ED Utilization and Self-Reported Symptoms in Community-Dwelling Older Adults

Patient-reported outcome measure use among older adults after emergency department care: A systematic review

BACKGROUND

Patient-reported outcome measures (PROMs) are gaining favor in clinical and research settings given their ability to capture a patient's symptom burden, functional status, and quality of life. Our objective in this systematic review was to summarize studies including PROMs assessed among older adults (age ≥ 65 years) after seeking emergency care.

METHODS

With the assistance of a medical librarian, we searched Ovid MEDLINE, PubMed, Embase, CINAHL, Web of Science-Core Collection, and Cochrane CENTRAL from inception through June 2023 for studies in which older adult ED patients had PROMs assessed in the post-emergency care time period. Independent reviewers performed title/abstract review, full-text screening, data extraction, study characteristic summarization, and risk-of-bias (RoB) assessments.

RESULTS

Our search strategy yielded 5153 studies of which 56 met study inclusion criteria. Within included studies, 304 unique PROM assessments were performed at varying time points after the ED visit, including 61 unique PROMs. The most commonly measured domain was physical function, assessed within the majority of studies (47/56; 84%), with measures including PROMs such as Katz activities of daily living (ADLs), instrumental ADLs, and the Barthel Index. PROMs were most frequently assessed at 1-3 months after an ED visit (113/304; 37%), greater than 6 months (91/304; 30%), and 4-6 months (88/304; 29%), with very few PROMs assessed within 1 month of the ED visit (12/304; 4%). Of the 16 interventional studies, two were determined to have a low RoB, four had moderate RoB, nine had high RoB, and one had insufficient information. Of the 40 observational studies, 10 were determined to be of good quality, 20 of moderate quality, and 10 of poor quality.

CONCLUSIONS

PROM assessments among older adults following an ED visit frequently measured physical function, with very few assessments occurring within the first 1 month after an ED visit.

Who Doesn't Come Home? Factors Influencing Mortality Among Long-Term Care Residents Transitioning to and From Emergency Departments in Two Canadian Cities

Residents of long-term care (LTC) whose deaths are imminent are likely to trigger a transfer to the emergency department (ED), which may not be appropriate. Using data from an observational study, we employed structural equation modeling to examine relationships among organizational and resident variables and death during transitions between LTC and ED. We identified 524 residents involved in 637 transfers from 38 LTC facilities and 2 EDs. Our model fit the data, (χ² = 72.91, df = 56, p = .064), explaining 15% variance in resident death. Sustained shortness of breath (SOB), persistent decreased level of consciousness (LOC) and high triage acuity at ED presentation were direct and significant predictors of death. The estimated model can be used as a framework for future research. Standardized reporting of SOB and changes in LOC, scoring of resident acuity in LTC and timely palliative care consultation for families in the ED, when they are present, warrant further investigation.

Interpreting Health Events in Big Data Using Qualitative Traditions

The training of artificial intelligence requires integrating real-world context and mathematical computations. To achieve efficacious smart health artificial intelligence, contextual clinical knowledge serving as ground truth is required. Qualitative methods are well-suited to lend consistent and valid ground truth. In this methods article, we illustrate the use of qualitative descriptive methods for providing ground truth when training an intelligent agent to detect Restless Leg Syndrome. We show how one interdisciplinary, inter-methodological research team used both sensor-based data and the participant's description of their experience with an episode of Restless Leg Syndrome for training the intelligent agent. We make the case for clinicians with qualitative research expertise to be included at the design table to ensure optimal efficacy of smart health artificial intelligence and a positive end-user experience.