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A Randomized Trial of Population-Based Clinical Decision Support to Manage Health and Resource Use for Medicaid Beneficiaries

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Abstract

To determine whether a clinical decision support system can favorably impact the delivery of emergency department and hospital services. Randomized clinical trial of three clinical decision support delivery modalities: email messages to care managers (email), printed reports to clinic administrators (report) and letters to patients (letter) conducted among 20,180 Medicaid beneficiaries in Durham County, North Carolina with follow-up through 9 months. Patients in the email group had fewer low-severity emergency department encounters vs. controls (8.1 vs. 10.6/100 enrollees, p < 0.001) with no increase in outpatient encounters or medical costs. Patients in the letter group had more outpatient encounters and greater outpatient and total medical costs. There were no treatment-related differences for patients in the reports group. Among patients <18 years, those in the email group had fewer low severity (7.6 vs. 10.6/100 enrollees, p < 0.001) and total emergency department encounters (18.3 vs. 23.5/100 enrollees, p < 0.001), and lower emergency department ($63 vs. $89, p = 0.002) and total medical costs ($1,736 vs. $2,207, p = 0.009). Patients who were ≥18 years in the letter group had greater outpatient medical costs. There were no intervention-related differences in patient-reported assessments of quality of life and medical care received. The effectiveness of clinical decision support messaging depended upon the delivery modality and patient age. Health IT interventions must be carefully evaluated to ensure that the resultant outcomes are aligned with expectations as interventions can have differing effects on clinical and economic outcomes.

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Acknowledgments

The authors would like to thank Allyn Meredith, MA, for her expert editorial assistance. Funding for this study was provided by grant R01HS015057 from the U.S. Agency for Healthcare Research and Quality, Rockville, MD. DFL and KK are part owners of Clinica Software, Inc., which holds the intellectual property rights, including a pending patent application, to a clinical decision support engine known as SEBASTIAN [17] that was used by the population health management interventions to identify patient care needs. Clinica is creating an open-source version of this CDS engine that has served as the basis of the Health Level 7 Decision Support Service standard. DFL is also a director of Clinica. ELE is an unpaid board member for QCIII, a software and services company specializing in information support and program development for cardiology service lines.

Trial Registration: http://clinicaltrials.gov/ct2/show/NCT00365885

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Authors and Affiliations

  1. Division of Clinical Informatics, Department of Community and Family Medicine, Duke University Medical Center, Durham, NC, USA

    David F. Lobach, Kensaku Kawamoto, Garry M. Silvey, Janese M. Willis & Eric L. Eisenstein

  2. Duke Clinical Research Institute, 2400 Pratt Street, Terrace Level 0311, Durham, NC, 27705, USA

    Kevin J. Anstrom, Rex Edwards & Eric L. Eisenstein

  3. Division of Community Health, Department of Community and Family Medicine, Duke University Medical Center, Durham, NC, USA

    Fred S. Johnson, Jessica Simo & Pam Phillips

  4. Department of Biostatistics, University of Washington, Seattle, WA, USA

    David R. Crosslin

Authors
  1. David F. Lobach
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  2. Kensaku Kawamoto
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  9. Pam Phillips
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  11. Eric L. Eisenstein
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Correspondence to Eric L. Eisenstein.

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Lobach, D.F., Kawamoto, K., Anstrom, K.J. et al. A Randomized Trial of Population-Based Clinical Decision Support to Manage Health and Resource Use for Medicaid Beneficiaries. J Med Syst 37, 9922 (2013). https://doi.org/10.1007/s10916-012-9922-3

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  • DOI: https://doi.org/10.1007/s10916-012-9922-3

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