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DOI: 10.1055/s-0038-1660849
Incorporating Guideline Adherence and Practice Implementation Issues into the Design of Decision Support for Beta-Blocker Titration for Heart Failure
Funding This study was supported by the U.S. Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Service (CRE 12–037). The views expressed are those of the authors and not those of the Department of Veterans Affairs or affiliated institutions.Publication History
18 December 2017
12 May 2018
Publication Date:
27 June 2018 (online)
![](https://www.thieme-connect.de/media/10.1055-s-00035026/201802/lookinside/thumbnails/10-1055-s-0038-1660849_170212ra-1.jpg)
Abstract
Background The recognition of and response to undertreatment of heart failure (HF) patients can be complicated. A clinical reminder can facilitate use of guideline-concordant β-blocker titration for HF patients with depressed ejection fraction. However, the design must consider the cognitive demands on the providers and the context of the work.
Objective This study's purpose is to develop requirements for a clinical decision support tool (a clinical reminder) by analyzing the cognitive demands of the task along with the factors in the Cabana framework of physician adherence to guidelines, the health information technology (HIT) sociotechnical framework, and the Promoting Action on Research Implementation in Health Services (PARIHS) framework of health services implementation. It utilizes a tool that extracts information from medical records (including ejection fraction in free text reports) to identify qualifying patients at risk of undertreatment.
Methods We conducted interviews with 17 primary care providers, 5 PharmDs, and 5 Registered Nurses across three Veterans Health Administration outpatient clinics. The interviews were based on cognitive task analysis (CTA) methods and enhanced through the inclusion of the Cabana, HIT sociotechnical, and PARIHS frameworks. The analysis of the interview data led to the development of requirements and a prototype design for a clinical reminder. We conducted a small pilot usability assessment of the clinical reminder using realistic clinical scenarios.
Results We identified organizational challenges (such as time pressures and underuse of pharmacists), knowledge issues regarding the guideline, and information needs regarding patient history and treatment status. We based the design of the clinical reminder on how to best address these challenges. The usability assessment indicated the tool could help the decision and titration processes.
Conclusion Through the use of CTA methods enhanced with adherence, sociotechnical, and implementation frameworks, we designed a decision support tool that considers important challenges in the decision and execution of β-blocker titration for qualifying HF patients at risk of undertreatment.
Keywords
clinical decision support - heart failure - computer systems development - primary health care - adrenergic β-antagonistsProtection of Human and Animal Subjects
This study was approved by the Baylor College of Medicine Institutional Review Board.
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References
- 1 Fang J, Mensah GA, Croft JB, Keenan NL. Heart failure-related hospitalization in the U.S., 1979 to 2004. J Am Coll Cardiol 2008; 52 (06) 428-434
- 2 Yancy CW, Jessup M, Bozkurt B. , et al; WRITING COMMITTEE MEMBERS; American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines. Circulation 2013; 128 (16) e240-e327
- 3 Yancy CW, Jessup M, Bozkurt B. , et al. 2017 ACC/AHA/HFSA focused update of the 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America. J Am Coll Cardiol 2017; 70 (06) 776-803
- 4 Lloyd-Jones D, Adams R, Carnethon M. , et al; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics--2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 2009; 119 (03) e21-e181
- 5 Roger VL, Go AS, Lloyd-Jones DM. , et al; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics--2011 update: a report from the American Heart Association. Circulation 2011; 123 (04) e18-e209
- 6 Hunt SA, Abraham WT, Chin MH. , et al. 2009 focused update incorporated into the ACC/AHA 2005 Guidelines for the Diagnosis and Management of Heart Failure in Adults: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines: developed in collaboration with the International Society for Heart and Lung Transplantation. Circulation 2009; 119 (14) e391-e479
- 7 Patel P, White DL, Deswal A. Translation of clinical trial results into practice: temporal patterns of β-blocker utilization for heart failure at hospital discharge and during ambulatory follow-up. Am Heart J 2007; 153 (04) 515-522
- 8 Metra M, Torp-Pedersen C, Cleland JGF. , et al; COMET investigators. Should beta-blocker therapy be reduced or withdrawn after an episode of decompensated heart failure? Results from COMET. Eur J Heart Fail 2007; 9 (09) 901-909
- 9 Packer M, Bristow MR, Cohn JN. , et al; U.S. Carvedilol Heart Failure Study Group. The effect of carvedilol on morbidity and mortality in patients with chronic heart failure. N Engl J Med 1996; 334 (21) 1349-1355
- 10 Krum H, Sackner-Bernstein JD, Goldsmith RL. , et al. Double-blind, placebo-controlled study of the long-term efficacy of carvedilol in patients with severe chronic heart failure. Circulation 1995; 92 (06) 1499-1506
- 11 Metra M, Nardi M, Giubbini R, Dei Cas L. Effects of short- and long-term carvedilol administration on rest and exercise hemodynamic variables, exercise capacity and clinical conditions in patients with idiopathic dilated cardiomyopathy. J Am Coll Cardiol 1994; 24 (07) 1678-1687
- 12 Fisher ML, Gottlieb SS, Plotnick GD. , et al. Beneficial effects of metoprolol in heart failure associated with coronary artery disease: a randomized trial. J Am Coll Cardiol 1994; 23 (04) 943-950
- 13 The Cardiac Insufficiency Bisoprolol Study (CIBIS). CIBIS Investigators and Committees. A randomized trial of beta-blockade in heart failure. Circulation 1994; 90 (04) 1765-1773
- 14 Adams Jr KF, Fonarow GC, Emerman CL. , et al; ADHERE Scientific Advisory Committee and Investigators. Characteristics and outcomes of patients hospitalized for heart failure in the United States: rationale, design, and preliminary observations from the first 100,000 cases in the Acute Decompensated Heart Failure National Registry (ADHERE). Am Heart J 2005; 149 (02) 209-216
- 15 Fonarow GC, Abraham WT, Albert NM. , et al; OPTIMIZE-HF Investigators and Hospitals. Factors identified as precipitating hospital admissions for heart failure and clinical outcomes: findings from OPTIMIZE-HF. Arch Intern Med 2008; 168 (08) 847-854
- 16 Swedberg K, Cleland J, Dargie H. , et al. Task Force for the Diagnosis and Treatment of Chronic Heart Failure of the European Society of Cardiology. Guidelines for the diagnosis and treatment of chronic heart failure: executive summary (update 2005): the Task Force for the Diagnosis and Treatment of Chronic Heart Failure of the European Society of Cardiology. Eur Heart J 2005; 26 (11) 1115-1140
- 17 Komajda M, Anker SD, Cowie MR. , et al; QUALIFY Investigators. Physicians' adherence to guideline-recommended medications in heart failure with reduced ejection fraction: data from the QUALIFY global survey. Eur J Heart Fail 2016; 18 (05) 514-522
- 18 Leslie SJ, Hartswood M, Meurig C. , et al. Clinical decision support software for management of chronic heart failure: development and evaluation. Comput Biol Med 2006; 36 (05) 495-506
- 19 Toth-Pal E, Wårdh I, Strender L-E, Nilsson G. A guideline-based computerised decision support system (CDSS) to influence general practitioners management of chronic heart failure. Inform Prim Care 2008; 16 (01) 29-39
- 20 de Vries AE, van der Wal MH, Nieuwenhuis MM. , et al. Perceived barriers of heart failure nurses and cardiologists in using clinical decision support systems in the treatment of heart failure patients. BMC Med Inform Decis Mak 2013; 13: 54
- 21 Smeets M, Van Roy S, Aertgeerts B, Vermandere M, Vaes B. Improving care for heart failure patients in primary care, GPs' perceptions: a qualitative evidence synthesis. BMJ Open 2016; 6 (11) e013459
- 22 Garvin JH, DuVall SL, South BR. , et al. Automated extraction of ejection fraction for quality measurement using regular expressions in Unstructured Information Management Architecture (UIMA) for heart failure. J Am Med Inform Assoc 2012; 19 (05) 859-866
- 23 Garvin JH, Elkin PL, Shen S. , et al. Automated quality measurement in Department of the Veterans Affairs discharge instructions for patients with congestive heart failure. J Healthc Qual 2013; 35 (04) 16-24
- 24 Ansari M, Shlipak MG, Heidenreich PA. , et al. Improving guideline adherence: a randomized trial evaluating strategies to increase β-blocker use in heart failure. Circulation 2003; 107 (22) 2799-2804
- 25 Gheorghiade M, Colucci WS, Swedberg K. β-blockers in chronic heart failure. Circulation 2003; 107 (12) 1570-1575
- 26 Østbye T, Yarnall KS, Krause KM, Pollak KI, Gradison M, Michener JL. Is there time for management of patients with chronic diseases in primary care?. Ann Fam Med 2005; 3 (03) 209-214
- 27 Bodenheimer T, Chen E, Bennett HD. Confronting the growing burden of chronic disease: can the U.S. health care workforce do the job?. Health Aff (Millwood) 2009; 28 (01) 64-74
- 28 Fuat A, Hungin APS, Murphy JJ. Barriers to accurate diagnosis and effective management of heart failure in primary care: qualitative study. BMJ 2003; 326 (7382): 196
- 29 Gurses AP, Marsteller JA, Ozok AA, Xiao Y, Owens S, Pronovost PJ. Using an interdisciplinary approach to identify factors that affect clinicians' compliance with evidence-based guidelines. Crit Care Med 2010; 38 (8, Suppl): S282-S291
- 30 Rycroft-Malone J. The PARIHS framework–a framework for guiding the implementation of evidence-based practice. J Nurs Care Qual 2004; 19 (04) 297-304
- 31 Sittig DF, Singh H. A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Qual Saf Health Care 2010; 19 (Suppl. 03) i68-i74
- 32 Carayon P, Schoofs Hundt A, Karsh B-T. , et al. Work system design for patient safety: the SEIPS model. Qual Saf Health Care 2006; 15 (Suppl. 01) i50-i58
- 33 Holden RJ, Carayon P, Gurses AP. , et al. SEIPS 2.0: a human factors framework for studying and improving the work of healthcare professionals and patients. Ergonomics 2013; 56 (11) 1669-1686
- 34 Cabana MD, Rand CS, Powe NR. , et al. Why don't physicians follow clinical practice guidelines? A framework for improvement. JAMA 1999; 282 (15) 1458-1465
- 35 Lau R, Stevenson F, Ong BN. , et al. Achieving change in primary care--causes of the evidence to practice gap: systematic reviews of reviews. Implement Sci 2016; 11: 40
- 36 Sheehan B, Nigrovic LE, Dayan PS. , et al; Pediatric Emergency Care Applied Research Network (PECARN). Informing the design of clinical decision support services for evaluation of children with minor blunt head trauma in the emergency department: a sociotechnical analysis. J Biomed Inform 2013; 46 (05) 905-913
- 37 Crandall B, Klein GA, Hoffman RR. Working Minds: A Practitioner's Guide to Cognitive Task Analysis. Cambridge, MA: MIT Press; 2006: 332
- 38 Militello LG, Dominguez CO, Lintern G, Klein G. The role of cognitive systems engineering in the systems engineering design process. Syst Eng 2009; 13 (03) 261-273
- 39 Patel VL, Kannampallil TG. Human factors and health information technology: current challenges and future directions. Yearb Med Inform 2014; 9 (01) 58-66
- 40 Weir CR, Rubin MA, Nebeker J, Samore M. Modeling the mind: how do we design effective decision-support?. J Biomed Inform 2017; 71S (Supplement): S1-S5
- 41 Bisantz A, Roth E. Analysis of cognitive work. Rev Hum Factors Ergon 2007; 3: 1-43
- 42 Militello LG, Hutton RJ. Applied cognitive task analysis (ACTA): a practitioner's toolkit for understanding cognitive task demands. Ergonomics 1998; 41 (11) 1618-1641
- 43 Potter S, Roth EM, Woods DD, Elm WC. Bootstrapping multiple converging cognitive task analysis techniques for system design. In: Schraagen JM, Chipman SF, Shalin VL. , eds. Cognitive Task Analysis. Mahwah, New Jersey: Lawrence Erlbaum Associates; 2000: 317-340
- 44 Rosland A-M, Nelson K, Sun H. , et al. The patient-centered medical home in the Veterans Health Administration. Am J Manag Care 2013; 19 (07) e263-e272
- 45 Steinman MA, Harlow JB, Massie BM, Kaboli PJ, Fung KZ, Heidenreich PA. Age and receipt of guideline-recommended medications for heart failure: a nationwide study of veterans. J Gen Intern Med 2011; 26 (10) 1152-1159
- 46 Klein GA, Calderwood R, MacGregor D, Inc KA, Springs Y. Critical decision method for eliciting knowledge. Syst Man Cybern IEEE Trans On. 1989; 19 (03) 462-472
- 47 Ritchie J, Spencer L. Qualitative data analysis for applied policy research. In: Bryman A, Burgess B. , eds. Analyzing Qualitative Data. New York: Routledge; 2002: 173-194
- 48 Gale NK, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol 2013; 13 (01) 117
- 49 Xiao Y, Parker SH, Manser T. Teamwork and collaboration. Rev Hum Factors Ergon 2013; 8 (01) 55-102
- 50 Rodriguez HP, Giannitrapani KF, Stockdale S, Hamilton AB, Yano EM, Rubenstein LV. Teamlet structure and early experiences of medical home implementation for veterans. J Gen Intern Med 2014; 29 (02) (Suppl. 02) S623-S631
- 51 True G, Stewart GL, Lampman M, Pelak M, Solimeo SL. Teamwork and delegation in medical homes: primary care staff perspectives in the Veterans Health Administration. J Gen Intern Med 2014; 29 (Suppl. 02) S632-S639
- 52 Lipshitz R. Rigor and relevance in NDM: how to study decision making rigorously with small Ns and without controls and (inferential) statistics. J Cogn Eng Decis Mak 2010; 4 (02) 99-112
- 53 Clinical Reminders Manager's Manual PXRM*2.0*53. Product Development, Office of Information and Technology, Department of Veterans Affairs; 2015. Available at: https://www.va.gov/vdl/documents/Clinical/CPRS-Clinical_Reminders/pxrm_2_mm.doc . Accessed October 20, 2017
- 54 Benally P, Smith MW, Brown C. , et al. Barriers to Automating Beta Blocker Titration in Heart Failure at the Point of Care. Poster session presented at: AMIA 2017 Annual Symposium; 2017 November 4-8; Washington, DC; 2017
- 55 Yazzie V, Smith M, Brown C. , et al. The use of informatic tools to improve CHF patients within the VA health care system. Association of American Indian Physicians 46th Annual Meeting, Shawnee, OK, July 29, 2017
- 56 Weir C, Brunker C, Butler J, Supiano MA. Making cognitive decision support work: Facilitating adoption, knowledge and behavior change through QI. J Biomed Inform 2017; 71S (Supplement): S32-S38
- 57 Saleem JJ, Patterson ES, Militello L, Render ML, Orshansky G, Asch SM. Exploring barriers and facilitators to the use of computerized clinical reminders. J Am Med Inform Assoc 2005; 12 (04) 438-447
- 58 Wyer Jr RS. Social Comprehension and Judgment: The Role of Situation Models, Narratives, and Implicit Theories. 1st ed. New York, NY: Psychology Press; 2004: 440
- 59 U.S. Department of Veterans Affairs. VA Secretary announces decision on next-generation Electronic Health Record. Available at: https://www.va.gov/opa/pressrel/pressrelease.cfm?id=2914 . Accessed October 19, 2017
- 60 Wright A, Sittig DF, Ash JS. , et al. Development and evaluation of a comprehensive clinical decision support taxonomy: comparison of front-end tools in commercial and internally developed electronic health record systems. J Am Med Inform Assoc 2011; 18 (03) 232-242