• Complex surgery
• Liver surgery
• Surgical procedure manager
• Workflow surgery

• Humans
• Workflow
• Hepatectomy/standards
• Hepatectomy/methods
• Pilot Projects
• Male
• Female
• Operative Time
• Middle Aged
• Aged
• Adult

• pp 18-15 Propatient
• Universitätsklinikum Leipzig (8929)

• critical care
• digital twin
• full-stack application architecture
• graph model
• virtual patient simulation

• centralized multipatient display
• dashboard
• user perception
• intensive and critical care
• performance improvement
• testing and evaluation
• translational research

• Humans
• Intensive Care Units
• Dashboard Systems

• artificial intelligence
• delirium
• intensive care unit
• intervention
• nurses

• R…D beijing municipal education commission
• capital medical university

• Aged
• Humans
• Middle Aged
• Activities of Daily Living
• Artificial Intelligence
• Delirium/drug therapy
• Delirium/prevention & control
• Intensive Care Units
• Nurse's Role
• Pilot Projects
• Randomized Controlled Trials as Topic

• R&D Program of Beijing Municipal Education Commission

• critical care
• medical education
• medical intensive care unit
• patient safety
• patient-specific modeling
• simulation training

• UL1 TR002377 NCATS NIH HHS

• clinical decision-making
• health information technology
• intensive care units
• patient care prioritization
• physiological monitoring
• visualization
• vital signs

• Humans
• Blood Pressure
• Heart Rate
• Intensive Care Units
• Respiratory Rate
• Vital Signs
• Monitoring, Physiologic

• Visual Blood
• blood gas analysis
• point-of-care diagnostic
• qualitative research
• situation awareness
• user-centered design
• visualization

• clinical decision support
• clinical informatics
• cognitive load
• electronic medical record
• intensive care unit

• ehr safety
• electronic health record
• information overload
• note bloat

• Electronic Health Records
• Humans
• Patient Safety
• Physicians
• Systematic Reviews as Topic

• Artificial intelligence
• Clinical decision support
• Clinical informatics
• Machine learning
• Pediatric intensive care unit
• Quality improvement

• electronic records
• risk model
• technology acceptance
• user interface

• Burn Units
• Critical care
• Decision Making
• Electronic Health Record
• Information Technology
• Patient Care Team
• Simulation

• EHR
• burnout
• critical care
• efficiency
• performance
• satisfaction

• Computer Graphics
• Electronic Health Records
• Humans
• Intensive Care Units/statistics & numerical data
• Machine Learning
• Medical Informatics/methods
• User-Computer Interface

• R01 LM012095 NLM NIH HHS
• T15 LM007059 NLM NIH HHS

• Cognitive load
• Healthcare
• NASA TLX
• Patient safety

• Adult
• Analgesics, Opioid/therapeutic use
• Electronic Health Records
• Female
• Health Personnel/psychology
• Humans
• Male
• Medication Therapy Management
• Patient Safety
• Task Performance and Analysis
• User-Computer Interface
• Work/psychology
• Workload/psychology

• P30 HS023535 AHRQ HHS

• Critical care
• Electronic medical records
• Information-seeking behavior
• Machine learning

• Electronic Health Records
• Humans
• Information Seeking Behavior
• Machine Learning
• Physicians/psychology

• R01 GM088224 NIGMS NIH HHS
• R01 LM012095 NLM NIH HHS
• T15 LM007059 NLM NIH HHS

• Clinical decision support
• Critical care (6 allowed)
• Electronic medical record
• Information display
• User-centered design
• User-computer interface

• R56 LM011925 NLM NIH HHS
• T15 LM007124 NLM NIH HHS

• Big Data
• Biomedical Research
• Decision Making
• Delivery of Health Care/standards
• Humans
• Information Dissemination
• Intensive Care Units/standards
• Self Report

• Electronic Health Records
• Evaluation
• Physicians
• Providers
• Satisfaction
• Usability

• Acute Kidney Injury
• Diagnostic Tests, Routine
• Electronic Health Records
• Humans
• Information Seeking Behavior
• Intensive Care Units
• Machine Learning
• Medical Records Systems, Computerized
• Physicians
• Respiratory Insufficiency
• User-Computer Interface

• R01 GM088224 NIGMS NIH HHS
• R01 LM012095 NLM NIH HHS
• T15 LM007059 NLM NIH HHS

• Electronic checklist
• ICU rounds
• Intensive care unit (ICU)
• Pediatrics

• Attitude of Health Personnel
• Child
• Critical Care/methods
• Equipment Design
• Humans
• Intensive Care Units, Pediatric
• Monitoring, Physiologic/methods
• Patient Compliance
• Prospective Studies
• Signal Processing, Computer-Assisted
• Software
• Teaching Rounds
• User-Computer Interface

Intensive care clinicians use several sources of data in order to inform decision-making. We set out to evaluate a new interactive data integration platform called T3™ made available for pediatric intensive care. Three primary functions are supported: tracking of physiologic signals, displaying trajectory, and triggering decisions, by highlighting data or estimating risk of patient instability. We designed a human factors study to identify interface usability issues, to measure ease of use, and to describe interface features that may enable or hinder clinical tasks.

Twenty-two participants, consisting of bedside intensive care physicians, nurses, and respiratory therapists, tested the T3™ interface in a simulation laboratory setting. Twenty tasks were performed with a true-to-setting, fully functional, prototype, populated with physiological and therapeutic intervention patient data. Primary data visualization was time series and secondary visualizations were: 1) shading out-of-target values, 2) mini-trends with exaggerated maxima and minima (sparklines), and 3) bar graph of a 16-parameter indicator. Task completion was video recorded and assessed using a use error rating scale. Usability issues were classified in the context of task and type of clinician. A severity rating scale was used to rate potential clinical impact of usability issues.

Time series supported tracking a single parameter but partially supported determining patient trajectory using multiple parameters. Visual pattern overload was observed with multiple parameter data streams. Automated data processing using shading and sparklines was often ignored but the 16-parameter data reduction algorithm, displayed as a persistent bar graph, was visually intuitive. However, by selecting or automatically processing data, triggering aids distorted the raw data that clinicians use regularly. Consequently, clinicians could not rely on new data representations because they did not know how they were established or derived.

Usability issues, observed through contextual use, provided directions for tangible design improvements of data integration software that may lessen use errors and promote safe use. Data-driven decision making can benefit from iterative interface redesign involving clinician-users in simulated environments. This study is a first step in understanding how software can support clinicians’ decision making with integrated continuous monitoring data. Importantly, testing of similar platforms by all the different disciplines who may become clinician users is a fundamental step necessary to understand the impact on clinical outcomes of decision aids.

The online version of this article (doi:10.1186/s12911-017-0520-7) contains supplementary material, which is available to authorized users.

Due to the rapidly evolving medical, technological, and technical possibilities, surgical procedures are becoming more and more complex. On the one hand, this offers an increasing number of advantages for patients, such as enhanced patient safety, minimal invasive interventions, and less medical malpractices. On the other hand, it also heightens pressure on surgeons and other clinical staff and has brought about a new policy in hospitals, which must rely on a great number of economic, social, psychological, qualitative, practical, and technological resources. As a result, medical disciplines, such as surgery, are slowly merging with technical disciplines. However, this synergy is not yet fully matured. The current information and communication technology in hospitals cannot manage the clinical and operational sequence adequately. The consequences are breaches in the surgical workflow, extensions in procedure times, and media disruptions. Furthermore, the data accrued in operating rooms (ORs) by surgeons and systems are not sufficiently implemented. A flood of information, “big data”, is available from information systems. That might be deployed in the context of Medicine 4.0 to facilitate the surgical treatment. However, it is unused due to infrastructure breaches or communication errors. Surgical process models (SPMs) alleviate these problems. They can be defined as simplified, formal, or semiformal representations of a network of surgery-related activities, reflecting a predefined subset of interest. They can employ different means of generation, languages, and data acquisition strategies. They can represent surgical interventions with high resolution, offering qualifiable and quantifiable information on the course of the intervention on the level of single, minute, surgical work-steps. The basic idea is to gather information concerning the surgical intervention and its activities, such as performance time, surgical instrument used, trajectories, movements, or intervention phases. These data can be gathered by means of workflow recordings. These recordings are abstracted to represent an individual surgical process as a model and are an essential requirement to enable Medicine 4.0 in the OR. Further abstraction can be generated by merging individual process models to form generic SPMs to increase the validity for a larger number of patients. Furthermore, these models can be applied in a wide variety of use-cases. In this regard, the term “modeling” can be used to support either one or more of the following tasks: “to describe”, “to understand”, “to explain”, to optimize”, “to learn”, “to teach”, or “to automate”. Possible use-cases are requirements analyses, evaluating surgical assist systems, generating surgeon-specific training-recommendation, creating workflow management systems for ORs, and comparing different surgical strategies. The presented chapter will give an introduction into this challenging topic, presenting different methods to generate SPMs from the workflow in the OR, as well as various use-cases, and state-of-the-art research in this field. Although many examples in the article are given according to SPMs that were computed based on observations, the same approaches can be easily applied to SPMs that were measured automatically and mined from big data.

• operating room
• surgical process
• surgical workflow
• workflow management

To develop and validate a phenotyping algorithm for the identification of patients with type 1 and type 2 diabetes mellitus (DM) preoperatively using routinely available clinical data from electronic health records.

We used first-order logic rules (if-then-else rules) to imply the presence or absence of DM types 1 and 2. The “if” clause of each rule is a conjunction of logical and, or predicates that provides evidence toward or against the presence of DM. The rule includes International Classification of Diseases, Ninth Revision, Clinical Modification diagnostic codes, outpatient prescription information, laboratory values, and positive annotation of DM in patients’ clinical notes. This study was conducted from March 2, 2015, through February 10, 2016. The performance of our rule-based approach and similar approaches proposed by other institutions was evaluated with a reference standard created by an expert reviewer and implemented for routine clinical care at an academic medical center.

A total of 4208 surgical patients (mean age, 52 years; males, 48%) were analyzed to develop the phenotyping algorithm. Expert review identified 685 patients (16.28% of the full cohort) as having DM. Our proposed method identified 684 patients (16.25%) as having DM. The algorithm performed well—99.70% sensitivity, 99.97% specificity—and compared favorably with previous approaches.

Among patients undergoing surgery, determination of DM can be made with high accuracy using simple, computationally efficient rules. Knowledge of patients’ DM status before surgery may alter physicians’ care plan and reduce postsurgical complications. Nevertheless, future efforts are necessary to determine the effect of first-order logic rules on clinical processes and patient outcomes.

• CCW, Chronic Condition Data Warehouse
• DDC, Durham Diabetes Coalition
• DM, diabetes mellitus
• EHR, electronic health record
• HbA1c of NYC, Hemoglobin A1c of New York City
• HbA1c, hemoglobin A1c
• ICD-9-CM, International Classification of Diseases, Ninth Revision, Clinical Modification
• MICS, Mayo Integrated Clinical Systems
• NLP, natural language processing
• SUPREME-DM, Surveillance, Prevention, and Management of Diabetes Mellitus
• T1DM, type 1 diabetes mellitus
• T2DM, type 2 diabetes mellitus
• eMERGE, Electronic Medical Records and Genomics

• alert
• computer applications
• outcomes
• sepsis

• Alert studies
• Electronic health record
• Implementation barriers
• Intensive care unit
• Sepsis
• Simulation studies

• R36 HS022799 AHRQ HHS

Resuscitation of critically ill patients is complex and potentially prone to diagnostic errors and therapeutic harm. The Checklist for early recognition and treatment of acute illness and injury (CERTAIN) is an electronic tool that aims to provide decision-support, charting, and prompting for standardization. This study sought to evaluate the validity and reliability of CERTAIN in a real-time Intensive Care Unit (ICU).

This was a prospective pilot study in the medical ICU of a tertiary care medical center. A total of thirty patient encounters over 2 months period were charted independently by two CERTAIN investigators. The inter-observer recordings and comparison to the electronic medical records (EMR) were used to evaluate reliability and validity, respectively. The primary outcome was reliability and validity measured using Cohen's Kappa statistic. Secondary outcomes included time to completion, user satisfaction, and learning curve.

A total of 30 patients with a median age of 59 (42–78) years and median acute physiology and chronic health evaluation III score of 38 (23–50) were included in this study. Inter-observer agreement was very good (κ = 0.79) in this study and agreement between CERTAIN and the EMR was good (κ = 0.5). CERTAIN charting was completed in real-time that was 121 (92–150) min before completion of EMR charting. The subjective learning curve was 3.5 patients without differences in providers with different levels of training.

CERTAIN provides a reliable and valid method to evaluate resuscitation events in real time. CERTAIN provided the ability to complete data in real-time.

• Checklist for early recognition and treatment of acute illness and injury
• checklists
• critical care
• reliability
• resuscitation

• Clinical calculator
• Clinical score
• Decision support tool
• Electronic medical record
• Medical calculator

• Electronic Health Records
• Financial Management, Hospital
• Health Services Needs and Demand
• Hospital Medicine
• Minnesota

• National Aeronautics and Space Administration task load index
• cognitive function
• mental fatigue
• pediatric intensive care unit
• repeatable episodic memory test

• critical illness
• intensive care unit
• patient care handoffs
• quality improvement

• Electronic dashboard
• Handoff
• Health information technology
• Needs assessment
• Rounding

• Hospital Administration/methods
• Humans
• Information Systems/organization & administration
• Intensive Care Units/organization & administration
• Needs Assessment
• Patient Handoff/organization & administration
• User-Computer Interface

• Data representation
• clinical decision support systems
• patient similarity
• visual scale

AIM: To examine the feasibility and validity of electronic generation of quality metrics in the intensive care unit (ICU).

METHODS: This minimal risk observational study was performed at an academic tertiary hospital. The Critical Care Independent Multidisciplinary Program at Mayo Clinic identified and defined 11 key quality metrics. These metrics were automatically calculated using ICU DataMart, a near-real time copy of all ICU electronic medical record (EMR) data. The automatic report was compared with data from a comprehensive EMR review by a trained investigator. Data was collected for 93 randomly selected patients admitted to the ICU during April 2012 (10% of admitted adult population). This study was approved by the Mayo Clinic Institution Review Board.

RESULTS: All types of variables needed for metric calculations were found to be available for manual and electronic abstraction, except information for availability of free beds for patient-specific time-frames. There was 100% agreement between electronic and manual data abstraction for ICU admission source, admission service, and discharge disposition. The agreement between electronic and manual data abstraction of the time of ICU admission and discharge were 99% and 89%. The time of hospital admission and discharge were similar for both the electronically and manually abstracted datasets. The specificity of the electronically-generated report was 93% and 94% for invasive and non-invasive ventilation use in the ICU. One false-positive result for each type of ventilation was present. The specificity for ICU and in-hospital mortality was 100%. Sensitivity was 100% for all metrics.

CONCLUSION: Our study demonstrates excellent accuracy of electronically-generated key ICU quality metrics. This validates the feasibility of automatic metric generation.

• Electronic medical record
• Quality indicators
• Critical care
• Information processing
• Datamart
• Automatic
• Intensive care
• Health care

• data visualization
• electronic health records
• informatics
• quality improvement
• status epilepticus

• Clinical decision support/CDS
• Computerized provider order entry/CPOE
• Electronic health record/EHR
• Intensive care
• Medical informatics
• Outcome
• Pediatric

• Automated alert systems
• Critical care
• Hospital
• Intensive care unit
• Sepsis

• Humans
• Intensive Care Units/standards
• Sepsis/therapy

• R36 HS022799 AHRQ HHS
• UH2 HL125119 NHLBI NIH HHS
• UH3 HL125119 NHLBI NIH HHS
• U01 HL121819 NHLBI NIH HHS

• Electronic medical record
• Implementation
• Intensive care unit
• Rounds
• Usability

• Critical Care
• Electronic Health Records
• Humans
• Intensive Care Units
• Machine Learning
• Physicians

• R01 GM088224 NIGMS NIH HHS
• R01 LM012095 NLM NIH HHS
• T15 LM007059 NLM NIH HHS

• Checklists
• Critical care
• Decision support systems
• Education
• Medical informatics

• Clinical data management
• Cluster randomization trial
• Electronic medical record
• Human-centered
• Implementation
• Intensive care unit
• Rounds

• 2014 EDM Forum Symposium
• Health Information Technology
• Learning Health System
• Quality Improvement
• Standardized Data Collection