Use,Impact, Weaknesses,and Advanced Features of Search Functions for Clinical Use in Electronic Health Records: A Scoping Review

Objective  Although vast amounts of patient information are captured in electronic health records (EHRs), effective clinical use of this information is challenging due to inadequate and inefficient access to it at the point of care. The purpose of this study was to conduct a scoping review of the literature on the use of EHR search functions within a single patient''s record in clinical settings to characterize the current state of research on the topic and identify areas for future study. Methods  We conducted a literature search of four databases to identify articles on within-EHR search functions or the use of EHR search function in the context of clinical tasks. After reviewing titles and abstracts and performing a full-text review of selected articles, we included 17 articles in the analysis. We qualitatively identified themes in those articles and synthesized the literature for each theme. Results  Based on the 17 articles analyzed, we delineated four themes: (1) how clinicians use search functions, (2) impact of search functions on clinical workflow, (3) weaknesses of current search functions, and (4) advanced search features. Our review found that search functions generally facilitate patient information retrieval by clinicians and are positively received by users. However, existing search functions have weaknesses, such as yielding false negatives and false positives, which can decrease trust in the results, and requiring a high cognitive load to perform an inclusive search of a patient''s record. Conclusion  Despite the widespread adoption of EHRs, only a limited number of articles describe the use of EHR search functions in a clinical setting, despite evidence that they benefit clinician workflow and productivity. Some of the weaknesses of current search functions may be addressed by enhancing EHR search functions with collaborative filtering.     

Clinician Perceptions on the Use of Free-Text Communication Orders

Objective  The aim of this study was to investigate (1) why ordering clinicians use free-text orders to communicate medication information; (2) what risks physicians and nurses perceive when free-text orders are used for communicating medication information; and (3) how electronic health records (EHRs) could be improved to encourage the safe communication of medication information. Methods  We performed semi-structured, scenario-based interviews with eight physicians and eight nurses. Interview responses were analyzed and grouped into common themes. Results  Participants described eight reasons why clinicians use free-text medication orders, five risks relating to the use of free-text medication orders, and five recommendations for improving EHR medication-related communication. Poor usability, including reduced efficiency and limited functionality associated with structured order entry, was the primary reason clinicians used free-text orders to communicate medication information. Common risks to using free-text orders for medication communication included the increased likelihood of missing orders and the increased workload on nurses responsible for executing orders. Discussion  Clinicians'' use of free-text orders is primarily due to limitations in the current structured order entry design. To encourage the safe communication of medication information between clinicians, the EHR''s structured order entry must be redesigned to support clinicians'' cognitive and workflow needs that are currently being addressed via the use of free-text orders. Conclusion  Clinicians'' use of free-text orders as a workaround to insufficient structured order entry can create unintended patient safety risks. Thoughtful solutions designed to address these workarounds can improve the medication ordering process and the subsequent medication administration process.     

Ambulatory Clinician's Guide to Inpatient Service: An Innovative Rapid Onboarding Strategy for the COVID-19 Pandemic

Background and Significance  When hospitals are subject to prolonged surges in patients, such as during the coronavirus disease 2019 (COVID-19) pandemic, additional clinicians may be needed to care for the rapid increase of acutely ill patients. How might we quickly prepare a large number of ambulatory-based clinicians to care for hospitalized patients using the inpatient workflow of the electronic health record (EHR)? Objectives  The aim of the study is to create a successful training intervention which prepares ambulatory-based clinicians as they transition to inpatient services. Methods  We created a training guide with embedded videos that describes the workflow of an inpatient clinician. We delivered this intervention via an e-mail hyperlink, a static hyperlink inside of the EHR, and an on-demand hyperlink within the EHR. Results  In anticipation of the first peak of inpatients with COVID-19 in April 2020, the training manual was accessed 261 times by 167 unique users as clinicians anticipated being called into service. As our institution has not yet needed to deploy ambulatory-based clinicians for inpatient service, usage data of the training document is still pending. Conclusion  We intend that our novel implementation of a multimedia, highly accessible onboarding document with access from points inside and outside of the EHR will improve clinician performance and serve as a helpful example to other organizations during the COVID-19 pandemic and beyond.     

Machine Learning for Detection of Correct Peripherally Inserted Central Catheter Tip Position from Radiology Reports in Infants

Background  In critically ill infants, the position of a peripherally inserted central catheter (PICC) must be confirmed frequently, as the tip may move from its original position and run the risk of hyperosmolar vascular damage or extravasation into surrounding spaces. Automated detection of PICC tip position holds great promise for alerting bedside clinicians to noncentral PICCs. Objectives  This research seeks to use natural language processing (NLP) and supervised machine learning (ML) techniques to predict PICC tip position based primarily on text analysis of radiograph reports from infants with an upper extremity PICC. Methods  Radiographs, containing a PICC line in infants under 6 months of age, were manually classified into 12 anatomical locations based on the radiologist''s textual report of the PICC line''s tip. After categorization, we performed a 70/30 train/test split and benchmarked the performance of seven different (neural network, support vector machine, the naïve Bayes, decision tree, random forest, AdaBoost, and K-nearest neighbors) supervised ML algorithms. After optimization, we calculated accuracy, precision, and recall of each algorithm''s ability to correctly categorize the stated location of the PICC tip. Results  A total of 17,337 radiographs met criteria for inclusion and were labeled manually. Interrater agreement was 99.1%. Support vector machines and neural networks yielded accuracies as high as 98% in identifying PICC tips in central versus noncentral position (binary outcome) and accuracies as high as 95% when attempting to categorize the individual anatomical location (12-category outcome). Conclusion  Our study shows that ML classifiers can automatically extract the anatomical location of PICC tips from radiology reports. Two ML classifiers, support vector machine (SVM) and a neural network, obtained top accuracies in both binary and multiple category predictions. Implementing these algorithms in a neonatal intensive care unit as a clinical decision support system may help clinicians address PICC line position.     

Privacy versus Convenience: A Historical Perspective,Analysis of Risks,and an Informatics Call to Action

Background  The pace of technological change dwarfs the pace of social and policy change. This mismatch allows for individual harm from lack of recognition of changes in societal context. The value of privacy has not kept pace with changes in technology over time; individuals seem to discount how loss of privacy can lead to directed personal harm. Objective  The authors examined individuals sharing personal data with mobile health applications (mHealth apps) and compared the current digital context to the historical context of harm. The authors make recommendations to informatics professionals to support consumers who wish to use mHealth apps in a manner that balances convenience with personal privacy to reduce the risk of harm. Methods  A literature search focused by a historical perspective of risk of harm was performed throughout the development of this paper. Two case studies highlight questions a consumer might ask to assess the risk of harm posed by mobile health applications. Results  A historical review provides the context for the collective human experience of harm. We then encapsulate current perceptions and views of privacy and list potential risks created by insufficient attention to privacy management. Discussion  The results provide a historical context for individuals to view the risk of harm and shed light on potential emotional, reputational, economic, and physical harms that can result from naïve use of mHealth apps. We formulate implications for clinical informaticists. Conclusion  Concepts of both harm and privacy have changed substantially over the past 20 years. Technology provides methods to invade privacy and cause harm unimaginable a few decades ago. Only recently have the consequences become clearer. The current regulatory framework is extremely limited. Given the risks of harm and limited awareness, we call upon informatics professionals to support more privacy education and protections and increase mHealth transparency about data usage.     

Multidisciplinary Sprint Program Achieved Specialty-Specific EHR Optimization in 20 Clinics

Objective  The objective of the study was to highlight and analyze the outcomes of software configuration requests received from Sprint, a comprehensive, clinic-centered electronic health record (EHR) optimization program. Methods  A retrospective review of 1,254 Sprint workbook requests identified (1) the responsible EHR team, (2) the clinical efficiency gained from the request, and (3) the EHR intervention conducted. Results  Requests were received from 407 clinicians and 538 staff over 31 weeks of Sprint. Sixty-nine percent of the requests were completed during the Sprint. Of all requests, 25% required net new build, 73% required technical investigation and/or solutions, and 2% of the requests were escalated to the vendor. The clinical specialty groups requested a higher percentage of items that earned them clinical review (16 vs. 10%) and documentation (29 vs. 23%) efficiencies compared with their primary care colleagues who requested slightly more order modifications (22 vs. 20%). Clinical efficiencies most commonly associated with workbook requests included documentation (28%), ordering (20%), in basket (17%), and clinical review (15%). Sprint user requests evaluated by ambulatory, hardware, security, and training teams comprised 80% of reported items. Discussion  Sprint requests were categorized as clean-up, break-fix, workflow investigation, or new build. On-site collaboration with clinical care teams permitted consensus-building, drove vetting, and iteration of EHR build, and led to goal-driven, usable workflows and EHR products. Conclusion  This program evaluation demonstrates the process by which optimization can occur and the products that result when we adhere to optimization principles in health care organizations.     

Evidence-Based Guidelines for Fatigue Risk Management in Emergency Medical Services

Abstract

Background: Administrators of Emergency Medical Services (EMS) operations lack guidance on how to mitigate workplace fatigue, which affects greater than half of all EMS personnel. The primary objective of the Fatigue in EMS Project was to create an evidence-based guideline for fatigue risk management tailored to EMS operations. Methods: Systematic searches were conducted from 1980 to September 2016 and guided by seven research questions framed in the Population, Intervention, Comparison, Outcome (PICO) framework. Teams of investigators applied inclusion criteria, which included limiting the retained literature to EMS personnel or similar shift worker groups. The expert panel reviewed summaries of the evidence based on the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) methodology. The panel evaluated the quality of evidence for each PICO question separately, considered the balance between benefits and harms, considered the values and preferences of the targeted population, and evaluated the resource requirements/needs. The GRADE Evidence-to-Decision (EtD) Framework was used to prepare draft recommendations based on the evidence, and the Content Validity Index (CVI) was used to quantify the panel's agreement on the relevance and clarity of each recommendation. CVI scores for relevance and clarity were measured separately on a 1–4 scale to indicate consensus/agreement among panel members and conclusion of recommendation development. Results: The EtD framework was applied to all 7 PICO questions, and the panel created 5 recommendations. PICO1: The panel recommends using fatigue/sleepiness survey instruments to measure and monitor fatigue in EMS personnel. PICO2: The panel recommends that EMS personnel work shifts shorter than 24 hours in duration. PICO3: The panel recommends that EMS personnel have access to caffeine as a fatigue countermeasure. PICO4: The panel recommends that, EMS personnel have the opportunity to nap while on duty to mitigate fatigue. PICO5: The panel recommends that EMS personnel receive education and training to mitigate fatigue and fatigue-related risks. The panel referenced insufficient evidence as the reason for making no recommendation linked to 2 PICO questions. Conclusions: Based on a review of the evidence, the panel developed a guideline with 5 recommendations for fatigue risk management in EMS operations.     

U.S. COVID-19 State Government Public Dashboards: An Expert Review

Background  In the United States, all 50 state governments deployed publicly viewable dashboards regarding the novel coronavirus disease 2019 (COVID-19) to track and respond to the pandemic. States dashboards, however, reflect idiosyncratic design practices based on their content, function, and visual design and platform. There has been little guidance for what state dashboards should look like or contain, leading to significant variation. Objectives  The primary objective of our study was to catalog how information, system function, and user interface were deployed across the COVID-19 state dashboards. Our secondary objective was to group and characterize the dashboards based on the information we collected using clustering analysis. Methods  For preliminary data collection, we developed a framework to first analyze two dashboards as a group and reach agreement on coding. We subsequently doubled coded the remaining 48 dashboards using the framework and reviewed the coding to reach total consensus. Results  All state dashboards included maps and graphs, most frequently line charts, bar charts, and histograms. The most represented metrics were total deaths, total cases, new cases, laboratory tests, and hospitalization. Decisions on how metrics were aggregated and stratified greatly varied across dashboards. Overall, the dashboards were very interactive with 96% having at least some functionality including tooltips, zooming, or exporting capabilities. For visual design and platform, we noted that the software was dominated by a few major organizations. Our cluster analysis yielded a six-cluster solution, and each cluster provided additional insights about how groups of states engaged in specific practices in dashboard design. Conclusion  Our study indicates that states engaged in dashboard practices that generally aligned with many of the goals set forth by the Centers for Disease Control and Prevention, Essential Public Health Services. We highlight areas where states fall short of these expectations and provide specific design recommendations to address these gaps.     

Moving Faster than the COVID-19 Pandemic: The Rapid,Digital Transformation of a Public Health System

Background  Queensland, Australia has been successful in containing the COVID-19 pandemic. Underpinning that response has been a highly effective virus containment strategy which relies on identification, isolation, and contact tracing of cases. The dramatic emergence of the COVID-19 pandemic rendered traditional paper-based systems for managing contact tracing no longer fit for purpose. A rapid digital transformation of the public health contact tracing system occurred to support this effort. Objectives  The objectives of the digital transformation were to shift legacy systems (paper or standalone electronic systems) to a digitally enabled public health system, where data are centered around the consumer rather than isolated databases. The objective of this paper is to outline this case study and detail the lessons learnt to inform and give confidence to others contemplating digitization of public health systems in response to the COVID-19 pandemic. Methods  This case study is set in Queensland, Australia. Universal health care is available. A multidisciplinary team was established consisting of clinical informaticians, developers, data strategists, and health information managers. An agile “pair-programming” approach was undertaken to application development and extensive change efforts were made to maximize adoption of the new digital workflows. Data governance and flows were changed to support rapid management of the pandemic. Results  The digital coronavirus application (DCOVA) is a web-based application that securely captures information about people required to quarantine and creates a multiagency secure database to support a successful containment strategy. Conclusion  Most of the literature surrounding digital transformation allows time for significant consultation, which was simply not possible under crisis conditions. Our observation is that staff was willing to adopt new digital systems because the reason for change (the COVID-19 pandemic) was clearly pressing. This case study highlights just how critical a unified purpose, is to successful, rapid digital transformation.     

A Perioperative Care Display for Understanding High Acuity Patients

Background  The data visualization literature asserts that the details of the optimal data display must be tailored to the specific task, the background of the user, and the characteristics of the data. The general organizing principle of a concept-oriented display is known to be useful for many tasks and data types. Objectives  In this project, we used general principles of data visualization and a co-design process to produce a clinical display tailored to a specific cognitive task, chosen from the anesthesia domain, but with clear generalizability to other clinical tasks. To support the work of the anesthesia-in-charge (AIC) our task was, for a given day, to depict the acuity level and complexity of each patient in the collection of those that will be operated on the following day. The AIC uses this information to optimally allocate anesthesia staff and providers across operating rooms. Methods  We used a co-design process to collaborate with participants who work in the AIC role. We conducted two in-depth interviews with AICs and engaged them in subsequent input on iterative design solutions. Results  Through a co-design process, we found (1) the need to carefully match the level of detail in the display to the level required by the clinical task, (2) the impedance caused by irrelevant information on the screen such as icons relevant only to other tasks, and (3) the desire for a specific but optional trajectory of increasingly detailed textual summaries. Conclusion  This study reports a real-world clinical informatics development project that engaged users as co-designers. Our process led to the user-preferred design of a single binary flag to identify the subset of patients needing further investigation, and then a trajectory of increasingly detailed, text-based abstractions for each patient that can be displayed when more information is needed.     

A Collaborative Partnership between the Multicenter Handoff Collaborative and an Electronic Health Record Vendor

Objectives  The operating room is a specialized, complex environment with many factors that can impede effective communication during transitions of care between anesthesia clinicians. We postulated that an efficient, accessible, standardized tool for intraoperative handoffs built into standard workflow would improve communication and handoff safety. Most institutions now use an electronic health record (EHR) system for patient care and have independently designed intraoperative handoff tools, but these home-grown tools are not scalable to other organizations and lack vendor-supported features. The goal of this project was to create a standardized, intraoperative handoff tool supported by EHR functionality. Methods  The Multicenter Handoff Collaborative, with support from the Anesthesia Patient Safety Foundation, created a working group of frontline anesthesia experts to collaborate with a development team from the EHR vendor (Epic Systems) to design a standardized intraoperative handoff tool. Over 2 years, the working group identified the critical elements for the tool and software usability, and the EHR team designed a standardized intraoperative handoff tool that is accessible to any institution using this EHR. Results  The first iteration of the intraoperative handoff tool was released in August 2019, with a second version in February 2020. The tool is standardized but customizable by individual institutions. Conclusion  We demonstrate that work on complex health care processes critical to patient safety, such as handoffs, can be performed on a national scale through cross-industry collaboration. Frontline experts can partner with health care industry vendors to design, build, and release a product on an accelerated timeline.     

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), c hanging 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.     

A Scoping Review of Health Information Technology in Clinician Burnout

Background  Clinician burnout is a prevalent issue in healthcare, with detrimental implications in healthcare quality and medical costs due to errors. The inefficient use of health information technologies (HIT) is attributed to having a role in burnout. Objective  This paper seeks to review the literature with the following two goals: (1) characterize and extract HIT trends in burnout studies over time, and (2) examine the evidence and synthesize themes of HIT''s roles in burnout studies. Methods  A scoping literature review was performed by following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines with two rounds of searches in PubMed, IEEE Xplore, ACM, and Google Scholar. The retrieved papers and their references were screened for eligibility by using developed inclusion and exclusion criteria. Data were extracted from included papers and summarized either statistically or qualitatively to demonstrate patterns. Results  After narrowing down the initial 945 papers, 36 papers were included. All papers were published between 2013 and 2020; nearly half of them focused on primary care ( n  = 16; 44.4%). The most commonly studied variable was electronic health record (EHR) practices (e.g., number of clicks). The most common study population was physicians. HIT played multiple roles in burnout studies: it can contribute to burnout; it can be used to measure burnout; or it can intervene and mitigate burnout levels. Conclusion  This scoping review presents trends in HIT-centered burnout studies and synthesizes three roles for HIT in contributing to, measuring, and mitigating burnout. Four recommendations were generated accordingly for future burnout studies: (1) validate and standardize HIT burnout measures; (2) focus on EHR-based solutions to mitigate clinician burnout; (3) expand burnout studies to other specialties and types of healthcare providers, and (4) utilize mobile and tracking technology to study time efficiency.     

Patients Are Knowledge Workers in the Clinical Information Space

Background  Limited research exists on patient knowledge/cognition or “getting inside patients'' heads.” Because patients possess unique and privileged knowledge, clinicians need this information to make patient-centered and coordinated treatment planning decisions. To achieve patient-centered care, we characterize patient knowledge and contributions to the clinical information space. Methods and Objectives  In a theoretical overview, we explore the relevance of patient knowledge to care provision, apply historical perspectives of knowledge acquisition to patient knowledge, propose a representation of patient knowledge types across the continuum of care, and include illustrative vignettes about Mr. Jones. We highlight how the field of human factors (a core competency of health informatics) provides a perspective and methods for eliciting and characterizing patient knowledge. Conclusion  Patients play a vital role in the clinical information space by possessing and sharing unique knowledge relevant to the clinical picture. Without a patient''s contributions, the clinical picture of the patient is incomplete. A human factors perspective informs patient-centered care and health information technology solutions to support clinical information sharing.     

Tracking Hospital Visitors/Chaperones during the COVID-19 Pandemic

Objective  The coronavirus disease (COVID-19) is an emerging infectious disease with strong infectious power and fatality rate. To protect national health, government agencies have regulations on hospital chaperoning and visiting. This article presents the development and implementation of a monitoring system for hospital visiting and chaperoning during the COVID-19 pandemic. The study aimed to create a hospital visiting and chaperoning monitor system that uses nation-wide data sources to more accurately screen hospital visitors and chaperones, assist contract tracing, and prevent transmission of severe acute respiratory syndrome coronavirus 2. Methods  This project was implemented in 57 ward units of an academic medical center. The system was connected to the National Health Insurance (NHI) system and Hospital Information System (HIS), and built on the data of everyone who accessed either the hospital or ward using an NHI smart card or national identification card. To shorten the time for manual identification, we also developed a new system of “app for appointment visits and chaperones” to make appointments online. Results  After the implementation of the system, data from visitors and chaperones in the nursing information system could be accessed. Given that all data were registered in the HIS visiting/chaperoning monitor system, an epidemic investigation could be performed whenever there was a confirmed case. Conclusion  Through the establishment of this system, people entering the ward can be accurately controlled, and all the contacts of potential cases can be traced.     

Patient Cohort Identification on Time Series Data Using the OMOP Common Data Model

Background  The identification of patient cohorts for recruiting patients into clinical trials requires an evaluation of study-specific inclusion and exclusion criteria. These criteria are specified depending on corresponding clinical facts. Some of these facts may not be present in the clinical source systems and need to be calculated either in advance or at cohort query runtime (so-called feasibility query). Objectives  We use the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) as the repository for our clinical data. However, Atlas, the graphical user interface of OMOP, does not offer the functionality to perform calculations on facts data. Therefore, we were in search for a different approach. The objective of this study is to investigate whether the Arden Syntax can be used for feasibility queries on the OMOP CDM to enable on-the-fly calculations at query runtime, to eliminate the need to precalculate data elements that are involved with researchers'' criteria specification. Methods  We implemented a service that reads the facts from the OMOP repository and provides it in a form which an Arden Syntax Medical Logic Module (MLM) can process. Then, we implemented an MLM that applies the eligibility criteria to every patient data set and outputs the list of eligible cases (i.e., performs the feasibility query). Results  The study resulted in an MLM-based feasibility query that identifies cases of overventilation as an example of how an on-the-fly calculation can be realized. The algorithm is split into two MLMs to provide the reusability of the approach. Conclusion  We found that MLMs are a suitable technology for feasibility queries on the OMOP CDM. Our method of performing on-the-fly calculations can be employed with any OMOP instance and without touching existing infrastructure like the Extract, Transform and Load pipeline. Therefore, we think that it is a well-suited method to perform on-the-fly calculations on OMOP.