Efficacy of a training intervention on the quality of practitioners' decision support for patients deciding about place of care at the end of life: A randomized control trial: Study protocol

<p>Abstract</p> <p>Background</p> <p>Most people prefer home palliation but die in an institution. Some experience decisional conflict when weighing options regarding place of care. Clinicians can identify patients' decisional needs and provide decision support, yet generally lack skills and confidence in doing so. This study aims to determine whether the quality of clinicians' decision support can be improved with a brief, theory-based, skills-building intervention.</p> <p>Theory</p> <p>The Ottawa Decision Support Framework (ODSF) guides an evidence based, practical approach to assist clinicians in providing high-quality decision support. The ODSF proposes that decisional needs [personal uncertainty, knowledge, values clarity, support, personal characteristics] strongly influence the quality of decisions patients make. Clinicians can improve decision quality by providing decision support to address decisional needs [clarify decisional needs, provide facts and probabilities, clarify values, support/guide deliberation, monitor/facilitate progress].</p> <p>Methods/Design</p> <p>The efficacy of a brief education intervention will be assessed in a two-phase study. In phase one a focused needs assessment will be conducted with key informants. Phase two is a randomized control trial where clinicians will be randomly allocated to an intervention or control group. The intervention, informed by the needs assessment, knowledge transfer best practices and the ODSF, comprises an online tutorial; an interactive skills building workshop; a decision support protocol; performance feedback, and educational outreach. Participants will be assessed: a) at baseline (quality of decision support); b) after the tutorial (knowledge); and c) four weeks after the other interventions (quality of decision support, intention to incorporate decision support into practice and perceived usefulness of intervention components). Between group differences in the primary outcome (quality of decision support scores) will be analyzed using ANOVA.</p> <p>Discussion</p> <p>Few studies have investigated the efficacy of an evidence-based, theory guided intervention aimed at assisting clinicians to strengthen their patient decision support skills. Expanding our understanding of how clinicians can best support palliative patients' decision-making will help to inform best practices in patient-centered palliative care. There is potential transferability of lessons learned to other care situations such as chronic condition management, advance directives and anticipatory care planning. Should the efficacy evaluation reveal clear improvements in the quality of decision support provided by clinicians who received the intervention, a larger scale implementation and effectiveness trial will be considered.</p> <p>Trial registration</p> <p>This study is registered as NCT00614003</p

A comparative analysis of centralized waiting lists for patients without a primary care provider implemented in six Canadian provinces: study protocol

Background: Having a regular primary care provider (i.e., family physician or nurse practitioner) is widely considered to be a prerequisite for obtaining healthcare that is timely, accessible, continuous, comprehensive, and well-coordinated with other parts of the healthcare system. Yet, 4.6 million Canadians, approximately 15% of Canada’s population, are unattached; that is, they do not have a regular primary care provider. To address the critical need for attachment, especially for more vulnerable patients, six Canadian provinces have implemented centralized waiting lists for unattached patients. These waiting lists centralize unattached patients’ requests for a primary care provider in a given territory and match patients with providers. From the little information we have on each province’s centralized waiting list, we know the way they work varies significantly from province to province. The main objective of this study is to compare the different models of centralized waiting lists for unattached patients implemented in six provinces of Canada to each other and to available scientific knowledge to make recommendations on ways to improve their design in an effort to increase attachment of patients to a primary care provider. Methods: A logic analysis approach developed in three steps will be used. Step 1: build logic models that describe each province’s centralized waiting list through interviews with key stakeholders in each province; step 2: develop a conceptual framework, separate from the provincially informed logic models, that identifies key characteristics of centralized waiting lists for unattached patients and factors influencing their implementation through a literature review and interviews with experts; step 3: compare the logic models to the conceptual framework to make recommendations to improve centralized waiting lists in different provinces during a pan Canadian face-to-face exchange with decision-makers, clinicians and researchers. Discussion: This study is based on an inter-provincial learning exchange approach where we propose to compare centralized waiting lists and analyze variations in strategies used to increase attachment to a regular primary care provider. Fostering inter-provincial healthcare systems connectivity to improve centralized waiting lists’ practices across Canada can lever attachment to a regular provider for timely access to continuous, comprehensive and coordinated healthcare for all Canadians and particular for those who are vulnerable.Applied Science, Faculty ofMedicine, Faculty ofOther UBCNon UBCNursing, School ofPopulation and Public Health (SPPH), School ofReviewedFacult

ISARIC-COVID-19 dataset: A Prospective, Standardized, Global Dataset of Patients Hospitalized with COVID-19

The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use