Investigating Adverse Event Free Admissions in Medicare Inpatients as a Patient Safety Indicator

King, A. J.; Bottle, Alex; Faiz, Omar; Aylin, Paul

doi:10.1097/sla.0000000000001792

Cited by 11 publications

(9 citation statements)

References 23 publications

(24 reference statements)

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“…The World Health Organization reports that thousands of people are affected by complications and adverse events (AEs) associated with caregiving, and that these events increase morbidity and mortality rates worldwide [ 1 – 3 ]. Complications in hospital are unfortunate for patients and expensive for healthcare systems [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Care complexity individual factors associated with adverse events and in-hospital mortality

et al. 2020

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Introduction Measuring the impact of care complexity on health outcomes, based on psychosocial, biological and environmental circumstances, is important in order to detect predictors of early deterioration of inpatients. We aimed to identify care complexity individual factors associated with selected adverse events and in-hospital mortality. Methods A multicenter, case-control study was carried out at eight public hospitals in Catalonia, Spain, from January 1, 2016 to December 31, 2017. All adult patients admitted to a ward or a step-down unit were evaluated. Patients were divided into the following groups based on the presence or absence of three adverse events (pressure ulcers, falls or aspiration pneumonia) and in-hospital mortality. The 28 care complexity individual factors were classified in five domains (developmental, mental-cognitive, psycho-emotional, sociocultural and comorbidity/complications). Adverse events and complexity factors were retrospectively reviewed by consulting patients' electronic health records. Multivariate logistic analysis was performed to identify factors associated with an adverse event and in-hospital mortality. Results A total of 183,677 adult admissions were studied. Of these, 3,973 (2.2%) patients experienced an adverse event during hospitalization (1,673 [0.9%] pressure ulcers; 1,217 [0.7%] falls and 1,236 [0.7%] aspiration pneumonia). In-hospital mortality was recorded in 3,996 patients (2.2%). After adjustment for potential confounders, the risk factors independently associated with both adverse events and in-hospital mortality were: mental status impairments, impaired adaptation, lack of caregiver support, old age, major chronic disease, hemodynamic instability, communication disorders, urinary or fecal incontinence, vascular

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Section: Introductionmentioning

confidence: 99%

Care complexity individual factors associated with adverse events and in-hospital mortality

et al. 2020

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“…Similarities between adverse event rates in our study compared with previous studies suggest that human error remains a significant unresolved cause of adverse events in health care delivery. 4,5,6,7,8,9,13,14,15,16,17,18 Specifically, compared with our currently reported adverse event rate of 3%, prior studies conducted as early as 2000 7,15,17,21,22,23,24,25,26,27,28,29,30 reporting a preventable or human error event rate ranging from approximately 3% to 4% support the need for interventions beyond current systems-based strategies if we are to achieve Six Sigma safety levels.…”

Section: Discussionmentioning

confidence: 99%

“…Nearly 3 decades after the seminal work of Reason, 10,11 Leape, 9 and others studying the effects of human error on health care delivery, to our knowledge, relatively little work has been undertaken to systematically analyze what we have termed human performance deficiencies (HPDs) and cognitive decision-making as a source of adverse events in medical and surgical care. [12][13][14][15][16][17][18] Furthermore, most human error studies predate the current era of systems-based health care risk-reduction strategies. [12][13][14][15][16][17][18] Analyses of the cognitive behavior of health care delivery teams and individual members of these teams in the modern era of systems-based safety nets for health care delivery may identify additional opportunities to enhance the safety of health care.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Human Performance Deficiencies Associated With Surgical Adverse Events

et al. 2019

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“…However, more than 50% of AEs in the ICU are preventable through timely medical interventions [2]. Therefore, it is important to predict the onset of AEs in ICU patients as early as possible [5].…”

Section: Adverse Events Predictionmentioning

confidence: 99%

Temporal Pattern Detection to Predict Adverse Events in Critical Care: Case Study With Acute Kidney Injury

Morid¹,

Sheng²,

Fiol³

et al. 2020

JMIR Med Inform

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Background More than 20% of patients admitted to the intensive care unit (ICU) develop an adverse event (AE). No previous study has leveraged patients’ data to extract the temporal features using their structural temporal patterns, that is, trends. Objective This study aimed to improve AE prediction methods by using structural temporal pattern detection that captures global and local temporal trends and to demonstrate these improvements in the detection of acute kidney injury (AKI). Methods Using the Medical Information Mart for Intensive Care dataset, containing 22,542 patients, we extracted both global and local trends using structural pattern detection methods to predict AKI (ie, binary prediction). Classifiers were built on 17 input features consisting of vital signs and laboratory test results using state-of-the-art models; the optimal classifier was selected for comparisons with previous approaches. The classifier with structural pattern detection features was compared with two baseline classifiers that used different temporal feature extraction approaches commonly used in the literature: (1) symbolic temporal pattern detection, which is the most common approach for multivariate time series classification; and (2) the last recorded value before the prediction point, which is the most common approach to extract temporal data in the AKI prediction literature. Moreover, we assessed the individual contribution of global and local trends. Classifier performance was measured in terms of accuracy (primary outcome), area under the curve, and F-measure. For all experiments, we employed 20-fold cross-validation. Results Random forest was the best classifier using structural temporal pattern detection. The accuracy of the classifier with local and global trend features was significantly higher than that while using symbolic temporal pattern detection and the last recorded value (81.3% vs 70.6% vs 58.1%; P<.001). Excluding local or global features reduced the accuracy to 74.4% or 78.1%, respectively (P<.001). Conclusions Classifiers using features obtained from structural temporal pattern detection significantly improved the prediction of AKI onset in ICU patients over two baselines based on common previous approaches. The proposed method is a generalizable approach to predict AEs in critical care that may be used to help clinicians intervene in a timely manner to prevent or mitigate AEs.

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Investigating Adverse Event Free Admissions in Medicare Inpatients as a Patient Safety Indicator

Abstract: Overall, only 60% of admissions were recorded as adverse event free. Multiple adverse events were common. Even if events are under recorded, this measure could provide an easily understandable and useful baseline for clinicians and managers.

Cited by 11 publications

References 23 publications

Care complexity individual factors associated with adverse events and in-hospital mortality

Care complexity individual factors associated with adverse events and in-hospital mortality

Analysis of Human Performance Deficiencies Associated With Surgical Adverse Events

Temporal Pattern Detection to Predict Adverse Events in Critical Care: Case Study With Acute Kidney Injury

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