Case-mix for assessing primary care value (CPCV)

The Effect of Primary Care Visits on Total Patient Care Cost: Evidence From the Veterans Health Administration

BACKGROUND

Since the 1980s, primary care (PC) in the US has been recognized as the backbone of healthcare providing comprehensive care to complex patients, coordinating care among specialists, and rendering preventive services to contain costs and improve clinical outcomes. However, the effect of PC visits on total patient care cost has been difficult to quantify.

OBJECTIVE

To assess the effect of PC visits on total patient care cost.

METHODS

This is a retrospective study of over 5 million patients assigned to a PC provider in the Veterans Health Administration (VHA) in each of the 4 fiscal years (FY 2016-2019). The main outcome of interest is total annual patient care cost. We assessed the effect of primary care visits on total patient care cost first by descriptive statistics, and then by multivariate regressions adjusting for severity of illness and other confounders. We conducted in-depth sensitivity analyses to validate the findings.

RESULTS

On average, each additional in-person PC visit was associated with a total cost reduction of $721 (per patient per year). The first PC visit was associated with the largest savings, $3976 on average, and a steady diminishing return was observed. Further, the higher the patient risk (severity of illness), the larger the cost reduction: Among the top 10% of high-risk patients, the first PC in-person visit was associated with a reduction of $16 406 (19%).

CONCLUSIONS

These findings, substantiated by our exhaustive sensitivity analyses, suggest that expanding PC capacity can significantly reduce overall health care costs and improve patient care outcomes given the former is a strong proxy of the latter.

Predicting High-risk and High-cost Patients for Proactive Intervention

BACKGROUND

It is well known that 20% of the patients incur 80% of health care costs and many diseases and complications can be prevented or ameliorated with prompt intervention. One of the well-recognized strategies for cost reduction and better outcomes is to predict or identify high-risk and high-cost (HRHC) patients for proactive intervention.

OBJECTIVE

The objective of this study was to develop a predictive model that can be used to identify HRHC patients more accurately for proactive intervention.

METHODS

This is an observational study using fiscal year (FY) 2018 administrative data to predict FY 2019 total cost at the patient level. All 5,676,248 patients who received care in both FYs 2018 and 2019 from the Veterans Health Administration were included in the analyses. The Veterans Health Administration Corporate Data Warehouse was our main data source. With split-sample analyses, 3 sets of patient comorbidities and 5 statistical models were assessed for the highest predictive power.

RESULTS

The Box-Cox regression using comorbidities designated by the expanded CCSR (Clinical Classifications Software Refined) groups as predictors yielded the highest predictive power. The R2 reached 0.51 and 0.37 for the transformed and raw scale cost, respectively.

CONCLUSIONS

The predictive model developed in this study exhibits substantially higher predictive power than what has been reported in the literature. The algorithm based on administrative data and a publicly available patient classification system can be readily implemented by other value-based health systems to identify HRHC patients for proactive intervention.