Sample selection in the face of design constraints: Use of clustering to define sample strata for qualitative research

Are value-based incentives driving behavior change to improve value?

OBJECTIVES

To understand physician organization (PO) responses to financial incentives for quality and total cost of care among POs that were exposed to a statewide multipayer value-based payment (VBP) program, and to identify challenges that POs face in advancing the goals of VBP.

STUDY DESIGN

Semistructured qualitative interviews and survey.

METHODS

We drew a stratified random sample of 40 multispecialty California POs (25% of the POs that were eligible for incentives). In-person interviews were conducted with physician leaders and a survey was administered on actions being taken to reduce costs and redesign care and to discuss the challenges to improving value. We performed a thematic analysis of interview transcripts to identify common actions taken and challenges to reducing costs.

RESULTS

VBP helps to promote care delivery transformation among POs, although efforts varied across organizations. Investments are occurring primarily in strategies to control hospital costs and redesign primary care, particularly for chronically ill patients; specialty care redesign is largely absent. Physician payment incentives for value remain small relative to total compensation, with continued emphasis on productivity. Challenges cited include the lack of a single enterprisewide electronic health records platform for information exchange, limited ability to influence specialists who were not exclusive to the organization, lack of payer cost and utilization data to manage costs, inability to recoup care redesign investments given the small size of VBP incentives, and lack of physician cost awareness.

CONCLUSIONS

Transformation could be advanced by strengthening financial incentives for value; engaging specialists in care redesign and delivering value; enhancing partnerships among POs, hospitals, and payers to align quality and cost actions; strengthening information exchange across providers; and applying other strategies to influence physician behavior.

Sample selection in the face of design constraints: Use of clustering to define sample strata for qualitative research

OBJECTIVE

To sample 40 physician organizations stratified on the basis of longitudinal cost of care measures for qualitative interviews in order to describe the range of care delivery structures and processes that are being deployed to influence the total costs of caring for patients.

DATA SOURCES

Three years of physician organization-level total cost of care data (n = 156 in California) from the Integrated Healthcare Association's value-based pay-for-performance program.

STUDY DESIGN

We fit total cost of care data using mixture and K-means clustering algorithms to segment the population of physician organizations into sampling strata based on 3-year cost trajectories (ie, cost curves).

PRINCIPAL FINDINGS

A mixture of multivariate normal distributions can classify physician organization cost curves into clusters defined by total cost level, shape, and within-cluster variation. K-means clustering does not accommodate differing levels of within-cluster variation and resulted in more clusters being allocated to unstable cost curves. A mixture of regressions approach focuses overly on anomalous trajectories and is sensitive to model coding.

CONCLUSIONS

Statistical clustering can be used to form sampling strata when longitudinal measures are of primary interest. Many clustering algorithms are available; the choice of the clustering algorithm can strongly impact the resulting strata because various algorithms focus on different aspects of the observed data.