Evaluation of Attention Switching and Duration of Electronic Inbox Work Among Primary Care Physicians.

Importance: Primary care physicians (PCPs) report multitasking during workdays while processing electronic inbox messages, but scant systematic information exists on attention switching and its correlates in the health care setting. Objectives: To describe PCPs' frequency of attention switching associated with electronic inbox work, identify potentially modifiable factors associated with attention switching and inbox work duration, and compare the relative association of attention switching and other factors with inbox work duration. Design, Setting, and Participants: This cross-sectional study of the work of 1275 PCPs in an integrated group serving 4.5 million patients used electronic health record (EHR) access logs from March 1 to 31, 2018, to evaluate PCPs' frequency of attention switching. Statistical analysis was performed from October 15, 2018, to August 28, 2020. Main Outcomes and Measures: Attention switching was defined as switching between the electronic inbox, other EHR work, and non-EHR periods. Inbox work duration included minutes spent on electronic inbox message views and related EHR tasks. Multivariable models controlled for the exposures. Results: The 1275 PCPs studied (721 women [56.5%]; mean [SD] age, 45.9 [8.5] years) had a mean (SD) of 9.0 (7.6) years of experience with the medical group and received a mean (SD) of 332.6 (148.3) (interquartile range, 252-418) new inbox messages weekly. On workdays, PCPs made a mean (SD) of 79.4 (21.8) attention switches associated with inbox work and did a mean (SD) 64.2 (18.7) minutes of inbox work over the course of 24 hours on workdays. In the model for attention switching, each additional patient secure message beyond the reference value was associated with 0.289 (95% CI, 0.217-0.362) additional switches, each additional results message was associated with 0.203 (95% CI, 0.127-0.278) additional switches, each additional request message was associated with 0.190 (95% CI, 0.124-0.257) additional switches, and each additional administrative message was associated with 0.262 (95% CI, 0.166-0.358) additional switches. Having a panel (a list of patients assigned to a primary care team) with more elderly patients (0.144 switches per percentage increase [95% CI, 0.009-0.278]) and higher inbox work duration (0.468 switches per additional minute of inbox work [95% CI, 0.411-0.524]) were also associated with higher attention switching involving the inbox. In the model for inbox work duration, each additional patient secure message beyond the reference value was associated with 0.151 (95% CI, 0.085-0.217) additional minutes, each additional results message was associated with 0.338 (95% CI, 0.272-0.404) additional minutes, each additional request message was associated with 0.101 (95% CI, 0.041-0.161) additional minutes, and each additional administrative message was associated with 0.179 (95% CI, 0.093-0.265) additional minutes. A higher percentage of the panel's patients initiating messages (0.386 minutes per percentage increase [95% CI, 0.026-0.745]) and attention switches (0.373 minutes per switch [95% CI, 0.328-0.419]) were also associated with higher inbox work duration. In addition, working at a medical center where all PCPs had high inbox work duration was independently associated with high or low inbox work duration. Conclusions and Relevance: This study suggests that PCPs make frequent attention switches during workdays while processing electronic inbox messages. Message quantity was associated with both attention switching and inbox work duration. Physician and patient panel characteristics had less association with attention switching and inbox work duration. Assisting PCPs with message quantity might help modulate both attention switching and inbox work duration.

Assessment of Electronic Health Record Search Patterns and Practices by Practitioners in a Large Integrated Health Care System.

Importance: The electronic health record (EHR) is a source of practitioner dissatisfaction in part because of challenges with information retrieval. To improve data accessibility, a better understanding of practitioners' information needs within individual patient records is needed. Objective: To assess EHR users' searches using data from a large integrated health care system. Design, Setting, and Participants: This retrospective cross-sectional analysis used EHR search data from Kaiser Permanente Northern California, an integrated health care delivery system with more than 4.4 million members. Users' EHR search activity data were obtained from April 1, 2018, to May 15, 2019. Main Outcomes and Measures: Search term frequency was grouped by user and practitioner types. Network analyses were performed of co-occurring search terms within a single search episode, and centrality measures for search terms (degree and betweenness centrality) were calculated. Results: A total of 12 313 047 search activities (including 4 328 330 searches and 7 984 717 result views) conducted by 34 735 unique users within 977 160 unique patient EHRs were identified. In aggregate, users searched for 208 374 unique search terms and conducted a median of 4 searches (interquartile range, 1-28 searches). Of all 97 367 active EHR users, 34 735 (35.7%) conducted at least 1 search. However, of all 12 968 active EHR physician users, 9801 (75.6%) conducted at least 1 search, and of all 1908 active pharmacist users, 1402 (73.5%) conducted at least 1 search. The top 3 most commonly searched terms were statin (75 017 searches [1.7%]), colonoscopy (73 545 [1.7%]), and pft (54 990 [1.3%]). However, wide variation in top searches were noted across practitioner groups. Terms searched most often with another term in a single linked search episode included statin, lisinopril, colonoscopy, gabapentin, and aspirin. Conclusions and Relevance: Although physicians and pharmacists were the most active users of EHR searches, search volume and frequently searched terms varied considerably by and within user role. Further customization of the EHR interface may help leverage users' search content and patterns to improve targeted information retrieval.

Piloting electronic medical record-based early detection of inpatient deterioration in community hospitals.

Patients who deteriorate in the hospital outside the intensive care unit (ICU) have higher mortality and morbidity than those admitted directly to the ICU. As more hospitals deploy comprehensive inpatient electronic medical records (EMRs), attempts to support rapid response teams with automated early detection systems are becoming more frequent. We aimed to describe some of the technical and operational challenges involved in the deployment of an early detection system. This 2-hospital pilot, set within an integrated healthcare delivery system with 21 hospitals, had 2 objectives. First, it aimed to demonstrate that severity scores and probability estimates could be provided to hospitalists in real time. Second, it aimed to surface issues that would need to be addressed so that deployment of the early warning system could occur in all remaining hospitals. To achieve these objectives, we first established a rationale for the development of an early detection system through the analysis of risk-adjusted outcomes. We then demonstrated that EMR data could be employed to predict deteriorations. After addressing specific organizational mandates (eg, defining the clinical response to a probability estimate), we instantiated a set of equations into a Java application that transmits scores and probability estimates so that they are visible in a commercially available EMR every 6 hours. The pilot has been successful and deployment to the remaining hospitals has begun. Journal of Hospital Medicine 2016;11:S18-S24. © 2016 Society of Hospital Medicine.

An electronic order set for acute myocardial infarction is associated with improved patient outcomes through better adherence to clinical practice guidelines.

BACKGROUND: Adherence to evidence-based recommendations for acute myocardial infarction (AMI) remains unsatisfactory. OBJECTIVE: Quantifying association between using an electronic AMI order set (AMI-OS) and hospital processes and outcomes. DESIGN: Retrospective cohort study. SETTING: Twenty-one community hospitals. PATIENTS: A total of 5879 AMI patients were hospitalized between September 28, 2008 and December 31, 2010. MEASUREMENTS: We ascertained whether patients were treated using the AMI-OS or individual orders (a la carte). Dependent process variables were use of evidence-based care; outcome variables were mortality and rehospitalization. RESULTS: Use of individual and combined therapies improved outcomes (eg, 50% lower odds of 30-day mortality for patients with ≥3 therapies). The 3531 patients treated using the AMI-OS were more likely to receive evidence-based therapies (eg, 50% received 5 different therapies vs 36% a la carte). These patients had lower 30-day mortality (5.7% vs 8.5%) than the 2348 treated using a la carte orders. Although AMI-OS patients' predicted mortality risk was lower (3.2%) than that of a la carte patients (4.8%), the association of improved processes and outcomes with the use of the AMI-OS persisted after risk adjustment. For example, after inverse probability weighting, the relative risk for inpatient mortality in the AMI-OS group was 0.67 (95% confidence interval: 0.52-0.86). Inclusion of use of recommended therapies in risk adjustment eliminated the benefit of the AMI-OS, highlighting its mediating effect on adherence to evidence-based treatment. CONCLUSIONS: Use of an electronic order set is associated with increased adherence to evidence-based care and better AMI outcomes.