Comparing the Accuracy of Traditional vs. FHIR®-Based Extraction of Electronic Health Record Data for Two Clinical Trials.

While pilots and production use of software based on the Health Level Seven (HL7®) Fast Healthcare Interoperability Resources (FHIR®) standard are increasing in clinical research, we lack consistent evaluative data on important outcomes, such as data accuracy. We compared the accuracy of EHR collected, FHIR® extracted data (called EHR-to-eCRF data collection) to traditional clinical trial data collection. The accuracy rate for EHR-collected data was significantly higher than for the same data collected through traditional methods. It is possible that EHR-collected (FHIR® extracted) data can substantially improve data quality in clinical studies while decreasing the burden on study sites.

A randomised controlled trial of the Learning Skills Together (LST) intervention to improve dementia family caregivers' self-efficacy with complex care.

BACKGROUND: Nearly two-thirds of family caregivers of persons living with Alzheimer's disease or related dementias (AD/ADRD) provide complex care, including medical care. Family caregivers typically receive little to no training on how to provide this care. Furthermore, family caregivers simultaneously grapple with the presence of behavioral and psychological symptoms of dementia (BPSD), diminished communication abilities, and comorbidities such as diabetes. We developed Learning Skills Together (LST), a 6-week digitally delivered psychoeducational program, to facilitate family caregiver abilities to administer complex care tasks. The goal of the present study is to test the efficacy of LST and to reduce adverse outcomes associated with caregiving, such as depressive symptomology and negative appraisal of BPSD. METHODS: To test the efficacy of LST, we will conduct a two-arm single-site randomized controlled trial (RCT) with N = 200 family caregivers of persons living with AD/ADRD. Eligible family caregivers will be randomly assigned to participate in either the LST intervention or a structurally equivalent control condition focused on healthy living. All family caregivers will complete four surveys, including a baseline survey administered prior to randomization, a post-intervention survey, and a 3- and 6-month follow-up survey to assess change in study outcomes. Between-group comparisons of each outcome will be evaluated using generalized estimating equation models. Mediation analyses will assess family caregiver self-efficacy as the intervention's mechanism of change in depressive symptomology and BPSD. We will also examine caregiver race, ethnicity, and gender as effect modifiers of the intervention. DISCUSSION: LST findings will inform the field of AD/ADRD and caregiving regarding optimally supporting family caregivers in managing complex care tasks. If efficacious, the LST intervention will support family caregivers in preserving their own mental health while providing complex care. TRIAL REGISTRATION: Clinical Trials.gov NCT05846984 . This study was registered on May 6, 2023.

A randomised controlled trial of the Learning Skills Together (LST) intervention to improve dementia family caregivers' self-efficacy with complex care.

Background: Nearly two-thirds of family caregivers of persons living with Alzheimer's disease or related dementias (AD/ADRD) provide complex care, including medical care. Family caregivers typically receive little to no training on how to provide this care. Furthermore, family caregivers simultaneously grapple with the presence of behavioral and psychological symptoms of dementia (BPSD), diminished communication abilities, and comorbidities such as diabetes. We developed Learning Skills Together (LST), a six-week digitally delivered psychoeducational program, to facilitate family caregiver abilities to administer complex care tasks. The goal of the present study is to test the efficacy of LST and to reduce adverse outcomes associated with caregiving, such as depressive symptomology and negative appraisal of BPSD. Methods: To test the efficacy of LST, we will conduct a two-arm single-site randomised controlled trial (RCT) with N = 200 family caregivers of persons living with AD/ADRD. Eligible family caregivers will be randomly assigned to participate in either the LST intervention or a structurally equivalent control condition focused on healthy living. All family caregivers will complete four surveys, including a baseline survey administered prior to randomisation, a post-intervention survey, and a three- and six-month follow-up survey to assess change in study outcomes. Between-group comparisons of each outcome will be evaluated using generalized estimating equation models. Mediation analyses will assess family caregiver self-efficacy as the intervention's mechanism of change in depressive symptomology and BPSD. We will also examine caregiver race, ethnicity, and gender as effect modifiers of the intervention. Discussion: LST findings will inform the field of AD/ADRD and caregiving regarding optimally supporting family caregivers in managing complex care tasks. If efficacious, the LST intervention will support family caregivers in preserving their own mental health while providing complex care.

Propensity score analysis for health care disparities: a deweighting approach.

BACKGROUND: Propensity score weighting is a useful tool to make causal or unconfounded comparisons between groups. According to the definition by the Institute of Medicine (IOM), estimates of health care disparities should be adjusted for health-status factors but not for socioeconomic status (SES) variables. There have been attempts to use propensity score weighting to generate estimates that are concordant with IOM's definition. However, the existing propensity score methods do not preserve SES distributions in minority and majority groups unless SES variables are independent of health status variables. METHODS: The present study introduces a deweighting method that uses two types of propensity scores. One is a function of all covariates of health status and SES variables and is used to weight study subjects to adjust for them. The other is a function of only the SES variables and is used to deweight the subjects to preserve the original SES distributions. RESULTS: The procedure of deweighting is illustrated using a dataset from a right heart catheterization (RHC) study, where it was used to examine whether there was a disparity between black and white patients in receiving RHC. The empirical example provided promising evidence that the deweighting method successfully preserved the marginal SES distributions for both racial groups but balanced the conditional distributions of health status given SES. CONCLUSIONS: Deweighting is a promising tool for implementing the IOM-definition of health care disparities. The method is expected to be broadly applied to quantitative research on health care disparities.

Project ECHO Brain Health: Assessing the Impact of a Pilot Program to Promote Self-Efficacy Among Community Health Workers.

This mixed-methods study sought to examine the impact of the Project ECHO Brain Health program on participating community health workers' (CHWs') self-efficacy to address dementia, promote brain health, and advocate for research among Latinx South Texas communities. Using an explanatory sequential design, quantitative data collected from pre- and post-program surveys were analyzed to inform the collection of qualitative data, followed by an interpretation of all findings to better understand the impact of the program on self-efficacy. Pre- and post-surveys were collected from 25 CHWs, 13 of whom later participated in individual interviews. There was a statistically significant increase in mean self-efficacy scores between the pre- and post-surveys among participants. Three categories reflecting the experiences of participants were identified from the qualitative data: addressing training needs; impact on CHWs and their practice; and community of learning. The findings suggest that Project ECHO Brain Health program is a timely intervention that may facilitate increased self-efficacy among CHWs as they navigate the impacts of dementia in their communities.

Erratum: Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration - CORRIGENDUM.

[This corrects the article DOI: 10.1017/cts.2024.2.].

Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration.

Introduction: The focus on social determinants of health (SDOH) and their impact on health outcomes is evident in U.S. federal actions by Centers for Medicare & Medicaid Services and Office of National Coordinator for Health Information Technology. The disproportionate impact of COVID-19 on minorities and communities of color heightened awareness of health inequities and the need for more robust SDOH data collection. Four Clinical and Translational Science Award (CTSA) hubs comprising the Texas Regional CTSA Consortium (TRCC) undertook an inventory to understand what contextual-level SDOH datasets are offered centrally and which individual-level SDOH are collected in structured fields in each electronic health record (EHR) system potentially for all patients. Methods: Hub teams identified American Community Survey (ACS) datasets available via their enterprise data warehouses for research. Each hub's EHR analyst team identified structured fields available in their EHR for SDOH using a collection instrument based on a 2021 PCORnet survey and conducted an SDOH field completion rate analysis. Results: One hub offered ACS datasets centrally. All hubs collected eleven SDOH elements in structured EHR fields. Two collected Homeless and Veteran statuses. Completeness at four hubs was 80%-98%: Ethnicity, Race; < 10%: Education, Financial Strain, Food Insecurity, Housing Security/Stability, Interpersonal Violence, Social Isolation, Stress, Transportation. Conclusion: Completeness levels for SDOH data in EHR at TRCC hubs varied and were low for most measures. Multiple system-level discussions may be necessary to increase standardized SDOH EHR-based data collection and harmonization to drive effective value-based care, health disparities research, translational interventions, and evidence-based policy.