Temporal Patterns of High-Spend Subgroups Can Inform Service Strategy for Medicare Advantage Enrollees.

BACKGROUND: Most healthcare costs are concentrated in a small proportion of individuals with complex social, medical, behavioral, and clinical needs that are poorly met by a fee-for-service healthcare system. Efforts to reduce cost in the top decile have shown limited effectiveness. Understanding patient subgroups within the top decile is a first step toward designing more effective and targeted interventions. OBJECTIVE: Segment the top decile based on spending and clinical characteristics and examine the temporal movement of individuals in and out of the top decile. DESIGN: Retrospective claims data analysis. PARTICIPANTS: UnitedHealthcare Medicare Advantage (MA) enrollees (N = 1,504,091) continuously enrolled from 2016 to 2019. MAIN MEASURES: Medical (physician, inpatient, outpatient) and pharmacy claims for services submitted for third-party reimbursement under Medicare Advantage, available as International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) and National Drug Codes (NDC) claims. KEY RESULTS: The top decile was segmented into three distinct subgroups characterized by different drivers of cost: (1) Catastrophic: acute events (acute myocardial infarction and hip/pelvic fracture), (2) persistent: medications, and (3) semi-persistent chronic conditions and frailty indicators. These groups show different patterns of spending across time. Each year, 79% of the catastrophic group dropped out of the top decile. In contrast, 68-70% of the persistent group and 36-37% of the semi-persistent group remained in the top decile year over year. These groups also show different 1-year mortality rates, which are highest among semi-persistent members at 17.5-18.5%, compared to 12% and 13-14% for catastrophic and persistent members, respectively. CONCLUSIONS: The top decile consists of subgroups with different needs and spending patterns. Interventions to reduce utilization and expenditures may show more effectiveness if they account for the different characteristics and care needs of these subgroups.

Castleman's disease in the orbit of a 17-year-old girl: a case report.

Castleman's disease is an atypical lympho proliferative disorder comprising hyaline vascular elements, plasma cells, or a mixture of both, which can present in unicentric or multicentric fashion. Resection of unicentric lesions is typically curative, but multicentric disease, also characterized by constitutional symptoms and a poorer prognosis, often requires treatment with chemotherapy, radiation, steroids, or immune modulators. Castleman's disease is rarely diagnosed in the orbit. The authors present the clinical and histopathological findings of a 17-year-old who was found to have a focal lesion in her orbit. She was successfully treated with surgical resection and was free of disease recurrence or other sequelae at 10-months follow up.

Predictive Models to Assess Risk of Persistent Opioid Use, Opioid Use Disorder, and Overdose.

BACKGROUND: This systematic review summarizes the development, accuracy, quality, and clinical utility of predictive models to assess the risk of opioid use disorder (OUD), persistent opioid use, and opioid overdose. METHODS: In accordance with Preferred Reporting Items for a Systematic Review and Meta-analysis guidelines, 8 electronic databases were searched for studies on predictive models and OUD, overdose, or persistent use in adults until June 25, 2023. Study selection and data extraction were completed independently by 2 reviewers. Risk of bias of included studies was assessed independently by 2 reviewers using the Prediction model Risk of Bias ASsessment Tool (PROBAST). RESULTS: The literature search yielded 3130 reports; after removing 199 duplicates, excluding 2685 studies after abstract review, and excluding 204 studies after full-text review, the final sample consisted of 41 studies that developed more than 160 predictive models. Primary outcomes included opioid overdose (31.6% of studies), OUD (41.4%), and persistent opioid use (17%). The most common modeling approach was regression modeling, and the most common predictors included age, sex, mental health diagnosis history, and substance use disorder history. Most studies reported model performance via the c statistic, ranging from 0.507 to 0.959; gradient boosting tree models and neural network models performed well in the context of their own study. One study deployed a model in real time. Risk of bias was predominantly high; concerns regarding applicability were predominantly low. CONCLUSIONS: Models to predict opioid-related risks are developed using diverse data sources and predictors, with a wide and heterogenous range of accuracy metrics. There is a need for further research to improve their accuracy and implementation.

Behavioral Health Decision Support Systems and User Interface Design in the Emergency Department.

OBJECTIVE: The objective of this qualitative study is to gauge physician sentiment about an emergency department (ED) clinical decision support (CDS) system implemented in multiple adult EDs within a university hospital system. This CDS system focuses on predicting patients' likelihood of ED recidivism and/or adverse opioid-related events. METHODS: The study was conducted among adult emergency physicians working in three EDs of a single academic health system in Rhode Island. Qualitative, semistructured interviews were conducted with ED physicians. Interviews assessed physicians' prior experience with predictive analytics, thoughts on the alert's placement, design, and content, the alert's overall impact, and potential areas for improvement. Responses were aggregated and common themes identified. RESULTS: Twenty-three interviews were conducted (11 preimplementation and 12 postimplementation). Themes were identified regarding each physician familiarity with predictive analytics, alert rollout, alert appearance and content, and on alert sentiments. Most physicians viewed these alerts as a neutral or positive EHR addition, with responses ranging from neutral to positive. The alert placement was noted to be largely intuitive and nonintrusive. The design of the alert was generally viewed positively. The alert's content was believed to be accurate, although the decision to respond to the alert's call-to-action was physician dependent. Those who tended to ignore the alert did so for a few reasons, including already knowing the information the alert contains, the alert offering information that is not relevant to this particular patient, and the alert not containing enough information to be useful. CONCLUSION: Ultimately, this alert appears to have a marginally positive effect on ED physician workflow. At its most beneficial, the alert reminded physicians to deeply consider the care provided to high-risk populations and to potentially adjust their care and referrals. At its least beneficial, the alert did not affect physician decision-making but was not intrusive to the point of negatively impacting workflow.