How are ontologies implemented to represent clinical practice guidelines in clinical decision support systems: protocol for a systematic review.

BACKGROUND: Clinical practice guidelines are statements which are based on the best available evidence, and their goal is to improve the quality of patient care. Integrating clinical practice guidelines into computer systems can help physicians reduce medical errors and help them to have the best possible practice. Guideline-based clinical decision support systems play a significant role in supporting physicians in their decisions. Meantime, system errors are the most critical concerns in designing decision support systems that can affect their performance and efficacy. A well-developed ontology can be helpful in this matter. The proposed systematic review will specify the methods, components, language of rules, and evaluation methods of current ontology-driven guideline-based clinical decision support systems. METHODS: This review will identify literature through searching MEDLINE (via Ovid), PubMed, EMBASE, Cochrane Library, CINAHL, ScienceDirect, IEEEXplore, and ACM Digital Library. Gray literature, reference lists, and citing articles of the included studies will be searched. The quality of the included studies will be assessed by the mixed methods appraisal tool (MMAT-version 2018). At least two independent reviewers will perform the screening, quality assessment, and data extraction. A third reviewer will resolve any disagreements. Proper data analysis will be performed based on the type of system and ontology engineering evaluation data. DISCUSSION: The study will provide evidence regarding applying ontologies in guideline-based clinical decision support systems. The findings of this systematic review will be a guide for decision support system designers and developers, technologists, system providers, policymakers, and stakeholders. Ontology builders can use the information in this review to build well-structured ontologies for personalized medicine. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42018106501.

Automating risk of bias assessment in systematic reviews: a real-time mixed methods comparison of human researchers to a machine learning system.

BACKGROUND: Machine learning and automation are increasingly used to make the evidence synthesis process faster and more responsive to policymakers' needs. In systematic reviews of randomized controlled trials (RCTs), risk of bias assessment is a resource-intensive task that typically requires two trained reviewers. One function of RobotReviewer, an off-the-shelf machine learning system, is an automated risk of bias assessment. METHODS: We assessed the feasibility of adopting RobotReviewer within a national public health institute using a randomized, real-time, user-centered study. The study included 26 RCTs and six reviewers from two projects examining health and social interventions. We randomized these studies to one of two RobotReviewer platforms. We operationalized feasibility as accuracy, time use, and reviewer acceptability. We measured accuracy by the number of corrections made by human reviewers (either to automated assessments or another human reviewer's assessments). We explored acceptability through group discussions and individual email responses after presenting the quantitative results. RESULTS: Reviewers were equally likely to accept judgment by RobotReviewer as each other's judgement during the consensus process when measured dichotomously; risk ratio 1.02 (95% CI 0.92 to 1.13; p = 0.33). We were not able to compare time use. The acceptability of the program by researchers was mixed. Less experienced reviewers were generally more positive, and they saw more benefits and were able to use the tool more flexibly. Reviewers positioned human input and human-to-human interaction as superior to even a semi-automation of this process. CONCLUSION: Despite being presented with evidence of RobotReviewer's equal performance to humans, participating reviewers were not interested in modifying standard procedures to include automation. If further studies confirm equal accuracy and reduced time compared to manual practices, we suggest that the benefits of RobotReviewer may support its future implementation as one of two assessors, despite reviewer ambivalence. Future research should study barriers to adopting automated tools and how highly educated and experienced researchers can adapt to a job market that is increasingly challenged by new technologies.

Do providers use computerized clinical decision support systems? A systematic review and meta-regression of clinical decision support uptake.

BACKGROUND: Computerized clinical decision support systems (CDSSs) are a promising knowledge translation tool, but often fail to meaningfully influence the outcomes they target. Low CDSS provider uptake is a potential contributor to this problem but has not been systematically studied. The objective of this systematic review and meta-regression was to determine reported CDSS uptake and identify which CDSS features may influence uptake. METHODS: Medline, Embase, CINAHL, and the Cochrane Database of Controlled Trials were searched from January 2000 to August 2020. Randomized, non-randomized, and quasi-experimental trials reporting CDSS uptake in any patient population or setting were included. The main outcome extracted was CDSS uptake, reported as a raw proportion, and representing the number of times the CDSS was used or accessed over the total number of times it could have been interacted with. We also extracted context, content, system, and implementation features that might influence uptake, for each CDSS. Overall weighted uptake was calculated using random-effects meta-analysis and determinants of uptake were investigated using multivariable meta-regression. RESULTS: Among 7995 citations screened, 55 studies involving 373,608 patients and 3607 providers met full inclusion criteria. Meta-analysis revealed that overall CDSS uptake was 34.2% (95% CI 23.2 to 47.1%). Uptake was only reported in 12.4% of studies that otherwise met inclusion criteria. Multivariable meta-regression revealed the following factors significantly associated with uptake: (1) formally evaluating the availability and quality of the patient data needed to inform CDSS advice; and (2) identifying and addressing other barriers to the behaviour change targeted by the CDSS. CONCLUSIONS AND RELEVANCE: System uptake was seldom reported in CDSS trials. When reported, uptake was low. This represents a major and potentially modifiable barrier to overall CDSS effectiveness. We found that features relating to CDSS context and implementation strategy best predicted uptake. Future studies should measure the impact of addressing these features as part of the CDSS implementation strategy. Uptake reporting must also become standard in future studies reporting CDSS intervention effects. REGISTRATION: Pre-registered on PROSPERO, CRD42018092337.

Emergency care with lay responders in underserved populations: a systematic review.

OBJECTIVE: To assess the individual and community health effects of task shifting for emergency care in low-resource settings and underserved populations worldwide. METHODS: We systematically searched 13 databases and additional grey literature for studies published between 1984 and 2019. Eligible studies involved emergency care training for laypeople in underserved or low-resource populations, and any quantitative assessment of effects on the health of individuals or communities. We conducted duplicate assessments of study eligibility, data abstraction and quality. We synthesized findings in narrative and tabular format. FINDINGS: Of 19 308 papers retrieved, 34 studies met the inclusion criteria from low- and middle-income countries (21 studies) and underserved populations in high-income countries (13 studies). Targeted emergency conditions included trauma, burns, cardiac arrest, opioid poisoning, malaria, paediatric communicable diseases and malnutrition. Trainees included the general public, non-health-care professionals, volunteers and close contacts of at-risk populations, all trained through in-class, peer and multimodal education and public awareness campaigns. Important clinical and policy outcomes included improvements in community capacity to manage emergencies (14 studies), patient outcomes (13 studies) and community health (seven studies). While substantial effects were observed for programmes to address paediatric malaria, trauma and opioid poisoning, most studies reported modest effect sizes and two reported null results. Most studies were of weak (24 studies) or moderate quality (nine studies). CONCLUSION: First aid education and task shifting to laypeople for emergency care may reduce patient morbidity and mortality and build community capacity to manage health emergencies for a variety of emergency conditions in underserved and low-resource settings.

Instruments to measure fear of COVID-19: a diagnostic systematic review.

BACKGROUND: The COVID-19 pandemic has become a source of fear across the world. Measuring the level or significance of fear in different populations may help identify populations and areas in need of public health and education campaigns. We were interested in diagnostic tests developed to assess or diagnose COVID-19-related fear or phobia. METHODS: We performed a systematic review of studies that examined instruments diagnosing or assessing fear or phobia of COVID-19 (PROSPERO registration: CRD42020197100). We utilized the Norwegian Institute of Public Health's Live map of covid-19 evidence, a database of pre-screened and pre-categorized studies. The Live map of covid-19 evidence identified references published since 1 December 2019 in MEDLINE, Embase, and the Centers for Disease Control and Prevention. Following biweekly searches, two researchers independently categorized all studies according to topic (seven main topics, 52 subordinate topics), population (41 available groups), study design, and publication type. For this review, we assessed for eligibility all studies that had been categorized to the topic "Experiences and perceptions, consequences; social, political, economic aspects" as of 25 September 2020, in addition to hand-searching included studies' reference lists. We meta-analyzed correlation coefficients of fear scores to the most common reference tests (self-reports of anxiety, depression, and stress), and reported additional concurrent validity to other reference tests such as specific phobias. We assessed study quality using the QUADAS-2 for the minority of studies that presented diagnostic accuracy statistics. RESULTS: We found 18 studies that validated fear instruments. Fifteen validated the Fear of COVID-19 scale (FCV-19S). We found no studies that proposed a diagnosis of fear of COVID-19 or a threshold of significant/clinical versus non-significant/subclinical fear. Study quality was low, with the most common potential biases related to sampling strategy and un-blinded data analysis. The FSV-19S total score correlated strongly with severe phobia (r = 0.703, 95%CI 0.634-0.761) in one study, and moderately with anxiety in a meta-analysis. CONCLUSIONS: The accuracy of the FSV-19S needs to be measured further using fear-related reference instruments, and future studies need to provide cut-off scores and normative values. Further evaluation of the remaining three instruments is required.

Coding Systems for Clinical Decision Support: Theoretical and Real-World Comparative Analysis.

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.

The mental health impact of the covid-19 pandemic on healthcare workers, and interventions to help them: A rapid systematic review.

The covid-19 pandemic has heavily burdened healthcare systems throughout the world. We performed a rapid systematic review to identify, assess and summarize research on the mental health impact of the covid-19 pandemic on HCWs (healthcare workers). We utilized the Norwegian Institute of Public Health's Live map of covid-19 evidence on 11 May and included 59 studies. Six reported on implementing interventions, but none reported on effects of the interventions. HCWs reported low interest in professional help, and greater reliance on social support and contact. 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. HCWs reported anxiety, depression, sleep problems, and distress during the covid-19 pandemic. We assessed the certainty of the estimates of prevalence of these symptoms as very low using GRADE. Most studies did not report comparative data on mental health symptoms before the pandemic or in the general population. There seems to be a mismatch between risk factors for adverse mental health outcomes among HCWs in the current pandemic, their needs and preferences, and the individual psychopathology focus of current interventions.

Computerized clinical decision support system for diabetes in primary care does not improve quality of care: a cluster-randomized controlled trial.

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.

A systematic review of trials evaluating success factors of interventions with computerised clinical decision support.

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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. TRIAL REGISTRATION: PROSPERO, CRD42016033738.

Involving general practice trainees in clinical practice guideline adaptation.

BACKGROUND: It is unclear whether it is feasible to involve residents in guideline development or adaptation. We designed a multifaceted training program that combines training sessions, a handbook and a documentation tool to assist general practice (GP)-trainees in the adaptation of clinical practice guidelines (CPGs). The aim of this study is to adapt a database of CPGs by involving GP-trainees and to build evidence-based practice (EBP) learning capacity. METHODS: We assessed each adaptation process and surveyed all GP-trainees who enrolled in our training program on their views on the program. They were asked to formulate an overall rating for the training and were asked to rate individual aspects of the training program (the training sessions, the handbook and the documentation tool). RESULTS: To date, 122 GP-trainees followed the training and have adapted 60 different CPGs. Overall quality of their work was good. Based on an assessment of the content of the documentation tool, 24 (40%) adapted CPGs rated as good quality and 30 (50%) rated as moderate quality. Only 3 adapted CPGs (5%) were evaluated as being of poor quality. 51 (42%) GP-trainees completed the survey on user satisfaction. 98% (50) of the GP-trainees found the training to be of good overall quality. 86% of the GP-trainees were satisfied with the handbook but satisfaction was lowest for the documentation tool (47% satisfied). CONCLUSION: It is possible to engage GP-trainees in CPG adaptation using a formal process when provided with training, feedback and documentation tools.

Development of a Tailored Intervention With Computerized Clinical Decision Support to Improve Quality of Care for Patients With Knee Osteoarthritis: Multi-Method Study.

BACKGROUND: Clinical practice patterns greatly diverge from evidence-based recommendations to manage knee osteoarthritis conservatively before resorting to surgery. OBJECTIVE: This study aimed to tailor a guideline-based computerized decision support (CDS) intervention that facilitates the conservative management of knee osteoarthritis. METHODS: Experts with backgrounds in clinical medicine, research, implementation, or health informatics suggested the most important recommendations for implementation, how to develop an implementation strategy, and how to form the CDS algorithms. In 6 focus group sessions, 8 general practitioners and 22 patients from Norway, Belgium, and Finland discussed the suggested CDS intervention and identified factors that would be most critical for the success of the intervention. The focus group moderators used the GUideline Implementation with DEcision Support checklist, which we developed to support consideration of CDS success factors. RESULTS: The experts prioritized 9 out of 22 recommendations for implementation. We formed the concept for 6 CDS algorithms to support implementation of these recommendations. The focus group suggested 59 unique factors that could affect the success of the presented CDS intervention. Five factors (out of the 59) were prioritized by focus group participants in every country, including the perceived potential to address the information needs of both patients and general practitioners; the credibility of CDS information; the timing of CDS for patients; and the need for personal dialogue about CDS between the general practitioner and the patient. CONCLUSIONS: The focus group participants supported the CDS intervention as a tool to improve the quality of care for patients with knee osteoarthritis through shared, evidence-based decision making. We aim to develop and implement the CDS based on these study results. Future research should address optimal ways to (1) provide patient-directed CDS, (2) enable more patient-specific CDS within the context of patient complexity, and (3) maintain user engagement with CDS over time.

The GUIDES checklist: development of a tool to improve the successful use of guideline-based computerised clinical decision support.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Electronic Laboratory Medicine ordering with evidence-based Order sets in primary care (ELMO study): protocol for a cluster randomised trial.

BACKGROUND: Laboratory testing is an important clinical act with a valuable role in screening, diagnosis, management and monitoring of diseases or therapies. However, inappropriate laboratory test ordering is frequent, burdening health care spending and negatively influencing quality of care. Inappropriate tests may also result in false-positive results and potentially cause excessive downstream activities. Clinical decision support systems (CDSSs) have shown promising results to influence the test-ordering behaviour of physicians and to improve appropriateness. Order sets, a form of CDSS where a limited set of evidence-based tests are proposed for a series of indications, integrated in a computerised physician order entry (CPOE) have been shown to be effective in reducing the volume of ordered laboratory tests but convincing evidence that they influence appropriateness is lacking. The aim of this study is to evaluate the effect of order sets on the quality and quantity of laboratory test orders by physicians. We also aim to evaluate the effect of order sets on diagnostic error and explore the effect on downstream or cascade activities. METHODS: We will conduct a cluster randomised controlled trial in Belgian primary care practices. The study is powered to measure two outcomes. We will primarily measure the influence of our CDSS on the appropriateness of laboratory test ordering. Additionally, we will also measure the influence on diagnostic error. We will also explore the effects of our intervention on cascade activities due to altered results of inappropriate tests. DISCUSSION: We have designed a study that should be able to demonstrate whether the CDSS aimed at diagnostic testing is not only able to influence appropriateness but also safe with respect to diagnostic error. These findings will influence a lager, nationwide implementation of this CDSS. TRIAL REGISTRATION: ClinicalTrials.gov, NCT02950142 .

Adapting a large database of point of care summarized guidelines: a process description.

RATIONALE, AIMS AND OBJECTIVES: Questions posed at the point of care (POC) can be answered using POC summarized guidelines. To implement a national POC information resource, we subscribed to a large database of POC summarized guidelines to complement locally available guidelines. Our challenge was in developing a sustainable strategy for adapting almost 1000 summarized guidelines. The aim of this paper was to describe our process for adapting a database of POC summarized guidelines. METHODS: An adaptation process based on the ADAPTE framework was tailored to be used by a heterogeneous group of participants. Guidelines were assessed on content and on applicability to the Belgian context. To improve efficiency, we chose to first aim our efforts towards those guidelines most important to primary care doctors. RESULTS: Over a period of 3 years, we screened about 80% of 1000 international summarized guidelines. For those guidelines identified as most important for primary care doctors, we noted that in about half of the cases, remarks were made concerning content. On the other hand, at least two-thirds of all screened guidelines required no changes when evaluating their local usability. CONCLUSIONS: Adapting a large body of POC summarized guidelines using a formal adaptation process is possible, even when faced with limited resources. This can be done by creating an efficient and collaborative effort and ensuring user-friendly procedures. Our experiences show that even though in most cases guidelines can be adopted without adaptations, careful review of guidelines developed in a different context remains necessary. Streamlining international efforts in adapting international POC information resources and adopting similar adaptation processes may lessen duplication efforts and prove more cost-effective.

Lower Extremity Near-infrared Spectroscopy After Popliteal Block For Orthopaedic Foot Surgery.

BACKGROUND: Noninvasive measurement of cutaneous tissue oxygenation using near-infrared spectroscopy (NIRS) has become common in peri-operative care. Following institution of peripheral nerve blocks, neurovascular alterations in the blocked region have been described. OBJECTIVE: The primary aim of this study encompassed the assessment of the influence of a popliteal block on changes in regional oxygen saturation (SrO2), and the location of most prominent changes. METHOD: We conducted a prospective randomised controlled trial. Hundred twenty patients who received a popliteal block for foot surgery were included. Popliteal block was performed under echographic guidance. The patients were randomized in 3 groups according to the location of the SrO2 electrodes on the legs. Bilateral SrO2 measurements were performed simultaneously. SrO2 in the operated leg and in the control leg was measured at baseline and 1, 5, 10, 15, and 30 minutes after the perineural injection. We quantified the evolution in SrO2 by calculating over time the differences in SrO2 values between the operated and control leg (=ΔSrO2). RESULTS: At 30 minutes, ΔSrO2 increased significantly (p<0.05) at the plantar side of the foot (11.3% ± 2.9%), above the ankle (4.9% ± 1.3%) and the popliteal fossa (3.6% ± 1.2%). CONCLUSION: At 30 minutes after institution of the popliteal block, ΔSrO2 was most prominent at the plantar side of the foot as compared with measurement performed above the ankle or under the knee.

Health effects of training laypeople to deliver emergency care in underserviced populations: a systematic review protocol.

INTRODUCTION: The Disease Control Priorities Project recommends emergency care training for laypersons in low-resource settings, but evidence for these interventions has not yet been systematically reviewed. This review will identify the individual and community health effects of educating laypeople to deliver prehospital emergency care interventions in low-resource settings. METHODS AND ANALYSIS: This systematic review addresses the following question: in underserviced populations and low-resource settings (P), does first aid or emergency care training or education for laypeople (I) confer any individual or community health benefit for emergency health conditions (O), in comparison with no training or other forms of education (C)? We restrict this review to studies reporting quantitatively measurable outcomes, and search 12 electronic bibliographic databases and grey literature sources. A team of expert content and methodology reviewers will conduct title and abstract screening and full-text review, using a custom-built online platform. Two investigators will independently extract methodological variables and outcomes related to patient-level morbidity and mortality and community-level effects on resilience or emergency care capacity. Two investigators will independently assess external validity, selection bias, performance bias, measurement bias, attrition bias and confounding. We will summarise the findings using a narrative approach to highlight similarities and differences between the gathered studies. ETHICS AND DISSEMINATION: Formal ethical approval is not required. RESULTS: The results will be disseminated through a peer-reviewed publication and knowledge translation strategy. REVIEW REGISTRATION NUMBER: CRD42014009685.

Tailoring implementation strategies for evidence-based recommendations using computerised clinical decision support systems: protocol for the development of the GUIDES tools.

BACKGROUND: A computerised clinical decision support system (CCDSS) is a technology that uses patient-specific data to provide relevant medical knowledge at the point of care. It is considered to be an important quality improvement intervention, and the implementation of CCDSS is growing substantially. However, the significant investments do not consistently result in value for money due to content, context, system and implementation issues. The Guideline Implementation with Decision Support (GUIDES) project aims to improve the impact of CCDSS through optimised implementation based on high-quality evidence-based recommendations. To achieve this, we will develop tools that address the factors that determine successful CCDSS implementation. METHODS/DESIGN: We will develop the GUIDES tools in four steps, using the methods and results of the Tailored Implementation for Chronic Diseases (TICD) project as a starting point: (1) a review of research evidence and frameworks on the determinants of implementing recommendations using CCDSS; (2) a synthesis of a comprehensive framework for the identified determinants; (3) the development of tools for use of the framework and (4) pilot testing the utility of the tools through the development of a tailored CCDSS intervention in Norway, Belgium and Finland. We selected the conservative management of knee osteoarthritis as a prototype condition for the pilot. During the process, the authors will collaborate with an international expert group to provide input and feedback on the tools. DISCUSSION: This project will provide guidance and tools on methods of identifying implementation determinants and selecting strategies to implement evidence-based recommendations through CCDSS. We will make the GUIDES tools available to CCDSS developers, implementers, researchers, funders, clinicians, managers, educators, and policymakers internationally. The tools and recommendations will be generic, which makes them scalable to a large spectrum of conditions. Ultimately, the better implementation of CCDSS may lead to better-informed decisions and improved care and patient outcomes for a wide range of conditions. PROTOCOL REGISTRATION: PROSPERO, CRD42016033738.