Patient Concerns about Medical Errors in Emergency Departments

Burroughs, Thomas E.; Waterman, Amy D.; Gallagher, Thomas H.; Waterman, Brian; Adams, David B.; Jeffe, Donna B.; Dunagan, W. Claiborne; Garbutt, Jane; Cohen, Max M.; Cira, Jane; Inguanzo, Joe; Fraser, Victoria J.

doi:10.1197/j.aem.2004.08.052

Cited by 49 publications

(32 citation statements)

References 40 publications

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“…In particular, in the emergency department (ED), up to 15% of patients are misdiagnosed (1). Medical malpractice litigation accounts for a high incidence of malpractice payments, and pediatricians are often the targets of such litigations (2).…”

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confidence: 99%

Supporting diagnostic decisions using hybrid and complementary data mining applications: a pilot study in the pediatric emergency department

Grigull

Lechner²

2012

Pediatr Res

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IntroductIon: This article demonstrates the capacity of a combination of different data mining (DM) methods to support diagnosis in pediatric emergency patients. By using a novel combination of these DM procedures, a computer-based diagnosis was created. methods: a support vector machine (sVM), artificial neural networks (aNNs), fuzzy logics, and a voting algorithm were simultaneously used to allocate a patient to one of 18 diagnoses (e.g., pneumonia, appendicitis). anonymized data sets of patients who presented in the emergency department (eD) of a pediatric care clinic were chosen. For each patient, 26 identical clinical and laboratory parameters were used (e.g., blood count, c-reactive protein) to finally develop the program. results: The combination of four DM operations arrived at a correct diagnosis in 98% of the cases, retrospectively. a subgroup analysis showed that the highest diagnostic accuracy was for appendicitis (97% correct diagnoses) and idiopathic thrombocytopenic purpura or erythroblastopenia (100% correct diagnoses). During the prospective testing, 81% of the patients were correctly diagnosed by the system. dIscussIon: The combination of these DM methods was suitable for proposing a diagnosis using both laboratory and clinical parameters. We conclude that an optimized combination of different but complementary DM methods might serve to assist medical decisions in the eD.

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confidence: 99%

Supporting diagnostic decisions using hybrid and complementary data mining applications: a pilot study in the pediatric emergency department

Grigull

Lechner²

2012

Pediatr Res

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“…In a multicentre series of interviews of patients discharged from the ED, the majority of ED patients indicated feeling relatively safe from medical errors, yet a significant percentage of patients expressed concern about a specific error during their ED encounter. 5 Only 1 previous study prospectively evaluated adverse events in patients discharged home from the ED. Two-week telephone follow-up of approximately 400 patients revealed a 6% incidence of adverse events, of which 63% led to an additional ED visit or hospitalization.…”

Section: Introductionmentioning

confidence: 99%

Errors, near misses and adverse events in the emergency department: What can patients tell us?

Friedman

Provan

Moore

et al. 2008

CJEM

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Objective: We sought to determine whether patients or their families could identify adverse events in the emergency department (ED), to characterize patient reports of errors and to compare patient reports to events recorded by health care providers. Methods: This was a prospective cohort study in a quaternary care inner city teaching hospital with approximately 40 000 annual visits. ED patients were recruited for participation in a standardized interview within 24 hours of ED discharge and a follow-up interview 3-7 days after discharge. Responses regarding events were tabulated and compared with physician and nurse notations in the medical record and hospital event reporting system. Results: Of 292 eligible patients, 201 (69%) were interviewed within 24 hours of ED discharge, and 143 (71% of interviewees) underwent a follow-up interview 3-7 days after discharge. Interviewees did not differ from the base ED population in terms of age, sex or language. Analysis of patient interviews identified 10 adverse events (5% incident rate; 95% confidence interval [CI] 2.41%-8.96%), 8 near misses (4% incident rate; 95% CI 1.73%-7.69%) and no medical errors. Of the 10 adverse events, 6 (60%) were characterized as preventable (2 raters; κ = 0.78, standard error [SE] 0.20; 95% CI 0.39-1.00; p = 0.01). Adverse events were primarily related to delayed or inadequate analgesia. Only 4 out of 8 (50%) near misses were intercepted by hospital personnel. The secondary interview elicited 2 out of 10 adverse events and 3 out of 8 near misses that had not been identified in the primary interview. No designation (0 out of 10) of an adverse event was recorded in the ED medical record or in the confidential hospital event reporting system. Conclusion: ED patients can identify adverse events affecting their care. Moreover, many of these events are not recorded in the medical record. Engaging patients and their family members in identification of errors may enhance patient safety. RÉSUMÉObjectif : Nous avons cherché à déterminer si les patients ou leurs familles pouvaient détecter des événements indésirables à l'urgence, à caractériser les rapports d'erreurs des patients et à comparer les rapports des patients aux événements consignés par les fournisseurs de soins de santé. Méthodes : Nous avons réalisé une étude de cohorte prospective dans un hôpital universitaire de soins quaternaires du centre-ville qui compte environ 40 000 visites annuellement. Nous avons recruté des patients à l'urgence pour participer à une entrevue normalisée dans les 24 heures suivant ORIGINAL RESEARCH • RECHERCHE ORIGINALE EM ADVANCES

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“…It has been shown that emergency department patients were attuned to environmental cues they perceived as linked to possible errors. 38 The staff culture required to ensure high levels of technical safety is also a culture that values empathy and patient centredness. Patient experience can be measured 39 and is an essential component of patient safety measurement.…”

Section: Patient Experiencementioning

confidence: 99%

Patient safety — a balanced measurement framework

Wakefield¹,

Jorm²

2009

Aust. Health Review

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Evidence of the unacceptably high incidence of patient harm associated with health care has resulted in patient safety becoming a major reform agenda. Despite significant investment by governments on strategies to reduce patient harm, confusion still exists on how to measure patient safety.While the goal of patient safety is harm prevention, most of the measurement focus has been on counting incident reports. The (ab)use of reported incident data to measure both technical safety performance (injury rates) and evaluate the effectiveness of safety improvement initiatives continues to confuse and mislead consumers, funders and providers of health care. This paper proposes a simple measurement framework for patient safety which balances the elements of: learning, action, performance, patient experience, and staff attitudes and behaviour. Application of this framework to current priority areas should be used as a basis for patient safety improvement at clinical unit, hospital, state and national levels.OVER THE PAST fifteen years, patient safety has become the focus of significant national and international health reform activity. Despite this, the measurement of patient safety has remained a challenge, particularly at jurisdictional level. This paper seeks to address this issue by proposing a simple patient safety measurement framework involving five measurement domains. All have limited scope, each being best for a specific purpose, but used together can assist an organisation in measuring and improving patient safety.Multiple patient safety measures have been proposed and combined; however, very few assess patient safety performance (true rate of patient harm). It has recently been suggested that "while most hospitals measure some aspect of patient safety, there may not be comprehensive measurement in up to 44% of hospitals", 1 (p. 39) yet these authors did not define or justify a set of measures that would constitute comprehensive measurement for safety. The practical framework outlined in this paper, while pragmatic in its scope, represents a comprehensive view of patient safety measurement. What is known about the topic? It is hard to measure safety performance, that is, the true adverse event or injury rate. Incident reports are often incorrectly used for this purpose. Measures of harm alone are not sufficient to help us determine how to improve safety (ie, for harm prevention). What does this paper add? This paper introduces a comprehensive measurement framework for patient safety. Measures are suggested for the complementary elements of: safety learning, safety performance, patient experience, and staff attitudes and behaviour. Use of measures for all these elements tells us why incidents occur, whether corrective action is being undertaken, the quanta of harm, whether patients feel safe and about the organisational safety culture. What are the implications for practitioners? Application of this measurement framework produces a comprehensive assessment of patient safety. Use of the framework will ens...

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Patient Concerns about Medical Errors in Emergency Departments

Cited by 49 publications

References 40 publications

Supporting diagnostic decisions using hybrid and complementary data mining applications: a pilot study in the pediatric emergency department

Supporting diagnostic decisions using hybrid and complementary data mining applications: a pilot study in the pediatric emergency department

Errors, near misses and adverse events in the emergency department: What can patients tell us?

Patient safety — a balanced measurement framework

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Patient Concerns about Medical Errors in Emergency Departments

Cited by 49 publications

References 40 publications

Supporting diagnostic decisions using hybrid and complementary data mining applications: a pilot study in the pediatric emergency department

Supporting diagnostic decisions using hybrid and complementary data mining applications: a pilot study in the pediatric emergency department

Errors, near misses and adverse events in the emergency department: What can patients tell us?

Patient safety &mdash; a balanced measurement framework

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Product

Resources

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Patient safety — a balanced measurement framework