Developing a Machine Learning Model to Predict Severe Chronic Obstructive Pulmonary Disease Exacerbations: Retrospective Cohort Study.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) poses a large burden on health care. Severe COPD exacerbations require emergency department visits or inpatient stays, often cause an irreversible decline in lung function and health status, and account for 90.3% of the total medical cost related to COPD. Many severe COPD exacerbations are deemed preventable with appropriate outpatient care. Current models for predicting severe COPD exacerbations lack accuracy, making it difficult to effectively target patients at high risk for preventive care management to reduce severe COPD exacerbations and improve outcomes. OBJECTIVE: The aim of this study is to develop a more accurate model to predict severe COPD exacerbations. METHODS: We examined all patients with COPD who visited the University of Washington Medicine facilities between 2011 and 2019 and identified 278 candidate features. By performing secondary analysis on 43,576 University of Washington Medicine data instances from 2011 to 2019, we created a machine learning model to predict severe COPD exacerbations in the next year for patients with COPD. RESULTS: The final model had an area under the receiver operating characteristic curve of 0.866. When using the top 9.99% (752/7529) of the patients with the largest predicted risk to set the cutoff threshold for binary classification, the model gained an accuracy of 90.33% (6801/7529), a sensitivity of 56.6% (103/182), and a specificity of 91.17% (6698/7347). CONCLUSIONS: Our model provided a more accurate prediction of severe COPD exacerbations in the next year compared with prior published models. After further improvement of its performance measures (eg, by adding features extracted from clinical notes), our model could be used in a decision support tool to guide the identification of patients with COPD and at high risk for care management to improve outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/13783.

Racial/Ethnic Disparities in Clinical Grading in Medical School.

Phenomenon: Performance during the clinical phase of medical school is associated with membership in the Alpha Omega Alpha Honor Medical Society, competitiveness for highly selective residency specialties, and career advancement. Although race/ethnicity has been found to be associated with clinical grades during medical school, it remains unclear whether other factors such as performance on standardized tests account for racial/ethnic differences in clinical grades. Identifying the root causes of grading disparities during the clinical phase of medical school is important because of its long-term impacts on the career advancement of students of color. Approach: To evaluate the association between race/ethnicity and clinical grading, we examined Medical Student Performance Evaluation (MSPE) summary words (Outstanding, Excellent, Very Good, Good) and 3rd-year clerkship grades among medical students at the University of Washington School of Medicine. The analysis included data from July 2010 to June 2015. Medical students were categorized as White, underrepresented minorities (URM), and non-URM minorities. Associations between MSPE summary words and clerkship grades with race/ethnicity were assessed using ordinal logistic regression models. Findings: Students who identified as White or female, students who were younger in age, and students with higher United States Medical Licensing Examination Step 1 scores or final clerkship written exam scores consistently received higher final clerkship grades. Non-URM minority students were more likely than White students (Adjusted Odds Ratio = 0.53), confidence interval [0.36, 0.76], p = .001, to receive a lower category MSPE summary word in analyses adjusting for student demographics (age, gender, maternal education), year, and United States Medical Licensing Examination Step 1 scores. Similarly, in four of six required clerkships, grading disparities (p < .05) were found to favor White students over either URM or non-URM minority students. In all analyses, after accounting for all available confounding variables, grading disparities favored White students. Insights: This single institution study is among the first to document racial/ethnic disparities in MSPE summary words and clerkship grades while accounting for clinical clerkship final written examinations. A national focus on grading disparities in medical school is needed to understand the scope of this problem and to identify causes and possible remedies.

Using a Constraint-Based Method to Identify Chronic Disease Patients Who Are Apt to Obtain Care Mostly Within a Given Health Care System: Retrospective Cohort Study.

BACKGROUND: For several major chronic diseases including asthma, chronic obstructive pulmonary disease, chronic kidney disease, and diabetes, a state-of-the-art method to avert poor outcomes is to use predictive models to identify future high-cost patients for preemptive care management interventions. Frequently, an American patient obtains care from multiple health care systems, each managed by a distinct institution. As the patient's medical data are spread across these health care systems, none has complete medical data for the patient. The task of building models to predict an individual patient's cost is currently thought to be impractical with incomplete data, which limits the use of care management to improve outcomes. Recently, we developed a constraint-based method to identify patients who are apt to obtain care mostly within a given health care system. Our method was shown to work well for the cohort of all adult patients at the University of Washington Medicine for a 6-month follow-up period. It is unknown how well our method works for patients with various chronic diseases and over follow-up periods of different lengths, and subsequently, whether it is reasonable to perform this predictive modeling task on the subset of patients pinpointed by our method. OBJECTIVE: To understand our method's potential to enable this predictive modeling task on incomplete medical data, this study assesses our method's performance at the University of Washington Medicine on 5 subgroups of adult patients with major chronic diseases and over follow-up periods of 2 different lengths. METHODS: We used University of Washington Medicine data for all adult patients who obtained care at the University of Washington Medicine in 2018 and PreManage data containing usage information from all hospitals in Washington state in 2019. We evaluated our method's performance over the follow-up periods of 6 months and 12 months on 5 patient subgroups separately-asthma, chronic kidney disease, type 1 diabetes, type 2 diabetes, and chronic obstructive pulmonary disease. RESULTS: Our method identified 21.81% (3194/14,644) of University of Washington Medicine adult patients with asthma. Around 66.75% (797/1194) and 67.13% (1997/2975) of their emergency department visits and inpatient stays took place within the University of Washington Medicine system in the subsequent 6 months and in the subsequent 12 months, respectively, approximately double the corresponding percentage for all University of Washington Medicine adult patients with asthma. The performance for adult patients with chronic kidney disease, adult patients with chronic obstructive pulmonary disease, adult patients with type 1 diabetes, and adult patients with type 2 diabetes was reasonably similar to that for adult patients with asthma. CONCLUSIONS: For each of the 5 chronic diseases most relevant to care management, our method can pinpoint a reasonably large subset of patients who are apt to obtain care mostly within the University of Washington Medicine system. This opens the door to building models to predict an individual patient's cost on incomplete data, which was formerly deemed impractical. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/13783.