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

Correspondence to:

Rajeev Chaudhry, MBBS,MPH
Associate Professor of Medicine, Division of Primary
Care Internal Medicine
Knowledge and Delivery Center
Mayo Clinic
200 First Street SW
Rochester, MN 55905
Phone: (507) 255-3956   

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.


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Conflict of interest

The authors report no conflict of interest relationships to industry.

  • References

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  • 2 Stone NJ, Robinson JG, Lichtenstein AH, Bairey Merz CN, Blum CB, Eckel RH, Goldberg AC, Gordon D, Levy D, Lloyd-Jones DM, McBride P, Schwartz JS, Shero ST, Smith SC, Watson K, Wilson PWF. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults. JACC 2014; 63 25_PA 2889-2934.
  • 3 Koopman RJ, Steege LMB, Moore JL, Clarke MA, Canfield SM, Kim MS, Belden JL. (2015) Physician Information Needs and Electronic Health Records (EHRs): Time to Reengineer the Clinic Note. J Am Board Fam Med 2015; 28 (Suppl. 03) 316-323.
  • 4 Tange HJ. The paper-based patient record: Is it really so bad?. Comput Methods Programs Biomed 1995; 48 (Suppl. 01) 127-131.
  • 5 Linzer M, Konrad TR, Douglas J, McMurray JE, Pathman DE, Williams ES, Schwartz MD, Gerrity M, Scheckler W, Bigby JA, Rhodes E. Managed care, time pressure, and physician job satisfaction: results from the physician worklife study. J Gen Intern Med 2003; 15 (Suppl. 07) 441-450.
  • 6 Yarnall KS, Pollak KI, Ostbye T, Krause KM, Michener JL. Primary care: is there enough time for prevention?. Am J Public Health 2003; 93 (Suppl. 04) 635-641.
  • 7 Oxentenko AS, West CP, Popkave C, Weinberger SE, Kolars JC. Time spent on clinical documentation: a survey of internal medicine residents and program directors. Arch Intern Med 2010; 170 (Suppl. 04) 377-380.
  • 8 Shipman SA, Sinsky CA. Expanding primary care capacity by reducing waste and improving the efficiency of care. Health Aff 2013; 32 (Suppl. 11) 1990-1997.
  • 9 Ahmed A, Chandra S, Herasevich V, Ognjen G, Pickering BW. The effect of two different electronic health record user interfaces on intensive care provider task load, errors of cognition, and performance. Crit Care Med 2011; 39 (Suppl. 07) 1626-1634.
  • 10 Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J Am Med Inform Assoc 2004; 11 (Suppl. 02) 104-112.
  • 11 Meeks DW, Smith MW, Taylor L, Sittig DF, Scott JM, Singh H. An analysis of electronic health record-related patient safety concerns. J Am Med Inform Assoc 2014; 21 (Suppl. 06) 1053-1059.
  • 12 Wang SV, Rogers JR, Jin Y, Bates DW, Fischer MA. Use of electronic healthcare records to identify complex patients with atrial fibrillation for targeted intervention. JAMIA 2016; ocw082.
  • 13 Persell SD, Dunne AP, Lloyd-Jones DM, Baker DW. Electronic health record-based cardiac risk assessment and identification of unmet preventive needs. Med Care 2009; 47 (Suppl. 04) 418.
  • 14 Maviglia SM, Zielstorff RD, Paterno M, Teich JM, Bates DW, Kuperman GJ. Automating complex guidelines for chronic disease: lessons learned. J Am Med Inform Assoc 2003; 10 (Suppl. 02) 154-165.

Correspondence to:

Rajeev Chaudhry, MBBS,MPH
Associate Professor of Medicine, Division of Primary
Care Internal Medicine
Knowledge and Delivery Center
Mayo Clinic
200 First Street SW
Rochester, MN 55905
Phone: (507) 255-3956   

  • References

  • 1 Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults.. Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on Detection, Evaluation, and Treatment of high blood cholesterol in adults (Adult Treatment Panel III). JAMA 2001; 285 (Suppl. 19) 2486.
  • 2 Stone NJ, Robinson JG, Lichtenstein AH, Bairey Merz CN, Blum CB, Eckel RH, Goldberg AC, Gordon D, Levy D, Lloyd-Jones DM, McBride P, Schwartz JS, Shero ST, Smith SC, Watson K, Wilson PWF. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults. JACC 2014; 63 25_PA 2889-2934.
  • 3 Koopman RJ, Steege LMB, Moore JL, Clarke MA, Canfield SM, Kim MS, Belden JL. (2015) Physician Information Needs and Electronic Health Records (EHRs): Time to Reengineer the Clinic Note. J Am Board Fam Med 2015; 28 (Suppl. 03) 316-323.
  • 4 Tange HJ. The paper-based patient record: Is it really so bad?. Comput Methods Programs Biomed 1995; 48 (Suppl. 01) 127-131.
  • 5 Linzer M, Konrad TR, Douglas J, McMurray JE, Pathman DE, Williams ES, Schwartz MD, Gerrity M, Scheckler W, Bigby JA, Rhodes E. Managed care, time pressure, and physician job satisfaction: results from the physician worklife study. J Gen Intern Med 2003; 15 (Suppl. 07) 441-450.
  • 6 Yarnall KS, Pollak KI, Ostbye T, Krause KM, Michener JL. Primary care: is there enough time for prevention?. Am J Public Health 2003; 93 (Suppl. 04) 635-641.
  • 7 Oxentenko AS, West CP, Popkave C, Weinberger SE, Kolars JC. Time spent on clinical documentation: a survey of internal medicine residents and program directors. Arch Intern Med 2010; 170 (Suppl. 04) 377-380.
  • 8 Shipman SA, Sinsky CA. Expanding primary care capacity by reducing waste and improving the efficiency of care. Health Aff 2013; 32 (Suppl. 11) 1990-1997.
  • 9 Ahmed A, Chandra S, Herasevich V, Ognjen G, Pickering BW. The effect of two different electronic health record user interfaces on intensive care provider task load, errors of cognition, and performance. Crit Care Med 2011; 39 (Suppl. 07) 1626-1634.
  • 10 Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J Am Med Inform Assoc 2004; 11 (Suppl. 02) 104-112.
  • 11 Meeks DW, Smith MW, Taylor L, Sittig DF, Scott JM, Singh H. An analysis of electronic health record-related patient safety concerns. J Am Med Inform Assoc 2014; 21 (Suppl. 06) 1053-1059.
  • 12 Wang SV, Rogers JR, Jin Y, Bates DW, Fischer MA. Use of electronic healthcare records to identify complex patients with atrial fibrillation for targeted intervention. JAMIA 2016; ocw082.
  • 13 Persell SD, Dunne AP, Lloyd-Jones DM, Baker DW. Electronic health record-based cardiac risk assessment and identification of unmet preventive needs. Med Care 2009; 47 (Suppl. 04) 418.
  • 14 Maviglia SM, Zielstorff RD, Paterno M, Teich JM, Bates DW, Kuperman GJ. Automating complex guidelines for chronic disease: lessons learned. J Am Med Inform Assoc 2003; 10 (Suppl. 02) 154-165.