Rational, aims and objectives: This study aimed to assess knowledge, attitude and practice (KAP) of using insulin pen in diabetes and determine the related factors to the KAP, adverse drug reactions (ADRs) and glycemic control status. Methods: In our descriptive study, 148 patients with diabetes were interviewed by 16-item questions on knowledge and 8 questions on attitude, and were asked to present the insulin pen injection technique with a sample insulin pen. Results: Proportions of patient having a good knowledge, positive attitude, and good practice were 45.9%, 78.4%, and 44.6%, respectively. The three most incorrect steps were skipping to prime pen needle (90.9%), not removing the used needle from the pen after using (87.8), and not holding for specific count time before withdrawal of pen needle from skin (50.7%). Patients having duration of insulin one year or more had better knowledge (p=0.025), more positive attitude (p=0.017), and better practice of insulin (p=0.042). Patients using insulin combined with oral diabetic medications or having history of using insulin vials had more positive attitude of insulin role (0.038). Frequency of having good knowledge was statistically significantly higher in participants who received counseling from health professionals previously (p=0.001). The study also found a positive correlation between good knowledge of patients and good practical skills (p<0.001). Patients with poor practice of insulin were more likely to have ADRs at the injection sites (p = 0.013). The worse knowledge of patients was significant related to the higher risk of hypoglycemia (p=0.001). KAP levels did not correlate with glycemic control status. Conclusions: Positive attitude in patients was recorded at a significant rate; however, knowledge and practice needs improvement. This study was helpful to shape the patient education and target specific patients for education.

Rationale, aims and objectives In the US, the reluctance of the federal government to impose a national stay-at-home policy in wake of COVID19 pandemic has left the decision of how to achieve social distancing to individual state governors. We hypothesized that in the absence of formal guidelines, the decision to close a state reflects the classic Weber-Fechner law of psychophysics- the amount by which a stimulus (such as number of cases or deaths) must increase in order to be noticed as a fraction of the intensity of that stimulus. Methods On April 12, 2020 we downloaded data from the New York Times database from all 50 states and the District of Columbia; by that time all but 7 states had issued the stay-at-home orders. We fitted the Weber-Fechner logarithmic function by regressing the log2 of cases and deaths respectively against the daily counts. We also conducted Cox regression analysis to determine if the probability of issuing the stay-at-home order increases proportionally as the number of cases or deaths increases. Results We found that the decision to issue the state-at-home order reflects the Weber-Fechner law. Both the number of infections (p=<0.0001; R2=0.79) and deaths (p<0.0001; R2=0.63) were significantly associated with the decision to issue the stay-at-home orders. The results indicate that for each doubling of infections or deaths, an additional 4 to 6 states will issue stay-at-home orders. Cox regression showed that when the number of deaths reached 256 and the number of infected people were over 16,000 the probability of issuing “stay-at-home” order was close to 100%. We found no difference in decision-making according to the political affiliation; the results remain unchanged on July 16,2020. Conclusions when there are not clearly articulated rules to follow, decision-makers resort to simple heuristics, in this case one consistent with the Weber-Fechner law.

Rationale aims and objectives Potentially preventable hospitalizations (PPH) are a challenge. What happens before hospital admission? Are there crucial tipping points before admissions in at-risk cohorts’ trajectories? HealthLinksChronicCare (HLCC) hospital risk-prediction algorithms using admission, diagnosis, and lifestyle data identifies at-patients. MW monitors HLCC patients with outbound phone calls using telehealth – the Patient Journey Record System with alerts representing a real-time anticipated risk of PPH. Health Coaches triage and intervene to optimize GP, hospital and community service utilization to reduce the risk of PPH. Aims To describe a time series of telehealth phone calls related to an acute admission ( 10 days) to investigate tipping points in self-reported biopsychosocial environmental concerns (total alerts) and or condition symptoms of concern (red alerts). Methods MW participants had an acute (non-surgical) admission and >44 calls between 23/12/16 - 11/10/17. The Patient Journey Record System (PaJR) and Victorian Admitted Episode Data/ Emergency Minimum Dataset provided longitudinal data. Descriptive time series analysis employed Pettitt’s homogeneity test to detect ‘tipping points’ using XLSTAT package. Findings One hundred three patients aged 74 ± 15.4 years, with 59% male and 61% female, provided 764 call records around admission(s) and 22,715 records over 10 months. Total alerts and red alerts were higher in the 10 days before and after admission. Total alerts significantly increased (tipped) at day 3 before hospitalisation persisting until 10 days. Red alerts increased (tipped) 1 day before admission and remained high following discharge. Discussion and Conclusion Self-report in phone calls describe a pre-hospital phase of ‘post-hospital syndrome’ (PHS), which began at least 10 days before admission and persisted after discharge. Wide-ranging health, psychosocial, and environmental concerns preceded a tipping point into acute symptoms. Telehealth monitoring of biopsychosocial, as well as disease, concerns require further investigation.

Our purpose is to assess epidemiological agent-based models– or ABMs - of the SARS-CoV-2 pandemic methodologically. The rapid spread of the outbreak requires fast-paced decision-making regarding mitigation measures. However, the evidence for the efficacy of non-pharmaceutical interventions such as imposed social distancing and school or workplace closures is scarce: few observational studies use quasi-experimental research designs, and conducting randomized controlled trials seems infeasible. Additionally, evidence from the previous coronavirus outbreaks of SARS and MERS lacks external validity, given the significant differences in contagiousness of those pathogens relative to SARS-CoV-2. To address the pressing policy questions that have emerged as a result of COVID-19, epidemiologists have produced numerous models that range from simple compartmental models to highly advanced agent-based models. These models have been criticized for involving simplifications and lacking empirical support for their assumptions. In order to address these voices and methodologically appraise epidemiological ABMs, we consider AceMod (the model of the COVID-19 epidemic in Australia) as an example of the modeling practice. Our case study shows that, although epidemiological ABMs involve simplifications of various sorts, the key characteristics of social interactions and the spread of SARS-CoV-2 are represented sufficiently accurately. This is the case because these modelers treat empirical results as inputs for constructing modeling assumptions and rules that the agents follow; and they use calibration to assert the adequacy to benchmark variables. Given this, we claim that the best epidemiological ABMs are models of actual mechanisms and deliver both mechanistic and difference-making evidence. Furthermore, the efficacy claims are not only internally valid but also adequately describe the effects of interventions in the targets of the models. We also discuss the limitations of ABMs and put forward policy recommendations.

Decision-making in musculoskeletal health care is complex, with discrepancy among clinical providers, and variation in the per cent of referrals for specialist care. To date there is an increased focus on specialist referrals, risking overuse of expensive testing and contributing to unnecessary treatment. This report will considerer the difficulties of primary care musculoskeletal decision-making using shoulder injuries as an example, presenting a solution based on multi-criteria decision-making analysis with online software. The associated issues involved in clinical decision-making are complex. Contributing to the components of complexity are; the aetiology of shoulders being multi-faceted, the experience and knowledge of providers, and the burden of patient demands. Notwithstanding, funding considerations, resource allocation availability, and other associated issues around clinical decision-making. Considering there are many facets involved in decision-making, this is an area where multi-criteria decision-making analysis (MCDM) may be appropriate. MCDM which uses mathematical analysis, is increasingly being used in health care and could be applied to the challenges of musculoskeletal care. MCDM has the potential to decrease variability in decision-making and be used as a tool to augment clinical decisions. Furthermore, the key clinical factors that decision-making is based on are to date unclear, therefore, this preliminary report offers a start towards moving to a clarification of the key factors, and an approach for implementing improved shoulder clinical care decision-making which could then be adapted and applied to other body sites.

Linden and Yarnold1 recently proposed classification tree analysis (CTA), a machine‐learning procedure, as an alternative to conventional methods for analyzing mediation effects in treatment-outcome research. They note that CTA may have a number of advantages in this regard. It requires no assumptions about the distribution of variables or the functional form of the best-fitting model, for example, thus affording greater potential flexibility in identifying complex forms of association among variables with varying scales of measurement (e.g., binary and continuous). The authors further argue that CTA, unlike conventional approaches to testing mediation, “will not generate a model if a treatment-mediator-outcome relationship does not exist” (p. 359) and, conversely, that CTA “will systematically identify a treatment-by-mediator interaction if it exists, as well as any other interaction between variables.” (p. 359). Using data from the Job Search Intervention Study (JOBS II), they find that structural equation modeling, a conventional approach to testing mediation, failed to indicate support for job-search self-efficacy as a mediator of the effects of the intervention on whether the study participant was reemployed at follow-up. In contrast, the authors conclude that CTA applied to the same set of variables revealed that job-search self-efficacy was a mediator of intervention effects on employment among those in the treatment group.There are, however, two problematic aspects of the authors’ approach. First, as they point out, causal inference of a mediational pathway depends on the assumption that the associations involved exist net of potential confounding variables. In a randomized control trial such as JOBS II, confounding of the association between the mediator and outcome is of particular concern.2 The authors seek to address this concern by conducting a CTA that includes a set of 9 potential confounders (e.g., income, age, initial level of depressive symptoms) as candidate predictors. This approach does not ensure statistical control for these variables, however, for at least two reasons. First, there is no guarantee that the variables involved will actually be included in the resulting classification tree; failing to meet the criterion for inclusion does not rule out the possibility that a given potential confounder or combination of such confounders nonetheless share an association with the mediator and outcome to an extent that renders their association non-significant. This concern proves pertinent to the authors’ analysis as only 3 of the potential confounders earn entry into the resulting classification tree for reemployment status. A second concern with the approach taken by the authors is that whatever covariates are included in the classification tree may be included in positions that are inadequate for the purpose of controlling for confounding of the mediator-outcome association. Gender, for example, in their analysis is included in the control group branch of the tree, whereas job-search self-efficacy, the potential mediator, is included only in the treatment group arm. As a further example, education is included on the treatment group arm, but only after job-search self-efficacy’s role in the model already has already been established through its inclusion on a higher tier of the model. An alternative approach for addressing confounding in the context of CTA would be to residualize the candidate mediator on possible confounders and then fit a classification tree with this adjusted variable. If the residualized mediator continues to discriminate across levels of the outcome, conditional on treatment status, it could be concluded that the mediate-outcome portion of the potential mediational pathway of interest is evident independent of the measured confounders. Applying this approach, I find that the job-search self-efficacy continues to emerge as a discriminating variable for reemployment in the treatment arm branch of the classification tree that I fit with the same JOBS-II data. However, it cannot be assumed that this more rigorous approach to taking into account confounders will always yield the same result as one in which they are merely included as candidate predictors within a CTA.A more fundamental concern with the CTA approach to identifying mediation employed by the authors is that the mediator in the predictor-mediator and mediator-outcome segments of the mediational pathway are not assured of having a consistent definition. This is a result of the optimal cut-point on the mediator being determined separately for the two segments, through an optimal discriminant analysis for the treatment-mediator segment and CTA for the mediator-outcome segment. Thus, in the case of the mediational pathway for reemployment, treatment status predicts a dichotomous measure of job-search self-efficacy determined by a cut-score of 3.92 (i.e., high job-search self-efficacy corresponds to values above 3.92 and low to values equal to and below 3.92), whereas the dichotomous measure of job-search self-efficacy predicting reemployment is determined by a different cut-score of 4.92. Yet, it is essential by definition that the mediator in a mediational pathway that is influenced by the initial variable in the pathway (in this case, treatment) be the same variable that then influences the outcome (in this case, reemployment). In this case, nearly one half of the sample (47.3%; n = 426) has a different high-low classification on the job-search self-efficacy, suggesting considering divergence between the versions of this variable determined by the respective cut-scores. One approach to addressing this concern would be to define a new mediator that reflects the overlapping portion of the differing definitions. In the present example, this could be a dichotomous measure of job-search self-efficacy defined by a cut-score of greater 3.92, which would ensure that all those with scores classified as relatively high on the measure continue to be classified as such; alternatively, a cut-score of 4.92 could be used if priority is given to ensuring that all those classified as low remain in this category. Still another option would be to use the mid-point between the two cut-scores of 4.42. One could then evaluate the mediational pathway of interest using one or more of these options. Applying this approach using PROC CAUSALMED in SAS3, allowing for the suggested treatment-mediator interaction and including all covariates, I find evidence of an indirect mediated pathway when using the lower bound cut-off score of 3.92 for job-search self-efficacy (Odds Ratio for natural indirect effect of .959, 95% CI limits of .893, .998), but not when using the other two cut-off scores, although the differences in estimates are admittedly small in this case (complete results are available upon request).To summarize, Linden and Yarnold (2018) make a significant contribution by introducing CTA as a promising strategy for identifying mediated effects in intervention research. Further refinements to their approach, however, are recommended to more fully incorporate fundamental assumptions that accompany all tests for mediation as well as to evaluate and confirm potential mediational pathways using conventional procedures.References1Linden A, Yarnold PR. Identifying causal mechanisms in health care interventions using classification tree analysis. J Eval Clin Pract. 2018;24:353–361. https://doi.org/10.1111/jep.128482Imai K, Keele L, Tingley D. A general approach to causal mediation analysis. Psychol Methods . 2010;15:309‐334.3Yung, Y, Lamm, M, Zhang, W. Causal Mediation Analysis with the CAUSALMED Procedure . Paper SAS1991-2018. Cary, NC: SAS Institute Inc.; 2018.

Inappropriate use of telemetry frequently occurs in the inpatient, non-intensive care unit setting. Recent telemetry practice standards have attempted to guide appropriate use and limit the overuse of this important resource, with limited success. Clinical-effectiveness studies of these practice standards have thus far not included care settings in which resident-physicians are the primary caregivers. Furthermore, studies of the role of nurses in appropriate telemetry use are lacking. In this report, we describe two interventions implemented on general internal medicine units of a large academic hospital over three years. The first intervention, or nurse-discontinuation protocol, allowed nurses to trigger the discontinuation of telemetry once the appropriate duration had passed according to practice standards. The second intervention, or physician-discontinuation protocol, instituted a Best Practice Advisory that notified the resident-physician via the electronic medical record when the appropriate telemetry duration for each patient had elapsed and suggested termination of telemetry. Results showed that the nurse-discontinuation protocol reduced mean patient time on telemetry and the physician-discontinuation protocol reduced mean telemetry orders per patient. These findings validate a prior study and expand on our understanding of telemetry use in the academic care setting in which trainees serve as the primary caregivers.

Rationale Assessing the performance of diagnostic tests requires evaluation of the amount of diagnostic uncertainty the test reduces (i.e. 0% - useless test, 100% - perfect test). Statistical measures currently dominating the evidence-based medicine (EBM) field and particularly meta-analysis (e.g. sensitivity and specificity), cannot explicitly measure this uncertainty reduction. Mutual information (MI), an information theory statistic, is a more appropriate metric for evaluating diagnostic tests as it explicitly quantifies uncertainty and, therefore, facilitates natural interpretation of a test’s value. In this paper, we propose the use of MI as a single measure to express diagnostic test performance and demonstrate how it can be used in meta-analysis of diagnostic test studies. Methods We use two cases from the literature to demonstrate the applicability of MI meta-analysis in assessing diagnostic performance. These cases are: 1) Meta-analysis of studies evaluating ultrasonography (US) to detect endometrial cancer and 2) meta-analysis of studies evaluating magnetic resonance angiography to detect arterial stenosis. Results Results produced by the MI meta-analyses are comparable to the results of meta-analyses based on traditionally used statistical measures. However, the results of MI are easier to understand as it relates directly to the extent of uncertainty a diagnostic test can reduce. For example, a US test diagnosing endometrial cancer is 40% specific and 94% sensitive. The combination of these values is difficult to interpret and may lead to inappropriate assessment (e.g. one could favour the test due to its high sensitivity, ignoring its low specificity). In terms of MI however, the test reduces diagnostic uncertainty by 10%, which is marginal and thus the test is clearly not very useful. Conclusions We have demonstrated the suitability of MI in assessing the performance of diagnostic tests, which can facilitate easier interpretation of the true utility of diagnostic tests.

TitleOn Metabolism: Ensuring Our Health Care System Remains “Ready-to-Hand”

Rationale, aims and objectives New therapies are increasingly approved by regulatory agencies such as the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) based on testing in non-randomized clinical trials. These treatments have typically displayed “dramatic effects” (i.e., effects that are considered large enough to obviate the combined effects of bias and random error). The agencies, however, have not identified how large these effects should be to avoid the need for further testing in randomized controlled trials (RCTs). We investigated the effect size that would circumvent the need for further RCTs testing by the regulatory agencies. We hypothesized that the approval of therapeutic interventions by regulators is based on heuristic decision-making whose accuracy can be best characterized by the application of signal detection theory (SDT). Methods We merged the EMA and FDA database of approvals based on non-RCT comparisons. We excluded duplicate entries between the two databases. We included a total of 134 approvals of drugs and devices based on non-RCTs. We integrated Weber-Fechner law of psychophysics and recognition heuristics within SDT to provide descriptive explanations of the decisions made by the FDA and EMA to approve new treatments based on non-randomized studies without requiring further testing in RCTs. Results Our findings suggest that when the difference between novel treatments and the historical control is at least one logarithm (base 10) of magnitude, the veracity of testing in non-RCTs seems to be established. Conclusion Drug developers and practitioners alike can use the change in one logarithm of effect size as a benchmark to decide if further testing in RCTs should be pursued, or as a guide to interpreting the results reported in non-randomized studies. However, further research would be useful to better characterize the threshold of effect size above which testing in RCTs is not needed.

Aims: The aim of this study was to investigate in detail the traumatic birth experiences of midwives in the delivery rooms, and their attitudes, reactions, and coping strategies. Methods: The design of the study is descriptive and the purposive sampling method was used. This approach is ideal for a preliminary exploration of the nature of a phenomenon. Between October 2018 and January 2019, semi-structured interviews were conducted with a purposeful sample of midwives. The research was carried out with the participation of 29 midwives, who work in labour and birth room. They were asked to describe a particular stressful situation they had experienced during the birth process, their emotions about the event, and their coping strategies and support systems. All interviews were digitally recorded, stored in a database, and transferred to MAX Qualitative Data Analysis 18.1.0 for analysis. Results: As a result of the content analysis, three main themes emerged: Psychological impact, defensive practice, and expectations from the hospital. It was revealed that, after the traumatic birth, midwives experienced highly emotional exhaustion in the form of sadness, flashbacks, guilt, fear, and empathy, and that they performed an increasingly defensive practice. Besides, midwives explicitly stated that they were not prepared enough for traumatic events and that most traumatic births were simply ignored in their workplace. Eventually, it was determined that midwives received support mostly from their colleagues in case of a traumatic birth. Conclusion(s): Midwives need to feel valued and be supported by their institutions in coping with emotional stress. Therefore, performing clinical inspections by experienced or specialist midwives may serve as a supporting framework for reducing defensive interventions.

Rationale, Aims and Objectives: Guidelines recommend inviting family members of intensive care unit (ICU) patients to rounds. We aimed to create a toolkit to support family participation in ICU bedside rounds, based upon evidence from research and in collaboration with ICU family member representatives and healthcare providers. Methods: A multi-method qualitative research program was conducted to provide an evidence-base. Ethnographic observations of rounds and interviews and focus groups with family members and ICU healthcare providers were analyzed for key themes, barriers and facilitators of participation, and suggestions. A full day workshop with family representatives and providers (physicians, nurses, social workers, and unit managers) from a diverse range of adult ICUs in Western Canada, including several community ICUs and a majority of large, urban ICUs enabled the collaborative development of key toolkit elements. Results: We have developed an evidence-informed approach to patient-and-family-centered rounds that highlights the importance of 6 key elements foundational to patient and family centered rounds: Invitation, Orientation, Engagement, Summary, Questions, and Communication Follow-Up. We describe strategies, techniques, and templates to optimize these elements and interactions so that communication is more meaningful, and to facilitate the ability of family members to adopt a meaningful role as contributing members of the care team. Conclusion: There is consensus on general strategies for facilitating family participation in rounds and meaningful communication between family and the healthcare team during rounds as an important element of the continuum of communication in the ICU. The incorporation of these elements should be standardized, though tailored to user needs.

Background: The health care delivery model in the United States does not work; it perpetuates unequal access to care, favors treatment over prevention, and contributes to persistent health disparities and lack of insurance. The historical lack of support in the United States for primary health care, universal health coverage, population health, addressing the social determinants of health, and community empowerment, creates opportunities for community health scientists to develop innovative solutions for addressing community health needs. Methods: We developed a model community health science approach combining community-oriented primary care (COPC), community-based participatory research (CBPR), asset-based community development, and service learning principles. The approach defines health as a social outcome, resulting from a combination of clinical science, collective responsibility, and informed social action. Results: From 2000-2020, we established partnerships with community organizations to reduce the risk of chronic disease in vulnerable minority communities. Our programs have provided structured community health science training for hundreds of physicians and other health care workers in training. Conclusion: As the U.S. begins to seek solutions to chronic health disparities and health inequities, community health science provides useful lessons in how to engage communities to address the deficits of the current system. Perhaps the greatest error that U.S. health care systems could make in trying to better address population health and the social determinants of health, would be ignoring the important community initiatives already underway in most local communities. Building partnerships based on local resources and ongoing social determinants of health initiatives is the key for medicine to meaningfully engage communities for reducing health disparities. This has been the greatest lesson we have learned during the past two decades, has provided the foundation for our community health science approach, and accounts for whatever success we have achieved.

Motion capture and analysis techniques are emerging in the surgical education and surgical education research literature as viable ways to augment the assessment of technical skills. In particular, these methods provide an opportunity to reveal objective information about the efficiency of surgical procedures, above and beyond the accuracy of procedural outcomes. One assessment that is very prevalent in the literature are counts of the number of movements a surgeon makes in completing a technical performance. In this commentary, the number of movements metric is explored from kinesiology and engineering perspectives; two disciplines that have contributed heavily to the development of rigorous motion analysis methods. Furthermore, the assumption that skill efficiency improves linearly as a learner progresses along the continuum of expertise is challenged. While movement efficiency does certainly improve, this assumption does not necessarily capture the way that learners flexibly prioritize particular aspects of performance in the intermediate stages of skill learning. By way of this commentary, important a priori decisions that should proceed effective motion capture and analysis are highlighted, a call for the standardization of procedures is made, and an opportunity to better understand the way that computerized movement analysis techniques may contribute (or be detrimental) to competency constructs in surgical education and assessment is realized.

The frantic search for a cure or prophylactic treatment of COVID-19 has unfortunately led to a dropping of the guard of many medical specialists resulting in widespread adoption of unproven treatment modalities. The recent article regarding the inconsistent physician attitudes towards hydroxychloroquine paints a depressing picture of the actual practices during the so-called era of evidence-based medicine. On this backdrop, we comment on how Romania (where this survey took place) has imposed some of the most severe lockdown measures in Europe, including the forced hospitalization of all confirmed with SARS-CoV-2 infection. Additionally, a therapeutic guideline was written into law, endorsing concomitant use of several drugs with unproven antiviral efficiency. This unprecedented situation has resulted in the sometimes indiscriminate prescription of off-label drugs, with a non-negligible risk of adverse reactions, especially in fragile patients with coexisting conditions. In light of the experience accrued in a COVID-19 dedicated unit, the authors discuss the importance of avoiding polypharmacy and administering all antiviral drugs within the confines of rigorously conducted clinical trials.

ABSTRACT Rationale, aims and objectives The primary aim of the study was to understand the mindset of doctors and pharmacists, as they embark upon prescribing in a polypharmacy and multi-morbidity context during routine practice at a hospital acute admissions unit. The study also aimed to evaluate to what extent attitudes, embedded within real-life decision-making scenarios, relate to existing theory and models of prescribing decisions. Methods Anonymised case studies were identified from the medical notes of patients aged 65 and over with conditions likely to be associated with multi-morbidity, medication issues and polypharmacy: namely: fall, urinary tract infection, confusion or lower respiratory tract infection. A total of 39 doctors based on the acute medical admissions unit and 9 pharmacists were recruited to one of three focus groups. Patient case-studies provided the context for discussion from which verbatim transcripts were thematically analysed using an interpretative, qualitative approach. Sub-themes were matched to Murshid and Mohaidin’s proposed model of physician prescribing decisions. Results Seven principal themes were identified that were associated with prescribing decisions on the acute medical unit, namely, ‘patient characteristics’, ‘drug characteristics’, ‘pharmacist factors’, ‘trustworthiness’, ’reliability of medication history, ‘competing pressures and priorities’ and ‘responsibilities of prescribers’. Conclusion Prescribing decisions on the acute medical admissions unit were influenced by a variety of factors, some of which have already been acknowledged within existing theories and models. The findings provisionally offer new insights, which, subject to confirmation by further research, bring to light three attitudinal characteristics that may impact negatively upon the quality of prescribing decisions. These include, first, how perceived poor reliability of medication history may result in information gaps that compromise prescribing decisions; second, how competing pressures and priorities restrict doctors’ aptitude to conduct a review of medication and finally, how doctors may rationalise the assignment of medication review to the GP.

Rationale. Evidence-based practice (EBP) can improve health care in underprivileged countries. Bolivia’s EBP movement is nascent and the factors contributing to better implementation in nursing are unknown. Aim. To explore Bolivian nurses’ readiness to engage in EBP while highlighting the facilitators and barriers for pursuing EBP. Method. International collaborators used a sequential explanatory mixed methods study. First, general trends were disclosed via a survey of 170 nurses in La Paz, Bolivia, holding at least a baccalaureate regarding their perceived beliefs about EBP. The survey identified facilitators and barriers for implementing EBP in acute and ambulatory settings. Second, qualitative data was gathered via a focus group of nine nurses with the purpose of enhancing the survey findings. Results. The survey results showed that nurses believe that engaging in EBP can improve their clinical practice. However, the nurses’ research behaviors were found to be infrequent. Lack of support from the nurses’ clinics and hospitals and from non-nursing professionals were identified as barriers for engaging in EBP. The qualitative results revealed underlying limitations to nurses’ clinical practice, including “feeling undervalued.” Conclusions. There is a dearth of EBP knowledge among Bolivian nurses stemming from a lack of preparation in EBP environments, including EBP training opportunities. This situation affects nurses’ professional dimensions of relational work, power, and collaboration. Collaborative research among educators, professional nursing societies, and local and international organizations could provide initiatives for implementing EBP, based on local health profiles. Key words: international collaboration, evidence-based practice, nurse-multidisciplinary relationship, barriers to EBP.

Rationale, aims and objectives: The diversity of safety signals (e.g., case reports, animal studies and observational studies) makes the assessment of the (un-)safety of a drug a formidable challenge. While frequentist viewpoints to uncertain inference struggle in aggregating these signals, the more flexible Bayesian approaches seem better suited for this quest. Artificial Intelligence (AI) offers great promise to these approaches for information retrieval, decision support and leaning probabilities from data. E-Synthesis is a Bayesian framework for drug safety assessments build on philosophical principles and considerations. It aims to aggregate all the available information, in order to provide a Bayesian probability of a drug causing an adverse reaction. We delineate and assess ways in which AI can support E-Synthesis. Results: We find that AI can help with information retrieval, usability (graphical decision making aids), learning Bayes factors from historical data, assessing quality of information and determining conditional probabilities for the so-called “indicators” of causation for E-Synthesis. Conclusions: Properly applied, AI can help the transition of philosophical principles and considerations concerning evidence aggregation for drug safety to a tool that can be used in practise.