Abstract
Current approaches to predicting health care costs generally rely on a single composite value of spending and focus on short time horizons. By contrast, examining patients' spending patterns using dynamic measures applied over longer periods may better identify patients with different spending and help target interventions to those with the greatest need. To classify patients by their long-term, dynamic health care spending patterns using a data-driven approach and assess the ability to predict spending patterns, particularly using characteristics that are potentially modifiable through intervention. This cohort study used a retrospective cohort design from a random nationwide sample of Medicare fee-for-service administrative claims data to identify beneficiaries aged 65 years or older with continuous eligibility from 2011 to 2013. Statistical analysis was performed from August 2018 to December 2019. Group-based trajectory modeling was applied to the claims data to classify the Medicare beneficiaries by their total health care spending patterns over a 2-year period. The ability to predict membership in each trajectory spending group was assessed using generalized boosted regression, a data mining approach to model building and prediction, with split-sample validation. Models were estimated using (1) prior-year predictors and (2) prior-year predictors potentially modifiable through intervention measured in the claims data. These models were evaluated using validated C-statistics. The relative influence of individual predictors in the models was evaluated. Among the 329 476 beneficiaries, the mean (SD) age was 76.0 (7.2) years and 190 346 (57.8%) were female. This final 5-group model included a minimal-user group (group 1, 37 572 individuals [11.4%]), a low-cost group (group 2, 48 575 individuals [14.7%]), a rising-cost group (group 3, 24 736 individuals [7.5%]), a moderate-cost group (group 4, 83 338 individuals [25.3%]), and a high-cost group (group 5, 135 255 individuals [41.2%]). Potentially modifiable characteristics strongly predicted these patterns (C-statistics range: 0.68-0.94). For groups with progressively increasing spending in particular, the most influential factors were number of medications (relative influence: 29.2), number of office visits (relative influence: 30.3), and mean medication adherence (relative influence: 33.6). Using a data-driven approach, distinct spending patterns were identified with high accuracy. The potentially modifiable predictors of membership in the rising-cost group represent important levers for early interventions that may prevent later spending increases. This approach could be adapted by organizations to target quality improvement interventions, particularly because numerous health care organizations are increasingly using these routinely collected data.
Highlights
With health care spending accounting for almost 18% of the US gross domestic product, identifying individuals who may benefit from interventions to address potentially avoidable spending has become a central priority for health insurers and health care professionals.[1]
Using a data-driven approach, distinct spending patterns were identified with high accuracy
Lauffenburger et al[11] observed 7 distinct, dynamic patterns of spending over a 1-year period in commercially insured beneficiaries, including individuals whose costs increased rapidly toward the end of the year and another group of high-cost individuals for whom spending decreased. These prior studies were conducted over a 1-year period, yet there may be dynamic patterns of spending over longer periods that may have implications both for whom to outreach for intervention and when to do so.[1,12]
Summary
Lauffenburger et al[11] observed 7 distinct, dynamic patterns of spending over a 1-year period in commercially insured beneficiaries, including individuals whose costs increased rapidly toward the end of the year and another group of high-cost individuals for whom spending decreased These prior studies were conducted over a 1-year period, yet there may be dynamic patterns of spending over longer periods that may have implications both for whom to outreach for intervention and when to do so.[1,12] For example, patients with the same clinical conditions who are hospitalized early during a 12-month period may differ meaningfully from those hospitalized later, both could be identified as having rising costs.[13,14] If these different spending patterns could be predicted using routinely collected data, the ability to better proactively differentiate patients with increasing or decreasing spending patterns could better target interventions to those who are at greatest need of improved health or cost containment.[15] The predictive accuracy of spending may be higher when evaluating a long-term, compared with a short-term, time horizon as seen for other outcomes.[16] we sought to classify patients according to their spending patterns over a 2-year period and to evaluate the ability to predict these spending groups using patient characteristics that are potentially modifiable
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