Assessment of the potential impact of a reminder system on the reduction of diagnostic errors: a quasi-experimental study

Ramnarayan, Padmanabhan; Roberts, Graham; Coren, Michael; Nanduri, Vasantha; Tomlinson, Amanda L.; Taylor, Paul; Wyatt, Jeremy C.; Britto, Joseph

doi:10.1186/1472-6947-6-22

Cited by 58 publications

(60 citation statements)

References 53 publications

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“…Four systems in current use (Isabel, DXplain, Diagnosis Pro and PEPID) were recently reviewed and evaluated on test cases 51. There have also been various evaluations of these systems and earlier counterparts (eg, QMR and Iliad) including retrospective52–58 and simulated cases53 59 60 as well as pre–post61 and prospective62 studies. In general, these studies—although not always rigorously performed—demonstrate that the systems include the gold standard diagnosis within the output list of up to 30 diagnoses in 70–95% of cases.…”

Section: Resultsmentioning

confidence: 99%

Use of health information technology to reduce diagnostic errors

2013

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BackgroundHealth information technology (HIT) systems have the potential to reduce delayed, missed or incorrect diagnoses. We describe and classify the current state of diagnostic HIT and identify future research directions.Methods A multi-pronged literature search was conducted using PubMed, Web of Science, backwards and forwards reference searches and contributions from domain experts. We included HIT systems evaluated in clinical and experimental settings as well as previous reviews, and excluded radiology computer-aided diagnosis, monitor alerts and alarms, and studies focused on disease staging and prognosis. Articles were organised within a conceptual framework of the diagnostic process and areas requiring further investigation were identified.ResultsHIT approaches, tools and algorithms were identified and organised into 10 categories related to those assisting: (1) information gathering; (2) information organisation and display; (3) differential diagnosis generation; (4) weighing of diagnoses; (5) generation of diagnostic plan; (6) access to diagnostic reference information; (7) facilitating follow-up; (8) screening for early detection in asymptomatic patients; (9) collaborative diagnosis; and (10) facilitating diagnostic feedback to clinicians. We found many studies characterising potential interventions, but relatively few evaluating the interventions in actual clinical settings and even fewer demonstrating clinical impact.ConclusionsDiagnostic HIT research is still in its early stages with few demonstrations of measurable clinical impact. Future efforts need to focus on: (1) improving methods and criteria for measurement of the diagnostic process using electronic data; (2) better usability and interfaces in electronic health records; (3) more meaningful incorporation of evidence-based diagnostic protocols within clinical workflows; and (4) systematic feedback of diagnostic performance.

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Section: Resultsmentioning

confidence: 99%

Use of health information technology to reduce diagnostic errors

2013

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“…These authors suggest that it is not sensible to measure student success by how often they give the correct diagnosis, that the cost of being incorrect must also be considered (Wainer and Mee 2004). Along a similar line, in studies of diagnostic support systems, Ramnarayan and colleagues have proposed a scoring model that assesses diagnostic quality rather than accuracy and considers the patient management plan as well as the diagnostic hypothesis set (Ramnarayan et al 2006). …”

Section: Discussionmentioning

confidence: 94%

Defining the correctness of a diagnosis: differential judgments and expert knowledge

Kanter

Brosenitsch

Mahoney

et al. 2010

Adv in Health Sci Educ

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Approaches that use a simulated patient case to study and assess diagnostic reasoning usually use the correct diagnosis of the case as a measure of success and as an anchor for other measures. Commonly, the correctness of a diagnosis is determined by the judgment of one or more experts. In this study, the consistency of experts' judgments of the correctness of a diagnosis, and the structure of knowledge supporting their judgments, were explored using a card sorting task. Seven expert pediatricians were asked to sort into piles the diagnoses proposed by 119 individuals who had worked through a simulated patient case of Haemophilus influenzae Type B (HIB) meningitis. The 119 individuals had varying experience levels. The expert pediatricians were asked to sort the proposed diagnoses by similarity of content, and then to order the piles based on correctness, relative to the known correct diagnosis (HIB meningitis). Finally, the experts were asked to judge which piles contained correct or incorrect diagnoses. We found that, contrary to previous studies, experts shared a common conceptual framework of the diagnostic domain being considered and were consistent in how they categorized the diagnoses. However, similar to previous studies, the experts differed greatly in their judgment of which diagnoses were correct. This study has important implications for understanding expert knowledge, for scoring performance on simulated or real patient cases, for providing feedback to learners in the clinical setting, and for establishing criteria that define what is correct in studies of diagnostic error and diagnostic reasoning.

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“…One popular diagnostic webbased DSS, Isabel, provides information in the form of additional diagnoses which the practitioner may or may not have considered in the assessment of a given patient (e.g. Ramnarayan, 2005;Ramnarayan et al, 2006;Bavdekar and Pawar, 2005). Isabel draws on cross-references from a range of medical textbooks; it has been found to perform quite well in terms of ''including [73%] all key diagnoses'' (Ramnarayan, 2005) as well as including the ''single expected'' diagnosis in over 90% of cases in various validation studies (Ramnarayan and Cronje, 2005).…”

Section: Diagnostic Decision Support In Medicinementioning

confidence: 99%