A Library of Logic Models to Explain How Interventions to Reduce Diagnostic Errors Work.
Journal
Journal of patient safety
ISSN: 1549-8425
Titre abrégé: J Patient Saf
Pays: United States
ID NLM: 101233393
Informations de publication
Date de publication:
01 12 2021
01 12 2021
Historique:
pubmed:
26
1
2018
medline:
19
2
2022
entrez:
26
1
2018
Statut:
ppublish
Résumé
We aimed to create a library of logic models for interventions to reduce diagnostic error. This library can be used by those developing, implementing, or evaluating an intervention to improve patient care, to understand what needs to happen, and in what order, if the intervention is to be effective. To create the library, we modified an existing method for generating logic models. The following five ordered activities to include in each model were defined: preintervention; implementation of the intervention; postimplementation, but before the immediate outcome can occur; the immediate outcome (usually behavior change); and postimmediate outcome, but before a reduction in diagnostic errors can occur. We also included reasons for lack of progress through the model. Relevant information was extracted about existing evaluations of interventions to reduce diagnostic error, identified by updating a previous systematic review. Data were synthesized to create logic models for four types of intervention, addressing five causes of diagnostic error in seven stages in the diagnostic pathway. In total, 46 interventions from 43 studies were included and 24 different logic models were generated. We used a novel approach to create a freely available library of logic models. The models highlight the importance of attending to what needs to occur before and after intervention delivery if the intervention is to be effective. Our work provides a useful starting point for intervention developers, helps evaluators identify intermediate outcomes, and provides a method to enable others to generate libraries for interventions targeting other errors.
Identifiants
pubmed: 29369895
pii: 01209203-202112000-00077
doi: 10.1097/PTS.0000000000000459
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e1223-e1233Informations de copyright
Copyright © 2018 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
The authors disclose no conflict of interest.
Références
Brown C, Hofer T, Johal A, et al. An epistemology of patient safety research: a framework for study design and interpretation. Part 1. Conceptualising and developing interventions. Qual Saf Health Care . 2008;17:158–162.
Colquhoun HL, Squires JE, Kolehmainen N, et al. Methods for designing interventions to change healthcare professionals’ behaviour: a systematic review. Implement Sci . 2017;12:30.
Brown HE, Atkin AJ, Panter J, et al. Family‐based interventions to increase physical activity in children: a systematic review, meta‐analysis and realist synthesis. Obes Rev . 2016;17:345–360.
Anderson LM, Petticrew M, Rehfuess E, et al. Using logic models to capture complexity in systematic reviews. Res Synth Methods . 2011;2:33–42.
Bruce BB, El-Kareh R, Ely JW, et al. Methodologies for evaluating strategies to reduce diagnostic error: report from the research summit at the 7th International Diagnostic Error in Medicine Conference. Diagnosis . 2016;3:1–7.
Foy R, Ovretveit J, Shekelle PG, et al. The role of theory in research to develop and evaluate the implementation of patient safety practices. BMJ Qual Saf . 2011;20:453–459.
Hoffmann TC, Glasziou PP, Boutron I, et al. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. BMJ . 2014;348:g1687.
Moher D, Hopewell S, Schulz KF, et al. CONSORT 2010 explanation and elaboration: updated guidelines for reporting parallel group randomised trials. J Clin Epidemiol . 2010;63:e1–e37.
Kneale D, Thomas J, Harris K. Developing and optimising the use of logic models in systematic reviews: exploring practice and good practice in the use of programme theory in reviews. PLoS One . 2015;10:e0142187.
McDonald KM, Matesic B, Contopoulos-Ioannidis DG, et al. Patient safety strategies targeted at diagnostic errors: a systematic review. Ann Intern Med . 2013;158(5 Part 2):381–389.
Royal S, Smeaton L, Avery AJ, et al. Interventions in primary care to reduce medication related adverse events and hospital admissions: systematic review and meta-analysis. Qual Saf Health Care . 2006;15:23–31.
Cottrell S, Watson D, Eyre TA, et al. Interventions to reduce wrong blood in tube errors in transfusion: a systematic review. Transfus Med Rev . 2013;27:197–205.
Shekelle PG, Pronovost PJ, Wachter RM, et al. The top patient safety strategies that can be encouraged for adoption now. Ann Intern Med . 2013;158(5 Part 2):365–368.
Reed JE, McNicholas C, Woodcock T, et al. Designing quality improvement initiatives: the action effect method, a structured approach to identifying and articulating programme theory. BMJ Qual Saf . 2014;23:1040–1048.
Medical Research Council. Developing and Evaluating Complex Interventions: New Guidance . London: Medical Research Council; 2008.
Zwaan L, de Bruijne M, Wagner C, et al. Patient record review of the incidence, consequences, and causes of diagnostic adverse events. Arch Intern Med . 2010;170:1015–1021.
Saber Tehrani AS, Lee H, Mathews SC, et al. 25-Year summary of US malpractice claims for diagnostic errors 1986-2010: an analysis from the National Practitioner Data Bank. BMJ Qual Saf . 2013;22:672–680.
Panesar SS, deSilva D, Carson-Stevens A, et al. How safe is primary care? A systematic review. BMJ Qual Saf . 2016;25:544–553.
Cresswell KM, Panesar SS, Salvilla SA, et al. Global research priorities to better understand the burden of iatrogenic harm in primary care: an international Delphi exercise. PLoS Med . 2013;10:e1001554.
Schiff GD, Kim S, Abrams R, et al. Advances in Patient Safety: From Research to ImplementationVolume 2: Concepts and Methodology . Henriksen K, Battles JB, Marks ES, et al., eds. Rockville, MD: Agency for Healthcare Research and Quality.
Gandhi TK, Kachalia A, Thomas EJ, et al. Missed and delayed diagnoses in the ambulatory setting: a study of closed malpractice claims. Ann Intern Med . 2006;145:488–496.
Singh H, Graber M, Kissam S, et al. System-related interventions to reduce diagnostic errors: a narrative review. BMJ Qual Saf . 2012;21:160–170.
Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci . 2011;6:42.
Norcross JC, Krebs PM, Prochaska JO. Stages of change. J Clin Psychol . 2011;67:143–154.
Hammick M, Dornan T, Steinert Y. Conducting a best evidence systematic review. Part 1: From idea to data coding. BEME Guide No 13. Med Teach . 2010;32:3–15.
Wilson D. Practical meta-analysis effect size calculator [Online calculator]. Available at: https://www.campbellcollaboration.org/this-is-a-web-based-effect-size-calculator/explore/this-is-a-web-based-effect-size-calculator . Accessed March 3, 2017.
Cohen J. Statistical Power Analysis for the Behavioral Sciences . Hillsdale, NJ: Lawrence Erlbaum; 1988.
Lewis G, Sharp D, Bartholomew J, et al. Computerized assessment of common mental disorders in primary care: effect on clinical outcome. Fam Pract . 1996;13:120–126.
Mueller CA, Klaassen-Mielke R, Penner E, et al. Disclosure of new health problems and intervention planning using a geriatric assessment in a primary care setting. Croat Med J . 2010;51:493–500.
Schriger DL, Gibbons PS, Langone CA, et al. Enabling the diagnosis of occult psychiatric illness in the emergency department: a randomized, controlled trial of the computerized, self-administered PRIME-MD diagnostic system. Ann Emerg Med . 2001;37:132–140.
Nicholl D, Yap CP, Cahill V, et al. The TOS study: can we use our patients to help improve clinical assessment? J R Coll Physicians Edinb . 2011;42:306–310.
Biffl WL, Harrington DT, Cioffi WG. Implementation of a tertiary trauma survey decreases missed injuries. J Trauma Acute Care Surg . 2003;54:38–44.
Keijzers GB, Campbell D, Hooper J, et al. A prospective evaluation of missed injuries in trauma patients, before and after formalising the trauma tertiary survey. World J Surg . 2014;38:222–232.
Lillo R, Salinas M, Lopez-Garrigos M, et al. Reducing preanalytical laboratory sample errors through educational and technological interventions. Clin Lab . 2012;58:911–917.
Hy Li, Huang XN, Yang YC, et al. Reduction of preanalytical errors in clinical laboratory through multiple aspects and whole course intervention measures. J Evid Based Med . 2014;7:172–177.
Turner HE, Deans KA, Kite A, et al. The effect of electronic ordering on pre-analytical errors in primary care. Ann Clin Biochem . 2013;50:485–488.
Hlabangana LT, Andronikou S. Short-term impact of pictorial posters and a crash course on radiographic errors for improving the quality of paediatric chest radiographs in an unsupervised unit—a pilot study for quality-assurance outreach. Pediatr Radiol . 2015;45:158–165.
Hopkins K, Huynh S, McNary C, et al. Reducing blood culture contamination rates: a systematic approach to improving quality of care. Am J Infect Control . 2013;41:1272–1274.
Ramirez P, Gordón M, Cortes C, et al. Blood culture contamination rate in an intensive care setting: effectiveness of an education-based intervention. Am J Infect Control . 2015;43:844–847.
Raab SS, Tworek JA, Souers R, et al. The value of monitoring frozen section-permanent section correlation data over time. Arch Pathol Lab Med . 2006;130:337–342.
Rosskopf AB, Dietrich TJ, Hirschmann A, et al. Quality management in musculoskeletal imaging: form, content, and diagnosis of knee MRI reports and effectiveness of three different quality improvement measures. Am J Roentgenol . 2015;204:1069–1074.
Sibbald M, de Bruin AB, van Merrienboer JJ. Checklists improve experts’ diagnostic decisions. Med Educ . 2013;47:301–308.
Sibbald M, De Bruin AB, van Merrienboer JJ. Finding and fixing mistakes: do checklists work for clinicians with different levels of experience? Adv Health Sci Educ Theory Pract . 2014;19:43–51.
Kundel HL, Nodine CF, Krupinski EA. Computer-displayed eye position as a visual aid to pulmonary nodule interpretation. Invest Radiol . 1990;25:890–896.
Tudor GR, Finlay DB. Error review: can this improve reporting performance? Clin Radiol . 2001;56:751–754.
Hosoe N, Rey JF, Imaeda H, et al. Evaluations of capsule endoscopy software in reducing the reading time and the rate of false negatives by inexperienced endoscopists. Clin Res Hepatol Gastroenterol . 2012;36:66–71.
Espinosa JA, Nolan TW. Reducing errors made by emergency physicians in interpreting radiographs: longitudinal study. BMJ . 2000;320:737–740.
Itri JN, Kang HC, Krishnan S, et al. Using focused missed-case conferences to reduce discrepancies in musculoskeletal studies interpreted by residents on call. Am J Roentgenol . 2011;197:W696–W705.
Dudley M, Channer K. Assessment of the value of technician reporting of electrocardiographs in an accident and emergency department. J Accid Emerg Med . 1997;14:307–310.
Rondonotti E, Soncini M, Girelli CM, et al. Can we improve the detection rate and interobserver agreement in capsule endoscopy? Dig Liver Dis . 2012;44:1006–1011.
Nishikawa RM, Schmidt RA, Linver MN, et al. Clinically missed cancer: how effectively can radiologists use computer-aided detection? Am J Roentgenol . 2012;198:708–716.
Goodacre S, Webster A, Morris F. Do computer generated ECG reports improve interpretation by accident and emergency senior house officers? Postgrad Med J . 2001;77:455–457.
Tsai TL, Fridsma DB, Gatti G. Computer decision support as a source of interpretation error: the case of electrocardiograms. J Am Med Inform Assoc . 2003;10:478–483.
Weatherburn G, Bryan S, Nicholas A, et al. The effect of a picture archiving and communications system (PACS) on diagnostic performance in the accident and emergency department. J Accid Emerg Med . 2000;17:180–184.
Rezvyy G, Parniakov A, Fedulova E, et al. Correcting biases in psychiatric diagnostic practice in Northwest Russia: comparing the impact of a general educational program and a specific diagnostic training program. BMC Med Educ . 2008;8:15.
Sherbino J, Kulasegaram K, Howey E, et al. Ineffectiveness of cognitive forcing strategies to reduce biases in diagnostic reasoning: a controlled trial. CJEM . 2014;16:34–40.
Monteiro SD, Sherbino J, Patel A, et al. Reflecting on diagnostic errors: taking a second look is not enough. J Gen Intern Med . 2015;30:1270–1274.
Myung SJ, Kang SH, Phyo SR, et al. Effect of enhanced analytic reasoning on diagnostic accuracy: a randomized controlled study. Med Teach , 2013;35:248–250.
Coderre S, Wright B, McLaughlin K. To think is good: querying an initial hypothesis reduces diagnostic error in medical students. Acad Med . 2010;85:1125–1129.
Mamede S, van Gog T, van den Berge K, et al. Effect of availability bias and reflective reasoning on diagnostic accuracy among internal medicine residents. JAMA . 2010;304:1198–1203.
Sibbald M, de Bruin AB, Cavalcanti RB, et al. Do you have to re-examine to reconsider your diagnosis? Checklists and cardiac exam. BMJ Qual Saf . 2013;22:333–338.
Ramnarayan P, Roberts GC, Coren M, et al. Assessment of the potential impact of a reminder system on the reduction of diagnostic errors: a quasi-experimental study. BMC Med Inform Decis Mak . 2006;6:22.
Ramnarayan P, Winrow A, Coren M, et al. Diagnostic omission errors in acute paediatric practice: impact of a reminder system on decision-making. BMC Med Inform Decis Mak . 2006;6:37.
Kostopoulou O, Lionis C, Angelaki A, et al. Early diagnostic suggestions improve accuracy of family physicians: a randomized controlled trial in Greece. Fam Pract . 2015;32:323–328.
Kostopoulou O, Rosen A, Round T, et al. Early diagnostic suggestions improve accuracy of GPs: a randomised controlled trial using computer-simulated patients. Br J Gen Pract . 2015;65:e49–e54.
Wellwood J, Johannessen S, Spiegelhalter D. How does computer-aided diagnosis improve the management of acute abdominal pain? Ann R Coll Surg Engl . 1992;74:40.
Chern CH, How CK, Wang LM, et al. Decreasing clinically significant adverse events using feedback to emergency physicians of telephone follow-up outcomes. Ann Emerg Med . 2005;45:15–23.
Segal MM, Williams MS, Gropman AL, et al. Evidence-based decision support for neurological diagnosis reduces errors and unnecessary workup. J Child Neurol . 2014;29:487–492.
Wexler JR, Swender PT, Tunnessen WW Jr, et al. Impact of a system of computer-assisted diagnosis. Initial evaluation of the hospitalized patient. Am J Dis Child . 1975;129:203–205.
Murphy DR, Wu L, Thomas EJ, et al. Electronic trigger-based intervention to reduce delays in diagnostic evaluation for cancer: a cluster randomized controlled trial. J Clin Oncol . 2015;33:3560–3567.
Graber ML, Franklin N, Gordon R. Diagnostic error in internal medicine. Arch Intern Med . 2005;165:1493–1499.
Wilson PF. Root Cause Analysis: A Tool for Total Quality Management . Milwaukee, Wisconsin: ASQ Quality Press; 1993.
Ovretveit JC, Shekelle PG, Dy SM, et al. How does context affect interventions to improve patient safety? An assessment of evidence from studies of five patient safety practices and proposals for research. BMJ Qual Saf . 2011;20:604–610.
Singh H, Schiff GD, Graber ML, et al. The global burden of diagnostic errors in primary care. BMJ Qual Saf . 2017;26:484–494.
Thomas J, O’Mara-Eves A, Brunton G. Using qualitative comparative analysis (QCA) in systematic reviews of complex interventions: a worked example. Syst Rev . 2014;3:67.
Michie S, West R, Campbell R, et al. ABC of Behaviour Change Theories . Sutton: Silverback Publishing; 2014.
Watson SI, Lilford RJ. Integrating multiple sources of evidence: a Bayesian perspective. In: Raine R, Fitzpatrick R, Barratt H, et al. eds. Challenges, solutions and future directions in the evaluation of service innovations in health care and public health . Southampton: NIHR; 2016.