Journal
JOURNAL OF MARKETING
Volume 84, Issue 3, Pages 1-27Publisher
SAGE PUBLICATIONS INC
DOI: 10.1177/0022242920913025
Keywords
causal forests; machine learning; personalized health care marketing; cancer screening; randomized field experiment
Categories
Funding
- National Cancer Institute [R01 CA222900]
- AHRQ [R24HS022418]
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Patients at risk for hepatocellular carcinoma or liver cancer should undergo semiannual screening tests to facilitate early detection, effective treatment options at lower cost, better recovery prognosis, and higher life expectancy. Health care institutions invest in direct-to-patient outreach marketing to encourage regular screening. They ask the following questions: (1) Does the effectiveness of outreach vary among patients and over time?; (2) What is the return on outreach?; and (3) Can patient-level targeted outreach increase the return? The authors use a multiperiod, randomized field experiment involving 1,800 patients. Overall, relative to the usual-care condition, outreach alone (outreach with patient navigation) increases screening completion rates by 10-20 (13-24) percentage points. Causal forests demonstrate that patient-level treatment effects vary substantially across periods and by patients' demographics, health status, visit history, health system accessibility, and neighborhood socioeconomic status, thereby facilitating the implementation of the targeted outreach program. A simulation shows that the targeted outreach program improves the return on the randomized outreach program by 74%-96% or $1.6 million to $2 million. Thus, outreach marketing provides a substantial positive payoff to the health care system.
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