Expiration of State Licensure Waivers and Out-of-State Telemedicine Relationships.

This cross-sectional study compares the use of telemedicine in states where COVID-19 pandemic­related licensure waivers expired vs states where waivers continued.

Subgroups of High-Cost Medicare Advantage Patients: an Observational Study.

BACKGROUND: There is a growing focus on improving the quality and value of health care delivery for high-cost patients. Compared to fee-for-service Medicare, less is known about the clinical composition of high-cost Medicare Advantage populations. OBJECTIVE: To describe a high-cost Medicare Advantage population and identify clinically and operationally significant subgroups of patients. DESIGN: We used a density-based clustering algorithm to group high-cost patients (top 10% of spending) according to 161 distinct demographic, clinical, and claims-based variables. We then examined rates of utilization, spending, and mortality among subgroups. PARTICIPANTS: Sixty-one thousand five hundred forty-six Medicare Advantage beneficiaries. MAIN MEASURES: Spending, utilization, and mortality. KEY RESULTS: High-cost patients (n = 6154) accounted for 55% of total spending. High-cost patients were more likely to be younger, male, and have higher rates of comorbid illnesses. We identified ten subgroups of high-cost patients: acute exacerbations of chronic disease (mixed); end-stage renal disease (ESRD); recurrent gastrointestinal bleed (GIB); orthopedic trauma (trauma); vascular disease (vascular); surgical infections and other complications (complications); cirrhosis with hepatitis C (liver); ESRD with increased medical and behavioral comorbidity (ESRD+); cancer with high-cost imaging and radiation therapy (oncology); and neurologic disorders (neurologic). The average number of inpatient days ranged from 3.25 (oncology) to 26.09 (trauma). Preventable spending (as a percentage of total spending) ranged from 0.8% (oncology) to 9.5% (complications) and the percentage of spending attributable to prescription medications ranged from 7.9% (trauma and oncology) to 77.0% (liver). The percentage of patients who were persistently high-cost ranged from 11.8% (trauma) to 100.0% (ESRD+). One-year mortality ranged from 0.0% (liver) to 25.8% (ESRD+). CONCLUSIONS: We identified clinically distinct subgroups of patients within a heterogeneous high-cost Medicare Advantage population using cluster analysis. These subgroups, defined by condition-specific profiles and illness trajectories, had markedly different patterns of utilization, spending, and mortality, holding important implications for clinical strategy.

Applying Machine Learning Algorithms to Segment High-Cost Patient Populations.

BACKGROUND: Efforts to improve the value of care for high-cost patients may benefit from care management strategies targeted at clinically distinct subgroups of patients. OBJECTIVE: To evaluate the performance of three different machine learning algorithms for identifying subgroups of high-cost patients. DESIGN: We applied three different clustering algorithms-connectivity-based clustering using agglomerative hierarchical clustering, centroid-based clustering with the k-medoids algorithm, and density-based clustering with the OPTICS algorithm-to a clinical and administrative dataset. We then examined the extent to which each algorithm identified subgroups of patients that were (1) clinically distinct and (2) associated with meaningful differences in relevant utilization metrics. PARTICIPANTS: Patients enrolled in a national Medicare Advantage plan, categorized in the top decile of spending (n = 6154). MAIN MEASURES: Post hoc discriminative models comparing the importance of variables for distinguishing observations in one cluster from the rest. Variance in utilization and spending measures. KEY RESULTS: Connectivity-based, centroid-based, and density-based clustering identified eight, five, and ten subgroups of high-cost patients, respectively. Post hoc discriminative models indicated that density-based clustering subgroups were the most clinically distinct. The variance of utilization and spending measures was the greatest among the subgroups identified through density-based clustering. CONCLUSIONS: Machine learning algorithms can be used to segment a high-cost patient population into subgroups of patients that are clinically distinct and associated with meaningful differences in utilization and spending measures. For these purposes, density-based clustering with the OPTICS algorithm outperformed connectivity-based and centroid-based clustering algorithms.