Voice response system to measure healthcare costs: A STAR*D Report

T. Michael Kashner, Madhukar H. Trivedi, Annie Wicker, Maurizio Fava, John H. Greist, James C. Mundt, Kathy Shores-Wilson, A. John Rush, Stephen R. Wisniewski

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Objective:To evaluate a telephone-operated, interactive voice response (IVR) system designed to collect use-of-care data from patients with major depression (UAC-IVR). Study Design: Patient self-reports from repeated IVR surveys were compared with provider records for 3789 patients with major depression at 41 clinical sites participating in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial.Methods: UAC-IVR responses were examined for consistency and compared with provider records to compute reporting biases and intraclass correlation coefficients. Predictors of inconsistent responses and reporting biases were based on mixed logistic and regression models adjusted for need and predisposing and enabling covariates, and corrected for nesting and repeated measures. Results: Inconsistent responses were found for 10% of calls and 21% of patients. Underreporting biases (-20%) and moderate agreement (intraclass correlation of 68%) were found when UAC-IVR responses were compared with medical records. IVR reporting biases were less for patients after 3 calls or more (experience), for patients with severe baseline symptoms (motivation), and for patients who gave consistent IVR responses (reliability). Bias was unrelated to treatment outcomes or demographic factors. Conclusion: Clinical managers should use IVR systems to collect service histories only after patients are properly trained and responses monitored for consistency and reporting biases.

Original languageEnglish (US)
Pages (from-to)153-162
Number of pages10
JournalAmerican Journal of Managed Care
Volume15
Issue number3
StatePublished - Mar 2009

ASJC Scopus subject areas

  • Health Policy

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