Barriers and facilitators to implementing cancer prevention clinical decision support in primary care: a qualitative study

Melissa L Harry, Anjali R Truitt, Daniel M Saman, Hillary A Henzler-Buckingham, Clayton I Allen, Kayla M Walton, Heidi L Ekstrom, Patrick J O'Connor, JoAnn M Sperl-Hillen, Joseph A Bianco, Thomas E Elliott, Melissa L Harry, Anjali R Truitt, Daniel M Saman, Hillary A Henzler-Buckingham, Clayton I Allen, Kayla M Walton, Heidi L Ekstrom, Patrick J O'Connor, JoAnn M Sperl-Hillen, Joseph A Bianco, Thomas E Elliott

Abstract

Background: In the United States, primary care providers (PCPs) routinely balance acute, chronic, and preventive patient care delivery, including cancer prevention and screening, in time-limited visits. Clinical decision support (CDS) may help PCPs prioritize cancer prevention and screening with other patient needs. In a three-arm, pragmatic, clinic-randomized control trial, we are studying cancer prevention CDS in a large, upper Midwestern healthcare system. The web-based, electronic health record (EHR)-linked CDS integrates evidence-based primary and secondary cancer prevention and screening recommendations into an existing cardiovascular risk management CDS system. Our objective with this study was to identify adoption barriers and facilitators before implementation in primary care.

Methods: We conducted semi-structured interviews guided by the Consolidated Framework for Implementation Research (CFIR) with 28 key informants employed by the healthcare organization in either leadership roles or the direct provision of clinical care. Transcribed interviews were analyzed using qualitative content analysis.

Results: EHR, CDS workflow, CDS users (providers and patients), training, and organizational barriers and facilitators were identified related to Intervention Characteristics, Outer Setting, Inner Setting, and Characteristics of Individuals CFIR domains.

Conclusion: Identifying and addressing key informant-identified barriers and facilitators before implementing cancer prevention CDS in primary care may support a successful implementation and sustained use. The CFIR is a useful framework for understanding pre-implementation barriers and facilitators. Based on our findings, the research team developed and instituted specialized training, pilot testing, implementation plans, and post-implementation efforts to maximize identified facilitators and address barriers.

Trial registration: clinicaltrials.gov , NCT02986230 , December 6, 2016.

Keywords: Cancer screening; Clinical decision support; Key informants; Pre-implementation; Primary and secondary prevention; Primary care; Qualitative.

Conflict of interest statement

The authors declare that they have no competing interests.

References

    1. Kwon HT, Ma GX, Gold RS, Atkinson NL, Wang MW. Primary care physicians’ cancer screening recommendation practices and perceptions of cancer risk of Asian Americans. Asian Pac J Cancer Prev. 2013;14:1999–2004. doi: 10.7314/APJCP.2013.14.3.1999.
    1. Wagner EH. Chronic disease management: what will it take to improve care for chronic illness? Eff Clin Pract. 1998;1:2–4.
    1. Osheroff JA, Teich JM, Levick D, Saldana L, Velasco FT, Sittig DF, et al. Improving outcomes with clinical decision support: an implementer’s guide. 2. Chicago: HIMSS; 2012.
    1. Berner ES. Clinical decision support systems: state of the art. Rockville: Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services; 2009. Report No.: 09–0069-EF. . Accessed 21 Nov 2018
    1. Elwyn G, Frosch D, Thomson R, Joseph-Williams N, Lloyd A, Kinnersley P, et al. Shared decision making: a model for clinical practice. J Gen Intern Med. 2012;27(10):1361–1367. doi: 10.1007/s11606-012-2077-6.
    1. Stacey D, Légaré F, Lewis K, Barry MJ, Bennett CL, Eden KB. Decision aids for people facing health treatment or screening decisions (review). Cochrane Database Syst Rev. v2017;1:CD001431. 10.1002/14651858.CD001431.pub5.
    1. Greene RA, Bates DW, Kawamoto K, Middleton B, Osheroff J, Shahar Y. Clinical decision support models and frameworks: seeking to address research issues underlying implementation successes and failures. J Biomed Inform. 2018;78:134–143. doi: 10.1016/j.jbi.2017.12.005.
    1. Roshanov PS, Fernandes N, Wilczynski JM, Hemens BJ, You JJ, Handler SM, et al. Features of effective computerized clinical decision support systems: meta-regression of 162 randomised trials. BMJ. 2013;346:f657. doi: 10.1136/bmj.f657.
    1. Sperl-Hillen JM, Crain AL, Margolis KL, Ekstrom HL, Appana D, Amundson G, 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. Meites E, Kempe A, Markowitz LE. Use of a 2-dose schedule for human papillomavirus vaccination - updated recommendations of the advisory committee on immunization practices. MMWR Morb Mortal Wkly Rep. 2016;65:1405–1408. doi: 10.15585/mmwr.mm6549a5.
    1. US Preventive Services Task Force . Final recommendation statement: breast cancer: screening. 2016.
    1. US Preventive Services Task Force . Final recommendation statement: cervical cancer: screening. 2018.
    1. US Preventive Services Task Force . Final recommendation statement: colorectal cancer: screening. 2017.
    1. US Preventive Services Task Force . Final recommendation statement: lung cancer: screening. 2016.
    1. Gail MH, Brinton LA, Byar DP, Corle DK, Green SB, Schairer C, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst. 1989;81:1879–1886. doi: 10.1093/jnci/81.24.1879.
    1. National Cancer Institute. n.d. Breast Cancer Risk Assessment Tool. . Accessed 1 May 2019.
    1. National Cancer Institute. n.d. Colorectal Cancer Risk Tool. . Accessed 1 May 2019.
    1. National Cancer Institute. n.d. Risk-based National Lung Cancer Screening Trial Outcomes Tool (RNOT). #/. Accessed 1 May 2019.
    1. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4:50. doi: 10.1186/1748-5908-4-50.
    1. Schreier M. Qualitative content analysis in practice. 1. Thousand Oakes: Sage; 2012.
    1. Cole AM, Esplin A, Baldwin LM. Adaptation of an evidence-based colorectal cancer screening program using the consolidated framework for implementation research. Prev Chronic Dis. 2015;12:150300. doi: 10.5888/pcd12.150300.
    1. Consolidated Framework for Implementation Research . Qualitative data. 2018.
    1. Broom A, Willis E. Competing paradigms and health research. In: Saks M, Allsop J, editors. Researching health: qualitative, quantitative and mixed methods. London: Sage Publications, LTD; 2007. pp. 16–31.
    1. . Yearly “Wellness” visits. n.d. . Accessed 30 Apr 2019.
    1. . What’s Medicare? What’s Medicaid? 2019.
    1. Van de Velde S, Heselmans A, Delvaux N, Brandt L, Marco-Ruiz L, Spitaels D, et al. A systematic review of trials evaluating success factors of interventions with computerised clinical decision support. Implement Sci. 2018;13(1):114. doi: 10.1186/s13012-018-0790-1.
    1. Medlock S, Wyatt JC, Patel VL, Shortliffe EH, Abu-Hanna A. Modeling information flows in clinical decision support: key insights for enhancing system effectiveness. J Am Med Inform Assoc. 2016;23(5):1001–1006. doi: 10.1093/jamia/ocv177.
    1. Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L, et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc. 2003;10(6):523–540. doi: 10.1197/jamia.M1370.
    1. Van de Velde S, Kunnamo I, Roshanov P, Kortteisto T, Aertgeerts B, Vandvik PO, et al. The GUIDES checklist: development of a tool to improve the successful use of guideline-based computerized clinical decision support. Implement Sci. 2018;13:86. doi: 10.1186/s13012-018-0772-3.
    1. Fraccaro Paolo, Arguello Castelerio Mercedes, Ainsworth John, Buchan Iain. Adoption of Clinical Decision Support in Multimorbidity: A Systematic Review. JMIR Medical Informatics. 2015;3(1):e4. doi: 10.2196/medinform.3503.

Source: PubMed

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