Stijn Van de Velde

Background: Implementing evidence‐based recommendations in anaesthesiology and critical care remains challenging due to guideline, workflow and contextual barriers. Guideline‐based computerised clinical decision support systems (CDSS), embedded in structured pathways linking international guidelines to Scandinavian‐endorsed recommendations and delivered via electronic health records, may help strengthen the evidence‐to‐practice pathway. This study aimed to explore perceived barriers and facilitators to their use. Methods: This qualitative study interviewed 10 clinicians (anaesthesiologists and intensivists; specialists and non‐specialists; < 40 and ≥ 40 years; male and female) and nine system‐level stakeholders (hospital and health authority representatives) from Denmark, Finland, Iceland, Norway and Sweden. We integrated endorsed international guidelines into a CDSS providing patient‐specific reminders linked to recommendations, as well as shared decision‐making tools and decision‐analytic components. Using a simulation application, participants explored the CDSS in a demo electronic health record. Through a framework analysis based on the GUIDES checklist, we categorised perspectives as barriers, facilitators or suggestions. Results: Clinicians and system‐level interest holders expressed broad interest in this multifunctional CDSS to support evidence‐based, patient‐centred care and appreciated the collaborative international pathway facilitated by digital tools. Key themes included the challenges hospitals face in translating international guidelines into up‐to‐date, locally adapted guidance. Artificial intelligence (AI) was perceived as having the potential to both enhance and challenge traditional CDSS. Opportunities were identified to strengthen shared decision‐making for major decisions through the proposed system. Finally, participants highlighted the potential value of using a decision‐analytic tool to prioritise reminders, although the tool was considered conceptually challenging in its current form and would require clearer presentation before being applied in CDSS. Conclusions: This prototype study indicates support for a multifunctional CDSS. Building on the barriers and facilitators identified in this study, interviewees advocated for real‐world evaluation with particular attention to sustainable implementation. Editorial Comment: This study aimed to describe how ICU clinicians experience interaction in a simulated electronic health record where there were chosen elements of decision support based on international treatment guidelines incorporated into the simulation. Participants were interviewed for their perceptions of what types of decision support might be most helpful to support their clinical work in future patient information and management systems.

Background: Effective clinical decision support systems require accurate translation of practice recommendations into machine-readable artifacts; developing code sets that represent clinical concepts are an important step in this process. Many clinical coding systems are currently used in electronic health records, and it is unclear whether all of these systems are capable of efficiently representing the clinical concepts required in executing clinical decision support systems. Objective: The aim of this study was to evaluate which clinical coding systems are capable of efficiently representing clinical concepts that are necessary for translating artifacts into executable code for clinical decision support systems. Methods: Two methods were used to evaluate a set of clinical coding systems. In a theoretical approach, we extracted all the clinical concepts from 3 preventive care recommendations and constructed a series of code sets containing codes from a single clinical coding system. In a practical approach using data from a real-world setting, we studied the content of 1890 code sets used in an internationally available clinical decision support system and compared the usage of various clinical coding systems. Results: SNOMED CT and ICD-10 (International Classification of Diseases, Tenth Revision) proved to be the most accurate clinical coding systems for most concepts in our theoretical evaluation. In our practical evaluation, we found that International Classification of Diseases (Tenth Revision) was most often used to construct code sets. Some coding systems were very accurate in representing specific types of clinical concepts, for example, LOINC (Logical Observation Identifiers Names and Codes) for investigation results and ATC (Anatomical Therapeutic Chemical Classification) for drugs. Conclusions: No single coding system seems to fulfill all the needs for representing clinical concepts for clinical decision support systems. Comprehensiveness of the coding systems seems to be offset by complexity and forms a barrier to usability for code set construction. Clinical vocabularies mapped to multiple clinical coding systems could facilitate clinical code set construction.

Background: The covid-19 pandemic has heavily burdened, and in some cases overwhelmed, healthcare systems throughout the world. Healthcare workers are not only at heightened risk of infection, but also of adverse mental health outcomes. Identification of organizational, collegial and individual risk and resilience factors impacting the mental health of healthcare workers are needed to inform preparedness planning and sustainable response. Methods: We performed a rapid systematic review to identify, assess and summarize available research on the mental health impact of the covid-19 pandemic on healthcare workers. On 11 May 2020, we utilized the Norwegian Institute of Public Health's Live map of covid-19 evidence, the visualization of a database of 20,738 screened studies, to identify studies for inclusion. We included studies reporting on any type of mental health outcome in any type of healthcare workers during the pandemic. We described interventions reported by the studies, and narratively summarized mental health-related outcomes, as study heterogeneity precluded meta-analysis. We assessed study quality using design-specific instruments. Results: We included 59 studies, reporting on a total of 54,707 healthcare workers. The prevalence of general psychological distress across the studies ranged from 7-97% (median 37%), anxiety 9-90% (median 24%), depression 5-51% (median 21%), and sleeping problems 34-65% (median 37%). Seven studies reported on implementing mental health interventions, and most focused on individual symptom reduction, but none reported on effects of the interventions. In most studies, healthcare workers reported low interest in and use of professional help, and greater reliance on social support and contact with family and friends. Exposure to covid-19 was the most commonly reported correlate of mental health problems, followed by female gender, and worry about infection or about infecting others. Social support correlated with less mental health problems. Discussion: Healthcare workers in a variety of fields, positions, and exposure risks are reporting anxiety, depression, sleep problems, and distress during the covid-19 pandemic, but most studies do not report comparative data on mental health symptoms. before the pandemic. There seems to be a mismatch between risk factors for adverse mental health outcomes among healthcare workers in the current pandemic and their needs and preferences, and the individual psychopathology focus of current interventions. Efforts to help healthcare workers sustain healthy relationships to colleagues, family and friends over time may be paramount to safeguard what is already an important source of support during the prolonged crisis. Expanding interventions' focus to incorporate organizational, collegial and family factors to support healthcare workers responding to the pandemic could improve acceptability and efficacy of interventions. Other: The protocol for this review is available online. No funding was received.

Background: The EBMeDS system is the computerized clinical decision support (CCDS) system of EBPNet, a national computerized point-of-care information service in Belgium. There is no clear evidence of more complex CCDS systems to manage chronic diseases in primary care practices (PCPs). The objective of this study was to assess the effectiveness of EBMeDS use in improving diabetes care. Methods: A cluster-randomized trial with before-and-after measurements was performed in Belgian PCPs over 1 year, from May 2017 to May 2018. We randomly assigned 51 practices to either the intervention group (IG), to receive the EBMeDS system, or to the control group (CG), to receive usual care. Primary and secondary outcomes were the 1-year pre- to post-implementation change in HbA1c, LDL cholesterol, and systolic and diastolic blood pressure. Composite patient and process scores were calculated. A process evaluation was added to the analysis. Results were analyzed at 6 and 12 months. Linear mixed models and logistic regression models based on generalized estimating equations were used where appropriate. Results: Of the 51 PCPs that were enrolled and randomly assigned (26 PCPs in the CG and 25 in the IG), 29 practices (3815 patients) were analyzed in the study: 2464 patients in the CG and 1351 patients in the IG. No change differences existed between groups in primary or secondary outcomes. Change difference between CG and IG after 1-year follow-up was − 0.09 (95% CI − 0.18; 0.01, p-value = 0.06) for HbA1c; 1.76 (95% CI − 0.46; 3.98, p-value = 0.12) for LDL cholesterol; and 0.13 (95% CI − 0.91; 1.16, p-value = 0.81) and 0.12 (95% CI − 1.25;1.49, p-value = 0.86) for systolic and diastolic blood pressure respectively. The odds ratio of the IG versus the CG for the probability of no worsening and improvement was 1.09 (95% CI 0.73; 1.63, p-value = 0.67) for the process composite score and 0.74 (95% CI 0.49; 1.12, p-value = 0.16) for the composite patient score. All but one physician was satisfied with the EBMeDS system. Conclusions: The CCDS system EBMeDS did not improve diabetes care in Belgian primary care. The lack of improvement was mainly caused by imperfections in the organizational context of Belgian primary care for chronic disease management and shortcomings in the system requirements for the correct use of the EBMeDS system (e.g., complete structured records). These shortcomings probably caused low-use rates of the system. Trial registration: ClinicalTrials.gov, NCT01830569, Registered 12 April 2013.

BACKGROUND Effective clinical decision support systems require accurate translation of practice recommendations into machine-readable artifacts; developing code sets that represent clinical concepts are an important step in this process. Many clinical coding systems are currently used in electronic health records, and it is unclear whether all of these systems are capable of efficiently representing the clinical concepts required in executing clinical decision support systems. OBJECTIVE The aim of this study was to evaluate which clinical coding systems are capable of efficiently representing clinical concepts that are necessary for translating artifacts into executable code for clinical decision support systems. METHODS Two methods were used to evaluate a set of clinical coding systems. In a theoretical approach, we extracted all the clinical concepts from 3 preventive care recommendations and constructed a series of code sets containing codes from a single clinical coding system. In a practical approach using data from a real-world setting, we studied the content of 1890 code sets used in an internationally available clinical decision support system and compared the usage of various clinical coding systems. RESULTS SNOMED CT and ICD-10 (International Classification of Diseases, Tenth Revision) proved to be the most accurate clinical coding systems for most concepts in our theoretical evaluation. In our practical evaluation, we found that International Classification of Diseases (Tenth Revision) was most often used to construct code sets. Some coding systems were very accurate in representing specific types of clinical concepts, for example, LOINC (Logical Observation Identifiers Names and Codes) for investigation results and ATC (Anatomical Therapeutic Chemical Classification) for drugs. CONCLUSIONS No single coding system seems to fulfill all the needs for representing clinical concepts for clinical decision support systems. Comprehensiveness of the coding systems seems to be offset by complexity and forms a barrier to usability for code set construction. Clinical vocabularies mapped to multiple clinical coding systems could facilitate clinical code set construction.

Computerised clinical decision support (CDS) can potentially better inform decisions, and it can help with the management of information overload. It is perceived to be a key component of a learning health care system. Despite its increasing implementation worldwide, it remains uncertain why the effect of CDS varies and which factors make CDS more effective. To examine which factors make CDS strategies more effective on a number of outcomes, including adherence to recommended practice, patient outcome measures, economic measures, provider or patient satisfaction, and medical decision quality. We identified randomised controlled trials, non-randomised trials, and controlled before-and-after studies that directly compared CDS implementation with a given factor to CDS without that factor by searching CENTRAL, MEDLINE, EMBASE, and CINAHL and checking reference lists of relevant studies. We considered CDS with any objective for any condition in any healthcare setting. We included CDS interventions that were either displayed on screen or provided on paper and that were directed at healthcare professionals or targeted at both professionals and patients. The reviewers screened the potentially relevant studies in duplicate. They extracted data and assessed risk of bias in independent pairs or individually followed by a double check by another reviewer. We summarised results using medians and interquartile ranges and rated our certainty in the evidence using the GRADE system. We identified 66 head-to-head trials that we synthesised across 14 comparisons of CDS intervention factors. Providing CDS automatically versus on demand led to large improvements in adherence. Displaying CDS on-screen versus on paper led to moderate improvements and making CDS more versus less patient-specific improved adherence modestly. When CDS interventions were combined with professional-oriented strategies, combined with patient-oriented strategies, or combined with staff-oriented strategies, then adherence improved slightly. Providing CDS to patients slightly increased adherence versus CDS aimed at the healthcare provider only. Making CDS advice more explicit and requiring users to respond to the advice made little or no difference. The CDS intervention factors made little or no difference to patient outcomes. The results for economic outcomes and satisfaction outcomes were sparse. Multiple factors may affect the success of CDS interventions. CDS may be more effective when the advice is provided automatically and displayed on-screen and when the suggestions are more patient-specific. CDS interventions combined with other strategies probably also improves adherence. Providing CDS directly to patients may also positively affect adherence. The certainty of the evidence was low to moderate for all factors. PROSPERO, CRD42016033738

Computerised decision support (CDS) based on trustworthy clinical guidelines is a key component of a learning healthcare system. Research shows that the effectiveness of CDS is mixed. Multifaceted context, system, recommendation and implementation factors may potentially affect the success of CDS interventions. This paper describes the development of a checklist that is intended to support professionals to implement CDS successfully. We developed the checklist through an iterative process that involved a systematic review of evidence and frameworks, a synthesis of the success factors identified in the review, feedback from an international expert panel that evaluated the checklist in relation to a list of desirable framework attributes, consultations with patients and healthcare consumers and pilot testing of the checklist. We screened 5347 papers and selected 71 papers with relevant information on success factors for guideline-based CDS. From the selected papers, we developed a 16-factor checklist that is divided in four domains, i.e. the CDS context, content, system and implementation domains. The panel of experts evaluated the checklist positively as an instrument that could support people implementing guideline-based CDS across a wide range of settings globally. Patients and healthcare consumers identified guideline-based CDS as an important quality improvement intervention and perceived the GUIDES checklist as a suitable and useful strategy. The GUIDES checklist can support professionals in considering the factors that affect the success of CDS interventions. It may facilitate a deeper and more accurate understanding of the factors shaping CDS effectiveness. Relying on a structured approach may prevent that important factors are missed.