Clinician perceptions of a clinical decision support system to reduce cardiovascular risk among prediabetes patients in a predominantly rural healthcare system

Daniel M Saman, Clayton I Allen, Laura A Freitag, Melissa L Harry, JoAnn M Sperl-Hillen, Jeanette Y Ziegenfuss, Jacob L Haapala, A Lauren Crain, Jay R Desai, Kris A Ohnsorg, Patrick J O'Connor, Daniel M Saman, Clayton I Allen, Laura A Freitag, Melissa L Harry, JoAnn M Sperl-Hillen, Jeanette Y Ziegenfuss, Jacob L Haapala, A Lauren Crain, Jay R Desai, Kris A Ohnsorg, Patrick J O'Connor

Abstract

Background: The early detection and management of uncontrolled cardiovascular risk factors among prediabetes patients can prevent cardiovascular disease (CVD). Prediabetes increases the risk of CVD, which is a leading cause of death in the United States. CVD clinical decision support (CDS) in primary care settings has the potential to reduce cardiovascular risk in patients with prediabetes while potentially saving clinicians time. The objective of this study is to understand primary care clinician (PCC) perceptions of a CDS system designed to reduce CVD risk in adults with prediabetes.

Methods: We administered pre-CDS implementation (6/30/2016 to 8/25/2016) (n = 183, 61% response rate) and post-CDS implementation (6/12/2019 to 8/7/2019) (n = 131, 44.5% response rate) independent cross-sectional electronic surveys to PCCs at 36 randomized primary care clinics participating in a federally funded study of a CVD risk reduction CDS tool. Surveys assessed PCC demographics, experiences in delivering prediabetes care, perceptions of CDS impact on shared decision making, perception of CDS impact on control of major CVD risk factors, and overall perceptions of the CDS tool when managing cardiovascular risk.

Results: We found few significant differences when comparing pre- and post-implementation responses across CDS intervention and usual care (UC) clinics. A majority of PCCs felt well-prepared to discuss CVD risk factor control with patients both pre- and post-implementation. About 73% of PCCs at CDS intervention clinics agreed that the CDS helped improve risk control, 68% reported the CDS added value to patient clinic visits, and 72% reported they would recommend use of this CDS system to colleagues. However, most PCCs disagreed that the CDS saves time talking about preventing diabetes or CVD, and most PCCs also did not find the clinical domains useful, nor did PCCs believe that the clinical domains were useful in getting patients to take action. Finally, only about 38% reported they were satisfied with the CDS.

Conclusions: These results improve our understanding of CDS user experience and can be used to guide iterative improvement of the CDS. While most PCCs agreed the CDS improves CVD and diabetes risk factor control, they were generally not satisfied with the CDS. Moreover, only 40-50% agreed that specific suggestions on clinical domains helped patients to take action. In spite of this, an overwhelming majority reported they would recommend the CDS to colleagues, pointing for the need to improve upon the current CDS.

Trial registration: NCT02759055 03/05/2016.

Keywords: Advanced practice provider; Cardiovascular disease; Cardiovascular risk; Clinical decision support; Diabetes; Dyslipidemia; Electronic medical record; Hypertension; Prediabetes; Primary care clinician.

Conflict of interest statement

The authors declare that they have no competing interests.

© 2022. The Author(s).

Figures

Fig. 1
Fig. 1
Patient view and printout of the clinical decision support system. Reprinted from Contemporary Clinical Trials, 114, Desai J, Saman D, Sperl-Hillen JM, Pratt R, Dehmer SP, Allen C, Ohnsorg K, Wuorio A, Appana D, Hitz P, Land A, Sharma R, Wilkinson L, Crain AL, Crabtree BF, Bianco J, O'Connor PJ. Implementing a prediabetes clinical decision support system in a large primary care system: Design, methods, and pre-implementation results, 106,686, Copyright (2022), with permission from Elsevier [20]
Fig. 2
Fig. 2
Provider view and printout of the clinical decision support system. Reprinted from Contemporary Clinical Trials, 114, Desai J, Saman D, Sperl-Hillen JM, Pratt R, Dehmer SP, Allen C, Ohnsorg K, Wuorio A, Appana D, Hitz P, Land A, Sharma R, Wilkinson L, Crain AL, Crabtree BF, Bianco J, O'Connor PJ, Implementing a prediabetes clinical decision support system in a large primary care system: Design, methods, and pre-implementation results, 106,686, Copyright (2022), with permission from Elsevier [20]

References

    1. Centers for Disease Control and Prevention (CDC). The surprising truth about prediabetes 2020. Available from: .
    1. Hostalek U. Global epidemiology of prediabetes - present and future perspectives. Clin Diabetes Endocrinol. 2019;5:5. doi: 10.1186/s40842-019-0080-0.
    1. Huang Y, Cai X, Mai W, Li M, Hu Y. Association between prediabetes and risk of cardiovascular disease and all cause mortality: systematic review and meta-analysis. BMJ. 2016;355:i5953. doi: 10.1136/bmj.i5953.
    1. Bright TJ, Wong A, Dhurjati R, Bristow E, Bastian L, Coeytaux RR, 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. Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005;293(10):1223–1238. doi: 10.1001/jama.293.10.1223.
    1. Harry ML, Saman DM, Allen CI, Ohnsorg KA, Sperl-Hillen JM, O'Connor PJ, et al. Understanding primary care provider attitudes and behaviors regarding cardiovascular disease risk and diabetes prevention in the northern midwest. Clin Diabetes. 2018;36(4):283–294. doi: 10.2337/cd17-0116.
    1. Saman DM, Walton KM, Harry ML, Asche SE, Truitt AR, Henzler-Buckingham HA, et al. Understanding primary care providers' perceptions of cancer prevention and screening in a predominantly rural healthcare system in the upper Midwest. BMC Health Serv Res. 2019;19(1):1019. doi: 10.1186/s12913-019-4872-9.
    1. Kandula NR, Moran MR, Tang JW, O'Brien MJ. Preventing diabetes in primary care: providers' perspectives about diagnosing and treating prediabetes. Clin Diabetes. 2018;36(1):59–66. doi: 10.2337/cd17-0049.
    1. Bhuyan SS, Chandak A, Gupta N, Isharwal S, LaGrange C, Mahmood A, et al. Patient-provider communication about prostate cancer screening and treatment: new evidence from the health information national trends survey. Am J Mens Health. 2017;11(1):134–146. doi: 10.1177/1557988315614082.
    1. Dunn AS, Shridharani KV, Lou W, Bernstein J, Horowitz CR. Physician-patient discussions of controversial cancer screening tests. Am J Prev Med. 2001;20(2):130–134. doi: 10.1016/S0749-3797(00)00288-9.
    1. Guerra CE, Jacobs SE, Holmes JH, Shea JA. Are physicians discussing prostate cancer screening with their patients and why or why not? A pilot study. J Gen Intern Med. 2007;22(7):901–907. doi: 10.1007/s11606-007-0142-3.
    1. Jia P, Zhang L, Chen J, Zhao P, Zhang M. The effects of clinical decision support systems on medication safety: an overview. PLOS ONE. 2016;11(12):e0167683. doi: 10.1371/journal.pone.0167683.
    1. Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med. 2020;3:17. doi: 10.1038/s41746-020-0221-y.
    1. Saleem JJ, Militello LG, Arbuckle N, Flanagan M, Haggstrom DA, Linder JA, et al. Provider perceptions of colorectal cancer screening clinical decision support at three benchmark institutions. In: AMIA Annual Symposium Proceedings. 2009. p. 558–62.
    1. Hunt DL, Haynes RB, Hanna SE, Smith K. Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review. JAMA. 1998;280(15):1339–1346. doi: 10.1001/jama.280.15.1339.
    1. Gonzalez ER, Vanderheyden BA, Ornato JP, Comstock TG. Computer-assisted optimization of aminophylline therapy in the emergency department. Am J Emerg Med. 1989;7(4):395–401. doi: 10.1016/0735-6757(89)90046-6.
    1. Han PK, Kobrin S, Breen N, Joseph DA, Li J, Frosch DL, et al. National evidence on the use of shared decision making in prostate-specific antigen screening. Ann Fam Med. 2013;11(4):306–314. doi: 10.1370/afm.1539.
    1. Marc DT, Khairat SS. Why do physicians have difficulty accepting clinical decision support systems? Stud Health Technol Inform. 2013;192:1202.
    1. Sperl-Hillen JM, Rossom RC, Kharbanda EO, Gold R, Geissal ED, Elliott TE, 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.
    1. Desai J, Saman D, Sperl-Hillen JM, Pratt R, Dehmer SP, Allen C, Ohnsorg K, Wuorio A, Appana D, Hitz P, Land A, Sharma R, Wilkinson L, Crain AL, Crabtree BF, Bianco J, O'Connor PJ. Implementing a prediabetes clinical decision support system in a large primary care system: design, methods, and pre-implementation results. Contemp Clin Trials. 2022;114:106686. doi: 10.1016/j.cct.2022.106686.
    1. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O'Neal L, et al. The REDCap consortium: building an international community of software platform partners. J Biomed Inform. 2019;95:103208. doi: 10.1016/j.jbi.2019.103208.
    1. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–381. doi: 10.1016/j.jbi.2008.08.010.
    1. Scholl I, Kriston L, Dirmaier J, et al. Development and psychometric properties of the shared decision making questionnaire: physician version (SDM-Q-Doc) Patient Educ Couns. 2012;88:284–290. doi: 10.1016/j.pec.2012.03.005.
    1. JB. SUS: a 'quick and dirty' usability scale. In: Jordan PW TB, Weerdmeester BA, McClelland AL, editor. Usability evaluation in industry. London: Taylor and Francis; 1996. p. 189–94.
    1. SAS Institute Inc. Version 9.4. Cary, North Carolina, USA. 2013.
    1. Harry ML, Saman DM, Truitt AR, Allen CI, Walton KM, O'Connor PJ, et al. Pre-implementation adaptation of primary care cancer prevention clinical decision support in a predominantly rural healthcare system. BMC Med Inform Decis Mak. 2020;20(1):117. doi: 10.1186/s12911-020-01136-8.
    1. O'Connor PJ, Sperl-Hillen JM, Rush WA, Johnson PE, Amundson GH, Asche SE, 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, Ekstrom HL, Appana D, Amundson GH, 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):1137–46. doi: 10.1093/jamia/ocy085.
    1. Salwei ME, Carayon P, Hoonakker PLT, Hundt AS, Wiegmann D, Pulia M, Patterson BW. Workflow integration analysis of a human factors-based clinical decision support in the emergency department. Appl Ergon. 2021;97:103498. doi: 10.1016/j.apergo.2021.103498.
    1. Harry ML, Truitt AR, Saman DM, Henzler-Buckingham HA, Allen CI, Walton KM, Ekstrom HL, O'Connor PJ, Sperl-Hillen JM, Bianco JA, Elliott TE. Barriers and facilitators to implementing cancer prevention clinical decision support in primary care: a qualitative study. BMC Health Serv Res. 2019;19(1):534.
    1. Pratt R, Saman DM, Allen C, Crabtree B, Ohnsorg K, Sperl-Hillen JM, Harry M, Henzler-Buckingham H, O'Connor PJ, Desai J. Assessing the implementation of a clinical decision support tool in primary care for diabetes prevention: a qualitative interview study using the Consolidated Framework for Implementation Science. BMC Med Inform Decis Mak. 2022;22(1):15.
    1. Gilmer TP, O'Connor PJ, Sperl-Hillen JM, Rush WA, Johnson PE, Amundson GH, 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.

Source: PubMed

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