JMIR Publications

ABSTRACT

Background:

For several major chronic diseases including asthma, chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), and diabetes, a state-of-the-art way to avert poor outcomes is to use predictive models to identify future high-cost patients for preemptive care management interventions. Frequently, an American patient obtains care from multiple healthcare systems, each managed by a distinct institution. As the patient’s medical data are spread across these healthcare systems, none of them has complete medical data for the patient. The task of building models to predict an individual patient’s cost is currently thought to be impractical on incomplete data, limiting the use of care management to improve outcomes. Recently, we developed a constraint-based method to pinpoint patients apt to obtain care mostly within a given healthcare system. Our method was shown to work well for the cohort of all adult patients at the University of Washington Medicine (UWM) for a 6-month follow-up period. It is unknown how our method performs on patients with various chronic diseases and over follow-up periods of different lengths, and subsequently whether it is reasonable to perform this predictive modeling task on the subset of patients pinpointed by our method.

Objective:

To understand our method’s potential to enable this predictive modeling task on incomplete medical data, this study assesses our method’s performance at the UWM on 5 subgroups of adult patients with major chronic diseases and over follow-up periods of 2 different lengths.

Methods:

We used UWM data for all adult patients who obtained care at the UWM in 2018 and PreManage data containing usage information of all hospitals in Washington state in 2019. We evaluated our method’s performance over the follow-up periods of 6 months and 12 months on 5 patient subgroups separately, one subgroup for each of 5 diseases: asthma, CKD, type 1 diabetes (T1D), type 2 diabetes (T2D), and COPD.

Results:

Our method identified 21.81% (3,194/14,644) of UWM adult patients with asthma. About 66.75% (797/1,194) and 67.13% (1,997/2,975) of their emergency department visits and inpatient stays took place within the UWM in the subsequent 6 months and in the subsequent 12 months, respectively, roughly double the corresponding percentage for all UWM adult patients with asthma. The performance for adult patients with CKD, adult patients with COPD, adult patients with T1D, and adult patients with T2D was reasonably similar to that for adult patients with asthma.

Conclusions:

For each of the 5 chronic diseases most relevant to care management, our method can pinpoint a reasonably large subset of patients apt to obtain care mostly within the UWM. This opens the door to building models to predict an individual patient’s cost on incomplete data, which was formerly deemed impractical.