期刊论文详细信息
BMC Medical Informatics and Decision Making
Implementation of multiple-domain covering computerized decision support systems in primary care: a focus group study on perceived barriers
Rudolf B. Kool3  Gert P. Westert3  Trudy van der Weijden2  Jan-Willem Weenink3  Marjolein Lugtenberg1 
[1] Scientific center for care and welfare (Tranzo), Tilburg School of Social and Behavioral Sciences, Tilburg University, Tilburg, 5000 LE, The Netherlands;School for Public Health and Primary Care (CAPHRI), Department of General Practice, Maastricht University, Maastricht, 6200 MD, The Netherlands;Scientific Institute for Quality of Healthcare (IQ healthcare), Radboud university medical center, Nijmegen, 6500 HB, The Netherlands
关键词: Implementation;    Interventions;    Barriers;    Primary care;    Clinical practice guidelines;    Clinical decision support;   
Others  :  1228368
DOI  :  10.1186/s12911-015-0205-z
 received in 2015-02-03, accepted in 2015-09-29,  发布年份 2015
PDF
【 摘 要 】

Background

Despite the widespread availability of computerized decision support systems (CDSSs) in various healthcare settings, evidence on their uptake and effectiveness is still limited. Most barrier studies focus on CDSSs that are aimed at a limited number of decision points within selected small-scale academic settings. The aim of this study was to identify the perceived barriers to using large-scale implemented CDSSs covering multiple disease areas in primary care.

Methods

Three focus group sessions were conducted in which 24 primary care practitioners (PCPs) participated (general practitioners, general practitioners in training and practice nurses), varying from 7 to 9 per session. In each focus group, barriers to using CDSSs were discussed using a semi-structured literature-based topic list. Focus group discussions were audio-taped and transcribed verbatim. Two researchers independently performed thematic content analysis using the software program Atlas.ti 7.0.

Results

Three groups of barriers emerged, related to 1) the users’ knowledge of the system, 2) the users’ evaluation of features of the system (source and content, format/lay out, and functionality), and 3) the interaction of the system with external factors (patient-related and environmental factors). Commonly perceived barriers were insufficient knowledge of the CDSS, irrelevant alerts, too high intensity of alerts, a lack of flexibility and learning capacity of the CDSS, a negative effect on patient communication, and the additional time and work it requires to use the CDSS.

Conclusions

Multiple types of barriers may hinder the use of large-scale implemented CDSSs covering multiple disease areas in primary care. Lack of knowledge of the system is an important barrier, emphasizing the importance of a proper introduction of the system to the target group. Furthermore, barriers related to a lack of integration into daily practice seem to be of primary concern, suggesting that increasing the system’s flexibility and learning capacity in order to be able to adapt the decision support to meet the varying needs of different users should be the main target of CDSS interventions.

【 授权许可】

   
2015 Lugtenberg et al.

【 预 览 】
附件列表
Files Size Format View
20151016020739323.pdf 1313KB PDF download
Fig. 2. 75KB Image download
Fig. 1. 64KB Image download
【 图 表 】

Fig. 1.

Fig. 2.

【 参考文献 】
  • [1]Hunt D, Haynes R, Hanna S, Smith K. Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review. JAMA. 1998; 280:1339-46.
  • [2]Committee on Quality of Health Care in America Institute of Medicine. To err is human: building a safer health system. Washington DC: National Academies Press; 2000.
  • [3]Committee on Quality of Health Care in America Institute of Medicine. Crossing the quality chasm: a new health system for the 21st century. Washington DC: National Academies Press; 2001.
  • [4]Kortteisto T, Komulainen J, Makela M, Kunnamo I, Kaila M. Clinical decision support must be useful, functional is not enough: a qualitative study of computer-based clinical decision support in primary care. BMC Health Serv Res. 2012; 12:349. BioMed Central Full Text
  • [5]Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E et al.. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006; 144:742-52.
  • [6]Johnston M, Langton K, Haynes R, Mathieu A. Effects of computer-based clinical decision support systems on clinician performance and patient outcome: a critical appraisal of research. Ann Intern Med. 1994; 120:135-42.
  • [7]Garg A, Adhikari N, McDonald H, Rosas-Arellano M, Devereaux P, Beyene J et al.. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005; 293:1223-38.
  • [8]Kawamoto K, Houlihan C, Balas E, Lobach D. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005; 330:765.
  • [9]Roshanov PS, Fernandes N, Wilczynski JM, Hemens BJ, You JJ, Handler SM et al. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. BMJ. 2013;346. doi:10.1136/bmj.f657.
  • [10]Moja L, Kwag KH, Lytras T, Bertizzolo L, Brandt L, Pecoraro V 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:e12-22.
  • [11]Roshanov P, Misra S, Gerstein H, Garg A, Sebaldt R, Mackay J et al.. Computerized clinical decision support systems for chronic disease management: A decision-maker-researcher partnership systematic review. Implement Sci. 2011; 6:92. BioMed Central Full Text
  • [12]Young J, Donahue M, Farquhar M, Simpson C, Rocker G. Using opioids to treat dyspnea in advanced COPD: Attitudes and experiences of family physicians and respiratory therapists. Can Fam Physician. 2012; 58:e401-e7.
  • [13]Nieuwlaat R, Connolly S, Mackay J, Weise-Kelly L, Navarro T, Wilczynski N et al.. Computerized clinical decision support systems for therapeutic drug monitoring and dosing: A decision-maker-researcher partnership systematic review. Implement Sci. 2011; 6:90. BioMed Central Full Text
  • [14]Sahota N, Lloyd R, Ramakrishna A, Mackay J, Prorok J, Weise-Kelly L et al.. Computerized clinical decision support systems for acute care management: A decision-maker-researcher partnership systematic review of effects on process of care and patient outcomes. Implement Sci. 2011; 6:91. BioMed Central Full Text
  • [15]Souza N, Sebaldt R, Mackay J, Prorok J, Weise-Kelly L, Navarro T et al.. 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. BioMed Central Full Text
  • [16]Haynes R, Wilczynski N. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: methods of a decision-maker-researcher partnership systematic review. Implement Sci. 2010; 5:12. BioMed Central Full Text
  • [17]Martens JD, van der Weijden T, Winkens RAG, Kester ADM, Geerts PJH, Evers SMAA et al.. Feasibility and acceptability of a computerised system with automated reminders for prescribing behaviour in primary care. Int J Med Inform. 2008; 77:199-207.
  • [18]Sittig D, Krall M, Dykstra R, Russell A, Chin H. A survey of factors affecting clinician acceptance of clinical decision support. BMC Med Inform Decis Mak. 2006; 6:6. BioMed Central Full Text
  • [19]Varonen H, Kortteisto T, Kaila M. Group ftES. What may help or hinder the implementation of computerized decision support systems (CDSSs): a focus group study with physicians. Fam Pract. 2008; 25:162-7.
  • [20]Rousseau N, McColl E, Newton J, Grimshaw J, Eccles M. Practice based, longitudinal, qualitative interview study of computerised evidence based guidelines in primary care. BMJ. 2003; 326:314.
  • [21]Zheng K, Padman R, Johnson MP, Diamond HS. Understanding technology adoption in clinical care: Clinician adoption behavior of a point-of-care reminder system. Int J Med Inform. 2005; 74:535-43.
  • [22]De Vries A, Van der Wal M, Nieuwenhuis M, De Jong R, Van Dijk R, Jaarsma T 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. BioMed Central Full Text
  • [23]Bryan C, Boren SA. The use and effectiveness of electronic clinical decision support tools in the ambulatory/primary care setting: a systematic review of the literature. Inform Prim Care. 2008; 16:79-91.
  • [24]Van Hassel DTP, Kenens RJ. Cijfers uit de registratie van huisartsen - peiling 2012. Utrecht, NIVEL; 2013.
  • [25]Prins F, Sluijsmans DA, Kirschner P. Feedback for General Practitioners in Training: Quality, Styles, and Preferences. Adv Health Sci Educ Theory Pract. 2006; 11:289-303.
  • [26]Heiligers PJM, Noordman J, Korevaar JC, Dorsman S, Hingstman L, Van Dulmen AM et al. Praktijkondersteuners in de huisartspraktijk (POH’s), klaar voor de toekomst? Utrecht: NIVEL; 2012.
  • [27]Nederlands Huisartsen Genootschap (NHG). www.nhg.org/ (2015). Accessed March 13, 2015.
  • [28]ExpertDoc. www.expertdoc.nl (2015). Accessed March 13, 2015.
  • [29]Krueger RA, Casey MA. Focus groups: a practical guide for applied research. Sage, Thousand Oaks, CA; 2000.
  • [30]Murphy E, Mattson B. Qualitative research and family practice: a marriage made in heaven? Fam Pract. 1992; 9:85-91.
  • [31]Mays N, Pope C. Qualitative research: rigour and qualitative research. BMJ. 1995; 311:109-12.
  • [32]Mays N, Pope C. Qualitative research in health care: assessing quality in qualitative research. BMJ. 2000; 320:50-2.
  • [33]Lugtenberg M, Westert G, Pasveer D, van der Weijden T, Kool R. Evaluating the uptake and effects of the computerized decision support system NHGDoc on quality of primary care: protocol for a large-scale cluster randomized controlled trial. Implem Sci. 2014; 9:145. BioMed Central Full Text
  • [34]Clark JP. How to peer review a qualitative manuscript. In: Peer Review in Health Sciences. 2nd ed. Godlee FJT, editor. BMJ Books, London; 2003: p.219-35.
  • [35]Cabana MD, Rand CS, Powe NR, Wu AW, Wilson MH, Abboud PA et al.. Why don't physicians follow clinical practice guidelines: a framework for improvement. JAMA. 1999; 282:1458-65.
  • [36]Grol R, Wensing M. Effective implementation: a model. Improving patient care: the implementation of change in clinical practice. Grol R, Wensing M, Eccles M, editors. Elsevier, Oxford; 2005.
  • [37]Goossens A, Bossuyt PMM, de Haan RJ. Physicians and Nurses Focus on Different Aspects of Guidelines When Deciding Whether to Adopt Them: An Application of Conjoint Analysis. Med Decis Making. 2008; 28:138-45.
  • [38]Kesselheim AS, Cresswell K, Phansalkar S, Bates DW, Sheikh A. Clinical Decision Support Systems Could Be Modified To Reduce ‘Alert Fatigue’ While Still Minimizing The Risk Of Litigation. Health Affairs. 2011; 30:2310-7.
  • [39]Van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of Drug Safety Alerts in Computerized Physician Order Entry. J Am Med Inform Ass. 2006; 13:138-47.
  • [40]Isaac T, Weissman JS, Davis RB, Massagli M, Cyrulik A, Sands DZ. Overrides of medication alerts in ambulatory care. Arch Intern Med. 2009;169:305–11. doi:10.1001/archinternmed.2008.551.
  • [41]Weingart SN, Toth M, Sands DZ, Aronson MD, Davis RB, Phillips RS. Physicians' decisions to override computerized drug alerts in primary care. Arch Intern Med. 2003; 163:2625-31.
  • [42]Sim J. Collecting and analysing qualitative data: issues raised by the focus group. J Adv Nurs. 1998; 28:345-52.
  • [43]Baker R, Camosso-Stefinovic J, Gillies C, Shaw E, Cheater F, Flottorp S et al.. Tailored interventions to overcome identified barriers to change: effects on professional practice and health care outcomes. 2010.
  • [44]Bosch M, Van der Weijden T, Wensing M, Grol R. Tailoring quality improvement interventions to identified barriers: a multiple case analysis. J Eval Clin Pract. 2007; 13:161-8.
  • [45]Horsky J, Schiff GD, Johnston D, Mercincavage L, Bell D, Middleton B. Interface design principles for usable decision support: A targeted review of best practices for clinical prescribing interventions. J Biomed Inform. 2012; 45:1202-16.
  文献评价指标  
  下载次数:66次 浏览次数:37次