Construction of the Digital Health Equity-Focused Implementation Research Conceptual Model - Bridging the Divide Between Equity-focused Digital Health and Implementation Research.

Digital health implementations and investments continue to expand. As the reliance on digital health increases, it is imperative to implement technologies with inclusive and accessible approaches. A conceptual model can be used to guide equity-focused digital health implementations to improve suitability and uptake in diverse populations. The objective of this study is expand an implementation model with recommendations on the equitable implementation of new digital health technologies. The Digital Health Equity-Focused Implementation Research (DH-EquIR) conceptual model was developed based on a rigorous review of digital health implementation and health equity literature. The Equity-Focused Implementation Research for Health Programs (EquIR) model was used as a starting point and merged with digital equity and digital health implementation models. Existing theoretical frameworks and models were appraised as well as individual equity-sensitive implementation studies. Patient and program-related concepts related to digital equity, digital health implementation, and assessment of social/digital determinants of health were included. Sixty-two articles were analyzed to inform the adaption of the EquIR model for digital health. These articles included digital health equity models and frameworks, digital health implementation models and frameworks, research articles, guidelines, and concept analyses. Concepts were organized into EquIR conceptual groupings, including population health status, planning the program, designing the program, implementing the program, and equity-focused implementation outcomes. The adapted DH-EquIR conceptual model diagram was created as well as detailed tables displaying related equity concepts, evidence gaps in source articles, and analysis of existing equity-related models and tools. The DH-EquIR model serves to guide digital health developers and implementation specialists to promote the inclusion of health-equity planning in every phase of implementation. In addition, it can assist researchers and product developers to avoid repeating the mistakes that have led to inequities in the implementation of digital health across populations.

Predicting Risk of Alzheimer's Diseases and Related Dementias with AI Foundation Model on Electronic Health Records.

Early identification of Alzheimer's disease (AD) and AD-related dementias (ADRD) has high clinical significance, both because of the potential to slow decline through initiating FDA-approved therapies and managing modifiable risk factors, and to help persons living with dementia and their families to plan before cognitive loss makes doing so challenging. However, substantial racial and ethnic disparities in early diagnosis currently lead to additional inequities in care, urging accurate and inclusive risk assessment programs. In this study, we trained an artificial intelligence foundation model to represent the electronic health records (EHR) data with a vast cohort of 1.2 million patients within a large health system. Building upon this foundation EHR model, we developed a predictive Transformer model, named TRADE, capable of identifying risks for AD/ADRD and mild cognitive impairment (MCI), by analyzing the past sequential visit records. Amongst individuals 65 and older, our model was able to generate risk predictions for various future timeframes. On the held-out validation set, our model achieved an area under the receiver operating characteristic (AUROC) of 0.772 (95% CI: 0.770, 0.773) for identifying the AD/ADRD/MCI risks in 1 year, and AUROC of 0.735 (95% CI: 0.734, 0.736) in 5 years. The positive predictive values (PPV) in 5 years among individuals with top 1% and 5% highest estimated risks were 39.2% and 27.8%, respectively. These results demonstrate significant improvements upon the current EHR-based AD/ADRD/MCI risk assessment models, paving the way for better prognosis and management of AD/ADRD/MCI at scale.

A Preliminary Validation of an Optimal Cutpoint in Total Number of Patient-Reported Symptoms in Head and Neck Cancer for Effective Alignment of Clinical Resources with Patients' Symptom Burden.

Background: Patients with head and neck cancer (HNC) often experience high symptom burden leading to lower quality of life (QoL). Objective: This study aims to conceptually model optimal cutpoint by examining where total number of patient-reported symptoms exceeds patients' coping capacity, leading to a decline in QoL in patients with HNC. Methods: Secondary data analysis of 105 individuals with HNC enrolled in a clinical usefulness study of the NYU Electronic Patient Visit Assessment (ePVA)©, a digital patient-reported symptom measure. Patients completed ePVA and European Organization for Research and Treatment of Cancer (EORTC©) QLQ-C30 v3.0. The total number of patient-reported symptoms was the sum of symptoms as identified by the ePVA questionnaire. Analysis of variance (ANOVA) was used to define optimal cutpoint. Results: Study participants had a mean age of 61.5, were primarily male (67.6%), and had Stage IV HNC (53.3%). The cutpoint of 10 symptoms was associated with significant decline of QoL (F= 44.8, P<.0001), dividing the population into categories of low symptom burden (< 10 symptoms) and high symptom burden (≥ 10 symptoms). Analyses of EORTC© function subscales supported the validity of 10 symptoms as the optimal cutpoint (Physical: F=28.3, P<.0001; Role: F=21.6, P<.0001; Emotional: F=9.5, P=.003; Social: F=33.1, P<.0001). Conclusions: In HNC, defining optimal cutpoints in the total number of patient-reported symptoms is feasible. Implications for Practice: Cutpoints in the total number of patient-reported symptoms may identify patients experiencing a high symptom burden from HNC. Foundational: Using optimal cutpoints of the total number of patient-reported symptoms may help effectively align clinical resources with patients' symptom burden.

Race and Ethnicity Are Related to Undesirable Home Health Care Outcomes in Seriously Ill Older Adults.

OBJECTIVES: Medicare Home Health Care (HHC) services are integral to the care of homebound seriously ill older adults requiring ongoing specialized medical care. Although disparities in health outcomes are well documented in inpatient and primary care, disparities experienced by historically marginalized racial and ethnic groups underrepresented in HHC are understudied. This study aimed to examine the relationship between individual characteristics and differences in HHC health outcomes for seriously ill older adults. DESIGN: Secondary data analysis, repeated measure. SETTING AND PARTICIPANTS: Seriously ill older adults who received HHC in 2016 in the HHC Outcome and Assessment Information Set (OASIS). METHODS: Start of care and discharge data from the 2016 HCC OASIS were used to examine the relationship between individual characteristics and differences in HHC health outcomes identified by the Centers for Medicare and Medicaid Services as key indicators of quality in HHC, including dyspnea, pain frequency, cognitive functioning, and presence of unhealed pressure ulcer stage II or higher. A generalized ordered logit model with partial proportional odds was used for the ordinal categorical outcomes and a logistic regression was used for the binary dependent variable. RESULTS: Findings indicated that of 227,402 seriously ill individuals with an HHC episode in 2016, those from underrepresented racial and ethnic groups had between 14% and 57% higher odds of worse health outcomes compared with non-Hispanic white patients with the exception of pain frequency. CONCLUSIONS AND IMPLICATIONS: For people living with serious illness, there are significant differences in Medicare HHC health outcomes when comparing underrepresented racial or ethnic beneficiaries with white counterparts. More research is needed to understand how health care processes such as referral patterns or time to care initiation, and structural factors such as HHC agency quality and neighborhood social deprivation are related to health differences observed in the population.

Improving Sleep Using Mentored Behavioral and Environmental Restructuring (SLUMBER).

OBJECTIVES: To evaluate the impact of a mentoring program to encourage staff-delivered sleep-promoting strategies on sleep, function, depression, and anxiety among skilled nursing facility (SNF) residents. DESIGN: Modified stepped-wedge unit-level intervention. SETTING AND PARTICIPANTS: Seventy-two residents (mean age 75 ± 15 years; 61.5% female, 41% non-Hispanic white, 35% Black, 20% Hispanic, 3% Asian) of 2 New York City urban SNFs. METHODS: Expert mentors provided SNF staff webinars, in-person workshops, and weekly sleep pearls via text messaging. Resident data were collected at baseline, post-intervention (V1), and 3-month follow-up (V2), including wrist actigraphy, resident behavioral observations, Pittsburgh Sleep Quality Index (PSQI), Patient Health Questionnaire-9 (PHQ-9) depression scale, Brief Anxiety and Depression Scale (BADS), Brief Cognitive Assessment Tool (BCAT), and select Minimum Data Set 3.0 (MDS 3.0) measures. Linear mixed models were fit for continuous outcomes and mixed-effects logistic models for binary outcomes. Outcomes were modeled as a function of time. Planned contrasts compared baseline to V1 and V2. RESULTS: There was significant improvement in PSQI scores from baseline to V1 (P = .009), and from baseline to V2 (P = .008). Other significant changes between baseline and V1 included decreased depression (PHQ-9) (P = .028), increased daytime observed out of bed (P ≤ .001), and increased daytime observed being awake (P < .001). At V2 (vs baseline) being observed out of bed decreased (P < .001). Daytime sleeping by actigraphy increased from baseline to V1 (P = .004), but not V2. MDS 3.0 activities of daily living and pain showed improvements by the second quarter following implementation of SLUMBER (P's ≤ .034). There were no significant changes in BADS or BCAT between baseline and V1 or V2. CONCLUSIONS AND IMPLICATIONS: SNF residents had improvements in sleep quality and depression with intervention, but improvements were not sustained at 3-month follow-up. The COVID-19 pandemic led to premature study termination, so full impacts remain unknown.

Implementation Outcomes for the SLUMBER Sleep Improvement Program in Long-Term Care.

OBJECTIVES: To describe the implementation of a mentored staff-delivered sleep program in nursing facilities. DESIGN: Modified stepped-wedge unit-level intervention. SETTING AND PARTICIPANTS: This program was implemented in 2 New York City nursing facilities, with partial implementation (due to COVID-19) in a third facility. METHODS: Expert mentors provided staff webinars, in-person workshops, and weekly sleep pearls via text messaging. We used the integrated Promoting Action on Research Implementation in Health Services (i-PARiHS) framework as a post hoc approach to describe key elements of the SLUMBER implementation. We measured staff participation in unit-level procedures and noted their commentary during unit workshops. RESULTS: We completed SLUMBER within 5 units across 2 facilities and held 15 leadership meetings before and during program implementation. Sessions on each unit included 3 virtual webinar presentations and 4 in-person workshops for each nursing shift, held over a period of 3 to 4 months. Staff attendance averaged >3 sessions per individual staff member. Approximately 65% of staff present on each unit participated in any given session. Text messaging was useful for engagement, educational reinforcement, and encouraging attendance. We elevated staff as experts in the care of their residents as a strategy for staff engagement and behavior change and solicited challenging cases from staff during workshops to provide strategies to address resident behavior and encourage adoption when successful. CONCLUSIONS AND IMPLICATIONS: Engaging staff, leadership, residents, and family of nursing facilities in implementing a multicomponent sleep quality improvement program is feasible for improving nursing facilities' sleep environment. The program required gaining trust at multiple levels through presence and empathy, and reinforcement mechanisms (primarily text messages). To improve scalability, SLUMBER could evolve from an interdisciplinary investigator-based approach to internal coaches in a train-the-trainer model to effectively and sustainably implement this program to improve sleep quality for facility residents.

An Evolutionary Concept Analysis of the "Fighter" in the Intensive Care Unit.

The purpose of this article was to analyze the concept of "the fighter in the intensive care unit (ICU)" per the scientific literature and the impact this mentality has on care administered in the ICU. A literature review and a concept analysis based on Rodger's evolutionary method were performed to identify surrogate terms, antecedents, attributes, and consequences pertaining to the "fighter" in the ICU. Thirteen articles with a focus on "the fighter" were included in this analysis. There is a strong desire to remain optimistic and maintain high spirits as a coping mechanism in the face of extreme prognostic uncertainty. Themes that emerged from the literature were the need to find inner strength and persist in the face of adversity. The concept of "the fighter in the ICU" can serve as either adaptive or maladaptive coping, depending on the larger clinical picture. Patient experiences in the ICU are fraught with physical and psychological distress. How the patient and family unit cope during this anxiety-provoking time is based on the individual. Maintaining optimism and identifying as a fighter can be healthy ways to adapt to the circumstances. This concept analysis highlights the importance of holistic care and instilling hope particularly as patients may be nearing the end of life.

Evaluating Large Language Models in Extracting Cognitive Exam Dates and Scores.

Importance: Large language models (LLMs) are crucial for medical tasks. Ensuring their reliability is vital to avoid false results. Our study assesses two state-of-the-art LLMs (ChatGPT and LlaMA-2) for extracting clinical information, focusing on cognitive tests like MMSE and CDR. Objective: Evaluate ChatGPT and LlaMA-2 performance in extracting MMSE and CDR scores, including their associated dates. Methods: Our data consisted of 135,307 clinical notes (Jan 12th, 2010 to May 24th, 2023) mentioning MMSE, CDR, or MoCA. After applying inclusion criteria 34,465 notes remained, of which 765 underwent ChatGPT (GPT-4) and LlaMA-2, and 22 experts reviewed the responses. ChatGPT successfully extracted MMSE and CDR instances with dates from 742 notes. We used 20 notes for fine-tuning and training the reviewers. The remaining 722 were assigned to reviewers, with 309 each assigned to two reviewers simultaneously. Inter-rater-agreement (Fleiss' Kappa), precision, recall, true/false negative rates, and accuracy were calculated. Our study follows TRIPOD reporting guidelines for model validation. Results: For MMSE information extraction, ChatGPT (vs. LlaMA-2) achieved accuracy of 83% (vs. 66.4%), sensitivity of 89.7% (vs. 69.9%), true-negative rates of 96% (vs 60.0%), and precision of 82.7% (vs 62.2%). For CDR the results were lower overall, with accuracy of 87.1% (vs. 74.5%), sensitivity of 84.3% (vs. 39.7%), true-negative rates of 99.8% (98.4%), and precision of 48.3% (vs. 16.1%). We qualitatively evaluated the MMSE errors of ChatGPT and LlaMA-2 on double-reviewed notes. LlaMA-2 errors included 27 cases of total hallucination, 19 cases of reporting other scores instead of MMSE, 25 missed scores, and 23 cases of reporting only the wrong date. In comparison, ChatGPT's errors included only 3 cases of total hallucination, 17 cases of wrong test reported instead of MMSE, and 19 cases of reporting a wrong date. Conclusions: In this diagnostic/prognostic study of ChatGPT and LlaMA-2 for extracting cognitive exam dates and scores from clinical notes, ChatGPT exhibited high accuracy, with better performance compared to LlaMA-2. The use of LLMs could benefit dementia research and clinical care, by identifying eligible patients for treatments initialization or clinical trial enrollments. Rigorous evaluation of LLMs is crucial to understanding their capabilities and limitations.

Emergency Nurses' Perceived Barriers and Solutions to Engaging Patients With Life-Limiting Illnesses in Serious Illness Conversations: A United States Multicenter Mixed-Method Analysis.

INTRODUCTION: This study aimed to assess emergency nurses' perceived barriers toward engaging patients in serious illness conversations. METHODS: Using a mixed-method (quant + QUAL) convergent design, we pooled data on the emergency nurses who underwent the End-of-Life Nursing Education Consortium training across 33 emergency departments. Data were extracted from the End-of-Life Nursing Education Consortium post-training questionnaire, comprising a 5-item survey and 1 open-ended question. Our quantitative analysis employed a cross-sectional design to assess the proportion of emergency nurses who report that they will encounter barriers in engaging seriously ill patients in serious illness conversations in the emergency department. Our qualitative analysis used conceptual content analysis to generate themes and meaning units of the perceived barriers and possible solutions toward having serious illness conversations in the emergency department. RESULTS: A total of 2176 emergency nurses responded to the survey. Results from the quantitative analysis showed that 1473 (67.7%) emergency nurses reported that they will encounter barriers while engaging in serious illness conversations. Three thematic barriers-human factors, time constraints, and challenges in the emergency department work environment-emerged from the content analysis. Some of the subthemes included the perceived difficulty of serious illness conversations, delay in daily throughput, and lack of privacy in the emergency department. The potential solutions extracted included the need for continued training, the provision of dedicated emergency nurses to handle serious illness conversations, and the creation of dedicated spaces for serious illness conversations. DISCUSSION: Emergency nurses may encounter barriers while engaging in serious illness conversations. Institutional-level policies may be required in creating a palliative care-friendly emergency department work environment.

Variation in Home Healthcare Use by Dementia Status Among a National Cohort of Older Adults.

BACKGROUND: Medicare-funded home healthcare (HHC) delivers skilled nursing, therapy, and related services through visits to the patient's home. Nearly one-third (31%) of HHC patients have diagnosed dementia, but little is currently known regarding how HHC utilization and care delivery differ for persons living with dementia (PLwD). METHODS: We drew on linked 2012-2018 Health and Retirement Study and Medicare claims for a national cohort of 1 940 community-living older adults. We described differences in HHC admission, length of stay, and referral source by patient dementia status and used weighted, multivariable logistic and negative binomial models to estimate the relationship between dementia and HHC visit type and intensity while adjusting for sociodemographic characteristics, health and functional status, and geographic/community factors. RESULTS: PLwD had twice the odds of using HHC during a 2-year observation period, compared to those without dementia (odds ratio [OR]: 2.03; p < .001). They were more likely to be referred to HHC without a preceding hospitalization (49.4% vs 32.1%; p < .001) and incurred a greater number of HHC episodes (1.4 vs 1.0; p < .001) and a longer median HHC length of stay (55.8 days vs 40.0 days; p < .001). Among post-acute HHC patients, PLwD had twice the odds of receiving social work services (unadjusted odds ratio [aOR]: 2.15; p = .008) and 3 times the odds of receiving speech-language pathology services (aOR: 2.92; p = .002). CONCLUSIONS: Findings highlight HHC's importance as a care setting for community-living PLwD and indicate the need to identify care delivery patterns associated with positive outcomes for PLwD and design tailored HHC clinical pathways for this patient subpopulation.

Neuropsychiatric symptoms in people living with dementia receiving home health services.

BACKGROUND: We sought to describe neuropsychiatric symptoms (NPS) among people living with dementia (PLWD) from diverse racial and ethnic groups receiving home health services while accounting for dementia severity, individual symptom prevalence, and neighborhood disadvantage. METHODS: A prospective study using cross-sectional data from n = 192 PLWD receiving skilled home healthcare in New Jersey enrolled in the Dementia Symptom Management at Home Program trial. We prospectively measured symptom prevalence with the Neuropsychiatric Inventory Questionnaire and dementia severity using the Quick Dementia Rating System. A one-way ANOVA determined NPS prevalence by dementia severity (mild, moderate, severe). Fisher's exact tests were used to assess the association of individual symptom prevalence with race and ethnicity and cross tabs to descriptively stratify individual symptom prevalence by dementia severity among groups. A Pearson correlation was performed to determine if a correlation existed among neighborhood disadvantages measured by the Area Deprivation Index (ADI) state decile scores and NPS prevalence and severity. RESULTS: Participants identified as non-Hispanic White (50%), non-Hispanic Black (30%), or Hispanic (13%). NPS were prevalent in 97% of participants who experienced 5.4 ± 2.6 symptoms with increased severity (10.8 ± 6.6) and care partner distress (13.8 ± 10.8). NPS increased with dementia severity (p = 0.004) with the greatest difference seen between individuals with mild dementia (4.3 ± 2.3) versus severe dementia (5.9 ± 2.3; p = 0.002). Few differences were found in symptom prevalence by racial and ethnic sub-groups. Nighttime behaviors were higher in non-Hispanic Black (78%), compared with non-Hispanic Whites (46%) with moderate dementia, p = 0.042. State ADI scores were not correlated with the number of NPS reported, or severity. CONCLUSIONS: NPS were prevalent and increased with dementia severity with commonalities among racial and ethnic groups with varying levels of neighborhood disadvantage. There is a need for effective methods for improving NPS identification, assessment, and management broadly for homebound PLWD.

Engagement, Advance Care Planning, and Hospice Use in a Telephonic Nurse-Led Palliative Care Program for Persons Living with Advanced Cancer.

Persons living with advanced cancer have intensive symptoms and psychosocial needs that often result in visits to the Emergency Department (ED). We report on program engagement, advance care planning (ACP), and hospice use for a 6-month longitudinal nurse-led, telephonic palliative care intervention for patients with advanced cancer as part of a larger randomized trial. Patients 50 years and older with metastatic solid tumors were recruited from 18 EDs and randomized to receive nursing calls focused on ACP, symptom management, and care coordination or specialty outpatient palliative care (ClinicialTrials.gov: NCT03325985). One hundred and five (50%) graduated from the 6-month program, 54 (26%) died or enrolled in hospice, 40 (19%) were lost to follow-up, and 19 (9%) withdrew prior to program completion. In a Cox proportional hazard regression, withdrawn subjects were more likely to be white and have a low symptom burden compared to those who did not withdraw. Two hundred eighteen persons living with advanced cancer were enrolled in the nursing arm, and 182 of those (83%) completed some ACP. Of the subjects who died, 43/54 (80%) enrolled in hospice. Our program demonstrated high rates of engagement, ACP, and hospice enrollment. Enrolling subjects with a high symptom burden may result in even greater program engagement.

"I Have a Lotta Sad Feelin'" - Unaddressed Mental Health Needs and Self-Support Strategies in Medicaid-Funded Assisted Living.

OBJECTIVE: To investigate mental health needs and barriers to seeking mental health support in Medicaid-funded Assisted Living Facility (M-ALF). DESIGN: A multimethod, qualitative-dominant descriptive design using questionnaires and semistructured interviews. SETTING AND PARTICIPANTS: The study occurred at a M-ALF in the Bronx, New York. A researcher in residence recruited 13 residents (11 Black or African American, 2 Asian) using purposive sampling. METHODS: Demographic data and mental health indicators (depression, anxiety, stress, hopelessness) were measured with questionnaires (Center for Epidemiological Studies Depression Scale, Edmonton Symptom Assessment System, Perceived Stress Scale, Beck Hopelessness Survey) and analyzed with descriptive statistics. Interviews were conducted between June and November 2021, transcribed, and analyzed using conventional content analysis. RESULTS: Thirteen residents (mean age: 73.4 years, mean length of stay: 3.5 years; range: 1.0-7.5) completed data collection. Quantitatively indicators of unmet mental health were common. Qualitatively, residents reported barriers to mental health access to address depression, anxiety, and substance use. This was accompanied by concerns surrounding loss of autonomy, mistrust for M-ALF organizational support, isolation and uncertainty about how to receive mental health support. Perspectives were shaped by past experiences with institutional living, serious illness, and being unhoused. Themes and subthemes were (1) mental health need (unmet mental health need, depression, and anxiety and seeking support through non-mental health resources) and (2) barriers to mental health support (dissatisfaction with M-ALF care, perceived threats to autonomy, desire for autonomy that leads to diminished care seeking). CONCLUSION AND IMPLICATIONS: Residents of M-ALF have mental health needs for which care is stymied by loss of autonomy, lack of resources, and the M-ALF environment. Residents use unconventional resources to address needs that may be neither efficient nor effective. Novel mental health interventions and processes are needed to improve mental health access and should prioritize residents' desire for autonomy and the unique circumstances of living in M-ALF.

Complex Care Needs at the End of Life for Seriously Ill Adults With Multiple Chronic Conditions.

Understanding the complex care needs of seriously ill adults with multiple chronic conditions with and without cancer is critical for the delivery of high-quality serious illness and palliative care at the end of life. The objective of this secondary data analysis of a multisite randomized clinical trial in palliative care was to elucidate the clinical profile and complex care needs of seriously ill adults with multiple chronic conditions and to highlight key differences among those with and without cancer at the end of life. Of the 213 (74.2%) older adults who met criteria for multiple chronic conditions (eg, 2 or more chronic conditions requiring regular care with limitations of daily living), 49% had a diagnosis of cancer. Hospice enrollment was operationalized as an indicator for severity of illness and allowed for the capture of complex care needs of those deemed to be nearing the end of life. Individuals with cancer had complex symptomatology with a higher prevalence of nausea, drowsiness, and poor appetite and end of life and lower hospice enrollment. Individuals with multiple chronic conditions without cancer had lower functional status, greater number of medications, and higher hospice enrollment. The care of seriously ill older adults with multiple chronic conditions requires tailored approaches to improve outcomes and quality of care across health care settings, particularly at the end of life.

Defining a taxonomy of Medicare-funded home-based clinical care using claims data.

BACKGROUND: As more Americans age in place, it is critical to understand care delivery in the home. However, data on the range of home-based services provided by Medicare is limited. We define a taxonomy of clinical care in the home funded through fee-for-service Medicare and methods to identify receipt of those services. METHODS: We analyzed Fee-for-service (FFS) Medicare claims data from a nationally-representative cohort of older adults, the National Health and Aging Trends Study (NHATS), to identify home-based clinical care. We included 6,664 NHATS enrollees age ≥ 70 and living in the community, observed an average of 3 times each on claims-linked NHATS surveys. We examined provider and service type of home-based clinical care to identify a taxonomy of 5 types: home-based medical care (physician, physician assistant, or nurse practitioner visits), home-based podiatry, skilled home health care (SHHC), hospice, and other fee-for-service (FFS) home-based care. We further characterized home-based clinical care by detailed care setting and visit types. RESULTS: From 2011-2016, 17.8%-20.8% of FFS Medicare beneficiaries age ≥ 70 received Medicare-funded home-based clinical care. SHHC was the most common service (12.8%-16.1%), followed by other FFS home-based care (5.5%-6.5%), home-based medical care (3.2%-3.9%), and hospice (2.6%-3.0%). Examination of the other-FFS home-based care revealed imaging/diagnostics and laboratory testing to be the most common service. CONCLUSIONS: We define a taxonomy of clinical care provided in the home, serving 1 in 5 FFS Medicare beneficiaries. This approach can be used to identify and address research and clinical care gaps in home-based clinical care delivery.

Improving sleep using mentored behavioral and environmental restructuring (SLUMBER): A randomized stepped-wedge design trial to evaluate a comprehensive sleep intervention in skilled nursing facilities.

INTRODUCTION: Poor sleep is ubiquitous in skilled nursing facilities (SNFs) and is associated with a myriad of negative symptoms. Non-pharmacological interventions can improve sleep, yet sustainability has not been demonstrated. The Improving Sleep Using Mentored Behavioral and Environmental Restructuring (SLUMBER) trial will test whether a staff mentoring approach to address resident sleep issues positively impacts sleep quality and whether improved sleep benefits mood, cognitive performance, and activity engagement for residents living in SNFs. INTERVENTION: This is a four-year hybrid type I effectiveness/implementation randomized stepped-wedge trial using a comprehensive sleep improvement program conducted in three urban SNFs. METHODS: We will provide SNF staff with sleep promotion strategies over a four-month intervention. Staff will have access to in-person workshops, webinars, weekly sleep pearls via text messaging, environmental data, and expert program mentors. We will consent residents for data collection (at baseline, end of intervention, and three- and six-months post-intervention) including resident observations, questionnaires, and wrist actigraphy (to objectively measure sleep). We will also use selected Minimum Data Set 3.0 (MDS) measures. CONCLUSION: SLUMBER uses a unique strategy to iteratively improve sleep interventions through SNF staff buy-in, expert mentoring, and technological supports within a quality improvement framework. As a stepped-wedge trial, the initial SNF units provide opportunities for program improvement in subsequent units, accounting for variation across resident populations at different sites. Protocol limitations include strategies which may require substantial customization for greater spread. A comprehensive staff training program that addresses both sleep quality and related symptoms has the opportunity for considerable dissemination. TRIAL REGISTRATION: USGOV Clinical Trials ID: NCT03327324.

Multiple Chronic Conditions among Seriously Ill Adults Receiving Palliative Care.

The objective of this study was to characterize multiple chronic conditions (MCCs) among seriously ill adults receiving palliative care at the end of life. A latent class analysis was conducted to identify latent subgroups of seriously ill older adults based on a baseline Charlson comorbidity index (CCI) measurement, a measure of comorbidity burden, and mortality risk. The three latent subgroups were: (1) low to moderate CCI with MCC, (2) high CCI with MCC, and (3) high CCI and metastatic cancer. The "low to moderate CCI and MCC" subgroup included older adults with chronic obstructive pulmonary disease (COPD), cardiovascular disease, congestive heart failure, myocardial infarction, dementia, diabetes, and lymphoma. A "high CCI and MCC" subgroup included individuals with severe illness including liver or renal disease among other MCCs. A "high CCI and metastatic cancer" included all participants with metastatic cancer. This study sheds light on the MCC profile of seriously ill adults receiving palliative care.

Potential sources of moral distress during COVID-19: Perspectives of hospice interdisciplinary teams.

OBJECTIVE: This study aimed to examine the impact of COVID-19 on hospice Interdisciplinary team (IDT) members' self-reported stress and identify possible sources of moral distress. METHODS: A cross-sectional survey was conducted using Qualtrics to understand the impact of COVID-19 on quality improvement initiative implementation and hospice IDT members' general and dementia-specific care provision. Directed qualitative content analysis was used to analyze hospice IDT members' responses from five open-ended survey questions that were indicative of stress and possible moral distress. RESULTS: The final sample consisted of 101 unique respondents and 175 comments analyzed. Three categories related to sources of moral distress based on hospice IDT member survey responses were identified: (1) impact of telehealth, personal protective equipment (PPE), and visit restrictions on relationships; (2) lack of COVID-19-specific skills; and (3) organizational climate. Sources of moral distress were categorized in 40% of all responses analyzed. SIGNIFICANCE OF RESULTS: This study is one of the first to document and confirm evidence of potential stress and moral distress amongst hospice IDT members during COVID-19. It is imperative given the possible negative impact on patient care and clinician well-being, that future research and interventions incorporate mechanisms to support clinicians' emotional and ethical attunement and support organizations to actively engage in practices that address clinician moral distress resulting from restrictive environments, such as the one necessitated by COVID-19.