Improving Patient-Reported Outcomes in Stroke Care using Remote Blood Pressure Monitoring and Telehealth.

BACKGROUND: Inequities in health care access leads to suboptimal medication adherence and blood pressure (BP) control. Informatics-based approaches may deliver equitable care and enhance self-management. Patient-reported outcomes (PROs) complement clinical measures to assess the impact of illness on patients' well-being in poststroke care. OBJECTIVES: The aim of this study was to determine the feasibility of incorporating PROs into Telehealth After Stroke Care (TASC) and to explore the effect of this team-based remote BP monitoring program on psychological distress and quality of life in an underserved urban setting. METHODS: Patients discharged home from a Comprehensive Stroke Center were randomized to TASC or usual care for 3 months. They were provided with a BP monitor and a tablet that wirelessly transmitted data to a cloud-based platform, which were integrated with the electronic health record. Participants who did not complete the tablet surveys were contacted via telephone or e-mail. We collected the Patient-Reported Outcomes Measurement Information System Managing Medications and Treatment (PROMIS-MMT), Patient Activation Measure (PAM), Neuro-QOL (Quality of Life in Neurological Disorders) Cognitive Function, Neuro-QOL Depression, and Patient Health Questionnaire-9 (PHQ-9). T-tests and linear regression were used to evaluate the differences in PRO change between the arms. RESULTS: Of the 50 participants, two-thirds were Hispanic or non-Hispanic Black individuals. Mechanisms of PRO submission for the arms included tablet (62 vs. 47%), phone (24 vs. 37%), tablet with phone coaching (10 vs. 16%), and e-mail (4 vs. 0%). PHQ-9 depressive scores were nominally lower in TASC at 3 months compared with usual care (2.7 ± 3.6 vs. 4.0 ± 4.1; p = 0.06). No significant differences were observed in PROMIS-MMT, PAM, or Neuro-QoL measures. CONCLUSION: Findings suggest the feasibility of collecting PROs through an interactive web-based platform. The team-based remote BP monitoring demonstrated a favorable impact on patients' well-being. Patients equipped with appropriate resources can engage in poststroke self-care to mitigate inequities in health outcomes.

Understanding the perceived role of electronic health records and workflow fragmentation on clinician documentation burden in emergency departments.

OBJECTIVE: Understand the perceived role of electronic health records (EHR) and workflow fragmentation on clinician documentation burden in the emergency department (ED). METHODS: From February to June 2022, we conducted semistructured interviews among a national sample of US prescribing providers and registered nurses who actively practice in the adult ED setting and use Epic Systems' EHR. We recruited participants through professional listservs, social media, and email invitations sent to healthcare professionals. We analyzed interview transcripts using inductive thematic analysis and interviewed participants until we achieved thematic saturation. We finalized themes through a consensus-building process. RESULTS: We conducted interviews with 12 prescribing providers and 12 registered nurses. Six themes were identified related to EHR factors perceived to contribute to documentation burden including lack of advanced EHR capabilities, absence of EHR optimization for clinicians, poor user interface design, hindered communication, increased manual work, and added workflow blockages, and five themes associated with cognitive load. Two themes emerged in the relationship between workflow fragmentation and EHR documentation burden: underlying sources and adverse consequences. DISCUSSION: Obtaining further stakeholder input and consensus is essential to determine whether these perceived burdensome EHR factors could be extended to broader contexts and addressed through optimizing existing EHR systems alone or through a broad overhaul of the EHR's architecture and primary purpose. CONCLUSION: While most clinicians perceived that the EHR added value to patient care and care quality, our findings underscore the importance of designing EHRs that are in harmony with ED clinical workflows to alleviate the clinician documentation burden.

Use of Real-Time Information to Predict Future Arrivals in the Emergency Department.

STUDY OBJECTIVE: We aimed to build prediction models for shift-level emergency department (ED) patient volume that could be used to facilitate prediction-driven staffing. We sought to evaluate the predictive power of rich real-time information and understand 1) which real-time information had predictive power and 2) what prediction techniques were appropriate for forecasting ED demand. METHODS: We conducted a retrospective study in an ED site in a large academic hospital in New York City. We examined various prediction techniques, including linear regression, regression trees, extreme gradient boosting, and time series models. By comparing models with and without real-time predictors, we assessed the potential gain in prediction accuracy from real-time information. RESULTS: Real-time predictors improved prediction accuracy on models without contemporary information from 5% to 11%. Among extensive real-time predictors examined, recent patient arrival counts, weather, Google trends, and concurrent patient comorbidity information had significant predictive power. Out of all the forecasting techniques explored, SARIMAX (Seasonal Autoregressive Integrated Moving Average with eXogenous factors) achieved the smallest out-of-sample the root mean square error (RMSE) of 14.656 and mean absolute prediction error (MAPE) of 8.703%. Linear regression was the second best, with out-of-sample RMSE and MAPE equal to 15.366 and 9.109%, respectively. CONCLUSION: Real-time information was effective in improving the prediction accuracy of ED demand. Practice and policy implications for designing staffing paradigms with real-time demand forecasts to reduce ED congestion were discussed.

A Computational Framework to Evaluate Emergency Department Clinician Task Switching in the Electronic Health Record Using Event Logs.

Workflow fragmentation, defined as task switching, may be one proxy to quantify electronic health record (EHR) documentation burden in the emergency department (ED). Few measures have been operationalized to evaluate task switching at scale. Theoretically grounded in the time-based resource-sharing model (TBRSM) which conceives task switching as proportional to the cognitive load experienced, we describe the functional relationship between cognitive load and the time and effort constructs previously applied for measuring documentation burden. We present a computational framework, COMBINE, to evaluate multilevel task switching in the ED using EHR event logs. Based on this framework, we conducted a descriptive analysis on task switching among 63 full-time ED physicians from one ED site using EHR event logs extracted between April-June 2021 (n=2,068,605 events) which were matched to scheduled shifts (n=952). On average, we found a high volume of event-level (185.8±75.3/hr) and within-(6.6±1.7/chart) and between-patient chart (27.5±23.6/hr) switching per shift worked.

Variability in Nursing Documentation Patterns across Patients' Hospital Stays.

This study explores the variability in nursing documentation patterns in acute care and ICU settings, focusing on vital signs and note documentation, and examines how these patterns vary across patients' hospital stays, documentation types, and comorbidities. In both acute care and critical care settings, there was significant variability in nursing documentation patterns across hospital stays, by documentation type, and by patients' comorbidities. The results suggest that nurses adapt their documentation practices in response to their patients' fluctuating needs and conditions, highlighting the need to facilitate more individualized care and tailored documentation practices. The implications of these findings can inform decisions on nursing workload management, clinical decision support tools, and EHR optimizations.

A Scoping Review of Integrated Medical Devices and Clinical Decision Support in the Acute Care Setting.

BACKGROUND: Seamless data integration between point-of-care medical devices and the electronic health record (EHR) can be central to clinical decision support systems (CDSS). OBJECTIVE: The objective of this scoping review is to (1) examine the existing evidence related to integrated medical devices, primarily medication pump devices, and associated clinical decision support (CDS) in acute care settings and (2) to identify how acute care clinicians may use device CDS in clinical decision-making. The rationale for this review is that integrated devices are ubiquitous in the acute care setting, and they generate data that may help to contribute to the situational awareness of the clinical team necessary to provide individualized patient care. METHODS: This scoping review was conducted using the Joanna Briggs Institute Manual for Evidence Synthesis and the Preferred Reporting Items for Systematic Reviews and Meta-analyses Extensions for Scoping Review guidelines. PubMed, CINAHL, IEEE Xplore, and Scopus databases were searched for scholarly, peer-reviewed journals indexed between January 1, 2010 and December 31, 2020. A priori inclusion criteria were established. RESULTS: Of the 1,924 articles screened, 18 were ultimately included for synthesis, and primarily included articles on devices such as intravenous medication pumps and vital signs machines. Clinical alarm burden was mentioned in most of the articles, and despite not including the term "medication" there were many articles about smart pumps being integrated with the EHR. The Revised Technology, Nursing & Patient Safety Conceptual Model provided the organizational framework. Ten articles described patient assessment, monitoring, or surveillance use. Three articles described patient protection from harm. Four articles described direct care use scenarios, all of which described insulin administration. One article described a hybrid situation of patient communication and monitoring. Most of the articles described devices and decision support primarily used by registered nurses (RNs). CONCLUSION: The articles in this review discussed devices and the associated CDSS that are used by clinicians, primarily RNs, in the daily provision of care for patients. Integrated device data provide insight into user-device interactions and help to illustrate health care processes, especially the activities when providing direct care to patients in an acute care setting. While there are CDSS designed to support the clinician while working with devices, RNs and providers may disregard this guidance, and defer to their own expertise. Additionally, if clinicians perceive CDSS as intrusive, they are at risk for alarm and alert fatigue if CDSS are not tailored to sync with the workflow of the end-user. Areas for future research include refining inclusion criteria to examine the evidence for devices and their CDS that are most likely used by other groups' health care professionals (i.e., doctors and therapists), using integrated device metadata and deep learning analytics to identify patterns in care delivery, and decision support tools for patients using their own personal data.

Primary Care Nurse Practitioner Work Environments and Hospitalizations and ED Use Among Chronically Ill Medicare Beneficiaries.

BACKGROUND: Nurse practitioners (NPs) play a critical role in delivering primary care, particularly to chronically ill elderly. Yet, many NPs practice in poor work environments which may affect patient outcomes. OBJECTIVE: We investigated the relationship between NP work environments in primary care practices and hospitalizations and emergency department (ED) use among chronically ill elderly. RESEARCH DESIGN: We used a cross-sectional design to collect survey data from NPs about their practices. The survey data were merged with Medicare claims data. SUBJECTS: In total, 979 primary care practices employing NPs and delivering care to chronically ill Medicare beneficiaries (n=452,931) from 6 US states were included. MEASURES: NPs completed the Nurse Practitioner-Primary Care Organizational Climate Questionnaire-a valid and reliable measure for work environment. Data on hospitalizations and ED use was obtained from Medicare claims. We used Cox regression models to estimate risk ratios. RESULTS: After controlling for covariates, we found statistically significant associations between practice-level NP work environment and 3 outcomes: Ambulatory Care Sensitive (ACS) ED visits, all-cause ED visits, and all-cause hospitalizations. With a 1-unit increase in the work environment score, the risk of an ACS-ED visit decreased by 4.4% [risk ratio (RR)=0.956; 99% confidence interval (CI): 0.918-0.995; P=0.004], an ED visit by 3.5% (RR=0.965; 99% CI: 0.933-0.997; P=0.005), and a hospitalization by 4.0% (RR=0.960;99% CI: 0.928-0.993; P=0.002). There was no relationship between NP work environment and ACS hospitalizations. CONCLUSION: Favorable NP work environments are associated with lower hospital and ED utilization. Practice managers should focus on NP work environments in quality improvement strategies.

Factors Influencing Clinician Trust in Predictive Clinical Decision Support Systems for In-Hospital Deterioration: Qualitative Descriptive Study.

BACKGROUND: Clinician trust in machine learning-based clinical decision support systems (CDSSs) for predicting in-hospital deterioration (a type of predictive CDSS) is essential for adoption. Evidence shows that clinician trust in predictive CDSSs is influenced by perceived understandability and perceived accuracy. OBJECTIVE: The aim of this study was to explore the phenomenon of clinician trust in predictive CDSSs for in-hospital deterioration by confirming and characterizing factors known to influence trust (understandability and accuracy), uncovering and describing other influencing factors, and comparing nurses' and prescribing providers' trust in predictive CDSSs. METHODS: We followed a qualitative descriptive methodology conducting directed deductive and inductive content analysis of interview data. Directed deductive analyses were guided by the human-computer trust conceptual framework. Semistructured interviews were conducted with nurses and prescribing providers (physicians, physician assistants, or nurse practitioners) working with a predictive CDSS at 2 hospitals in Mass General Brigham. RESULTS: A total of 17 clinicians were interviewed. Concepts from the human-computer trust conceptual framework-perceived understandability and perceived technical competence (ie, perceived accuracy)-were found to influence clinician trust in predictive CDSSs for in-hospital deterioration. The concordance between clinicians' impressions of patients' clinical status and system predictions influenced clinicians' perceptions of system accuracy. Understandability was influenced by system explanations, both global and local, as well as training. In total, 3 additional themes emerged from the inductive analysis. The first, perceived actionability, captured the variation in clinicians' desires for predictive CDSSs to recommend a discrete action. The second, evidence, described the importance of both macro- (scientific) and micro- (anecdotal) evidence for fostering trust. The final theme, equitability, described fairness in system predictions. The findings were largely similar between nurses and prescribing providers. CONCLUSIONS: Although there is a perceived trade-off between machine learning-based CDSS accuracy and understandability, our findings confirm that both are important for fostering clinician trust in predictive CDSSs for in-hospital deterioration. We found that reliance on the predictive CDSS in the clinical workflow may influence clinicians' requirements for trust. Future research should explore the impact of reliance, the optimal explanation design for enhancing understandability, and the role of perceived actionability in driving trust.

The mediating role of anxiety/depression symptoms between adverse childhood experiences (ACEs) and somatic symptoms in adolescents.

INTRODUCTION: This study examines the relationships among recent adverse childhood experiences (ACEs), somatic symptoms, and anxiety/depression symptoms during adolescence and whether anxiety/depression symptoms mediate the relationship between ACEs and somatic symptoms. METHODS: Longitudinal prospective data from the Longitudinal Studies of Child Abuse and Neglect study of 1354 children and their primary caregivers in the United States was used in this study. A longitudinal cross-lagged path analysis among recent ACEs, anxiety/depression symptoms, and somatic symptoms at three points during adolescence (ages 12, 14, and 16 years) was conducted. RESULTS: The sample was 51% female and 53% African American. The results indicated significant concurrent associations between recent ACEs and increased anxiety/depression symptoms at ages 12, 14, and 16 (ß = .27, p < .001; ß = .15, p < .001; ß = .07, p < .05) and between anxiety/depression symptoms and increased somatic symptoms at ages 12, 14, and 16 years (ß = .44, p < .001; ß = .39, p < .001; ß = .49, p < .001). Moreover, anxiety/depression symptoms significantly mediated the relationship between recent ACEs and concurrent somatic symptoms at ages 12, 14, and 16 years (ß = .12, p < .001; ß = .06, p < .001; ß = .04, p < .05). However, there was no significant relationship between recent ACEs and somatic symptoms. CONCLUSION: The findings suggest that anxiety/depression symptoms mediate the concurrent relationships between recent ACEs and somatic symptoms at ages 12, 14, and 16. Clinicians should consider assessing anxiety/depression symptoms and possible concurrent exposure to ACEs when caring for adolescents who present with somatic symptoms.

Using Time Series Clustering to Segment and Infer Emergency Department Nursing Shifts from Electronic Health Record Log Files.

Few computational approaches exist for abstracting electronic health record (EHR) log files into clinically meaningful phenomena like clinician shifts. Because shifts are a fundamental unit of work recognized in clinical settings, shifts may serve as a primary unit of analysis in the study of documentation burden. We conducted a proof- of-concept study to investigate the feasibility of a novel approach using time series clustering to segment and infer clinician shifts from EHR log files. From 33,535,585 events captured between April-June 2021, we computationally identified 43,911 potential shifts among 2,285 (74.2%) emergency department nurses. On average, computationally-identified shifts were 10.6±3.1 hours long. Based on data distributions, we classified these shifts based on type: day, evening, night; and length: 12-hour, 8-hour, other. We validated our method through manual chart review of computationally-identified 12-hour shifts achieving 92.0% accuracy. Preliminary results suggest unsupervised clustering methods may be a reasonable approach for rapidly identifying clinician shifts.

Clinician and Health Care Leaders' Experiences with-and Perceptions of-COVID-19 Documentation Reduction Policies and Practices.

BACKGROUND: Substantial strategies to reduce clinical documentation were implemented by health care systems throughout the coronavirus disease-2019 (COVID-19) pandemic at national and local levels. This natural experiment provides an opportunity to study the impact of documentation reduction strategies on documentation burden among clinicians and other health professionals in the United States. OBJECTIVES: The aim of this study was to assess clinicians' and other health care leaders' experiences with and perceptions of COVID-19 documentation reduction strategies and identify which implemented strategies should be prioritized and remain permanent post-pandemic. METHODS: We conducted a national survey of clinicians and health care leaders to understand COVID-19 documentation reduction strategies implemented during the pandemic using snowball sampling through professional networks, listservs, and social media. We developed and validated a 19-item survey leveraging existing post-COVID-19 policy and practice recommendations proposed by Sinsky and Linzer. Participants rated reduction strategies for impact on documentation burden on a scale of 0 to 100. Free-text responses were thematically analyzed. RESULTS: Of the 351 surveys initiated, 193 (55%) were complete. Most participants were informaticians and/or clinicians and worked for a health system or in academia. A majority experienced telehealth expansion (81.9%) during the pandemic, which participants also rated as highly impactful (60.1-61.5) and preferred that it remain (90.5%). Implemented at lower proportions, documenting only pertinent positives to reduce note bloat (66.1 ± 28.3), changing compliance rules and performance metrics to eliminate those without evidence of net benefit (65.7 ± 26.3), and electronic health record (EHR) optimization sprints (64.3 ± 26.9) received the highest impact scores compared with other strategies presented; support for these strategies widely ranged (49.7-63.7%). CONCLUSION: The results of this survey suggest there are many perceived sources of and solutions for documentation burden. Within strategies, we found considerable support for telehealth, documenting pertinent positives, and changing compliance rules. We also found substantial variation in the experience of documentation burden among participants.

Characterizing Multitasking and Workflow Fragmentation in Electronic Health Records among Emergency Department Clinicians: Using Time-Motion Data to Understand Documentation Burden.

BACKGROUND: The impact of electronic health records (EHRs) in the emergency department (ED) remains mixed. Dynamic and unpredictable, the ED is highly vulnerable to workflow interruptions. OBJECTIVES: The aim of the study is to understand multitasking and task fragmentation in the clinical workflow among ED clinicians using clinical information systems (CIS) through time-motion study (TMS) data, and inform their applications to more robust and generalizable measures of CIS-related documentation burden. METHODS: Using TMS data collected among 15 clinicians in the ED, we investigated the role of documentation burden, multitasking (i.e., performing physical and communication tasks concurrently), and workflow fragmentation in the ED. We focused on CIS-related tasks, including EHRs. RESULTS: We captured 5,061 tasks and 877 communications in 741 locations within the ED. Of the 58.7 total hours observed, 44.7% were spent on CIS-related tasks; nearly all CIS-related tasks focused on data-viewing and data-entering. Over one-fifth of CIS-related task time was spent on multitasking. The mean average duration among multitasked CIS-related tasks was shorter than non-multitasked CIS-related tasks (20.7 s vs. 30.1 s). Clinicians experienced 1.4 ± 0.9 task switches/min, which increased by one-third when multitasking. Although multitasking was associated with a significant increase in the average duration among data-entering tasks, there was no significant effect on data-viewing tasks. When engaged in CIS-related task switches, clinicians were more likely to return to the same CIS-related task at higher proportions while multitasking versus not multitasking. CONCLUSION: Multitasking and workflow fragmentation may play a significant role in EHR documentation among ED clinicians, particularly among data-entering tasks. Understanding where and when multitasking and workflow fragmentation occurs is a crucial step to assessing potentially burdensome clinician tasks and mitigating risks to patient safety. These findings may guide future research on developing more scalable and generalizable measures of CIS-related documentation burden that do not necessitate direct observation techniques (e.g., EHR log files).

Nursing Documentation Variation Across Different Medical Facilities Within an Integrated Healthcare System.

The purpose of this study was to demonstrate nursing documentation variation based on electronic health record design and its relationship with different levels of care by reviewing how various flowsheet measures, within the same electronic health record across an integrated healthcare system, are documented in different types of medical facilities. Flowsheet data with information on patients who were admitted to academic medical centers, community hospitals, and rehabilitation centers were used to calculate the frequency of flowsheet entries documented. We then compared the distinct flowsheet measures documented in five flowsheet templates across the different facilities. We observed that each type of healthcare facility appeared to have distinct clinical care foci and flowsheet measures documented differed within the same template based on facility type. Designing flowsheets tailored to study settings can meet the needs of end users and increase documentation efficiency by reducing time spent on unrelated flowsheet measures. Furthermore, this process can save nurses time for direct patient care.

Measurement of clinical documentation burden among physicians and nurses using electronic health records: a scoping review.

BACKGROUND: . OBJECTIVE: Electronic health records (EHRs) are linked with documentation burden resulting in clinician burnout. While clear classifications and validated measures of burnout exist, documentation burden remains ill-defined and inconsistently measured. We aim to conduct a scoping review focused on identifying approaches to documentation burden measurement and their characteristics. MATERIALS AND METHODS: Based on Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Extension for Scoping Reviews (ScR) guidelines, we conducted a scoping review assessing MEDLINE, Embase, Web of Science, and CINAHL from inception to April 2020 for studies investigating documentation burden among physicians and nurses in ambulatory or inpatient settings. Two reviewers evaluated each potentially relevant study for inclusion/exclusion criteria. RESULTS: Of the 3482 articles retrieved, 35 studies met inclusion criteria. We identified 15 measurement characteristics, including 7 effort constructs: EHR usage and workload, clinical documentation/review, EHR work after hours and remotely, administrative tasks, cognitively cumbersome work, fragmentation of workflow, and patient interaction. We uncovered 4 time constructs: average time, proportion of time, timeliness of completion, activity rate, and 11 units of analysis. Only 45.0% of studies assessed the impact of EHRs on clinicians and/or patients and 40.0% mentioned clinician burnout. DISCUSSION: Standard and validated measures of documentation burden are lacking. While time and effort were the core concepts measured, there appears to be no consensus on the best approach nor degree of rigor to study documentation burden. CONCLUSION: Further research is needed to reliably operationalize the concept of documentation burden, explore best practices for measurement, and standardize its use.

Extubation during extracorporeal membrane oxygenation in adults: An international qualitative study on experts' opinions.

BACKGROUND: Extracorporeal membrane oxygenation in adults (adult-ECMO), a modification of cardiopulmonary bypass is increasingly used. Liberation from mechanical ventilation, or extubation, during adult-ECMO remains a challenge. OBJECTIVES: This study aimed to understand expert perceptions of the reasonableness of extubation during adult-ECMO and the usefulness of an extubation clinical practice guideline (ECPG). METHODS: Homogeneous purposive sampling, focus groups, and interviews with a discussion guide, and direct content, thematic analysis were used. RESULTS: Fourteen volunteers participated with different educational levels (79% Doctor of Medicine, 14% Registered Nurse, 7% Nurse Practitioner), from high-volume ECMO centers of various annual ECMO runs (50% 30-49 ECMO/year, 36% 50-99 ECMO/year, 14% >100 ECMO/year) worldwide (64% North America, 21% South America, 7% Europe, 7% Asia). Seven themes were identified: paucity of evidence, mindsets towards using an ECPG, barriers, criteria and benefits of extubation, culture towards extubation and vision of the future. Participants recommended aiming for extubation based on patient selection, and a standardized extubation approach with an ECPG or team decision-making. CONCLUSION: Application of adult-ECMO is expanding, during which extubation remains difficult. Experts recommend two methods of a standardized extubation approach.

Clinician involvement in research on machine learning-based predictive clinical decision support for the hospital setting: A scoping review.

OBJECTIVE: The study sought to describe the prevalence and nature of clinical expert involvement in the development, evaluation, and implementation of clinical decision support systems (CDSSs) that utilize machine learning to analyze electronic health record data to assist nurses and physicians in prognostic and treatment decision making (ie, predictive CDSSs) in the hospital. MATERIALS AND METHODS: A systematic search of PubMed, CINAHL, and IEEE Xplore and hand-searching of relevant conference proceedings were conducted to identify eligible articles. Empirical studies of predictive CDSSs using electronic health record data for nurses or physicians in the hospital setting published in the last 5 years in peer-reviewed journals or conference proceedings were eligible for synthesis. Data from eligible studies regarding clinician involvement, stage in system design, predictive CDSS intention, and target clinician were charted and summarized. RESULTS: Eighty studies met eligibility criteria. Clinical expert involvement was most prevalent at the beginning and late stages of system design. Most articles (95%) described developing and evaluating machine learning models, 28% of which described involving clinical experts, with nearly half functioning to verify the clinical correctness or relevance of the model (47%). DISCUSSION: Involvement of clinical experts in predictive CDSS design should be explicitly reported in publications and evaluated for the potential to overcome predictive CDSS adoption challenges. CONCLUSIONS: If present, clinical expert involvement is most prevalent when predictive CDSS specifications are made or when system implementations are evaluated. However, clinical experts are less prevalent in developmental stages to verify clinical correctness, select model features, preprocess data, or serve as a gold standard.

Characterizing and Visualizing Display and Task Fragmentation in the Electronic Health Record: Mixed Methods Design.

BACKGROUND: The complexity of health care data and workflow presents challenges to the study of usability in electronic health records (EHRs). Display fragmentation refers to the distribution of relevant data across different screens or otherwise far apart, requiring complex navigation for the user's workflow. Task and information fragmentation also contribute to cognitive burden. OBJECTIVE: This study aims to define and analyze some of the main sources of fragmentation in EHR user interfaces (UIs); discuss relevant theoretical, historical, and practical considerations; and use granular microanalytic methods and visualization techniques to help us understand the nature of fragmentation and opportunities for EHR optimization or redesign. METHODS: Sunburst visualizations capture the EHR navigation structure, showing levels and sublevels of the navigation tree, allowing calculation of a new measure, the Display Fragmentation Index. Time belt visualizations present the sequences of subtasks and allow calculation of proportion per instance, a measure that quantifies task fragmentation. These measures can be used separately or in conjunction to compare EHRs as well as tasks and subtasks in workflows and identify opportunities for reductions in steps and fragmentation. We present an example use of the methods for comparison of 2 different EHR interfaces (commercial and composable) in which subjects apprehend the same patient case. RESULTS: Screen transitions were substantially reduced for the composable interface (from 43 to 14), whereas clicks (including scrolling) remained similar. CONCLUSIONS: These methods can aid in our understanding of UI needs under complex conditions and tasks to optimize EHR workflows and redesign.