Can we quantify harm in general practice records? An assessment of precision and power using computer simulation

BMC Medical Research Methodology

Paul Bowie²  Catherine O’Donnell³  Paul Johnson¹  Carl de Wet³ 

[1] Robertson Centre for Biostatistics, Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK;NHS Education for Scotland, 2 Central Quay, Glasgow, G3 8BW, UK;General Practice & Primary Care, Institute of Health & Wellbeing, College of Medical, Veterinary and Life Science, University of Glasgow, Glasgow, UK

关键词: Harm;    Power;    Precision;    Clinical record review;    Trigger tool;    Primary care;    General practice;    Patient safety;   

Others  :  1126034
DOI  :  10.1186/1471-2288-13-39

 received in 2012-11-01, accepted in 2013-02-25,  发布年份 2013

【 摘 要 】

Background

Estimating harm rates for specific patient populations and detecting significant changes in them over time are essential if patient safety in general practice is to be improved. Clinical record review (CRR) is arguably the most suitable method for these purposes, but the optimal values and combinations of its parameters (such as numbers of records and practices) remain unknown. Our aims were to: 1. Determine and quantify CRR parameters; 2. Assess the precision and power of feasible CRR scenarios; and 3. Quantify the minimum requirements for adequate precision and acceptable power.

Method

We explored precision and power of CRR scenarios using Monte Carlo simulation. A range of parameter values were combined in 864 different CRR scenarios, with 1000 random data sets generated for each, and harm rates were estimated and tested for change over time by fitting a generalised linear model with a Poisson response.

Results

CRR scenarios with ≥100 detected harm incidents had harm rate estimates with acceptable precision. Harm reductions of 20% or ≥50% were detected with adequate power by those CRR scenarios with at least 100 and 500 harm incidents respectively. The number of detected harm incidents was dependent on the baseline harm rate multiplied by: the period of time reviewed in each record; number of records reviewed per practice; number of practices who reviewed records; and the number of times each record was reviewed.

Conclusion

We developed a simple formula to calculate the minimum values of CRR parameters required to achieve adequate precision and acceptable power when monitoring harm rates. Our findings have practical implications for health care decision-makers, leaders and researchers aiming to measure and reduce harm at regional or national level.

【 授权许可】

   
2013 de Wet et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

Figure 3.

【 参考文献 】

• [1]The Health Foundation: Evidence scan: Levels of harm. Published Jan 2011. London; Available at: http://www.health.org.uk/public/cms/75/76/313/2593/Levels%20of%20harm.pdf?realName=PYiXMz.pdf webcite. Acessed 30/10/2012
• [2]Healthcare Improvement Scotland: Patient Safety in Primary Care. 2012. Available at: http://www.healthcareimprovementscotland.org/programmes/patient_safety/patient_safety_in_primary_care.aspx webcite. Accessed 28/09/20120
• [3]The Health Foundation: Improving safety in primary care. 2011. Available at: http://www.health.org.uk/publications/improving-safety-in-primary-care/ webcite. Accessed 01/03/2013
• [4]Bates DW: Mountains in the clouds: patient safety research. Qual Saf Health Care 2008, 17(3):156-157.
• [5]Vincent C, Neale G, Woloshynowych M, et al.: Adverse events in British hospitals: preliminary retrospective record review. BMJ 2001, 322(7285):517-519.
• [6]Brennan TA, Leape LL, Laird NM, Hebert L, Localio AR, Lawthers AG: Incidence of adverse events and negligence in hospitalized patients: results of the Harvard Medical Practice Study I. 1991. Qual Saf Health Care 2004, 13(2):145-151. discussion 151–2
• [7]Resar RK, Rozich JD, Classen D: Methodology and rationale for the measurement of harm with trigger tools. Qual Saf Health Care 2003, 12(Suppl 2):39-45.
• [8]Sharek PJ, Parry G, Goldmann D, Bones K, Hackbarth A, Resar R, et al.: Performance characteristics of a methodology to quantify adverse events over time in hospitalized patients. Health Serv Res 2011, 46(2):654-678.
• [9]Field TS, Gurwitz JH, Harrold LR, Rothschild JM, Debellis K, Seger AC, et al.: Strategies for detecting adverse drug events among older persons in the ambulatory setting. J Am Med Inform Assoc 2004, 11(6):492-498. Nov-Dec
• [10]Wetzels R, Wolters R, van Weel C, Wensing M: Mix of methods is needed to identify adverse events in general practice: a prospective observational study. BMC Fam Pract 2008, 9:35. BioMed Central Full Text
• [11]Christiaans-Dingelhoff I, Smits M, Zwaan L, Lubberding S, Van DW, Wagner C: To what extent are adverse events found in patient records reported by patients and healthcare professionals via complaints, claims and incident reports? BMC Health Serv Res 2011, 11(1):49. BioMed Central Full Text
• [12]Classen DC, Lloyd RC, Provost L, Griffin FA, Resar R: Development and Evaluation of the Institute for Healthcare Improvement Global Trigger Tool. J Pt Saf 2008, 4(3):169-177.
• [13]Griffin FA, Classen DC: Detection of adverse events in surgical patients using the Trigger Tool approach. Qual Saf Health Care 2008, 17(4):253-258.
• [14]Matlow A, Flintoft V, Orrbine E, Brady-Fryer B, Cronin CM, Nijssen-Jordan C, et al.: The development of the Canadian paediatric trigger tool for identifying potential adverse events. Healthc Q 2005, 8(Spec No):90-93.
• [15]Sharek PJ, Horbar JD, Mason W, Bisarya H, Thurm CW, Suresh G, et al.: Adverse events in the neonatal intensive care unit: development, testing, and findings of an NICU-focused trigger tool to identify harm in North American NICUs. Pediatrics 2006, 118(4):1332-1340.
• [16]Takata GS, Mason W, Taketomo C, Logsdon T, Sharek PJ: Development, testing, and findings of a pediatric-focused trigger tool to identify medication-related harm in US children's hospitals. Pediatrics 2008, 121(4):e927-e935.
• [17]de Wet C, Bowie P: The preliminary development and testing of a global trigger tool to detect error and patient harm in primary-care records. Postgrad Med J 2009, 85(1002):176-180.
• [18]Singh R, McLean-Plunckett EA, Kee R, Wisniewski A, Cadzow R, Okazaki S, et al.: Experience with a trigger tool for identifying adverse drug events among older adults in ambulatory primary care. Qual Saf Health Care 2009, 18(3):199-204.
• [19]Gaal S, Verstappen W, Wolters R, Lankveld H, van Weel C, Wensing M: Prevalence and consequences of patient safety incidents in general practice in the Netherlands: a retrospective medical record review study. Implement Sci 2011, 6(1):37. BioMed Central Full Text
• [20]Bowie P, Halley L, Gillies J, Houston N, de Wet C: Searching primary care records for predefined triggers may expose latent risks and adverse events. Clin Risk 2012, 18(January):13-18.
• [21]de Wet C, Bowie P: Screening electronic patient records to detect preventable harm: a trigger tool for primary care. QPC 2011, 19(2):115-125.
• [22]Information Services Division Scotland: Practices and their Populations. 2012. Available at: http://www.isdscotland.org/Health-Topics/General-Practice/Practices-and-Their-Populations/ webcite. Accessed 09/28, 2012
• [23]Bowie P, de Wet C, Pringle M: Significant event analysis guidance for primary care teams. 2011. Available at: http://gptraining.dundee.ac.uk/docs/2011/NES%20SEA%20Guide%20FinalVersion%202011.pdf:1-48 webcite. Accessed 01/03/2013
• [24]de Wet C, Johnson P, Mash R, McConnachie A, Bowie P: Measuring perceptions of safety climate in primary care: a cross-sectional study. J Eval Clin Pract 2012, 18:135-142.
• [25]Larsen K, Peterson JH, Budtz-Jorgensen E, Endahl L: Interpreting parameters in the logistic regression model with random effects. Biometrics 2000, 56(3):909-914.
• [26]Clayton D, Kaldor J: Empirical Bayes estimates of age-standardaized relative risks for use in disease mapping. Biometrics 1987, 43(3):671-681.
• [27]RR Development Core Team: R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing; 2011.
• [28]Bates D, Maechler M, Bolker B: lme4: linear mixed-effects models using S4 classes. R package version 0.999375-39. 2011. Available at: http://CRAN.R-project.org/package=lme4 webcite. Accessed 01/03/2013
• [29]Venables WN, Ripley BD: Modern Applied Statistics with S. 4th edition. New York: Springer; 2002.
• [30]Hadfield JD: MCMC Methods for multi-response generalized linear mixed models: the MCMCglmm R package. J Stat Softw 2010, 33(2):1-22.
• [31]Carter M: Measuring harm levels with the Global Trigger Tool. Clin Risk 2010, 16(4):122-126.
• [32]Landrigan CP, Parry GJ, Bones CB, Hackbarth AD, Goldmann DA, Sharek PJ: Temporal trends in rates of patient harm resulting from medical care. N Engl J Med 2010, 363(22):2124-2134.
• [33]Barber ND, Alldred DP, Raynor DK, Dickinson R, Garfield S, Jesson B, et al.: Care homes' use of medicines study: prevalence, causes and potential harm of medication errors in care homes for older people. QSHC 2009, 18:341-346.
• [34]Morris CJ, Rodgers S, Hammersley VS, Avery AJ, Cantrill JA: Indicators for preventable drug related morbidity: application in primary care. Qual Saf Health Care 2004, 13(3):181-185.
• [35]Scott I, Phelps G: Measurement for improvement: getting one to follow the other. Intern Med J 2009, 39(6):347-351.
• [36]Siriwardena AN: Quality improvement projects for appraisal and revalidation of general practitioners. Quality Prim Care 2011, 19:205-209.