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BackgroundSignificant chronic disease challenges exist among older adults. However, most older adults want to remain at home even if their health conditions challenge their ability to live independently. Yet publicly funded home care resources are scarce, private home care is expensive, and family/friend caregivers have limited capacity. Many older adults with chronic illness would require institutional care without the support from family member/friend caregivers. This role raises the risk of physical health problems, stress, burnout, and depression. Passive remote monitoring (RM), the use of sensors that do not require any action by the individual for the system to work, may increase the older adult’s ability to live independently while also providing support and peace of mind to both the client and the family member/friend caregiver. ObjectiveThis paper presents the protocol of a study conducted in two provinces in Canada to investigate the impact of RM along with usual home care (the intervention) versus usual home care alone (control) on older adults with complex care. The primary outcome for this study is the occurrence of and time to events such as trips to emergency, short-term admission to the hospital, terminal admission to the hospital awaiting admission to long-term care, and direct admission to long-term care. The secondary outcomes for this study are (1) health care costs, (2) client functional status and quality of life in the home, (3) family/friend caregiver stress, and (4) family/friend caregiver functional health status. MethodsThe design for this study is an unblinded pragmatic randomized controlled trial (PRCT) with two parallel arms in two geographic strata (Ontario and Nova Scotia). Quantitative and qualitative methodologies will be used to address the study objectives. This PRCT is conceptually informed by the principles of client-centered care and viewing the family as the client and aims at providing supported self-management. ResultsThis study is supported by the Canadian Institutes for Health Research. A primary completion date is anticipated in fall 2022. ConclusionsFindings from this real-world rigorous randomized trial will support Canadian decision-makers, providers, and clients and their caregivers in assessing the health, well-being, and economic benefits and the social and technological challenges of integrating RM technologies to support older adults to stay in their home, including evaluating the impact on the burden of care experienced by family/friend caregivers. With an aging population, this technology may reduce institutionalization and promote safe and independent living for the elderly as long as possible. Trial RegistrationInternational Standard Randomised Controlled Trial Number (ISRCTN) 79884651; http://www.isrctn.com/ISRCTN79884651 International Registered Report Identifier (IRRID)DERR1-10.2196/15027

Context: Electronic health records (EHR) provide an opportunity for developing decision support and other types of learning health system (LHS) initiatives. Careful understanding of the population of interest and how clients are represented in the data is essential for problem selection and for effective study design and analysis of data to solve the problem. The Alliance for Healthier Communities is one of the first primary care LHS in North America, serving complex, at-risk clients through Community Health Centres (CHCs) across Ontario, Canada. We propose that to properly understand their electronic health record data both simple statistical techniques commonly seen in descriptive epidemiology and more complex techniques from artificial intelligence will be useful. Objective: To summarize characteristics of ongoing primary care clients served by CHCs for the purpose of informing future LHS initiatives. Study Design and Analysis: Table-based summaries are given for all outcome measures. An Ising model is used to identify condition co-occurrences, non-negative matrix factorization is used to examine care provider teams, and time series clustering is used to explore care frequency patterns. Setting or Dataset: EHR data from all CHCs across Ontario, Canada in 2009- 2019. Population Studied: Clients over 18 years old in 2009 who indicated a CHC as their primary care provider and had at least one encounter in 2009-2019. Intervention/Instrument: N/A. Outcome Measures: Sociodemographic characteristics (from structured client characteristic table, e.g., age, education), clinical characteristics (20 chronic conditions, four acute conditions, multimorbidity), and health care use characteristics (providers involved, complexity, frequency). Results: There were 221,047 eligible clients. Social determinants and clinical conditions are most prominent in CHCs serving those most at risk in urban settings; the former are also more prevalent in clients with multimorbidity. Physician and nursing types provide most care, with heterogeneous combinations of other providers. There is notable within- and between- client variability in care complexity and frequency. We identify implications for future analyses such as cohort building and risk prediction. Conclusions: Simple statistical and artificial intelligence techniques can be used to effectively describe a complex primary care population; this population-level overview provides a foundation for future LHS initiatives., Presenters Jacqueline Kueper, PhD, MSc, Jennifer Rayner, PhD, Merrick Zwarenstein, MD, PhD, Dan [...]

PURPOSE To measure the effectiveness of a 4-month interdisciplinary multifaceted intervention based on a change in care delivery for patients with multimorbidity in primary care practices. METHODS A pragmatic randomized controlled trial with a mixed-methods design in patients aged 18 to 80 years with 3 or more chronic conditions from 7 family medicine groups (FMGs) in Quebec, Canada. Health care professionals (nurses, nutritionists, kinesiologists) from the FMGs were trained to deliver the patient-centered intervention based on a motivational approach and self-management support. Primary outcomes: self-management (Health Education Impact Questionnaire); and self-efficacy. Secondary outcomes: health status, quality of life, and health behaviors. Quantitative analyses used multi-level mixed effects and generalized linear mixed models controlling for clustering within FMGs. We also conducted in-depth interviews with patients, family members, and health care professionals. RESULTS The trial randomized 284 patients (144 in intervention group, 140 in control group). The groups were comparable. After 4 months, the intervention showed a neutral effect for the primary outcomes. There was significant improvement in 2 health behaviors (healthy eating with odds ratios [OR] 4.36; P = .006, and physical activity with OR 3.43; P = .023). The descriptive qualitative evaluation revealed that the patients reinforced their self-efficacy and improved their self-management which was divergent from the quantitative results. CONCLUSIONS Quantitatively, this intervention showed a neutral effect on the primary outcomes and substantial improvement in 2 health behaviors as secondary outcomes. Qualitatively, the intervention was evaluated as positive. The combination of qualitative and quantitative designs proved to be a good design for evaluating this complex intervention. Key words: interdisciplinary research; multimorbidity; patient care team; pragmatic clinical trial, INTRODUCTION Much effort has been deployed internationally to improve care and outcomes for persons with multimorbidity in primary care. The Patient-Centered Innovations for Persons with Multimorbidity research program, funded by [...]

PURPOSE Rapid increases in technology and data motivate the application of artificial intelligence (AI) to primary care, but no comprehensive review exists to guide these efforts. Our objective was to assess the nature and extent of the body of research on AI for primary care. METHODS We performed a scoping review, searching 11 published or gray literature databases with terms pertaining to AI (eg, machine learning, bayes* network) and primary care (eg, general pract*, nurse). We performed title and abstract and then full-text screening using Covidence. Studies had to involve research, include both AI and primary care, and be published in English. We extracted data and summarized studies by 7 attributes: purpose(s); author appointment(s); primary care function(s); intended end user(s); health condition(s); geographic location of data source; and AI subfield(s). RESULTS Of 5,515 unique documents, 405 met eligibility criteria. The body of research focused on developing or modifying AI methods (66.7%) to support physician diagnostic or treatment recommendations (36.5% and 13.8%), for chronic conditions, using data from higher-income countries. Few studies (14.1%) had even a single author with a primary care appointment. The predominant AI subfields were supervised machine learning (40.0%) and expert systems (22.2%). CONCLUSIONS Research on AI for primary care is at an early stage of maturity. For the field to progress, more interdisciplinary research teams with end-user engagement and evaluation studies are needed., INTRODUCTION Artificial intelligence (AI) research began in the 1950s, and public, professional, and commercial recognition of its potential for adoption in health care settings is growing. (1-7) This application includes [...]

Abstract Background Poor interprofessional communication in hospital is deemed to cause significant patient harm. Although recognition of this issue is growing, protocols are being implemented to solve this problem without empirical research on the interprofessional communication interactions that directly underpin patient care. We report here the first large qualitative study of directly-observed talk amongst professions in general internal medicine wards, describing the content and usual conversation partners, with the aim of understanding the mechanisms by which current patterns of interprofessional communications may impact on patient care. Methods Qualitative study with 155 hours of data-collection, including observation and one-on-one shadowing, ethnographic and semi-structured interviews with physicians, nurses, and allied health professionals in the General Internal Medicine (GIM) wards of two urban teaching hospitals in Canada. Data were coded and analysed thematically with a focus on collaborative interactions between health professionals in both interprofessional and intraprofesional contexts. Results Physicians in GIM wards communicated with other professions mainly in structured rounds. Physicians’ communications were terse, consisting of reports, requests for information, or patient-related orders. Non-physician observations were often overlooked and interprofessional discussion was rare. Intraprofessional interactions among allied health professions, and between nursing, as well as interprofessional interactions between nursing and allied health were frequent and deliberative in character, but very few such discussions involved physicians, whose deliberative interactions were almost entirely with other physicians. Conclusion Without interprofessional problem identification and discussion, physician decisions take place in isolation. While this might be suited to protocol-driven care for patients whose conditions were simple and courses predictable, it may fail complex patients in GIM who often need tailored, interprofessional decisions on their care.Interpersonal communication training to increase interprofessional deliberation may improve efficiency, patient-centredness and outcomes of care in hospitals. Also, electronic communications tools which reduce cognitive burden and facilitate the sharing of clinical observations and orders could help physicians to engage more in non-medical deliberation. Such interventions should take into account real-world power differentials between physicians and other health professions.