Appl Clin Inform 2017; 08(01): 124-136
DOI: 10.4338/ACI-2016-07-RA-0114
Research Article
Schattauer GmbH

Effect of a Novel Clinical Decision Support Tool on the Efficiency and Accuracy of Treatment Recommendations for Cholesterol Management

Marianne R Scheitel
1  Knowledge Management and Delivery Center, Mayo Clinic, Rochester, MN, USA
,
Maya E Kessler
2  Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN
,
Jane L Shellum
1  Knowledge Management and Delivery Center, Mayo Clinic, Rochester, MN, USA
,
Steve G Peters
3  Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN
,
Dawn S Milliner
4  Department of Medicine, Division of Nephrology, Mayo Clinic, Rochester, MN
,
Hongfang Liu
5  Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN
,
Ravikumar Komandur Elayavilli
5  Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN
,
Karl A Poterack
6  Department of Anesthesiology, Mayo Clinic Hospital, Phoenix, AZ
,
Timothy A Miksch
6  Department of Anesthesiology, Mayo Clinic Hospital, Phoenix, AZ
,
Jennifer J Boysen
1  Knowledge Management and Delivery Center, Mayo Clinic, Rochester, MN, USA
,
Ron A Hankey
8  UDP Specialized Data Services, Mayo Clinic, Rochester, MN
,
Rajeev Chaudhry
1  Knowledge Management and Delivery Center, Mayo Clinic, Rochester, MN, USA
2  Division of Primary Care Internal Medicine, Mayo Clinic, Rochester, MN
› Author Affiliations
Further Information

Publication History

Received: 15 July 2016

Accepted: 02 February 2016

Publication Date:
20 December 2017 (online)

  

Summary

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 MayoExpertAd-visor, 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.