Sound psychometric properties of a short new screening tool for patient safety climate: applying a Rasch model analysis.

BACKGROUND: WHO recommends repeated measurement of patient safety climate in health care and to support monitoring an 11 item questionnaire on sustainable safety engagement (HSE) has been developed by the Swedish Association of Local Authorities and Regions. This study aimed to validate the psychometric properties of the HSE. METHODS: Survey responses (n = 761) from a specialist care provider organization in Sweden was used to evaluate psychometric properties of the HSE 11-item questionnaire. A Rasch model analysis was applied in a stepwise process to evaluate evidence of validity and precision/reliability in relation to rating scale functioning, internal structure, response processes, and precision in estimates. RESULTS: Rating scales met the criteria for monotonical advancement and fit. Local independence was demonstrated for all HSE items. The first latent variable explained 52.2% of the variance. The first ten items demonstrated good fit to the Rasch model and were included in the further analysis and calculation of an index measure based on the raw scores. Less than 5% of the respondents demonstrated low person goodness-of-fit. Person separation index > 2. The flooring effect was negligible and the ceiling effect 5.7%. No differential item functioning was shown regarding gender, time of employment, role within organization or employee net promotor scores. The correlation coefficient between the HSE mean value index and the Rasch-generated unidimensional measures of the HSE 10-item scale was r = .95 (p < .01). CONCLUSIONS: This study shows that an eleven-item questionnaire can be used to measure a common dimension of staff perceptions on patient safety. The responses can be used to calculate an index that enables benchmarking and identification of at least three different levels of patient safety climate. This study explores a single point in time, but further studies may support the use of the instrument to follow development of the patient safety climate over time by repeated measurement.

Emergency department crowding and hospital transformation during COVID-19, a retrospective, descriptive study of a university hospital in Stockholm, Sweden.

OBJECTIVES: COVID-19 presents challenges to the emergency care system that could lead to emergency department (ED) crowding. The Huddinge site at the Karolinska university hospital (KH) responded through a rapid transformation of inpatient care capacity together with changing working methods in the ED. The aim is to describe the KH response to the COVID-19 crisis, and how ED crowding, and important input, throughput and output factors for ED crowding developed at KH during a 30-day baseline period followed by the first 60 days of the COVID-19 outbreak in Stockholm Region. METHODS: Different phases in the development of the crisis were described and identified retrospectively based on major events that changed the conditions for the ED. Results were presented for each phase separately. The outcome ED length of stay (ED LOS) was calculated with mean and 95% confidence intervals. Input, throughput, output and demographic factors were described using distributions, proportions and means. Pearson correlation between ED LOS and emergency ward occupancy by phase was estimated with 95% confidence interval. RESULTS: As new working methods were introduced between phase 2 and 3, ED LOS declined from mean (95% CI) 386 (373-399) minutes to 307 (297-317). Imaging proportion was reduced from 29 to 18% and admission rate increased from 34 to 43%. Correlation (95% CI) between emergency ward occupancy and ED LOS by phase was 0.94 (0.55-0.99). CONCLUSIONS: It is possible to avoid ED crowding, even during extreme and quickly changing conditions by leveraging previously known input, throughput and output factors. One key factor was the change in working methods in the ED with higher competence, less diagnostics and increased focus on rapid clinical admission decisions. Another important factor was the reduction in bed occupancy in emergency wards that enabled a timely admission to inpatient care. A key limitation was the retrospective study design.

Data Quality Governance at the Emergency Department: A Qualitative Study Influenced by Grounded Theory in Nine Swedish Emergency Departments.

Vital Sign Data Quality is essential for successful implementation of clinical decision support systems in emergency care. Studies have shown that data quality is inadequate and needs improvement. This study shows that data quality is dependent on both technical and human factors and provides a conceptual model of data quality governance and improvement in the emergency department.

Is it possible to quickly identify acutely unwell patients who can be safely managed as outpatients? The need for a "Universal Safe to Discharge Score".

If scores or algorithms were developed that quickly identified patients who are bound to have 100% survival, if even only for a few days, more patients could be safely discharged from emergency department, this eliminating the risks of hospitalization for many patients. This hypothesis proposes that it is possible to develop a "Universal Safe to Discharge Score", and suggests how it might be developed and validated.

Man vs machine in emergency medicine - a study on the effects of manual and automatic vital sign documentation on data quality and perceived workload, using observational paired sample data and questionnaires.

BACKGROUND: Emergency medicine is characterized by a high patient flow where timely decisions are essential. Clinical decision support systems have the potential to assist in such decisions but will be dependent on the data quality in electronic health records which often is inadequate. This study explores the effect of automated documentation of vital signs on data quality and workload. METHODS: An observational study of 200 vital sign measurements was performed to evaluate the effects of manual vs automatic documentation on data quality. Data collection using questionnaires was performed to compare the workload on wards using manual or automatic documentation. RESULTS: In the automated documentation time to documentation was reduced by 6.1 min (0.6 min vs 7.7 min, p <  0.05) and completeness increased (98% vs 95%, p <  0.05). Regarding workflow temporal demands were lower in the automatic documentation workflow compared to the manual group (50 vs 23, p <  0.05). The same was true for frustration level (64 vs 33, p <  0.05). The experienced reduction in temporal demands was in line with the anticipated, whereas the experienced reduction in frustration was lower than the anticipated (27 vs 54, p < 0.05). DISCUSSION: The study shows that automatic documentation will improve the currency and the completeness of vital sign data in the Electronic Health Record while reducing workload regarding temporal demands and experienced frustration. The study also shows that these findings are in line with staff anticipations but indicates that the anticipations on the reduction of frustration may be exaggerated among the staff. The open-ended answers indicate that frustration focus will change from double documentation of vital signs to technical aspects of the automatic documentation system.

Exploring Vital Sign Data Quality in Electronic Health Records with Focus on Emergency Care Warning Scores.

BACKGROUND: Computerized clinical decision support and automation of warnings have been advocated to assist clinicians in detecting patients at risk of physiological instability. To provide reliable support such systems are dependent on high-quality vital sign data. Data quality depends on how, when and why the data is captured and/or documented. OBJECTIVES: This study aims to describe the effects on data quality of vital signs by three different types of documentation practices in five Swedish emergency hospitals, and to assess data fitness for calculating warning and triage scores. The study also provides reference data on triage vital signs in Swedish emergency care. METHODS: We extracted a dataset including vital signs, demographic and administrative data from emergency care visits (n=335027) at five Swedish emergency hospitals during 2013 using either completely paper-based, completely electronic or mixed documentation practices. Descriptive statistics were used to assess fitness for use in emergency care decision support systems aiming to calculate warning and triage scores, and data quality was described in three categories: currency, completeness and correctness. To estimate correctness, two further categories - plausibility and concordance - were used. RESULTS: The study showed an acceptable correctness of the registered vital signs irrespectively of the type of documentation practice. Completeness was high in sites where registrations were routinely entered into the Electronic Health Record (EHR). The currency was only acceptable in sites with a completely electronic documentation practice. CONCLUSION: Although vital signs that were recorded in completely electronic documentation practices showed plausible results regarding correctness, completeness and currency, the study concludes that vital signs documented in Swedish emergency care EHRs cannot generally be considered fit for use for calculation of triage and warning scores. Low completeness and currency were found if the documentation was not completely electronic.

How to improve vital sign data quality for use in clinical decision support systems? A qualitative study in nine Swedish emergency departments.

BACKGROUND: Vital sign data are important for clinical decision making in emergency care. Clinical Decision Support Systems (CDSS) have been advocated to increase patient safety and quality of care. However, the efficiency of CDSS depends on the quality of the underlying vital sign data. Therefore, possible factors affecting vital sign data quality need to be understood. This study aims to explore the factors affecting vital sign data quality in Swedish emergency departments and to determine in how far clinicians perceive vital sign data to be fit for use in clinical decision support systems. A further aim of the study is to provide recommendations on how to improve vital sign data quality in emergency departments. METHODS: Semi-structured interviews were conducted with sixteen physicians and nurses from nine hospitals and vital sign documentation templates were collected and analysed. Follow-up interviews and process observations were done at three of the hospitals to verify the results. Content analysis with constant comparison of the data was used to analyse and categorize the collected data. RESULTS: Factors related to care process and information technology were perceived to affect vital sign data quality. Despite electronic health records (EHRs) being available in all hospitals, these were not always used for vital sign documentation. Only four out of nine sites had a completely digitalized vital sign documentation flow and paper-based triage records were perceived to provide a better mobile workflow support than EHRs. Observed documentation practices resulted in low currency, completeness, and interoperability of the vital signs. To improve vital sign data quality, we propose to standardize the care process, improve the digital documentation support, provide workflow support, ensure interoperability and perform quality control. CONCLUSIONS: Vital sign data quality in Swedish emergency departments is currently not fit for use by CDSS. To address both technical and organisational challenges, we propose five steps for vital sign data quality improvement to be implemented in emergency care settings.