Effect of a novel clinical decision support tool on the efficiency and accuracy of treatment recommendations for cholesterol management

Marianne R. Scheitel, Maya E. Kessler, Jane L. Shellum, Steve G. Peters, Dawn S. Milliner, Hongfang D Liu, Ravikumar Komandur Elayavilli, Karl A. Poterack, Timothy A. Miksch, Jennifer J. Boysen, Ron A. Hankey, Rajeev Chaudhry

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Background: The 2013 American College of Cardiology / American Heart Association Guidelines for the Treatment of Blood Cholesterol emphasize treatment based on cardiovascular risk. But finding time in a primary care visit to manually calculate cardiovascular risk and prescribe treatment based on risk is challenging. We developed an informatics-based clinical decision support tool, MayoExpertAdvisor, to deliver automated cardiovascular risk scores and guideline-based treatment recommendations based on patient-specific data in the electronic heath record. Objective: To assess the impact of our clinical decision support tool on the efficiency and accuracy of clinician calculation of cardiovascular risk and its effect on the delivery of guideline-consistent treatment recommendations. Methods: Clinicians were asked to review the EHR records of selected patients. We evaluated the amount of time and the number of clicks and keystrokes needed to calculate cardiovascular risk and provide a treatment recommendation with and without our clinical decision support tool. We also compared the treatment recommendation arrived at by clinicians with and without the use of our tool to those recommended by the guidelines. Results: Clinicians saved 3 minutes and 38 seconds in completing both tasks with MayoExpertAdvisor, used 94 fewer clicks and 23 fewer key strokes, and improved accuracy from the baseline of 60.61% to 100% for both the risk score calculation and guideline-consistent treatment recommendation. Conclusion: Informatics solution can greatly improve the efficiency and accuracy of individualized treatment recommendations and have the potential to increase guideline compliance.

Original languageEnglish (US)
Pages (from-to)124-136
Number of pages13
JournalApplied Clinical Informatics
Volume8
Issue number1
DOIs
StatePublished - 2017

Fingerprint

Clinical Decision Support Systems
Cholesterol
Guidelines
Informatics
Therapeutics
Blood
Compliance
Primary Health Care
Stroke

Keywords

  • Ambulatory care information systems
  • Clinical decision support system
  • Electronic health records
  • Knowledge delivery
  • Knowledge management
  • Testing and evaluation of health information technology

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics
  • Health Information Management

Cite this

Effect of a novel clinical decision support tool on the efficiency and accuracy of treatment recommendations for cholesterol management. / Scheitel, Marianne R.; Kessler, Maya E.; Shellum, Jane L.; Peters, Steve G.; Milliner, Dawn S.; Liu, Hongfang D; Elayavilli, Ravikumar Komandur; Poterack, Karl A.; Miksch, Timothy A.; Boysen, Jennifer J.; Hankey, Ron A.; Chaudhry, Rajeev.

In: Applied Clinical Informatics, Vol. 8, No. 1, 2017, p. 124-136.

Research output: Contribution to journalArticle

Scheitel, MR, Kessler, ME, Shellum, JL, Peters, SG, Milliner, DS, Liu, HD, Elayavilli, RK, Poterack, KA, Miksch, TA, Boysen, JJ, Hankey, RA & Chaudhry, R 2017, 'Effect of a novel clinical decision support tool on the efficiency and accuracy of treatment recommendations for cholesterol management', Applied Clinical Informatics, vol. 8, no. 1, pp. 124-136. https://doi.org/10.4338/ACI-2016-07-RA-0114
Scheitel, Marianne R. ; Kessler, Maya E. ; Shellum, Jane L. ; Peters, Steve G. ; Milliner, Dawn S. ; Liu, Hongfang D ; Elayavilli, Ravikumar Komandur ; Poterack, Karl A. ; Miksch, Timothy A. ; Boysen, Jennifer J. ; Hankey, Ron A. ; Chaudhry, Rajeev. / Effect of a novel clinical decision support tool on the efficiency and accuracy of treatment recommendations for cholesterol management. In: Applied Clinical Informatics. 2017 ; Vol. 8, No. 1. pp. 124-136.
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