Patient Safety Issues From Information Overload in Electronic Medical Records

Exploring potential drivers of patient engagement with their health data through digital platforms: A scoping review

BACKGROUND

Patient engagement when providing patient access to health data results from an interaction between the available tools and individual capabilities. The recent digital advancements of the healthcare field have altered the manifestation and importance of patient engagement. However, a comprehensive assessment of what factors contribute to patient engagement remain absent. In this review article, we synthesised the most frequently discussed factors that can foster patient engagement with their health data.

METHODS

A scoping review was conducted in MEDLINE, Embase, and Google Scholar. Relevant data were synthesized within 7 layers using a thematic analysis: (1) social and demographic factors, (2) patient ability factors, (3) patient motivation factors, (4) factors related to healthcare professionals' attitudes and skills, (5) health system factors, (6) technological factors, and (7) policy factors.

RESULTS

We identified 5801 academic and 200 Gy literature records, and included 292 (4.83%) in this review. Overall, 44 factors that can affect patient engagement with their health data were extracted. We extracted 6 social and demographic factors, 6 patient ability factors, 12 patient motivation factors, 7 factors related to healthcare professionals' attitudes and skills, 4 health system factors, 6 technological factors, and 3 policy factors.

CONCLUSIONS

Improving patient engagement with their health data enables the development of patient-centered healthcare, though it can also exacerbate existing inequities. While expanding patient access to health data is an important step towards fostering shared decision-making in healthcare and subsequently empowering patients, it is important to ensure that these developments reach all sectors of the community.

The role of information systems in emergency department decision-making-a literature review

OBJECTIVES

Healthcare providers employ heuristic and analytical decision-making to navigate the high-stakes environment of the emergency department (ED). Despite the increasing integration of information systems (ISs), research on their efficacy is conflicting. Drawing on related fields, we investigate how timing and mode of delivery influence IS effectiveness. Our objective is to reconcile previous contradictory findings, shedding light on optimal IS design in the ED.

MATERIALS AND METHODS

We conducted a systematic review following PRISMA across PubMed, Scopus, and Web of Science. We coded the ISs' timing as heuristic or analytical, their mode of delivery as active for automatic alerts and passive when requiring user-initiated information retrieval, and their effect on process, economic, and clinical outcomes.

RESULTS

Our analysis included 83 studies. During early heuristic decision-making, most active interventions were ineffective, while passive interventions generally improved outcomes. In the analytical phase, the effects were reversed. Passive interventions that facilitate information extraction consistently improved outcomes.

DISCUSSION

Our findings suggest that the effectiveness of active interventions negatively correlates with the amount of information received during delivery. During early heuristic decision-making, when information overload is high, physicians are unresponsive to alerts and proactively consult passive resources. In the later analytical phases, physicians show increased receptivity to alerts due to decreased diagnostic uncertainty and information quantity. Interventions that limit information lead to positive outcomes, supporting our interpretation.

CONCLUSION

We synthesize our findings into an integrated model that reveals the underlying reasons for conflicting findings from previous reviews and can guide practitioners in designing ISs in the ED.

Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing

OBJECTIVE

Leverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to review when seeing oncology patients.

MATERIALS AND METHODS

We trained logistic regression models using note metadata and a Term Frequency Inverse Document Frequency (TF-IDF) text representation. We evaluated performance with precision, recall, F1, AUC, and a clinical qualitative assessment.

RESULTS

The metadata only model achieved an AUC 0.930 and the metadata and TF-IDF model an AUC 0.937. Qualitative assessment revealed a need for better text representation and to further customize predictions for the user.

DISCUSSION

Our model effectively surfaces the top 10 notes a clinician wants to review when seeing an oncology patient. Further studies can characterize different types of clinician users and better tailor the task for different care settings.

CONCLUSION

EHR audit logs can provide important relevance data for training ML models that assist with note-writing in the oncology setting.

Centralized Multipatient Dashboards' Impact on Intensive Care Unit Clinician Performance and Satisfaction: A Systematic Review

BACKGROUND

 Intensive care unit (ICU) clinicians encounter frequent challenges with managing vast amounts of fragmented data while caring for multiple critically ill patients simultaneously. This may lead to increased provider cognitive load that may jeopardize patient safety.

OBJECTIVES

 This systematic review assesses the impact of centralized multipatient dashboards on ICU clinician performance, perceptions regarding the use of these tools, and patient outcomes.

METHODS

 A literature search was conducted on February 9, 2023, using the EBSCO CINAHL, Cochrane Central Register of Controlled Trials, Embase, IEEE Xplore, MEDLINE, Scopus, and Web of Science Core Collection databases. Eligible studies that included ICU clinicians as participants and tested the effect of dashboards designed for use by multiple users to manage multiple patients on user performance and/or satisfaction compared with the standard practice. We narratively synthesized eligible studies following the SWiM (Synthesis Without Meta-analysis) guidelines. Studies were grouped based on dashboard type and outcomes assessed.

RESULTS

 The search yielded a total of 2,407 studies. Five studies met inclusion criteria and were included. Among these, three studies evaluated interactive displays in the ICU, one study assessed two dashboards in the pediatric ICU (PICU), and one study examined centralized monitor in the PICU. Most studies reported several positive outcomes, including reductions in data gathering time before rounds, a decrease in misrepresentations during multidisciplinary rounds, improved daily documentation compliance, faster decision-making, and user satisfaction. One study did not report any significant association.

CONCLUSION

 The multipatient dashboards were associated with improved ICU clinician performance and were positively perceived in most of the included studies. The risk of bias was high, and the certainty of evidence was very low, due to inconsistencies, imprecision, indirectness in the outcome measure, and methodological limitations. Designing and evaluating multipatient tools using robust research methodologies is an important focus for future research.

Assessing the Usability and Feasibility of Digital Assistant Tools for Direct Support Professionals: Participatory Design and Pilot-Testing

BACKGROUND

The United States is experiencing a direct support professional (DSP) crisis, with demand far exceeding supply. Although generating documentation is a critical responsibility, it is one of the most wearisome aspects of DSPs' jobs. Technology that enables DSPs to log informal time-stamped notes throughout their shift could help reduce the burden of end-of-shift documentation and increase job satisfaction, which in turn could improve the quality of life of the individuals with intellectual and developmental disabilities (IDDs) whom DSPs support. However, DSPs, with varied ages, levels of education, and comfort using technology, are not likely to adopt tools that detract from caregiving responsibilities or increase workload; therefore, technological tools for them must be relatively simple, extremely intuitive, and provide highly valued capabilities.

OBJECTIVE

This paper describes the development and pilot-testing of a digital assistant tool (DAT) that enables DSPs to create informal notes throughout their shifts and use these notes to facilitate end-of-shift documentation. The purpose of the pilot study was to assess the usability and feasibility of the DAT.

METHODS

The research team applied an established user-centered participatory design process to design, develop, and test the DAT prototypes between May 2020 and April 2023. Pilot-testing entailed having 14 DSPs who support adults with IDDs use the first full implementation of the DAT prototypes during 2 or 3 successive work shifts and fill out demographic and usability questionnaires.

RESULTS

Participants used the DAT prototypes to create notes and help generate end-of-shift reports. The System Usability Scale score of 81.79 indicates that they found the prototypes easy to use. Survey responses imply that using the DAT made it easier for participants to produce required documentation and suggest that they would adopt the DAT if this tool were available for daily use.

CONCLUSIONS

Simple technologies such as the DAT prototypes, which enable DSPs to use mobile devices to log time-stamped notes throughout their shift with minimal effort and use the notes to help write reports, have the potential to both reduce the burden associated with producing documentation and enhance the quality (level of detail and accuracy) of this documentation. This could help to increase job satisfaction and reduce turnover in DSPs, both of which would help improve the quality of life of the individuals with IDDs whom they support. The pilot test results indicate that DSPs found the DAT easy to use. Next steps include (1) producing more robust versions of the DAT with additional capabilities, such as storing data locally on mobile devices when Wi-Fi is not available; and (2) eliciting input from agency directors, families, and others who use data about adults with IDDs to help care for them to ensure that data produced by DSPs are relevant and useful.

More isn't always better: Technology in the intensive care unit

BACKGROUND

Clinical care in modern intensive care units (ICUs) combines multidisciplinary expertise and a complex array of technologies. These technologies have clearly advanced the ability of clinicians to do more for patients, yet so much equipment also presents the possibility for cognitive overload.

PURPOSE

The aim of this study was to investigate clinicians' experiences with and perceptions of technology in ICUs.

METHODOLOGY/APPROACH

We analyzed qualitative data from 30 interviews with ICU clinicians and frontline managers within four ICUs.

RESULTS

Our interviews identified three main challenges associated with technology in the ICU: (a) too many technologies and too much data; (b) inconsistent and inaccurate technologies; and (c) not enough integration among technologies, alignment with clinical workflows, and support for clinician identities. To address these challenges, interviewees highlighted mitigation strategies to address both social and technical systems and to achieve joint optimization.

CONCLUSION

When new technologies are added to the ICU, they have potential both to improve and to disrupt patient care. To successfully implement technologies in the ICU, clinicians' perspectives are crucial. Understanding clinicians' perspectives can help limit the disruptive effects of new technologies, so clinicians can focus their time and attention on providing care to patients.

PRACTICE IMPLICATIONS

As technology and data continue to play an increasingly important role in ICU care, everyone involved in the design, development, approval, implementation, and use of technology should work together to apply a sociotechnical systems approach to reduce possible negative effects on clinical care for critically ill patients.

Physicians, know thy patient

Person-centered care is presently the standard healthcare model, which emphases shared clinical decision-making, patient autonomy and empowerment. However, many aspects of the modern-day clinical practice such as the increased reliance on medical technologies, artificial intelligence, and teleconsultation have significantly altered the quality of patient-physician communications. Moreover, many countries are facing an aging population with longer life expectancies but increasingly complex medical comorbidities, which, coupled with medical subspecialization and competing health systems, often lead to fragmentation of clinical care. In this article, I discuss what it truly means for a clinician to know a patient, which is, in fact, a highly intricate skill that is necessary to meet the high bar of person-centered care. I suggest that this can be achieved through the implementation of a holistic biopsychosocial model of clinical consultation at the physician level and fostering coordinated and continuity of care at the health systems level.

Addressing Alert Fatigue by Replacing a Burdensome Interruptive Alert with Passive Clinical Decision Support

BACKGROUND

Recognizing that alert fatigue poses risks to patient safety and clinician wellness, there is a growing emphasis on evaluation and governance of electronic health record clinical decision support (CDS). This is particularly critical for interruptive alerts to ensure that they achieve desired clinical outcomes while minimizing the burden on clinicians. This study describes an improvement effort to address a problematic interruptive alert intended to notify clinicians about patients needing coronavirus disease 2019 (COVID) precautions and how we collaborated with operational leaders to develop an alternative passive CDS system in acute care areas.

OBJECTIVES

Our dual aim was to reduce the alert burden by redesigning the CDS to adhere to best practices for decision support while also improving the percent of admitted patients with symptoms of possible COVID who had appropriate and timely infection precautions orders.

METHODS

Iterative changes to CDS design included adjustment to alert triggers and acknowledgment reasons and development of a noninterruptive rule-based order panel for acute care areas. Data on alert burden and appropriate precautions orders on symptomatic admitted patients were followed over time on run and attribute (p) and individuals-moving range control charts.

RESULTS

At baseline, the COVID alert fired on average 8,206 times per week with an alert per encounter rate of 0.36. After our interventions, the alerts per week decreased to 1,449 and alerts per encounter to 0.07 equating to an 80% reduction for both metrics. Concurrently, the percentage of symptomatic admitted patients with COVID precautions ordered increased from 23 to 61% with a reduction in the mean time between COVID test and precautions orders from 19.7 to -1.3 minutes.

CONCLUSION

CDS governance, partnering with operational stakeholders, and iterative design led to successful replacement of a frequently firing interruptive alert with less burdensome passive CDS that improved timely ordering of COVID precautions.

Exploring the reliability of inpatient EMR algorithms for diabetes identification

INTRODUCTION

Accurate identification of medical conditions within a real-time inpatient setting is crucial for health systems. Current inpatient comorbidity algorithms rely on integrating various sources of administrative data, but at times, there is a considerable lag in obtaining and linking these data. Our study objective was to develop electronic medical records (EMR) data-based inpatient diabetes phenotyping algorithms.

MATERIALS AND METHODS

A chart review on 3040 individuals was completed, and 583 had diabetes. We linked EMR data on these individuals to the International Classification of Disease (ICD) administrative databases. The following EMR-data-based diabetes algorithms were developed: (1) laboratory data, (2) medication data, (3) laboratory and medications data, (4) diabetes concept keywords and (5) diabetes free-text algorithm. Combined algorithms used or statements between the above algorithms. Algorithm performances were measured using chart review as a gold standard. We determined the best-performing algorithm as the one that showed the high performance of sensitivity (SN), and positive predictive value (PPV).

RESULTS

The algorithms tested generally performed well: ICD-coded data, SN 0.84, specificity (SP) 0.98, PPV 0.93 and negative predictive value (NPV) 0.96; medication and laboratory algorithm, SN 0.90, SP 0.95, PPV 0.80 and NPV 0.97; all document types algorithm, SN 0.95, SP 0.98, PPV 0.94 and NPV 0.99.

DISCUSSION

Free-text data-based diabetes algorithm can yield comparable or superior performance to a commonly used ICD-coded algorithm and could supplement existing methods. These types of inpatient EMR-based algorithms for case identification may become a key method for timely resource planning and care delivery.

The unfulfilled promises of electronic health records

We provide a brief update on the current evidence on electronic health records' benefits, risks, and potential harms through a rapid narrative review. Many of the promised benefits of electronic health records have not yet been realised. Electronic health records are often not user-friendly. To enhance their potential, electronic health record platforms should be continuously evaluated and enhanced by carefully considering feedback from all stakeholders.

Patient Information Summarization in Clinical Settings: Scoping Review

BACKGROUND

Information overflow, a common problem in the present clinical environment, can be mitigated by summarizing clinical data. Although there are several solutions for clinical summarization, there is a lack of a complete overview of the research relevant to this field.

OBJECTIVE

This study aims to identify state-of-the-art solutions for clinical summarization, to analyze their capabilities, and to identify their properties.

METHODS

A scoping review of articles published between 2005 and 2022 was conducted. With a clinical focus, PubMed and Web of Science were queried to find an initial set of reports, later extended by articles found through a chain of citations. The included reports were analyzed to answer the questions of where, what, and how medical information is summarized; whether summarization conserves temporality, uncertainty, and medical pertinence; and how the propositions are evaluated and deployed. To answer how information is summarized, methods were compared through a new framework "collect-synthesize-communicate" referring to information gathering from data, its synthesis, and communication to the end user.

RESULTS

Overall, 128 articles were included, representing various medical fields. Exclusively structured data were used as input in 46.1% (59/128) of papers, text in 41.4% (53/128) of articles, and both in 10.2% (13/128) of papers. Using the proposed framework, 42.2% (54/128) of the records contributed to information collection, 27.3% (35/128) contributed to information synthesis, and 46.1% (59/128) presented solutions for summary communication. Numerous summarization approaches have been presented, including extractive (n=13) and abstractive summarization (n=19); topic modeling (n=5); summary specification (n=11); concept and relation extraction (n=30); visual design considerations (n=59); and complete pipelines (n=7) using information extraction, synthesis, and communication. Graphical displays (n=53), short texts (n=41), static reports (n=7), and problem-oriented views (n=7) were the most common types in terms of summary communication. Although temporality and uncertainty information were usually not conserved in most studies (74/128, 57.8% and 113/128, 88.3%, respectively), some studies presented solutions to treat this information. Overall, 115 (89.8%) articles showed results of an evaluation, and methods included evaluations with human participants (median 15, IQR 24 participants): measurements in experiments with human participants (n=31), real situations (n=8), and usability studies (n=28). Methods without human involvement included intrinsic evaluation (n=24), performance on a proxy (n=10), or domain-specific tasks (n=11). Overall, 11 (8.6%) reports described a system deployed in clinical settings.

CONCLUSIONS

The scientific literature contains many propositions for summarizing patient information but reports very few comparisons of these proposals. This work proposes to compare these algorithms through how they conserve essential aspects of clinical information and through the "collect-synthesize-communicate" framework. We found that current propositions usually address these 3 steps only partially. Moreover, they conserve and use temporality, uncertainty, and pertinent medical aspects to varying extents, and solutions are often preliminary.

Sociotechnical Work System Approach to Occupational Fatigue

INTRODUCTION TO THE PROBLEM

Occupational fatigue is a characteristic of excessive workload and depicts the limited capacity to complete demands. The impact of occupational fatigue has been studied outside of health care in fields such as transportation and heavy industry. Research in health care professionals such as physicians, medical residents, and nurses has demonstrated the potential for occupational fatigue to affect patient, employee, and organizational outcomes. A conceptual framework of occupational fatigue that is informed by a sociotechnical systems approach is needed to (1) describe the multidimensional facets of occupational fatigue, (2) explore individual and work system factors that may affect occupational fatigue, and (3) anticipate downstream implications of occupational fatigue on employee well-being, patient safety, and organizational outcomes.

CONCEPTUAL FRAMEWORK OF OCCUPATIONAL FATIGUE

The health care professional occupational fatigue conceptual framework is outlined following the Systems Engineering Initiative for Patient Safety (SEIPS) model and adapted from the Conceptual Model of Occupational Fatigue in Nursing. Future research may apply this conceptual framework to health care professionals as a tool to describe occupational fatigue, identify the causes, and generate solutions. Interventions to mitigate and resolve occupational fatigue must address the entire sociotechnical system, not just individual or employee changes.

Non-Invasive Mapping of Cerebral Autoregulation Using Near-Infrared Spectroscopy: A Study Protocol

The ability of cerebral vessels to maintain a fairly constant cerebral blood flow is referred to as cerebral autoregulation (CA). Using near-infrared spectroscopy (NIRS) paired with arterial blood pressure (ABP) monitoring, continuous CA can be assessed non-invasively. Recent advances in NIRS technology can help improve the understanding of continuously assessed CA in humans with high spatial and temporal resolutions. We describe a study protocol for creating a new wearable and portable imaging system that derives CA maps of the entire brain with high sampling rates at each point. The first objective is to evaluate the CA mapping system's performance during various perturbations using a block-trial design in 50 healthy volunteers. The second objective is to explore the impact of age and sex on regional disparities in CA using static recording and perturbation testing in 200 healthy volunteers. Using entirely non-invasive NIRS and ABP systems, we hope to prove the feasibility of deriving CA maps of the entire brain with high spatial and temporal resolutions. The development of this imaging system could potentially revolutionize the way we monitor brain physiology in humans since it would allow for an entirely non-invasive continuous assessment of regional differences in CA and improve our understanding of the impact of the aging process on cerebral vessel function.

An Electronic Medical Record Pocket Guide for Incoming Internal Medicine Interns: Perceptions and Impact on Patient Information Gathering

OBJECTIVES

The transition from medical student to intern is associated with a significant increase in workloads and responsibilities. This includes using the electronic medical record (EMR), which can lead to challenges in information gathering and patient care; however, no formal residency interventions exist in the use of an EMR for information gathering, with most EMR training occurring in the clinical setting. The present study aimed to improve information gathering on patient care and enhance the confidence of Internal Medicine interns in information gathering.

METHODS

We performed a pre- and postprospective study in July 2021. All of the Internal Medicine interns at our community hospital were included. A pre- and postassessment to evaluate interns' confidence was distributed to participants during orientation week and at the end of the inpatient Internal Medicine rotation. A pre- and postconfidence assessment was collected at the beginning and end, respectively, of each intern's inpatient Internal Medicine rotation.

RESULTS

Seventeen (85%) interns completed both the preassessment and postassessment. Use of an EMR guide led to a significant increase in completeness of patient information gathering (preassessment: 73.2% ± 18.4% vs post-EMR guide: 94.7% ± 7.4%, P < 0.001) and in intern confidence (P = 0.001).

CONCLUSIONS

The use of an EMR guide was well received among Internal Medicine interns and led to increased completeness in patient information gathering. Residency programs may benefit from developing an EMR guide to improving the transition of interns during residency.

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.

Electronic Health Record Use and Perceptions among Urologic Surgeons

OBJECTIVE

Electronic health records (EHRs) have become widely adopted with increasing emphasis on improving care delivery. Improvements in surgery may be limited by specialty-specific issues that impact EHR usability and engagement. Accordingly, we examined EHR use and perceptions in urology, a diverse surgical specialty.

METHODS

We conducted a national, sequential explanatory mixed methods study. Through the 2019 American Urological Association Census, we surveyed urologic surgeons on EHR use and perceptions and then identified associated characteristics through bivariable and multivariable analyses. Using purposeful sampling, we interviewed 25 urologists and applied coding-based thematic analysis, which was then integrated with survey findings.

RESULTS

Among 2,159 practicing urologic surgeons, 2,081 (96.4%) reported using an EHR. In the weighted sample (n = 12,366), over 90% used the EHR for charting, viewing results, and order entry with most using information exchange functions (59.0-79.6%). In contrast, only 35.8% felt the EHR increases clinical efficiency, whereas 43.1% agreed it improves patient care, which related thematically to information management, administrative burden, patient safety, and patient-surgeon interaction. Quantitatively and qualitatively, use and perceptions differed by years in practice and practice type with more use and better perceptions among more recent entrants into the urologic workforce and those in academic/multispecialty practices, who may have earlier EHR exposure, better infrastructure, and more support.

CONCLUSION

Despite wide and substantive usage, EHRs engender mixed feelings, especially among longer-practicing surgeons and those in lower-resourced settings (e.g., smaller and private practices). Beyond reducing administrative burden and simplifying information management, efforts to improve care delivery through the EHR should focus on surgeon engagement, particularly in the community, to boost implementation and user experience.

Analysis of Chest X-ray for COVID-19 Diagnosis as a Use Case for an HPC-Enabled Data Analysis and Machine Learning Platform for Medical Diagnosis Support

The COVID-19 pandemic shed light on the need for quick diagnosis tools in healthcare, leading to the development of several algorithmic models for disease detection. Though these models are relatively easy to build, their training requires a lot of data, storage, and resources, which may not be available for use by medical institutions or could be beyond the skillset of the people who most need these tools. This paper describes a data analysis and machine learning platform that takes advantage of high-performance computing infrastructure for medical diagnosis support applications. This platform is validated by re-training a previously published deep learning model (COVID-Net) on new data, where it is shown that the performance of the model is improved through large-scale hyperparameter optimisation that uncovered optimal training parameter combinations. The per-class accuracy of the model, especially for COVID-19 and pneumonia, is higher when using the tuned hyperparameters (healthy: 96.5%; pneumonia: 61.5%; COVID-19: 78.9%) as opposed to parameters chosen through traditional methods (healthy: 93.6%; pneumonia: 46.1%; COVID-19: 76.3%). Furthermore, training speed-up analysis shows a major decrease in training time as resources increase, from 207 min using 1 node to 54 min when distributed over 32 nodes, but highlights the presence of a cut-off point where the communication overhead begins to affect performance. The developed platform is intended to provide the medical field with a technical environment for developing novel portable artificial-intelligence-based tools for diagnosis support.