Medication Safety Event Reporting: Factors That Contribute to Safety Events During Times of Organizational Stress.

BACKGROUND: Incident reports submitted during times of organizational stress may reveal unique insights. PURPOSE: To understand the insights conveyed in hospital incident reports about how work system factors affected medication safety during a coronavirus disease-2019 (COVID-19) surge. METHODS: We randomly selected 100 medication safety incident reports from an academic medical center (December 2020 to January 2021), identified near misses and errors, and classified contributing work system factors using the Human Factors Analysis and Classification System-Healthcare. RESULTS: Among 35 near misses/errors, incident reports described contributing factors (mean 1.3/report) involving skill-based errors (n = 20), communication (n = 8), and tools/technology (n = 4). Reporters linked 7 events to COVID-19. CONCLUSIONS: Skill-based errors were the most common contributing factors for medication safety events during a COVID-19 surge. Reporters rarely deemed events to be related to COVID-19, despite the tremendous strain of the surge on nurses. Future efforts to improve the utility of incident reports should emphasize the importance of describing work system factors.

Digital Tools Designed to Obtain the History of Present Illness From Patients: Scoping Review.

BACKGROUND: Many medical conditions, perhaps 80% of them, can be diagnosed by taking a thorough history of present illness (HPI). However, in the clinical setting, situational factors such as interruptions and time pressure may cause interactions with patients to be brief and fragmented. One solution for improving clinicians' ability to collect a thorough HPI and maximize efficiency and quality of care could be to use a digital tool to obtain the HPI before face-to-face evaluation by a clinician. OBJECTIVE: Our objective was to identify and characterize digital tools that have been designed to obtain the HPI directly from patients or caregivers and present this information to clinicians before a face-to-face encounter. We also sought to describe outcomes reported in testing of these tools, especially those related to usability, efficiency, and quality of care. METHODS: We conducted a scoping review using predefined search terms in the following databases: MEDLINE, CINAHL, PsycINFO, Web of Science, Embase, IEEE Xplore Digital Library, ACM Digital Library, and ProQuest Dissertations & Theses Global. Two reviewers screened titles and abstracts for relevance, performed full-text reviews of articles meeting the inclusion criteria, and used a pile-sorting procedure to identify distinguishing characteristics of the tools. Information describing the tools was primarily obtained from identified peer-reviewed sources; in addition, supplementary information was obtained from tool websites and through direct communications with tool creators. RESULTS: We identified 18 tools meeting the inclusion criteria. Of these 18 tools, 14 (78%) used primarily closed-ended and multiple-choice questions, 1 (6%) used free-text input, and 3 (17%) used conversational (chatbot) style. More than half (10/18, 56%) of the tools were tailored to specific patient subpopulations; the remaining (8/18, 44%) tools did not specify a target subpopulation. Of the 18 tools, 7 (39%) included multilingual support, and 12 (67%) had the capability to transfer data directly into the electronic health record. Studies of the tools reported on various outcome measures related to usability, efficiency, and quality of care. CONCLUSIONS: The HPI tools we identified (N=18) varied greatly in their purpose and functionality. There was no consensus on how patient-generated information should be collected or presented to clinicians. Existing tools have undergone inconsistent levels of testing, with a wide variety of different outcome measures used in evaluation, including some related to usability, efficiency, and quality of care. There is substantial interest in using digital tools to obtain the HPI from patients, but the outcomes measured have been inconsistent. Future research should focus on whether using HPI tools can lead to improved patient experience and health outcomes, although surrogate end points could instead be used so long as patient safety is monitored.

Quantifying Emergency Physician Interruptions due to Electrocardiogram Review.

BACKGROUND: Interruptions are recognized as potentially harmful to safety and efficiency, and are especially prevalent in the emergency department (ED) setting. Policies urging immediate review of all electrocardiograms (ECGs) may lead to numerous and frequent interruptions. OBJECTIVE: We assessed the role of ECG review as a source of ED interruptions to characterize a potential target for interventions. METHODS: We analyzed emergency physician time use during the course of a clinical shift using a time-and-motion design. A research assistant observed a convenience sample of shifts, observing and logging transitions between different tasks using an electronic device. Instances of ECG review were tallied, with start and ending times of ECG review recorded to the nearest second. An ECG review was considered an interruption if the immediate prior and subsequent tasks were the same. RESULTS: Twenty shifts were observed for a total of 149 h. There were 211 ECG reviews, (mean rate 1.4 per hour), with more frequent review among physicians staffing a zone with higher-acuity patients (2.8 per hour), where clustering of multiple ECG reviews in succession was more common. Seventy-five percent of ECG reviews required < 30 s. Of all 211 ECG reviews, 102 (48%) were an interruption. The tasks most frequently interrupted were electronic medical record system use (68 of 102, 67%) and communicating with ED staff in person (18 of 102, 18%). CONCLUSIONS: Review of ECGs was a substantial driver of interruptions for emergency physicians. Interventions to integrate ECG review more naturally into physician workflow may improve patient safety by reducing these interruptions.

Comparing the Safety Action Feedback and Engagement (SAFE) Loop with an established incident reporting system: Study protocol for a pragmatic cluster randomized controlled trial.

Background: Incident reporting is widely used in hospitals to improve patient safety, but current reporting systems do not function optimally. The utility of incident reports is limited because hospital staff may not know what to report, may fear retaliation, and may doubt whether administrators will review reports and respond effectively. Methods: This is a clustered randomized controlled trial of the Safety Action Feedback and Engagement (SAFE) Loop, an intervention designed to transform hospital incident reporting systems into effective tools for improving patient safety. The SAFE Loop has six key attributes: obtaining nurses' input about which safety problems to prioritize on their unit; focusing on learning about selected high-priority events; training nurses to write more informative event reports; prompting nurses to report high-priority events; integrating information about events from multiple sources; and providing feedback to nurses on findings and mitigation plans. The study will focus on medication errors and randomize 20 nursing units at a large academic/community hospital in Los Angeles. Outcomes include: (1) incident reporting practices (rates of high-priority reports, contributing factors described in reports), (2) nurses' attitudes toward incident reporting, and (3) rates of high-priority events. Quantitative analyses will compare changes in outcomes pre- and post-implementation between the intervention and control nursing units, and qualitative analyses will explore nurses' experiences with implementation. Conclusion: If effective, SAFE Loop will have several benefits: increasing nurses' engagement with reporting, producing more informative reports, enabling safety leaders to understand problems, designing system-based solutions more effectively, and lowering rates of high-priority patient safety events.

Comprehensive Pharmacist-led Transitions-of-care Medication Management around Hospital Discharge Adds Modest Cost Relative to Usual Care: Time-and-Motion Cost Analysis.

Optimal medication management is important during hospitalization and at discharge because post-discharge adverse drug events (ADEs) are common, often preventable, and contribute to patient harms, healthcare utilization, and costs. Conduct a cost analysis of a comprehensive pharmacist-led transitions-of-care medication management intervention for older adults during and after hospital discharge. Twelve intervention components addressed medication reconciliation, medication review, and medication adherence. Trained, experienced pharmacists delivered the intervention to older adults with chronic comorbidities at 2 large U.S. academic centers. To quantify and categorize time spent on the intervention, we conducted a time-and-motion analysis of study pharmacists over 36 sequential workdays (14 519 min) involving 117 patients. For 40 patients' hospitalizations, we observed all intervention activities. We used the median minutes spent and pharmacist wages nationally to calculate cost per hospitalization (2020 U.S. dollars) from the hospital perspective, relative to usual care. Pharmacists spent a median of 66.9 min per hospitalization (interquartile range 46.1-90.1), equating to $101 ($86 to $116 in sensitivity analyses). In unadjusted analyses, study site was associated with time spent (medians 111 and 51.8 min) while patient primary language, discharge disposition, number of outpatient medications, and patient age were not. In this cost analysis, comprehensive medication management around discharge cost about $101 per hospitalization, with variation across sites. This cost is at least an order of magnitude less than published costs associated with ADEs, hospital readmissions, or other interventions designed to reduce readmissions. Work is ongoing to assess the current intervention's effectiveness.

Using body temperature and variables commonly available in the EHR to predict acute infection: a proof-of-concept study showing improved pretest probability estimates for acute COVID-19 infection among discharged emergency department patients.

OBJECTIVES: Obtaining body temperature is a quick and easy method to screen for acute infection such as COVID-19. Currently, the predictive value of body temperature for acute infection is inhibited by failure to account for other readily available variables that affect temperature values. In this proof-of-concept study, we sought to improve COVID-19 pretest probability estimation by incorporating covariates known to be associated with body temperature, including patient age, sex, comorbidities, month, and time of day. METHODS: For patients discharged from an academic hospital emergency department after testing for COVID-19 in March and April of 2020, we abstracted clinical data. We reviewed physician documentation to retrospectively generate estimates of pretest probability for COVID-19. Using patients' COVID-19 PCR test results as a gold standard, we compared AUCs of logistic regression models predicting COVID-19 positivity that used: (1) body temperature alone; (2) body temperature and pretest probability; (3) body temperature, pretest probability, and body temperature-relevant covariates. Calibration plots and bootstrap validation were used to assess predictive performance for model #3. RESULTS: Data from 117 patients were included. The models' AUCs were: (1) 0.69 (2) 0.72, and (3) 0.76, respectively. The absolute difference in AUC was 0.029 (95% CI -0.057 to 0.114, p=0.25) between model 2 and 1 and 0.038 (95% CI -0.021 to 0.097, p=0.10) between model 3 and 2. CONCLUSIONS: By incorporating covariates known to affect body temperature, we demonstrated improved pretest probability estimates of acute COVID-19 infection. Future work should be undertaken to further develop and validate our model in a larger, multi-institutional sample.

The safety of home discharge for low-risk emergency department patients presenting with coronavirus-like symptoms during the COVID-19 pandemic: A retrospective cohort study.

Objective: There is minimal evidence describing outcomes for emergency department (ED) patients with suspected coronavirus disease 2019 (COVID-19) infection who are not hospitalized. The study objective was to assess 30-day outcomes (ED revisit, admission, ICU admission, and death) for low-risk patients discharged after ED evaluation for COVID-19. Methods: This was a retrospective cohort study of patients triaged to a COVID-19 surge area within an urban ED and discharged between March 12 and April 6. Physicians were encouraged to discharge patients if they were well-appearing with few comorbidities. Data were collected from review of medical records and phone follow-up, and the analysis was descriptive. Results: Of 452 patients, the median age was 38, and 61.7% had no comorbidities. Chest radiographs were performed for 50.4% of patients and showed infiltrates in 14% of those tested. Polymerase chain reaction testing was performed for 28.3% of patients during the index ED visit and was positive in 35.9% of those tested. Follow-up was achieved for 75.4% of patients. ED revisits occurred for 13.7% of patients. The inpatient admission rate at 30 days was 4.6%, with 0.7% requiring intensive care. Median number of days between index ED evaluation and return for admission was 5 (interquartile range 3-7, range 1-17). There were no known deaths. Conclusions: A minority of low-risk patients with suspected COVID-19 will require hospitalization after being discharged home from the ED. Outpatient management is likely safe for well-appearing patients with normal vital signs, but patients should be instructed to return for worsening symptoms including labored breathing. Future work is warranted to develop and validate ED disposition guidelines.