Derivation and validation of the Personal Support Algorithm: an evidence-based framework to inform allocation of personal support services in home and community care.

BACKGROUND: Personal support services enable many individuals to stay in their homes, but there are no standard ways to classify need for functional support in home and community care settings. The goal of this project was to develop an evidence-based clinical tool to inform service planning while allowing for flexibility in care coordinator judgment in response to patient and family circumstances. METHODS: The sample included 128,169 Ontario home care patients assessed in 2013 and 25,800 Ontario community support clients assessed between 2014 and 2016. Independent variables were drawn from the Resident Assessment Instrument-Home Care and interRAI Community Health Assessment that are standardised, comprehensive, and fully compatible clinical assessments. Clinical expertise and regression analyses identified candidate variables that were entered into decision tree models. The primary dependent variable was the weekly hours of personal support calculated based on the record of billed services. RESULTS: The Personal Support Algorithm classified need for personal support into six groups with a 32-fold difference in average billed hours of personal support services between the highest and lowest group. The algorithm explained 30.8% of the variability in billed personal support services. Care coordinators and managers reported that the guidelines based on the algorithm classification were consistent with their clinical judgment and current practice. CONCLUSIONS: The Personal Support Algorithm provides a structured yet flexible decision-support framework that may facilitate a more transparent and equitable approach to the allocation of personal support services.

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.

Using standard clinical assessments for home care to identify vulnerable populations before, during, and after disasters.

OBJECTIVES: Several studies have shown the increased vulnerability of and disproportionate mortality rate among frail community-dwelling older adults as a result of emergencies and disasters. This article will discuss the applicability of the Vulnerable Persons at Risk (VPR) and VPR Plus decision support algorithms designed based on the Resident Assessment Instrument-Home Care (RAI-HC) to identify the most vulnerable community-dwelling (older) adults. DESIGN: A sample was taken from the Ontario RAI-HC database by selecting unique home care clients with assessments closest to December 31, 2014 (N = 275,797). Statistical methods used include cross tabulation, bivariate logistic regression as well as Kaplan-Meier survival plotting and Cox proportional hazards ratios calculations. RESULTS: The VPR and VPR Plus algorithms, were highly predictive of mortality, long-term care admission and hospitalization in ordinary circumstances. This provides a good indication of the strength of the algorithms in identifying vulnerable persons at times of emergencies. CONCLUSIONS: Access to real-time person-level information of persons with functional care needs is a vital enabler for emergency responders in prioritizing and allocating resources during a disaster, and has great utility for emergency planning and recovery efforts. The development of valid and reliable algorithms supports the rapid identification and response to vulnerable community-dwelling persons for all phases of emergency management.

Clinical Complexity and Use of Antipsychotics and Restraints in Long-Term Care Residents with Parkinson's Disease.

BACKGROUND: Patients with Parkinson's disease (PD) and/or Parkinsonism are affected by a complex burden of comorbidity. Many ultimately require institutional care, where they may be subject to the application of physical restraints or the prescription of antipsychotic medications, making them more vulnerable to adverse outcomes. OBJECTIVES: The objectives of this paper are to: 1) describe the clinical complexity of older institutionalized persons with PD; and 2) examine patterns and predictors of restraint use and prescription of antipsychotics in this population. METHODS: Population-based cross-sectional cohort study. Residents with PD and/or Parkinsonism living in long-term care (LTC) facilities in 6 Canadian provinces and 1 Northern Territory and Complex Continuing Care (CCC) facilities in Manitoba and Ontario, Canada. The RAI MDS 2.0 instrument was used to assess all LTC residents and CCC residents. Clinical characteristics and the prevalence of major comorbidities were examined. Multivariate modeling was used to identify the characteristics of PD residents most associated with the prescription of antipsychotics and the use of restraints in LTC and CCC facilities. RESULTS: Residents with PD in LTC and CCC exhibit a high prevalence of dementia, major psychiatric disorders, stroke, heart failure, chronic obstructive pulmonary disease and diabetes mellitus. More than 90% of LTC and CCC residents with PD had cognitive impairment; with more than half having moderate to severe impairment. Residents with PD were more likely to receive antipsychotics than those without PD. Antipsychotic use was associated with psychosis and aggressive behaviours, but also with unsteady gait and higher comorbidity and medication count. Similarly, although more common in CCC than LTC facilities, both psychosis and aggressive behaviours were associated with restraint use, as was greater cognitive and functional impairment, and urinary incontinence. Younger age, male gender, and lower physician access were all associated with greater antipsychotic and restraint use. CONCLUSIONS: LTC and CCC residents with PD are very complex medically. Use of antipsychotics and restraints is common, and their use is often associated with factors other than psychosis or aggression.