Bmc Med Inform Decis
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Bmc Med Inform Decis · Apr 2021
Observational StudyReady for SDM: evaluating a train-the-trainer program to facilitate implementation of SDM training in Norway.
Healthcare providers need training to implement shared decision making (SDM). In Norway, we developed "Ready for SDM", a comprehensive SDM curriculum tailored to various healthcare providers, settings, and competence levels, including a course targeting interprofessional healthcare teams. The overall aim was to evaluate a train-the-trainer (TTT) program for healthcare providers wanting to offer this course within their hospital trust. ⋯ Findings suggest that the TTT is a feasible approach for supporting large-scale training in SDM. Our study informed us about how to improve the advanced course. Further research shall investigate the efficacy of the training in the context of a comprehensive multifaceted strategy for implementing SDM in clinical practice.
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Bmc Med Inform Decis · Apr 2021
Construction of a demand and capacity model for intensive care and hospital ward beds, and mortality from COVID-19.
This paper describes a model for estimating COVID-19 related excess deaths that are a direct consequence of insufficient hospital ward bed and intensive care unit (ICU) capacity. ⋯ Mortality is increased when hospital demand exceeds available capacity. No excess deaths from breaching capacity would be expected under the 'Optimistic' scenario. The 'Middling' scenario could result in some excess deaths-up to a 0.7% increase relative to the total number of deaths. The 'Pessimistic' scenario would have resulted in significant excess deaths. Our sensitivity analysis indicated a range between 49,178 (17% increase) and 103,735 (35.8% increase). Given the new variant, the pessimistic scenario appeared increasingly likely and could have resulted in a substantial increase in the number of COVID-19 deaths. In the event, it would appear that capacity was not breached at any stage at a national level with no excess deaths. it will remain unclear if minor local capacity breaches resulted in any small number of excess deaths.
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Bmc Med Inform Decis · Apr 2021
Application of openEHR archetypes to automate data quality rules for electronic health records: a case study.
Ensuring data is of appropriate quality is essential for the secondary use of electronic health records (EHRs) in research and clinical decision support. An effective method of data quality assessment (DQA) is automating data quality rules (DQRs) to replace the time-consuming, labor-intensive manual process of creating DQRs, which is difficult to guarantee standard and comparable DQA results. This paper presents a case study of automatically creating DQRs based on openEHR archetypes in a Chinese hospital to investigate the feasibility and challenges of automating DQA for EHR data. ⋯ It's feasible to create DQRs automatically based on openEHR archetypes. This study evaluated the coverage of the auto-created DQRs to a typical DQA task of Chinese hospitals, the CARSES. The challenges of automating DQR creation were identified, such as quality requirements based on semantic, and complex constraints of multiple elements. This research can enlighten the exploration of DQR auto-creation and contribute to the automatic DQA.