1106 — The Impact of Multi-System Healthcare Use on Classification of Medically-Complex, High-Risk Veterans

Lead/Presenter: Franya Hutchins,  CHERP
All Authors: Hutchins F (VA Center for Health Equity Research and Promotion, VA Pittsburgh Health Care System), Rosland AM (VA Center for Health Equity Research and Promotion, VA Pittsburgh Health Care System) Zhao X (VA Center for Health Equity Research and Promotion, VA Pittsburgh Health Care System) Zhang H (VA Center for Health Equity Research and Promotion, VA Pittsburgh Health Care System) Thorpe J (VA Center for Health Equity Research and Promotion, VA Pittsburgh Health Care System)

Objectives:
Latent class analysis (LCA) is a popular empiric approach for describing clinically-distinct groups among medically complex patients, and is used to design programs for high-risk patient populations. However, patients seeking care in multiple healthcare systems may have missing diagnoses across systems, leading to misclassification. We evaluated the impact of multi-system use on the accuracy and composition of patient multimorbidity groups among Medicare-eligible, high-risk Veterans in the Veterans Health Administration (VA).

Methods:
Eligible patients were VA primary care users ages 65 and older, and in the top decile of predicted one-year VA hospitalization risk in 2018 (n = 558,864). Diagnoses of 26 chronic conditions coded in VA encounters and Medicare claims over the previous 24 months were input into latent class analysis models. In a random 10% sample (n = 56,008), we compared the resulting model fit, class profiles, and patient assignments from models using VA-only data versus VA plus Medicare data.

Results:
Over half (57%) of patients had some Medicare-billed care in addition to their VA care. For each chronic condition observed, fewer than 50% of the diagnoses reported in at least one health system were recorded in both. Using a six-class model, we labeled groups based on prevalent diagnoses: Substance Use Disorders (7% of patients), Mental Health (15%), Heart (22%), Diabetes (16%), Malignant Tumor (14%), and High Complexity (10%). The remaining 16% of patients were classified as “unassigned” due to low match probability to any group. The addition of Medicare data improved model fit statistics and decreased the number of “unassigned” patients to 12%. Over 70% of patients assigned to the Substance, Mental Health, High Complexity, and Malignant Tumor groups using VA systems data were assigned to the same group when Medicare data were added. However, 42% of the Heart group and 15% of the Diabetes group were assigned instead to a new group characterized by multiple cardiometabolic conditions.

Implications:
Older adult high-risk patients in the VA healthcare system were sorted into clinically-useful groups based on chronic condition diagnoses using empirical clustering. The addition of diagnoses reported in Medicare improved model accuracy and altered the clinical profiles of groups.

Impacts:
Accessing or accounting for multi-system data will be key to the success of group-tailored interventions for high-risk, medically-complex Veterans.