Dataset Information

Development and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit.

ABSTRACT: Aim: To develop models that predict the presence of medication errors (MEs) (prescription, preparation, administration, and monitoring) using machine learning in NICU patients. Design: Prospective, observational cohort study randomized with machine learning (ML) algorithms. Setting: A 22-bed capacity NICU in Ankara, Turkey, between February 2020 and July 2021. Results: A total of 11,908 medication orders (28.9 orders/patient) for 412 NICU patients (5.53 drugs/patient/day) who received 2,280 prescriptions over 32,925 patient days were analyzed. At least one physician-related ME and nurse-related ME were found in 174 (42.2%) and 235 (57.0%) of the patients, respectively. The parameters that had the highest correlation with ME occurrence and subsequently included in the model were: total number of drugs, anti-infective drugs, nervous system drugs, 5-min APGAR score, postnatal age, alimentary tract and metabolism drugs, and respiratory system drugs as patient-related parameters, and weekly working hours of nurses, weekly working hours of physicians, and number of nurses' monthly shifts as care provider-related parameters. The obtained model showed high performance to predict ME (AUC: 0.920; 95% CI: 0.876-0.970) presence and is accessible online (http://softmed.hacettepe.edu.tr/NEO-DEER_Medication_Error/). Conclusion: This is the first developed and validated model to predict the presence of ME using work environment and pharmacotherapy parameters with high-performance ML algorithms in NICU patients. This approach and the current model hold the promise of implementation of targeted/precision screening to prevent MEs in neonates. Clinical Trial Registration: ClinicalTrials.gov, identifier NCT04899960.

SUBMITTER: Yalcın N

PROVIDER: S-EPMC10140576 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Development and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit.

Yalçın Nadir N Kaşıkcı Merve M Çelik Hasan Tolga HT Allegaert Karel K Demirkan Kutay K Yiğit Şule Ş Yurdakök Murat M

Frontiers in pharmacology 20230414

Aim: To develop models that predict the presence of medication errors (MEs) (prescription, preparation, administration, and monitoring) using machine learning in NICU patients. Design: Prospective, observational cohort study randomized with machine learning (ML) algorithms. Setting: A 22-bed capacity NICU in Ankara, Turkey, between February 2020 and July 2021. Results: A total of 11,908 medication orders (28.9 orders/patient) for 412 NICU patients (5.53 drugs/patient/ ...[more]

PMID: 37124199

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Project description:ObjectiveTo improve neonatal patient safety through automated detection of medication administration errors (MAEs) in high alert medications including narcotics, vasoactive medication, intravenous fluids, parenteral nutrition, and insulin using the electronic health record (EHR); to evaluate rates of MAEs in neonatal care; and to compare the performance of computerized algorithms to traditional incident reporting for error detection.MethodsWe developed novel computerized algorithms to identify MAEs within the EHR of all neonatal patients treated in a level four neonatal intensive care unit (NICU) in 2011 and 2012. We evaluated the rates and types of MAEs identified by the automated algorithms and compared their performance to incident reporting. Performance was evaluated by physician chart review.ResultsIn the combined 2011 and 2012 NICU data sets, the automated algorithms identified MAEs at the following rates: fentanyl, 0.4% (4 errors/1005 fentanyl administration records); morphine, 0.3% (11/4009); dobutamine, 0 (0/10); and milrinone, 0.3% (5/1925). We found higher MAE rates for other vasoactive medications including: dopamine, 11.6% (5/43); epinephrine, 10.0% (289/2890); and vasopressin, 12.8% (54/421). Fluid administration error rates were similar: intravenous fluids, 3.2% (273/8567); parenteral nutrition, 3.2% (649/20124); and lipid administration, 1.3% (203/15227). We also found 13 insulin administration errors with a resulting rate of 2.9% (13/456). MAE rates were higher for medications that were adjusted frequently and fluids administered concurrently. The algorithms identified many previously unidentified errors, demonstrating significantly better sensitivity (82% vs. 5%) and precision (70% vs. 50%) than incident reporting for error recognition.ConclusionsAutomated detection of medication administration errors through the EHR is feasible and performs better than currently used incident reporting systems. Automated algorithms may be useful for real-time error identification and mitigation.

Project description:ImportanceIncoming text messages and calls on nurses' mobile telephones may interrupt medication administration, but whether such interruptions are associated with errors has not been established.ObjectiveTo assess whether a temporal association exists between mobile telephone interruptions and subsequent errors by pediatric intensive care unit (PICU) nurses during medication administration.Design, setting, and participantsA retrospective cohort study was performed using telecommunications and electronic health record data from a PICU in a children's hospital. Data were collected from August 1, 2016, through September 30, 2017. Participants included 257 nurses and the 3308 patients to whom they administered medications.ExposuresPrimary exposures were incoming telephone calls and text messages received on the institutional mobile telephone assigned to the nurse in the 10 minutes leading up to a medication administration attempt. Secondary exposures were the nurse's PICU experience, work shift (day vs night), nurse to patient ratio, and level of patient care required.Main outcomes and measuresPrimary outcome, errors during medication administration, was a composite of reported medication administration errors and bar code medication administration error alerts generated when nurses attempted to give medications without active orders for the patient whose bar code they scanned.ResultsParticipants included 257 nurses, of whom 168 (65.4%) had 6 months or more of PICU experience; and 3308 patients, of whom 1839 (55.6%) were male, 1539 (46.5%) were white, and 2880 (87.1%) were non-Hispanic. The overall rate of errors during 238 540 medication administration attempts was 3.1% (95% CI, 3.0%-3.3%) when nurses were uninterrupted by incoming telephone calls and 3.7% (95% CI, 3.4%-4.0%) when they were interrupted by such calls. During day shift, the odds ratios (ORs) for error when interrupted by calls (compared with uninterrupted) were 1.02 (95% CI, 0.92-1.13; P = .73) among nurses with 6 months or more of PICU experience and 1.22 (95% CI, 1.00-1.47; P = .046) among nurses with less than 6 months of experience. During night shift, the ORs for error when interrupted by calls were 1.35 (95% CI, 1.16-1.57; P < .001) among nurses with 6 months or more of PICU experience and 1.53 (95% CI, 1.16-2.03; P = .003) among nurses with less than 6 months of experience. Nurses administering medications to 1 or more patients receiving mechanical ventilation and arterial catheterization while caring for at least 1 other patient had an increased risk of error (OR, 1.21; 95% CI, 1.03-1.42; P = .02). Incoming text messages were not associated with error (OR, 0.97; 95% CI, 0.92-1.02; P = .22).Conclusions and relevanceThis study's findings suggest that incoming telephone call interruptions may be temporally associated with medication administration errors among PICU nurses. Risk of error varied by shift, experience, nurse to patient ratio, and level of patient care required.

Project description:Despite efforts in improving medication safety, medication administration errors are still common, resulting in significant clinical and economic impact. Studies conducted using a valid and reliable tool to assess clinical impact are lacking, and to the best of our knowledge, studies evaluating the economic impact of medication administration errors among neonates are not yet available. Therefore, this study aimed to determine the potential clinical and economic impact of medication administration errors in neonatal intensive care units and identify the factors associated with these errors. A national level, multi centre, prospective direct observational study was conducted in the neonatal intensive care units of five Malaysian public hospitals. The nurses preparing and administering the medications were directly observed. After the data were collected, two clinical pharmacists conducted independent assessments to identify errors. An expert panel of healthcare professionals assessed each medication administration error for its potential clinical and economic outcome. A validated visual analogue scale was used to ascertain the potential clinical outcome. The mean severity index for each error was subsequently calculated. The potential economic impact of each error was determined by averaging each expert's input. Multinomial logistic regression and multiple linear regression were used to identify factors associated with the severity and cost of the errors, respectively. A total of 1,018 out of 1,288 (79.0%) errors were found to be potentially moderate in severity, while only 30 (2.3%) were found to be potentially severe. The potential economic impact was estimated at USD 27,452.10. Factors significantly associated with severe medication administration errors were the medications administered intravenously, the presence of high-alert medications, unavailability of a protocol, and younger neonates. Moreover, factors significantly associated with moderately severe errors were intravenous medication administration, younger neonates, and an increased number of medications administered. In the multiple linear regression analysis, the independent variables found to be significantly associated with cost were the intravenous route of administration and the use of high-alert medications. In conclusion, medication administration errors were judged to be mainly moderate in severity costing USD 14.04 (2.22-22.53) per error. This study revealed important insights and highlights the need to implement effective error reducing strategies to improve patient safety among neonates in the neonatal intensive care unit.

Project description:BackgroundNeonatal intensive care units are high-risk settings where medication errors can occur and cause harm to this fragile segment of patients. This multicenter qualitative study was conducted to describe medication errors that occurred in neonatal intensive care units in Palestine from the perspectives of healthcare providers.MethodsThis exploratory multicenter qualitative study was conducted and reported in adherence to the consolidated criteria for reporting qualitative research checklist. Semi-structured in-depth interviews were conducted with healthcare professionals (4 pediatricians/neonatologists and 11 intensive care unit nurses) who provided care services for patients admitted to neonatal intensive care units in Palestine. An interview schedule guided the semi-structured in-depth interviews. The qualitative interpretive description approach was used to thematically analyze the data.ResultsThe total duration of the interviews was 282 min. The healthcare providers described their experiences with 41 different medication errors. These medication errors were categorized under 3 categories and 10 subcategories. Errors that occurred while preparing/diluting/storing medications were related to calculations, using a wrong solvent/diluent, dilution errors, failure to adhere to guidelines while preparing the medication, failure to adhere to storage/packaging guidelines, and failure to adhere to labeling guidelines. Errors that occurred while prescribing/administering medications were related to inappropriate medication for the neonate, using a different administration technique from the one that was intended, and administering a different dose from the one that was intended. Errors that occurred after administering the medications were related to failure to adhere to monitoring guidelines.ConclusionIn this multicenter study, pediatricians/neonatologists and neonatal intensive care unit nurses described medication errors occurring in intensive care units in Palestine. Medication errors occurred in different stages of the medication process: preparation/dilution/storage, prescription/administration, and monitoring. Further studies are still needed to quantify medication errors occurring in the neonatal intensive care units and investigate if the designed strategies could be effective in minimizing the medication errors.

Dataset Information

Development and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit.

Publications

Development and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit.

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