The aim of this meta-ethnography is to appraise the types and uses of theories relative to end-of-life decision making and to develop a conceptual framework to describe end-of-life decision making among patients with advanced cancers, heart failure, and amyotrophic lateral sclerosis (ALS) and their caregivers or providers. We used PubMed, Embase, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases to extract English-language articles published between January 2002 and April 2015. Forty-three articles were included. The most common theories included decision-making models ( n = 14) followed by family-centered ( n = 11) and behavioral change models ( n = 7). A conceptual framework was developed using themes including context of decision making, communication and negotiation of decision making, characteristics of decision makers, goals of decision making, options and alternatives, and outcomes. Future research should enhance and apply these theories to guide research to develop patient-centered decision-making programs that facilitate informed and shared decision making at the end of life among patients with advanced illness and their caregivers.

Knowledge levels were high in all participants (mean objective = 3.7 on a 5-point scale, SD = 1.02; mean subjective = 9.3 on a 10-point scale, SD = 1.29). There was a significant relationship between objective knowledge and perceived understanding (r = 0.56, P = .001); however, the study map itself had no significant effect on objective or perceived understanding.

Multimorbidity – the presence of two or more chronic health conditions – is common among older adults. Despite this, relatively little is known about the epidemiology of specific sequences of disease onset that occurs in mid-life and older adults over time. This may be attributed to the sheer number of possible permutations, which is difficult to handle with traditional methods. This is a retrospective cohort study using the Health & Retirement Study (HRS), a nationally-representative panel survey of aging. The study population included all adults age 50 and older that had no reported chronic disease at baseline (n=5567). We use a data mining algorithm, Sequential Pattern Discovery using Equivalence classes (SPADE), to identify all possible sequences of eight self-reported age-related chronic diseases: hypertension, arthritis, diabetes, cancer, stroke, heart disease, chronic lung disease, and psychiatric disorders. There were 67 unique sequences of disease identified that occurred in at least 1% of the study population. The most common two event sequence was Arthritis=>Hypertension (15.5% of all subjects), and the second most common was Hypertension=>Arthritis (9.6%). The most common three-way sequence was Arthritis=>Hypertension=>Heart Disease (1.8%). Arthritis=>Stroke occurred in 1.5% of subjects and was associated with the highest mortality rate (71.3% of subjects died). Sequential pattern mining allows for the discovery of longitudinal patterns of disease that frequently occur in older adults and advancements in our understanding of the epidemiology of multimorbidity. Future applications may include predicting a given patient's disease trajectory based on their life course and disease history.

Electronic health records (EHR) data are increasingly used to inform clinical care decisions, assess quality of care, and identify patients at high-risk of poor outcomes (e.g. readmission). Functional measures—including mobility and the ability to perform activities of daily living (ADLs)—are key indicators associated with health-related quality of life and chronic disease management in older adults. The goal of this analysis was to quantify the extent that measures of function are used in a national pool of structured EHR data. We used 2017-2019 data from IBM Watson Health Explorys, representing EHR data from 27 health systems and 360 hospitals nationwide (n=5,224,530 adults age 65 and older). Structured EHR data were mapped to SNOMED-CT codes that identifed six categories of function: mobility, fine motor, gross motor, large muscle, ADLs, and instrumental ADLs. Results indicated that only 3 of the 6 categories were used: ADLs (4.2% of study population), mobility (3.2%), and gross motor skills (2.4%). Fine motor, IADLs, and large muscle function were not recorded in any patients. These results indicate that functional measures appear to be under-reported in structured EHR data when compared to published estimates of the population prevalence. In conclusion, measures of function and mobility remain largely unused in structured EHR data, likely because this information is either not assessed, unavailable for inclusion, or is captured in a non-structured format (e.g. clinical notes). Comprehensive functional measures need to be added to EHRs to assess quality and improve delivery and outcomes in older adult patients.

A growing proportion of the population in the United States are older adults, and they are frequent users of convenient care clinics. This is particularly true for older adults from systematically marginalized communities who may be at greater risk for chronic health conditions and unmet healthcare needs. These trends render convenient care clinics critical sites to reimagine the provision of care for older adults. To improve the quality of care the CVS MinuteClinic implemented the Institute of Healthcare Improvement’s Age-Friendly Health Systems 4Ms Framework (assessing What Matters, Medication, Mentation, and Mobility). Research to evaluate two interventions (reflective learning and virtual clinic participation) to improve healthcare providers’ (HCP) uptake of the 4Ms was implemented. After the interventions, HCPs were provided a self-administered online survey to capture qualitative reports of reflections on learning and implementing the 4Ms. A total of 32 providers from both groups completed the survey. Using a codebook thematic analysis approach, facilitators and barriers for implementation were identified as were HCPs’ innovative approaches to consistently and effectively implement the 4Ms. Facilitator themes were the presence of resources (e.g., peer coaches) and the perceived value of the 4Ms model. Barrier themes included time, documentation issues, workflow challenges, HCP stress, and HCP comfort. Innovation themes included designing efficient workflows, scaffolded 4Ms learning, and collaboration. These findings underscore the supports/innovations to amplify and challenges to address to support HCPs in learning and implementing the 4Ms to improve the quality of care for older adults.