Dataset Information

Use of simulation to assess electronic health record safety in the intensive care unit: a pilot study.

ABSTRACT:

Objective

To establish the role of high-fidelity simulation training to test the efficacy and safety of the electronic health record (EHR)-user interface within the intensive care unit (ICU) environment.

Design

Prospective pilot study.

Setting

Medical ICU in an academic medical centre.

Participants

Postgraduate medical trainees.

Interventions

A 5-day-simulated ICU patient was developed in the EHR including labs, hourly vitals, medication administration, ventilator settings, nursing and notes. Fourteen medical issues requiring recognition and subsequent changes in management were included. Issues were chosen based on their frequency of occurrence within the ICU and their ability to test different aspects of the EHR-user interface. ICU residents, blinded to the presence of medical errors within the case, were provided a sign-out and given 10 min to review the case in the EHR. They then presented the case with their management suggestions to an attending physician. Participants were graded on the number of issues identified. All participants were provided with immediate feedback upon completion of the simulation.

Primary and secondary outcomes

To determine the frequency of error recognition in an EHR simulation. To determine factors associated with improved performance in the simulation.

Results

38 participants including 9 interns, 10 residents and 19 fellows were tested. The average error recognition rate was 41% (range 6-73%), which increased slightly with the level of training (35%, 41% and 50% for interns, residents, and fellows, respectively). Over-sedation was the least-recognised error (16%); poor glycemic control was most often recognised (68%). Only 32% of the participants recognised inappropriate antibiotic dosing. Performance correlated with the total number of screens used (p=0.03).

Conclusions

Despite development of comprehensive EHRs, there remain significant gaps in identifying dangerous medical management issues. This gap remains despite high levels of medical training, suggesting that EHR-specific training may be beneficial. Simulation provides a novel tool in order to both identify these gaps as well as foster EHR-specific training.

SUBMITTER: March CA

PROVIDER: S-EPMC3641430 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

Use of simulation to assess electronic health record safety in the intensive care unit: a pilot study.

March Christopher A CA Steiger David D Scholl Gretchen G Mohan Vishnu V Hersh William R WR Gold Jeffrey A JA

BMJ open 20130410 4

<h4>Objective</h4>To establish the role of high-fidelity simulation training to test the efficacy and safety of the electronic health record (EHR)-user interface within the intensive care unit (ICU) environment.<h4>Design</h4>Prospective pilot study.<h4>Setting</h4>Medical ICU in an academic medical centre.<h4>Participants</h4>Postgraduate medical trainees.<h4>Interventions</h4>A 5-day-simulated ICU patient was developed in the EHR including labs, hourly vitals, medication administration, ventil ...[more]

PMID: 23578685

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Project description:RationalePatients transferred from the intensive care unit to the wards who are later readmitted to the intensive care unit have increased length of stay, healthcare expenditure, and mortality compared with those who are never readmitted. Improving risk stratification for patients transferred to the wards could have important benefits for critically ill hospitalized patients.ObjectivesWe aimed to use a machine-learning technique to derive and validate an intensive care unit readmission prediction model with variables available in the electronic health record in real time and compare it to previously published algorithms.MethodsThis observational cohort study was conducted at an academic hospital in the United States with approximately 600 inpatient beds. A total of 24,885 intensive care unit transfers to the wards were included, with 14,962 transfers (60%) in the training cohort and 9,923 transfers (40%) in the internal validation cohort. Patient characteristics, nursing assessments, International Classification of Diseases, Ninth Revision codes from prior admissions, medications, intensive care unit interventions, diagnostic tests, vital signs, and laboratory results were extracted from the electronic health record and used as predictor variables in a gradient-boosted machine model. Accuracy for predicting intensive care unit readmission was compared with the Stability and Workload Index for Transfer score and Modified Early Warning Score in the internal validation cohort and also externally using the Medical Information Mart for Intensive Care database (n = 42,303 intensive care unit transfers).ResultsEleven percent (2,834) of discharges to the wards were later readmitted to the intensive care unit. The machine-learning-derived model had significantly better performance (area under the receiver operating curve, 0.76) than either the Stability and Workload Index for Transfer score (area under the receiver operating curve, 0.65), or Modified Early Warning Score (area under the receiver operating curve, 0.58; P value < 0.0001 for all comparisons). At a specificity of 95%, the derived model had a sensitivity of 28% compared with 15% for Stability and Workload Index for Transfer score and 7% for the Modified Early Warning Score. Accuracy improvements with the derived model over Modified Early Warning Score and Stability and Workload Index for Transfer were similar in the Medical Information Mart for Intensive Care-III cohort.ConclusionsA machine learning approach to predicting intensive care unit readmission was significantly more accurate than previously published algorithms in both our internal validation and the Medical Information Mart for Intensive Care-III cohort. Implementation of this approach could target patients who may benefit from additional time in the intensive care unit or more frequent monitoring after transfer to the hospital ward.

Project description:BackgroundNursing care is a critical determinant of patient outcomes in the intensive care unit (ICU). Most studies of nursing care have focused on nursing characteristics aggregated across the ICU (eg, unit-wide nurse-to-patient ratios, education, and working environment). In contrast, relatively little work has focused on the influence of individual nurses and their characteristics on patient outcomes. Such research could provide granular information needed to create evidence-based nurse assignments, where a nurse's unique skills are matched to each patient's needs. To date, research in this area is hindered by an inability to link individual nurses to specific patients retrospectively and at scale.ObjectiveThis study aimed to determine the feasibility of using nurse metadata from the electronic health record (EHR) to retrospectively determine nurse-patient assignments in the ICU.MethodsWe used EHR data from 38 ICUs in 18 hospitals from 2018 to 2020. We abstracted data on the time and frequency of nurse charting of clinical assessments and medication administration; we then used those data to iteratively develop a deterministic algorithm to identify a single ICU nurse for each patient shift. We examined the accuracy and precision of the algorithm by performing manual chart review on a randomly selected subset of patient shifts.ResultsThe analytic data set contained 5,479,034 unique nurse-patient charting times; 748,771 patient shifts; 87,466 hospitalizations; 70,002 patients; and 8,134 individual nurses. The final algorithm identified a single nurse for 97.3% (728,533/748,771) of patient shifts. In the remaining 2.7% (20,238/748,771) of patient shifts, the algorithm either identified multiple nurses (4,755/748,771, 0.6%), no nurse (14,689/748,771, 2%), or the same nurse as the prior shift (794/748,771, 0.1%). In 200 patient shifts selected for chart review, the algorithm had a 93% accuracy (ie, correctly identifying the primary nurse or correctly identifying that there was no primary nurse) and a 94.4% precision (ie, correctly identifying the primary nurse when a primary nurse was identified). Misclassification was most frequently due to patient transitions in care location, such as ICU transfers, discharges, and admissions.ConclusionsMetadata from the EHR can accurately identify individual nurse-patient assignments in the ICU. This information enables novel studies of ICU nurse staffing at the individual nurse-patient level, which may provide further insights into how nurse staffing can be leveraged to improve patient outcomes.

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Dataset Information

Use of simulation to assess electronic health record safety in the intensive care unit: a pilot study.

Objective

Design

Setting

Participants

Interventions

Primary and secondary outcomes

Results

Conclusions

Publications

Use of simulation to assess electronic health record safety in the intensive care unit: a pilot study.

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