The Safety Assurance Factors for Electronic Health Record (EHR) Resilience (SAFER) Guides provide recommendations to healthcare organizations for conducting proactive self-assessments of the safety and effectiveness of their EHR implementation and use. Originally released in 2014, they were last updated in 2016. In 2022, the Centers for Medicare and Medicaid Services required their annual attestation by US hospitals. OBJECTIVES: This case study describes how SAFER Guide recommendations were updated to align with current evidence and clinical practice. MATERIALS AND METHODS: Over nine months, a multidisciplinary team updated SAFER Guides through literature reviews, iterative feedback, and online meetings. RESULTS: We reduced the number of recommended practices across all Guides by 40% and consolidated 9 Guides into 8 to maximize ease of use, feasibility, and utility. We provide a 4-level evidence grading hierarchy for each recommendation and a new 5-point rating scale to self-assess implementation status of the recommendation. We included 429 citations of which 289 (67%) were published since the 2016 revision. DISCUSSION: SAFER Guides were revised to offer EHR best practices, adaptable to unique organizational needs, with interactive content available at: https://www.healthit.gov/topic/safety/safer-guides. CONCLUSION: Revisions ensure that the 2025 SAFER Guides represent the best available current evidence for EHR developers and healthcare organizations.

BACKGROUND: Studies examining the effects of computerized order entry (CPOE) on medication ordering errors demonstrate that CPOE does not consistently prevent these errors as intended. We used the Agency for Healthcare Research and Quality (AHRQ) Network of Patient Safety Databases (NPSD) to investigate the frequency and degree of harm of reported events that occurred at the ordering stage, characterized by error type. MATERIALS AND METHODS: This was a retrospective observational study of safety events reported by healthcare systems in participating patient safety organizations from 6/2010 through 12/2020. All medication and other substance ordering errors reported to NPSD via common format v1.2 between 6/2010 through 12/2020 were analyzed. We aggregated and categorized the frequency of reported medication ordering errors by error type, degree of harm, and demographic characteristics. RESULTS: A total of 12 830 errors were reported during the study period. Incorrect dose accounted for 3812 errors (29.7%), followed by incorrect medication 2086 (16.3%), and incorrect duration 765 (6.0%). Of 5282 events that reached the patient and had a known level of severity, 12 resulted in death, 4 resulted in severe harm, 45 resulted in moderate harm, 341 resulted in mild harm, and 4880 resulted in no harm. CONCLUSION: Incorrect dose and incorrect drug orders were the most commonly reported and harmful types of medication ordering errors. Future studies should aim to develop and test interventions focused on CPOE to prevent medication ordering errors, prioritizing wrong-dose and wrong-drug errors.

A prior randomized controlled trial (RCT) showed no significant difference in wrong-patient errors between clinicians assigned to a restricted electronic health record (EHR) configuration (limiting to 1 record open at a time) versus an unrestricted EHR configuration (allowing up to 4 records open concurrently). However, it is unknown whether an unrestricted EHR configuration is more efficient. This substudy of the RCT compared clinician efficiency between EHR configurations using objective measures. All clinicians who logged onto the EHR during the substudy period were included. The primary outcome measure of efficiency was total active minutes per day. Counts were extracted from audit log data, and mixed-effects negative binomial regression was performed to determine differences between randomized groups. Incidence rate ratios (IRRs) were calculated with 95% confidence intervals (CIs). Among a total of 2556 clinicians, there was no significant difference between unrestricted and restricted groups in total active minutes per day (115.1 vs 113.3 min, respectively; IRR, 0.99; 95% CI, 0.93-1.06), overall or by clinician type and practice area.

BACKGROUND: Problem lists represent an integral component of high-quality care. However, they are often inaccurate and incomplete. We studied the effects of alerts integrated into the inpatient and outpatient computerized provider order entry systems to assist in adding problems to the problem list when ordering medications that lacked a corresponding indication. METHODS: We analyzed medication orders from 2 healthcare systems that used an innovative indication alert. We collected data at site 1 between December 2018 and January 2020, and at site 2 between May and June 2021. We reviewed random samples of 100 charts from each site that had problems added in response to the alert. Outcomes were: (1) alert yield, the proportion of triggered alerts that led to a problem added and (2) problem accuracy, the proportion of problems placed that were accurate by chart review. RESULTS: Alerts were triggered 131 134, and 6178 times at sites 1 and 2, respectively, resulting in a yield of 109 055 (83.2%) and 2874 (46.5%), P< .001. Orders were abandoned, for example, not completed, in 11.1% and 9.6% of orders, respectively, P<.001. Of the 100 sample problems, reviewers deemed 88% ± 3% and 91% ± 3% to be accurate, respectively, P = .65, with a mean of 90% ± 2%. CONCLUSIONS: Indication alerts triggered by medication orders initiated in the absence of a justifying diagnosis were useful for populating problem lists, with yields of 83.2% and 46.5% at 2 healthcare systems. Problems were placed with a reasonable level of accuracy, with 90% ± 2% of problems deemed accurate based on chart review.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020. MATERIALS AND METHODS: For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)-positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model's calibration and evaluated feature importances to interpret model output. RESULTS: The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve-MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve-MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition. DISCUSSION: Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients. CONCLUSIONS: We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results.

To reduce the risk of wrong-patient errors, safety experts recommend allowing only one patient chart to be open at a time. Due to the lack of empirical evidence, the number of allowable open charts is often based on anecdotal evidence or institutional preference, and hence varies across institutions. Using an interrupted time series analysis of intercepted wrong-patient medication orders in an emergency department during 2010-2016 (83.6 intercepted wrong-patient events per 100 000 orders), we found no significant decrease in the number of intercepted wrong-patient medication orders during the transition from a maximum of 4 open charts to a maximum of 2 (b = -0.19, P = .33) and no significant increase during the transition from a maximum of 2 open charts to a maximum of 4 (b = 0.08, P = .67). These results have implications regarding decisions about allowable open charts in the emergency department in relation to the impact on workflow and efficiency.

To reduce the risk of wrong-patient errors, safety experts recommend limiting the number of patient records providers can open at once in electronic health records (EHRs). However, it is unknown whether health care organizations follow this recommendation or what rationales drive their decisions. To address this gap, we conducted an electronic survey via 2 national listservs. Among 167 inpatient and outpatient study facilities using EHR systems designed to open multiple records at once, 44.3% were configured to allow ≥3 records open at once (unrestricted), 38.3% allowed only 1 record open (restricted), and 17.4% allowed 2 records open (hedged). Decision-making centered on efforts to balance safety and efficiency, but there was disagreement among organizations about how to achieve that balance. Results demonstrate no consensus on the number of records to be allowed open at once in EHRs. Rigorous studies are needed to determine the optimal number of records that balances safety and efficiency.

Objective: To examine medication errors potentially related to computerized prescriber order entry (CPOE) and refine a previously published taxonomy to classify them. Materials and Methods: We reviewed all patient safety medication reports that occurred in the medication ordering phase from 6 sites participating in a United States Food and Drug Administration-sponsored project examining CPOE safety. Two pharmacists independently reviewed each report to confirm whether the error occurred in the ordering/prescribing phase and was related to CPOE. For those related to CPOE, we assessed whether CPOE facilitated (actively contributed to) the error or failed to prevent the error (did not directly cause it, but optimal systems could have potentially prevented it). A previously developed taxonomy was iteratively refined to classify the reports. Results: Of 2522 medication error reports, 1308 (51.9%) were related to CPOE. Of these, CPOE facilitated the error in 171 (13.1%) and potentially could have prevented the error in 1137 (86.9%). The most frequent categories of "what happened to the patient" were delays in medication reaching the patient, potentially receiving duplicate drugs, or receiving a higher dose than indicated. The most frequent categories for "what happened in CPOE" included orders not routed to or received at the intended location, wrong dose ordered, and duplicate orders. Variations were seen in the format, categorization, and quality of reports, resulting in error causation being assignable in only 403 instances (31%). Discussion and Conclusion: Errors related to CPOE commonly involved transmission errors, erroneous dosing, and duplicate orders. More standardized safety reporting using a common taxonomy could help health care systems and vendors learn and implement prevention strategies.

OBJECTIVE: To evaluate systems for estimating and preventing wrong-patient electronic orders in computerized physician order entry systems with a two-phase study. MATERIALS AND METHODS: In phase 1, from May to August 2010, the effectiveness of a 'retract-and-reorder' measurement tool was assessed that identified orders placed on a patient, promptly retracted, and then reordered by the same provider on a different patient as a marker for wrong-patient electronic orders. This tool was then used to estimate the frequency of wrong-patient electronic orders in four hospitals in 2009. In phase 2, from December 2010 to June 2011, a three-armed randomized controlled trial was conducted to evaluate the efficacy of two distinct interventions aimed at preventing these errors by reverifying patient identification: an 'ID-verify alert', and an 'ID-reentry function'. RESULTS: The retract-and-reorder measurement tool effectively identified 170 of 223 events as wrong-patient electronic orders, resulting in a positive predictive value of 76.2% (95% CI 70.6% to 81.9%). Using this tool it was estimated that 5246 electronic orders were placed on wrong patients in 2009. In phase 2, 901 776 ordering sessions among 4028 providers were examined. Compared with control, the ID-verify alert reduced the odds of a retract-and-reorder event (OR 0.84, 95% CI 0.72 to 0.98), but the ID-reentry function reduced the odds by a larger magnitude (OR 0.60, 95% CI 0.50 to 0.71). DISCUSSION AND CONCLUSION: Wrong-patient electronic orders occur frequently with computerized provider order entry systems, and electronic interventions can reduce the risk of these errors occurring.

OBJECTIVE: The aim of this study was to measure the effect of an electronic heparin-induced thrombocytopenia (HIT) alert on provider ordering behaviors and on patient outcomes. MATERIALS AND METHODS: A pop-up alert was created for providers when an individual's platelet values had decreased by 50% or to <100,000/mm(3) in the setting of recent heparin exposure. The authors retrospectively compared inpatients admitted between January 24, 2008 and August 24, 2008 to a control group admitted 1 year prior to the HIT alert. The primary outcome was a change in HIT antibody testing. Secondary outcomes included an assessment of incidence of HIT antibody positivity, percentage of patients started on a direct thrombin inhibitor (DTI), length of stay and overall mortality. RESULTS: There were 1006 and 1081 patients in the control and intervention groups, respectively. There was a 33% relative increase in HIT antibody test orders (p=0.01), and 33% more of these tests were ordered the first day after the criteria were met when a pop-up alert was given (p=0.03). Heparin was discontinued in 25% more patients in the alerted group (p=0.01), and more direct thrombin inhibitors were ordered for them (p=0.03). The number who tested HIT antibody-positive did not differ, however, between the two groups (p=0.99). The length of stay and mortality were similar in both groups. CONCLUSIONS: The HIT alert significantly impacted provider behaviors. However, the alert did not result in more cases of HIT being detected or an improvement in overall mortality. Our findings do not support implementation of a computerized HIT alert.