Objective To describe HARVEST, a novel point-of-care patient summarization and visualization tool, and to conduct a formative evaluation study to assess its effectiveness and gather feedback for iterative improvements. Materials and methods HARVEST is a problem-based, interactive, temporal visualization of longitudinal patient records. Using scalable, distributed natural language processing and problem salience computation, the system extracts content from the patient notes and aggregates and presents information from multiple care settings. Clinical usability was assessed with physician participants using a timed, task-based chart review and questionnaire, with performance differences recorded between conditions (standard data review system and HARVEST). Results HARVEST displays patient information longitudinally using a timeline, a problem cloud as extracted from notes, and focused access to clinical documentation. Despite lack of familiarity with HARVEST, when using a task-based evaluation, performance and time-to-task completion was maintained in patient review scenarios using HARVEST alone or the standard clinical information system at our institution. Subjects reported very high satisfaction with HARVEST and interest in using the system in their daily practice. Discussion HARVEST is available for wide deployment at our institution. Evaluation provided informative feedback and directions for future improvements. Conclusions HARVEST was designed to address the unmet need for clinicians at the point of care, facilitating review of essential patient information. The deployment of HARVEST in our institution allows us to study patient record summarization as an informatics intervention in a real-world setting. It also provides an opportunity to learn how clinicians use the summarizer, enabling informed interface and content iteration and optimization to improve patient care.

Background As adoption of electronic health records continues to increase, there is an opportunity to incorporate clinical documentation as well as laboratory values and demographics into risk prediction modeling. Objective The authors develop a risk prediction model for chronic kidney disease (CKD) progression from stage III to stage IV that includes longitudinal data and features drawn from clinical documentation. Methods The study cohort consisted of 2908 primary-care clinic patients who had at least three visits prior to January 1, 2013 and developed CKD stage III during their documented history. Development and validation cohorts were randomly selected from this cohort and the study datasets included longitudinal inpatient and outpatient data from these populations. Time series analysis (Kalman filter) and survival analysis (Cox proportional hazards) were combined to produce a range of risk models. These models were evaluated using concordance, a discriminatory statistic. Results A risk model incorporating longitudinal data on clinical documentation and laboratory test results (concordance 0.849) predicts progression from state III CKD to stage IV CKD more accurately when compared to a similar model without laboratory test results (concordance 0.733, P<.001), a model that only considers the most recent laboratory test results (concordance 0.819, P < .031) and a model based on estimated glomerular filtration rate (concordance 0.779, P < .001). Conclusions A risk prediction model that takes longitudinal laboratory test results and clinical documentation into consideration can predict CKD progression from stage III to stage IV more accurately than three models that do not take all of these variables into consideration.

Background: Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease. Objective: Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department. Methods: Data were collected from patients with COVID-19 who were admitted to Northwell Health acute care hospitals and were discharged, died, or spent a minimum of 48 hours in the hospital between March 1 and May 11, 2020. Of 11,525 patients, 933 (8.1%) were placed on invasive mechanical ventilation within 48 hours of admission. Variables used by the models included clinical and laboratory data commonly collected in the emergency department. We trained and validated three predictive models (two based on XGBoost and one that used logistic regression) using cross-hospital validation. We compared model performance among all three models as well as an established early warning score (Modified Early Warning Score) using receiver operating characteristic curves, precision-recall curves, and other metrics. Results: The XGBoost model had the highest mean accuracy (0.919; area under the curve=0.77), outperforming the other two models as well as the Modified Early Warning Score. Important predictor variables included the type of oxygen delivery used in the emergency department, patient age, Emergency Severity Index level, respiratory rate, serum lactate, and demographic characteristics. Conclusions: The XGBoost model had high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19.

Objective: This study examined the association between BMI and clinical outcomes among patients with coronavirus disease 2019 (COVID‐19) infection.Methods: A total of 10,861 patients with COVID‐19 infection who were admitted to the Northwell Health system hospitals between March 1, 2020, and April 27, 2020, were included in this study. BMI was classified as underweight, normal weight, overweight, and obesity classes I, II, and III. Primary outcomes were invasive mechanical ventilation (IMV) and death.Results: A total of 243 (2.2%) patients were underweight, 2,507 (23.1%) were normal weight, 4,021 (37.0%) had overweight, 2,345 (21.6%) had obesity class I, 990 (9.1%) had obesity class II, and 755 (7.0%) had obesity class III. Patients who had overweight (odds ratio [OR] = 1.27 [95% CI: 1.11‐1.46]), obesity class I (OR = 1.48 [95% CI: 1.27‐1.72]), obesity class II (OR = 1.89 [95% CI: 1.56‐2.28]), and obesity class III (OR = 2.31 [95% CI: 1.88‐2.85]) had an increased risk of requiring IMV. Underweight and obesity classes II and III were statistically associated with death (OR = 1.44 [95% CI: 1.08‐1.92]; OR = 1.25 [95% CI: 1.03‐1.52]; OR = 1.61 [95% CI: 1.30‐2.00], respectively). Among patients who were on IMV, BMI was not associated with inpatient deaths.Conclusions: Patients who are underweight or who have obesity are at risk for mechanical ventilation and death, suggesting that pulmonary complications (indicated by IMV) are a significant contributor for poor outcomes in COVID‐19 infection.