Objective To evaluate early lessons from a remote patient monitoring engagement and education technology solution for patients with COVID-19 symptoms. Materials and Methods A COVID-19-specific remote patient monitoring solution (GetWell Loop) was offered to patients with COVID-19 symptoms. The program engaged patients and provided educational materials and the opportunity to share concerns. Alerts were resolved through a virtual care workforce of providers and medical students. Results Between March 18 and April 20, 2020, 2,255 of 3,701 (60.93%) patients with COVID-19 symptoms enrolled resulting in over 2,303 alerts, 4,613 messages, 13 hospital admissions, and 91 emergency room visits. A satisfaction survey was given to 300 patient respondents, 74% of whom would be extremely likely to recommend their doctor. Discussion This program provided a safe and satisfying experience for patients while minimizing COVID-19 exposure and in-person healthcare utilization. Conclusion Remote patient monitoring appears to be an effective approach for managing COVID-19 symptoms at home.

Background High-quality clinical notes are essential to effective clinical communication. However, electronic clinical notes are often long, difficult to review, and contain information that is potentially extraneous or out of date. Additionally, many clinicians write electronic clinical notes using customized templates, resulting in notes with significant variability in structure. There is a need to understand better how clinicians review electronic notes and how note structure variability may impact clinicians' note-reviewing experiences. Objective This article aims to understand how physicians review electronic clinical notes and what impact section order has on note-reviewing patterns. Materials and Methods We conducted an experiment utilizing an electronic health record (EHR) system prototype containing four anonymized patient cases, each composed of nine progress notes that were presented with note sections organized in different orders to different subjects (i.e., Subjective, Objective, Assessment, and Plan, Assessment, Plan, Subjective, and Objective, Subjective, Assessment, Objective, and Plan, and Mixed). Participants, who were mid-level residents and fellows, reviewed the cases and provided a brief summary after reviewing each case. Time-related data were collected and analyzed using descriptive statistics. Surveys were administered and interviews regarding experiences reviewing notes were collected and analyzed qualitatively. Results Qualitatively, participants reported challenges related to reviewing electronic clinical notes. Experimentally, time spent reviewing notes varied based on the note section organization. Consistency in note section organization improved performance (e.g., less scrolling and searching) compared with Mixed section organization when reviewing progress notes. Discussion Clinicians face significant challenges reviewing electronic clinical notes. Our findings support minimizing extraneous information in notes, removing information that can be found in other parts of the EHR, and standardizing the display and order of note sections to improve clinicians' note review experience. Conclusion Our findings support the need to improve EHR note design and presentation to support optimal note review patterns for clinicians.

Objective: The objective of this study is to demonstrate the feasibility of applying word embeddings to expand the terminology of dietary supplements (DS) using over 26 million clinical notes. Methods: Word embedding models (ie, word2vec and GloVe) trained on clinical notes were used to predefine a list of top 40 semantically related terms for each of 14 commonly used DS. Each list was further evaluated by experts to generate semantically similar terms. We investigated the effect of corpus size and other settings (ie, vector size and window size) as well as the 2 word embedding models on performance for DS term expansion. We compared the number of clinical notes (and patients they represent) that were retrieved using the word embedding expanded terms to both the baseline terms and external DS sources exandped terms. Results: Using the word embedding models trained on clinical notes, we could identify 1–12 semantically similar terms for each DS. Using the word embedding exandped terms, we were able to retrieve averagely 8.39% more clinical notes and 11.68% more patients for each DS compared with 2 sets of terms. The increasing corpus size results in more misspellings, but not more semantic variants brand names. Word2vec model is also found more capable of detecting semantically similar terms than GloVe. Conclusion: Our study demonstrates the utility of word embeddings on clinical notes for terminology expansion on 14 DS. We propose that this method can be potentially applied to create a DS vocabulary for downstream applications, such as information extraction.

INTRODUCTION Elderly trauma patients are at high risk for mortality, even when presenting with minor injuries. Previous prognostic models are poorly used because of their reliance on elements unavailable during the index hospitalization. The purpose of this study was to develop a predictive algorithm to accurately estimate in-hospital mortality using easily available metrics. METHODS The National Trauma Databank was used to identify patients 65 years and older. Data were split into derivation (2007–2013) and validation (2014–2015) data sets. There was no overlap between data sets. Factors included age, comorbidities, physiologic parameters, and injury types. A two-tiered scoring system to predict in-hospital mortality was developed: a quick elderly mortality after trauma (qEMAT) score for use at initial patient presentation and a full EMAT (fEMAT) score for use after radiologic evaluation. The final model (stepwise forward selection, p < 0.05) was chosen based on calibration and discrimination analysis. Calibration (Brier score) and discrimination (area under the receiving operating characteristic curve [AuROC]) were evaluated. Because National Trauma Databank did not include blood product transfusion, an element of the Geriatric Trauma Outcome Score (GTOS), a regional trauma registry was used to compare qEMAT versus GTOS. A mobile-based application is currently available for cost-free utilization. RESULTS A total of 840,294 patients were included in the derivation data set and 427,358 patients in the validation data set. The fEMAT score (median, 91; S.D., 82–102) included 26 factors, and the qEMAT score included eight factors. The AuROC was 0.86 for fEMAT (Brier, 0.04) and 0.84 for qEMAT. The fEMAT outperformed other trauma mortality prediction models (e.g., Trauma and Injury Severity Score—Penetrating and Trauma and Injury Severity Score—Blunt, age + Injury Severity Score). The qEMAT outperformed the GTOS (AuROC, 0.87 vs. 0.83). CONCLUSION The qEMAT and fEMAT accurately estimate the probability of in-hospital mortality and can be easily calculated on admission. This information could aid in deciding transfer to tertiary referral center, patient/family counseling, and palliative care utilization. LEVEL OF EVIDENCE Epidemiological Study, level IV.