Effect of Clinical Decision Support at Community Health Centers on the Risk of Cardiovascular Disease: A Cluster Randomized Clinical Trial

Rachel Gold, Annie E Larson, JoAnn M Sperl-Hillen, David Boston, Christina R Sheppler, John Heintzman, Carmit McMullen, Mary Middendorf, Deepika Appana, Vijayakumar Thirumalai, Ann Romer, Julianne Bava, James V Davis, Nadia Yosuf, Jenny Hauschildt, Kristin Scott, Susan Moore, Patrick J O'Connor, Rachel Gold, Annie E Larson, JoAnn M Sperl-Hillen, David Boston, Christina R Sheppler, John Heintzman, Carmit McMullen, Mary Middendorf, Deepika Appana, Vijayakumar Thirumalai, Ann Romer, Julianne Bava, James V Davis, Nadia Yosuf, Jenny Hauschildt, Kristin Scott, Susan Moore, Patrick J O'Connor

Abstract

Importance: Management of cardiovascular disease (CVD) risk in socioeconomically vulnerable patients is suboptimal; better risk factor control could improve CVD outcomes.

Objective: To evaluate the impact of a clinical decision support system (CDSS) targeting CVD risk in community health centers (CHCs).

Design, setting, and participants: This cluster randomized clinical trial included 70 CHC clinics randomized to an intervention group (42 clinics; 8 organizations) or a control group that received no intervention (28 clinics; 7 organizations) from September 20, 2018, to March 15, 2020. Randomization was by CHC organization accounting for organization size. Patients aged 40 to 75 years with (1) diabetes or atherosclerotic CVD and at least 1 uncontrolled major risk factor for CVD or (2) total reversible CVD risk of at least 10% were the population targeted by the CDSS intervention.

Interventions: A point-of-care CDSS displaying real-time CVD risk factor control data and personalized, prioritized evidence-based care recommendations.

Main outcomes and measures: One-year change in total CVD risk and reversible CVD risk (ie, the reduction in 10-year CVD risk that was considered achievable if 6 key risk factors reached evidence-based levels of control).

Results: Among the 18 578 eligible patients (9490 [51.1%] women; mean [SD] age, 58.7 [8.8] years), patients seen in control clinics (n = 7419) had higher mean (SD) baseline CVD risk (16.6% [12.8%]) than patients seen in intervention clinics (n = 11 159) (15.6% [12.3%]; P < .001); baseline reversible CVD risk was similarly higher among patients seen in control clinics. The CDSS was used at 19.8% of 91 988 eligible intervention clinic encounters. No population-level reduction in CVD risk was seen in patients in control or intervention clinics; mean reversible risk improved significantly more among patients in control (-0.1% [95% CI, -0.3% to -0.02%]) than intervention clinics (0.4% [95% CI, 0.3% to 0.5%]; P < .001). However, when the CDSS was used, both risk measures decreased more among patients with high baseline risk in intervention than control clinics; notably, mean reversible risk decreased by an absolute 4.4% (95% CI, -5.2% to -3.7%) among patients in intervention clinics compared with 2.7% (95% CI, -3.4% to -1.9%) among patients in control clinics (P = .001).

Conclusions and relevance: The CDSS had low use rates and failed to improve CVD risk in the overall population but appeared to have a benefit on CVD risk when it was consistently used for patients with high baseline risk treated in CHCs. Despite some limitations, these results provide preliminary evidence that this technology has the potential to improve clinical care in socioeconomically vulnerable patients with high CVD risk.

Trial registration: ClinicalTrials.gov Identifier: NCT03001713.

Conflict of interest statement

Conflict of Interest Disclosures: Dr Gold reported receiving grants from the National Institutes of Health (NIH) during the conduct of the study. Dr Boston reported receiving grants from the National Heart, Lung, and Blood Institute (NHLBI) during the conduct of the study. Dr Sheppler reported receiving grants from the NHLBI during the conduct of the study. Dr Heintzman reported grants from the National Institute on Aging during the conduct of the study and serving as lead clinician scientist at OCHIN Inc, a nonprofit network of community health centers. Dr McMullen reported receiving grants from the NIH during the conduct of the study. Ms Middendorf reported receiving grants from the NHLBI during the conduct of the study. Mr Thirumalai reported receiving grants from HealthPartners Institute during the conduct of the study. Ms Romer reported receiving grants from the NHLBI during the conduct of the study. Ms Bava reported receiving grants from the NHLBI during the conduct of the study. Ms Yosuf reported receiving grants from the NHLBI during the conduct of the study. Ms Hauschildt reported receiving grants from the NHLBI during the conduct of the study. Ms Moore reported receiving grants from the NHLBI during the conduct of the study. No other disclosures were reported.

Figures

Figure.. Flow Diagram of Participating Organizations
Figure.. Flow Diagram of Participating Organizations

References

    1. Davis AM, Vinci LM, Okwuosa TM, Chase AR, Huang ES. Cardiovascular health disparities: a systematic review of health care interventions. Med Care Res Rev. 2007;64(5)(suppl):29S-100S. doi:10.1177/1077558707305416
    1. Graham G. Disparities in cardiovascular disease risk in the United States. Curr Cardiol Rev. 2015;11(3):238-245. doi:10.2174/1573403X11666141122220003
    1. Lewey J, Choudhry NK. The current state of ethnic and racial disparities in cardiovascular care: lessons from the past and opportunities for the future. Curr Cardiol Rep. 2014;16(10):530. doi:10.1007/s11886-014-0530-3
    1. Mueller M, Purnell TS, Mensah GA, Cooper LA. Reducing racial and ethnic disparities in hypertension prevention and control: what will it take to translate research into practice and policy? Am J Hypertens. 2015;28(6):699-716. doi:10.1093/ajh/hpu233
    1. Centers for Disease Control and Prevention, National Center for Health Statistics . Health, United States spotlight—racial and ethnic disparities in heart disease. April 2019. Accessed September 21, 2020.
    1. Schultz WM, Kelli HM, Lisko JC, et al. . Socioeconomic status and cardiovascular outcomes: challenges and interventions. Circulation. 2018;137(20):2166-2178. doi:10.1161/CIRCULATIONAHA.117.029652
    1. Koopman RJ, Kochendorfer KM, Moore JL, et al. . A diabetes dashboard and physician efficiency and accuracy in accessing data needed for high-quality diabetes care. Ann Fam Med. 2011;9(5):398-405. doi:10.1370/afm.1286
    1. Parchman ML, Pugh JA, Romero RL, Bowers KW. Competing demands or clinical inertia: the case of elevated glycosylated hemoglobin. Ann Fam Med. 2007;5(3):196-201. doi:10.1370/afm.679
    1. Yawn B, Goodwin MA, Zyzanski SJ, Stange KC. Time use during acute and chronic illness visits to a family physician. Fam Pract. 2003;20(4):474-477. doi:10.1093/fampra/cmg425
    1. Beasley JW, Wetterneck TB, Temte J, et al. . Information chaos in primary care: implications for physician performance and patient safety. J Am Board Fam Med. 2011;24(6):745-751. doi:10.3122/jabfm.2011.06.100255
    1. Karsh BT, Holden RJ, Alper SJ, Or CK. A human factors engineering paradigm for patient safety: designing to support the performance of the healthcare professional. Qual Saf Health Care. 2006;15(suppl 1):i59-i65. doi:10.1136/qshc.2005.015974
    1. Wickens CD. Multiple resources and mental workload. Hum Factors. 2008;50(3):449-455. doi:10.1518/001872008X288394
    1. Altmann EM, Gray WD. Forgetting to remember: the functional relationship of decay and interference. Psychol Sci. 2002;13(1):27-33. doi:10.1111/1467-9280.00405
    1. Committee on Patient Safety and Health Information Technology . Health IT and Patient Safety: Building Safer Systems for Better Care. Institute of Medicine; 2011.
    1. Stead WW, Lin HS, eds. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. National Academies Press; 2009.
    1. Jean-Jacques M, Persell SD, Thompson JA, Hasnain-Wynia R, Baker DW. Changes in disparities following the implementation of a health information technology-supported quality improvement initiative. J Gen Intern Med. 2012;27(1):71-77. doi:10.1007/s11606-011-1842-2
    1. Goud R, de Keizer NF, ter Riet G, et al. . Effect of guideline based computerised decision support on decision making of multidisciplinary teams: cluster randomised trial in cardiac rehabilitation. BMJ. 2009;338:b1440. doi:10.1136/bmj.b1440
    1. López L, Green AR, Tan-McGrory A, King R, Betancourt JR. Bridging the digital divide in health care: the role of health information technology in addressing racial and ethnic disparities. Jt Comm J Qual Patient Saf. 2011;37(10):437-445. doi:10.1016/S1553-7250(11)37055-9
    1. Jaffe MG, Lee GA, Young JD, Sidney S, Go AS. Improved blood pressure control associated with a large-scale hypertension program. JAMA. 2013;310(7):699-705. doi:10.1001/jama.2013.108769
    1. Shaw KM, Handler J, Wall HK, Kanter MH. Improving blood pressure control in a large multiethnic California population through changes in health care delivery, 2004-2012. Prev Chronic Dis. 2014;11:E191. doi:10.5888/pcd11.140173
    1. Ash JS, Sittig DF, Guappone KP, et al. . Recommended practices for computerized clinical decision support and knowledge management in community settings: a qualitative study. BMC Med Inform Decis Mak. 2012;12(1):6. doi:10.1186/1472-6947-12-6
    1. Ash JS, Sittig DF, Dykstra R, et al. . Identifying best practices for clinical decision support and knowledge management in the field. Stud Health Technol Inform. 2010;160(pt 2):806-810.
    1. Bright TJ, Wong A, Dhurjati R, et al. . Effect of clinical decision-support systems: a systematic review. Ann Intern Med. 2012;157(1):29-43. doi:10.7326/0003-4819-157-1-201207030-00450
    1. Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005;330(7494):765. doi:10.1136/bmj.38398.500764.8F
    1. Lobach D, Sanders GD, Bright TJ, et al. . Enabling health care decisionmaking through clinical decision support and knowledge management. Evid Rep Technol Assess (Full Rep). 2012;(203):1-784.
    1. Souza NM, Sebaldt RJ, Mackay JA, et al. ; CCDSS Systematic Review Team . Computerized clinical decision support systems for primary preventive care: a decision-maker–researcher partnership systematic review of effects on process of care and patient outcomes. Implement Sci. 2011;6:87. doi:10.1186/1748-5908-6-87
    1. Roshanov PS, You JJ, Dhaliwal J, et al. ; CCDSS Systematic Review Team . Can computerized clinical decision support systems improve practitioners’ diagnostic test ordering behavior? a decision-maker–researcher partnership systematic review. Implement Sci. 2011;6:88. doi:10.1186/1748-5908-6-88
    1. Jaspers MW, Smeulers M, Vermeulen H, Peute LW. Effects of clinical decision-support systems on practitioner performance and patient outcomes: a synthesis of high-quality systematic review findings. J Am Med Inform Assoc. 2011;18(3):327-334. doi:10.1136/amiajnl-2011-000094
    1. Cleveringa FG, Gorter KJ, van den Donk M, van Gijsel J, Rutten GE. Computerized decision support systems in primary care for type 2 diabetes patients only improve patients’ outcomes when combined with feedback on performance and case management: a systematic review. Diabetes Technol Ther. 2013;15(2):180-192. doi:10.1089/dia.2012.0201
    1. Moja L, Kwag KH, Lytras T, et al. . Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis. Am J Public Health. 2014;104(12):e12-e22. doi:10.2105/AJPH.2014.302164
    1. Murphy EV. Clinical decision support: effectiveness in improving quality processes and clinical outcomes and factors that may influence success. Yale J Biol Med. 2014;87(2):187-197.
    1. Sperl-Hillen JM, Rossom RC, Kharbanda EO, et al. . Priorities wizard: multisite web-based primary care clinical decision support improved chronic care outcomes with high use rates and high clinician satisfaction rates. EGEMS (Wash DC). 2019;7(1):9. doi:10.5334/egems.284
    1. 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(1):12-21. doi:10.1370/afm.1196
    1. Sperl-Hillen JM, Crain AL, Margolis KL, et al. . Clinical decision support directed to primary care patients and providers reduces cardiovascular risk: a randomized trial. J Am Med Inform Assoc. 2018;25(9):1137-1146. doi:10.1093/jamia/ocy085
    1. O’Connor P. Opportunities to increase the effectiveness of EHR-based diabetes clinical decision support. Appl Clin Inform. 2011;2(3):350-354. doi:10.4338/ACI-2011-05-IE-0032
    1. Sperl-Hillen JM, Crain AL, Ekstrom HL, Margolis KL, O'Connor PJ. A clinical decision support system promotes shared decision-making and cardiovascular risk factor management. J Patient Cent Res Rev. 2017;4(3):158-159. doi:10.17294/2330-0698.1490
    1. Bibbins-Domingo K, Grossman DC, Curry SJ, et al. ; US Preventive Services Task Force . Statin use for the primary prevention of cardiovascular disease in adults: US preventive services task force recommendation statement. JAMA. 2016;316(19):1997-2007. doi:10.1001/jama.2016.15450
    1. Whelton PK, Carey RM, Aronow WS, et al. . 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: a report of the American College of Cardiology/American Heart Association task force on clinical practice guidelines. Hypertension. 2018;71(6):e13-e115.
    1. American Diabetes Association . 6. Glycemic targets: standards of medical care in diabetes-2018. Diabetes Care. 2018;41(suppl 1):S55-S64. doi:10.2337/dc18-S006
    1. Obesity Expert Panel . Managing overweight and obesity in adults: systematic evidence review, 2013. National Heart, Lung, and Blood Institute, National Institutes of Health, US Department of Health and Human Services. Accessed December 8, 2020.
    1. U.S. Preventive Services Task Force . Final recommendation statement; tobacco smoking cessation in adults, including pregnant persons: interventions. January 19, 2021. Accessed April 8, 2019.
    1. Rothwell PM, Cook NR, Gaziano JM, et al. . Effects of aspirin on risks of vascular events and cancer according to bodyweight and dose: analysis of individual patient data from randomised trials. Lancet. 2018;392(10145):387-399. doi:10.1016/S0140-6736(18)31133-4
    1. Casey DE Jr, Thomas RJ, Bhalla V, et al. . 2019 AHA/ACC clinical performance and quality measures for adults with high blood pressure: a report of the American College of Cardiology/American Heart Association task force on performance measures. Circ Cardiovasc Qual Outcomes. 2019;12(11):e000057. doi:10.1161/HCQ.0000000000000057
    1. Gilmer TP, O’Connor PJ, Sperl-Hillen JM, et al. . Cost-effectiveness of an electronic medical record based clinical decision support system. Health Serv Res. 2012;47(6):2137-2158. doi:10.1111/j.1475-6773.2012.01427.x
    1. Dudl RJ, Wang MC, Wong M, Bellows J. Preventing myocardial infarction and stroke with a simplified bundle of cardioprotective medications. Am J Manag Care. 2009;15(10):e88-e94.
    1. Wong W, Jaffe M, Wong M, Dudl RJ. Community implementation and translation of Kaiser Permanente’s cardiovascular disease risk-reduction strategy. Perm J. 2011;15(1):36-41. doi:10.7812/TPP/10-115
    1. Feldstein AC, Perrin NA, Unitan R, et al. . Effect of a patient panel-support tool on care delivery. Am J Manag Care. 2010;16(10):e256-e266.
    1. Lillie-Blanton M, Rushing OE, Ruiz S, Mayberry R, Boone L. Racial/ethnic differences in cardiac care: the weight of the evidence [summary report]. September 29, 2002. Accessed November 4, 2021.
    1. Spranger CB, Ries AJ, Berge CA, Radford NB, Victor RG. Identifying gaps between guidelines and clinical practice in the evaluation and treatment of patients with hypertension. Am J Med. 2004;117(1):14-18. doi:10.1016/j.amjmed.2004.01.024
    1. Gold R, Middendorf M, Heintzman J, et al. . Challenges involved in establishing a web-based clinical decision support tool in community health centers. Healthc (Amst). 2020;8(4):100488. doi:10.1016/j.hjdsi.2020.100488
    1. Goff DC Jr, Lloyd-Jones DM, Bennett G, et al. . 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines. J Am Coll Cardiol. 2014;63(25, pt B):2935-2959. doi:10.1016/j.jacc.2013.11.005
    1. Goff DC Jr, Lloyd-Jones DM, Bennett G, et al. ; American College of Cardiology/American Heart Association Task Force on Practice Guidelines . 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129(25)(suppl 2):S49-S73. doi:10.1161/01.cir.0000437741.48606.98
    1. Heselmans A, Delvaux N, Laenen A, et al. . Computerized clinical decision support system for diabetes in primary care does not improve quality of care: a cluster-randomized controlled trial. Implement Sci. 2020;15(1):5. doi:10.1186/s13012-019-0955-6
    1. Ancker JS, Kern LM, Edwards A, et al. ; HITEC Investigators . Associations between healthcare quality and use of electronic health record functions in ambulatory care. J Am Med Inform Assoc. 2015;22(4):864-871. doi:10.1093/jamia/ocv030
    1. Hernán MA, Hernández-Díaz S. Beyond the intention-to-treat in comparative effectiveness research. Clin Trials. 2012;9(1):48-55. doi:10.1177/1740774511420743
    1. Li F, Zaslavsky AM, Landrum MB. Propensity score weighting with multilevel data. Stat Med. 2013;32(19):3373-3387. doi:10.1002/sim.5786
    1. Colantonio LD, Richman JS, Carson AP, et al. . Performance of the atherosclerotic cardiovascular disease pooled cohort risk equations by social deprivation status. J Am Heart Assoc. 2017;6(3):e005676. doi:10.1161/JAHA.117.005676
    1. Lloyd-Jones DM, Braun LT, Ndumele CE, et al. . Use of risk assessment tools to guide decision-making in the primary prevention of atherosclerotic cardiovascular disease: a special report from the American Heart Association and American College of Cardiology. Circulation. 2019;139(25):e1162-e1177. doi:10.1161/CIR.0000000000000638
    1. Kharbanda EO, Asche SE, Sinaiko A, et al. . Evaluation of an electronic clinical decision support tool for incident elevated BP in adolescents. Acad Pediatr. 2018;18(1):43-50. doi:10.1016/j.acap.2017.07.004
    1. Buntin MB, Burke MF, Hoaglin MC, Blumenthal D. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff (Millwood). 2011;30(3):464-471. doi:10.1377/hlthaff.2011.0178

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