Studying Workflow and Workarounds in Electronic Health Record-Supported Work to Improve Health System Performance

Technology in the management of diabetes in hospitalised adults

Suboptimal glycaemic management in hospitals has been associated with adverse clinical outcomes and increased financial costs to healthcare systems. Despite the availability of guidelines for inpatient glycaemic management, implementation remains challenging because of the increasing workload of clinical staff and rising prevalence of diabetes. The development of novel and innovative technologies that support the clinical workflow and address the unmet need for effective and safe inpatient diabetes care delivery is still needed. There is robust evidence that the use of diabetes technology such as continuous glucose monitoring and closed-loop insulin delivery can improve glycaemic management in outpatient settings; however, relatively little is known of its potential benefits and application in inpatient diabetes management. Emerging data from clinical studies show that diabetes technologies such as integrated clinical decision support systems can potentially mediate safer and more efficient inpatient diabetes care, while continuous glucose sensors and closed-loop systems show early promise in improving inpatient glycaemic management. This review aims to provide an overview of current evidence related to diabetes technology use in non-critical care adult inpatient settings. We highlight existing barriers that may hinder or delay implementation, as well as strategies and opportunities to facilitate the clinical readiness of inpatient diabetes technology in the future.

The user-centered design and development of a childhood and adolescent obesity Electronic Health Record tool, a mixed-methods study

Background

Childhood and adolescent obesity are persistent public health issues in the United States. Childhood obesity Electronic Health Record (EHR) tools strengthen provider-patient relationships and improve outcomes, but there are currently limited EHR tools that are linked to adolescent mHealth apps. This study is part of a larger study entitled, CommitFit, which features both an adolescent-targeted mobile health application (mHealth app) and an ambulatory EHR tool. The CommitFit mHealth app was designed to be paired with the CommitFit EHR tool for integration into clinical spaces for shared decision-making with patients and clinicians.

Objectives

The objective of this sub-study was to identify the functional and design needs and preferences of healthcare clinicians and professionals for the development of the CommitFit EHR tool, specifically as it relates to childhood and adolescent obesity management.

Methods

We utilized a user-centered design process with a mixed-method approach. Focus groups were used to assess current in-clinic practices, deficits, and general beliefs and preferences regarding the management of childhood and adolescent obesity. A pre- and post-focus group survey helped assess the perception of the design and functionality of the CommitFit EHR tool and other obesity clinic needs. Iterative design development of the CommitFit EHR tool occurred throughout the process.

Results

A total of 12 healthcare providers participated throughout the three focus group sessions. Two themes emerged regarding EHR design: (1) Functional Needs, including Enhancing Clinical Practices and Workflow, and (2) Visualization, including Colors and Graphs. Responses from the surveys (n = 52) further reflect the need for Functionality and User-Interface Design by clinicians. Clinicians want the CommitFit EHR tool to enhance in-clinic adolescent lifestyle counseling, be easy to use, and presentable to adolescent patients and their caregivers. Additionally, we found that clinicians preferred colors and graphs that improved readability and usability. During each step of feedback from focus group sessions and the survey, the design of the CommitFit EHR tool was updated and co-developed by clinicians in an iterative user-centered design process.

Conclusion

More research is needed to explore clinician actual user analytics for the CommitFit EHR tool to evaluate real-time workflow, design, and function needs. The effectiveness of the CommitFit mHealth and EHR tool as a weight management intervention needs to be evaluated in the future.

Improving electronic health record documentation and use to promote evidence-based pediatric care

OBJECTIVE

Electronic health records (EHRs) often lack the necessary functionalities to support the full implementation of national clinical guidelines for pediatric care outlined in the American Academy of Pediatrics Bright Futures Guidelines. Using HealthySteps (HS), an evidence-based pediatric primary care program, as an exemplar, this study aimed to enhance pediatric EHRs, identify facilitators and barriers to EHR enhancements, and improve data quality for delivering clinical care as part of HS implementation and evidence building.

METHODS

Three HS sites-each differing in location, setting, number of children served, and mix of child insurance coverage-participated in the study. Sites received technical assistance to support data collection and EHR updates. A comprehensive evaluation, including a process evaluation and outcomes monitoring, was conducted to gauge progress toward implementing study data requirements over time. Data sources included administrative records, surveys, and interviews.

RESULTS

All sites enhanced their EHRs yet relied on supplemental data systems to track care coordination. Sites improved documentation of required data, demonstrating reductions in missing data and increases in extractable data between baseline and follow-up assessments. For example, the percentage of missing social-emotional screening results ranged from 0% to 8.0% at study conclusion. Facilitators and barriers to EHR enhancements included organizational supports, leadership, and capacity building.

CONCLUSIONS

With significant investment of time and resources, practices modified their EHRs to better capture services aligned with HS and Bright Futures. However, more scalable digital solutions are necessary to support EHR updates to help drive improvements in clinical care and outcomes for children and families.

Re-Envisioning Electronic Health Records to Optimize Patient-Centered Cancer Care, Quality, Surveillance, and Research

Electronic health records (EHRs) are a significant advancement over paper records. However, the full potential of EHRs for improving care quality, patient outcomes, surveillance, and research in cancer care is yet to be realized. The organic evolution of EHRs has resulted in a number of unanticipated consequences including increased time spent by clinicians interfacing with the EHR for daily workflows. Patient access to clinicians and their records has been an important advancement in patient-centered care; however, this has brought to light additional gaps and challenges in EHRs meeting these needs. A significant challenge for EHR design and physician workflows is how best to meet the complex goals and priorities of various stakeholders including providers, researchers, patients, health systems, payors, and regulatory agencies. The National Cancer Policy Forum convened a 2022 workshop, "Innovations in Electronic Health Records for Oncology Care, Research and Surveillance," to address these challenges and to facilitate collaboration across all user groups with the goal of re-envisioning EHRs that will better support shared goals of improving patient outcomes and advancing cancer care and research without overburdening clinicians with administrative tasks. Here, we summarize the current EHR ecosystem as discussed at the workshop and highlight opportunities to improve EHR contributions to oncology evidence and care.

Guidance for reporting analyses of metadata on electronic health record use

INTRODUCTION

Research on how people interact with electronic health records (EHRs) increasingly involves the analysis of metadata on EHR use. These metadata can be recorded unobtrusively and capture EHR use at a scale unattainable through direct observation or self-reports. However, there is substantial variation in how metadata on EHR use are recorded, analyzed and described, limiting understanding, replication, and synthesis across studies.

RECOMMENDATIONS

In this perspective, we provide guidance to those working with EHR use metadata by describing 4 common types, how they are recorded, and how they can be aggregated into higher-level measures of EHR use. We also describe guidelines for reporting analyses of EHR use metadata-or measures of EHR use derived from them-to foster clarity, standardization, and reproducibility in this emerging and critical area of research.

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.

The Design of the Electronic Health Record in Type 1 Diabetes Centers: Implications for Metrics and Data Availability for a Quality Collaborative

BACKGROUND

Systematic and comprehensive data acquisition from the electronic health record (EHR) is critical to the quality of data used to improve patient care. We described EHR tools, workflows, and data elements that contribute to core quality metrics in the Type 1 Diabetes Exchange Quality Improvement Collaborative (T1DX-QI).

METHOD

We conducted interviews with quality improvement (QI) representatives at 13 T1DX-QI centers about their EHR tools, clinic workflows, and data elements.

RESULTS

All centers had access to structured data tools, nine had access to patient questionnaires and two had integration with a device platform. There was significant variability in EHR tools, workflows, and data elements, thus the number of available metrics per center ranged from four to 17 at each site. Thirteen centers had information about glycemic outcomes and diabetes technology use. Seven centers had measurements of additional self-management behaviors. Centers captured patient-reported outcomes including social determinants of health (n = 9), depression (n = 11), transition to adult care (n = 7), and diabetes distress (n = 3). Various stakeholders captured data including health care professionals, educators, medical assistants, and QI coordinators. Centers that had a paired staffing model in clinic encounters distributed the burden of data capture across the health care team and was associated with a higher number of available data elements.

CONCLUSIONS

The lack of standardization in EHR tools, workflows, and data elements captured resulted in variability in available metrics across centers. Further work is needed to support measurement and subsequent improvement in quality of care for individuals with type 1 diabetes.

Clinical named entity recognition and relation extraction using natural language processing of medical free text: A systematic review

BACKGROUND

Natural Language Processing (NLP) applications have developed over the past years in various fields including its application to clinical free text for named entity recognition and relation extraction. However, there has been rapid developments the last few years that there's currently no overview of it. Moreover, it is unclear how these models and tools have been translated into clinical practice. We aim to synthesize and review these developments.

METHODS

We reviewed literature from 2010 to date, searching PubMed, Scopus, the Association of Computational Linguistics (ACL), and Association of Computer Machinery (ACM) libraries for studies of NLP systems performing general-purpose (i.e., not disease- or treatment-specific) information extraction and relation extraction tasks in unstructured clinical text (e.g., discharge summaries).

RESULTS

We included in the review 94 studies with 30 studies published in the last three years. Machine learning methods were used in 68 studies, rule-based in 5 studies, and both in 22 studies. 63 studies focused on Named Entity Recognition, 13 on Relation Extraction and 18 performed both. The most frequently extracted entities were "problem", "test" and "treatment". 72 studies used public datasets and 22 studies used proprietary datasets alone. Only 14 studies defined clearly a clinical or information task to be addressed by the system and just three studies reported its use outside the experimental setting. Only 7 studies shared a pre-trained model and only 8 an available software tool.

DISCUSSION

Machine learning-based methods have dominated the NLP field on information extraction tasks. More recently, Transformer-based language models are taking the lead and showing the strongest performance. However, these developments are mostly based on a few datasets and generic annotations, with very few real-world use cases. This may raise questions about the generalizability of findings, translation into practice and highlights the need for robust clinical evaluation.

Development and Validation of SafeHIT: An Instrument to Assess the Self-Reported Safe Use of Health Information Technology

BACKGROUND

Implementing health information technology (HIT) may cause unintended consequences and safety risks when incorrectly designed and used. Yet, the tools to assess self-reported safe use of HIT are not well established.

OBJECTIVE

This study aims to develop and validate SafeHIT, an instrument to assess self-reported safe use of HIT among health care practitioners.

METHODS

Systematic literature review and a semistructured interview with 31 experts were adopted to generate SafeHIT instrument items. In total, 450 physicians from various departments at three Malaysian public hospitals participated in the questionnaire survey to validate SafeHIT. Exploratory factor analysis and confirmatory factor analysis (CFA) were undertaken to explore the items that best represent a specific construct and to confirm the reliability and validity of the SafeHIT, respectively.

RESULTS

The final SafeHIT consisted of 14 constructs and 58 items in total. The result of the CFA confirmed that all constructs demonstrated adequate convergent and discriminant validity.

CONCLUSION

A reliable and valid theoretically underpinned measure of determinants of safe HIT use behavior has been developed. Understanding external factors that influence safe HIT use is useful for developing targeted interventions that favor the quality and safety of health care.

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.

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.

Measuring the Cognitive Effort Associated with Task Switching in Routine EHR-based Tasks

OBJECTIVE

Clinical work involves performing overlapping, time-sensitive tasks that frequently require clinicians to switch their attention between multiple tasks. We developed a methodological approach using EHR-based audit logs to determine switch costs-the cognitive burden associated with task switching-and assessed its magnitude during routine EHR-based clinical tasks.

METHOD

Physician trainees (N=75) participated in a longitudinal study where they provided access to their EHR-based audit logs. Physicians' audit log actions were used to create a taxonomy of EHR tasks. These tasks were transformed into task sequences and the time spent on each task in a sequence was computed. Within these task sequences, instances of task switching (i.e., switching from one task to the next) and non-switching were identified. The primary outcome of interest was the time spent on a post-switch task. Using a mixed-effects regression model, we compared the durations of post-switch and non-switch tasks.

RESULTS

2,781,679 audit log events over 117,822 sessions from 75 physicians were analyzed. Physicians spent most time on chart review (Median (IQR)=5,439 (2,492-8,336) seconds), note review (1,936 (827-3,321) seconds), and navigating the EHR interface (1,048 (365.5-2,006) seconds) daily. Post task switch activity times were greater for documentation (Median increase=5 seconds), order entry (Median increase=3 seconds) and results review (Median increase=3 seconds). Mixed-effects regression showed that time spent on tasks were longer following a task switch (β=0.03; 95% CI_lower= 0.027, CI_upper=0.034), with greater post-swtich task times for imaging, order entry, note review, handoff, note entry, chart review and best practice advisory tasks.

DISCUSSION

Increased task switching time-an indicator of the cognitive burden associated with switching between tasks-is prevalent in routine EHR-based tasks. We discuss the cumulative impact of incremental switch costs have on overall EHR workload, wellness, and error rates. Relying on theoretical cognitive foundations, we suggest pragmatic design considerations for mitigating the effects of cognitive burden associated with task switching.

A qualitative study of the dark and bright sides of physicians' electronic health record work outside work hours

BACKGROUND

The introduction of electronic health records (EHRs) has contributed considerably to EHR work outside work (WOW) hours for physicians. Prior research has identified the pressures associated with stress resulting from EHR WOW, yet developing a nuanced understanding of how physicians appraise and respond to this stress, and the resulting impacts, remains absent from the literature.

PURPOSE

Grounded in the technostress model, this study takes a qualitative approach to explore both the pressures and opportunities associated with EHR WOW.

METHODS

Thematic analysis of data from semistructured interviews was utilized to examine the pressures and opportunities associated with EHR WOW among primary care pediatricians (n = 15) affiliated with a large Midwestern pediatric health system.

RESULTS

The physicians in this study regularly spent time working in the EHR outside work hours. They felt the EHR contributed to their documentation burden, which ultimately increased their EHR WOW, and reported a sense of burden from ubiquitous EHR availability. Conversely, they appreciated the flexibility the EHR provided in terms of work-life balance. Suggestions for improvement under the direct purview of practice management included enhanced EHR usability, improvements in workflow during work hours to free up time to document, and more training on both EHR documentation strategies and ongoing software upgrades.

CONCLUSION

Physicians perceive that the EHR exerts certain pressures while affording new opportunities and conveniences. This study provides evidence of both the pressures and opportunities of EHR WOW and their effect on physician well-being.

PRACTICE IMPLICATIONS

Specific opportunities are identified for health administrators to enable physicians to better manage EHR WOW.

Monitoring the Implementation of Tobacco Cessation Support Tools: Using Novel Electronic Health Record Activity Metrics

BACKGROUND

Clinical decision support (CDS) tools in electronic health records (EHRs) are often used as core strategies to support quality improvement programs in the clinical setting. Monitoring the impact (intended and unintended) of these tools is crucial for program evaluation and adaptation. Existing approaches for monitoring typically rely on health care providers' self-reports or direct observation of clinical workflows, which require substantial data collection efforts and are prone to reporting bias.

OBJECTIVE

This study aims to develop a novel monitoring method leveraging EHR activity data and demonstrate its use in monitoring the CDS tools implemented by a tobacco cessation program sponsored by the National Cancer Institute's Cancer Center Cessation Initiative (C3I).

METHODS

We developed EHR-based metrics to monitor the implementation of two CDS tools: (1) a screening alert reminding clinic staff to complete the smoking assessment and (2) a support alert prompting health care providers to discuss support and treatment options, including referral to a cessation clinic. Using EHR activity data, we measured the completion (encounter-level alert completion rate) and burden (the number of times an alert was fired before completion and time spent handling the alert) of the CDS tools. We report metrics tracked for 12 months post implementation, comparing 7 cancer clinics (2 clinics implemented the screening alert and 5 implemented both alerts) within a C3I center, and identify areas to improve alert design and adoption.

RESULTS

The screening alert fired in 5121 encounters during the 12 months post implementation. The encounter-level alert completion rate (clinic staff acknowledged completion of screening in EHR: 0.55; clinic staff completed EHR documentation of screening results: 0.32) remained stable over time but varied considerably across clinics. The support alert fired in 1074 encounters during the 12 months. Providers acted upon (ie, not postponed) the support alert in 87.3% (n=938) of encounters, identified a patient ready to quit in 12% (n=129) of encounters, and ordered a referral to the cessation clinic in 2% (n=22) of encounters. With respect to alert burden, on average, both alerts fired over 2 times (screening alert: 2.7; support alert: 2.1) before completion; time spent postponing the screening alert was similar to completing (52 vs 53 seconds) the alert, and time spent postponing the support alert was more than completing (67 vs 50 seconds) the alert per encounter. These findings inform four areas where the alert design and use can be improved: (1) improving alert adoption and completion through local adaptation, (2) improving support alert efficacy by additional strategies including training in provider-patient communication, (3) improving the accuracy of tracking for alert completion, and (4) balancing alert efficacy with the burden.

CONCLUSIONS

EHR activity metrics were able to monitor the success and burden of tobacco cessation alerts, allowing for a more nuanced understanding of potential trade-offs associated with alert implementation. These metrics can be used to guide implementation adaptation and are scalable across diverse settings.

Electronic Health Records' Support for Primary Care Physicians' Situation Awareness: A Metanarrative Review

OBJECTIVE

Situation awareness (SA) refers to people's perception and understanding of their dynamic environment. In primary care, reduced SA among physicians increases errors in clinical decision-making and, correspondingly, patients' risk of experiencing adverse outcomes. Our objective was to understand the extent to which electronic health records (EHRs) support primary care physicians (PCPs)' SA during clinical decision-making.

METHOD

We conducted a metanarrative review of papers in selected academic databases, including CINAHL and MEDLINE. Eligible studies included original peer-reviewed research published between January 2012 and August 2020 on PCP-EHR interactions. We iteratively queried, screened, and summarized literature focused on EHRs supporting PCPs' clinical decision-making and care management for adults. Then, we mapped findings to an established SA framework to classify external factors (individual, task, and system) affecting PCPs' levels of SA (1-Perception, 2-Comprehension, and 3-Projection) and identified SA barriers.

RESULTS

From 1504 articles identified, we included and synthesized 19 studies. Study designs were largely noninterventional. Studies described EHR workflow misalignments, usability issues, and communication challenges. EHR information, including lab results and care plans, was characterized as incomplete, untimely, or irrelevant. Unmet information needs made it difficult for PCPs to obtain even basic SA, Level 1 SA. Prevalent barriers to PCPs developing SA with EHRs were errant mental models, attentional tunneling, and data overload.

CONCLUSION

Based on our review, EHRs do not support the development of higher levels of SA among PCPs. Review findings suggest SA-oriented design processes for health information technology could improve PCPs' SA, satisfaction, and decision-making.

Unintended consequences of pharmacy information systems: A case study

BACKGROUND

Pharmacy information systems (PhIS) can cause medication errors that pharmacists may overlook due to their increased workload and lack of understanding of maintaining information quality. This study seeks to identify factors influencing unintended consequences of PhIS and how they affect the information quality, which can pose a risk to patient safety.

MATERIALS AND METHODS

This qualitative, explanatory case study evaluated PhIS in ambulatory pharmacies in a hospital and a clinic. Data were collected through observations, interviews, and document analysis. We applied the socio-technical interactive analysis (ISTA) framework to investigate the socio-technical interactions of pharmacy information systems that lead to unintended consequences. We then adopted the human-organization-process-technology-fit (HOPT-fit) framework to identify their contributing and dominant factors, misfits, and mitigation measures.

RESULTS

We identified 28 unintended consequences of PhIS, their key contributing factors, and their interrelations with the systems. The primary causes of unintended consequences include system rigidity and complexity, unclear knowledge, understanding, skills, and purpose of using the system, use of hybrid paper and electronic documentation, unclear and confusing transitions, additions and duplication of tasks and roles in the workflow, and time pressure, causing cognitive overload and workarounds. Recommended mitigating mechanisms include human factor principles in system design, data quality improvement for PhIS in terms of effective use of workspace, training, PhIS master data management, and communication by standardizing workarounds.

CONCLUSION

Threats to information quality emerge in PhIS because of its poor design, a failure to coordinate its functions and clinical tasks, and pharmacists' lack of understanding of the system use. Therefore, safe system design, fostering awareness in maintaining the information quality of PhIS and cultivating its safe use in organizations is essential to ensure patient safety. The proposed evaluation approach facilitates the evaluator to identify complex socio-technical interactions and unintended consequences factors, impact, and mitigation mechanisms.

Characterizing the macrostructure of electronic health record work using raw audit logs: an unsupervised action embeddings approach

Raw audit logs provide a comprehensive record of clinicians' activities on an electronic health record (EHR) and have considerable potential for studying clinician behaviors. However, research using raw audit logs is limited because they lack context for clinical tasks, leading to difficulties in interpretation. We describe a novel unsupervised approach using the comparison and visualization of EHR action embeddings to learn context and structure from raw audit log activities. Using a dataset of 15 767 634 raw audit log actions performed by 88 intern physicians over 6 months of EHR use across inpatient and outpatient settings, we demonstrated that embeddings can be used to learn the situated context for EHR-based work activities, identify discrete clinical workflows, and discern activities typically performed across diverse contexts. Our approach represents an important methodological advance in raw audit log research, facilitating the future development of metrics and predictive models to measure clinician behaviors at the macroscale.

Professionals as Change Agents or Instruments of Reproduction? Medical Residents’ Reasoning for Not Sharing the Electronic Health Record Screen with Patients

The stability of physicians’ authority over patients despite decades of changes in medicine conflicts with newer institutionalist accounts of professionals as change agents rather than instruments of reproduction. We analyzed whether the cultural scripts that twenty-one residents used to justify their approach to a new change, the electronic health record (EHR), signaled a leveling of the patient-physician hierarchy. Residents are intriguing because their position makes them open to change. Indeed, residents justified using the EHR in ways that level the patient-physician hierarchy, but also offered rationales that sustain it. For the latter, residents described using the EHR to substantiate their expertise, situate themselves as brokers between patients and the technology, and preserve the autonomy of clinicians. Our findings highlight how professionals with little direct experience before a change can selectively apply incumbent scripts to sustain extant structures, while informing newer institutionalist accounts of professionals and the design of EHR systems.

Priorities to accelerate workflow automation in health care

Inefficient workflows affect many health care stakeholders including patients, caregivers, clinicians, and staff. Widespread health information technology adoption and modern computing provide opportunities for more efficient health care workflows through automation. The Office of the National Coordinator for Health Information Technology (ONC) led a multidisciplinary effort with stakeholders across health care and experts in industrial engineering, computer science, and finance to explore opportunities for automation in health care. The effort included semistructured key informant interviews, a review of relevant literature, and a workshop to understand automation lessons across nonhealth care industries that could be applied to health care. In this article, we describe considerations for advancing workflow automation in health care that were identified through these activities. We also discuss a set of six priorities and related strategies developed through the ONC-led effort and highlight the role the informatics and research communities have in advancing each priority and the strategies.

The Limitations of User-and Human-Centered Design in an eHealth Context and How to Move Beyond Them

Human-centered design (HCD) is widely regarded as the best design approach for creating eHealth innovations that align with end users’ needs, wishes, and context and has the potential to impact health care. However, critical reflections on applying HCD within the context of eHealth are lacking. Applying a critical eye to the use of HCD approaches within eHealth, we present and discuss 9 limitations that the current practices of HCD in eHealth innovation often carry. The limitations identified range from limited reach and bias to narrow contextual and temporal focus. Design teams should carefully consider if, how, and when they should involve end users and other stakeholders in the design process and how they can combine their insights with existing knowledge and design skills. Finally, we discuss how a more critical perspective on using HCD in eHealth innovation can move the field forward and offer 3 directions of inspiration to improve our design practices: value-sensitive design, citizen science, and more-than-human design. Although value-sensitive design approaches offer a solution to some of the biased or limited views of traditional HCD approaches, combining a citizen science approach with design inspiration and imagining new futures could widen our view on eHealth innovation. Finally, a more-than-human design approach will allow eHealth solutions to care for both people and the environment. These directions can be seen as starting points that invite and support the field of eHealth innovation to do better and to try and develop more inclusive, fair, and valuable eHealth innovations that will have an impact on health and care.

Improving usability of Electronic Health Records in a UK Mental Health setting: a feasibility study

Background

Electronic Health Records (EHRs) can help clinicians to plan, document and deliver care for patients in healthcare services. When used consistently, EHRs can advance patient safety and quality, and reduce clinician’s workload. However, usability problems can make it difficult for clinicians to use EHRs effectively, which can negatively impact both healthcare professionals and patients.

Objective

To improve usability of EHRs within a mental health service in the UK.

Methods

This was a feasibility study conducted with two mental health teams. A mixed-methods approach was employed. Focus group discussions with clinicians identified existing usability problems in EHRs and changes were made to address these problems. Updated EHR assessment forms were evaluated by comparing the following measures pre and post changes: (1) usability testing to monitor time spent completing and duplicating patient information in EHRs, (2) clinician’s experience of using EHRs, and (3) proportion of completed EHR assessment forms.

Results

Usability testing with clinicians (n = 3) showed that the time taken to complete EHR assessment forms and time spent duplicating patient information decreased. Clinician’s experience of completing EHR assessment forms also significantly improved post changes compared to baseline (n = 71; p < 0.005). There was a significant increase in completion of most EHR forms by both teams after EHR usability improvements (all at p < 0.01).

Conclusions

Usability improvements to EHRs can reduce the time taken to complete forms, advance clinician’s experience and increase usage of EHRs. It is important to engage healthcare professionals in the usability improvement process of EHRs in mental health services.

Supplementary information

The online version contains supplementary material available at 10.1007/s10916-022-01832-0.

Increasing Nursing Documentation Efficiency With Wearable Sensors for Pressure Injury Prevention

BACKGROUND

Documentation presents an overwhelming burden to bedside clinical nurses. Nurses must manually enter several hundred data points into electronic health record flow sheets, taking time from direct patient care and introducing opportunity for documentation errors.

LOCAL PROBLEM

A patient record audit revealed a significant gap in documented patient repositioning events. This quality improvement initiative evaluated automated repositioning documentation via a wearable sensor system.

METHODS

A pretest-posttest design was used to examine retrospectively collected manual documentation and prospectively collected sensor documentation of patient repositioning events in a 148-bed rural community hospital. Repositioning documentation manually entered into electronic health records during the baseline period (January 1 to February 28, 2018) was compared with automatic, sensor-based repositioning documentation during the implementation period (corresponding months in 2019 and 2020 to eliminate seasonality).

RESULTS

A convenience sample of 105 patient records was reviewed. The mean documented patient repositioning interval was 6.6 hours in the baseline period and 2.4 hours in the implementation period. The improvement was most pronounced in patients with obesity, whose mean repositioning interval improved from 9.4 hours to 2.5 hours. Documentation compliance (actual vs expected repositioning documentation) was 31% with manual documentation and 82% with automatic sensor-based documentation.

CONCLUSIONS

Repositioning was documented more than 2.5 times as frequently with sensor technology as with manual data entry. Body position and reasons for delayed repositioning events were documented more completely with sensor technology. Automated documentation may improve the accuracy of electronic health records and reduce the documentation burden for nurses.

Electronic health records, mobile health, and the challenge of improving global health

Technology continues to impact healthcare around the world. This provides great opportunities, but also risks. These risks are compounded in low-resource settings where errors in planning and implementation may be more difficult to overcome. Global Health Informatics provides lessons in both opportunities and risks by building off of general Global Health. Global Health Informatics also requires a thorough understanding of the local environment and the needs of low-resource settings. Forming effective partnerships and following the lead of local experts are necessary for sustainability; it also ensures that the priorities of the local community come first. There is an opportunity for partnerships between low-resource settings and high income areas that can provide learning opportunities to avoid the pitfalls that plague many digital health systems and learn how to properly implement technology that truly improves healthcare.

Workflow Automation for a Virtual Hypertension Management Program

Objectives  Hypertension is a modifiable risk factor for numerous comorbidities and treating hypertension can greatly improve health outcomes. We sought to increase the efficiency of a virtual hypertension management program through workflow automation processes.

Methods  We developed a customer relationship management (CRM) solution at our institution for the purpose of improving processes and workflow for a virtual hypertension management program and describe here the development, implementation, and initial experience of this CRM system.

Results  Notable system features include task automation, patient data capture, multi-channel communication, integration with our electronic health record (EHR), and device integration (for blood pressure cuffs). In the five stages of our program (intake and eligibility screening, enrollment, device configuration/setup, medication titration, and maintenance), we describe some of the key process improvements and workflow automations that are enabled using our CRM platform, like automatic reminders to capture blood pressure data and present these data to our clinical team when ready for clinical decision making. We also describe key limitations of CRM, like balancing out-of-the-box functionality with development flexibility. Among our first group of referred patients, 76% (39/51) preferred email as their communication method, 26/51 (51%) were able to enroll electronically, and 63% of those enrolled (32/51) were able to transmit blood pressure data without phone support.

Conclusion  A CRM platform could improve clinical processes through multiple pathways, including workflow automation, multi-channel communication, and device integration. Future work will examine the operational improvements of this health information technology solution as well as assess clinical outcomes.

Reducing Nurse Practitioner Turnover in Home Based Primary Care: A Department of Veterans Affairs Quality Improvement Project

Access to healthcare is challenging for both Veterans and the nation's general population. To keep up with national primary healthcare needs, the Department of Veteran Affairs (VA) implemented Home Based Primary Care (HBPC). After a structure remodel at a Texas VA medical center, 40% of nurse practitioners (NPs) left the HBPC department in one year. The Anticipated Turnover Scale and the Misener NP Job Satisfaction Scale were administered online (n = 7), and results were used to complete a program evaluation. Forty-three percent of participants indicated intent to leave, and 56% of answers indicated job dissatisfaction. Seven categories were identified to mitigate voluntary turnover: Recognition; Shared governance; Orientation; Full practice authority; Collaboration; Organizational workflow maps; and Mentoring. Implementation of recommendations resulting from this project may help retain NPs in both VA and non-VA organizations, reduce organizational costs, support optimal patient outcomes, and increase access to healthcare.

An artificial intelligence platform to optimize workflow during ovarian stimulation and IVF: process improvement and outcome-based predictions

RESEARCH QUESTION

Can workflow during IVF be facilitated by artificial intelligence to limit monitoring during ovarian stimulation to a single day and enable level-loading of retrievals?

DESIGN

The dataset consisted of 1591 autologous cycles in unique patients with complete data including age, FSH, oestradiol and anti-Müllerian concentrations, follicle counts and body mass index. Observations during ovarian stimulation included oestradiol concentrations and follicle diameters. An algorithm was designed to identify the single best day for monitoring and predict trigger day options and total number of oocytes retrieved.

RESULTS

The mean error to predict the single best day for monitoring was 1.355 days. After identifying the single best day for evaluation, the algorithm identified the trigger date and range of three oocyte retrieval days specified by the earliest and the latest day on which the number of oocytes retrieved was minimally changed with a variance of 0-3 oocytes. Accuracy for prediction of total number of oocytes with baseline testing alone or in combination with data on the day of observation was 0.76 and 0.80, respectively. The sensitivities for estimating the total number and number of mature oocytes based solely on pre-IVF profiles in group I (0-10) were 0.76 and 0.78, and in group II (>10) 0.76 and 0.81, respectively.

CONCLUSIONS

A first-iteration algorithm is described designed to improve workflow, minimize visits and level-load embryology work. This algorithm enables decisions at three interrelated nodal points for IVF workflow management to include monitoring on the single best day, assign trigger days to enable a range of 3 days for level-loading and estimate oocyte number.

An interview study with medical scribes on how their work may alleviate clinician burnout through delegated health IT tasks

OBJECTIVES

To understand how medical scribes' work may contribute to alleviating clinician burnout attributable directly or indirectly to the use of health IT.

MATERIALS AND METHODS

Qualitative analysis of semistructured interviews with 32 participants who had scribing experience in a variety of clinical settings.

RESULTS

We identified 7 categories of clinical tasks that clinicians commonly choose to offload to medical scribes, many of which involve delegated use of health IT. These range from notes-taking and computerized data entry to foraging, assembling, and tracking information scattered across multiple clinical information systems. Some common characteristics shared among these tasks include: (1) time-consuming to perform; (2) difficult to remember or keep track of; (3) disruptive to clinical workflow, clinicians' cognitive processes, or patient-provider interactions; (4) perceived to be low-skill "clerical" work; and (5) deemed as adding no value to direct patient care.

DISCUSSION

The fact that clinicians opt to "outsource" certain clinical tasks to medical scribes is a strong indication that performing these tasks is not perceived to be the best use of their time. Given that a vast majority of healthcare practices in the US do not have the luxury of affording medical scribes, the burden would inevitably fall onto clinicians' shoulders, which could be a major source for clinician burnout.

CONCLUSIONS

Medical scribes help to offload a substantial amount of burden from clinicians-particularly with tasks that involve onerous interactions with health IT. Developing a better understanding of medical scribes' work provides useful insights into the sources of clinician burnout and potential solutions to it.

Investigation of usability problems of electronic medical record systems in the emergency department

BACKGROUND

Despite the benefits of using electronic medical record (EMR) systems, existing studies show that many healthcare providers are uncertain regarding their usability. The usability issues of these systems decrease their efficiency, discourage clinicians, and cause dissatisfaction among patients, which may result in safety risks and harm.

OBJECTIVE

The aim of this study was to collect and analyze EMR system usability problems from actual users. Practical user interface guidelines were presented based on the medical practices of these users.

METHODS

Employing an online questionnaire with a seven-point Likert scale, usability issues of EMR systems were collected from 200 emergency department healthcare providers (103 physicians (medical doctors) and 97 nurses) from South Korea.

RESULTS

The most common usability problem among the physicians and nurses was generating in-patient selection. This pertained to the difficulty in finding the required information on-screen because of poor visibility and a lack of distinctiveness.

CONCLUSIONS

The major problems of EMR systems and their causes were identified. It is recommended that intensive visual enhancement of EMR system interfaces should be implemented to support user tasks. By providing a better understanding of the current usability problems among medical practitioners, the results of this study can be useful for developing EMR systems with increased effectiveness and efficiency.

Digital systems for improving outcomes in patients with primary immune defects

PURPOSE OF REVIEW

Healthcare has already been impacted by the fourth industrial revolution exemplified by tip of spear technology, such as artificial intelligence and quantum computing. Yet, there is much to be accomplished as systems remain suboptimal, and full interoperability of digital records is not realized. Given the footprint of technology in healthcare, the field of clinical immunology will certainly see improvements related to these tools.

RECENT FINDINGS

Biomedical informatics spans the gamut of technology in biomedicine. Within this distinct field, advances are being made, which allow for engineering of systems to automate disease detection, create computable phenotypes and improve record portability. Within clinical immunology, technologies are emerging along these lines and are expected to continue.

SUMMARY

This review highlights advancements in digital health including learning health systems, electronic phenotyping, artificial intelligence and use of registries. Technological advancements for improving diagnosis and care of patients with primary immunodeficiency diseases is also highlighted.

Tailoring EHRs for Specific Working Environments Improves Work Well-Being of Physicians

Electronic health records (EHRs) have an impact on physicians’ well-being and stress levels. We studied physicians’ experiences with EHRs and their experienced time pressure and self-rated stress by an electronic questionnaire sent to Finnish physicians aged under 65 in 2017. Our sample was 2980 physicians working in the public sector, health care centers (35.5%) or hospitals (64.5%). Experienced technical problems were positively associated with experienced time pressure, whereas user-friendliness of the EHRs was negatively associated with experienced time pressure. Low perceived support for internal cooperation was associated with high levels of time pressure in hospitals. Those experiencing high levels of technical problems were 1.3 times more likely to experience stress compared to those experiencing low levels of technical problems. Better user-friendliness of the EHRs was associated with lower levels of self-rated stress. In both working environments but more strongly in primary health care, technical problems were associated with self-rated stress. Technical problems and user-friendliness of EHRs are the main factors associated with time pressure and self-rated stress. Health care environments differ in the nature of workflow having different demands on the EHRs. Developing EHR systems should consider the special needs of different environments and workflows, enabling better work well-being amongst physicians.