Clinical decision support in emergency medicine : exploring the prerequisites

A clinical decision support system is a technical system that combines individual patient data and evidence-based clinical knowledge to give advice and support to clinicians. For quite a long time, the emergence of such systems has been predicted and expected to impact health care dramatically by improving both quality and productivity. Three factors make Swedish emergency medicine an interesting context which could be mature for the introduction of clinical decision support systems. Firstly, Sweden is a leader in the implementation of health care information technology, and the coverage of electronic health records is around 100% in the country. Secondly, emergency medicine is a field with high patient turnover, frequent decisions, and substantial impact on patient outcome. Thirdly, although there are abundant publications on clinical decision support system development and implementation in general, there is less knowledge of such systems in the urgent care context. Therefore, this doctoral project aimed to explore the prerequisites prior to implementation of clinical decision support systems in emergency medicine. This thesis is based on a mixed-methods design and consists of four individual studies. Proctor’s conceptual model of implementation research was used as a framework for the project. Study I included semi-structured interviews with 16 medical doctors and nurses from nine Swedish emergency departments. Content analysis was used to describe factors affecting vital sign data quality in emergency care. Study II extracted vital signs from 330 000 emergency department visits to assess the effects of different documentation workflows on data quality. Study III prospectively explored 200 vital sign measurements from 50 emergency care visits to evaluate the impact of manual and automated documentation on vital sign data quality. Study III also used data from an adapted NASA TLX questionnaire to compare the workload of clinical staff (n=70) in manual and automatic documentation. Study IV used semi-structured interviews with 14 emergency medicine physicians from three different sites. Content analysis was used to explore participants’ expectations and concerns regarding clinical decision support systems. There are three main results and conclusions from the research. Firstly, documentation of vital signs in the emergency department is still surprisingly paper-based, which makes vital sign data unfit for reuse in clinical decision support. Secondly, automation of vital sign documentation is feasible in emergency care and should improve data quality and reduce workload. Thirdly, enthusiasts towards decision support are at risk of disappointment with the level of innovation in the currently available decision support systems, and this may affect the implementation strategy negatively

Factors influencing the quality of vital signs data in electronic health records: a qualitative study

AIMS AND OBJECTIVES: The purpose of this study was to investigate reasons for inadequate documentation of vital signs in an electronic health record. BACKGROUND: Monitoring vital signs is crucial to detecting and responding to patient deterioration. The ways in which vital signs are documented in electronic health records have received limited attention in the research literature. A previous study revealed that vital signs in an electronic health record were incomplete and inconsistent. DESIGN: Qualitative study. METHODS: Qualitative study. Data were collected by observing (68 hours) and interviewing nurses (n=11) and doctors (n=3), and analysed by thematic analysis to examine processes for measuring, documenting and retrieving vital signs in four clinical settings in a 353-bed hospital. RESULTS: We identified two central reasons for inadequate vital sign documentation. First, there was an absence of firm guidelines for observing patients' vital signs, resulting in inconsistencies in the ways vital signs were recorded. Second, there was a lack of adequate facilities in the electronic health record for recording vital signs. This led to poor presentation of vital signs in the electronic health record and to staff creating paper 'workarounds'. CONCLUSIONS: This study demonstrated inadequate routines and poor facilities for vital sign documentation in an electronic health record, and makes an important contribution to knowledge by identifying problems and barriers that may occur. Further, it has demonstrated the need for improved facilities for electronic documentation of vital signs. RELEVANCE TO CLINICAL PRACTICE: patient safety may have been compromised because of poor presentation of vital signs. Thus, our results emphasised the need for standardised routines for monitoring patients. In addition, designers should consult the clinical end-users in order to optimise facilities for electronic documentation of vital signs. This could have a positive impact on clinical practice and thus improve patient safety

Kvinnlig representation i regeringar : En kvantitativ studie om koalitionsregeringars påverkan på kvinnlig representation iOECD-länders regeringar

Political representation is important for gender equality. Women are underrepresented at alll evels of power around the world. Much of the previous research has been studying female representation in parliament, this essay will therefore focus on female representation in governments. The purpose of the study is to test the theory about coalition governments increasing female representation in governments in a contemporary OECD countries context. By including control variables in the analysis the study also aims to create a broader knowledge of how the factors affect the relationship between coalition government and women in governments and how it affects female representation in governments in general. The results indicated that coalition governments do not create a higher proportion of women in the current governments. Electoral system, the government's left-wing positions, the proportion of women in parliament and the total number of ministers can explain almost fifty percent of the variance of women in governments. Electoral systems and the proportion of women in parliament are the most important factors for female representation in governments.2021-06-04</p

Digital Representation of Audio

This report focuses on lossless compression of audio, which is usually built on predictioncoding. The aim of the report is to summarize five lossless compression algorithms, presentand compare their methods of compression, and propose what a future format could look like.Information is gathered and presented in the form integrative literature review, with snowballsampling starting from a convenience sample. The results are presented in a timeline and atable analyzing the formats’ methods of compression. The study finds that older formats werebuilt to minimize the usage of the limited processing power and still deliver high compressionrate. Newer formats use heavier, but more effective compression algorithms and predictionmethods. The results show that prediction coding via the LPC (Linear Predictive Coding)method will probably not be the preferred method in the future as the compression rate-gaindiminishes with higher orders of prediction. The prediction presented in this study is that in thefuture lossless formats will use predictive coding, where sine-functions will work as predictors.The study also predicts that compression of PCM audio will not be interesting for long, astechnological advancements will render the compression redundant when storage capacity andbandwidth grows. Instead, it predicts that the representation of analog signals in a digitalenvironment will be the new frontier in audio research and proposes the BEA format, whereanalog signals are stored as Sine functions

Digital Representation of Audio

This report focuses on lossless compression of audio, which is usually built on predictioncoding. The aim of the report is to summarize five lossless compression algorithms, presentand compare their methods of compression, and propose what a future format could look like.Information is gathered and presented in the form integrative literature review, with snowballsampling starting from a convenience sample. The results are presented in a timeline and atable analyzing the formats’ methods of compression. The study finds that older formats werebuilt to minimize the usage of the limited processing power and still deliver high compressionrate. Newer formats use heavier, but more effective compression algorithms and predictionmethods. The results show that prediction coding via the LPC (Linear Predictive Coding)method will probably not be the preferred method in the future as the compression rate-gaindiminishes with higher orders of prediction. The prediction presented in this study is that in thefuture lossless formats will use predictive coding, where sine-functions will work as predictors.The study also predicts that compression of PCM audio will not be interesting for long, astechnological advancements will render the compression redundant when storage capacity andbandwidth grows. Instead, it predicts that the representation of analog signals in a digitalenvironment will be the new frontier in audio research and proposes the BEA format, whereanalog signals are stored as Sine functions

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

Abstract 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

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 &gt; 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 &lt; .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.Funding Agencies|Karolinska InstituteKarolinska Institutet</p

Clinical applications of artificial intelligence in sepsis: A narrative review

Many studies have been published on a variety of clinical applications of artificial intelligence (AI) for sepsis, while there is no overview of the literature. The aim of this review is to give an overview of the literature and thereby identify knowledge gaps and prioritize areas with high priority for further research. A literature search was conducted in PubMed from inception to February 2019. Search terms related to AI were combined with terms regarding sepsis. Articles were included when they reported an area under the receiver operator characteristics curve (AUROC) as outcome measure. Fifteen articles on diagnosis of sepsis with AI models were included. The best performing model reached an AUROC of 0.97. There were also seven articles on prognosis, predicting mortality over time with an AUROC of up to 0.895. Finally, there were three articles on assistance of treatment of sepsis, where the use of AI was associated with the lowest mortality rates. Of the articles, twenty-two were judged to be at high risk of bias or had major concerns regarding applicability. This was mostly because predictor variables in these models, such as blood pressure, were also part of the definition of sepsis, which led to overestimation of the performance. We conclude that AI models have great potential for improving early identification of patients who may benefit from administration of antibiotics. Current AI prediction models to diagnose sepsis are at major risks of bias when the diagnosis criteria are part of the predictor variables in the model. Furthermore, generalizability of these models is poor due to overfitting and a lack of standardized protocols for the construction and validation of the models. Until these problems have been resolved, a large gap remains between the creation of an AI algorithm and its implementation in clinical practice