The effect of predictive analytics-driven interventions on healthcare utilization. Academic Article

abstract

• This paper studies a commercial insurer-driven intervention to improve resource allocation. The insurer developed a claims-based algorithm to derive a member-level healthcare utilization risk score. Members with the highest scores were contacted by a care management team tasked with closing gaps in care. The number of members outreached was dictated by resource availability and not by severity, creating a set of arbitrary cutoff points, separating treated and untreated members with very similar predicted risk scores. Using a regression discontinuity approach, we find evidence that predictive analytics-driven interventions directed at high-risk individuals reduced emergency room and specialist visits, yet not hospitalizations.

authors

• Ukert, Benjamin

published proceedings

• J Health Econ

altmetric score

• 30.2

author list (cited authors)

• David, G., Smith-McLallen, A., & Ukert, B.

citation count

• 12

complete list of authors

• David, Guy||Smith-McLallen, Aaron||Ukert, Benjamin

publication date

• March 2019

publisher

• Elsevier  Publisher

published in

• Journal of Health Economics  Journal

keywords

• Aged
• Aged, 80 And Over
• Algorithms
• Cost Containment
• Cost Control
• Female
• Forecasting
• Health Care Costs
• Health Resources
• Hospitalization
• Humans
• Insurance, Health
• Male
• Patient Acceptance Of Health Care
• Population Health Management
• Predictive Analytics
• Reimbursement Mechanisms
• Risk Assessment

PubMed Central ID

• 30818095

Digital Object Identifier (DOI)

• 10.1016/j.jhealeco.2019.02.002

start page

• 68

end page

• 79

volume

• 64

URL

• http://dx.doi.org/10.1016/j.jhealeco.2019.02.002