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DOI: 10.4338/ACI-2016-07-RA-0114
Effect of a Novel Clinical Decision Support Tool on the Efficiency and Accuracy of Treatment Recommendations for Cholesterol Management
Correspondence to:
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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Keywords
Clinical decision support system - ambulatory care information systems - testing and evaluation of health information technology - electronic health records - knowledge delivery - knowledge management
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Conflict of interest
The authors report no conflict of interest relationships to industry.
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References
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- 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:
-
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.