emergency medicine: a multi-centre study Validation of a diagnostic reminder system in (original) (raw)
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Improving decision making in the emergency department with simple decision aids
PsycEXTRA Dataset, 2014
Objective. To investigate diagnostic accuracy in patient histories involving nonspecific complaints and the extent to which characteristics of physicians and structural properties of patient histories are associated with accuracy. Methods. Six histories of patients presenting to the emergency department (ED) with nonspecific complaints were provided to 112 physicians: 36 ED physicians, 50 internists, and 26 family practitioners. Physicians listed the 3 most likely diagnoses for each history and indicated which cue(s) they considered crucial. Four weeks later, a subset of 20 physicians diagnosed the same 6 histories again. For each history, experts had previously determined the correct diagnoses and the diagnostic cues. Results. Accuracy ranged from 14% to 64% correct diagnoses (correct diagnosis listed as the most likely) and from 29% to 87% correct differential diagnoses (correct diagnosis listed in the differential). Acute care physicians (ED physicians and internists) included the correct diagnosis in the differential in, on average, 3.4 histories, relative to 2.6 for the family practitioners (P = 0.001, d = .75). Diagnostic performance was fairly reliable (r = .61, P \ 0.001). Clinical experience was negatively correlated with diagnostic accuracy (r = -.25, P = 0.008). Two structural properties of patient histories-cue consensus and cue substitutability-were significantly associated with diagnostic accuracy, whereas case difficulty was not. Finally, prevalence of diagnosis also proved significantly correlated with accuracy. Conclusions. Average diagnostic accuracy in cases with nonspecific complaints far exceeds chance performance, and accuracy varies with medical specialty. Analyzing cue properties in patient histories can help shed light on determinants of diagnostic performance and thus suggest ways to enhance physicians' ability to accurately diagnose cases with nonspecific complaints. P atients presenting to the emergency department (ED) with nonspecific complaints, such as weakness, fatigue, or dizziness, pose a challenge to emergency physicians' diagnostic decision-making process. For instance, researchers involved in the Basel Non-Specific Complaints (BANC) Study 1 observed in unpublished data that in the ED, the misdiagnosis rate in cases involving nonspecific complaints is about 53%, relative to an overall rate of less than 10%. This high rate of errors matters because nonspecific complaints can be associated with life-threatening conditions that require prompt intervention to prevent further deterioration of the patient's health status. 1 Moreover, according to a large study, up to 20% of elderly patients presenting to the ED report nonspecific complaints. A key component in the process of diagnosing patients with nonspecific complaints is the patient history. 3 The information encapsulated therein guides the diagnostician's initial decision-making process. To investigate the properties of patient histories that affect diagnosticians' judgment, we presented original patient histories, as recorded by the admitting emergency physician, 4 to physicians with various medical specialties. We aimed to investigate 3 questions: First, is diagnosis of nonspecific complaints presenting at the ED better than chance? Second, does diagnostic accuracy relate to physicians'
2006
Background: Computerized decision support systems (DSS) have mainly focused on improving clinicians' diagnostic accuracy in unusual and challenging cases. However, since diagnostic omission errors may predominantly result from incomplete workup in routine clinical practice, the provision of appropriate patient-and context-specific reminders may result in greater impact on patient safety. In this experimental study, a mix of easy and difficult simulated cases were used to assess the impact of a novel diagnostic reminder system (ISABEL) on the quality of clinical decisions made by various grades of clinicians during acute assessment.
BMC Medical Informatics and Decision Making, 2006
Background: Diagnostic error is a significant problem in specialities characterised by diagnostic uncertainty such as primary care, emergency medicine and paediatrics. Despite wide-spread availability, computerised aids have not been shown to significantly improve diagnostic decision-making in a real world environment, mainly due to the need for prolonged system consultation. In this study performed in the clinical environment, we used a Web-based diagnostic reminder system that provided rapid advice with free text data entry to examine its impact on clinicians' decisions in an acute paediatric setting during assessments characterised by diagnostic uncertainty.
International Journal of Interactive Multimedia and Artificial Intelligence
One of the biggest challenges for the management of the emergency department (ED) is to expedite the management of patients since their arrival for those with low priority pathologies selected by the classification systems, generating unnecessary saturation of the ED. Diagnostic decision support systems (DDSS) can be a powerful tool to guide diagnosis, facilitate correct classification and improve patient safety. Patients who attended the ED of a tertiary hospital with the preconditions of Manchester Triage system level of low priority (levels 3, 4 and 5), and with one of the five most frequent causes for consultation: dyspnea, chest pain, gastrointestinal bleeding, general discomfort and abdominal pain, were interviewed by an independent researcher with a DDSS, the Mediktor system. After the interview, we compare the Manchester triage and the final diagnoses made by the ED with the triage and diagnostic possibilities ordered by probability obtained by the Mediktor system, respectively. In a final sample of 214 patients, the urgency assignment made by both systems does not match exactly, which could indicate a different classification model, but there were no statistically significant differences between the assigned levels (S = 0.059, p = 0.442). The diagnostic accuracy between the final diagnosis and any of the first 10 Mediktor diagnoses was of 76.5%, for the first five diagnoses was 65.4%, for the first three diagnoses was 58%, and the exact match with the first diagnosis was 37.9%. The classification of Mediktor in this segment of patients shows that a higher level of severity corresponds to a greater number of hospital admissions, hospital readmissions and emergency screenings at 30 days, although without statistical significance. It is expected that this type of applications may be useful as a complement to the triage, to accelerate the diagnostic approach, to improve the request for appropriate complementary tests in a protocolized action model and to reduce waiting times in the ED.
Performance of a Web-Based Clinical Diagnosis Support System for Internists
Journal of General Internal Medicine, 2007
BACKGROUND: Clinical decision support systems can improve medical diagnosis and reduce diagnostic errors. Older systems, however, were cumbersome to use and had limited success in identifying the correct diagnosis in complicated cases. OBJECTIVE: To measure the sensitivity and speed of "Isabel" (Isabel Healthcare Inc., USA), a new web-based clinical decision support system designed to suggest the correct diagnosis in complex medical cases involving adults. METHODS: We tested 50 consecutive Internal Medicine case records published in the New England Journal of Medicine. We first either manually entered 3 to 6 key clinical findings from the case (recommended approach) or pasted in the entire case history. The investigator entering key words was aware of the correct diagnosis. We then determined how often the correct diagnosis was suggested in the list of 30 differential diagnoses generated by the clinical decision support system. We also evaluated the speed of data entry and results recovery. RESULTS: The clinical decision support system suggested the correct diagnosis in 48 of 50 cases (96%) with key findings entry, and in 37 of the 50 cases (74%) if the entire case history was pasted in. Pasting took seconds, manual entry less than a minute, and results were provided within 2-3 seconds with either approach. CONCLUSIONS: The Isabel clinical decision support system quickly suggested the correct diagnosis in almost all of these complex cases, particularly with key finding entry. The system performed well in this experimental setting and merits evaluation in more natural settings and clinical practice.
BMJ Open
IntroductionComputerised diagnostic decision support systems (CDDS) suggesting differential diagnoses to physicians aim to improve clinical reasoning and diagnostic quality. However, controlled clinical trials investigating their effectiveness and safety are absent and the consequences of its use in clinical practice are unknown. We aim to investigate the effect of CDDS use in the emergency department (ED) on diagnostic quality, workflow, resource consumption and patient outcomes.Methods and analysisThis is a multicentre, outcome assessor and patient-blinded, cluster-randomised, multiperiod crossover superiority trial. A validated differential diagnosis generator will be implemented in four EDs and randomly allocated to a sequence of six alternating intervention and control periods. During intervention periods, the treating ED physician will be asked to consult the CDDS at least once during diagnostic workup. During control periods, physicians will not have access to the CDDS and di...
Using voluntary reports from physicians to learn from diagnostic errors in emergency medicine
Emergency Medicine Journal, 2015
Objectives Diagnostic errors are common in the emergency department (ED), but few studies have comprehensively evaluated their types and origins. We analysed incidents reported by ED physicians to determine disease conditions, contributory factors and patient harm associated with ED-related diagnostic errors. Methods Between 1 March 2009 and 31 December 2013, ED physicians reported 509 incidents using a department-specific voluntary incident-reporting system that we implemented at two large academic hospitalaffiliated EDs. For this study, we analysed 209 incidents related to diagnosis. A quality assurance team led by an ED physician champion reviewed each incident and interviewed physicians when necessary to confirm the presence/absence of diagnostic error and to determine the contributory factors. We generated descriptive statistics quantifying disease conditions involved, contributory factors and patient harm from errors. Results Among the 209 incidents, we identified 214 diagnostic errors associated with 65 unique diseases/ conditions, including sepsis (9.6%), acute coronary syndrome (9.1%), fractures (8.6%) and vascular injuries (8.6%). Contributory factors included cognitive (n=317), system related (n=192) and non-remedial (n=106). Cognitive factors included faulty information verification (41.3%) and faulty information processing (30.6%) whereas system factors included high workload (34.4%) and inefficient ED processes (40.1%). Non-remediable factors included atypical presentation (31.3%) and the patients' inability to provide a history (31.3%). Most errors (75%) involved multiple factors. Major harm was associated with 34/209 (16.3%) of reported incidents. Conclusions Most diagnostic errors in ED appeared to relate to common disease conditions. While sustaining diagnostic error reporting programmes might be challenging, our analysis reveals the potential value of such systems in identifying targets for improving patient safety in the ED.
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium, 2003
Clinical decision support systems (CDSS) can impact both diagnostic and therapeutic decision-making, but physicians sometimes fail to heed the appropriate CDSS advice, or become influenced in a negative way by the CDSS. This study examined the relationships among clinicians' prior diagnostic accuracy, the performance of a diagnostic CDSS, and how the CDSS influenced the accuracy of the clinician's subsequent diagnoses. Results showed that (1) clinicians who already were considering the correct diagnosis prior to using the CDSS were more likely to get the CDSS to produce the correct diagnosis in a prominent position than those not considering it initially; (2) physicians are strongly anchored by their initial diagnoses prior to using the CDSS; and (3) changes in the clinicians' diagnoses after using the CDSS are related to presence or absence of the correct diagnosis in the top 10 diagnoses displayed by the CDSS.
Effectiveness of the Quick Medical Reference as a diagnostic tool
CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne, 1999
A number of computer-based systems with diagnostic capabilities have been developed for internal medicine. Quick Medical Reference (QMR) is one such program. The authors describe key features of QMR and report on their study of its effectiveness as a diagnostic tool. They investigated how frequently the correct diagnosis would appear among the 5 highest ranked diagnoses generated by QMR. The charts of 1144 consecutive patients admitted to a teaching unit were retrospectively screened. Eligible cases included those referred for investigation of an undiagnosed illness with an objectively proven final diagnosis (n = 154). Two physicians familiar with, but not experts in, the use of QMR entered clinical information abstracted from the patients' charts into the program. Physician A obtained the correct diagnosis in 62 (40%) of the 154 cases, and physician B was successful in 56 (36%) of the cases. The authors use study cases to illustrate QMR's strengths and weaknesses.