• adverse drug events
• decision support
• machine learning
• predictive modelling

• Humans
• United States/epidemiology
• Inpatients
• Tertiary Care Centers
• Hospitalization
• Emergency Service, Hospital
• Machine Learning

• bioinformatics
• biosensors
• digital health
• precision medicine
• telehealth

• Delivery of Health Care
• Biomedical Technology

• Allergy
• Artificial intelligence
• Computer-assisted decision-making, Telehealth
• Immunology
• Telemedicine

• Humans
• Diagnosis, Computer-Assisted
• Telemedicine
• Electronic Health Records

• Child
• Humans
• Adolescent
• Anti-Bacterial Agents/therapeutic use
• Retrospective Studies
• Pharmacies
• Drug Prescriptions
• Pharyngitis/drug therapy

• adverse drug reactions
• management
• patient self-reporting
• prevention
• severity

• Humans
• Cross-Sectional Studies
• Adverse Drug Reaction Reporting Systems
• Pharmacovigilance
• Outpatients
• Drug-Related Side Effects and Adverse Reactions
• Delivery of Health Care

• adverse drug events
• clinical decision support systems
• inappropriate prescriptions
• validation studies

• Decision Support Systems, Clinical
• Drug-Related Side Effects and Adverse Reactions/prevention & control
• Humans
• Medication Review
• Outcome Assessment, Health Care
• Potentially Inappropriate Medication List

• medication
• risk score
• polypharmacy
• mortality

• Adult
• Drug-Related Side Effects and Adverse Reactions
• Electronic Health Records
• Female
• Humans
• Middle Aged
• Proportional Hazards Models
• Retrospective Studies
• Risk Factors
• United States/epidemiology

• Clinical decision support system
• Medication
• Patient outcomes
• Significant factors
• Systematic review

• coronary heart disease
• health informatics
• information management
• medical history
• myocardial infarction
• risk management

• Acute Pain/diagnosis
• Acute Pain/epidemiology
• Adult
• Aged
• Chest Pain/diagnosis
• Chest Pain/epidemiology
• Electrocardiography/methods
• Emergency Service, Hospital
• Female
• Follow-Up Studies
• Humans
• Incidence
• Male
• Medical History Taking
• Middle Aged
• Prospective Studies
• Risk Assessment/methods
• Risk Factors
• Self Report
• Sweden/epidemiology

• Karolinska Institutet
• Robert Bosch Stiftung
• Stiftelsen Hjärtat (Stockholm, Sweden)

• Biological targets
• Drug-drug interaction
• Market Basket Analysis
• Rule discovery

To improve medication surveillance and provide pharmacotherapeutic support in University Hospitals Leuven, a back-office clinical service, called “Check of Medication Appropriateness” (CMA), was developed, consisting of clinical rule based screening for medication inappropriateness. The aim of this study is twofold: 1) describing the development of CMA and 2) evaluating the preliminary results, more specifically the number of clinical rule alerts, number of actions on the alerts and acceptance rate by physicians.

CMA focuses on patients at risk for potentially inappropriate medication and involves the daily checking by a pharmacist of high-risk prescriptions generated by advanced clinical rules integrating patient specific characteristics with details on medication. Pharmacists’ actions are performed by adding an electronic note in the patients’ medical record or by contacting the physician by phone. A retrospective observational study was performed to evaluate the primary outcomes during an 18-month study period.

39,481 clinical rule alerts were checked by pharmacists for which 2568 (7%) electronic notes were sent and 637 (1.6%) phone calls were performed. 37,782 (96%) alerts were checked within four pharmacotherapeutic categories: drug use in renal insufficiency (25%), QTc interval prolonging drugs (11%), drugs with a restricted indication or dosing (14%) and overruled very severe drug-drug interactions (50%). The emergency department was a frequently involved ward and anticoagulants are the drug class for which actions are most frequently carried out. From the 458 actions performed for the four abovementioned categories, 69% were accepted by physicians.

These results demonstrate the added value of CMA to support medication surveillance in synergy with already integrated basic clinical decision support and bedside clinical pharmacy. Otherwise, the study also highlighted a number of limitations, allowing improvement of the service.

The online version of this article (10.1186/s12911-019-0748-5) contains supplementary material, which is available to authorized users.

• Check of medication appropriateness (CMA)
• Clinical decision support (CDSS)
• Clinical rules
• Clinical validation of prescriptions
• Computerized physician order entry (CPOE)
• Medication surveillance

• electronic health records
• patient harm
• patient safety
• review, systematic

• adverse drug event
• adverse drug event surveillance
• automated medication monitoring system
• critically ill
• drug related hazardous condition
• intensive care unit
• medication safety
• trigger alert

Computerized decision support systems have raised a lot of hopes and expectations in the field of order entry. Although there are numerous studies reporting positive impacts, concerns are increasingly high about alert fatigue and effective impacts of these systems. One of the root causes of fatigue alert reported is the low clinical relevance of these alerts.

The objective of this systematic review was to assess the reported positive predictive value (PPV), as a proxy to clinical relevance, of decision support systems in computerized provider order entry (CPOE).

A systematic search of the scientific literature published between February 2009 and March 2015 on CPOE, clinical decision support systems, and the predictive value associated with alert fatigue was conducted using PubMed database. Inclusion criteria were as follows: English language, full text available (free or pay for access), assessed medication, direct or indirect level of predictive value, sensitivity, or specificity. When possible with the information provided, PPV was calculated or evaluated.

Additive queries on PubMed retrieved 928 candidate papers. Of these, 376 were eligible based on abstract. Finally, 26 studies qualified for a full-text review, and 17 provided enough information for the study objectives. An additional 4 papers were added from the references of the reviewed papers. The results demonstrate massive variations in PPVs ranging from 8% to 83% according to the object of the decision support, with most results between 20% and 40%. The best results were observed when patients’ characteristics, such as comorbidity or laboratory test results, were taken into account. There was also an important variation in sensitivity, ranging from 38% to 91%.

There is increasing reporting of alerts override in CPOE decision support. Several causes are discussed in the literature, the most important one being the clinical relevance of alerts. In this paper, we tried to assess formally the clinical relevance of alerts, using a near-strong proxy, which is the PPV of alerts, or any way to express it such as the rate of true and false positive alerts. In doing this literature review, three inferences were drawn. First, very few papers report direct or enough indirect elements that support the use or the computation of PPV, which is a gold standard for all diagnostic tools in medicine and should be systematically reported for decision support. Second, the PPV varies a lot according to the typology of decision support, so that overall rates are not useful, but must be reported by the type of alert. Finally, in general, the PPVs are below or near 50%, which can be considered as very low.

• decision support systems, clinical
• medical order entry systems
• predictive value of tests
• sensitivity
• specificity
• system, medication alert

• acute kidney injury
• electronic alerts
• outcome

• alert
• computer applications
• outcomes
• sepsis

• antimicrobial resistance
• health information technology
• review

• Anti-Bacterial Agents/therapeutic use
• Decision Support Systems, Clinical
• Electronic Prescribing
• Humans
• Medical Order Entry Systems

• MC_PC_13040 Medical Research Council
• MR/K007017/1 Medical Research Council
• RP-PG-1209-10099 Department of Health

• Alert fatigue
• Decision support system
• Methods of alert delivery
• Notification
• Sepsis

Clinical decision support (CDS) systems with automated alerts integrated into electronic medical records demonstrate efficacy for detecting medication errors (ME) and adverse drug events (ADEs). Critically ill patients are at increased risk for ME, ADEs and serious negative outcomes related to these events. Capitalizing on CDS to detect ME and prevent adverse drug related events has the potential to improve patient outcomes. The key to an effective medication safety surveillance system incorporating CDS is advancing the signals for alerts by using trajectory analyses to predict clinical events, instead of waiting for these events to occur. Additionally, incorporating cutting-edge biomarkers into alert knowledge in an effort to identify the need to adjust medication therapy portending harm will advance the current state of CDS. CDS can be taken a step further to identify drug related physiological events, which are less commonly included in surveillance systems. Predictive models for adverse events that combine patient factors with laboratory values and biomarkers are being established and these models can be the foundation for individualized CDS alerts to prevent impending ADEs.

• Drug-related side effects and adverse reactions
• Decision support systems
• Clinical
• Medication errors
• Patient safety
• Clinical pharmacy information systems
• Intensive care units
• Critical care
• Adverse drug event
• Clinical decision support systems

• P30 AG024827 NIA NIH HHS
• R18 HS024208 AHRQ HHS

• Adverse Drug Reaction Reporting Systems
• Attitude of Health Personnel
• Clinical Pharmacy Information Systems
• Data Collection
• Decision Support Systems, Clinical
• Drug Interactions
• Focus Groups
• Humans
• Los Angeles
• Medical Records Systems, Computerized
• Medication Errors/prevention & control
• Pharmacy Service, Hospital
• Primary Health Care/methods

• Academic Medical Centers
• Adult
• Decision Support Systems, Clinical/organization & administration
• Drug Therapy, Computer-Assisted
• Glomerular Filtration Rate
• Hospitalization
• Humans
• Medication Errors/prevention & control
• Medication Systems, Hospital/organization & administration
• New York City
• Renal Insufficiency/drug therapy

• Health Occupations
• Hospital Information Systems/organization & administration
• Hospitals, University
• Japan
• Medication Errors/classification
• Medication Errors/methods
• Medication Errors/prevention & control
• Medication Systems, Hospital/organization & administration
• Risk Management/classification
• Risk Management/methods

Clinical decision support systems, including electronic alerts, ideally provide immediate and relevant patient-specific information to improve clinical decision-making. Despite the growing capabilities of such alerts in conjunction with an expanding electronic medical record, there is a paucity of information regarding their perceived usefulness. We surveyed healthcare providers' opinions concerning the practicality and efficacy of a specific text-based automated electronic alert for acute kidney injury (AKI) in a single hospital during a randomized trial of AKI alerts.

Providers who had received at least one electronic AKI alert in the previous 6 months, as part of a separate randomized controlled trial (clinicaltrials.gov #01862419), were asked to complete a survey concerning their opinions about this specific AKI alert system. Individual approval of the alert system was defined by a provider's desire to continue receiving the alert after termination of the trial.

A total of 98 individuals completed the survey, including 62 physicians, 27 pharmacists and 7 non-physician providers. Sixty-nine percent of responders approved the alert, with no significant difference among the various professions (P = 0.28). Alert approval was strongly correlated with the belief that the alerts improved patient care (P < 0.0001), and negatively correlated with the belief that alerts did not provide novel information (P = 0.0001). With each additional 30 days of trial duration, odds of approval decreased by 20% (3–35%) (P = 0.02).

The alert system was generally well received, although approval waned with time. Approval was correlated with the belief that this type of alert improved patient care. These findings suggest that perceived efficacy is critical to the success of future alert trials.

• acute kidney injury
• alert
• alert fatigue
• approval
• clinical decision support

Although evidence has suggested that computerized drug-drug interaction alert systems may reduce the occurrence of drug-drug interactions, the numerous reminders and alerts generated by such systems could represent an excessive burden for clinicians, resulting in a high override rate of not only unimportant, but also important alerts.

We analyzed physicians’ responses to alerts of relative contraindications and contraindications for coadministration in a computerized drug-drug interaction alert system at Hokkaido University Hospital. In this system, the physician must enter a password to override an alert and continue an order. All of the drug-drug interaction alerts generated between December 2011 and November 2012 at Hokkaido University Hospital were included in this study.

The system generated a total of 170 alerts of relative contraindications and contraindication for coadministration; 59 (34.7 %) of the corresponding orders were cancelled after the alert was accepted, and 111 (65.3 %) were overridden. The most frequent contraindication alert was for the combination of 3-hydroxy-3-methylglutaryl–coenzyme A reductase inhibitors and fibrates. No incidents involving drug-drug interactions were reported among patients who were prescribed contraindicated drug pairs after an override.

Although computerized drug-drug interaction alert systems that require password overrides appear useful for promoting medication safety, having to enter passwords to override alerts may represent an excessive burden for the prescribing physician. Therefore, both patient safety and physicians’ workloads should be taken into consideration in future designs of computerized drug-drug interaction alert systems.

The evidence regarding oral contraceptives and its effectiveness with concomitant ingestion of antibiotics is conflicting. Until evidence becomes clearer, patients should be aware of this possible interaction. The aim of this study was to assess the knowledge and the source of information about this interaction in GP patients in Austria.

Within the framework of the APRES study, 20 Austrian GPs were purposefully selected from among a GP research network and were asked to recruit 200 patients each. The patient cohort was asked to complete a questionnaire. Subsequent analysis included descriptive statistics, statistical tests and logistic regression models.

Overall, 3280 questionnaires could be used for analysis. Of these, 29.7 % (n = 974) of patients acknowledged an awareness of the interaction of antibiotics with OCPs. Women under the age of 46 years acknowledged this interaction in 52.3 % of cases. Positive associations for the belief in an existing interaction in women were identified with age (OR 2.2) and having read the package inserts (OR 1.6). Further, belief was recognized in males based on age (OR 2.5) and tertiary education (OR 2.0). The main source of information regarding antibiotics was the GP (55.9 %).

Less than one-third of all participants and half of the women in the reproductive age acknowledged an interaction between antibiotics and OCPs. Since the GP is the main source of information, this finding depicts a large potential for knowledge transfer within the primary health care setting. A multifaceted strategy is needed at both the population and the GP level to improve awareness and to address these educational gaps.

• Adverse events
• Clinical decision support system
• Knowledge base
• Order entry system
• Renal failure

• Nursing homes
• adverse drug event
• clinical decision support systems
• therapeutic drug monitoring

• Adverse Drug Reaction Reporting Systems/standards
• Communication
• Data Collection
• Decision Support Systems, Clinical
• Humans
• Laboratories
• Nursing Homes
• Physicians
• Reference Values

• K07 AG033174 NIA NIH HHS
• P30 AG024827 NIA NIH HHS
• UL1 TR000005 NCATS NIH HHS
• R01 AG027017 NIA NIH HHS
• R01 LM011838 NLM NIH HHS
• K12HS019461 AHRQ HHS
• K01AG044433 NIA NIH HHS
• R01 HS018721 AHRQ HHS
• KL2 RR024154 NCRR NIH HHS
• P30AG024827 NIA NIH HHS
• R01HS018721 AHRQ HHS
• K12 HS019461 AHRQ HHS
• KL2 TR000146 NCATS NIH HHS
• KL2 RR024154-01 NCRR NIH HHS
• K07AG033174 NIA NIH HHS
• R01AG027017 NIA NIH HHS
• K01 AG044433 NIA NIH HHS

• Spectrum Medical
• cardiopulmonary bypass
• compliance alert
• reaction time

• Accidental Falls/statistics & numerical data
• Algorithms
• Bed Occupancy/statistics & numerical data
• Humans
• Medical Informatics/methods
• Medication Errors/statistics & numerical data
• Models, Statistical
• Patient Safety/statistics & numerical data
• Pressure Ulcer
• Regression Analysis

Confusion between similar drug names is a common cause of potentially harmful medication errors. Interventions to prevent these errors at the point of prescribing have had limited success. The purpose of this study is to measure whether indication alerts at the time of computerized physician order entry (CPOE) can intercept drug name confusion errors.

A retrospective observational study of alerts provided to prescribers in a public, tertiary hospital and ambulatory practice with medication orders placed using CPOE. Consecutive patients seen from April 2006 through February 2012 were eligible if a clinician received an indication alert during ordering. A total of 54,499 unique patients were included. The computerized decision support system prompted prescribers to enter indications when certain medications were ordered without a coded indication in the electronic problem list. Alerts required prescribers either to ignore them by clicking OK, to place a problem in the problem list, or to cancel the order. Main outcome was the proportion of indication alerts resulting in the interception of drug name confusion errors. Error interception was determined using an algorithm to identify instances in which an alert triggered, the initial medication order was not completed, and the same prescriber ordered a similar-sounding medication on the same patient within 5 minutes. Similarity was defined using standard text similarity measures. Two clinicians performed chart review of all cases to determine whether the first, non-completed medication order had a documented or non-documented, plausible indication for use. If either reviewer found a plausible indication, the case was not considered an error. We analyzed 127,458 alerts and identified 176 intercepted drug name confusion errors, an interception rate of 0.14±.01%.

Indication alerts intercepted 1.4 drug name confusion errors per 1000 alerts. Institutions with CPOE should consider using indication prompts to intercept drug name confusion errors.

• Antineoplastic Agents/therapeutic use
• Clinical Trials as Topic
• Humans
• Medication Errors/prevention & control
• Records

• Administrative Data
• Comparative Effectiveness and Safety Research
• Drug Safety
• International Classification of Diseases
• Patient Safety

• Databases, Factual
• Drug-Related Side Effects and Adverse Reactions
• Humans
• Information Storage and Retrieval/methods
• International Classification of Diseases

• CDS
• CPOE
• Clinical decision support
• EMR
• Hypertension
• Indication based
• Problem list

• CPOE
• Decision Support Systems, Clinical
• Electronic Health Record
• Electronic Medical Record
• Medical Order Entry System
• Medication Alert System

• adverse events
• developing countries
• drug adverse events
• healthcare acquired infections
• patient safety
• quality health care
• surgical adverse events

• indication based prescribing
• medication errors
• cpoe

• Academic Medical Centers
• Decision Support Systems, Clinical
• Drug Therapy, Computer-Assisted
• Humans
• Illinois
• Medical Order Entry Systems
• Medication Errors/prevention & control

• U19 HS021093 AHRQ HHS
• U19HS021093 AHRQ HHS

• Artificial Intelligence
• Biomedical Technology
• Computational Biology/methods
• Computational Biology/trends
• Data Collection
• Decision Making, Computer-Assisted
• Decision Support Systems, Clinical
• Decision Support Techniques
• Dentistry/methods
• Emergency Medicine
• Humans
• Image Processing, Computer-Assisted
• Intensive Care Units
• Medical Informatics/methods
• Neoplasms/therapy
• Radiology/methods

• Active surveillance
• Drug toxicity
• Spontaneous reporting

• Adverse Drug Reaction Reporting Systems
• Drug-Related Side Effects and Adverse Reactions
• Female
• Hospitals, Teaching
• Humans
• Male
• Middle Aged
• Pharmacovigilance
• Republic of Korea
• Retrospective Studies

During the past decade, the U.S. health care system has faced increasing challenges in delivering high quality of care, ensuring patient safety, providing access to care, and maintaining manageable costs. While reform progresses at a national level, health care providers have a responsibility and obligation to advance quality and safety. In 2009, the authors implemented a department-wide Clinical Quality Program. This Program comprised of an inter-disciplinary group of providers and staff working together to ensure the highest quality of patient care. The following methodology was followed to establish the Program: (1) Identifying the Department's quality improvement (QI) and patient safety priorities based on reviewing prior performance data; (2) Aligning the Department's priorities with institutional goals to select mutually significant initiatives; (3) Finalizing the goals for improvement based on departmental priorities, existing expertise and resources; (4) Launching the Program through an inter-disciplinary retreat that emphasizes open dialogue, innovative solutions, and fostering leadership in frontline providers; (5) Sustaining the QI initiatives through proactive performance review and management of barriers; and (6) Celebrating success to empower providers to remain engaged. Several challenges are inherent to the implementation of a clinical quality program, including lack of time and expertise, and the hierarchical nature of medicine, which can create a barrier to teamwork. This Program illustrates that improvement can lead to a sustainable clinical quality program and culture change.

• Clinical quality program
• Health care reform
• Quality improvement