Multistate Competing Risk Analysis of Transition Back to the Community Among Long-Term Care Home (LTC) Destined Patients: A Brief Report.

OBJECTIVE: The demand for long-term care in community and facilitybased settings in Canada is expected to increase with population growth. The Toronto Grace Health Center piloted an intervention program that aims to support return to the community of acute hospital patients designated for LTC placement. We investigated whether this program was effective in transitioning the program patients back to their homes in the community and the factors associated with transitioning patients to different destinations. METHOD: We performed a competing risk multi-state analysis of 111 patients enrolled into the Harbour Light (HL) transitional unit program between January 2020 and June 2023. RESULTS: At the time of the study, 92 enrolled patients had been discharged and of those these, 48.9% (45) were successfully transitioned back to their private home in the community. The remaining 51.1% (46) were discharged to other destinations. Being a female was the only positive predictor of transitioning back home. Higher CPS scores (HR 0.53, 95% CI 0.31-0.88), PADDRS scale of 1+, and higher ADL Hierarchy scale, strongly predicted lower odds of transitioning back to the community. CONCLUSION: Within the context of rising LTC bed demand and lengthy waiting time in Canada, with appropriate measures, this program successfully transitioned LTC home bound persons back to their homes. If replicable on a large scale, this could provide short and long-term solution to LTC bed demand in Canada.

Novel Smart Sensor Platform for Monitoring Multiple Pressure Injury Risk Factors: A Feasibility Study in a Post-Acute Care Facility.

INTRODUCTION: Posture, temperature, and moisture have been identified as critical modifiable risk factors in pressure injury (PI) development. Microclimate is defined as temperature and humidity at the interface of the support surface and body. To our knowledge, no studies have used sensor technology to measure these parameters simultaneously in real time. Continuous monitoring of repositioning and microclimate provide real-time actionable insights to help deliver personalized care and measure the effectiveness of interventions. OBJECTIVE: To evaluate the ability of a smart surface platform to collect and document clinical data on monitoring patients' movement and microclimate simultaneously and to compare data generated to nursing observations in order to construct an algorithm that is expected to evolve over time: (1) comparing the blinded data from nurses interacting with the patients and the system; and (2) data being collected is validating an algorithm that is expected to become more accurate over time. MATERIALS AND METHODS: This prospective, descriptive single-site trial was conducted at a tertiary care facility in a large urban centre in Canada. Patients identified at risk of PIs received standard of care while placed on the smart surface for timed intervals. Nurses' assessment data were collected at three hourly timepoints using a comprehensive tool developed for the study. Sensors monitored patients' interface pressure moisture and temperature every four seconds. A comparative statistical analysis was conducted between the two datasets retrospectively. RESULTS: The study included a total of 104 participants; mean age of 59 years (range 21-92, ± 19.15). Sensor monitoring hours (1,407) generated 1,101,780 frames of surface data. A total of 511 nursing assessments were recorded during the study period. Sensor-generated data correlated strongly with nurse-collected data at cross-sectional intervals. There was a high level of agreement between information collected from sensors and nursing assessments: 94.7% for moisture (p<0.05), and 87.1% for temperature (p<0.05). Nurse-recorded posture assessments were compared to the smart surface platform interface pressure visualizations to determine the device's posture detection, resulting in a 92% accordance (matching 552 out of 600 nurse postures), with a binomial test determining the posture results to be statistically significant (p<0.05) (CI 95%). In addition, moisture events were matched to nurse assessments with 94.7% in accordance, identifying 39 bladder incontinence and 93 non-urinary moisture events (125 total events captured out of 132). CONCLUSION: The technology's ability to capture PI risk factors supports nursing practice. Supplementary data generated has the potential to improve efficiency of professional caregiver workflow and patient outcomes by informing targeted microclimate management strategies and decreasing unnecessary interventions. The large volume of data collected will be used as a basis for artificial intelligence applications with the potential to inform other clinical decision-making areas.

Evaluation of a staff training programme to reimplement a comprehensive health assessment.

BACKGROUND: Health information systems with applications in patient care planning and decision support depend on high-quality data. A postacute care hospital in Ontario, Canada, conducted data quality assessment and focus group interviews to guide the development of a cross-disciplinary training programme to reimplement the Resident Assessment Instrument-Minimum Data Set (RAI-MDS) 2.0 comprehensive health assessment into the hospital's clinical workflows. METHODS: A hospital-level data quality assessment framework based on time series comparisons against an aggregate of Ontario postacute care hospitals was used to identify areas of concern. Focus groups were used to evaluate assessment practices and the use of health information in care planning and clinical decision support. The data quality assessment and focus groups were repeated to evaluate the effectiveness of the training programme. RESULTS: Initial data quality assessment and focus group indicated that knowledge, practice and cultural barriers prevented both the collection and use of high-quality clinical data. Following the implementation of the training, there was an improvement in both data quality and the culture surrounding the RAI-MDS 2.0 assessment. CONCLUSIONS: It is important for facilities to evaluate the quality of their health information to ensure that it is suitable for decision-making purposes. This study demonstrates the use of a data quality assessment framework that can be applied for quality improvement planning.

Predicting Adverse Outcomes After Discharge From Complex Continuing Care Hospital Settings to the Community.

PURPOSE OF STUDY: The purpose was to identify risk and protective factors assessed at complex continuing care (CCC) admission that were associated with three adverse outcomes (death, readmission, and incidence of or failure to improve possible depression) for persons discharged from CCC to the community with home care services. PRIMARY PRACTICE SETTINGS: CCC, home care, community. METHODOLOGY AND SAMPLE: The sample included all CCC patients in Ontario assessed with the Resident Assessment Instrument-Minimum Data Set 2.0 between January 2003 and December 2010 and who were subsequently assessed with the Resident Assessment Instrument-Home Care within 6 months of discharge to the community (n = 9,940). Separate multivariable logistic regression models were developed for each outcome. RESULTS: Within 6 months, 4.9% of the sample had died, 6.5% were readmitted to any Ontario CCC facility, and 13.7% showed symptoms of new possible depression or failure to improve possible depression. Heart failure, chronic obstructive pulmonary disease (COPD), health instability, intravenous/tube feed, and pressure ulcer were associated with increased risk of death. Difficulty with comprehension, possible depression, COPD, unstable conditions, acute episode or flare-up, short-term prognosis, worsening self-sufficiency, and having either patient or caregiver optimistic about discharge were associated with increased risk of readmission. Existing depressive symptoms or depression, unsettled relationships, multimorbidity, and polypharmacy were associated with risk for incidence of or failure to improve possible depression. Optimism about rehabilitation potential and high social engagement were protective against readmission and depressive outcomes, respectively. IMPLICATIONS FOR CASE MANAGEMENT PRACTICE: Person-level clinical data collected on admission to CCC can be used to identify high-risk patients and trigger early discharge planning processes and other in-home interventions. These results support the sharing of information between settings, and highlight key areas in which care teams in CCC and case managers in home care organizations can work together to support the transition to home and potentially reduce adverse postdischarge outcomes.