The aim of the study was to evaluate the sensitivity and resource efficiency of a partially automated adverse event (AE) surveillance system for routine patient safety efforts in hospitals with limited resources.Methods: Twenty-eight automated triggers from the hospital information system's clinical and administrative databases identified cases that were then filtered by exclusion criteria per trigger and then reviewed by an interdisciplinary team. The system, developed and implemented using in-house resources, was applied for 45 days of surveillance, for all hospital inpatient admissions (N = 1107). Each trigger was evaluated for its positive predictive value (PPV). Furthermore, the sensitivity of the surveillance system (overall and by AE category) was estimated relative to incidence ranges in the literature. Results:The surveillance system identified a total of 123 AEs among 283 reviewed medical records, yielding an overall PPV of 52%. The tool showed variable levels of sensitivity across and within AE categories when compared with the literature, with a relatively low overall sensitivity estimated between 21% and 44%. Adverse events were detected in 23 of the 36 AE categories defined by an established harm classification system. Furthermore, none of the detected AEs were voluntarily reported. Conclusions:The surveillance system showed variable sensitivity levels across a broad range of AE categories with an acceptable PPV, overcoming certain limitations associated with other harm detection methods. The number of cases captured was substantial, and none had been previously detected or voluntarily reported. For hospitals with limited resources, this methodology provides valuable safety information from which interventions for quality improvement can be formulated.
ObjectiveThe aim of the study is to evaluate the performance of a biomarker-based machine learning (ML) model (not including vital signs) derived from reviewed rapid response team (RRT) activations in predicting all-cause deterioration in general wards patients.DesignThis is a retrospective single-institution study. All consecutive adult patients’ cases on noncritical wards identified by RRT calls occurring at least 24 hours after patient admission, between April 2018 and June 2020, were included. The cases were reviewed and labeled for clinical deterioration by a multidisciplinary expert consensus panel. A supervised learning approach was adopted based on a set of biomarkers and demographic data available in the patient’s electronic medical record (EMR).SettingThe setting is a 250-bed tertiary university hospital with a basic EMR, with adult (>18 y) patients on general wards.PatientsThe study analyzed the cases of 514 patients for which the RRT was activated. Rapid response teams were extracted from the hospital telephone log data. Two hundred eighteen clinical deterioration cases were identified in these patients after expert chart review and complemented by 146 “nonevent” cases to build the training and validation data set.InterventionsNoneMeasurements and Main ResultsThe best performance was achieved with the random forests algorithm, with a maximal area under the receiver operating curve of 0.90 and F1 score of 0.85 obtained at prediction time T0–6h, slightly decreasing but still acceptable (area under the receiver operating curve, >0.8; F1 score, >0.75) at T0–42h. The system outperformed most classical track-and-trigger systems both in terms of prediction performance and prediction horizon.ConclusionsIn hospitals with a basic EMR, a biomarker-based ML model could be used to predict clinical deterioration in general wards patients earlier than classical track-and-trigger systems, thus enabling appropriate clinical interventions for patient safety and improved outcomes.
Objectives: This study aimed to prospectively validate an application that automates the detection of broad categories of hospital adverse events (AEs) extracted from a basic hospital information system, and to efficiently mobilize resources to reduce the level of acquired patient harm.Methods: Data were collected from an internally designed software, extracting results from 14 triggers indicative of patient harm, querying clinical and administrative databases including all inpatient admissions (n = 8760) from October 2019 to June 2020. Representative samples of the triggered cases were clinically validated using chart review by a consensus expert panel. The positive predictive value (PPV) of each trigger was evaluated, and the detection sensitivity of the surveillance system was estimated relative to incidence ranges in the literature. Results:The system identified 394 AEs among 946 triggered cases, associated with 291 patients, yielding an overall PPVof 42%. Variability was observed among the trigger PPVs and among the estimated detection sensitivities across the harm categories, the highest being for the healthcare-associated infections. The median length of stay of patients with an AE showed to be significantly higher than the median for the overall patient population. Conclusions:This application was able to identify AEs across a broad spectrum of harm categories, in a real-time manner, while reducing the use of resources required by other harm detection methods. Such a system could serve as a promising patient safety tool for AE surveillance, allowing for timely, targeted, and resource-efficient interventions, even for hospitals with limited resources.
Aim: To evaluate the contribution of medical imaging request forms as trigger tools to detect patient adverse event (AE) occurring during hospitalization. Material and Methods: This is a retrospective study in a single institution. Between January and June 2019, the hospital information system (HIS) was fetched for request forms of radiological examinations performed for inpatients >48 hours after the admission date. The investigated request forms were: Doppler ultrasound of the upper limbs, Doppler ultrasound of the lower limbs, and the repetition of three consecutive requests of chest radiographs within 24 hrs, to detect upper or lower limb venous thrombosis, or AEs related to the respiratory system, respectively. Patients’ medical charts and radiological examinations were evaluated to document the presence or absence of an AE. The frequencies of AEs in the three groups of trigger tools were compared to corresponding control groups, matched according to age, sex and length of stay. Results: Among a total of 2798 hospital admissions during the study period, there were 74 files triggered by the three types of radiological request forms. There were 6/24 AE (25%) related to upper limb venous thrombosis, 4/33 (12.1%) AE related to lower limb venous thrombosis, and 6/17 (35.3%) AE related to the respiratory system. For all the trigger tools, the frequency of AE in the study groups was significantly higher than that in the control groups. Conclusion: Medical imaging requests could be used as potential trigger tools to detect adverse events related to hospital stay.
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