Person-centered integrated care for people living with kidney disease and multimorbidity: exploring challenges and opportunities
Bibliographic record
Abstract
Background: People with chronic kidney disease (CKD) and other chronic conditions commonly experience fragmented care. A person-centered integrated care (PC-IC) approach has shown to effectively address care delivery and health outcomes for other populations. However, evidence to guide the application of PC-IC in patients with early-stage CKD and multimorbidity remains limited.To understand challenges and opportunities to address PC-IC for early-stage CKD and multimorbidity we engaged people with lived experience, including patients, caregivers, healthcare providers, and organizational leaders in Alberta, Canada. Approach: Our diverse multidisciplinary team includes two patient partners with lived experience (a patient and caregiver). They supported the grant applications, project planning, material development, and recruitment. The patient partners continue to provide guidance on interpretation of results and knowledge mobilization. We partnered with Essenburgh, leaders in the study of integrated care who developed the Rainbow Model of Integrated Care (RMIC) Framework and continue to collaborate, incorporating their international experience. Various organizations including Alberta Strategy for Patient Oriented Research, Can-SOLVE CKD Network, Primary Health Care Integrated Network, and Alberta Health Services (via the Medicine Strategic Clinical Network) supported the project including recruitment and facilitating community connections.This is a multi-phase, multi-methods patient-oriented study to co-design a PC-IC model of care. Our initial phases of work included conducting an online survey, using the validated RMIC Measurement Tool (RMIC-MT; May to November 2023) to measure the delivery of integrated care. We invited patients, caregivers, healthcare providers (HCPs), and decision-makers over the age of 8 and living in Alberta, Canada. We conducted descriptive and comparative analyses based on background characteristics (e.g., roles, settings, health status complexity). Next, we interviewed this population to contextualize our survey findings (9 completed, anticipating total of 30 interviews). We will use the Framework Method of analysis based on the RMIC Framework. Results: A total of 97 participants completed the survey. Participants included people with early-stage CKD and at least one comorbidity (n=24, 25%), caregivers (n=2, 2%), and HCPs (n=6, 63%). Many participants live in urban (n=62, 70%) compared to rural settings (n=26, 30%), and most HCPs were nephrologists (n=23, 4%) and primary care physicians (n=2, 38%). Overall, patients/caregivers and HCPs rated their experience with person-centered care moderately (3.98/5 and 4.8/5, respectively). The lowest scored items were care coordination between different providers (patients/caregivers; 3.29/5) and regional healthcare laws and regulations (HCPs; 2.94/5). Overall, Alberta received lower integrated care scores than the international benchmark.Preliminary interview results indicate current successes (e.g., multidisciplinary teams, provider to patient/caregiver communication) and challenges (e.g., information sharing between specialty and primary care) to implementing PC-IC. Implications: This research adds to existing knowledge on understanding the current state of PC-IC for early-stage CKD and multimorbidity management through a validated measurement tool and supplementary interviews. Our findings demonstrate opportunities to co-create an innovative PC-IC approach that would align services and resources to meet patient needs.Simultaneously, we are conducting a scoping review to identify existing PC-IC strategies at the micro-, meso-, and macro-levels employed internationally for this population. We will use these findings to inform a pan-Canadian survey aimed at identifying feasible PC-IC strategies.Our results from this current work inform the next phases of work including prioritizing barriers to PC-IC and identifying and leveraging feasible strategies to reimagine a model of care that addresses identified gaps.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".