A machine learning technology for addressing medication-related risk in older, multimorbid patients.

OBJECTIVES: To evaluate the FeelBetter machine learning system's ability to accurately identify older patients with multimorbidity at Brigham and Women's Hospital at highest risk of medication-associated emergency department (ED) visits and hospitalizations, and to assess the system's ability to provide accurate medication recommendations for these patients. STUDY DESIGN: Retrospective cohort study. METHODS: The system uses medications, demographics, diagnoses, laboratory results, health care utilization patterns, and costs to stratify patients' risk of ED visits and hospitalizations. Patients were assigned 1 of 22 risk levels based on their system-generated risk percentile of either ED visits or hospitalizations. Logistic regression models were used to estimate the odds of ED visits and hospitalizations associated with each successive risk level compared with the 45th to 50th percentiles. After stratification, 100 high-risk (95th-100th percentiles) and 100 medium-risk (45th-55th percentiles) patients were randomly selected for generation of medication recommendations. Two clinical pharmacists reviewed the system-generated medication recommendations for these patients. RESULTS: Logistic regression models predicting 3-month utilization showed that compared with the 45th to 50th percentiles, patients in the top 1% risk percentile had ORs of 7.9 and 17.3 for ED visits and hospitalizations, respectively. The first 5 high-priority medications on each patient's medication list were associated with a mean (SD) of 6.65 (4.09) warnings. Of 1290 warnings reviewed, 1151 (89.2%) were assessed as correct. CONCLUSIONS: The FeelBetter system effectively stratifies older patients with multimorbidity at risk of ED use and hospitalizations. Medication recommendations provided by the system are largely accurate and can potentially be beneficial for patient care.

Straight to the point: evaluation of a Point of Care Information (POCI) resource in answering disease-related questions.

Objective: To evaluate the ability of DynaMedex, an evidence-based drug and disease Point of Care Information (POCI) resource, in answering clinical queries using keyword searches. Methods: Real-world disease-related questions compiled from clinicians at an academic medical center, DynaMedex search query data, and medical board review resources were categorized into five clinical categories (complications & prognosis, diagnosis & clinical presentation, epidemiology, prevention & screening/monitoring, and treatment) and six specialties (cardiology, endocrinology, hematology-oncology, infectious disease, internal medicine, and neurology). A total of 265 disease-related questions were evaluated by pharmacist reviewers based on if an answer was found (yes, no), whether the answer was relevant (yes, no), difficulty in finding the answer (easy, not easy), cited best evidence available (yes, no), clinical practice guidelines included (yes, no), and level of detail provided (detailed, limited details). Results: An answer was found for 259/265 questions (98%). Both reviewers found an answer for 241 questions (91%), neither found the answer for 6 questions (2%), and only one reviewer found an answer for 18 questions (7%). Both reviewers found a relevant answer 97% of the time when an answer was found. Of all relevant answers found, 68% were easy to find, 97% cited best quality of evidence available, 72% included clinical guidelines, and 95% were detailed. Recommendations for areas of resource improvement were identified. Conclusions: The resource enabled reviewers to answer most questions easily with the best quality of evidence available, providing detailed answers and clinical guidelines, with a high level of replication of results across users.

Virtual Scribes and Physician Time Spent on Electronic Health Records.

Importance: Time on the electronic health record (EHR) is associated with burnout among physicians. Newer virtual scribe models, which enable support from either a real-time or asynchronous scribe, have the potential to reduce the burden of the EHR and EHR-related documentation. Objective: To characterize the association of use of virtual scribes with changes in physicians' EHR time and note and order composition and to identify the physician, scribe, and scribe response factors associated with changes in EHR time upon virtual scribe use. Design, Setting, and Participants: Retrospective, pre-post quality improvement study of 144 physicians across specialties who had used a scribe for at least 3 months from January 2020 to September 2022, were affiliated with Brigham and Women's Hospital and Massachusetts General Hospital, and cared for patients in the outpatient setting. Data were analyzed from November 2022 to January 2024. Exposure: Use of either a real-time or asynchronous virtual scribe. Main Outcomes: Total EHR time, time on notes, and pajama time (5:30 pm to 7:00 am on weekdays and nonscheduled weekends and holidays), all per appointment; proportion of the note written by the physician and team contribution to orders. Results: The main study sample included 144 unique physicians who had used a virtual scribe for at least 3 months in 152 unique scribe participation episodes (134 [88.2%] had used an asynchronous scribe service). Nearly two-thirds of the physicians (91 physicians [63.2%]) were female and more than half (86 physicians [59.7%]) were in primary care specialties. Use of a virtual scribe was associated with significant decreases in total EHR time per appointment (mean [SD] of 5.6 [16.4] minutes; P < .001) in the 3 months after vs the 3 months prior to scribe use. Scribe use was also associated with significant decreases in note time per appointment and pajama time per appointment (mean [SD] of 1.3 [3.3] minutes; P < .001 and 1.1 [4.0] minutes; P = .004). In a multivariable linear regression model, the following factors were associated with significant decreases in total EHR time per appointment with a scribe use at 3 months: practicing in a medical specialty (-7.8; 95% CI, -13.4 to -2.2 minutes), greater baseline EHR time per appointment (-0.3; 95% CI, -0.4 to -0.2 minutes per additional minute of baseline EHR time), and decrease in the percentage of the note contributed by the physician (-9.1; 95% CI, -17.3 to -0.8 minutes for every percentage point decrease). Conclusions and Relevance: In 2 academic medical centers, use of virtual scribes was associated with significant decreases in total EHR time, time spent on notes, and pajama time, all per appointment. Virtual scribes may be particularly effective among medical specialists and those physicians with greater baseline EHR time.