Evidence-based guidelines and decision support services: A discussion and evaluation in triple assessment of suspected breast cancer

V Patkar, C Hurt, R Steele, S Love, A Purushotham, M Williams, R Thomson, J Fox, V Patkar, C Hurt, R Steele, S Love, A Purushotham, M Williams, R Thomson, J Fox

Abstract

Widespread health service goals to improve consistency and safety in patient care have prompted considerable investment in the development of evidence-based clinical guidelines. Computerised decision support (CDS) systems have been proposed as a means to implement guidelines in practice. This paper discusses the general concept in oncology and presents an evaluation of a CDS system to support triple assessment (TA) in breast cancer care. Balanced-block crossover experiment and questionnaire study. One stop clinic for symptomatic breast patients. Twenty-four practising breast clinicians from United Kingdom National Health Service hospitals. A web-based CDS system. Clinicians made significantly more deviations from guideline recommendations without decision support (60 out of 120 errors without CDS; 16 out of 120 errors with CDS, P < 0.001). Ignoring minor deviations, 16 potentially critical errors arose in the no-decision-support arm of the trial compared with just one (P = 0.001) when decision support was available. Opinions of participating clinicians towards the CDS tool became more positive after they had used it (P < 0.025). The use of decision support capabilities in TA may yield significant measurable benefits for quality and safety of patient care. This is an important option for improving compliance with evidence-based practice guidelines.

Figures

Figure 1
Figure 1
Tallis representation of TA workflow showing the main plan. The decision nodes represented by circles are embedded at various points in the workflow.
Figure 2
Figure 2
TADS screen with decision support enabled, showing decision options for the imaging for one case, to be taken after medical history and examination. The system recommends an ultrasound scan but recommends against mammography and against doing nothing. For the decision option ‘Do a mammogram of both breasts’, arguments for and against have been expanded to show the justifying evidence (an option available to the clinician for all decisions, options and arguments). Links are provided to the relevant supporting literature, which can be accessed by the user if required (e.g. from PubMed).
Figure 3
Figure 3
TADS screen with decision support disabled, showing options for imaging after medical history and examination have been presented.

References

    1. Aylin P, Tanna S, Bottle A, Jarman B (2004) How often adverse events are reported in English hospital statistics? BMJ 329: 369.
    1. Bloom BS, de Pouvourville N, Chatre S, Jayadevappa R, Weinberg D (2004) Breast cancer treatment in clinical practice compared to best evidence and practice guidelines. Br J Cancer 90(1): 26–30
    1. Bury J, Hurt C, Roy A, Cheesman L, Bradburn M, Cross S, Fox J, Saha V (2005) LISA: a web-based decision-support system for trial management of childhood acute lymphoblastic leukaemia. Br J Haematol 129(6): 746–754
    1. Cabana MD, Rand CS, Powe NR, Wu AW, Wilson MH, Abboud PA, Rubin HR (1999) Why don't physicians follow clinical practice guidelines? A framework for improvement. JAMA 282: 1458–1465
    1. Commission for Health Improvement/Audit Commission (2001) National Service Framework Assessments No.1. NHS Cancer Care in England and Wales. London, UK: Commission for Health Improvement, (accessed November 01 2006 )
    1. Corrigan JM, Donaldson MS, Kohn LT, Maguire SK, Pike KC (2001) A New Health System for the 21st Century. Washington, DC: Institute of Medicine, National Academy of Sciences, National Academies Press; Crossing the Quality Chasm
    1. Emery J, Walton R, Murphy M, Austoker J, Yudkin P, Chapman C, Coulson A, Glasspool D, Fox J (2000) Computer support for interpreting family histories of breast and ovarian cancer in primary care: comparative study with simulated cases. BMJ 321(7252): 28–32
    1. Fervers B, Hardy J, Blanc-Vincent MP, Theobald S, Bataillard A, Farsi F, Gory G, Debuiche S, Guillo S, Renaud-Salis JL, Pinkerton R, Bey P, Philip T (2001) SOR: project methodology. Br J Cancer 84(Suppl 2): 8–16
    1. Field MJ, Lohr KN (eds) (1990) Institute of Medicine Committee to Advise the Public Health Service on Clinical Practice Guidelines. Clinical practice guidelines: directions for a new program. Washington DC: National Academy Press
    1. Fleiss JL (1981) Statistical methods for rates and proportions 2nd edn, New York: Wiley
    1. Fox J, Das S (2000) Safe and Sound: Artificial Intelligence in Hazardous Applications. MIT Press: Cambridge
    1. Fox J, Patkar V, Thomas R (2006) Decision support for healthcare: the PROforma evidence base. Inform Prim Care 14(1): 49–54
    1. Freemantle N, Harvey EL, Wolf F, Grimshaw JM, Grilli R, Bero LA (1996) The effectiveness of printed educational materials in changing the behaviour of health care professionals Cochrane Collaboration. Cochrane Library. Issue 3. Oxford: Update Software
    1. Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, Sam J, Haynes RB (2005) Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 293(10): 1223–1238
    1. Grimshaw J, Eccles M, Walker A, Thomas R (2002) Changing physician's behaviour: what works and thoughts on getting more things to work. J Cont Educ Health Prof 22: 237–243
    1. Grol R, Grimshaw J (2003) From best evidence to best practice: effective implementation of change. Lancet 362: 1225–1230
    1. Hippisley-Cox J, Pringle M, Cater R, Wynn A, Hammersley V, Coupland C, Hapgood R, Horsfield P, Teasdale S, Johnson C (2003) The electronic patient record in primary care – regression or progression? A cross-sectional study. BMJ 326: 1439–1443
    1. James BC, Hammond MEH (2000) The challenge of variation in medical practice. Arch Pathol Lab Med 125: 1001–1003
    1. Kohn LT, Corrigan JM, Donaldson MS (eds) (1999) To Err Is Human: Building a Safer Health System, Committee on Quality of Health Care in America. Washington, DC: Institute of medicine, National Academy Press
    1. Manotti C, Moia M, Palareti G, Pengo V, Ria L, Dettori AG (2001) Effect of computer-aided management on the quality of treatment in anticoagulated patients: a prospective, randomized, multicenter trial of APROAT (Automated PRogram for Oral Anticoagulant Treatment). Haematologica 86: 1060–1070
    1. McNemar Q (1947) Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12: 153–157
    1. O'Hagan JJ, Davies LJ, Pears RK (1986) The use of simulated patients in the assessment of actual clinical performance in general practice. NZ Med J 99: 948–951
    1. Selker HP, Beshansky JR, Griffith JL, Aufderheide TP, Ballin DS, Bernard SA, Crespo SG, Feldman JA, Fish SS, Gibler WB, Kiez DA, McNutt RA, Moulton AW, Ornato JP, Podrid PJ, Pope JH, Salem DN, Sayre MR, Woolard RH (1998) Use of the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI) to assist with triage of patients with chest pain or other symptoms suggestive of acute cardiac ischemia: a multicenter, controlled clinical trial. Ann Intern Med 129: 845–855
    1. Shiffman RN, Michel G, Essaihi A, Thornquist E (2004) Bridging the guideline implementation gap: a systematic, document-centered approach to guideline implementation. J Am Med Inform Assoc 11(5): 418–426
    1. Taylor P, Fox J, Pokropek AT (1999) The development and evaluation of CADMIUM: a prototype system to assist in the interpretation of mammograms. Med Image Anal 3(4): 321–337
    1. The AGREE collaboration (2003) Development and validation of an international appraisal instrument for assessing the quality of clinical practice guidelines: the AGREE project. Qual Saf Health Care 12: 18–23

Source: PubMed

Sponsors en medewerkers

Medische omstandigheden

Geneesmiddelinterventies

3
Abonneren