Exploring patterns of error in acute care using framework analysis

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.

A Literature Study of Medical Simulations for Non-Technical Skills Training in Emergency Medicine: Twenty Years of Progress, an Integrated Research Framework, and Future Research Avenues

Medical simulations have led to extensive developments in emergency medicine. Apart from the growing number of applications and research efforts in patient safety, few studies have focused on modalities, research methods, and professions via a synthesis of simulation studies with a focus on non-technical skills training. Intersections between medical simulation, non-technical skills training, and emergency medicine merit a synthesis of progress over the first two decades of the 21st century. Drawing on research from the Web of Science Core Collection's Science Citation Index Expanded and Social Science Citation Index editions, results showed that medical simulations were found to be effective, practical, and highly motivating. More importantly, simulation-based education should be a teaching approach, and many simulations are utilised to substitute high-risk, rare, and complex circumstances in technical or situational simulations. (1) Publications were grouped by specific categories of non-technical skills, teamwork, communication, diagnosis, resuscitation, airway management, anaesthesia, simulation, and medical education. (2) Although mixed-method and quantitative approaches were prominent during the time period, further exploration of qualitative data would greatly contribute to the interpretation of experience. (3) High-fidelity dummy was the most suitable instrument, but the tendency of simulators without explicitly stating the vendor selection calls for a standardised training process. The literature study concludes with a ring model as the integrated framework of presently known best practices and a broad range of underexplored research areas to be investigated in detail.

Twelve tips for developing a systematic acute care curriculum for medical students

There are inadequacies in the practice-readiness of junior doctors for providing acute care in areas of clinical deterioration. In addition, the existing undergraduate curricula are fragmented in how acute care is taught in medical schools. We propose twelve tips for developing a systematic acute care curriculum, including what to teach, how to teach it and, how to assess. Furthermore, we propose and incorporate an acute care learning dashboard as an assessment tool which collates and demonstrates the occurrence of learning, faculty feedback, and students' reflection. We also summarise the existing online resources available for acute care training. We hope to address the existing issues and improve acute care training to prepare the graduates to become practice-ready professionals.

A Human Factors Approach to Vaginal Retained Foreign Objects

STUDY OBJECTIVE

To apply a structured human factors analysis to understand conditions contributing to vaginal retained foreign objects (RFO).

DESIGN

All potential vagina RFO events from January 1, 2000, to May 21, 2019, were analyzed by trained human factors researchers. Each narrative was reviewed to identify contributing factors, classified using the Human Factors Analysis and Classification System for Healthcare (HFACS-Healthcare).

SETTING

An 890-bed, academic medical center in Southern California.

PATIENTS

Patients who underwent a vaginal procedure in which a vaginal RFO-related event occurred were included in this study. However, no patient information was included, only the relevant details from their procedures.

INTERVENTIONS

No interventions were developed or implemented.

MEASUREMENTS AND MAIN RESULTS

Over the 19-year period, 45 events were reported. The most common items were vaginal packing and vaginal sponges (53.33%).  Less frequently retained items involved broken instruments (20.20%).  The majority of cases were laparoscopic hysterectomies or vaginal deliveries.  Based on HFACS, 75 contributing factors were identified, consisting primarily of preconditions for unsafe acts (communication challenges, coordination breakdowns and issues with the design of tools/technology) and unsafe acts (errors).

CONCLUSION

While rare, vaginal RFOs do occur. The top two contributing factors were skill-based errors and communication breakdowns. Both types of errors can be addressed and improved with human factors interventions, including simulation, teamwork training, and streamlining workflow to reduce the opportunity for errors.

Predicting Future Care Requirements Using Machine Learning for Pediatric Intensive and Routine Care Inpatients

Supplemental Digital Content is available in the text.

OBJECTIVES:

Develop and compare separate prediction models for ICU and non-ICU care for hospitalized children in four future time periods (6–12, 12–18, 18–24, and 24–30 hr) and assess these models in an independent cohort and simulated children’s hospital.

DESIGN:

Predictive modeling used cohorts from the Health Facts database (Cerner Corporation, Kansas City, MO).

SETTING:

Children hospitalized in ICUs.

PATIENTS:

Children with greater than or equal to one ICU admission (n = 20,014) and randomly selected routine care children without ICU admission (n = 20,130) from 2009 to 2016 were used for model development and validation. An independent 2017–2018 cohort consisted of 80,089 children.

INTERVENTIONS:

None.

MEASUREMENT AND MAIN RESULTS:

Initially, we undersampled non-ICU patients for development and comparison of the models. We randomly assigned 64% of patients for training, 8% for validation, and 28% for testing in both clinical groups. Two additional validation cohorts were tested: a simulated children’s hospitals and the 2017–2018 cohort. The main outcome was ICU care or non-ICU care in four future time periods based on physiology, therapy, and care intensity. Four independent, sequential, and fully connected neural networks were calibrated to risk of ICU care at each time period. Performance for all models in the test sample were comparable including sensitivity greater than or equal to 0.727, specificity greater than or equal to 0.885, accuracy greater than 0.850, area under the receiver operating characteristic curves greater than or equal to 0.917, and all had excellent calibration (all R²s > 0.98). Model performance in the 2017–2018 cohort was sensitivity greater than or equal to 0.545, specificity greater than or equal to 0.972, accuracy greater than or equal to 0.921, area under the receiver operating characteristic curves greater than or equal to 0.946, and R²s greater than or equal to 0.979. Performance metrics were comparable for the simulated children’s hospital and for hospitals stratified by teaching status, bed numbers, and geographic location.

CONCLUSIONS:

Machine learning models using physiology, therapy, and care intensity predicting future care needs had promising performance metrics. Notably, performance metrics were similar as the prediction time periods increased from 6–12 hours to 24–30 hours.

Impact of repeated simulation on learning curve characteristics of residents exposed to rare life threatening situations

Background

Little is known about the learning curve characteristics of residents undertaking simulation-based education. It is important to understand the time for acquisition and decay of knowledge and skills needed to manage rare and difficult clinical situations.

Method

Ten anaesthesiology residents underwent simulation-based education to manage a cannot intubate cannot ventilate scenario during general anaesthesia for caesarean section. Their performance was measured using an assessment tool and debriefed by two experienced anaesthesiologists. The parameters against which the performance was judged were grouped into preoperative assessment, preoperative patient care, equipment availability, induction sequence, communication and adherence to airway algorithm protocol. The scenario was repeated at 6 and 12 months thereafter. The residents’ acquisition of knowledge, technical and non-technical skills were assessed and compared at baseline, 6 months and end of 12 months.

Result

The skills of preoperative assessment, preoperative care and communication quickly improved but the specific skill of managing a difficult airway as measured by adherence to an airway algorithm required more than 6 months (CI at 6 vs 12 months: −3.4 to –0.81, p=0.016). The skills of preoperative assessment and preoperative care improved to a higher level quickly and were retained at this improved level. Communication (CI at 0 vs 6 months: −3.78 to −0.22, p=0.045 and at 6 vs 12 months : −3.39 to −1.49, p=0.007) and difficult airway management skill were slower to improve but continued to do so over the 12 months. The compliance to machine check was more gradual and showed an improvement at 12 months.

Conclusion

Our study is unique in analysing the learning curve characteristics of different components of a failed obstetric airway management skill. Repeated simulations over a longer period of time help in better reinforcement, retention of knowledge, recapitulation and implementation of technical and non-technical skills.

A Systematic Framework for Analyzing Observation Data in Patient-Centered Registries: Case Study for Patients With Depression

Background

Patient-centered registries are essential in population-based clinical care for patient identification and monitoring of outcomes. Although registry data may be used in real time for patient care, the same data may further be used for secondary analysis to assess disease burden, evaluation of disease management and health care services, and research. The design of a registry has major implications for the ability to effectively use these clinical data in research.

Objective

This study aims to develop a systematic framework to address the data and methodological issues involved in analyzing data in clinically designed patient-centered registries.

Methods

The systematic framework was composed of 3 major components: visualizing the multifaceted and heterogeneous patient-centered registries using a data flow diagram, assessing and managing data quality issues, and identifying patient cohorts for addressing specific research questions.

Results

Using a clinical registry designed as a part of a collaborative care program for adults with depression at Mayo Clinic, we were able to demonstrate the impact of the proposed framework on data integrity. By following the data cleaning and refining procedures of the framework, we were able to generate high-quality data that were available for research questions about the coordination and management of depression in a primary care setting. We describe the steps involved in converting clinically collected data into a viable research data set using registry cohorts of depressed adults to assess the impact on high-cost service use.

Conclusions

The systematic framework discussed in this study sheds light on the existing inconsistency and data quality issues in patient-centered registries. This study provided a step-by-step procedure for addressing these challenges and for generating high-quality data for both quality improvement and research that may enhance care and outcomes for patients.

International Registered Report Identifier (IRRID)

DERR1-10.2196/18366

An Active Shooter in Your Hospital: A Novel Method to Develop a Response Policy Using In Situ Simulation and Video Framework Analysis

Hospital shootings (Code Silver) are events that pose extreme risk to staff, patients, and visitors. Hospitals are faced with unique challenges to train staff and develop protocols to manage these high-risk events. In situ simulation is an innovative technique that can evaluate institutional responses to emergent situations. This study highlights the design of an active shooter in situ simulation conducted at a Canadian level-1 trauma center to test a Code Silver active shooter protocol response. We further apply a modified framework analysis to extract latent safety threats (LSTs) from the simulation using ethnographic observation of the response by law enforcement, hospital security, logistics, and medical personnel.The video-based framework analysis identified 110 LSTs, which were assigned hazard scores, highlighting 3 high-risk LSTs that did not have effective control measures or were not easily discoverable. These included lack of security during patient transport, inadequate situational awareness outside the clinical area, and poor coordination of critical tasks among interprofessional team members. In situ simulation is a novel approach to support the design and implementation of similar events at other institutions. Findings from ethnographic observations and a video-based analysis form a structured framework to address safety, logistical, and medical response considerations.