Characterizing managing physicians by claims sequences in episodes of care.

Value-based healthcare in the US is a payment structure that ties reimbursement to quality rather than volume alone. One model of value-based care is the Tennessee Division of TennCare's Episodes of Care program, which groups common health conditions into episodes using specified time windows, medical code sets and quality metrics as defined in each episode's Detailed Business Requirements [1,2]. Tennessee's program assigns responsibility for an episode to a managing physician, presenting a unique opportunity to study physician variability in cost and quality within these structured episodes. This paper proposes a pipeline for analysis demonstrated using a cohort of 599 Outpatient and Non-Acute Inpatient Cholecystectomy episodes managed by BlueCross BlueShield of Tennessee in 2016. We sorted episode claims by date of service, then calculated the pairwise Levenshtein distance between all episodes. Next, we adjusted the resulting matrix by cost dissimilarity and performed agglomerative clustering. We then examined the lowest and highest average episode cost clusters for patterns in cost and quality. Our results indicate that the facility type where the surgery takes place is important: outpatient ambulatory care center for the lowest cost cluster, and hospital operating room for the highest cost cluster. Average patient risk scores were higher in the highest cost cluster than the lowest cost cluster. Readmission rate (a quality metric tied to managing physician performance) was low for the whole cohort. Lastly, we explain how our analytical pipeline can be generalized and extended to domains beyond Episodes of Care.

Development and validation of a predictive model for critical illness in adult patients requiring hospitalization for COVID-19.

BACKGROUND: Identifying factors that can predict severe disease in patients needing hospitalization for COVID-19 is crucial for early recognition of patients at greatest risk. OBJECTIVE: (1) Identify factors predicting intensive care unit (ICU) transfer and (2) develop a simple calculator for clinicians managing patients hospitalized with COVID-19. METHODS: A total of 2,685 patients with laboratory-confirmed COVID-19 admitted to a large metropolitan health system in Georgia, USA between March and July 2020 were included in the study. Seventy-five percent of patients were included in the training dataset (admitted March 1 to July 10). Through multivariable logistic regression, we developed a prediction model (probability score) for ICU transfer. Then, we validated the model by estimating its performance accuracy (area under the curve [AUC]) using data from the remaining 25% of patients (admitted July 11 to July 31). RESULTS: We included 2,014 and 671 patients in the training and validation datasets, respectively. Diabetes mellitus, coronary artery disease, chronic kidney disease, serum C-reactive protein, and serum lactate dehydrogenase were identified as significant risk factors for ICU transfer, and a prediction model was developed. The AUC was 0.752 for the training dataset and 0.769 for the validation dataset. We developed a free, web-based calculator to facilitate use of the prediction model (https://icucovid19.shinyapps.io/ICUCOVID19/). CONCLUSION: Our validated, simple, and accessible prediction model and web-based calculator for ICU transfer may be useful in assisting healthcare providers in identifying hospitalized patients with COVID-19 who are at high risk for clinical deterioration. Triage of such patients for early aggressive treatment can impact clinical outcomes for this potentially deadly disease.