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.

Rationale, aims and objectives The main purpose of this paper is to measure the efficiency and ranking of medical diagnostic laboratories by applying a Network Data Envelopment Analysis. Methods In this study, each medical diagnostic laboratory is considered as a decision making unit (DMU) and a network data envelopment analysis (NDEA) model is utilized to calculate the efficiency of each medical diagnostic laboratory. Therefore, we design a series four-stage system composed of three main laboratory processes (the pre-test process, the test process and the post-test process). We also consider sustainability criteria in order to cover social, economic, and environmental problems of health care organizations. Results The results show that three of the 22 considered laboratories are efficient. Therefore, the network DEA approach can lead to performance scores and ultimately real ranking. Also, the average efficiency scores show that the decrease of the reception unit’s efficiency results in a decrease of the efficiency of each laboratory. Therefore, the laboratories can increase the number of patients. Along with the intermediate values of the reception unit and the sampling unit, the efficiency of the reception unit increases, which results in an increase for the overall efficiency of each laboratory. Conclusion The proposed model can appropriately help the administrators and managers to identify inefficient units in their laboratory and ultimately improve the laboratory performance.

Purpose This paper aims to elucidate the factors that play into physicians’ experience of receiving practice data and to subsequently develop a model that describes how individuals may interact with the data they receive. Methods In a prior study, we conducted a needs analysis of 105 physicians in the Hamilton-Niagara area in order to understand which data metrics were most valuable to physicians. Using these results, we designed an interview guide to study physicians’ perspectives on audit and feedback. By intentional sampling, we recruited 15 physicians amongst gender groups, types of practice (academic vs community), and duration of practice. The interviews were conducted by a single author and transcribed without identifiers. We then began with an open coding analysis for all of the transcripts, and thereafter conducted axial coding to group the data into larger themes. Results Several environmental and personal attributes were identified as either enabling or counterproductive attributes for participant improvement. The final proposed model identifies different zones of engagement on the basis of both the individual practitioner’s growth mindset and the quality of the existing data system. In the highest engagement zone, the mindset of the collective leadership is one of growth. Systemic supports are in place which potentiates learning that may come from an individual motivated to use their own data. Conclusion Our model shows how data feedback systems and individual growth-oriented mindsets interact to augment or hinder clinical practice improvement. This model provides important guidance to academic and administrative structures looking to develop appropriate performance feedback systems with clinicians.

Rationale and Objectives Medical humanities are becoming increasingly popular, required, and recognized as positively impacting medical education and medical practice. However, the extent of medical humanities teaching in medical schools is largely unknown. We aimed to review medical school curricula in Canada, the UK, and the US. Our secondary objective was to compare the inclusion of medical humanities in the curricula with rankings of medical schools. Methods We searched the curriculum websites of all accredited medical schools in Canada, the UK, and the US to check which medical humanities topics were taught, and whether they were mandatory or optional. We then noted rankings both by Times Higher Education and U.S. News and World Report and calculated the average rank. We formally explored whether there was an association between average medical school ranking and medical humanities offerings using Spearman’s correlation and inverse variance weighting meta-analysis. Results We identified 18 accredited medical school programmes in Canada, 41 in the UK, and 156 in the US. Of these, 9 (56%) in Canada, 34 (73%) in the UK and 124 (79%) in the US offered at least one medical humanity that was not ethics. The most common medical humanities were Unspecified Medical Humanities, History, and Literature (Canada), Sociology and Social Medicine, Unspecified Medical Humanities, and Art (UK), and Unspecified Medical Humanities, Literature, and History (US). There was a negative relationship between the ranking of the medical school and whether they offered medical humanities. Conclusions The extent and content of medical humanities offerings at accredited medical schools in Canada, the UK, and the US varies. The quality of our analysis was limited by the data provided on the Universities’ curriculum websites. Given the potential for medical humanities to improve medical education and medical practice, this variation should be investigated further.

Evidence-Based Medicine proposes a prescriptive model of physician decision-making in which “best evidence” is used to guide best practice. And yet, proponents of EBM acknowledge that EBM fails to offer a systematic theory of physician decision-making. In this paper, we explore how physicians from the neurology and emergency medicine communities have responded to an evolving body of evidence surrounding the acute treatment of patients with ischemic stroke. Through analysis of this case study, we argue that EBM’s vision of evidence-based medical decision-making fails to appreciate a process that we have termed epistemic evaluation. Namely, physicians are required to interpret and apply any knowledge — even what EBM would term “best evidence” — in light of their own knowledge, background and experience. This is consequential for EBM as understanding what physicians do and why they do it would appear to be essential to achieving optimal practice in accordance with best evidence.

Rereading Popper’s “The Logic of scientific discovery”, at his 120th anniversary, brings some thoughts regarding the diagnostic process and decision making in medicine from the viewpoint of the classical scientific method. In recent years physicians are increasingly becoming technical experts who base their decision-making on uniform criteria, guidelines and classifications but unfortunately have moved away from understanding the basic concepts in the philosophy of science. This raises an ethically and philosophically important issue; what does a medical diagnosis mean? Is this an absolute or a relative truth? The implications of this question are enormous in terms of prognosis and treatment. Both patients and physicians should be educated about the nature of the diagnostic process.

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.

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.

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.

Since news of COVID-19 outbreak hit the mainstream media, I have received several calls from acquaintances about if and how they should be worried. I suspect many readers of the Journal of Evaluation in Clinical Practice have experienced the same. What makes communicating the risk difficult can be illustrated through a recent assignment I gave to my undergraduate class focused on how we use science in public policy. I asked the students to identify claims in the media regarding the virus and then search the literature to assess the level of support for such claims. Suffice it to say, they found several claims unsupported, and several others to be inconclusive. Not very good grounding for providing definitive (or even satisfying) advice.

Rationale, aims and objectives: Consistent data demonstrates negative psychological effects of caregiving on front-line health professionals. Evidence that psychological resilience factors can help minimise distress and the potential for low-cost interventions have created interest in resilience-based development programmes; yet evidence of perceived value amongst health professionals is lacking. This study explored health professionals’ experiences and perceptions of a novel, resilience-based intervention designed to pro-actively prepare staff for coping with error; to investigate their perceptions of what resilience meant to them, the relevance of the intervention, and impact of participation on ability to cope with error. Method: Semi-structured interviews 4-6 weeks post intervention with 23 randomly selected participants from seven cohorts (midwives, paediatricians, obstetrians/gynaecologists, paramedics) and trainees (physician associates, mammographers, sonographers). Thematic analysis of interview data. Findings: Participants reported various interpretations of, and a shift in perception regarding what the concept of psychological resilience meant to them and their practice. These included for example, resilience as a positive or negative concept and their awareness and response to a range of personal, organisational and system factors influencing personal resilience. They valued the prophylactic, clinically relevant, interactive and applied nature of the intervention; having developed and applied valuable skills beyond the context of involvement in error, noting that individuals needed to be willing to explore their own coping mechanisms and human fallibility to gain maximum benefit. There was also consensus that whilst proactively developing individual level psychological resilience is important, so too is addressing the organisational and system factors that affect staff resilience which are outside individual staff control. Conclusion: Enhancing resilience appears to be considered useful in supporting staff to prepare for coping with error and the wider emotional burden of clinical work, but such interventions require integration into wider system approaches to reduce the burden of clinical work for health professionals.

Despite the great promises that artificial intelligence (AI) holds for health care, the uptake of such technologies into medical practice is slow. In this paper, we focus on the epistemological issues arising from the development and implementation of a class of AI for clinical practice, namely clinical decision support systems (CDSS). We will first provide an overview of the epistemic tasks of medical professionals, and then analyse which of these tasks can be supported by CDSS, while also explaining why some of them should remain the territory of human experts. Clinical decision-making involves a reasoning process in which clinicians combine different types of information into a coherent and adequate ‘picture of the patient’ that enables them to draw explainable and justifiable conclusions for which they bear epistemological responsibility. Therefore, we suggest that it is more appropriate to think of a CDSS as clinical reasoning support systems (CRSS). Developing CRSS that support clinicians’ reasoning process therefore requires that: 1) CRSSs are developed on the basis of relevant and well-processed data; and 2) the system facilitates an interaction with the clinician. Therefore, medical experts must collaborate closely with AI experts developing the CRSS. In addition, responsible use of an CRSS requires that the data generated by the CRSS is empirically justified through an empirical link with the individual patient. In practice, this means that the system indicates what factors contributed to arriving at an advice, allowing the user (clinician) to evaluate whether these factors are medically plausible and applicable to the patient. Finally, we defend that proper implementation of CRSS allows combining human and artificial intelligence into hybrid intelligence, were both perform clearly delineated and complementary empirical tasks. Whereas CDRSs can assist with statistical reasoning and finding patterns in complex data, it is the clinicians’ task to interpret, integrate and contextualise.

In the United States chronic illnesses have become a way of life for multiple generations – they are the number one cause of death and disability (accounting for more than 70% of deaths), 60% of American adults have at least one chronic disease, and 40% have multiple chronic conditions. Although multiple factors contribute to the growth in chronic disease prevalence, a major factor has been overreliance on health care systems for promoting health and preventing disease. Large health care systems are ill equipped for this role since they are designed to detect, treat, and manage disease, not to promote health or address the underlying causes of disease. Improving health outcomes in the U.S. will require implementing broad-based prevention strategies combining biological, behavioral, and societal variables that move beyond clinical care. According to community medicine, clinical care alone cannot create, support, or maintain health. Rather, health can only ensue from combining clinical care with epidemiology and community organization, because health is a social outcome resulting from a combination of clinical science, collective responsibility, and informed social action. During the past 20 years, our team has developed an operational community medicine approach known as community health science. Our model provides a simple framework for integrating clinical care, population health, and community organization, using community-based participatory research (CBPR) practices for developing place-based initiatives. In the present paper, we present a brief overview of the model and describe its evolution, applications, and outcomes in two major urban environments. The paper demonstrates means for integrating the social determinants of health into collaborative place-based approaches, for aligning community assets and reducing health disparities. We conclude by discussing how asset-based community development can promote social connectivity and improve health, and discuss how our approach reflects the emerging national consensus on the importance of place-based population system change.

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.

Health professions education is in constant pursuit of new ways of teaching and assessment in order to improve the training of healthcare professionals. Educators are often challenged with designing, implementing, and evaluating programs in the context of their professional practice, particularly those in response to dynamic and emerging social needs. This article explores the synergies and intersections of two approaches -- quality improvement and program evaluation -- and the potential utility of their combinations within our field to design, evaluate, and most importantly, improve educational programming. We argue that the inclusion of established quality improvement frameworks within program evaluation provides a proven mechanism for driving change, can optimize programming within the multi-contextual education systems, and, ultimately, that these two approaches are complementary to one another. These combinations hold great promise for optimizing programming in alignment with social missions, where it has been difficult for institutions worldwide to generate and capture evidence of social accountability.

Background and Objective Clinical Practice Guidelines (CPGs) provide evidence-based recommendations to healthcare professionals, policy makers, patients and other stakeholders. Mexico is the biggest producer of CPGs in Latin America and Caribbean countries. The National Healthcare Technology Excellence Center (acronym in Spanish: CENETEC) is responsible for the CPG development, adaptation and update. The aim of this study was to assess the Mexican CPG quality and adherence to the GRADE framework. Study design We conducted a descriptive cross-sectional study of 86 CPGs representing all the CPGs produced by CENETEC between 2015 and 2017 and published in an online database called “Catalogo Maestro”. We performed quality assessment with the online AGREE II tool and assessed the reporting on the GRADE framework. Results Of the 86 CPGs, 34 were published in 2015, 21 in 2016 and 31 in 2017. The overall quality by AGREE II proved a median of 16.6% (Min 16.6%, Max 50%). Of the 86 CPGs, 25 (29%) used the GRADE framework; adherence to GRADE standards was, however, inconsistent and generally poor. Conclusion CPGs produced by CENETEC during this period had a low score by AGREE II standards and low adherence to the GRADE framework. A concerted initiative could rapidly improve CENETEC guidelines.

Background: Lack of time has consistently been reported as a major barrier to effective research evidence-uptake into clinical practice. There has been no research to our knowledge that explores time as a barrier within the Transtheoretical model of Stages of Change (SoC), to better understand the processes of physiotherapists’ uptake of clinical practice guidelines (CPG). This paper explores the concept of lack of time as a barrier for CPG uptake for physiotherapists at different SoC. Methods: A 6-step process is presented to determine the best-fit SoC for 31 physiotherapy interviewees. This process used an amalgamation of interview findings and socio-demographic data, which was layered onto the SoC and previously identified time-barriers to CPG uptake (few staff; high workload; access to CPGs; evidence-based practice as priority in clinical practice; “time is money” attitude; and knowledge on the use of CPGs). Results: The analysis process highlighted the complexities of assigning individuals to a SoC. A model of time management for better CPG uptake is proposed which is a novel approach to assist evidence implementalists and clinicians alike to determine how to progress through the SoC and barriers to improve CPG uptake. Conclusions: To the authors’ knowledge, this is the first attempt at exploring the construct of (lack of) time for CPG-uptake in relation to the physiotherapists’ readiness to behaviour change. This study shows that ‘lack of time’ is a euphemism for quite different barriers, which map to different stages of readiness to embrace current best evidence into physiotherapy practice. By understanding what is meant by ‘lack of time’, it may indicate specific support required by physiotherapists at different stages of changing these behaviours.

Background: Most studies on the transition from pediatric to adult care focus on practices at a single institution. We examine the transition for young adults with type 1 diabetes across an entire Canadian province with a small, mostly rural population: Newfoundland and Labrador (NL). Our aim is to determine a comprehensive picture of how transition is occurring in one jurisdiction and explore potential methods for improvement. Methods: A provincial diabetes database and hospital admission data were reviewed for a cohort of young adults who transitioned into adult care to determine the number of transfers occurring, patient characteristics, and the number of diabetes-related hospitalizations. Semi-structured interviews with pediatric and adult diabetes providers were conducted to determine the current process of transition and identify ways for improvement, including the potential role of family physicians. Results: Between 2008 and 2013, 93 patients with type 1 diabetes transitioned into adult care. Fifteen interviews were conducted across the province’s four regional health authorities. Various models of transition care are being employed, reflecting staff and resource availability. While no structured transition program was identified, many providers were comfortable with their current transition processes. Suggested improvements included more structured processes, shared educational resources, and a dedicated transfer clinic. Conclusions: In a province with a relatively small number of patients who transition out of pediatric care annually, we found different approaches for transitioning them into adult care, but this variation may not negatively impact patient outcomes.