filoVision - using deep learning and tip markers to automate filopodia analysis.

Filopodia are slender, actin-filled membrane projections used by various cell types for environment exploration. Analyzing filopodia often involves visualizing them using actin, filopodia tip or membrane markers. Due to the diversity of cell types that extend filopodia, from amoeboid to mammalian, it can be challenging for some to find a reliable filopodia analysis workflow suited for their cell type and preferred visualization method. The lack of an automated workflow capable of analyzing amoeboid filopodia with only a filopodia tip label prompted the development of filoVision. filoVision is an adaptable deep learning platform featuring the tools filoTips and filoSkeleton. filoTips labels filopodia tips and the cytosol using a single tip marker, allowing information extraction without actin or membrane markers. In contrast, filoSkeleton combines tip marker signals with actin labeling for a more comprehensive analysis of filopodia shafts in addition to tip protein analysis. The ZeroCostDL4Mic deep learning framework facilitates accessibility and customization for different datasets and cell types, making filoVision a flexible tool for automated analysis of tip-marked filopodia across various cell types and user data.

Understanding the Information Needs of Pharmacy Staff Using CancelRx: A Qualitative Study of the Use of Prescription E-cancellation.

Background: Although electronic prescription cancellation such as via CancelRx can facilitate critical communication between prescribers and pharmacy staff about discontinued medications, there is little work that explores whether CancelRx meets the needs of pharmacy staff users. Objective: This study leverages qualitative interviews with pharmacy staff to address the following question: When medication changes are made by a prescriber using CancelRx, what information is needed by pharmacy staff to make correct and effective decisions in their roles in medication management? Methods: We conducted an inductive thematic analysis of interviews with 11 pharmacy staff members (pharmacists and pharmacy technicians) across three outpatient community pharmacy sites within an academic health care system. Results: Three information needs themes were consistently identified by both pharmacists and pharmacy technicians: prescriber intent when initiating the CancelRx, clinical rationale for the medication change, and intended medication regimen. Notably, both pharmacists and pharmacy technicians often reported seeking multiple information needs not fully addressed by CancelRx in the electronic health record (EHR) to achieve the shared goals of correct dispensing of medications and supporting patient self-management. Conclusions: Our qualitative analysis reveals that outpatient community pharmacy staff in an academic health care system often seek additional information from the (EHR) following medication changes communicated by CancelRx to meet their information needs. Ideally, the prescriber would provide sufficient information through CancelRx to automatically identify all discontinued prescriptions. These limitations highlight the need for design features that support routine communication of needed information at the time of a medication change, such as structured data elements.

Prevalence of lifestyle-related behavioral information in claims data in the U.S.

OBJECTIVE: Given their association with varying health risks, lifestyle-related behaviors are essential to consider in population-level disease prevention. Health insurance claims are a key source of information for population health analytics, but the availability of lifestyle information within claims data is unknown. Our goal was to assess the availability and prevalence of data items that describe lifestyle behaviors across several domains within a large U.S. claims database. METHODS: We conducted a retrospective, descriptive analysis to determine the availability of the following claims-derived lifestyle domains: nutrition, eating habits, physical activity, weight status, emotional wellness, sleep, tobacco use, and substance use. To define these domains, we applied a serial review process with three physicians to identify relevant diagnosis and procedure codes within claims for each domain. We used enrollment files and medical claims from a large national U.S. health plan to identify lifestyle relevant codes filed between 2016 and 2020. We calculated the annual prevalence of each claims-derived lifestyle domain and the proportion of patients by count within each domain. RESULTS: Approximately half of all members within the sample had claims information that identified at least one lifestyle domain (2016 = 41.9%; 2017 = 46.1%; 2018 = 49.6%; 2019 = 52.5%; 2020 = 50.6% of patients). Most commonly identified domains were weight status (19.9-30.7% across years), nutrition (13.3-17.8%), and tobacco use (7.9-9.8%). CONCLUSION: Our study demonstrates the feasibility of using claims data to identify key lifestyle behaviors. Additional research is needed to confirm the accuracy and validity of our approach and determine its use in population-level disease prevention.

Obesity Management in Adults: A Review.

Importance: Obesity affects approximately 42% of US adults and is associated with increased rates of type 2 diabetes, hypertension, cardiovascular disease, sleep disorders, osteoarthritis, and premature death. Observations: A body mass index (BMI) of 25 or greater is commonly used to define overweight, and a BMI of 30 or greater to define obesity, with lower thresholds for Asian populations (BMI ≥25-27.5), although use of BMI alone is not recommended to determine individual risk. Individuals with obesity have higher rates of incident cardiovascular disease. In men with a BMI of 30 to 39, cardiovascular event rates are 20.21 per 1000 person-years compared with 13.72 per 1000 person-years in men with a normal BMI. In women with a BMI of 30 to 39.9, cardiovascular event rates are 9.97 per 1000 person-years compared with 6.37 per 1000 person-years in women with a normal BMI. Among people with obesity, 5% to 10% weight loss improves systolic blood pressure by about 3 mm Hg for those with hypertension, and may decrease hemoglobin A1c by 0.6% to 1% for those with type 2 diabetes. Evidence-based obesity treatment includes interventions addressing 5 major categories: behavioral interventions, nutrition, physical activity, pharmacotherapy, and metabolic/bariatric procedures. Comprehensive obesity care plans combine appropriate interventions for individual patients. Multicomponent behavioral interventions, ideally consisting of at least 14 sessions in 6 months to promote lifestyle changes, including components such as weight self-monitoring, dietary and physical activity counseling, and problem solving, often produce 5% to 10% weight loss, although weight regain occurs in 25% or more of participants at 2-year follow-up. Effective nutritional approaches focus on reducing total caloric intake and dietary strategies based on patient preferences. Physical activity without calorie reduction typically causes less weight loss (2-3 kg) but is important for weight-loss maintenance. Commonly prescribed medications such as antidepressants (eg, mirtazapine, amitriptyline) and antihyperglycemics such as glyburide or insulin cause weight gain, and clinicians should review and consider alternatives. Antiobesity medications are recommended for nonpregnant patients with obesity or overweight and weight-related comorbidities in conjunction with lifestyle modifications. Six medications are currently approved by the US Food and Drug Administration for long-term use: glucagon-like peptide receptor 1 (GLP-1) agonists (semaglutide and liraglutide only), tirzepatide (a glucose-dependent insulinotropic polypeptide/GLP-1 agonist), phentermine-topiramate, naltrexone-bupropion, and orlistat. Of these, tirzepatide has the greatest effect, with mean weight loss of 21% at 72 weeks. Endoscopic procedures (ie, intragastric balloon and endoscopic sleeve gastroplasty) can attain 10% to 13% weight loss at 6 months. Weight loss from metabolic and bariatric surgeries (ie, laparoscopic sleeve gastrectomy and Roux-en-Y gastric bypass) ranges from 25% to 30% at 12 months. Maintaining long-term weight loss is difficult, and clinical guidelines support the use of long-term antiobesity medications when weight maintenance is inadequate with lifestyle interventions alone. Conclusion and Relevance: Obesity affects approximately 42% of adults in the US. Behavioral interventions can attain approximately 5% to 10% weight loss, GLP-1 agonists and glucose-dependent insulinotropic polypeptide/GLP-1 receptor agonists can attain approximately 8% to 21% weight loss, and bariatric surgery can attain approximately 25% to 30% weight loss. Comprehensive, evidence-based obesity treatment combines behavioral interventions, nutrition, physical activity, pharmacotherapy, and metabolic/bariatric procedures as appropriate for individual patients.

Fumarase Deficiency and Its Effect on Infertility: A Case Series.

Background: Fumarase deficiency is an autosomal recessive condition characterized by severe neurologic abnormalities due to homozygous mutations in the fumarate hydratase (FH) gene. Heterozygous carriers of FH mutations have increased risk of developing uterine fibroids that can be associated with hereditary leiomyomatosis and renal cell cancer (HLRCC). The association between FH mutations and infertility remains uncertain. The objective of our study was to characterize the infertility diagnoses, treatments, and outcomes in women presenting to a fertility center who were found to be carriers of fumarase deficiency based on the presence of heterozygous FH mutations. Case Presentation: A retrospective case series was conducted including 10 women presenting to an academic fertility center who were found to be FH carriers based on genetic carrier screening. Of the 9 women who were engaged in further workup, 2 had imaging results consistent with uterine fibroids. One woman underwent hysteroscopic myomectomy prior to two courses of ovulation induction with timed intercourse (OI/TIC) followed by one successful cycle of IVF. Of the remaining patients, only 1 woman successfully delivered after a cycle of ovulation induction with intrauterine insemination (OI/IUI). Other patients pursuing OI/IUI, OI/TIC, or monitored natural cycles had unsuccessful experiences. Conclusion: Patients with infertility who are offered genetic testing should be screened for FH mutations, as the carriers are at risk of developing HLRCC-associated uterine fibroids, which can influence fertility and pregnancy. Additional research is needed to investigate the impacts of FH mutations on infertility.

Use of medications associated with weight change among participants in the All of Us research programme.

Our objective was to describe the use of medications associated with weight change among US adults with overweight/obesity, including anti-obesity medications (AOMs), weight-loss-promoting and weight-gain-promoting medications. We performed a cross-sectional analysis of data from the nationwide All of Us Research Programme. We included adults with measured body mass index (BMI) ≥ 27 kg/m2 enrolled between 2018 and 2022 across the United States. We used linked electronic health record data to determine medication use ±12 months of BMI measure. Our 132 057 participants had mean age 54 years and mean BMI 34 kg/m2 ; 60% of participants were women, 62% White, and 32% Black. Only 1% used any AOM, and 14% used at least one weight-loss-promoting medication. We found that 36% used at least one weight-gain-promoting medication, and approximately 20% used multiple weight-gain-promoting medications. While AOMs are underutilized by participants with overweight/obesity, weight-gain-promoting medication use is common. Our results raise concern about potential iatrogenic weight gain from medications. Future research is needed to estimate the long-term effect of weight-gain-promoting medications on weight status and determine whether weight-loss benefits occur with their discontinuation. Clinician education on AOMs and weight-loss-promoting medications may be needed to increase their use.

Natural Language Processing of Nursing Notes: An Integrative Review.

Natural language processing includes a variety of techniques that help to extract meaning from narrative data. In healthcare, medical natural language processing has been a growing field of study; however, little is known about its use in nursing. We searched PubMed, EMBASE, and CINAHL and found 689 studies, narrowed to 43 eligible studies using natural language processing in nursing notes. Data related to the study purpose, patient population, methodology, performance evaluation metrics, and quality indicators were extracted for each study. The majority (86%) of the studies were conducted from 2015 to 2021. Most of the studies (58%) used inpatient data. One of four studies used data from open-source databases. The most common standard terminologies used were the Unified Medical Language System and Systematized Nomenclature of Medicine, whereas nursing-specific standard terminologies were used only in eight studies. Full system performance metrics (eg, F score) were reported for 61% of applicable studies. The overall number of nursing natural language processing publications remains relatively small compared with the other medical literature. Future studies should evaluate and report appropriate performance metrics and use existing standard nursing terminologies to enable future scalability of the methods and findings.

Creating community: Expanding access to psychosocial programs for Latina breast cancer survivors.

OBJECTIVE: Breast cancer survivors often derive benefits from psychosocial interventions, but less is known about Latina women's experiences. Given the disproportionately high disease burden faced by Latina survivors, it is critical to examine ways to enhance access for this population. Thus, the present study aimed to (a) examine women's perceptions of factors associated with effective delivery of a psychosocial program designed for Spanish speaking women with limited access to care, and (b) identify the mechanisms by which the program enhanced women's psychological well-being. METHODS: In a qualitative study, in-depth interviews were conducted with 15 immigrant Latina breast cancer survivors who previously received psychosocial services at a community-based organization. Grounded theory was used to analyze the data. RESULTS: Through open, axial, and selective coding, we arrived at the core category achieving a sense of community. The psychosocial program promoted access and enhanced women's psychological well-being by creating a sense of community among participants. This was facilitated by three primary aspects of service provision: access factors, a holistic approach to health, and therapeutic factors imparted through a biweekly support group. CONCLUSIONS: Psychosocial services promoted a sense of community among Latina breast cancer survivors while reflecting their cultural values and unique psychosocial needs. Findings may guide the development of interventions to increase access to care, enhance health outcomes, and create and maintain a sense of community among medically underserved populations.

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

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

Homophilic Interaction Between Transmembrane-JAM-A and Soluble JAM-A Regulates Thrombo-Inflammation: Implications for Coronary Artery Disease.

Genetic predisposition through F11R-single-nucleotide variation (SNV) influences circulatory soluble junctional adhesion molecule-A (sJAM-A) levels in coronary artery disease (CAD) patients. Homozygous carriers of the minor alleles (F11R-SNVs rs2774276, rs790056) show enhanced levels of thrombo-inflammatory sJAM-A. Both F11R-SNVs and sJAM-A are associated with worse prognosis for recurrent myocardial infarction in CAD patients. Platelet surface-associated JAM-A correlate with platelet activation markers in CAD patients. Activated platelets shed transmembrane-JAM-A, generating proinflammatory sJAM-A and JAM-A-bearing microparticles. Platelet transmembrane-JAM-A and sJAM-A as homophilic interaction partners exaggerate thrombotic and thrombo-inflammatory platelet monocyte interactions. Therapeutic strategies interfering with this homophilic interface may regulate thrombotic and thrombo-inflammatory platelet response in cardiovascular pathologies where circulatory sJAM-A levels are elevated.

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

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

Utility of an Electronic Health Record Report to Identify Patients with Delays in Testing for Poorly Controlled Diabetes.

BACKGROUND: Reducing hemoglobin A1c (HbA1c) is essential for patients with poorly controlled diabetes. However, delays in HbA1c testing are common, and incomplete electronic health record (EHR) reports hinder identification of patients who are overdue. This study sought to quantify how often an EHR report correctly identifies patients with HbA1c testing delays and to describe potential contributing factors. METHODS: Using an EHR report, the researchers identified adult patients who had an HbA1c > 9.0% between October 2017 and March 2018 and a suspected delay (for example, another HbA1c had not resulted within six months). A retrospective chart review of 200 randomly selected records was performed to confirm delays in testing. Secondary measures were collected from 93 charts to evaluate associated factors. RESULTS: A total of 685 patients with suspected delays were identified. On chart review (N = 200), 82.0% were confirmed. Nine percent of patients had a timely repeat result, but the result was not in a discrete field within the EHR. Another 8.5% were never expected to return. Among a subset of confirmed delays, patients often received lifestyle counseling, but less than half had documented discussions about repeat glycemic testing. Also, 74.2% had a timely follow-up appointment scheduled but the majority (85.5%) were missed. CONCLUSION: Most suspected delays in HbA1c testing were confirmed; however, a substantial minority were misclassified due to missing data or follow-up care outside the health system. Current solutions to improve data quality for HbA1c are labor intensive and highlight the need for better integration of health care data. Missed appointments were commonly noted among patients with delays in care and are a potential target for improvement.

Identification of Prediabetes Discussions in Unstructured Clinical Documentation: Validation of a Natural Language Processing Algorithm.

BACKGROUND: Prediabetes affects 1 in 3 US adults. Most are not receiving evidence-based interventions, so understanding how providers discuss prediabetes with patients will inform how to improve their care. OBJECTIVE: This study aimed to develop a natural language processing (NLP) algorithm using machine learning techniques to identify discussions of prediabetes in narrative documentation. METHODS: We developed and applied a keyword search strategy to identify discussions of prediabetes in clinical documentation for patients with prediabetes. We manually reviewed matching notes to determine which represented actual prediabetes discussions. We applied 7 machine learning models against our manual annotation. RESULTS: Machine learning classifiers were able to achieve classification results that were close to human performance with up to 98% precision and recall to identify prediabetes discussions in clinical documentation. CONCLUSIONS: We demonstrated that prediabetes discussions can be accurately identified using an NLP algorithm. This approach can be used to understand and identify prediabetes management practices in primary care, thereby informing interventions to improve guideline-concordant care.

Predictors of posttraumatic growth among hematopoietic cell transplant recipients.

OBJECTIVE: Prior research has shown that cancer survivors often report positive psychological changes from the experience of cancer, or posttraumatic growth (PTG). However, few studies have focused on PTG in cancer patients recovering from hematopoietic cell transplantation (HCT). The present study measured PTG at specific milestones during the year following HCT and investigated psychosocial and treatment-related factors that may hinder or facilitate PTG. METHODS: Participants (N = 430) completed assessments of PTG, social support, and coping pre-transplant. Posttraumatic growth was also assessed at 1, 3, 6, and 12 months post-transplant. Information about treatment regimen and post-transplant complications was abstracted from medical records. Mixed-effects linear regression models were used to evaluate the extent to which pre-transplant social support, coping approaches, treatment intensity, and post-transplant complications predicted PTG. RESULTS: Compared to pre-transplant, PTG scores were significantly higher at 6- and 12-month post-transplant. Greater pre-transplant social support significantly predicted greater PTG across the assessment points. Use of emotional engagement coping strategies also strongly predicted post-transplant PTG. Conversely, coping styles characterized by emotional avoidance generally were not predictive of PTG. No treatment-related factors or post-transplant complications were predictive of PTG. CONCLUSIONS: Findings indicate that supportive social relationships and coping by engaging with difficult emotions may facilitate PTG following HCT. Moreover, these factors were more important than medical characteristics in explaining PTG. Findings may guide the development of interventions to enhance positive psychological outcomes after HCT.

The Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) Clinical Decision Support Early Warning System: Protocol for a Cluster Randomized Pragmatic Clinical Trial.

BACKGROUND: Every year, hundreds of thousands of inpatients die from cardiac arrest and sepsis, which could be avoided if those patients' risk for deterioration were detected and timely interventions were initiated. Thus, a system is needed to convert real-time, raw patient data into consumable information that clinicians can utilize to identify patients at risk of deterioration and thus prevent mortality and improve patient health outcomes. The overarching goal of the COmmunicating Narrative Concerns Entered by Registered Nurses (CONCERN) study is to implement and evaluate an early warning score system that provides clinical decision support (CDS) in electronic health record systems. With a combination of machine learning and natural language processing, the CONCERN CDS utilizes nursing documentation patterns as indicators of nurses' increased surveillance to predict when patients are at the risk of clinical deterioration. OBJECTIVE: The objective of this cluster randomized pragmatic clinical trial is to evaluate the effectiveness and usability of the CONCERN CDS system at 2 different study sites. The specific aim is to decrease hospitalized patients' negative health outcomes (in-hospital mortality, length of stay, cardiac arrest, unanticipated intensive care unit transfers, and 30-day hospital readmission rates). METHODS: A multiple time-series intervention consisting of 3 phases will be performed through a 1-year period during the cluster randomized pragmatic clinical trial. Phase 1 evaluates the adoption of our algorithm through pilot and trial testing, phase 2 activates optimized versions of the CONCERN CDS based on experience from phase 1, and phase 3 will be a silent release mode where no CDS is viewable to the end user. The intervention deals with a series of processes from system release to evaluation. The system release includes CONCERN CDS implementation and user training. Then, a mixed methods approach will be used with end users to assess the system and clinician perspectives. RESULTS: Data collection and analysis are expected to conclude by August 2022. Based on our previous work on CONCERN, we expect the system to have a positive impact on the mortality rate and length of stay. CONCLUSIONS: The CONCERN CDS will increase team-based situational awareness and shared understanding of patients predicted to be at risk for clinical deterioration in need of intervention to prevent mortality and associated harm. TRIAL REGISTRATION: ClinicalTrials.gov NCT03911687; https://clinicaltrials.gov/ct2/show/NCT03911687. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30238.

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

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

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

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