Incidence of adverse drug events and potential adverse drug events. Implications for prevention. ADE Prevention Study Group

Bates, David W.

doi:10.1001/jama.274.1.29

Cited by 1,247 publications

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“…For AEs, the two independent physician-adjudicators then determined Bpreventability^and Bameliorability.^Preventable means the injury would not have occurred at all had medical management been better, while ameliorable means the injury could have been lessened in severity or duration had the error not occurred. AEs were also categorized by effects on functional status, by severity (significant, serious, life-threatening, or fatal), 17 duration, organ systems involved, and additional health care utilization (of note, an AE may be detected during a planned post-discharge physician visit and not the cause of any additional utilization). Lastly, we utilized a standard approach to classify the type of AE.…”

Section: Review Of Health Recordsmentioning

confidence: 99%

Post-Discharge Adverse Events Among Urban and Rural Patients of an Urban Community Hospital: A Prospective Cohort Study

et al. 2015

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BACKGROUND: There has been little research to examine post-discharge adverse events (AEs) in rural patients discharged from community hospitals. OBJECTIVE: We aimed to determine the rate of postdischarge AEs, classify the types of post-discharge AEs, and identify risk factors for post-discharge AEs in urban and rural patients. DESIGN: This was a prospective cohort study of patients at risk for post-discharge adverse events from December 2011 through October 2012. PATIENTS: Six hundred and eighty-four patients who were under the care of hospitalist physicians and were being discharged home, spoke English, and could be contacted after discharge, were admitted to the medical service. Patients were stratified as urban/rural using zip code of residence. Rural patients were oversampled to ensure equal enrollment of urban and rural patients. MAIN MEASURES:The main outcome of the study was post-discharge AEs based on structured telephone interviews, health record review, and adjudication by two blinded, trained physicians using a previously established methodology. RESULTS: Over 28 % of 684 patients experienced postdischarge AEs, most of which were either preventable or ameliorable. There was no difference in the incidence of post-discharge AEs in urban versus rural patients (ARR 1.04 95 % CI 0.82 -1.32 ), but post-discharge AEs were associated with hypertension, type 2 diabetes mellitus, and number of secondary discharge diagnoses only in urban patients. CONCLUSIONS: Post-discharge AEs were common in both urban and rural patients and many were preventable or ameliorable. Potentially different risk factors for AEs in urban versus rural patients suggests the need for further research into the underlying causes. Different interventions may be required in urban versus rural patients to improve patient safety during transitions in care.KEY WORDS: medical errors; adverse events; quality of care; transitional care.

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Section: Review Of Health Recordsmentioning

confidence: 99%

Post-Discharge Adverse Events Among Urban and Rural Patients of an Urban Community Hospital: A Prospective Cohort Study

et al. 2015

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“…Anaesthesiologists prepare and administer more drugs than doctors from any other specialty and work in environments where medication errors are most common and most likely to result in harm to patients [25,26]. We have an important role to play in arguing for improvements in drug labelling and must continue to research novel ways of reducing medication error and improving patient safety.…”

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confidence: 99%

Dose calculation and medication error – why are we still weakened by strengths?

Wheeler

2004

Eur J Anaesthesiol

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“…[1][2][3] Although only a variable fraction of ADR is related to interactions, most clinically relevant interactions are known and should therefore be avoidable given sufficient awareness at the time of drug prescription. [4,5] Consequently, a number of clinical decision support software (CDSS) systems that are able to identify concomitantly prescribed interacting drugs and alert the prescriber were developed in recent years.…”

Section: Introductionmentioning

confidence: 99%

“…Comparative performance of two drug interaction screening programmes analysing a cross-sectional prescription dataset of 84,625 psychiatric inpatients. Drug Safety, 36 (4) alerts in 48,302 patients. Only 2,154 unique interactions were identified by both programs, but overlap increased with higher risk rating.…”

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Comparative Performance of Two Drug Interaction Screening Programmes Analysing a Cross-Sectional Prescription Dataset of 84,625 Psychiatric Inpatients

et al. 2013

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BACKGROUND: Clinical decision support software (CDSS) solutions can automatically identify drug interactions and thereby aim to improve drug safety. However, data on the comparative performance of different CDSS to detect and appropriately classify interactions in real-life prescription datasets is limited. OBJECTIVE: The aim of this study was to compare the results from two different CDSS analysing the pharmacotherapy of a large population of psychiatric inpatients for drug interactions. METHODS: We performed mass analyses of cross-sectional patient-level prescriptions from 84,625 psychiatric inpatients using two CDSS -MediQ and ID PHARMA CHECK(®). Interactions with the highest risk ratings and the most frequent ratings were reclassified according to the Zurich Interaction System (ZHIAS), a multidimensional classification that incorporates the OpeRational ClassificAtion of Drug Interactions (ORCA) and served as a reference standard. RESULTS: MediQ reported 6,133 unique interacting combinations responsible for 270,617 alerts affecting 63,454 patients. ID PHARMA CHECK(®) issued 5,400 interactions and 157,489 alerts in 48,302 patients. Only 2,154 unique interactions were identified by both programmes, but overlap increased with higher risk rating. MediQ reported high-risk interactions in 2.5 % of all patients, compared with 5 % according to ID PHARMA CHECK(®). The positive predictive value for unique major alerts to be (provisionally) contraindicated according to ORCA was higher for MediQ (0.63) than for either of the two ID PHARMA CHECK(®) components (0.42 for hospINDEX and 0.30 for ID MACS). MediQ reported more interactions, and ID PHARMA CHECK(®) tended to classify interactions into a higher risk class, but overall both programmes identified a similar number of (provisionally) contraindicated interactions according to ORCA criteria. Both programmes identified arrhythmia as the most frequent specific risk associated with interactions in psychiatric patients. CONCLUSIONS: CDSS can be used for mass-analysis of prescription data and thereby support quality management. However, in clinical practice CDSS impose an overwhelming alert burden on the prescriber, and prediction of clinical relevance remains a major challenge. Only a small subset of yet to be determined alerts appears suitable for automated display in clinical routine. DOI: https://doi.org/10.1007/s40264-013-0027-9Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-76756 Accepted Version Originally published at: Zorina, Olesya I; Haueis, Patrick; Greil, Waldemar; Grohmann, Renate; Kullak-Ublick, Gerd A; Russmann, Stefan (2013). Comparative performance of two drug interaction screening programmes analysing a cross-sectional prescription dataset of 84,625 psychiatric inpatients. Drug Safety, 36(4):247-258.

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Incidence of adverse drug events and potential adverse drug events. Implications for prevention. ADE Prevention Study Group

Cited by 1,247 publications

References 0 publications

Post-Discharge Adverse Events Among Urban and Rural Patients of an Urban Community Hospital: A Prospective Cohort Study

Post-Discharge Adverse Events Among Urban and Rural Patients of an Urban Community Hospital: A Prospective Cohort Study

Dose calculation and medication error – why are we still weakened by strengths?

Comparative Performance of Two Drug Interaction Screening Programmes Analysing a Cross-Sectional Prescription Dataset of 84,625 Psychiatric Inpatients

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