Dementia Ideal Care: Ecosystem Map of Best Practices and Care Pathways Enhanced by Technology and Community.

Background: Globally, much work has been done by nonprofit, private, and academic groups to develop best practices for the care of people living with dementia (PLWD), including Alzheimer's disease. However, these best practices reside in disparate repositories and tend to focus on one phase of the patient journey or one relevant group. Objective: To fill this gap, we developed a Dementia Ideal Care Map that everyone in the dementia ecosystem can use as an actionable tool for awareness, policy development, funding, research, training, service delivery, and technology design. The intended audience includes (and not limited to) policymakers, academia, industry, technology developers, health system leaders, clinicians, social service providers, patient advocates, PLWD, their families, and communities at large. Methods: A search was conducted for published dementia care best practices and quality measures, which were then summarized in a visual diagram. The draft diagram was analyzed to identify barriers to ideal care. Then, additional processes, services, technologies, and quality measures to overcome those challenges were brainstormed. Feedback was then obtained from experts. Results: The Dementia Ideal Care Map summarizes the ecosystem of over 200 best practices, nearly 100 technology enablers, other infrastructure, and enhanced care pathways in one comprehensive diagram. It includes psychosocial interventions, care partner support, community-based organizations; awareness, risk reduction; initial detection, diagnosis, ongoing medical care; governments, payers, health systems, businesses, data, research, and training. Conclusions: Dementia Ideal Care Map is a practical tool for planning and coordinating dementia care. This visualized ecosystem approach can be applied to other conditions.

Establishing a Research Agenda on Mobile Health Technologies and Later-Life Pain Using an Evidence-Based Consensus Workshop Approach.

The rapid growth of mobile health (mHealth) devices holds substantial potential for improving care and care outcomes in all patient populations, including older adults with pain. However, existing research reflects a substantial gap in knowledge about how to design, evaluate, and disseminate devices to optimally address the many challenges associated with managing pain in older persons. Given these knowledge gaps, we sought to develop a set of practice-based research priorities to facilitate innovation in this field. We employed the Cornell Research-Practice Consensus Workshop Model, an evidence-based approach to generating research priorities. Sixty participants attended the conference, where stakeholder groups included older adults with pain and their caregivers, behavioral and social scientists, healthcare providers, pain experts, and specialists in mHealth and health policy. Participants generated 13 recommendations classified into 2 categories: 1) implications for designing research on mHealth among older adults (eg, conduct research on ways to enhance accessibility of mHealth tools among diverse groups of older adults with pain, expand research on mHealth sensing applications), and 2) implementation of mHealth technology into practice and associated regulatory issues (eg, promote research on ways to initiate/sustain patient behavior change, expand research on mHealth cybersecurity and privacy issues). PERSPECTIVE: This report highlights a set of research priorities in the area of mHealth and later-life pain derived from the joint perspectives of researchers and key stakeholder groups. Addressing these priorities could help to improve the quality of care delivered to older adults with pain.

Factors predicting rehospitalization of elderly patients in a postacute skilled nursing facility rehabilitation program.

OBJECTIVE: To examine potential risk factors for rehospitalization of skilled nursing facility (SNF) rehabilitation patients. DESIGN: Retrospective review of rehabilitation charts. SETTING: SNF rehabilitation beds (n=114) at a 514-bed urban, academic nursing home that receives patients from tertiary care hospitals. PARTICIPANTS: Consecutive rehabilitation patients (n=50) who were rehospitalized during days 4 to 30 of rehabilitation, compared with a matched group of rehabilitation patients (n=50) who were discharged without rehospitalization. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE: Data on potential risk factors were collected: demographics, medical history, conditions associated with preceding hospitalization, and initial rehabilitation examination and laboratory values. The clinical conditions precipitating rehospitalizations were noted. RESULTS: Sixty-two percent of rehospitalizations were related to complications or recurrence of the same medical condition that was treated during the preceding hospitalization. The rehospitalized group had significantly more comorbidities including anemia (P=.001) and malignant solid tumors (P<.001), index hospitalizations involving a gastrointestinal condition (P=.001), needed more assistance with eating (P=.001) and walking (P=.03), and had lower hemoglobin (P=.002) and albumin levels (P<.001). A logistic regression model found that the strongest predictors for rehospitalization are a history of a malignant solid tumor (odds ratio [OR]=10.10), a recent hospitalization involving gastrointestinal conditions (OR=4.62), and a low serum albumin level (with each unit decrease in albumin, the odds of rehospitalization are 4 times greater [OR=.24, P=.005]). CONCLUSIONS: Comorbid conditions, reasons for index hospitalization, and laboratory values are associated with an increased risk for rehospitalization. Further studies are needed to identify high-risk elderly patients and target interventions to minimize rehospitalizations.

Changes in health-related quality of life and factors predicting long-term outcomes in older adults admitted to intensive care units.

OBJECTIVES: The aims of this study were to determine predictors of survival after hospital discharge and to describe the impact of intensive care unit admission on health-related quality of life at 6 months after hospital discharge in older adults admitted to intensive care units. DESIGN: Prospective longitudinal observational study with administered questionnaire. SETTINGS AND PATIENTS: Patients 65 yrs of age and older who were admitted to the medical, surgical, and coronary intensive care units for >24 hrs in a large urban teaching hospital system from August 2007 to May 2008 with a follow-up period ending April 2009. INTERVENTIONS: Administered questionnaire to patients or proxies. MEASUREMENTS AND MAIN RESULTS: Four hundred eighty-four patients 65 yrs old and older were enrolled. Data were collected on demographics, comorbidities, intensive care unit admission diagnoses, Acute Physiology and Chronic Health Evaluation II and Sequential Organ Failure Assessment score, Glasgow Coma Scale score at intensive care unit admission, intensive care unit interventions, and disposition after hospital discharge. A health-related quality of life survey was administered to patients, their proxies, or caregivers at intensive care unit admission, and to hospital survivors at 6 months after hospital discharge. Three hundred sixty-seven (75.8%) and 318 (65.7%) of enrolled patients were alive at hospital discharge and at 6 months, respectively. Mean age of survivors was 77.8 ± 8.5. Independent predictors of death at 6 months were: number of days during the 30 days before hospitalization that the patient felt their "physical health was not good" on the health-related quality of life survey [odds ratio = 1.08; confidence interval 1.04-1.12], a higher Acute Physiology and Chronic Health Evaluation II score [odds ratio = 1.09; 95% confidence interval 1.06-1.12], and chronic pulmonary disease as a comorbidity [odds ratio = 2.22; 95% confidence interval 1.04-4.78]. Of the 318 survivors at 6 months after hospital discharge, 297 (93.4%) completed the health-related quality of life questionnaire. When assessing whether changes in health-related quality of life over time were affected by age in our study cohort of 65 yrs old and older, we found that the oldest survivors, age 86.3 yrs old and older, had worse health-related quality of life over time, including more days spent with poor physical health (p < .004) and mental health (p < .001), while the youngest survivors, age 65-69.3 yrs old, showed improvement in health-related quality of life with fewer days spent with poor physical health (p < .004) and mental health (p < .001) at follow-up compared to baseline. These differences remained after adjusting for severity of illness and other potential confounders. CONCLUSIONS: One-third of adults 65 yrs old and older admitted to the intensive care unit die within 6 months of hospital discharge. Among survivors at 6 months, health-related quality of life has significantly worsened over time in the oldest patients but improved in the youngest. Our study in a large cohort of mixed intensive care unit patients identifies additional prognostic factors and significant quality of life information in intensive care unit survivors well after hospital discharge. This additional information may guide clinicians in their discussions with patients, families, and other providers as they decide on what treatments and interventions to pursue.