Kerry Kuluski

Introduction: Opioids and benzodiazepines (BZDs) are commonly prescribed for managing pain, anxiety, and sleep‐related problems following a hip fracture. Both opioids and BZDs carry risks of persistent dependence, falls, and mortality with long‐term use. Despite these risks, Canada lacks standardized prescribing guidelines for older adults with hip fracture. We examined population‐level trends in opioids and BZDs dispensing before hospitalization and up to 1‐year post‐discharge among older adults admitted for hip fractures in Ontario, Canada. Methods: We conducted a retrospective cohort study using linked health administrative data from ICES, Ontario, Canada. The cohort included individuals aged 66 years and older, residing in Ontario, admitted for an acute hip fracture between April 1, 2008, and March 31, 2024, and who survived for at least 275 days in the following year. Our primary outcome was post‐discharge opioids and BZDs dispensed, defined as ≥ 1 prescription dispensed within 1–365 days following discharge. Secondary outcomes included pre‐hospitalization dispensing, concurrent dispensing, and morphine equivalent dose. Results: A total of 114,055 eligible older adults were admitted with hip fractures between 2008 and 2024. Overall, BZDs dispensing declined from 33.7% in 2008 to 13.3% in 2024, while opioids dispensing peaked in 2015 at 55.8% and decreased to 44.6% in 2024. Hydromorphone and combination drug therapy were the most frequently dispensed opioid drug classes during pre‐hospitalization and post‐discharge periods following a hip fracture. Concurrent opioids and BZDs dispensed following a hip fracture decreased from 3.7% in 2008 to 1% in 2024. Among individuals with 90 days of continuous opioid dispensed, most received < 50 mg average daily morphine equivalents. Conclusion: We observed a substantial reduction in BZDs dispensing. However, opioids dispensing remained consistently high. These findings underscore the need for future research on initiatives to reduce opioids use in post‐fracture care, while recognizing that opioids remain an effective pain control drug.

Introduction: Learning Health Systems (LHS) offer a pathway to continuously improve care by integrating data and lived experience, yet few practical examples demonstrate how LHS can be operationalized in hospital settings in ways that embed the voices of patients and families. This manuscript describes how patient experience (PX) data are integrated into the design of a new model of care (MoC) that responds to both system demands and the realities of patients and families. Methods: Researchers partnered with clinical leaders to integrate patient and family voices into the design of a new MoC using PX survey data, interviews with patients and families admitted to medicine units, and naturalistic walk‐throughs of each medicine unit. Data collection explored care priorities, interactions with staff, physical environments, and reflections on positive and challenging aspects of care. Analysis followed a three‐step iterative process: unit‐level synthesis, cross‐unit thematic comparison, and data integration. Results: In total, 390 patients participated in the PX survey and 19 patients/families participated in interviews. Patients situated their experiences within a health care system under strain, highlighting both strengths and gaps. Many expressed appreciation for nurses' compassion, but emotional support for fears, anxieties, and worries was inconsistent, particularly when staff were overstretched or transient. Long wait times for transport, procedures, or basic care needs were reported as sources of anxiety and diminished dignity. Naturalistic walk‐throughs documented how crowding, noise, lighting, and wayfinding shaped patients' comfort and privacy. Conclusions: This work provides a practical example of how an LHS approach can be operationalized by embedding the voices of patients and families into care redesign. Findings from the PX survey and interviews are directly shaping a new MoC at Trillium Health Partners (THP) that is evidence‐informed and co‐designed with patients and families, providing a practical example of how PX data can drive continuous, system‐level learning.

Introduction: Health systems are increasingly using population segmentation for planning to organise populations into segments with similar health characteristics. However, given that these data-driven approaches rely on available data, they fail to encompass important social determinants of health, thereby making decision-making challenging. Segmentation is powerful, but little is known about how missing information could be incorporated to more accurately characterise segments. Description: We demonstrate a process that uses personas [i.e., fictional stories] to apply the outputs of population segmentation in Ontario, Canada. First, we created personas using available data to represent individuals in a population segment. Second, we conducted focus groups with service users, caregivers, and providers to understand what information was missing from the persona. We conducted two iterative rounds of focus groups with 27 participants to test and assess this process on the frailty population segment. Results: Findings revealed the preferred design and types of information to present in a persona to represent individuals from a segment. Participants also identified what information was pertinent or missing to better understand the frailty population segment in Ontario. Conclusion: Personas can be useful for health system planners as a mechanism to incorporate community perspectives to enhance data-driven segments for planning.

BACKGROUND Co-design is a collaborative process spanning the lifecycle of a design project that uses creativity, reflexivity, and iterative learning to support feedback, adaptation, and shared understanding during intervention development. Co-design aims to involve diverse groups, including patients, caregivers, clinicians, administrators, community members, researchers, designers, and policy actors. Evidence suggests co-designed interventions may be more engaging, acceptable, and feasible to implement than those developed using traditional approaches. Despite the growing adoption of co-design, considerable variation remains in how it is understood, implemented, and reported, limiting its potential to support meaningful and equitable improvements in health interventions. OBJECTIVE To map the scope and nature of the literature describing the models, theories, and frameworks (MTFs) used to co-design complex health interventions and synthesize their key characteristics. This review constitutes Phase 1 of the Preferred Components for Co-design in Research (PRECISE) initiative, which aims to enhance conceptual clarity, methodological rigour, and transparency in reporting co-design studies for complex health interventions. METHODS A scoping review was conducted following JBI guidance and reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Seven databases were searched from 1972 to 19 October 2023. Screening and data extraction were done independently and in duplicate. Publications were eligible if they reported co-design, co-production, or co-creation approaches and applied a model, theory, or framework to guide co-design of complex interventions. Extracted data were synthesized descriptively using counts and proportions. RESULTS A total of 130 publications were included. Co-design terminology use was inconsistent: 62.3% of publications used the term “co-design,” 7.7% “co-production”, 6.9% “co-creation”, and 16.2% did not use any co-approach term in their methodology. Among publications using a co-approach term, only 31.2% provided an associated citation. Fewer than two-thirds (60.0%) reported reciprocal collaboration with co-design partners. Among these, “developing the intervention” was the most commonly reported role (89.5%), while involvement in analysis and dissemination was less frequently reported. Reported co-design principles were variable, with "use participatory means" being the most prevalent (93.9%). Regarding MTFs, 70.0% of publications used a design-only MTF (e.g., pre-2021 Medical Research Council framework). Only three studies (2.3%) used a co-only MTF. Nearly half (42.3%) reported using a co + design MTF (e.g., Double Diamond). Of those using a co + design MTF, 67.3% operationalized its elements in the study context. CONCLUSIONS This scoping review reveals significant variability in how co-design is defined, conceptualized, and applied in the development of complex health interventions, with limited use of and application of MTFs. These findings highlight the necessity for clearer conceptual bases and more transparent reporting. As Phase 1 of the PRECISE guideline initiative, this review lays the empirical groundwork for developing reporting standards for co-design research. CLINICALTRIAL The Open Science Framework on June 12, 2023 (osf.io/prd3t).

Background: Amid growing demands and constrained health care resources, effective hospital bed capacity management is crucial. Delayed hospital discharge, where patients remain in the hospital beyond the need for acute care, strains resources, affects patient outcomes, and reduces system efficiency. Predicting such delays facilitates early interventions to avert them and alleviate burdens on patients, care partners, hospitals, and the broader health care system. Objective: This study aimed to develop comprehensive predictive analytics for delayed discharges among older adults using explainable machine learning to boost transparency and interpretability, while integrating fairness to mitigate algorithmic biases. Methods: Leveraging longitudinal data from over 2 decades in Ontario, Canada, we applied extreme gradient boosting and logistic regression models to predict delayed discharges within 90 days post–acute care. Data preprocessing included a 2-year look-back for clinical histories and balanced sampling to address class imbalance. Model performance was assessed via area under the receiver operating characteristic curve, calibration, and clinical utility. Fairness was evaluated across sex, urban or rural residence, and residential instability using several threshold-free metrics. Explainability was examined at the global model level (via partial dependence plots and permutation feature importance) and locally (via Shapley Additive Explanations, breakdown, and ceteris paribus methods), with principal component analysis used to cluster key features for high-risk patients. Results: The extreme gradient boosting model outperformed logistic regression, achieving an area under the receiver operating characteristic curve of 0.82 on the test set, with acceptable within-group and cross-group ranking fairness across subgroups. Explainability clustering analyses identified functional and cognitive declines (eg, care support needs, dementia, and mobility issues) and regional disparities as primary drivers of high-risk predictions. Bias mitigation improved calibration parity, especially when stratifying by residential instability, underscoring the trade-offs policymakers must weigh between accuracy, fairness, and explainability. Conclusions: This study demonstrates the potential of responsible artificial intelligence in health care, emphasizing the need to balance predictive accuracy, equity, and interpretability. It uncovers systemic gaps and offers actionable insights for enhanced discharge planning, resource optimization, and equitable care delivery.

Background/Objectives: Military identity serves as a foundational lens through which service members navigate the events of everyday military and civilian life. However, the very process that cultivates a sense of unity and purpose can be a double-edged sword in civilian life. Although the prevalence and transition needs are known, few studies have explored how chronic pain specifically disrupts military identity in depth. This qualitative study explores three distinct trajectories through which Veterans with chronic pain experience identity change. Methods: This study used narrative inquiry involving two sets of in-depth interviews with 20 Veterans. Reflexive thematic analysis was employed to describe and differentiate three distinct trajectories of chronic pain. Results: Veterans with chronic pain experience identity change through three overlapping pain trajectories: (1) traumatic injury -> immediate discharge; (2) misdiagnosed/non-traumatic injury -> delayed discharge; and (3) cumulative wear and tear -> gradual discharge. Regardless of trajectory, chronic pain consistently disrupted military identity and forced Veterans to confront tensions between institutional expectations of stoicism and combat readiness and the physical realities of chronic pain during military service. Those interviewed described experiencing fragmented institutional support, uneven access to care, and the systemic invalidation of pain that did not conform to military ideals. Conclusions: These findings underscore the need for Veteran-centred approaches, including responsive services, comprehensive pain science education throughout military careers, early detection of conditions that can lead to chronic pain, and flexible care pathways tailored to the nuances of each pain trajectory and grounded in military culture and lifestyle.

BACKGROUND Amid growing demands and constrained health care resources, effective hospital bed capacity management is crucial. Delayed hospital discharge, where patients remain in the hospital beyond the need for acute care, strains resources, affects patient outcomes, and reduces system efficiency. Predicting such delays facilitates early interventions to avert them and alleviate burdens on patients, care partners, hospitals, and the broader health care system. OBJECTIVE This study aimed to develop comprehensive predictive analytics for delayed discharges among older adults using explainable machine learning to boost transparency and interpretability, while integrating fairness to mitigate algorithmic biases. METHODS Leveraging longitudinal data from over 2 decades in Ontario, Canada, we applied extreme gradient boosting and logistic regression models to predict delayed discharges within 90 days post–acute care. Data preprocessing included a 2-year look-back for clinical histories and balanced sampling to address class imbalance. Model performance was assessed via area under the receiver operating characteristic curve, calibration, and clinical utility. Fairness was evaluated across sex, urban or rural residence, and residential instability using several threshold-free metrics. Explainability was examined at the global model level (via partial dependence plots and permutation feature importance) and locally (via Shapley Additive Explanations, breakdown, and ceteris paribus methods), with principal component analysis used to cluster key features for high-risk patients. RESULTS The extreme gradient boosting model outperformed logistic regression, achieving an area under the receiver operating characteristic curve of 0.82 on the test set, with acceptable within-group and cross-group ranking fairness across subgroups. Explainability clustering analyses identified functional and cognitive declines (eg, care support needs, dementia, and mobility issues) and regional disparities as primary drivers of high-risk predictions. Bias mitigation improved calibration parity, especially when stratifying by residential instability, underscoring the trade-offs policymakers must weigh between accuracy, fairness, and explainability. CONCLUSIONS This study demonstrates the potential of responsible artificial intelligence in health care, emphasizing the need to balance predictive accuracy, equity, and interpretability. It uncovers systemic gaps and offers actionable insights for enhanced discharge planning, resource optimization, and equitable care delivery.

Background: Although many people nearing the end of life wish to die at home, many patients experience re‐hospitalisation and hospital death. No end‐of‐life hospital‐to‐home interventions have been developed with patients and caregivers, and none have been tested in Canada. Through an iterative, participatory design approach, we codesigned an intervention in partnership with potential users of the final intervention: patients, family caregivers (FCs) and healthcare providers (HCPs). Objective: This study (ACEPATH) aimed to use a patient, FC and HCP engaged codesign process to continue to iterate and refine an intervention for transition from hospital to home in preparation for a pilot implementation. Methods: The codesign process consisted of: (1) Development of codesign workshop (CDW) materials; (2) CDWs with patients and/or their FCs, who iterated our team's previously developed checklists and reference materials; (3) Low‐fidelity prototyping sessions with hospital and community HCPs, who provided feedback on the low‐fidelity prototype, the guidebook (that combined the refined checklists and guides) and identified HCPs to facilitate the guidebook; and (4) High‐fidelity prototyping sessions entailed simulated interactions between an HCP and a patient/FC using the intervention, accompanied by discussion for feedback. Results: Participants identified several areas for refinement to enhance the relevance, clarity and acceptability of the guidebook intervention. Patients and FCs refined and organised questions into specific ‘moments’ that would be helpful for conversations with HCPs during their transition home. HCPs identified social workers, hospital home care coordinators and community home care coordinators as the best fit for facilitating completion of the guidebook at three moments (preparing to leave the hospital, immediately before discharge and getting comfortable at home). Conclusions: We successfully codesigned a guidebook for hospital‐to‐home transitions that was amenable to patients, FCs and HCPs. The next steps will entail piloting the guidebook to evaluate its acceptability, appropriateness, feasibility, costs and fidelity. Patient or Public Contribution: Patients and FCs who had lived/living experiences with hospital‐to‐home transitions near the end of life participated in codesign workshops and high‐fidelity prototyping sessions. We used codesign to ensure the final intervention was aligned with participants' needs and experiences and would hopefully improve aspects of the hospital‐to‐home transition that are important to them.

Background: It is reported that women are more likely to be caregivers than men, experience a higher burden of care and increased emotional health sequelae as a result. Social location (a person's gender, culture, ethnicity, etc.) is known to influence caregiving experiences. However, there is limited work that draws attention to how cultural and linguistic diversity shapes the experiences and expectations of informal caregivers.Objective: The authors aimed to study how to reallocate health and social service resources to better support older adults ageing in place. However, some participants felt strongly about the role of gender. This report addresses the gap for better understanding (1) how gender influences informal caregiving for older adults ageing at home in Ontario, Canada, and (2) how culture may influence gendered caregiving expectations for this population.Design: A critical social justice paradigm and balance of care framework guided the research. Focus groups (15) and one‐one‐one interviews (7) were carried out. A collaborative approach to codebook thematic analysis was conducted.Setting and Participants: This study was carried out in Peel, a diverse region in Ontario, Canada. 42 individuals participated in the study (14 older adults, 10 caregivers and 18 healthcare providers).Findings: Four themes were found regarding the role of gender in caregiving: (1) women caregivers as catalysts for ageing in place, (2) gender norms, generational standards and the societal expectation for women to be caregivers, (3) the intersection of culture and gender on caregiving for older adults and (4) health service workforce as women‐dominant and linguistically diverse.Discussion and Conclusion: Service needs not currently met by Canada's healthcare system often become absorbed by women caregivers who facilitate ageing in place. Further research is required to better understand: (1) how a larger breadth of communities experience the intersection of gender and culture in the care of older adults in Ontario, Canada, and (2) how to better harness the diversity within Canada's homecare workforce to allow for cultural, linguistic and/or gender alignment with older adult clients.Patient or Public Contribution: Patients and caregivers were research participants; however, the focus groups were co‐design sessions, in which participants built and shaped personas and care packages.