Electronically Screening Discharge Summaries for Adverse Medical Events

Murff, Harvey J.; Forster, Alan J.; Peterson, Josh F.; Fiskio, Julie M.; Heiman, Heather L.; Bates, David W.

doi:10.1197/jamia.m1201

Cited by 88 publications

(49 citation statements)

References 38 publications

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“…Only two studies used automated methods: one compared automated with traditional manual triggers [23] and another electronically screened discharge summary text [41]. These did not report proportion of errors that were diagnostic in nature.…”

Section: Resultsmentioning

confidence: 99%

Clinical criteria to screen for inpatient diagnostic errors: a scoping review

Shenvi

El‐Kareh

2014

Diagnosis

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Diagnostic errors are common and costly, but difficult to detect. “Trigger” tools have promise to facilitate detection, but have not been applied specifically for inpatient diagnostic error. We performed a scoping review to collate all individual “trigger” criteria that have been developed or validated that may indicate that an inpatient diagnostic error has occurred. We searched three databases and screened 8568 titles and abstracts to ultimately include 33 articles. We also developed a conceptual framework of diagnostic error outcomes using real clinical scenarios, and used it to categorize the extracted criteria. Of the multiple criteria we found related to inpatient diagnostic error and amenable to automated detection, the most common were death, transfer to a higher level of care, arrest or “code”, and prolonged length of hospital stay. Several others, such as abrupt stoppage of multiple medications or change in procedure, may also be useful. Validation for general adverse event detection was done in 15 studies, but only one performed validation for diagnostic error specifically. Automated detection was used in only two studies. These criteria may be useful for developing diagnostic error detection tools.

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Section: Resultsmentioning

confidence: 99%

Clinical criteria to screen for inpatient diagnostic errors: a scoping review

Shenvi

El‐Kareh

2014

Diagnosis

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“…More recently, researchers have started to use alternative and potentially more accurate approaches to AE detection, such as natural language processing (NLP) of dictated electronic radiology reports or discharge summaries [22,23]. NLP refers to automated methods for converting free-text data into computer-understandable format [24].…”

Section: Literature Review Pilot Work: Novel Methods Of Ae Detectionmentioning

confidence: 99%

Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol

Rochefort

Buckeridge

Forster

2015

Implementation Science

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BackgroundAdverse events are associated with significant morbidity, mortality and cost in hospitalized patients. Measuring adverse events is necessary for quality improvement, but current detection methods are inaccurate, untimely and expensive. The advent of electronic health records and the development of automated methods for encoding and classifying electronic narrative data, such as natural language processing, offer an opportunity to identify potentially better methods. The objective of this study is to determine the accuracy of using automated methods for detecting three highly prevalent adverse events: a) hospital-acquired pneumonia, b) catheter-associated bloodstream infections, and c) in-hospital falls.Methods/designThis validation study will be conducted at two large Canadian academic health centres: the McGill University Health Centre (MUHC) and The Ottawa Hospital (TOH). The study population consists of all medical, surgical and intensive care unit patients admitted to these centres between 2008 and 2014. An automated detection algorithm will be developed and validated for each of the three adverse events using electronic data extracted from multiple clinical databases. A random sample of MUHC patients will be used to develop the automated detection algorithms (cohort 1, development set). The accuracy of these algorithms will be assessed using chart review as the reference standard. Then, receiver operating characteristic curves will be used to identify optimal cut points for each of the data sources. Multivariate logistic regression and the areas under curve (AUC) will be used to identify the optimal combination of data sources that maximize the accuracy of adverse event detection. The most accurate algorithms will then be validated on a second random sample of MUHC patients (cohort 1, validation set), and accuracy will be measured using chart review as the reference standard. The most accurate algorithms validated at the MUHC will then be applied to TOH data (cohort 2), and their accuracy will be assessed using a reference standard assessment of the medical chart.DiscussionThere is a need for more accurate, timely and efficient measures of adverse events in acute care hospitals. This is a critical requirement for evaluating the effectiveness of preventive interventions and for tracking progress in patient safety through time.

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“…One interesting study by Murff et al was carried out in 2003 [28]. The authors used keyword queries to detect adverse events in electronic screening discharge summaries.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Patient Safety through Human-Computer Information Retrieval on the Example of German-Speaking Surgical Reports

Stocker¹,

Marzi²,

Matula

et al. 2014

2014 25th International Workshop on Database and Expert Systems Applications

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In view of the high number of deaths and complication rates of major surgical procedures worldwide, surgical safety is described as a substantial global public-health concern. Naturally, patient safety has become an international priority. The increasing amount of electronically available clinical documents holds great potential for the computational analysis of large repositories. However, most of this data is in textual form and the clinical domain is a challenging field for the appliance of natural language processing. This is particularly the case if you deal with a language other than English, due to the little attention from the international research community. In this project, we are concerned with the utilization of a Germanspeaking operative report repository for the purpose of risk management and patient safety research. In this particular paper we focus on the description of our information retrieval approach. We investigated the thought process of a domain expert in order to derive his information of interest and describe a facet-based way to navigate this kind of information in the form of extracted phrases. Initial results and feedback has been very promising, but a formal evaluation is still missing.

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Electronically Screening Discharge Summaries for Adverse Medical Events

Cited by 88 publications

References 38 publications

Clinical criteria to screen for inpatient diagnostic errors: a scoping review

Clinical criteria to screen for inpatient diagnostic errors: a scoping review

Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol

Enhancing Patient Safety through Human-Computer Information Retrieval on the Example of German-Speaking Surgical Reports

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