Journal of Medical Systems

, 37:9922 | Cite as

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

  • David F. Lobach
  • Kensaku Kawamoto
  • Kevin J. Anstrom
  • Garry M. Silvey
  • Janese M. Willis
  • Fred S. Johnson
  • Rex Edwards
  • Jessica Simo
  • Pam Phillips
  • David R. Crosslin
  • Eric L. EisensteinEmail author
Original Paper


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.


Clinical decision support Population health Care coordination Medical resource use Medical costs 



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:


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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • David F. Lobach
    • 1
  • Kensaku Kawamoto
    • 1
  • Kevin J. Anstrom
    • 2
  • Garry M. Silvey
    • 1
  • Janese M. Willis
    • 1
  • Fred S. Johnson
    • 3
  • Rex Edwards
    • 2
  • Jessica Simo
    • 3
  • Pam Phillips
    • 3
  • David R. Crosslin
    • 4
  • Eric L. Eisenstein
    • 1
    • 2
    Email author
  1. 1.Division of Clinical Informatics, Department of Community and Family MedicineDuke University Medical CenterDurhamUSA
  2. 2.Duke Clinical Research InstituteDurhamUSA
  3. 3.Division of Community Health, Department of Community and Family MedicineDuke University Medical CenterDurhamUSA
  4. 4.Department of BiostatisticsUniversity of WashingtonSeattleUSA

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