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Nordyke RJ, Motyka J, Patterson JA. The Association of 340B Program Drug Margins with Covered Entity Characteristics. INQUIRY : A JOURNAL OF MEDICAL CARE ORGANIZATION, PROVISION AND FINANCING 2025; 62:469580251324051. [PMID: 40123227 PMCID: PMC11938891 DOI: 10.1177/00469580251324051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2024] [Revised: 01/24/2025] [Accepted: 02/10/2025] [Indexed: 03/25/2025]
Abstract
The 340B Drug Pricing Program aims to help facilities serving low-income and uninsured patients to stretch scarce resources by allowing covered entities to purchase outpatient drugs at federally mandated discounted rates while often receiving reimbursement for them at higher rates by commercial payers and Medicare. Despite increasing focus on the expansion and impact of the program, profit margins under 340B have not been fully explored. We aimed to examine drug-, facility-, and geographic-level factors that influence drug margins among 340B covered entities. We conducted a cross-sectional analysis of predictors of facility-level 340B margins for 5 drug classes in a multivariable regression model using 2021 data linked across multiple proprietary and public datasets. Regression results show that drug, facility characteristics, and geographic healthcare market-level characteristics influence drug margins under the 340B program. Adjusted 340B margins were higher in hospital outpatient departments than free-standing offices (ie, hospital-affiliated physician offices and independent, 340B eligible clinics) and among covered entities in more concentrated (ie, less competitive) markets. Covered entity market power, quantified by a facility-level measure of non-340B drug margins indicating pricing power, and area wealth were both associated with higher 340B drug margins. Margins on 340B drugs were higher among facilities in stronger bargaining positions and those serving wealthier areas. These findings add to the growing body of literature on expansions of the 340B program into more affluent communities, informing calls for reforms to ensure the 340B program serves low-income and uninsured patients.
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Affiliation(s)
- Robert J. Nordyke
- Petauri LLC, formerly National Pharmaceutical Council, Washington, DC, USA
| | - James Motyka
- National Pharmaceutical Council, Washington, DC, USA
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Panchangam PVR, A T, B U T, Maniaci MJ. Machine Learning-Based Prediction of Readmission Risk in Cardiovascular and Cerebrovascular Conditions Using Patient EMR Data. Healthcare (Basel) 2024; 12:1497. [PMID: 39120200 PMCID: PMC11311788 DOI: 10.3390/healthcare12151497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Revised: 07/19/2024] [Accepted: 07/26/2024] [Indexed: 08/10/2024] Open
Abstract
The primary objective of this study was to develop a risk-based readmission prediction model using the EMR data available at discharge. This model was then validated with the LACE plus score. The study cohort consisted of about 310,000 hospital admissions of patients with cardiovascular and cerebrovascular conditions. The EMR data of the patients consisted of lab results, vitals, medications, comorbidities, and admit/discharge settings. These data served as the input to an XGBoost model v1.7.6, which was then used to predict the number of days until the next readmission. Our model achieved remarkable results, with a precision score of 0.74 (±0.03), a recall score of 0.75 (±0.02), and an overall accuracy of approximately 82% (±5%). Notably, the model demonstrated a high accuracy rate of 78.39% in identifying the patients readmitted within 30 days and 80.81% accuracy for those with readmissions exceeding six months. The model was able to outperform the LACE plus score; of the people who were readmitted within 30 days, only 47.70 percent had a LACE plus score greater than 70, and, for people with greater than 6 months, only 10.09 percent had a LACE plus score less than 30. Furthermore, our analysis revealed that the patients with a higher comorbidity burden and lower-than-normal hemoglobin levels were associated with increased readmission rates. This study opens new doors to the world of differential patient care, helping both clinical decision makers and healthcare providers make more informed and effective decisions. This model is comparatively more robust and can potentially substitute the LACE plus score in cardiovascular and cerebrovascular settings for predicting the readmission risk.
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Affiliation(s)
| | - Tejas A
- Data Science Team, Saigeware Inc., Karnataka 560070, India; (T.A.); (T.B.U.)
| | - Thejas B U
- Data Science Team, Saigeware Inc., Karnataka 560070, India; (T.A.); (T.B.U.)
| | - Michael J. Maniaci
- Enterprise Physician Lead, Advanced Care at Home Program, Mayo Clinic Hospital, Jacksonville, FL 32224, USA;
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Quinn J, Bodenstab HM, Wo E, Parrish RH. Medication Management Through Collaborative Practice for Children With Medical Complexity: A Prospective Case Series. J Pediatr Pharmacol Ther 2024; 29:119-129. [PMID: 38596413 PMCID: PMC11001202 DOI: 10.5863/1551-6776-29.2.119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 11/08/2023] [Indexed: 04/11/2024]
Abstract
OBJECTIVE Care coordination for children and youth with special health care needs and medical complexity (CYSHCN-CMC), especially medication management, is difficult for providers, parents/caregivers, and -patients. This report describes the creation of a clinical pharmacotherapy practice in a pediatric long-term care facility (pLTCF), application of standard operating procedures to guide comprehensive medication management (CMM), and establishment of a collaborative practice agreement (CPA) to guide drug therapy. METHODS In a prospective case series, 102 patients characterized as CYSHCN-CMC were included in this pLTCF quality improvement project during a 9-month period. RESULTS Pharmacists identified, prevented, or resolved 1355 drug therapy problems (DTP) with an average of 13 interventions per patient. The patients averaged 9.5 complex chronic medical conditions with a -median length of stay of 2815 days (7.7 years). The most common medications discontinued due to pharmacist assessment and recommendation included diphenhydramine, albuterol, sodium phosphate enema, ipratropium, and metoclopramide. The average number of medications per patient was reduced from 23 to 20. A pharmacoeconomic analysis of 244 of the interventions revealed a monthly direct cost savings of $44,304 ($434 per patient per month) and monthly cost avoidance of $48,835 ($479 per patient per month). Twenty-eight ED visits/admissions and 61 clinic and urgent care visits were avoided. Hospital -readmissions were reduced by 44%. Pharmacist recommendations had a 98% acceptance rate. CONCLUSIONS Use of a CPA to conduct CMM in CYSHCN-CMC decreased medication burden, resolved, and prevented adverse events, reduced health care-related costs, reduced hospital readmissions and was well-accepted and implemented collaboratively with pLTCF providers.
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Affiliation(s)
- Jena Quinn
- Perfecting Peds (JQ, HMB, EW), Haddon Heights, NJ
| | - Heather Monk Bodenstab
- Perfecting Peds (JQ, HMB, EW), Haddon Heights, NJ
- Medical Affairs (HMB), Sobi, Waltham, MA
| | - Emily Wo
- Perfecting Peds (JQ, HMB, EW), Haddon Heights, NJ
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Hoffman GJ, Alexander NB, Ha J, Nguyen T, Min LC. Medicare's Hospital Readmission Reduction Program reduced fall-related health care use: An unexpected benefit? Health Serv Res 2024; 59:e14246. [PMID: 37806664 PMCID: PMC10771912 DOI: 10.1111/1475-6773.14246] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023] Open
Abstract
OBJECTIVE To assess whether Medicare's Hospital Readmissions Reduction Program (HRRP) was associated with a reduction in severe fall-related injuries (FRIs). DATA SOURCES AND STUDY SETTING Secondary data from Medicare were used. STUDY DESIGN Using an event study design, among older (≥65) Medicare fee-for-service beneficiaries, we assessed changes in 30- and 90-day FRI readmissions before and after HRRP's announcement (April 2010) and implementation (October 2012) for conditions targeted by the HRRP (acute myocardial infarction [AMI], congestive heart failure [CHF], and pneumonia) versus "non-targeted" (gastrointestinal) conditions. We tested for modification by hospitals with "high-risk" before HRRP and accounted for potential upcoding. We also explored changes in 30-day FRI readmissions involving emergency department (ED) or outpatient care, care processes (length of stay, discharge destination, and primary care visit), and patient selection (age and comorbidities). DATA COLLECTION Not applicable. PRINCIPAL FINDINGS We identified 1.5 million (522,596 pre-HRRP, 514,844 announcement, and 474,029 implementation period) index discharges. After its announcement, HRRP was associated with 12%-20% reductions in 30- and 90-day FRI readmissions for patients with CHF (-0.42 percentage points [ppt], p = 0.02; -1.53 ppt, p < 0.001) and AMI (-0.35, p = 0.047; -0.97, p = 0.001). Two years after implementation, HRRP was associated with reductions in 90-day FRI readmission for AMI (-1.27 ppt, p = 0.01) and CHF (-0.98 ppt, p = 0.02) patients. Results were similar for hospitals at higher versus lower baseline risk of FRI readmission. After HRRP's announcement, decreases were observed in home health (AMI: -2.43 ppt, p < 0.001; CHF: -8.83 ppt, p < 0.001; pneumonia: -1.97 ppt, p < 0.001) and skilled nursing facility referrals (AMI: -5.95 ppt, p < 0.001; CHF: -3.19 ppt, p < 0.001; pneumonia: -10.27 ppt, p < 0.001). CONCLUSIONS HRRP was associated with reductions in FRIs, primarily for HF and pneumonia patients. These decreases may reflect improvements in transitional care including changes in post-acute referral patterns that benefit patients at risk for falls.
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Affiliation(s)
- Geoffrey J. Hoffman
- Department of Systems, Populations and LeadershipUniversity of Michigan School of NursingAnn ArborMichiganUSA
- Institute for Healthcare Policy and InnovationUniversity of MichiganAnn ArborMichiganUSA
| | - Neil B. Alexander
- Department of Medicine, Division of Geriatric and Palliative MedicineUniversity of MichiganAnn ArborMichiganUSA
- Geriatric Research Education and Clinical Care Center (GRECC)VA Medical CenterAnn ArborMichiganUSA
| | - Jinkyung Ha
- Division of Geriatric and Palliative Medicine, Department of Internal MedicineUniversity of MichiganAnn ArborMichiganUSA
| | - Thuy Nguyen
- Department of Health Policy and ManagementUniversity of Michigan School of Public HealthAnn ArborMichiganUSA
| | - Lillian C. Min
- Institute for Healthcare Policy and InnovationUniversity of MichiganAnn ArborMichiganUSA
- Department of Medicine, Division of Geriatric and Palliative MedicineUniversity of MichiganAnn ArborMichiganUSA
- Veterans Affairs Center for Clinical Management and Research (CCMR)VA Medical CenterAnn ArborMichiganUSA
- VA Center for Clinical Management ResearchAnn Arbor VA Healthcare SystemAnn ArborMichiganUSA
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Fusco K, Sharma Y, Hakendorf P, Thompson C. The Impact of Weight Loss Prior to Hospital Readmission. J Clin Med 2023; 12:jcm12093074. [PMID: 37176515 PMCID: PMC10179303 DOI: 10.3390/jcm12093074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/14/2023] [Accepted: 04/20/2023] [Indexed: 05/15/2023] Open
Abstract
Hospital readmissions place a burden on hospitals. Reducing the readmission number and duration will help reduce the burden. Weight loss might affect readmission risk, especially the risk of an early (<30 days) readmission. This study sought to identify the predictors and the impact of weight loss prior to a delayed readmission (>30 days). Body mass index (BMI) was measured during the index admission and first readmission. Patients, after their readmission, were assessed retrospectively to identify the characteristics of those who had lost >5% weight prior to that readmission. Length of stay (LOS), time spent in the intensive care unit (ICU) and the one-year mortality of those patients who lost weight were compared to the outcomes of those who remained weight-stable using multilevel mixed-effects regression adjusting for BMI, Charlson comorbidity index (CCI), ICU hours and relative stay index (RSI). Those who were at risk of weight loss prior to readmission were identifiable based upon their age, BMI, CCI and LOS. Of 1297 patients, 671 (51.7%) remained weight-stable and 386 (29.7%) lost weight between admissions. During their readmission, those who had lost weight had a significantly higher LOS (IRR 1.17; 95% CI 1.12, 1.22: p < 0.001), RSI (IRR 2.37; 95% CI 2.27, 2.47: p < 0.001) and an increased ICU LOS (IRR 2.80; 95% CI 2.65, 2.96: p < 0.001). This study indicates that weight loss prior to a delayed readmission is predictable and leads to worse outcomes during that readmission.
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Affiliation(s)
- Kellie Fusco
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Yogesh Sharma
- Department of General Medicine, Division of Medicine, Cardiac & Critical Care, Flinders Medical Centre, Bedford Park, SA 5042, Australia
| | - Paul Hakendorf
- College of Medicine and Public Health, Flinders University, Adelaide, SA 5001, Australia
| | - Campbell Thompson
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
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The Implementation of a Geriatrics Co-Management Model of Care Reduces Hospital Length of Stay. Healthcare (Basel) 2022; 10:healthcare10112160. [DOI: 10.3390/healthcare10112160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 10/10/2022] [Accepted: 10/27/2022] [Indexed: 11/16/2022] Open
Abstract
(1) Background: Older adults comprise a large proportion of hospitalized patients. Many are frail and require complex care. Geriatrics has developed models of care specific to this inpatient population. Our objective was to demonstrate the effect of a geriatric co-management team on clinical administrative indicators of care in Clinical Teaching Units (CTUs) that have adopted the Age-friendly Hospital (AFH) principles in Brazilian hospitals. (2) Methods: Following 3 months of implementation of the AFH principles in CTUs, two periods of the same 6 months of two consecutive years were compared. (3) Results: The total number of participants in the study was 641 and 743 in 2015 and 2016, respectively. Average length of patient-stay (length of stay: 8.7 ± 2.7 vs. 5.4 ± 1.7 days) and number of monthly complaints (44.2 ± 6.5 vs. 13.5 ± 2.2) were significantly lower with the co-management model. Number of homecare service referrals/month was also significantly higher (2.5 ± 1 vs. 38.3 ± 6.3). The 30-day readmission rates and total hospital costs per patient remained unchanged. (4) Conclusion: The presence of a geriatric co-management team in CTUs is of added benefit to increase the efficiency of the AFH for vulnerable older inpatients with reduced LOS and increased referrals to homecare services without increasing hospital costs.
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Jun-O'Connell AH, Grigoriciuc E, Silver B, Kobayashi KJ, Osgood M, Moonis M, Henninger N. Association between the LACE+ index and unplanned 30-day hospital readmissions in hospitalized patients with stroke. Front Neurol 2022; 13:963733. [PMID: 36277929 PMCID: PMC9581259 DOI: 10.3389/fneur.2022.963733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 08/24/2022] [Indexed: 11/13/2022] Open
Abstract
Background The LACE+ index is used to predict unplanned 30-day hospital readmissions, but its utility to predict 30-day readmission in hospitalized patients with stroke is unknown. Methods We retrospectively analyzed 1,657 consecutive patients presenting with ischemic or hemorrhagic strokes, included in an institutional stroke registry between January 2018 and August 2020. The primary outcome of interest was unplanned 30-day readmission for any reason after index hospitalization for stroke. The 30-day readmission risk was categorized by LACE+ index to high risk (≥78), medium-to-high risk (59–77), medium risk (29–58), and low risk (≤ 28). Kaplan-Meier analysis, Log rank test, and multivariable Cox regression analysis (with backward elimination) were used to determine whether the LACE+ score was an independent predictor for 30-day unplanned readmission. Results The overall 30-day unplanned readmission rate was 11.7% (194/1,657). The median LACE+ score was higher in the 30-day readmission group compared to subjects that had no unplanned 30-day readmission [74 (IQR 67–79) vs. 70 (IQR 62–75); p < 0.001]. On Kaplan-Meier analysis, the high-risk group had the shortest 30-day readmission free survival time as compared to medium and medium-to-high risk groups (p < 0.01, each; statistically significant). On fully adjusted multivariable Cox-regression, the highest LACE+ risk category was independently associated with the unplanned 30-day readmission risk (per point: HR 1.67 95%CI 1.23–2.26, p = 0.001). Conclusion Subjects in the high LACE+ index category had a significantly greater unplanned 30-day readmission risk after stroke as compared to lower LACE+ risk groups. This supports the validity of the LACE+ scoring system for predicting unplanned readmission in subjects with stroke. Future studies are warranted to determine whether LACE+ score-based risk stratification can be used to devise early interventions to mitigate the risk for unplanned readmission.
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Affiliation(s)
- Adalia H. Jun-O'Connell
- Departments of Neurology, University of Massachusetts Chan Medical School, Worcester, MA, United States
- *Correspondence: Adalia H. Jun-O'Connell
| | - Eliza Grigoriciuc
- Departments of Neurology, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Brian Silver
- Departments of Neurology, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Kimiyoshi J. Kobayashi
- Departments of Internal Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Marcey Osgood
- Departments of Neurology, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Majaz Moonis
- Departments of Neurology, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Nils Henninger
- Departments of Neurology, University of Massachusetts Chan Medical School, Worcester, MA, United States
- Departments of Psychiatry, University of Massachusetts Chan Medical School, Worcester, MA, United States
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Jha AK, Ojha CP, Krishnan AM, Paul TK. Thirty-day readmission in patients with heart failure with preserved ejection fraction: Insights from the nationwide readmission database. World J Cardiol 2022; 14:473-482. [PMID: 36187428 PMCID: PMC9523271 DOI: 10.4330/wjc.v14.i9.473] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 06/16/2022] [Accepted: 07/27/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND There are rising numbers of patients who have heart failure with preserved ejection fraction (HFpEF). Poorly understood pathophysiology of heart failure with preserved and reduced ejection fraction and due to a sparsity of studies, the management of HFpEF is challenging.
AIM To determine the hospital readmission rate within 30 d of acute or acute on chronic heart failure with preserved ejection fraction and its effect on mortality and burden on health care in the United States.
METHODS We performed a retrospective study using the Agency for Health-care Research and Quality Health-care Cost and Utilization Project, Nationwide Readmissions Database for the year 2017. We collected data on hospital readmissions of 60514 adults hospitalized for acute or acute on chronic HFpEF. The primary outcome was the rate of all-cause readmission within 30 d of discharge. Secondary outcomes were cause of readmission, mortality rate in readmitted and index patients, length of stay, total hospitalization costs and charges. Independent risk factors for readmission were identified using Cox regression analysis.
RESULTS The thirty day readmission rate was 21%. Approximately 9.17% of readmissions were in the setting of acute on chronic diastolic heart failure. Hypertensive chronic kidney disease with heart failure (1245; 9.7%) was the most common readmission diagnosis. Readmitted patients had higher in-hospital mortality (7.9% vs 2.9%, P = 0.000). Our study showed that Medicaid insurance, higher Charlson co-morbidity score, patient admitted to a teaching hospital and longer hospital stay were significant variables associated with higher readmission rates. Lower readmission rate was found in residents of small metropolitan or micropolitan areas, older age, female gender, and private insurance or no insurance were associated with lower risk of readmission.
CONCLUSION We found that patients hospitalized for acute or acute on chronic HFpEF, the thirty day readmission rate was 21%. Readmission cases had a higher mortality rate and increased healthcare resource utilization. The most common cause of readmission was cardio-renal syndrome.
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Affiliation(s)
- Anil Kumar Jha
- Internal Medicine, Lowell General Hospital, Lowell, MA 01852, United States
| | - Chandra P Ojha
- Department of Medicine, Texas Tech University Health Sciences Center, El Paso, TX 79905, United States
| | - Anand M Krishnan
- Department of Cardiovascular Disease, Larner College of Medicine at the University of Vermont, Burlington, VT 05405, United States
| | - Timir K Paul
- Department of Clinical Education, University of Tennessee Health Sciences Center at Nashville, Nashville, TN 37025, United States
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Xie J, Zhang B, Ma J, Zeng D, Lo-Ciganic J. Readmission Prediction for Patients with Heterogeneous Medical History: A Trajectory-Based Deep Learning Approach. ACM TRANSACTIONS ON MANAGEMENT INFORMATION SYSTEMS 2022. [DOI: 10.1145/3468780] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Hospital readmission refers to the situation where a patient is re-hospitalized with the same primary diagnosis within a specific time interval after discharge. Hospital readmission causes $26 billion preventable expenses to the U.S. health systems annually and often indicates suboptimal patient care. To alleviate those severe financial and health consequences, it is crucial to proactively predict patients’ readmission risk. Such prediction is challenging because the evolution of patients’ medical history is dynamic and complex. The state-of-the-art studies apply statistical models which use static predictors in a period, failing to consider patients’ heterogeneous medical history. Our approach –
Trajectory-BAsed DEep Learning (TADEL)
– is motivated to tackle the deficiencies of the existing approaches by capturing dynamic medical history. We evaluate TADEL on a five-year national Medicare claims dataset including 3.6 million patients per year over all hospitals in the United States, reaching an F1 score of 87.3% and an AUC of 88.4%. Our approach significantly outperforms all the state-of-the-art methods. Our findings suggest that health status factors and insurance coverage are important predictors for readmission. This study contributes to IS literature and analytical methodology by formulating the trajectory-based readmission prediction problem and developing a novel deep-learning-based readmission risk prediction framework. From a health IT perspective, this research delivers implementable methods to assess patients’ readmission risk and take early interventions to avoid potential negative consequences.
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Affiliation(s)
- Jiaheng Xie
- Lerner College of Business & Economics, University of Delaware, Newark, DE, USA
| | - Bin Zhang
- Eller College of Management, University of Arizona, Tucson, AZ, USA
| | - Jian Ma
- University of Colorado, Colorado Springs, Colorado Springs CO, USA
| | - Daniel Zeng
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Jenny Lo-Ciganic
- Department of Pharmaceutical Outcomes & Policy, University of Florida, FL
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Ismayl M, Machanahalli Balakrishna A, Walters RW, Pajjuru VS, Goldsweig AM, Aboeata A. In-hospital mortality and readmission after ST-elevation myocardial infarction in nonagenarians: A nationwide analysis from the United States. Catheter Cardiovasc Interv 2022; 100:5-16. [PMID: 35568973 DOI: 10.1002/ccd.30227] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 04/14/2022] [Accepted: 05/03/2022] [Indexed: 12/28/2022]
Abstract
OBJECTIVES To assess readmission rates in nonagenarians (age ≥ 90 years) with ST-elevation myocardial infarction (STEMI) following primary percutaneous coronary intervention (pPCI) versus no pPCI. BACKGROUND There are limited data exploring readmissions following STEMI in nonagenarians undergoing pPCI versus no pPCI. METHODS We retrospectively analyzed the Nationwide Readmissions Database to identify nonagenarians hospitalized with STEMI. We divided the cohort into two groups based on pPCI status. We compared mortality during index hospitalization and during 30-day readmission, readmission rates, and causes of readmissions. RESULTS We identified 58,231 nonagenarian STEMI hospitalizations between 2010 and 2018, of which 18,809 (32.3%) included pPCI, and 39,422 (67.7%) had no pPCI. Unadjusted unplanned 30-day readmission was higher in pPCI cohort (21.0% vs. 15.4%, p < 0.001). However, mortality during index hospitalization and during 30-day readmission were significantly lower in pPCI cohort (15.8% vs. 32.2%, p < 0.001; 7.4% vs. 14.2%, p < 0.001, respectively). After adjusting for baseline characteristics, hospitalizations that included pPCI had 25% greater odds of unplanned 30-day readmission (adjusted odds ratio [aOR]: 1.25, 95% confidence interval [CI]: 1.12-1.39, p < 0.001) and 49% lower odds of in-hospital mortality during index hospitalization (aOR: 0.51, 95% CI: 0.46-0.56, p < 0.001). Heart failure was the most common cause of readmission in both cohorts followed by myocardial infarction. CONCLUSIONS In nonagenarians with STEMI, pPCI is associated with slightly higher 30-day readmission but significantly lower mortality during index hospitalization and during 30-day readmission than no pPCI. Given the overwhelming mortality benefit with pPCI, further research is necessary to optimize the utilization of pPCI while reducing readmissions following STEMI in nonagenarians.
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Affiliation(s)
- Mahmoud Ismayl
- Department of Medicine, Creighton University School of Medicine, Omaha, Nebraska, USA
| | | | - Ryan W Walters
- Division of Clinical Research and Evaluative Sciences, Department of Medicine, Creighton University School of Medicine, Omaha, Nebraska, USA
| | - Venkata S Pajjuru
- Division of Cardiology, Department of Medicine, Creighton University School of Medicine, Omaha, Nebraska, USA
| | - Andrew M Goldsweig
- Division of Cardiology, Department of Medicine, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Ahmed Aboeata
- Division of Cardiology, Department of Medicine, Creighton University School of Medicine, Omaha, Nebraska, USA
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Pathway Home™ for High Utilizers of Psychiatric Inpatient Services: Impact on Inpatient Days and Outpatient Engagement. Community Ment Health J 2022; 58:415-419. [PMID: 34655367 DOI: 10.1007/s10597-021-00902-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 10/02/2021] [Indexed: 10/20/2022]
Abstract
OBJECTIVE This study examined the impact of Pathway Home™ (PH) transition services for high utilizers of psychiatric hospitalization on inpatient days and outpatient engagement post-hospital discharge. METHODS This case series study of forty PH graduates (5/22/2015-8/31/2018) used Medicaid claims to assess psychiatric inpatient days-per-month, average proportion of months with psychiatric emergency room, outpatient, and health home care management services. T-tests compared three time periods: the year prior, during, and after enrollment. RESULTS Graduates had significantly fewer psychiatric inpatient days/month during (M = 1.84, p < 0.001) and after PH enrollment (M = 1.88, p < 0.001) compared to prior to enrollment (M = 7.1), while emergency services were stable. Outpatient visits increased from 45% prior to 76% during enrollment (p < 0.001) and was sustained on follow-up (67%, p = 0.008). A similar pattern emerged for health home services (32%, 60%, and 50%). CONCLUSION PH is a promising approach for improving outcomes for high utilizers of psychiatric inpatient services, with sustained impact on follow-up.
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Deshpande OA, Tawfik JA, Namavar AA, Nguyen KP, Vangala SS, Romero T, Parikh NN, Dowling EP. A Prospective Observational Study Assessing the Impacts of Health Literacy and Psychosocial Determinants of Health on 30-day Readmission Risk. J Patient Exp 2022; 9:23743735221079140. [PMID: 35187225 PMCID: PMC8855411 DOI: 10.1177/23743735221079140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Our objective was to assess the utility of an assessment battery capturing health literacy (HL) and biopsychosocial determinants of health in predicting 30-day readmission in comparison to a currently well-adopted readmission risk calculator. We also sought to capture the distribution of inpatient HL, with emphasis on inadequate and marginal HL (an intermediate HL level). A prospective observational study was conducted to obtain HL and biopsychosocial data on general medicine inpatients admitted to the UCLA health system. Five hundred thirty-seven subjects were tracked prospectively for 30-day readmission after index hospitalization. HL was significantly better at predicting readmission compared to LACE + (Length, admission acuity, comorbidities, emergency room visits) alone (P = .013). A multivariate model including education, insurance, and language comfort was a strong predictor of adequate HL (P < .001). In conclusion, HL offered significant improvement in risk stratification in comparison to LACE + alone. Patients with marginal HL were high-risk, albeit difficult to characterize. Incorporating robust HL and biopsychosocial determinant assessments may allow hospital systems to allocate educational resources towards at-risk patients, thereby mitigating readmission risk.
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Affiliation(s)
- Ojas A Deshpande
- University of California, Los Angeles, CA, USA.,California Health Sciences University College of Osteopathic Medicine, San Bernardino, CA, USA
| | - John A Tawfik
- University of California, Los Angeles, CA, USA.,California Health and Science University - School of Osteopathic Medicine, Clovis, CA, USA
| | - Aram A Namavar
- University of California, Los Angeles, CA, USA.,University of California, San Diego, CA, USA
| | | | | | | | - Neil N Parikh
- University of California, Los Angeles, CA, USA.,University of California, Los Angeles Health Department of Medicine, Los Angeles, CA, USA
| | - Erin P Dowling
- University of California, Los Angeles, CA, USA.,University of California, Los Angeles Health Department of Medicine, Los Angeles, CA, USA
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13
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Bravo J, Buta FL, Talina M, Silva-Dos-Santos A. Avoiding revolving door and homelessness: The need to improve care transition interventions in psychiatry and mental health. Front Psychiatry 2022; 13:1021926. [PMID: 36226101 PMCID: PMC9548635 DOI: 10.3389/fpsyt.2022.1021926] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 09/07/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Joana Bravo
- Department of Psychiatry, Hospital Vila Franca de Xira, Vila Franca de Xira, Portugal
| | - Francisco Lima Buta
- Department of Psychiatry, Hospital Vila Franca de Xira, Vila Franca de Xira, Portugal
| | - Miguel Talina
- NOVA Medical School, NOVA University of Lisbon, Lisboa, Portugal
| | - Amílcar Silva-Dos-Santos
- Department of Psychiatry, Hospital Vila Franca de Xira, Vila Franca de Xira, Portugal.,NOVA Medical School, NOVA University of Lisbon, Lisboa, Portugal.,Hospital CUF Tejo, Lisbon, Portugal
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14
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Krause TM, Hamden R, Schaefer CM. The Impact of Personal and Historical Factors That Contribute to Medicare Readmissions. Popul Health Manag 2021; 25:375-383. [PMID: 34748435 DOI: 10.1089/pop.2021.0106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Medicare readmissions from the 5% national sample of Medicare Fee For Service claims were assessed to identify the contribution of various demographic or personal health history risk factors to the liklihood of readmission within 30 days of discharge. The Medicare population was evaluated as 2 cohorts based on their eligibility status: age (71.9%) or disability (18.1%). Roughly 12% of admissions for the aged population resulted in a readmission within 30 days, whereas ∼18% was true of the disabled population. Patients with a history of frequent emergency department visits or an urgent index admission had an increased risk for a readmission in both groups of beneficiaries. An important indicator for readmission risk was discharge status from the index hospital stay. In both categories of beneficiaries, individuals who were discharged against medical advice were 1.6 times more likely to experience a readmission. Most importantly, personal and social determinants of health (SDOH) documented preadmission and postdischarge were more evident for the disabled population than the aged. Furthermore, when pre- and postadmission spans for SDOH conditions were examined, (6 months before initial admission to 30 days postadmission), both the aged and disabled populations were statistically significantly more likely to experience readmissions if they had an SDOH diagnosis.
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Affiliation(s)
- Trudy Millard Krause
- Department of Management, Policy and Community Health, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Randa Hamden
- Department of Management, Policy and Community Health, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Caroline M Schaefer
- Department of Management, Policy and Community Health, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
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15
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Lo YT, Liao JCH, Chen MH, Chang CM, Li CT. Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms. BMC Med Inform Decis Mak 2021; 21:288. [PMID: 34670553 PMCID: PMC8527795 DOI: 10.1186/s12911-021-01639-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 09/22/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Early unplanned hospital readmissions are associated with increased harm to patients, increased medical costs, and negative hospital reputation. With the identification of at-risk patients, a crucial step toward improving care, appropriate interventions can be adopted to prevent readmission. This study aimed to build machine learning models to predict 14-day unplanned readmissions. METHODS We conducted a retrospective cohort study on 37,091 consecutive hospitalized adult patients with 55,933 discharges between September 1, 2018, and August 31, 2019, in an 1193-bed university hospital. Patients who were aged < 20 years, were admitted for cancer-related treatment, participated in clinical trial, were discharged against medical advice, died during admission, or lived abroad were excluded. Predictors for analysis included 7 categories of variables extracted from hospital's medical record dataset. In total, four machine learning algorithms, namely logistic regression, random forest, extreme gradient boosting, and categorical boosting, were used to build classifiers for prediction. The performance of prediction models for 14-day unplanned readmission risk was evaluated using precision, recall, F1-score, area under the receiver operating characteristic curve (AUROC), and area under the precision-recall curve (AUPRC). RESULTS In total, 24,722 patients were included for the analysis. The mean age of the cohort was 57.34 ± 18.13 years. The 14-day unplanned readmission rate was 1.22%. Among the 4 machine learning algorithms selected, Catboost had the best average performance in fivefold cross-validation (precision: 0.9377, recall: 0.5333, F1-score: 0.6780, AUROC: 0.9903, and AUPRC: 0.7515). After incorporating 21 most influential features in the Catboost model, its performance improved (precision: 0.9470, recall: 0.5600, F1-score: 0.7010, AUROC: 0.9909, and AUPRC: 0.7711). CONCLUSIONS Our models reliably predicted 14-day unplanned readmissions and were explainable. They can be used to identify patients with a high risk of unplanned readmission based on influential features, particularly features related to diagnoses. The operation of the models with physiological indicators also corresponded to clinical experience and literature. Identifying patients at high risk with these models can enable early discharge planning and transitional care to prevent readmissions. Further studies should include additional features that may enable further sensitivity in identifying patients at a risk of early unplanned readmissions.
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Affiliation(s)
- Yu-Tai Lo
- Department of Geriatrics and Gerontology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan (R.O.C.)
| | - Jay Chie-Hen Liao
- Institute of Data Science, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan (R.O.C.)
| | - Mei-Hua Chen
- Department of Geriatrics and Gerontology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan (R.O.C.)
| | - Chia-Ming Chang
- Department of Geriatrics and Gerontology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan (R.O.C.).,Department of Medicine and Institute of Gerontology, College of Medicine, National Cheng Kung University, Tainan, Taiwan (R.O.C.)
| | - Cheng-Te Li
- Institute of Data Science, National Cheng Kung University, No. 1, University Road, Tainan City 701, Taiwan (R.O.C.).
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16
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Alzeer AH, Althemery A, Alsaawi F, Albalawi M, Alharbi A, Alzahrani S, Alabdulaali D, Alabdullatif R, Tash A. Using machine learning to reduce unnecessary rehospitalization of cardiovascular patients in Saudi Arabia. Int J Med Inform 2021; 154:104565. [PMID: 34509027 DOI: 10.1016/j.ijmedinf.2021.104565] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 08/30/2021] [Accepted: 08/30/2021] [Indexed: 12/19/2022]
Abstract
OBJECTIVES Patient readmission is a costly and preventable burden on healthcare systems. The main objective of this study was to develop a machine-learning classification model to identify cardiovascular patients with a high risk of readmission. METHODS Inpatient data were collected from 48 Ministry of Health hospitals (MOH) in Saudi Arabia from 2016 to 2019. Cardiovascular disease (CVD)-related diagnoses were defined as congestive heart failure (HF), ischemic heart disease (IHD), cardiac arrhythmias (CA), and valvular diseases (VD). Hospitalization days, daily hospitalization price, and the price of each basic and medical service provided were used to calculate the healthcare utilization cost. We employed a Python machine-learning model to identify all-cause 30-day CVD-related readmissions using the International Classification of Diseases, Revision 10 classification system (ICD10) as the gold standard. Demographics, comorbidities, and healthcare utilization were used as the independent variables. RESULTS From 2016 to 2019, we identified 403,032 hospitalized patients from 48 hospitals in 13 administrative regions of Saudi Arabia. Out of these patients, 17,461 had a history of hospital admission for cardiovascular reasons. The total direct cost of overall hospitalizations was 1.6 B international dollars (I$) with an average of I$ 3,156 per hospitalization, whereas CVD-related readmission costs were estimated to be I$ 14.9 M, with an average of I$ 7,600 per readmission. Finally, an empirical approach was followed to test several algorithms to identify patients at high risk of readmission. The comparison indicated that the decision-tree algorithm correctly classified 2,336 instances (926 readmitted and 1,410 not readmitted) and showed a higher F1 score than other models (64%), with a recall of 71% and precision of 57%. CONCLUSION This study identified IHD as the most prevalent CVD, and hypertension and diabetes were found to be the most common comorbidities among hospitalized CVD patients. Compared to general encounters, readmission encounters were nearly two times higher on average among the study population. Furthermore, we concluded that a machine-learning model can be used to identify CVD patients at a high risk of readmission. Further research is required to develop more accurate models based on clinical notes and laboratory results.
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Affiliation(s)
- Abdullah H Alzeer
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia.
| | - Abdullah Althemery
- Department of Clinical Pharmacy, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia.
| | - Fahad Alsaawi
- Department of Data Services, Lean Business Services, Riyadh, Saudi Arabia.
| | - Marwan Albalawi
- Department of Digital Health, Lean Business Services, Riyadh, Saudi Arabia.
| | - Abdulaziz Alharbi
- Department of Data Services, Lean Business Services, Riyadh, Saudi Arabia.
| | - Somayah Alzahrani
- Department of Data Services, Lean Business Services, Riyadh, Saudi Arabia.
| | - Deema Alabdulaali
- Department of Data Services, Lean Business Services, Riyadh, Saudi Arabia.
| | | | - Adel Tash
- Cardiac Services Development, Ministry of Health, Riyadh, Saudi Arabia; National Heart Center, Saudi Health Council, Riyadh, Saudi Arabia.
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17
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Factors associated with early 14-day unplanned hospital readmission: a matched case-control study. BMC Health Serv Res 2021; 21:870. [PMID: 34433448 PMCID: PMC8390214 DOI: 10.1186/s12913-021-06902-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 08/17/2021] [Indexed: 11/17/2022] Open
Abstract
Background/Purpose Early unplanned hospital readmissions are burdensome health care events and indicate low care quality. Identifying at-risk patients enables timely intervention. This study identified predictors for 14-day unplanned readmission. Methods We conducted a retrospective, matched, case–control study between September 1, 2018, and August 31, 2019, in an 1193-bed university hospital. Adult patients aged ≥ 20 years and readmitted for the same or related diagnosis within 14 days of discharge after initial admission (index admission) were included as cases. Cases were 1:1 matched for the disease-related group at index admission, age, and discharge date to controls. Variables were extracted from the hospital’s electronic health records. Results In total, 300 cases and 300 controls were analyzed. Six factors were independently associated with unplanned readmission within 14 days: previous admissions within 6 months (OR = 3.09; 95 % CI = 1.79–5.34, p < 0.001), number of diagnoses in the past year (OR = 1.07; 95 % CI = 1.01–1.13, p = 0.019), Malnutrition Universal Screening Tool score (OR = 1.46; 95 % CI = 1.04–2.05, p = 0.03), systolic blood pressure (OR = 0.98; 95 % CI = 0.97–0.99, p = 0.01) and ear temperature within 24 h before discharge (OR = 2.49; 95 % CI = 1.34–4.64, p = 0.004), and discharge with a nasogastric tube (OR = 0.13; 95 % CI = 0.03–0.60, p = 0.009). Conclusions Factors presented at admission (frequent prior hospitalizations, multimorbidity, and malnutrition) along with factors presented at discharge (clinical instability and the absence of a nasogastric tube) were associated with increased risk of early 14-day unplanned readmission.
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18
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Care Strategies for Reducing Hospital Readmissions Using Stochastic Programming. Healthcare (Basel) 2021; 9:healthcare9080940. [PMID: 34442079 PMCID: PMC8393874 DOI: 10.3390/healthcare9080940] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2021] [Revised: 07/13/2021] [Accepted: 07/20/2021] [Indexed: 12/29/2022] Open
Abstract
A hospital readmission occurs when a patient has an unplanned admission to a hospital within a specific time period of discharge from an earlier or initial hospital stay. Preventable readmissions have turned into a critical challenge for the healthcare system globally, and hospitals seek care strategies that reduce the readmission burden. Some countries have developed hospital readmission reduction policies, and in some cases, these policies impose financial penalties for hospitals with high readmission rates. Decision models are needed to help hospitals identify care strategies that avoid financial penalties, yet maintain balance among quality of care, the cost of care, and the hospital’s readmission reduction goals. We develop a multi-condition care strategy model to help hospitals prioritize treatment plans and allocate resources. The stochastic programming model has probabilistic constraints to control the expected readmission probability for a set of patients. The model determines which care strategies will be the most cost-effective and the extent to which resources should be allocated to those initiatives to reach the desired readmission reduction targets and maintain high quality of care. A sensitivity analysis was conducted to explore the value of the model for low- and high-performing hospitals and multiple health conditions. Model outputs are valuable to hospitals as they examine the expected cost of hitting its target and the expected improvement to its readmission rates.
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19
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Stein LK, Mocco J, Fifi J, Jette N, Tuhrim S, Dhamoon MS. Correlations Between Physician and Hospital Stroke Thrombectomy Volumes and Outcomes: A Nationwide Analysis. Stroke 2021; 52:2858-2865. [PMID: 34092122 DOI: 10.1161/strokeaha.120.033312] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
[Figure: see text].
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Affiliation(s)
- Laura K Stein
- Department of Neurology (L.K.S., J.F., N.J., S.T., M.S.D.), Icahn School of Medicine at Mount Sinai, NY
| | - J Mocco
- Department of Neurosurgery (J.M., J.F.), Icahn School of Medicine at Mount Sinai, NY
| | - Johanna Fifi
- Department of Neurology (L.K.S., J.F., N.J., S.T., M.S.D.), Icahn School of Medicine at Mount Sinai, NY.,Department of Neurosurgery (J.M., J.F.), Icahn School of Medicine at Mount Sinai, NY
| | - Nathalie Jette
- Department of Neurology (L.K.S., J.F., N.J., S.T., M.S.D.), Icahn School of Medicine at Mount Sinai, NY.,Department of Population Health Science and Policy (N.J.), Icahn School of Medicine at Mount Sinai, NY
| | - Stanley Tuhrim
- Department of Neurology (L.K.S., J.F., N.J., S.T., M.S.D.), Icahn School of Medicine at Mount Sinai, NY
| | - Mandip S Dhamoon
- Department of Neurology (L.K.S., J.F., N.J., S.T., M.S.D.), Icahn School of Medicine at Mount Sinai, NY
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20
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Kowalkowski M, Eaton T, McWilliams A, Tapp H, Rios A, Murphy S, Burns R, Gutnik B, O'Hare K, McCurdy L, Dulin M, Blanchette C, Chou SH, Halpern S, Angus DC, Taylor SP. Protocol for a two-arm pragmatic stepped-wedge hybrid effectiveness-implementation trial evaluating Engagement and Collaborative Management to Proactively Advance Sepsis Survivorship (ENCOMPASS). BMC Health Serv Res 2021; 21:544. [PMID: 34078374 PMCID: PMC8170654 DOI: 10.1186/s12913-021-06521-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 05/12/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Sepsis survivors experience high morbidity and mortality, and healthcare systems lack effective strategies to address patient needs after hospital discharge. The Sepsis Transition and Recovery (STAR) program is a navigator-led, telehealth-based multicomponent strategy to provide proactive care coordination and monitoring of high-risk patients using evidence-driven, post-sepsis care tasks. The purpose of this study is to evaluate the effectiveness of STAR to improve outcomes for sepsis patients and to examine contextual factors that influence STAR implementation. METHODS This study uses a hybrid type I effectiveness-implementation design to concurrently test clinical effectiveness and gather implementation data. The effectiveness evaluation is a two-arm, pragmatic, stepped-wedge cluster randomized controlled trial at eight hospitals in North Carolina comparing clinical outcomes between sepsis survivors who receive Usual Care versus care delivered through STAR. Each hospital begins in a Usual Care control phase and transitions to STAR in a randomly assigned sequence (one every 4 months). During months that a hospital is allocated to Usual Care, all eligible patients will receive usual care. Once a hospital transitions to STAR, all eligible patients will receive STAR during their hospitalization and extending through 90 days from discharge. STAR includes centrally located nurse navigators using telephonic counseling and electronic health record-based support to facilitate best-practice post-sepsis care strategies including post-discharge review of medications, evaluation for new impairments or symptoms, monitoring existing comorbidities, and palliative care referral when appropriate. Adults admitted with suspected sepsis, defined by clinical criteria for infection and organ failure, are included. Planned enrollment is 4032 patients during a 36-month period. The primary effectiveness outcome is the composite of all-cause hospital readmission or mortality within 90 days of discharge. A mixed-methods implementation evaluation will be conducted before, during, and after STAR implementation. DISCUSSION This pragmatic evaluation will test the effectiveness of STAR to reduce combined hospital readmissions and mortality, while identifying key implementation factors. Results will provide practical information to advance understanding of how to integrate post-sepsis management across care settings and facilitate implementation, dissemination, and sustained utilization of best-practice post-sepsis management strategies in other heterogeneous healthcare delivery systems. TRIAL REGISTRATION NCT04495946 . Submitted July 7, 2020; Posted August 3, 2020.
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Affiliation(s)
- Marc Kowalkowski
- Center for Outcomes Research and Evaluation, Atrium Health, 1300 Scott Ave, Charlotte, NC, 28203, USA.
| | - Tara Eaton
- Center for Outcomes Research and Evaluation, Atrium Health, 1300 Scott Ave, Charlotte, NC, 28203, USA
| | - Andrew McWilliams
- Center for Outcomes Research and Evaluation, Atrium Health, 1300 Scott Ave, Charlotte, NC, 28203, USA.,Department of Internal Medicine, Atrium Health, Charlotte, USA
| | - Hazel Tapp
- Department of Family Medicine, Atrium Health, Charlotte, USA
| | - Aleta Rios
- Ambulatory Care Management, Atrium Health, Charlotte, USA
| | | | - Ryan Burns
- Center for Outcomes Research and Evaluation, Atrium Health, 1300 Scott Ave, Charlotte, NC, 28203, USA
| | - Bella Gutnik
- Center for Outcomes Research and Evaluation, Atrium Health, 1300 Scott Ave, Charlotte, NC, 28203, USA
| | | | - Lewis McCurdy
- Division of Infectious Disease, Department of Internal Medicine, Atrium Health, Charlotte, USA
| | - Michael Dulin
- Academy for Population Health Innovation, University of North Carolina Charlotte & Mecklenburg County Public Health Department, Charlotte, USA.,Department of Public Health Sciences, University of North Carolina Charlotte, Charlotte, USA
| | - Christopher Blanchette
- Department of Public Health Sciences, University of North Carolina Charlotte, Charlotte, USA.,Health Economics and Outcomes Research Strategy, Novo Nordisk, Plainsboro Township, USA
| | - Shih-Hsiung Chou
- Center for Outcomes Research and Evaluation, Atrium Health, 1300 Scott Ave, Charlotte, NC, 28203, USA
| | - Scott Halpern
- Palliative and Advanced Illness Research (PAIR) Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.,Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Derek C Angus
- Clinical Research, Investigation, and Systems Modeling of Acute illness (CRISMA) Center, University of Pittsburgh, Pittsburgh, USA.,Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, USA
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21
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Ayvaci M, Cavusoglu H, Kim Y, Raghunathan S. Designing Payment Contracts for Healthcare Services to Induce Information Sharing: The Adoption and the Value of Health Information Exchanges (HIEs). MIS QUART 2021. [DOI: 10.25300/misq/2021/14809] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Recent initiatives to improve healthcare quality and reduce costs have centered around payment mechanisms and IT-enabled health information exchanges (HIEs). Such initiatives profoundly influence both providers’ choices in terms of healthcare effort levels and HIE adoption and patients’ choice of providers. Using a game-theoretical model of a healthcare setup, we examine the role of payment models in aligning providers’ and patients’ incentives for realizing socially optimal (i.e., first-best) choices. We show that the traditional fee-for-service (FFS) payment model does not necessarily induce the first-best solution. The pay-for-performance (P4P) model may induce the first-best solution under some conditions if provider switching by patients during a health episode is socially suboptimal, making provider coordination less of an issue. We identify an episode-based payment (EBP) model that can always induce the first-best solution. The proposed EBP model reduces to the P4P model if the P4P model induces the first-best solution. In other cases, the first-best inducing EBP model is multilateral in the sense that the payment to a provider depends not only on the provider’s own efforts and outcomes but also on those of other providers. Furthermore, the payment in this EBP model is sequence dependent in the sense that payment to a provider is contingent upon whether the patient visits a given provider first or second. We show that the proposed EBP model achieves the lowest healthcare cost, not necessarily at the expense of care quality or provider payment, relative to FFS and P4P. Although our proposed contract is complex, it sets an optimality baseline when evaluating simpler contracts and also characterizes aspects of payment that need to be captured for socially desirable actions. We further show that the value of HIEs depends critically on the payment model as well as on the social desirability of patient switching. Under all three payment models, the HIE value is higher when switching by at least some patients is desirable than when switching by any patient is undesirable. Moreover, the HIE value is highest under the FFS model and lowest under the P4P model. Hence, assessing the value of HIEs in isolation from the underlying payment mechanism and patient-switching behavior may result in under- or overestimation of the HIE value. Therefore, as payment models evolve over time, there is a real need to reevaluate the HIE value and the government subsidies that induce providers to adopt HIEs.
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22
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Lin Z, Han H, Wu C, Wei X, Ruan Y, Zhang C, Cao Y, He J. Discharge Against Medical Advice in Acute Ischemic Stroke: the Risk of 30-Day Unplanned Readmission. J Gen Intern Med 2021; 36:1206-1213. [PMID: 33559060 PMCID: PMC8131431 DOI: 10.1007/s11606-020-06366-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Accepted: 11/25/2020] [Indexed: 11/26/2022]
Abstract
BACKGROUND Discharge against medical advice may be associated with more readmissions. OBJECTIVE To evaluate DAMA in patients with acute ischemic stroke (AIS) and identify the relationship between DAMA and 30-day unplanned readmissions. DESIGN A retrospective cohort study. PARTICIPANTS The National Readmission Database was used to identify inpatients with a primary diagnosis of AIS who were either discharged home or DAMA between 2010 and 2017 in the USA. MEASURES Demographic features, hospital type, comorbidities, stroke risk factors, severity indices, and treatments were compared between patients discharged routinely and DAMA. Multivariable logistic regression was used to evaluate predictors of DAMA, and a double robust inverse probability of treatment weighting method was used to assess the association between DAMA and 30-day unplanned readmissions. KEY RESULTS Overall, 1,335,484 patients with AIS were included, of whom 2.09% (n = 27,892) were DAMA. The prevalence of DAMA in AIS patients increased from 1.65 in 2010 to 2.57% in 2017. The rates of 30-day unplanned readmissions for DAMA and non-DAMA patients were 16.81% and 7.78%, respectively. Patients with drug abuse, alcohol abuse, smoking, prior stroke, psychoses, and intravenous thrombolysis had greater odds of DAMA. DAMA was associated with all-cause readmissions (OR, 2.04; 95% CI, 2.01-2.07) and remained a strong predictor for transient ischemic attack/stroke-specific and cardiac-specific causes of readmissions. CONCLUSIONS Although the DAMA rate is low in AIS patients, DAMA is a risk factor for all-cause and recurrent stroke-specific readmissions. Future studies are needed to address issues around compliance and engagement with health care to reduce DAMA.
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Affiliation(s)
- Zhen Lin
- Department of Health Statistics, Second Military Medical University, Shanghai, China
| | - Hedong Han
- Department of Health Statistics, Second Military Medical University, Shanghai, China
- Department of Respiratory and Critical Care Medicine , Jinling Hospital Nanjing University School of Medicine , 210002, Nanjing, China
| | - Cheng Wu
- Department of Health Statistics, Second Military Medical University, Shanghai, China
| | - Xin Wei
- Department of Cardiology, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Yiming Ruan
- Department of Health Statistics, Second Military Medical University, Shanghai, China
| | - Chenxu Zhang
- Department of Health Statistics, Second Military Medical University, Shanghai, China
| | - Yang Cao
- Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Jia He
- Department of Health Statistics, Second Military Medical University, Shanghai, China.
- Tongji University School of Medicine, Shanghai, China.
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23
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Braet DJ, Smith JB, Bath J, Kruse RL, Vogel TR. Risk factors associated with 30-day hospital readmission after carotid endarterectomy. Vascular 2021; 29:61-68. [PMID: 32628069 PMCID: PMC7782206 DOI: 10.1177/1708538120937955] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
OBJECTIVE The current study evaluated all-cause 30-day readmissions after carotid endarterectomy. METHODS Patients undergoing carotid endarterectomy were selected from the Cerner Health Facts® database using ICD-9-CM procedure codes from their index admission. Readmission within 30 days of discharge was determined. Chi-square analysis determined characteristics of the index admission (demographics, diagnoses, postoperative medications, and laboratory results) associated with readmission. Multivariate logistic regression models were used to identify characteristics independently associated with readmission. RESULTS In total, 5257 patients undergoing elective carotid endarterectomy were identified. Readmission within 30 days was 3.1%. After multivariable adjustment, readmission was associated with end-stage renal disease (OR: 3.21, 95% CI: 1.01-10.2), hemorrhage or hematoma (OR: 2.34, 95% CI: 1.15-4.77), procedural complications (OR: 3.07, 95% CI: 1.24-7.57), use of bronchodilators (OR: 1.48, 95% CI: 1.03-2.11), increased Charlson index scores (OR: 1.22, 95% CI: 1.08-1.38), and electrolyte abnormalities (hyponatremia < 135 mEq/L (OR: 1.69, 95% CI: 1.07-2.67) and hypokalemia less than 3.7 mEq/L (OR: 2.26, 95% CI: 1.03-4.98)). CONCLUSIONS Factors associated with readmission following carotid endarterectomy included younger age, increased comorbidity burden, end-stage renal disease, electrolyte disorders, the use of bronchodilators, and complications including bleeding (hemorrhage or hematoma). Of note, in this real-world study, only 40% of the patients received protamine, despite evidence-based literature demonstrating the reduced risk of bleeding complications. As healthcare moves towards quality of care-driven reimbursement, physician modifiable targets such as protamine utilization to reduce bleeding are greatly needed to reduce readmission, and failure to reduce preventable physician-driven complications after carotid interventions may be associated with decreased reimbursement.
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Affiliation(s)
- Drew J. Braet
- Division of Vascular Surgery, University of Missouri, School of Medicine, Columbia, Missouri
| | - Jamie B. Smith
- Department of Family and Community Medicine, University of Missouri, School of Medicine, Columbia, Missouri
| | - Jonathan Bath
- Division of Vascular Surgery, University of Missouri, School of Medicine, Columbia, Missouri
| | - Robin L. Kruse
- Department of Family and Community Medicine, University of Missouri, School of Medicine, Columbia, Missouri
| | - Todd R. Vogel
- Division of Vascular Surgery, University of Missouri, School of Medicine, Columbia, Missouri
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Lee NJ, Fields MW, Boddapati V, Cerpa M, Dansby J, Lin JD, Sardar ZM, Lehman R, Lenke L. The risks, reasons, and costs for 30- and 90-day readmissions after fusion surgery for adolescent idiopathic scoliosis. J Neurosurg Spine 2021; 34:245-253. [PMID: 33157526 DOI: 10.3171/2020.6.spine20197] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 06/29/2020] [Indexed: 11/06/2022]
Abstract
OBJECTIVE With the continued evolution of bundled payment plans, there has been a greater focus within orthopedic surgery on quality metrics up to 90 days of care. Although the Centers for Medicare and Medicaid Services does not currently penalize hospitals based on their pediatric readmission rates, it is important to understand the drivers for unplanned readmission to improve the quality of care and reduce costs. METHODS The National Readmission Database provides a nationally representative sample of all discharges from US hospitals and allows follow-up across hospitals up to 1 calendar year. Adolescents (age 10-18 years) who underwent idiopathic scoliosis surgery from 2012 to 2015 were included. Patients were separated into those with and those without readmission within 30 days or between 31 and 90 days. Demographics, operative conditions, hospital factors, and surgical outcomes were compared using the chi-square test and t-test. Independent predictors for readmissions were identified using stepwise multivariate logistic regression. RESULTS A total of 30,677 patients underwent adolescent idiopathic scoliosis surgery from 2012 to 2015. The rates of 30- and 90-day readmissions were 2.9% and 1.4%, respectively. The mean costs associated with the index admission and 30- and 90-day readmissions were $60,680, $23,567, and $16,916, respectively. Common risk factors for readmissions included length of stay > 5 days, obesity, neurological disorders, and chronic use of antiplatelets or anticoagulants. The index admission complications associated with readmissions were unintended durotomy, syndrome of inappropriate antidiuretic hormone, and superior mesenteric artery syndrome. Hospital factors, discharge disposition, and operative conditions appeared to be less important for readmission risk. The top reasons for 30-day and 90-day readmissions were wound infection (34.7%) and implant complications (17.3%), respectively. Readmissions requiring a reoperation were significantly higher for those that occurred between 31 and 90 days after the index readmission. CONCLUSIONS Readmission rates were low for both 30- and 90-day readmissions for adolescent idiopathic scoliosis surgery patients. Nevertheless, readmissions are costly and appear to be associated with potentially modifiable risk factors, although some risk factors remain potentially unavoidable.
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Khan S, Kaltenmeier C, Hrebinko K, Nassour I, Hoehn RS, Medich DS, Zureikat A, Tohme S. Readmission After Surgical Resection for Colon and Rectal Cancers: A Retrospective Cohort Study. Am Surg 2021; 88:1118-1130. [PMID: 33517684 DOI: 10.1177/0003134820988810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Colorectal adenocarcinoma is a leading cause of cancer mortality worldwide, often requiring patients to undergo anatomy-altering surgical interventions leading to increased postoperative readmission. Hospital readmission rates have been correlated with increased mortality. Therefore, it is important to understand the association between 30-day readmission rates and mortality as well as the factors associated with increased readmission rates. STUDY DESIGN This is a retrospective review utilizing data from the National Cancer Database. Our primary outcomes of interest were 30- and 90-day mortality rates. Our primary independent variable of interest was 30-day readmission. RESULTS Between 2010 and 2016, 207 299 patients underwent surgery for rectal cancer and 754 895 for colon cancer. The readmission rates within 30 days of discharge were 5.4% and 5.5% for patients after surgery for rectal or colon cancer, respectively. 30-day readmission was not associated with 30-day mortality, but it was independently associated with increased 90-day mortality and inferior long-term survival for both cohorts (P = .001). Independent risk factors significantly associated with increased readmission included race, non-private insurance, and low income. CONCLUSION This study provides a large, up-to-date, and comprehensive analysis of readmission rates for colon and rectal cancers. We demonstrate that socioeconomic factors are associated with increased 30-day readmission. 30-day readmission is also independently associated with increased 90-day mortality as well as lower overall survival rates. Our study supports the need for implementation of programs that support patients of lower socioeconomic status undergoing surgery to further decrease readmission rates and mortality.
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Affiliation(s)
- Sidrah Khan
- Department of Surgery, 6614University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Katherine Hrebinko
- Department of Surgery, 6614University of Pittsburgh, Pittsburgh, PA, USA
| | - Ibrahim Nassour
- Department of Surgery, 6614University of Pittsburgh, Pittsburgh, PA, USA
| | - Richard S Hoehn
- Department of Surgery, 6614University of Pittsburgh, Pittsburgh, PA, USA
| | - David S Medich
- Department of Surgery, 6614University of Pittsburgh, Pittsburgh, PA, USA
| | - Amer Zureikat
- Department of Surgery, 6614University of Pittsburgh, Pittsburgh, PA, USA
| | - Samer Tohme
- Department of Surgery, 6614University of Pittsburgh, Pittsburgh, PA, USA
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Schipmann S, Suero Molina E, Windheuser J, Doods J, Schwake M, Wilbers E, Alsofy SZ, Warneke N, Stummer W. The 30-day readmission rate in neurosurgery-a useful indicator for quality assessment? Acta Neurochir (Wien) 2020; 162:2659-2669. [PMID: 32495079 DOI: 10.1007/s00701-020-04382-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Accepted: 04/29/2020] [Indexed: 12/18/2022]
Abstract
BACKGROUND A shift in how we evaluate healthcare outcomes has driven the introduction of quality indicators as potential parameters to evaluate value-based healthcare delivery. So far, only few studies have been performed evaluating quality indicators in the context of neurosurgery, especially in the European region. The purpose of this study was to evaluate the 30-day readmission rate, identify reasons for readmission regarding the various neurosurgical diagnoses, and discuss the usefulness of this rate as a potential quality indicator. METHODS During a 6-year period, a total of 8878 hospitalized patients in our neurosurgical department were retrospectively analyzed and included in this study. Reasons for readmission were identified. Patients' diagnoses and baseline characteristics were obtained in order to identify possible risk factors for readmission. RESULTS The 30-day readmission rate was 2.9%. The most common reason for unplanned readmissions were surgical site infections. The reasons for readmissions varied significantly between the different underlying neurosurgical diseases (p < 0.001). Multivariate logistic regression revealed hydrocephalus (OR, 4) and shorter length of stay during index admission (OR, 0.9) as risk factors for readmission. CONCLUSIONS We provided an analysis of reasons for readmission for various neurosurgical diseases in a large patient spectrum in Germany. Although readmission rates are easy to track and an attractive tool for quality assessment, the rate alone cannot be seen as an adequate measure for quality in neurosurgery as it lacks a homogenous definition and depends on the underlying health care system. In addition, strategies for risk adjustment are required.
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Affiliation(s)
- Stephanie Schipmann
- Department of Neurosurgery, University Hospital Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany.
| | - Eric Suero Molina
- Department of Neurosurgery, University Hospital Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
| | - Julia Windheuser
- Department of Neurosurgery, University Hospital Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
| | - Justin Doods
- Institute of Medical Informatics, University Hospital Münster, Münster, Germany
| | - Michael Schwake
- Department of Neurosurgery, University Hospital Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
| | - Eike Wilbers
- Department of Neurosurgery, St. Barbara-Hospital, Academic Hospital of Westphalian Wilhelm-University Münster, Hamm, Germany
| | - Samer Zawy Alsofy
- Department of Neurosurgery, St. Barbara-Hospital, Academic Hospital of Westphalian Wilhelm-University Münster, Hamm, Germany
- Department of Medicine, Faculty of Health, Witten/Herdecke University, Witten, Germany
| | - Nils Warneke
- Department of Neurosurgery, University Hospital Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
| | - Walter Stummer
- Department of Neurosurgery, University Hospital Münster, Albert-Schweitzer-Campus 1, 48149, Münster, Germany
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Wu S, Cui X, Zhang S, Tian W, Liu J, Wu Y, Wu M, Han Y. Economic burden of readmission due to postoperative cerebrospinal fluid leak in Chinese patients. J Comp Eff Res 2020; 9:1105-1115. [PMID: 33112181 DOI: 10.2217/cer-2020-0067] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Aim: This real-world data study investigated the economic burden and associated factors of readmissions for cerebrospinal fluid leakage (CSFL) post-cranial, transsphenoidal, or spinal index surgeries. Methods: Costs of CSFL readmissions and index hospitalizations during 2014-2018 were collected. Readmission cost was measured as absolute cost and as percentage of index hospitalization cost. Factors associated with readmission cost were explored using generalized linear models. Results: Readmission cost averaged US$2407-6106, 35-94% of index hospitalization cost. Pharmacy costs were the leading contributor. Generalized linear models showed transsphenoidal index surgery and surgical treatment for CSFL were associated with higher readmission costs. Conclusion: CSFL readmissions are a significant economic burden in China. Factors associated with higher readmission cost should be monitored.
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Affiliation(s)
| | - Xin Cui
- Shanghai Information Center for Health, Shanghai, PR China
| | - Shaoyu Zhang
- Shanghai Information Center for Health, Shanghai, PR China
| | - Wenqi Tian
- Shanghai Information Center for Health, Shanghai, PR China
| | - Jiazhen Liu
- Shanghai Information Center for Health, Shanghai, PR China
| | - Yiqing Wu
- Johnson & Johnson Medical Shanghai, Shanghai, PR China
| | - Man Wu
- Johnson & Johnson Medical Shanghai, Shanghai, PR China
| | - Yi Han
- Health Economics Research Institute, Sun Yat-Sen University, Guangzhou, Guangdong Province, PR China
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Geniş B, Coşar B, Arikan Z. Readmission Rates and Causes Within The First Six Months After Discharge in Patients with Alcohol Addiction. ACTA ACUST UNITED AC 2020; 58:57-62. [PMID: 33795954 DOI: 10.29399/npa.25077] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Accepted: 07/02/2020] [Indexed: 11/07/2022]
Abstract
Introduction Readmission rate is an important criterion that evaluates the quality of treatment and care. In this study, it was aimed to determine the rates of readmission and variables predicting readmission in patients with alcohol addiction. Methods The study sample consisted of 264 alcohol addiction patients with recurrent admissions between 2005-2017 at the Gazi University Hospital Alcohol and Drug Addiction Clinic. In the study, ICD-10 diagnostic classification was used. The differences between the medical comorbidity and psychiatric comorbidity of the patients during the first and second admissions were analyzed. Results The average age of the study sample was 51.45±12.04 and 89% (n=235) were male. In the second admission, the comorbid headaches (p=0.001), psychotic symptoms (p=0.013), anxiety disorder (p=0.003) and substance addiction (p=0.027) were significantly higher, and the length of hospital stay was shorter. In the first six months, 24.2% (n=64) of the sample was hospitalized again. While the comorbidity of anxiety disorder increased the risk of readmission within six months 2.2-fold (OR=2.240; p=0.031), the short duration of hospitalization (less than 35 days) increased the risk of readmission 2-fold (OR=0.492; p=0.026). Discussion Patients with a short hospital stay have an increased risk of readmission within the first 6 months after discharge. Policies that reduce the length of hospital stay in health services should be reviewed. However, it is noteworthy that in the second admission of patients with alcohol dependence, the diagnosis of drug addiction is added. To prevent this, issues related to substance abuse prevention should be addressed during the treatment stages of alcohol dependence.
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Affiliation(s)
- Bahadır Geniş
- Çaycuma State Hospital, Department of Psychiatry, Zonguldak, Turkey
| | - Behçet Coşar
- Gazi Univesity, Faculty of Medicine, Department of Psychiatry, Ankara, Turkey
| | - Zehra Arikan
- Gazi Univesity, Faculty of Medicine, Department of Psychiatry, Ankara, Turkey
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Li CY, Karmarkar A, Lin YL, Kuo YF, Ottenbacher KJ. Hospital Readmissions Reduction Program and Post-Acute Care: Implications for Service Delivery and 30-Day Hospital Readmission. J Am Med Dir Assoc 2020; 21:1504-1508.e1. [PMID: 32660855 PMCID: PMC7529906 DOI: 10.1016/j.jamda.2020.05.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Revised: 05/10/2020] [Accepted: 05/13/2020] [Indexed: 11/20/2022]
Abstract
OBJECTIVES Examine whether the introduction of the Hospital Readmissions Reduction Program (HRRP) is associated with changes in post-acute care (PAC) use and 30-day readmission. DESIGN A retrospective cohort study examined data prepassage, preimplementation, and postimplementation of the HRRP. SETTING AND PARTICIPANTS In total, 7,851,430 Medicare beneficiaries discharged from 5116 acute hospitals to PAC settings including inpatient rehabilitation, skilled nursing, home health, or a long-term care hospital during 2007‒2015. We examined HRRP-targeted conditions (acute myocardial infarction, heart failure, and pneumonia) and nontargeted conditions (ischemic stroke, total hip arthroplasty/total knee arthroplasty, and hip/femur fractures). MEASURES The hospital-level of quarterly PAC use and the association with 30-day risk-standardized readmission rates. Outcomes were calculated for HRRP-targeted and nontargeted conditions/diagnoses across 3 phases of HRRP implementation. RESULTS An increase in quarterly PAC use was significantly (P < .001) associated with a decrease in 30-day risk-standardized readmission rates for acute myocardial infarction, heart failure, and hip/femur fracture. In contrast, an increase in quarterly PAC use was significantly associated with an increase in readmission rate for total hip arthroplasty/total knee arthroplasty (P < 001). PAC quarterly use and readmission rates varied significantly during implementation periods for HRRP- targeted and nontargeted conditions. CONCLUSIONS AND IMPLICATIONS The impact on readmission after PAC for selected impairment groups may be mediated by the type of PAC services received and whether the diagnoses is included in the HRRP. Additional research is necessary to determine if a reduction in readmission is associated with inclusion in the HRRP or is a side effect related to diagnostic group and/or type of PAC services received.
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Affiliation(s)
- Chih-Ying Li
- Department of Occupational Therapy, University of Texas Medical Branch, Galveston, TX.
| | - Amol Karmarkar
- Division of Rehabilitation Sciences, University of Texas Medical Branch, Galveston, TX
| | - Yu-Li Lin
- Department of Preventive Medicine and Community Health, University of Texas Medical Branch, Galveston, TX
| | - Yong-Fang Kuo
- Department of Preventive Medicine and Community Health, University of Texas Medical Branch, Galveston, TX; Sealy Center on Aging, University of Texas Medical Branch, Galveston, TX
| | - Kenneth J Ottenbacher
- Division of Rehabilitation Sciences, University of Texas Medical Branch, Galveston, TX; Sealy Center on Aging, University of Texas Medical Branch, Galveston, TX
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Hoffman GJ, Tilson S, Yakusheva O. The Financial Impact of an Avoided Readmission for Teaching and Safety-Net Hospitals Under Medicare's Hospital Readmission Reduction Program. Med Care Res Rev 2020; 77:324-333. [PMID: 30141374 PMCID: PMC6656617 DOI: 10.1177/1077558718795733] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
We examined the financial incentives to avoid readmissions under Medicare's Hospital Readmission Reduction Program for safety-net hospitals (SNHs) and teaching hospitals (THs) compared with other hospitals. Using Medicare's FY2016 Hospital Compare and readmissions data for 2,465 hospitals, we tested for differential revenue gains for SNHs (n = 658) relative to non-SNHs (n = 1,807), and for major (n = 231) and minor (n = 591) THs relative to non-THs (n = 1,643). We examined hospital-level factors predicting differences in revenue gains by hospital type. The revenue gains of an avoided readmission were 10% to 15% greater for major THs compared with non-THs ($18,047 vs. $15,478 for acute myocardial infarction) but no different for SNHs compared with non-SNHs. The greater revenue gains for THs were strongly positively predicted by hospitals' poor initial readmission performance. We found little evidence that the Hospital Readmission Reduction Program creates disincentives for SNHs and THs to invest in readmission reduction efforts, and THs have greater returns from readmissions avoidance than non-THs.
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Affiliation(s)
| | | | - Olga Yakusheva
- University of Michigan School of Nursing, Ann Arbor, USA
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Nathan AS, Martinez JR, Giri J, Navathe AS. Observational study assessing changes in timing of readmissions around postdischarge day 30 associated with the introduction of the Hospital Readmissions Reduction Program. BMJ Qual Saf 2020; 30:493-499. [PMID: 32694145 DOI: 10.1136/bmjqs-2019-010780] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 05/18/2020] [Accepted: 06/24/2020] [Indexed: 11/04/2022]
Abstract
BACKGROUND The Hospital Readmissions Reduction Program (HRRP) initially penalised hospitals for excess readmission within 30 days of discharge for acute myocardial infarction (AMI), congestive heart failure (CHF) or pneumonia (PNA) and was expanded in subsequent years to include readmissions for chronic obstructive pulmonary disease, elective total hip arthroplasty, total knee arthroplasty and coronary artery bypass graft surgery. We assessed whether HRRP was associated with delays in readmissions from immediately before the 30-day penalty threshold to just after it. METHODS We included Medicare fee-for-service beneficiaries discharged between 1 January 2007 and 31 October 2015. Readmissions were assessed until December 31, 2015. The study period was divided into three phases: January 2007 to March 2009 (pre-HRRP), April 2009 to September 2012 (implementation) and October 2012 to December 2015 (penalty). We estimated additional readmissions between postdischarge days 31-35 compared with days 26-30 using a negative binomial difference-in-differences model, comparing target HRRP versus non-HRRP conditions at the same hospital in the same month in the pre-HRRP and penalty phases. RESULTS HRRP was not associated with a significant difference in AMI readmissions between postdischarge days 31-35 versus postdischarge days 26-30 for each hospital in the penalty phase, as compared with non-HRRP conditions and the pre-HRRP phase (p=0.19). There were statistically significant increases in readmissions CHF (0.040%, 95% CI 0.024% to 0.056%, p<0.01), PNA (0.022%, 95% CI 0.002% to 0.042%, p=0.03) and stroke (0.035%, 95% CI 0.010% to 0.060%, p<0.01); however, these readmissions represent <0.01% of readmissions during this time period. CONCLUSION We did not identify consistently significant associations between HRRP and delayed readmissions, and importantly, any findings suggesting delayed readmissions were extremely small and unlikely to be clinically relevant.
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Affiliation(s)
- Ashwin S Nathan
- Division of Cardiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA .,Cardiovascular Quality, Outcomes and Evaluative Research Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Joseph R Martinez
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jay Giri
- Division of Cardiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Cardiovascular Quality, Outcomes and Evaluative Research Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Corporal Michael J. Cresencz VA Medical Center, Philadelphia, Pennsylvania, USA
| | - Amol S Navathe
- Corporal Michael J. Cresencz VA Medical Center, Philadelphia, Pennsylvania, USA.,Department of Medical Ethics and Health Policy, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Singh S, Eguchi M, Min SJ, Fischer S. Outcomes of Patients With Cancer Discharged to a Skilled Nursing Facility After Acute Care Hospitalization. J Natl Compr Canc Netw 2020; 18:856-865. [PMID: 32634778 PMCID: PMC8370039 DOI: 10.6004/jnccn.2020.7534] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Accepted: 01/10/2020] [Indexed: 11/17/2022]
Abstract
BACKGROUND After discharge from an acute care hospitalization, patients with cancer may choose to pursue rehabilitative care in a skilled nursing facility (SNF). The objective of this study was to examine receipt of anticancer therapy, death, readmission, and hospice use among patients with cancer who discharge to an SNF compared with those who are functionally able to discharge to home or home with home healthcare in the 6 months after an acute care hospitalization. METHODS A population-based cohort study was conducted using the SEER-Medicare database of patients with stage II-IV colorectal, pancreatic, bladder, or lung cancer who had an acute care hospitalization between 2010 and 2013. A total of 58,770 cases were identified and patient groups of interest were compared descriptively using means and standard deviations for continuous variables and frequencies and percentages for categorical variables. Logistic regression was used to compare patient groups, adjusting for covariates. RESULTS Of patients discharged to an SNF, 21%, 17%, and 2% went on to receive chemotherapy, radiotherapy, and targeted chemotherapy, respectively, compared with 54%, 28%, and 6%, respectively, among patients discharged home. Fifty-six percent of patients discharged to an SNF died within 6 months of their hospitalization compared with 36% discharged home. Thirty-day readmission rates were 29% and 28% for patients discharged to an SNF and home, respectively, and 12% of patients in hospice received <3 days of hospice care before death regardless of their discharge location. CONCLUSIONS Patients with cancer who discharge to an SNF are significantly less likely to receive subsequent oncologic treatment of any kind and have higher mortality compared with patients who discharge to home after an acute care hospitalization. Further research is needed to understand and address patient goals of care before discharge to an SNF.
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Affiliation(s)
- Sarguni Singh
- 1Division of Hospital Medicine, University of Colorado Denver
| | | | | | - Stacy Fischer
- 4Division of General Internal Medicine, University of Colorado Denver, Aurora, Colorado
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Hoffman GJ, Yakusheva O. Association Between Financial Incentives in Medicare's Hospital Readmissions Reduction Program and Hospital Readmission Performance. JAMA Netw Open 2020; 3:e202044. [PMID: 32242906 PMCID: PMC7125432 DOI: 10.1001/jamanetworkopen.2020.2044] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
IMPORTANCE The strongest evidence for the effectiveness of Medicare's Hospital Readmissions Reduction Program (HRRP) involves greater reductions in readmissions for hospitals receiving penalties compared with those not receiving penalties. However, the HRRP penalty is an imperfect measure of hospitals' marginal incentive to avoid a readmission for HRRP-targeted diagnoses. OBJECTIVES To assess the association between hospitals' condition-specific incentives and readmission performance and to examine the responsiveness of hospitals to condition-specific incentives compared with aggregate penalty amounts. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort analysis used Medicare readmissions data from 2823 US short-term acute care hospitals participating in HRRP to compare 3-year (fiscal years 2016-2019) follow-up readmission performance according to tertiles of hospitals' baseline (2016) marginal incentives for each of 5 HRRP-targeted conditions (acute myocardial infarction, heart failure, chronic obstructive pulmonary disease, pneumonia, and hip and/or knee surgery). MAIN OUTCOMES AND MEASURES Linear regression models were used to estimate mean change in follow-up readmission performance, measured using the excess readmissions ratio, with baseline condition-specific incentives and aggregate penalty amounts. RESULTS Of 2823 hospitals that participated in the HRRP from baseline to follow-up, 2280 (81%) had more than 1 excess readmission for 1 or more applicable condition and 543 (19%) did not have any excess readmissions. The mean (SD) financial incentive to reduce readmissions for incentivized hospitals ranged from $8762 ($3699) to $58 158 ($26 198) per 1 avoided readmission. Hospitals with greater incentives for readmission avoidance had greater decreases in readmissions compared with hospitals with smaller incentives (45% greater for pneumonia, 172% greater for acute myocardial infarction, 40% greater for hip and/or knee surgery, 32% greater for chronic obstructive pulmonary disease, and 13% greater for heart failure), whereas hospitals with no incentives had increases in excess readmissions of 4% to 7% (median, 4% [percentage change for nonincentivized hospitals was 3.7% for pneumonia, 4.2% for acute myocardial infarction, 7.1% for hip and/or knee surgery, 3.7% for chronic obstructive pulmonary disease, and 3.7% for heart failure]; P < .001). During the study period, each additional $5000 in the incentive amount was associated with a 0.6- to 1.3-percentage point decrease, or up to a 26% decrease, in excess readmissions (P < .001). Regression to the mean explained approximately one-third of the results depending on the condition examined. CONCLUSIONS AND RELEVANCE The findings suggest that improvements in readmission avoidance are more strongly associated with incentives from the HRRP than with aggregate penalty amounts, suggesting that the program has elicited sizeable changes. Worsened performance among hospitals with small or no incentives may indicate the need for reconsideration of the program's lack of financial rewards for high-performing hospitals.
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MESH Headings
- Acute Disease
- Arthroplasty, Replacement, Hip/economics
- Arthroplasty, Replacement, Hip/statistics & numerical data
- Arthroplasty, Replacement, Knee/economics
- Arthroplasty, Replacement, Knee/statistics & numerical data
- Economics, Hospital/statistics & numerical data
- Heart Failure/economics
- Heart Failure/epidemiology
- Hospitals/statistics & numerical data
- Humans
- Medicare/economics
- Motivation/ethics
- Myocardial Infarction/economics
- Myocardial Infarction/epidemiology
- Patient Readmission/economics
- Patient Readmission/trends
- Pneumonia/economics
- Pneumonia/epidemiology
- Pulmonary Disease, Chronic Obstructive/economics
- Pulmonary Disease, Chronic Obstructive/epidemiology
- Retrospective Studies
- United States/epidemiology
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Affiliation(s)
- Geoffrey J. Hoffman
- Department of Systems, Populations and Leadership, University of Michigan School of Nursing, Ann Arbor
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor
| | - Olga Yakusheva
- Department of Systems, Populations and Leadership, University of Michigan School of Nursing, Ann Arbor
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor
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Shaaban AN, Dias SS, Muggli Z, Peleteiro B, Martins MRO. Risk of Readmission Among HIV Patients in Public Portuguese Hospitals: Longitudinal Multilevel Population-Based Study. Front Public Health 2020; 8:15. [PMID: 32154201 PMCID: PMC7049668 DOI: 10.3389/fpubh.2020.00015] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Accepted: 01/17/2020] [Indexed: 02/06/2023] Open
Abstract
Background: Thirty-day hospital readmission is receiving growing attention as an indicator of the quality of hospital care. Understanding factors associated with 30-day hospital readmission among HIV patients in Portugal is essential given the high burden cost of HIV hospitalizations in Portugal, a country suffering from financial constrains for almost 10 years. Objectives: We aimed to estimate the 30-day hospital readmission rates among HIV patients in Portugal and to identify its determinants using population-based data for Portuguese public hospitals. Study Design: A multilevel longitudinal population-based study. Methods: Between January 2009 and December 2014, a total of 37,134 registered discharges in the Portuguese National Health Service (NHS) facilities with HIV/AIDS as a main or secondary cause of admission were analyzed. Logistic regression was used to compare 30-day hospital readmission categories by computing odds ratio (OR) and corresponding 95% confidence intervals (95% CIs). A normal random effects model was used to determine unmeasured factors specific to each hospital. Results: A total of 4914 (13.2%, 95% CI: 12.9%-13.6%) hospitalizations had a subsequent 30-day readmission. Hospitalizations that included exit against medical opinion (OR = 1.18, 95% CI: 1.01-1.39), scheduled admissions (OR = 1.71, 95% CI: 1.58-1.85), and tuberculosis infection (OR = 1.20, 95% CI: 1.05-1.38) exhibited a higher risk of hospitalizations with subsequent 30-day readmission. In contrast, hospitalizations that included females (OR = 0.87, 95% CI: 0.81-0.94), a transfer to another facility (OR = 0.78, 95% CI: 0.67-0.91), and having a responsible financial institution (OR = 0.63, 95% CI: 0.55-0.72) exhibited a lower risk of hospitalizations with subsequent 30-day readmission. Hospitalizations associated with higher number of diagnosis, older ages, or hospitalizations during the economic crisis showed an increasing trend of 30-day readmission, whereas an opposite trend was observed for hospitalizations with higher number of procedures. Significant differences exist between hospital quality, adjusting for other factors. Conclusion: This study analyzes the indicators of 30-day hospital readmission among HIV patients in Portugal and provides useful information for enlightening policymakers and health care providers for developing health policies that can reduce costs associated with HIV hospitalizations.
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Affiliation(s)
- Ahmed N. Shaaban
- Global Health and Tropical Medicine, Institute of Hygiene and Tropical Medicine, NOVA University of Lisboa, Lisbon, Portugal
| | - Sara S. Dias
- EpiDoC Unit – CEDOC, NOVA Medical School – Universidade Nova de Lisboa (NMS-UNL), Lisbon, Portugal
- ciTechCare, Escola Superior de Saúde De Leiria (ESSLei), Instituto Politécnico de Leiria (IPLeiria), Leiria, Portugal
| | - Zelia Muggli
- Global Health and Tropical Medicine, Institute of Hygiene and Tropical Medicine, NOVA University of Lisboa, Lisbon, Portugal
| | - Bárbara Peleteiro
- EPIUnit - Instituto de Saúde Pública, Universidade do Porto, Porto, Portugal
- Departamento de Ciências da Saúde Pública e Forenses e Educação Médica, Faculdade de Medicina da Universidade do Porto, Porto, Portugal
| | - Maria Rosario O. Martins
- Global Health and Tropical Medicine, Institute of Hygiene and Tropical Medicine, NOVA University of Lisboa, Lisbon, Portugal
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Papadopoulos A, Stark RB. Does Home Health Care Increase the Probability of 30-Day Hospital Readmissions? Interpreting Coefficient Sign Reversals, or Their Absence, in Binary Logistic Regression Analysis. AM STAT 2020. [DOI: 10.1080/00031305.2019.1704873] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Alecos Papadopoulos
- Department of Economics, Athens University of Economics and Business, Athens, Greece
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Shahian DM, Liu X, Mort EA, Normand SLT. The association of hospital teaching intensity with 30-day postdischarge heart failure readmission and mortality rates. Health Serv Res 2020; 55:259-272. [PMID: 31916243 DOI: 10.1111/1475-6773.13248] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVE To investigate risk-adjusted, 30-day postdischarge heart failure mortality and readmission rates stratified by hospital teaching intensity. DATA SOURCES AND STUDY SETTING A total of 709 221 Medicare fee-for-service beneficiaries discharged from 3135 US hospitals between 1/1/2013 and 11/30/2014 with a principal diagnosis of heart failure. STUDY DESIGN Hospitals were classified as Council of Teaching Hospitals and Health Systems (COTH) major teaching hospitals, non-COTH teaching hospitals, and nonteaching hospitals. Hospital teaching status was linked with MedPAR patient data and FY2016 Hospital Readmission Reduction Program penalties. Index hospitalization survival probabilities were estimated with hierarchical logistic regression and used to stratify index hospitalization survivors into severity deciles. Decile-specific models were estimated for 30-day postdischarge readmission and mortality. Thirty-day postdischarge outcomes were estimated by teaching intensity and penalty categories. PRINCIPAL FINDINGS Averaged across deciles, adjusted 30-day COTH hospital readmission rates were, on a relative scale ([COTH minus nonteaching] ÷ nonteaching), 1.63 percent higher (95% CI: 0.89 percent, 2.25 percent) than at nonteaching hospitals, but their average adjusted 30-day postdischarge mortality rates were 11.55 percent lower (95% CI: -13.78 percent, -9.37 percent). Penalized COTH hospitals had the highest readmission rates of all categories (23.99 percent [95% CI: 23.50 percent, 24.49 percent]) but the lowest 30-day postdischarge mortality (8.30 percent [95% CI: 7.99 percent, 8.57 percent] vs 9.84 percent [95% CI: 9.69 percent, 9.99 percent] for nonpenalized, nonteaching hospitals). CONCLUSIONS Heart failure readmission penalties disproportionately impact major teaching hospitals and inadequately credit their better postdischarge survival.
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Affiliation(s)
- David M Shahian
- Center for Quality and Safety, Division of Cardiac Surgery, Department of Surgery, Massachusetts General Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Xiu Liu
- Center for Quality and Safety, Massachusetts General Hospital, Boston, Massachusetts
| | - Elizabeth A Mort
- Harvard Medical School, Boston, Massachusetts.,Department of Medicine, Center for Quality and Safety, Massachusetts General Hospital, Boston, Massachusetts
| | - Sharon-Lise T Normand
- Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts.,Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
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Hugar LA, Borza T, Oerline MK, Hollenbeck BK, Skolarus TA, Jacobs BL. Resurrecting immortal-time bias in the study of readmissions. Health Serv Res 2019; 55:273-276. [PMID: 31880314 DOI: 10.1111/1475-6773.13252] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
OBJECTIVE To compare readmission rates as measured by the Centers for Medicare and Medicaid Services and the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) methods. DATA SOURCES 20 percent sample of national Medicare data for patients undergoing cystectomy, colectomy, abdominal aortic aneurysm (AAA) repair, and total knee arthroplasty (TKA) between 2010 and 2014. STUDY DESIGN Retrospective cohort study comparing 30-day readmission rates. DATA COLLECTION/EXTRACTION METHODS Patients undergoing cystectomy, colectomy, abdominal aortic aneurysm repair, and total knee arthroplasty between 2010 and 2014 were identified. PRINCIPAL FINDINGS Cystectomy had the highest and total knee arthroplasty had the lowest readmission rate. The NSQIP measure reported significantly lower rates for all procedures compared to the CMS measure, which reflects an immortal-time bias. CONCLUSIONS We found significantly different readmission rates across all surgical procedures when comparing CMS and NSQIP measures. Longer length of stay exacerbated these differences. Uniform outcome measures are needed to eliminate ambiguity and synergize research and policy efforts.
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Affiliation(s)
- Lee A Hugar
- Department of Urology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Tudor Borza
- Dow Health Services Research Division, Department of Urology, University of Michigan, Ann Arbor, Michigan
| | - Mary K Oerline
- Dow Health Services Research Division, Department of Urology, University of Michigan, Ann Arbor, Michigan
| | - Brent K Hollenbeck
- Dow Health Services Research Division, Department of Urology, University of Michigan, Ann Arbor, Michigan
| | - Ted A Skolarus
- Dow Health Services Research Division, Department of Urology, University of Michigan, Ann Arbor, Michigan.,Center for Clinical Management Research, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan
| | - Bruce L Jacobs
- Department of Urology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.,School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
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Doyle J, Graves J, Gruber J. Evaluating Measures of Hospital Quality:Evidence from Ambulance Referral Patterns. THE REVIEW OF ECONOMICS AND STATISTICS 2019; 101:841-852. [PMID: 32601511 PMCID: PMC7323928 DOI: 10.1162/rest_a_00804] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Hospital quality measures are crucial to a key idea behind health care payment reforms: "paying for quality" instead of quantity. Nevertheless, such measures face major criticisms largely over the potential failure of risk adjustment to overcome endogeneity concerns when ranking hospitals. In this paper we test whether patients treated at hospitals that score higher on commonly-used quality measures have better health outcomes in terms of rehospitalization and mortality. To compare similar patients across hospitals in the same market, we exploit ambulance company preferences as an instrument for hospital choice. We find that a variety of measures used by insurers to measure provider quality are successful: choosing a high-quality hospital compared to a low-quality hospital results in 10-15% better outcomes.
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Thirty-day Emergency Department Utilization after Distal Radius Fracture Treatment: Identifying Predictors and Variation. PLASTIC AND RECONSTRUCTIVE SURGERY-GLOBAL OPEN 2019; 7:e2416. [PMID: 31741813 PMCID: PMC6799403 DOI: 10.1097/gox.0000000000002416] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 05/29/2019] [Indexed: 11/26/2022]
Abstract
Supplemental Digital Content is available in the text. Unplanned hospital visits are costly and may indicate reduced care quality. In this analysis, we aim to investigate the emergency department (ED) utilization for patients 30 days after treatment for a distal radius fracture (DRF) with an emphasis on DRF-related diagnoses of complications and examine nationwide variation in returns to the ED after treatment.
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Lin KP, Chen JH, Lu FP, Wen CJ, Chan DCD. The impact of early comprehensive geriatric screening on the readmission rate in an acute geriatric ward: a quasi-experimental study. BMC Geriatr 2019; 19:285. [PMID: 31651249 PMCID: PMC6813968 DOI: 10.1186/s12877-019-1312-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 10/10/2019] [Indexed: 12/11/2022] Open
Abstract
Background Unplanned readmission is an important healthcare quality issue. We studied the effect of a comprehensive geriatric screen (CGS) in the early admission course followed by a comprehensive geriatric assessment on readmission rates in elderly patients. Methods This quasi-experimental study with a historical comparison group was conducted in the geriatric ward of a referral centre in northern Taiwan. Older adults (aged > = 65 y/o) admitted from June 2013 to December 2013 were recruited for the geriatric screen group (N = 377). Patients admitted to the same ward from July 2011 to June 2012 were selected for the historical group (N = 380). The CGS was administered within the first 48 h after admission and was followed by a comprehensive geriatric assessment (CGA). Confounding risk factors included age, gender, Charlson comorbidity index, Barthel index score and medical utilization (length of stay and number of admissions), which were controlled using logistic regression models. We also developed a scoring system to identify the group that would potentially benefit the most from the early CGS. Results The 30-day readmission rate was significantly lower in the early CGS group than in the historical comparison group (11.4% vs 16.9%, p = 0.03). After adjusting for confounding variables, the hazard ratio of the early CGS group was 0.64 (95% CI 0.43–0.95). After scoring the potential benefit to the patients in the early CGS group, the log rank test showed a significant difference (p = 0.001 in the high-potential group and p = 0.98 in the low-potential group). Conclusion An early CGS followed by a CGA may significantly reduce the 30-day readmission rate of elderly patients.
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Affiliation(s)
- Kun-Pei Lin
- Department of Geriatrics and Gerontology, National Taiwan University Hospital, Taipei, Taiwan.,Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Jen-Hau Chen
- Department of Geriatrics and Gerontology, National Taiwan University Hospital, Taipei, Taiwan.,Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Feng-Ping Lu
- Department of Geriatrics and Gerontology, National Taiwan University Hospital, Taipei, Taiwan.,Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Chiung-Jung Wen
- Department of Geriatrics and Gerontology, National Taiwan University Hospital, Taipei, Taiwan.,Department of Family Medicine, National Taiwan University Hospital, Taipei, Taiwan
| | - Ding-Cheng Derrick Chan
- Department of Geriatrics and Gerontology, National Taiwan University Hospital, Taipei, Taiwan. .,Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan. .,National Taiwan University Hospital Chu-Tung Branch, No. 1, Changde St., Taipei, 100, Taiwan.
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41
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Gruneir A, Fung K, Fischer HD, Bronskill SE, Panjwani D, Bell CM, Dhalla I, Rochon PA, Anderson G. Care setting and 30-day hospital readmissions among older adults: a population-based cohort study. CMAJ 2019; 190:E1124-E1133. [PMID: 30249758 DOI: 10.1503/cmaj.180290] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2018] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Despite the fact that many older adults receive home or long-term care services, the effect of these care settings on hospital readmission is often overlooked. Efforts to reduce hospital readmissions, including capacity planning and targeting of interventions, require clear data on the frequency of and risk factors for readmission among different populations of older adults. METHODS We identified all adults older than 65 years discharged from an unplanned medical hospital stay in Ontario between April 2008 and December 2015. We defined 2 preadmission care settings (community, long-term care) and 3 discharge care settings (community, home care, long-term care) and used multinomial regression to estimate associations with 30-day readmission (and death as a competing risk). RESULTS We identified 701 527 individuals (mean age 78.4 yr), of whom 414 302 (59.1%) started in and returned to the community. Overall, 88 305 in dividuals (12.6%) were re admitted within 30 days, but this proportion varied by care setting combination. Relative to individuals returning to the community, those discharged to the community with home care (adjusted odds ratio [OR] 1.43, 95% confidence interval [CI] 1.39-1.46) and those returning to long-term care (adjusted OR 1.35, 95% CI 1.27-1.43) had a greater risk of readmission, whereas those newly admitted to long-term care had a lower risk of readmission (adjusted OR 0.68, 95% CI 0.63-0.72). INTERPRETATION In Ontario, about 40% of older people were discharged from hospital to either home care or long-term care. These discharge settings, as well as whether an individual was admitted to hospital from long-term care, have important implications for understanding 30-day readmission rates. System planning and efforts to reduce readmission among older adults should take into account care settings at both admission and discharge.
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Affiliation(s)
- Andrea Gruneir
- Department of Family Medicine (Gruneir), University of Alberta, Edmonton, Alta.; ICES (Gruneir, Fung, Fischer, Bronskill, Bell, Rochon, Anderson); Women's College Research Institute (Gruneir, Bronskill, Panjwani, Rochon), Women's College Hospital; Institute of Health Policy, Management and Evaluation (Bronskill, Bell, Dhalla, Rochon, Anderson) and Department of Medicine (Dhalla), University of Toronto; Department of Medicine (Bell, Dhalla), Li Ka Shing Knowledge Institute, St. Michael's Hospital; Health Quality Ontario (Dhalla), Toronto, Ont.
| | - Kinwah Fung
- Department of Family Medicine (Gruneir), University of Alberta, Edmonton, Alta.; ICES (Gruneir, Fung, Fischer, Bronskill, Bell, Rochon, Anderson); Women's College Research Institute (Gruneir, Bronskill, Panjwani, Rochon), Women's College Hospital; Institute of Health Policy, Management and Evaluation (Bronskill, Bell, Dhalla, Rochon, Anderson) and Department of Medicine (Dhalla), University of Toronto; Department of Medicine (Bell, Dhalla), Li Ka Shing Knowledge Institute, St. Michael's Hospital; Health Quality Ontario (Dhalla), Toronto, Ont
| | - Hadas D Fischer
- Department of Family Medicine (Gruneir), University of Alberta, Edmonton, Alta.; ICES (Gruneir, Fung, Fischer, Bronskill, Bell, Rochon, Anderson); Women's College Research Institute (Gruneir, Bronskill, Panjwani, Rochon), Women's College Hospital; Institute of Health Policy, Management and Evaluation (Bronskill, Bell, Dhalla, Rochon, Anderson) and Department of Medicine (Dhalla), University of Toronto; Department of Medicine (Bell, Dhalla), Li Ka Shing Knowledge Institute, St. Michael's Hospital; Health Quality Ontario (Dhalla), Toronto, Ont
| | - Susan E Bronskill
- Department of Family Medicine (Gruneir), University of Alberta, Edmonton, Alta.; ICES (Gruneir, Fung, Fischer, Bronskill, Bell, Rochon, Anderson); Women's College Research Institute (Gruneir, Bronskill, Panjwani, Rochon), Women's College Hospital; Institute of Health Policy, Management and Evaluation (Bronskill, Bell, Dhalla, Rochon, Anderson) and Department of Medicine (Dhalla), University of Toronto; Department of Medicine (Bell, Dhalla), Li Ka Shing Knowledge Institute, St. Michael's Hospital; Health Quality Ontario (Dhalla), Toronto, Ont
| | - Dilzayn Panjwani
- Department of Family Medicine (Gruneir), University of Alberta, Edmonton, Alta.; ICES (Gruneir, Fung, Fischer, Bronskill, Bell, Rochon, Anderson); Women's College Research Institute (Gruneir, Bronskill, Panjwani, Rochon), Women's College Hospital; Institute of Health Policy, Management and Evaluation (Bronskill, Bell, Dhalla, Rochon, Anderson) and Department of Medicine (Dhalla), University of Toronto; Department of Medicine (Bell, Dhalla), Li Ka Shing Knowledge Institute, St. Michael's Hospital; Health Quality Ontario (Dhalla), Toronto, Ont
| | - Chaim M Bell
- Department of Family Medicine (Gruneir), University of Alberta, Edmonton, Alta.; ICES (Gruneir, Fung, Fischer, Bronskill, Bell, Rochon, Anderson); Women's College Research Institute (Gruneir, Bronskill, Panjwani, Rochon), Women's College Hospital; Institute of Health Policy, Management and Evaluation (Bronskill, Bell, Dhalla, Rochon, Anderson) and Department of Medicine (Dhalla), University of Toronto; Department of Medicine (Bell, Dhalla), Li Ka Shing Knowledge Institute, St. Michael's Hospital; Health Quality Ontario (Dhalla), Toronto, Ont
| | - Irfan Dhalla
- Department of Family Medicine (Gruneir), University of Alberta, Edmonton, Alta.; ICES (Gruneir, Fung, Fischer, Bronskill, Bell, Rochon, Anderson); Women's College Research Institute (Gruneir, Bronskill, Panjwani, Rochon), Women's College Hospital; Institute of Health Policy, Management and Evaluation (Bronskill, Bell, Dhalla, Rochon, Anderson) and Department of Medicine (Dhalla), University of Toronto; Department of Medicine (Bell, Dhalla), Li Ka Shing Knowledge Institute, St. Michael's Hospital; Health Quality Ontario (Dhalla), Toronto, Ont
| | - Paula A Rochon
- Department of Family Medicine (Gruneir), University of Alberta, Edmonton, Alta.; ICES (Gruneir, Fung, Fischer, Bronskill, Bell, Rochon, Anderson); Women's College Research Institute (Gruneir, Bronskill, Panjwani, Rochon), Women's College Hospital; Institute of Health Policy, Management and Evaluation (Bronskill, Bell, Dhalla, Rochon, Anderson) and Department of Medicine (Dhalla), University of Toronto; Department of Medicine (Bell, Dhalla), Li Ka Shing Knowledge Institute, St. Michael's Hospital; Health Quality Ontario (Dhalla), Toronto, Ont
| | - Geoff Anderson
- Department of Family Medicine (Gruneir), University of Alberta, Edmonton, Alta.; ICES (Gruneir, Fung, Fischer, Bronskill, Bell, Rochon, Anderson); Women's College Research Institute (Gruneir, Bronskill, Panjwani, Rochon), Women's College Hospital; Institute of Health Policy, Management and Evaluation (Bronskill, Bell, Dhalla, Rochon, Anderson) and Department of Medicine (Dhalla), University of Toronto; Department of Medicine (Bell, Dhalla), Li Ka Shing Knowledge Institute, St. Michael's Hospital; Health Quality Ontario (Dhalla), Toronto, Ont
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Abstract
Surgeons are increasingly under pressure to measure and improve their quality. While there is broad consensus that we ought to track surgical quality, there is far less agreement about which metrics matter most. This article reviews the important statistical concepts of case mix and chance as they apply to understanding the observed wide variation in surgical quality. We then discuss the benefits and drawbacks of current measurement strategies through the framework of structure, process, and outcomes approaches. Finally, we describe emerging new metrics, such as video evaluation and network optimization, that are likely to take on an increasingly important role in the future of measuring surgical quality.
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Affiliation(s)
- Andrew M Ibrahim
- University of Michigan School of Medicine, Ann Arbor, Michigan 48109;
| | - Justin B Dimick
- Center for Healthcare Outcomes and Policy, University of Michigan, Ann Arbor, Michigan 48105;
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Shaaban AN, Martins MRO. The Importance of Improving the Quality of Care Among HIV/AIDS Hospitalizations in Portugal. Front Public Health 2019; 7:266. [PMID: 31572706 PMCID: PMC6753230 DOI: 10.3389/fpubh.2019.00266] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Accepted: 08/30/2019] [Indexed: 02/05/2023] Open
Affiliation(s)
- Ahmed N Shaaban
- Global Health and Tropical Medicine, Institute of Hygiene and Tropical Medicine, NOVA University of Lisbon, Lisbon, Portugal.,EPIUnit-Instituto de Saúde Pública, Universidade Do Porto, Porto, Portugal
| | - Maria Rosario O Martins
- Global Health and Tropical Medicine, Institute of Hygiene and Tropical Medicine, NOVA University of Lisbon, Lisbon, Portugal
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Khoury H, Sanaiha Y, Rudasill SE, Mardock AL, Sareh S, Benharash P. Readmissions Following Isolated Coronary Artery Bypass Graft Surgery in the United States (from the Nationwide Readmissions Database 2010 to 2014). Am J Cardiol 2019; 124:205-210. [PMID: 31104778 DOI: 10.1016/j.amjcard.2019.04.018] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Revised: 04/01/2019] [Accepted: 04/05/2019] [Indexed: 01/23/2023]
Abstract
Readmission following cardiac surgery is associated with poor outcomes and increased healthcare expenditure. However, a nationwide understanding of the incidence, cost, causes, and predictors of 30-day readmission following coronary artery bypass grafting is limited. The Nationwide Readmissions Database was used to identify all adult patients who underwent isolated coronary artery bypass grafting (CABG) with no other concomitant surgery between 2010 and 2014. The primary outcome was all-cause readmission within 30 days of discharge after surgery. Risk-adjusted multivariable analyses were used to develop a model of readmission risk. Of 855,836 patients, 95,504 (11.2%) had an emergent 30-day readmission following CABG. The most common causes of readmission were related to respiratory complications (17.1%), infection (13.5%), and heart failure (11.9%). Readmission cost an average of $13,392 per patient, accounting for an estimated annual cost of over $250 million. Independent predictors of 30-day readmission encompassed female gender (odds ratio [OR] 1.27; 95% confidence interval [CI] 1.24 to 1.31), emergent index admission (OR 1.29; 95% CI 1.25 to 1.33), and preoperative co-morbidities, including atrial fibrillation (OR 1.24; 95% CI 1.21 to 1.28), liver disease (OR 1.29; 95% CI 1.17 to 1.41), renal failure (OR 1.38; 95% CI 1.34 to 1.43), among others. CABG performed at a high CABG volume hospital was protective of readmission (OR 0.95; 95% CI 0.91 to 0.99). In conclusion, we characterized using a national sample the incidence, causes, costs, and predictors of 30-day readmission following CABG. Targeting modifiable risk factors for readmission should be a priority to reduce rates of readmission and decrease healthcare expenditure.
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Mejia de Grubb MC, Salemi JL, Gonzalez SJ, Chima CC, Kowalchuk AA, Zoorob RJ. Opioid, cocaine, and amphetamine use disorders are associated with higher30-day inpatient readmission rates in the United States. Subst Abus 2019; 41:365-374. [PMID: 31295052 DOI: 10.1080/08897077.2019.1635964] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Background: Patients with substance use disorders (SUDs) are more likely to experience serious health problems, high healthcare utilization, and premature death. However, little is known about the contribution of SUDs to medical 30-day readmission risk. We examined the association between SUDs and 30-day all cause readmission among non-pregnant adult in-patients in the US. Methods: We conducted a retrospective study using 2010-2014 data from the Nationwide Readmissions Database. Our primary focus was on opioid use compared to stimulant use (cocaine and amphetamine) identified by ICD-9-CM diagnosis codes in index hospitalizations. Multivariable logistic regression models were used to estimate adjusted odds ratios and 95% CI representing the association between substance use and 30-day readmission, overall and stratified by the principal reason for the index hospitalization. Results: Nearly 118 million index hospitalizations were included in the study, 4% were associated with opioid or stimulant use disorder. Readmission rates for users (19.5%) were higher than for nonusers (15.7%), with slight variation by the type of substance used: cocaine (21.8%), opioid (19.0%), and amphetamine (17.5%). After adjusting for key demographic, socioeconomic, clinical, and health system characteristics, SUDs and stimulant use disorders increased the odds of 30-day all-cause readmission by 20%. Conclusions: Reducing the frequency of inpatient readmission is an important goal for improving the quality of care and ensuring proper transition to residential/outpatient care among patients with SUDs. Differences between groups may suggest directions for further investigation into the distinct needs and challenges of hospitalized opioid- and other drug-exposed patients.
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Affiliation(s)
- Maria C Mejia de Grubb
- Department of Family and Community Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Jason L Salemi
- Department of Family and Community Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Sandra J Gonzalez
- Department of Family and Community Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Charles C Chima
- Department of Family and Community Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Alicia A Kowalchuk
- Department of Family and Community Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Roger J Zoorob
- Department of Family and Community Medicine, Baylor College of Medicine, Houston, Texas, USA
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Kharfan-Dabaja MA, Alvarnas JC. Recognizing Risk Factors Associated With Unplanned 30-Day Readmissions in Hematopoietic Cell Transplantation: An Opportunity to Develop Cost-Containment Strategies. JAMA Netw Open 2019; 2:e196463. [PMID: 31276171 DOI: 10.1001/jamanetworkopen.2019.6463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Mohamed A Kharfan-Dabaja
- Division of Hematology-Oncology and Blood and Marrow Transplantation Program, Mayo Clinic, Jacksonville, Florida
| | - Joseph C Alvarnas
- Department of Hematology/Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, California
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Dhakal B, Giri S, Levin A, Rein L, Fenske TS, Chhabra S, Shah NN, Szabo A, D'Souza A, Pasquini M, Hari P, Hamadani M. Factors Associated With Unplanned 30-Day Readmissions After Hematopoietic Cell Transplantation Among US Hospitals. JAMA Netw Open 2019; 2:e196476. [PMID: 31276175 DOI: 10.1001/jamanetworkopen.2019.6476] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
IMPORTANCE Hematopoietic cell transplantation (HCT) is a therapeutic strategy in the management of several hematological cancers. Limited data exist on the incidence and predictors of 30-day readmission after HCT. OBJECTIVE To measure the incidence of and risk factors associated with 30-day readmission following HCT in the United States. DESIGN, SETTING, AND PARTICIPANTS This cohort study examined patient data from the US population-based Nationwide Readmissions Database. All adults (age ≥18 years) who underwent autologous (auto-) or allogenic (allo-) HCT in US hospitals between January 1, 2012, and November 30, 2014, were included. The analysis was performed from June 2018 to February 2019. MAIN OUTCOMES AND MEASURES The main outcome was 30-day readmission rates for auto-HCT and allo-HCT. Factors associated with readmission, including baseline demographic characteristics and disease- and hospital-related characteristics (including annual case volume), were measured. RESULTS A total of 28 356 index admissions for auto-HCT in 244 centers (191 low-volume, 38 medium-volume, and 15 high-volume centers) and 17 217 index admissions for allo-HCT in 211 centers (161 low-volume, 37 medium-volume, and 13 high-volume centers) were identified during the study period. The overall 30-day readmission rates were 11.6% for auto-HCT and 24.4% for allo-HCT. The odds of readmission were significantly higher in low-volume hospitals compared with high-volume hospitals (adjusted odds ratio [aOR], 1.69; 95% CI, 1.08-2.64 for auto-HCT and aOR, 1.41; 95% CI, 1.09-1.82 for allo-HCT) but comparable to medium-volume hospitals (aOR, 1.06; 95% CI, 0.62-1.83 for auto-HCT and aOR, 1.19; 95% CI, 0.90-1.57 for allo-HCT). Other factors associated with readmission for auto-HCT included younger age (aOR for age ≥50 vs <49 years, 0.82; 95% CI, 0.68-0.98), female sex (aOR, 1.21; 95% CI, 1.06-1.36), disease type (aOR for other vs myeloma, 1.37; 95% CI, 1.06-1.77), and Elixhauser comorbidity index score (aOR for ≥20 vs 0, 1.5; 95% CI, 1.17-1.93). For allo-HCT, factors associated with readmission included disease type (aOR for acute lymphoblastic leukemia vs acute myelogenous leukemia, 1.30; 95% CI, 1.04-1.62), insurance (aOR for Medicare vs private, 1.18; 95% CI, 1.02-1.36), and Elixhauser comorbidity index score (aOR for 1-9 vs 0, 1.2; 95% CI, 1.04-1.39). Infections, neutropenic fever, and gastrointestinal symptoms were the most common reasons for readmission for both types of HCT. CONCLUSIONS AND RELEVANCE This study found substantial rates of readmission for both types of HCT and an inverse association between hospital HCT volume and 30-day readmission. These results may provide guidance when developing quality indicators and policies penalizing hospitals for HCT readmission.
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Affiliation(s)
- Binod Dhakal
- Division of Hematology and Oncology, Medical College of Wisconsin, Milwaukee
| | - Smith Giri
- Division of Hematology and Oncology, Yale University, New Haven, Connecticut
| | - Adam Levin
- Division of Hematology and Oncology, Medical College of Wisconsin, Milwaukee
| | - Lisa Rein
- Division of Biostatistics, Medical College of Wisconsin, Milwaukee
| | - Timothy S Fenske
- Division of Hematology and Oncology, Medical College of Wisconsin, Milwaukee
| | - Saurabh Chhabra
- Division of Hematology and Oncology, Medical College of Wisconsin, Milwaukee
| | - Nirav N Shah
- Division of Hematology and Oncology, Medical College of Wisconsin, Milwaukee
| | - Aniko Szabo
- Division of Biostatistics, Medical College of Wisconsin, Milwaukee
| | - Anita D'Souza
- Division of Hematology and Oncology, Medical College of Wisconsin, Milwaukee
| | - Marcelo Pasquini
- Division of Hematology and Oncology, Medical College of Wisconsin, Milwaukee
| | - Parameswaran Hari
- Division of Hematology and Oncology, Medical College of Wisconsin, Milwaukee
| | - Mehdi Hamadani
- Division of Hematology and Oncology, Medical College of Wisconsin, Milwaukee
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Kim LD, Pfoh ER, Hu B, Kou L, Knowlton LM, Staudenmayer K, Rothberg MB. Derivation and Validation of a Model to Predict 30-Day Readmission in Surgical Patients Discharged to Skilled Nursing Facility. J Am Med Dir Assoc 2019; 20:1086-1090.e2. [PMID: 31176675 DOI: 10.1016/j.jamda.2019.04.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 04/05/2019] [Accepted: 04/16/2019] [Indexed: 10/26/2022]
Abstract
OBJECTIVES To identify factors associated with 30-day all-cause readmission rates in surgical patients discharged to skilled nursing facilities (SNFs), and derive and validate a risk score. DESIGN Retrospective cohort. SETTING AND PARTICIPANTS Patients admitted to 1 tertiary hospital's surgical services between January 1, 2011, and December 31, 2014 and subsequently discharged to 110 SNFs within a 25-mile radius of the hospital. The first 2 years were used for the derivation set and the last 2 for validation. METHODS Data were collected on 30-day all cause readmissions, patient demographics, procedure and surgical service, comorbidities, laboratory tests, and prior health care utilization. Multivariate regression was used to identify risk factors for readmission. RESULTS During the study period, 2405 surgical patients were discharged to 110 SNFs, and 519 (21.6%) of these patients experienced readmission within 30 days. In a multivariable regression model, hospital length of stay [odds ratio (OR) per day: 1.03, 95% confidence interval (CI) 1.02-1.04], number of hospitalizations in past year (OR 1.24 per hospitalization, 95% CI 1.18-1.31), nonelective surgery (OR 1.33, 95% CI 1.18-1.65), low-risk service (orthopedic/spine service) (OR 0.32, 95% CI 0.25-0.42), and intermediate-risk service (cardiothoracic surgery/urology/gynecology/ear, nose, throat) (OR 0.69, 95% CI 0.53-0.88) were associated with all-cause readmissions. The model had a C index of 0.71 in the validation set. Using the following risk score [0.8 × (hospital length of stay) + 7 × (number of hospitalizations in past year) +10 for nonelective surgery, +36 for high-risk surgery, and +20 for intermediate-risk surgery], a score of >40 identified patients at high risk of 30-day readmission (35.8% vs 12.6%, P < .001). CONCLUSIONS/IMPLICATIONS Among surgical patients discharged to an SNF, a simple risk score with 4 parameters can accurately predict the risk of 30-day readmission.
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Affiliation(s)
- Luke D Kim
- Center for Geriatric Medicine, Medicine Institute, Cleveland Clinic, Cleveland, OH.
| | - Elizabeth R Pfoh
- Center for Value-Based Care Research, Medicine Institute, Cleveland Clinic, Cleveland, OH
| | - Bo Hu
- Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
| | - Lei Kou
- Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH
| | - Lisa M Knowlton
- Department of Trauma, Acute Care Surgery and Surgical Critical Care, Stanford University Medical Center, Stanford, CA
| | - Kristan Staudenmayer
- Department of Trauma, Acute Care Surgery and Surgical Critical Care, Stanford University Medical Center, Stanford, CA
| | - Michael B Rothberg
- Center for Value-Based Care Research, Medicine Institute, Cleveland Clinic, Cleveland, OH
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LaHue SC, Douglas VC, Kuo T, Conell CA, Liu VX, Josephson SA, Angel C, Brooks KB. Association between Inpatient Delirium and Hospital Readmission in Patients ≥ 65 Years of Age: A Retrospective Cohort Study. J Hosp Med 2019; 14:201-206. [PMID: 30933669 PMCID: PMC6628723 DOI: 10.12788/jhm.3130] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/17/2018] [Accepted: 11/20/2018] [Indexed: 01/04/2023]
Abstract
BACKGROUND Delirium affects more than seven million hospitalized adults in the United States annually. However, its impact on postdischarge healthcare utilization remains unclear. OBJECTIVE To determine the association between delirium and 30-day hospital readmission. DESIGN A retrospective cohort study. SETTING A general community medical and surgical hospital. PATIENTS All adults who were at least 65 years old, without a history of delirium or alcohol-related delirium, and were hospitalized from September 2010 to March 2015. MEASUREMENTS The patients deemed at risk for or displaying symptoms of delirium were screened by nurses using the Confusion Assessment Method with a followup by a staff psychiatrist for a subset of screen-positive patients. Patients with delirium confirmed by a staff psychiatrist were compared with those without delirium. The primary outcome was the 30-day readmission rate. The secondary outcomes included emergency department (ED) visits 30 days postdischarge, mortality during hospitalization and 30 days postdischarge, and discharge location. RESULTS The cohort included 718 delirious patients and 7,927 nondelirious patients. Using an unweighted multivariable logistic regression, delirium was determined to be significantly associated with the increased odds of readmission within 30 days of discharge (odds ratio (OR): 2.60; 95% CI, 1.96-3.44; P < .0001). Delirium was also significantly (P < .0001) associated with ED visits within 30 days postdischarge (OR: 2.18; 95% CI: 1.77-2.69) and discharge to a facility (OR: 2.52; 95% CI: 2.09-3.01). CONCLUSIONS Delirium is a significant predictor of hospital readmission, ED visits, and discharge to a location other than home. Delirious patients should be targeted to reduce postdischarge healthcare utilization.
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Affiliation(s)
- Sara C LaHue
- Department of Neurology, School of Medicine, University of California, San Francisco, California
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California
- Corresponding Author: Sara Catherine LaHue, MD; E-mail: ; Telephone: 415-476-1489
| | - Vanja C. Douglas
- Department of Neurology, School of Medicine, University of California, San Francisco, California
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California
| | - Teresa Kuo
- Department of Medicine, Kaiser Permanente San Francisco Medical Center, San Francisco, California
| | - Carol A Conell
- Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - Vincent X Liu
- Division of Research, Kaiser Permanente Northern California, Oakland, California
| | - S Andrew Josephson
- Department of Neurology, School of Medicine, University of California, San Francisco, California
- Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California
| | - Clay Angel
- Department of Hospital Medicine, Kaiser Permanente San Rafael Medical Center, San Rafael, California
| | - Kristen B Brooks
- Department of Psychiatry, Kaiser Permanente San Rafael Medical Center, San Rafael, California
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Mojadidi MK, Mahmoud AN, Mahtta D, Zaman MO, Elgendy IY, Elgendy AY, Agarwal N, Patel NK, Gertz ZM, Wayangankar SA, Lew DC, Jneid H, Don CW, Meier B, Tobis JM. Incidence and Causes of 30-day Readmissions after Surgical Versus Percutaneous Secundum Atrial Septal Defect Closure: A United States Nationwide Analysis. STRUCTURAL HEART 2019. [DOI: 10.1080/24748706.2018.1559963] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Affiliation(s)
- Mohammad K. Mojadidi
- Division of Cardiology, Department of Medicine, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Ahmed N. Mahmoud
- Division of Cardiology, Department of Medicine, University of Florida College of Medicine, Gainesville, Florida, USA
| | - Dhruv Mahtta
- Department of Medicine, University of Florida College of Medicine, Gainesville, Florida, USA
| | - Muhammad O. Zaman
- Division of Cardiology, Department of Medicine, University of Florida College of Medicine, Gainesville, Florida, USA
| | - Islam Y. Elgendy
- Division of Cardiology, Department of Medicine, University of Florida College of Medicine, Gainesville, Florida, USA
| | - Akram Y. Elgendy
- Division of Cardiology, Department of Medicine, University of Florida College of Medicine, Gainesville, Florida, USA
| | - Nayan Agarwal
- Interventional Cardiology, Cardiovascular Institute of the South, Houma, Louisiana, USA
| | - Nimesh K. Patel
- Division of Cardiology, Department of Medicine, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Zachary M. Gertz
- Division of Cardiology, Department of Medicine, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Siddharth A. Wayangankar
- Division of Cardiology, Department of Medicine, University of Florida College of Medicine, Gainesville, Florida, USA
| | - David C. Lew
- Florida Heart and Vascular Center, Leesburg, Florida, USA
| | - Hani Jneid
- Division of Cardiology and Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Creighton W. Don
- Division of Cardiology, Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Bernhard Meier
- Department of Cardiology, University Hospital of Bern, Bern, Switzerland
| | - Jonathan M. Tobis
- Program in Interventional Cardiology, Division of Cardiology, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California, USA
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