Applying Sequential Analytic Methods to Self-Reported Information to Anticipate Care Needs

Limiting and Promoting Resident Self-Care in Assisted Living

OBJECTIVES

Assisted living (AL) residents often manage multiple chronic conditions, functional and/or cognitive decline along with their individual needs and preferences for a full life. Although residents participate in their own care, little is known about their self-care activities and how to support them. This analysis focuses on residents' self-care and theorizing the dynamic, socially embedded process of negotiating self-care.

METHODS

We analyze data from a grounded theory study informed by the Convoys of Care model. Participants included 50 focal residents and 169 paid and unpaid convoy members in eight AL homes; each resident convoy was followed up for 2 years. Data collection included participant observation, interviews, and resident record review.

RESULTS

To the extent possible, most AL residents were involved in self-care related to activities of daily living, health promotion, and social, emotional, and mental well-being. Residents and care partners engaged in a dynamic process of limiting and promoting self-care activities. Multiple factors influenced self-care, including residents' past self-care behaviors, caregiver fear and availability, and the availability of services and supports.

DISCUSSION

Strategies for promoting self-care must involve residents and care partners and include convoy education in collaborative goal-setting, prioritizing care that supports the goals, and putting resources in place to support goal achievement.

Using Self-Reported Data to Segment Older Adult Populations with Complex Care Needs

Background:

Tailored care management requires effectively segmenting heterogeneous populations into actionable subgroups. Using patient reported data may help identify groups with care needs not revealed in traditional clinical data.

Methods:

We conducted retrospective segmentation analyses of 9,617 Kaiser Permanente Colorado members age 65 or older at risk for high utilization due to advanced illness and geriatric issues who had completed a Medicare Health Risk Assessment (HRA) between 2014 and 2017. We separately applied clustering methods and latent class analyses (LCA) to HRA variables to identify groups of individuals with actionable profiles that may inform care management. HRA variables reflected self-reported quality of life, mood, activities of daily living (ADL), urinary incontinence, falls, living situation, isolation, financial constraints, and advance directives. We described groups by demographic, utilization, and clinical characteristics.

Results:

Cluster analyses produced a 14-cluster solution and LCA produced an 8-class solution reflecting groups with identifiable care needs. Example groups included: frail individuals with memory impairment less likely to live independently, those with poor physical and mental well-being and ADL limitations, those with ADL limitations but good mental and physical well-being, and those with few health or other limitations differentiated by age, presence or absence of a documented advance directive, and tobacco use.

Conclusions:

Segmenting populations with complex care needs into meaningful subgroups can inform tailored care management. We found groups produced through cluster methods to be more intuitive, but both methods produced actionable information. Applying these methods to patient-reported data may make care more efficient and patient-centered.

Systematic review of high-cost patients' characteristics and healthcare utilisation

OBJECTIVES

To investigate the characteristics and healthcare utilisation of high-cost patients and to compare high-cost patients across payers and countries.

DESIGN

Systematic review.

DATA SOURCES

PubMed and Embase databases were searched until 30 October 2017.

ELIGIBILITY CRITERIA AND OUTCOMES

Our final search was built on three themes: 'high-cost', 'patients', and 'cost' and 'cost analysis'. We included articles that reported characteristics and utilisation of the top-X% (eg, top-5% and top-10%) patients of costs of a given population. Analyses were limited to studies that covered a broad range of services, across the continuum of care. Andersen's behavioural model was used to categorise characteristics and determinants into predisposing, enabling and need characteristics.

RESULTS

The studies pointed to a high prevalence of multiple (chronic) conditions to explain high-cost patients' utilisation. Besides, we found a high prevalence of mental illness across all studies and a prevalence higher than 30% in US Medicaid and total population studies. Furthermore, we found that high costs were associated with increasing age but that still more than halve of high-cost patients were younger than 65 years. High costs were associated with higher incomes in the USA but with lower incomes elsewhere. Preventable spending was estimated at maximally 10% of spending. The top-10%, top-5% and top-1% high-cost patients accounted for respectively 68%, 55% and 24% of costs within a given year. Spending persistency varied between 24% and 48%. Finally, we found that no more than 30% of high-cost patients are in their last year of life.

CONCLUSIONS

High-cost patients make up the sickest and most complex populations, and their high utilisation is primarily explained by high levels of chronic and mental illness. High-cost patients are diverse populations and vary across payer types and countries. Tailored interventions are needed to meet the needs of high-cost patients and to avoid waste of scarce resources.