Download PDF
Appl Clin Inform 2019; 10(03): 421-445
DOI: 10.1055/s-0039-1692186
Research Article
Georg Thieme Verlag KG Stuttgart · New York

Physicians Voluntarily Using an EHR-Based CDS Tool Improved Patients' Guideline-Related Statin Prescription Rates: A Retrospective Cohort Study

Timothy S. Chang
1   Department of Neurology, University of California, Los Angeles, Los Angeles, California, United States
,
Ashwin Buchipudi∗
2   Information Services and Solutions, University of California, Los Angeles, Los Angeles, California, United States
,
Gregg C. Fonarow∗
3   Division of Cardiology, Department of Medicine, University of California, Los Angeles, Los Angeles, California, United States
,
Michael A. Pfeffer∗
4   Division of General Internal Medicine, Department of Medicine, University of California, Los Angeles, Los Angeles, California, United States
,
Jennifer S. Singer∗
5   Department of Urology, University of California, Los Angeles, Los Angeles, California, United States
,
Eric M. Cheng∗
1   Department of Neurology, University of California, Los Angeles, Los Angeles, California, United States
› Author Affiliations Funding This research was supported by NIH National Center for Advancing Translational Science (NCATS) UCLA CTSI Grant Number UL1TR001881.
Further Information

Publication History

02 January 2019

26 April 2019

Publication Date:
19 June 2019 (online)

Also available at
   Permissions and Reprints

Abstract

Background In 2013, the American College of Cardiology (ACC) and the American Heart Association (AHA) released a revised guideline on statin therapy initiation. The guideline included a 10-year risk calculation based on regression modeling, which made hand calculation infeasible. Compliance to the guideline has been suboptimal, as many patients were recommended but not prescribed statin therapy. Clinical decision support (CDS) tools may improve statin guideline compliance. Few statin guideline CDS tools evaluated clinical outcome.

Objectives We determined if use of a CDS tool, the statin macro, was associated with increased 2013 ACC/AHA statin guideline compliance at the level of statin prescription versus no statin prescription. We did not determine if each patient's statin prescription met ACC/AHA 2013 therapy intensity recommendations (high vs. moderate vs. low).

Methods The authors developed a clinician-initiated, EHR-embedded statin macro command (“statin macro”) that displayed the 2013 ACC/AHA statin guideline recommendation in the electronic health record documentation. We included patients who had a primary care visit during the study period (January 1–June 30, 2016), were eligible for statin therapy based on the ACC/AHA guideline prior to the study period, and were not prescribed statin therapy prior to the study period. We tested the association of macro usage and statin therapy prescription during the study period using relative risk and mixed effect logistic regression.

Results Subjects included 11,877 patients seen in primary care, who were retrospectively recommended statin therapy at study initiation based on the ACC/AHA guideline, but who had not received statin therapy. During the study period, 125 clinicians used the statin macro command for 389 of the 11,877 patients (3.2%). Of the 389 patients for whom that statin macro was used, 108 patients (28%) had a statin prescribed during the study period. Of the 11,488 for whom the statin macro was not used, 1,360 (13%) patients received a clinician-prescribed statin (relative risk 2.3, p < 0.001). Controlling for patient covariates and clinicians, statin macro usage was significantly associated with statin therapy prescription (odds ratio 2.86, p < 0.001).

Conclusion Although the statin macro had low uptake, its use was associated with a greater rate of statin prescriptions (dosage not determined) for patients whom 2013 ACC/AHA guidelines required statin therapy.

Keywords

electronic health records - decision support techniques - anticholesteremic agents - atherosclerosis

Protection of Human and Animal Subjects

The University of California, Los Angeles Institutional Review Board approved a waiver of authorization for this study (IRB#: 16–001676).


Supplementary Material

 

  • References

  • 1 Grol R, Grimshaw J. From best evidence to best practice: effective implementation of change in patients' care. Lancet 2003; 362 (9391): 1225-1230
    CrossrefPubMedGoogle Scholar
  • 2 Stone NJ, Robinson JG, Lichtenstein AH. , et al; American College of Cardiology/American Heart Association Task Force on Practice Guidelines. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 2014; 129 (25) (Suppl. 02) S1-S45
    CrossrefPubMedGoogle Scholar
  • 3 Housholder-Hughes SD, Martin MM, McFarland MR, Creech CJ, Shea MJ. Healthcare provider compliance with the 2013 ACC/AHA Adult Cholesterol Guideline recommendation for high-intensity dose statins for patients with coronary artery disease. Heart Lung 2017; 46 (04) 328-333
    CrossrefPubMedGoogle Scholar
  • 4 Bavishi A, Howard T, Kim JP. , et al. Treatment gap in primary prevention patients presenting with acute coronary syndrome. Am J Cardiol 2019; 123 (03) 368-374
    CrossrefPubMedGoogle Scholar
  • 5 Fischer F, Lange K, Klose K, Greiner W, Kraemer A. Barriers and strategies in guideline implementation-a scoping review. Healthcare (Basel) 2016; 4 (03) 36
    CrossrefPubMedGoogle Scholar
  • 6 Carthey J, Walker S, Deelchand V, Vincent C, Griffiths WH. Breaking the rules: understanding non-compliance with policies and guidelines. BMJ 2011; 343: d5283
    CrossrefPubMedGoogle Scholar
  • 7 National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). 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) final report. Circulation 2002; 106 (25) 3143-3421
    CrossrefPubMedGoogle Scholar
  • 8 D'Agostino Sr RB, Vasan RS, Pencina MJ. , et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 2008; 117 (06) 743-753
    CrossrefPubMedGoogle Scholar
  • 9 Hippisley-Cox J, Coupland C, Vinogradova Y, Robson J, May M, Brindle P. Derivation and validation of QRISK, a new cardiovascular disease risk score for the United Kingdom: prospective open cohort study. BMJ 2007; 335 (7611): 136
    CrossrefPubMedGoogle Scholar
  • 10 American College of Cardiology. ASCVD Risk Estimator. 2013 . Available at: http://tools.cardiosource.org/ASCVD-Risk-Estimator/ . Accessed December 1, 2013
    PubMedGoogle Scholar
  • 11 American College of Cardiology. ASCVD Risk Calculator. 2013 . Available at: https://professional.heart.org/professional/GuidelinesStatements/ASCVDRiskCalculator/UCM_457698_ASCVD-Risk-Calculator.jsp . Accessed December 1, 2013
    PubMedGoogle Scholar
  • 12 North F, Fox S, Chaudhry R. Clinician time used for decision making: a best case workflow study using cardiovascular risk assessments and Ask Mayo Expert algorithmic care process models. BMC Med Inform Decis Mak 2016; 16: 96
    CrossrefPubMedGoogle Scholar
  • 13 Fretheim A, Flottorp S, Oxman A. Effect of interventions for implementing clinical practice guidelines. Oslo, Norway: The Norwegian Institute of Public Health (NIPH); 2015
    Google Scholar
  • 14 Sequist TD, Gandhi TK, Karson AS. , et al. A randomized trial of electronic clinical reminders to improve quality of care for diabetes and coronary artery disease. J Am Med Inform Assoc 2005; 12 (04) 431-437
    CrossrefPubMedGoogle Scholar
  • 15 Smith MW, Brown C, Virani SS. , et al. Incorporating guideline adherence and practice implementation issues into the design of decision support for beta-blocker titration for heart failure. Appl Clin Inform 2018; 9 (02) 478-489
    Article in Thieme ConnectPubMedGoogle Scholar
  • 16 Jones BE, Collingridge DS, Vines CG. , et al. CDS in a learning health care system: identifying physicians' reasons for rejection of best-practice recommendations in pneumonia through computerized clinical decision support. Appl Clin Inform 2019; 10 (01) 1-9
    Article in Thieme ConnectPubMedGoogle Scholar
  • 17 Au L, Oster A, Yeh GH, Magno J, Paek HM. Utilizing an electronic health record system to improve vaccination coverage in children. Appl Clin Inform 2010; 1 (03) 221-231
    Article in Thieme ConnectPubMedGoogle Scholar
  • 18 Van de Velde S, Heselmans A, Delvaux N. , et al. A systematic review of trials evaluating success factors of interventions with computerised clinical decision support. Implement Sci 2018; 13 (01) 114
    CrossrefPubMedGoogle Scholar
  • 19 Roshanov PS, Fernandes N, Wilczynski JM. , et al. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. BMJ 2013; 346: f657
    CrossrefPubMedGoogle Scholar
  • 20 Aspry KE, Furman R, Karalis DG. , et al. Effect of health information technology interventions on lipid management in clinical practice: a systematic review of randomized controlled trials. J Clin Lipidol 2013; 7 (06) 546-560
    CrossrefPubMedGoogle Scholar
  • 21 Scheitel MR, Kessler ME, Shellum JL. , et al. Effect of a novel clinical decision support tool on the efficiency and accuracy of treatment recommendations for cholesterol management. Appl Clin Inform 2017; 8 (01) 124-136
    Article in Thieme ConnectPubMedGoogle Scholar
  • 22 Bright TJ, Wong A, Dhurjati R. , et al. Effect of clinical decision-support systems: a systematic review. Ann Intern Med 2012; 157 (01) 29-43
    Google Scholar
  • 23 Epic Systems. Epic. Verona, WI: Epic Systems; 2016
    Google Scholar
  • 24 Rattay KT, Ramakrishnan M, Atkinson A, Gilson M, Drayton V. Use of an electronic medical record system to support primary care recommendations to prevent, identify, and manage childhood obesity. Pediatrics 2009; 123 (Suppl. 02) S100-S107
    CrossrefPubMedGoogle Scholar
  • 25 Young A. ‘SWINEUPDATE’: using EMR charting tools as a clinical decision support tool during the H1N1 outbreak. WMJ 2010; 109 (04) 222-223
    PubMedGoogle Scholar
  • 26 O'Connor PJ, Sperl-Hillen JM, Rush WA. , et al. Impact of electronic health record clinical decision support on diabetes care: a randomized trial. Ann Fam Med 2011; 9 (01) 12-21
    CrossrefPubMedGoogle Scholar
  • 27 Tierney WM, Overhage JM, Murray MD. , et al. Effects of computerized guidelines for managing heart disease in primary care. J Gen Intern Med 2003; 18 (12) 967-976
    CrossrefPubMedGoogle Scholar
  • 28 van Wyk JT, van Wijk MAM, Sturkenboom MCJM, Mosseveld M, Moorman PW, van der Lei J. Electronic alerts versus on-demand decision support to improve dyslipidemia treatment: a cluster randomized controlled trial. Circulation 2008; 117 (03) 371-378
    CrossrefPubMedGoogle Scholar
  • 29 Lester WT, Grant RW, Barnett GO, Chueh HC. Randomized controlled trial of an informatics-based intervention to increase statin prescription for secondary prevention of coronary disease. J Gen Intern Med 2006; 21 (01) 22-29
    CrossrefPubMedGoogle Scholar
  • 30 Van de Velde S, Kunnamo I, Roshanov P. , et al; GUIDES expert panel. The GUIDES checklist: development of a tool to improve the successful use of guideline-based computerised clinical decision support. Implement Sci 2018; 13 (01) 86
    CrossrefPubMedGoogle Scholar
  • 31 Weiskopf NG, Weng C. Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. J Am Med Inform Assoc 2013; 20 (01) 144-151
    CrossrefPubMedGoogle Scholar
  • 32 Johnson SG, Speedie S, Simon G, Kumar V, Westra BL. Application of an ontology for characterizing data quality for a secondary use of EHR data. Appl Clin Inform 2016; 7 (01) 69-88
    Article in Thieme ConnectPubMedGoogle Scholar
  • 33 Bates D, Mächler M, Bolker B. , et al. Fitting linear mixed-effects models using lme4. J Stat Softw 2015; 67: 1-48
    CrossrefPubMedGoogle Scholar
  • 34 Rubin DB. Statistical matching using file concatenation with adjusted weights and multiple imputations. J Bus Econ Stat 1986; 4: 87-94
    PubMedGoogle Scholar
  • 35 Rubin DB. Multiple Imputation for Nonresponse in Surveys. New York: John Wiley and Sons; 1987
    Google Scholar
  • 36 Van Buuren S. Flexible Imputation of Missing Data. Boca Raton, FL: Chapman and Hall/CRC; 2018
    Google Scholar
  • 37 Harrell Jr FE. Hmisc: Harrell Miscellaneous. Nashville, TN: Vanderbilt University; 2019
    Google Scholar
  • 38 R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2018
    Google Scholar
  • 39 Eccles M, McColl E, Steen N. , et al. Effect of computerised evidence based guidelines on management of asthma and angina in adults in primary care: cluster randomised controlled trial. BMJ 2002; 325 (7370): 941
    CrossrefPubMedGoogle Scholar
  • 40 Hobbs FD, Delaney BC, Carson A, Kenkre JE. A prospective controlled trial of computerized decision support for lipid management in primary care. Fam Pract 1996; 13 (02) 133-137
    CrossrefPubMedGoogle Scholar
  • 41 Liberati EG, Ruggiero F, Galuppo L. , et al. What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation. Implement Sci 2017; 12 (01) 113
    CrossrefPubMedGoogle Scholar
  • 42 Moxey A, Robertson J, Newby D, Hains I, Williamson M, Pearson SA. Computerized clinical decision support for prescribing: provision does not guarantee uptake. J Am Med Inform Assoc 2010; 17 (01) 25-33
    CrossrefPubMedGoogle Scholar
  • 43 Kouri A, Yamada J, Gupta S. Identifying factors related to user uptake of computerized clinical decision support systems: a systematic review and meta-regression. PROSPERO 2018; CRD42018092337 . Available at: http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42018092337 . Accessed May 17, 2019
    PubMedGoogle Scholar
  • 44 Virani SS, Pokharel Y, Steinberg L. , et al. Provider understanding of the 2013 ACC/AHA cholesterol guideline. J Clin Lipidol 2016; 10 (03) 497-504.e4
    CrossrefPubMedGoogle Scholar
  • 45 Ridker PM, Cook NR. Statins: new American guidelines for prevention of cardiovascular disease. Lancet 2013; 382 (9907): 1762-1765
    CrossrefPubMedGoogle Scholar
  • 46 Pencina MJ, Navar-Boggan AM, D'Agostino Sr RB. , et al. Application of new cholesterol guidelines to a population-based sample. N Engl J Med 2014; 370 (15) 1422-1431
    CrossrefPubMedGoogle Scholar
  • 47 Amin NP, Martin SS, Blaha MJ, Nasir K, Blumenthal RS, Michos ED. Headed in the right direction but at risk for miscalculation: a critical appraisal of the 2013 ACC/AHA risk assessment guidelines. J Am Coll Cardiol 2014; 63 (25 Pt A): 2789-2794
    CrossrefPubMedGoogle Scholar
  • 48 Otvos JD, Mora S, Shalaurova I, Greenland P, Mackey RH, Goff Jr DC. Clinical implications of discordance between low-density lipoprotein cholesterol and particle number. J Clin Lipidol 2011; 5 (02) 105-113
    CrossrefPubMedGoogle Scholar
  • 49 Erqou S, Kaptoge S, Perry PL. , et al; Emerging Risk Factors Collaboration. Lipoprotein(a) concentration and the risk of coronary heart disease, stroke, and nonvascular mortality. JAMA 2009; 302 (04) 412-423
    CrossrefPubMedGoogle Scholar
  • 50 Setia S, Fung SS-W, Waters DD. Doctors' knowledge, attitudes, and compliance with 2013 ACC/AHA guidelines for prevention of atherosclerotic cardiovascular disease in Singapore. Vasc Health Risk Manag 2015; 11: 303-310
    PubMedGoogle Scholar
  • 51 Rehr CA, Wong A, Seger DL, Bates DW. Determining inappropriate medication alerts from “inaccurate warning” overrides in the intensive care unit. Appl Clin Inform 2018; 9 (02) 268-274
    Article in Thieme ConnectPubMedGoogle Scholar
  • 52 Ancker JS, Edwards A, Nosal S, Hauser D, Mauer E, Kaushal R. ; with the HITEC Investigators. Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC Med Inform Decis Mak 2017; 17 (01) 36
    CrossrefPubMedGoogle Scholar
  • 53 Yoshida E, Fei S, Bavuso K, Lagor C, Maviglia S. The value of monitoring clinical decision support interventions. Appl Clin Inform 2018; 9 (01) 163-173
    Article in Thieme ConnectPubMedGoogle Scholar
  • 54 Grundy SM, Stone NJ, Bailey AL. , et al. AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of blood cholesterol: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol 2018; 2018: 25709
    PubMedGoogle Scholar