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Background: The electronic health record (EHR) is central to medical informatics. Its use is also recognized as an important skill for future clinicians. Typically, medical students' first exposure to an EHR is when they start their clinical internships, and medical informatics students may or may not get experience with an EHR before graduation. We describe the process of implementing an open-source EHR in two curricula: Medicine and Medical informatics. For medical students, the primary goals were to allow students to practice analyzing information from the EHR, creating therapeutic plans, and communicating with their colleagues via the EHR before they start their first clinical rotations. For medical informatics students, the primary goal was to give students hands-on experience with creating decision support in an EHR., Approach: We used the OpenMRS electronic health record with a custom decision support module based on Arden Syntax. Medical students needed a secure, stable environment to practice medical reasoning. Medical informatics students needed a more isolated system to experiment with the EHR's internal configuration. Both student groups needed synthetic patient cases that were realistic, but in different aspects. For medical students, it is essential that these cases are clinically consistent, and events unfold in a logical order. By contrast, synthetic data for medical informatics students should mimic the data quality problems found in real patient data., Outcomes: Medical informatics students show more mature reasoning about data quality issues and workflow integration than prior to using the EHR. Comments on both course evaluations have been positive, including comments on how working with a real-world EHR provides a realistic experience., Conclusion: The open-source EHR OpenMRS has proven to be a valuable addition to both the medicine and medical informatics curriculum. Both sets of students experience use of the EHR as giving them valuable, realistic learning experiences., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022. Published by Elsevier B.V.)

Background: Shared decision making (SDM) can be beneficial for patients, healthcare professionals, but is often not applied in practice. A clinical decision support system (CDSS) can facilitate SDM. However, CDSS acceptance rates are rather low. One context in which SDM between a general practitioner (GP) and patient regarding medication can be of great value is older patients' medication-related fall risk. Applying user-centered design to optimally tailor the CDSS to the needs and wishes of GPs can help overcome the low CDSS-acceptance rates. The current study aims to learn GPs' needs and wishes for a CDSS focused on diminishing medication-related fall risk., Materials and Methods: Participants were recruited through the Amsterdam Academic Network of General Practice and were sent a web-lecture as preparation. Three online focus groups with a total of 13 GPs were performed and were led by two moderators. The focus groups were recorded and transcribed verbatim. Transcripts were analyzed using Atlas.ti., Results: GPs' views on the workflow, risk presentation and advice of the system were elicited. The fit with the GPs' workflow was elaborately discussed, for instance how the CDSS could support the selection of patients at risk. GPs articulated a strong preference for a visual risk presentation, in the form of a gradient scale ranging from bright green to dark red. Furthermore, they preferred receiving both medication-related and non-medication-related advice, which should be presented on request., Discussion: The findings provide a valuable insight into GPs' needs and wishes for a CDSS focused on medication-related fall prevention. This will inform the design of a first prototype of the CDSS which will be subjected to usability tests. The findings of this study can also be used to support the development of medication-related CDSSs in a broader context., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: A medication-related Clinical Decision Support System (CDSS) is an application that analyzes patient data to provide assistance in medication-related care processes. Despite its potential to improve the clinical decision-making process, evidence shows that clinicians do not always use CDSSs in such a way that their potential can be fully realized. This systematic literature review provides an overview of frequently-reported barriers and facilitators for acceptance of medication-related CDSS., Materials and Methods: Search terms and MeSH headings were developed in collaboration with a librarian, and database searches were conducted in Medline, Scopus, Embase and Web of Science Conference Proceedings. After screening 5404 records and 140 full papers, 63 articles were included in this review. Quality assessment was performed for all 63 included articles. The identified barriers and facilitators are categorized within the Human, Organization, Technology fit (HOT-fit) model., Results: A total of 327 barriers and 291 facilitators were identified. Results show that factors most often reported were related to (a lack of) usefulness and relevance of information, and ease of use and efficiency of the system., Discussion: This review provides a valuable insight into a broad range of barriers and facilitators for using a medication-related CDSS as perceived by clinicians. The results can be used as a stepping stone in future studies developing medication-related CDSSs., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Purpose: To assess the extent to which clinical rules (CRs) can be implemented for automatic evaluation of quality of care in general practice., Methods: We assessed 81 clinical rules (CRs) adapted from a subset of Assessing Care of Vulnerable Elders (ACOVE) clinical rules, against Dutch College of General Practitioners (NHG) data model. Each CR was analyzed using the Logical Elements Rule METHOD: (LERM). LERM is a stepwise method of assessing and formalizing clinical rules for decision support. Clinical rules that satisfied the criteria outlined in the LERM method were judged to be implementable in automatic evaluation in general practice., Results: Thirty-three out of 81 (40.7%) Dutch-translated ACOVE clinical rules can be automatically evaluated in electronic medical record systems. Seven out of 7 CRs (100%) in the domain of diabetes can be automatically evaluated, 9/17 (52.9%) in medication use, 5/10 (50%) in depression care, 3/6 (50%) in nutrition care, 6/13 (46.1%) in dementia care, 1/6 (16.6%) in end of life care, 2/13 (15.3%) in continuity of care, and 0/9 (0%) in the fall-related care. Lack of documentation of care activities between primary and secondary health facilities and ambiguous formulation of clinical rules were the main reasons for the inability to automate the clinical rules., Conclusion: Approximately two-fifths of the primary care Dutch ACOVE-based clinical rules can be automatically evaluated. Clear definition of clinical rules, improved GP database design and electronic linkage of primary and secondary healthcare facilities can improve prospects of automatic assessment of quality of care. These findings are relevant especially because the Netherlands has very high automation of primary care., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: Reasons for intentional non-adherence to guidelines are largely unknown. The objective of this systematic review was to gain insight into and categorize reasons for intentional non-adherence and their validity. Non-adherence might be a conscious choice by either the clinician or the patient, and is not influenced by external factors (e.g. lack of knowledge or resources). We use the term intentional non-adherence to describe this class of reasons for not following guideline recommendations., Methods: Two independent reviewers examined MEDLINE citations for studies that investigated reasons for guideline non-adherence. The obtained articles were assessed for relevance and quality. Our search yielded 2912 articles, of which 16 matched our inclusion criteria and quality requirements. We planned to determine an overall ranking of categories of non-adherence., Results: Seven studies investigated clinical reasons and performed adjudication, while nine studies did not perform adjudication. Non-adherence varied between 8.2% and 65.3%. Meta-analysis proved unfeasible due to heterogeneity of study methodologies. The percentage of reasons deemed valid by adjudication ranged from 6.6% to 93.6%. Guideline non-adherence was predominantly valid; contra-indications and patient preference were most often reported as reasons for intentional non-adherence., Conclusion: We found a wide range of rates of non-adherence to clinical guidelines. This non-adherence is often supported by valid reasons, mainly related to contra-indications and patient preference. Therefore, we submit that many guideline deviations are intentional and these deviations do not necessarily impact quality of care., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Objectives: To propose a combined disease management and process modeling approach for evaluating and improving care processes, and demonstrate its usability and usefulness in a real-world fall management case study., Methods: We identified essential disease management related concepts and mapped them into explicit questions meant to expose areas for improvement in the respective care processes. We applied the disease management oriented questions to a process model of a comprehensive real world fall prevention and treatment program covering primary and secondary care. We relied on interviews and observations to complete the process models, which were captured in UML activity diagrams. A preliminary evaluation of the usability of our approach by gauging the experience of the modeler and an external validator was conducted, and the usefulness of the method was evaluated by gathering feedback from stakeholders at an invitational conference of 75 attendees., Results: The process model of the fall management program was organized around the clinical tasks of case finding, risk profiling, decision making, coordination and interventions. Applying the disease management questions to the process models exposed weaknesses in the process including: absence of program ownership, under-detection of falls in primary care, and lack of efficient communication among stakeholders due to missing awareness about other stakeholders' workflow. The modelers experienced the approach as usable and the attendees of the invitational conference found the analysis results to be valid., Conclusions: The proposed disease management view of process modeling was usable and useful for systematically identifying areas of improvement in a fall management program. Although specifically applied to fall management, we believe our case study is characteristic of various disease management settings, suggesting the wider applicability of the approach., (Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.)

Purpose: The aim of this study was to create a step-by-step method for transforming clinical rules for use in decision support, and to validate this method for usability and reliability., Methods: A sample set of clinical rules was identified from the relevant literature. Using an iterative approach with a focus group of mixed clinical and informatics experts, a method was developed for assessing and formalizing clinical rules. Two assessors then independently applied the method to a separate validation set of rules. Usability was assessed in terms of the time required and the error rate, and reliability was assessed by comparing the results of the two assessors., Results: The resulting method, called the Logical Elements Rule Method, consists of 7 steps: (1) restate the rule proactively; (2) restate the rule as a logical statement (preserving key phrases); (3) assess for conflict between rules; (4) identify concepts which are not needed; (5) classify concepts as crisp or fuzzy, find crisp definitions corresponding to fuzzy concepts, and extract data elements from crisp concepts; (6) identify rules which are related by sharing patients, actions, etc.; (7) determine availability of data in local systems. Validation showed that the method was usable with rules from various sources and clinical conditions, and reliable between users provided that the users agree on a terminology and agree on when the rule will be evaluated., Conclusions: A method is presented to assist in assessing clinical rules for their amenability to decision support, and formalizing the rules for implementation. Validation shows that the method is usable and reliable between users. Use of a terminology increases reliability but also the error rate. The method is useful for future developers of systems which offer decision support based on clinical rules., (2011 Elsevier Ireland Ltd. All rights reserved.)