Longitudinal Evaluation of a Statewide Quality Improvement Program for Nursing Homes

OBJECTIVES

The National Academies of Sciences, Engineering, and Medicine (NASEM) Nursing Home Quality report recommends that states "develop and operate state-based…technical assistance programs…to help nursing homes…improve care and…operations." The Quality Improvement Program for Missouri (QIPMO) is one such program. This longitudinal evaluation examined and compared differences in quality measures (QMs) and nursing home (NH) characteristics based on intensity of QIPMO services used.

DESIGN

A descriptive study compared key QMs of clinical care, facility-level characteristics, and differing QIPMO service intensity use. QIPMO services include on-site clinical consultation by expert nurses; evidence-based practice information; teaching NHs use of quality improvement (QI) methods; and guiding their use of Centers for Medicare and Medicaid Services (CMS)-prepared QM comparative feedback reports to improve care.

SETTING AND PARTICIPANTS

All Missouri NHs (n = 510) have access to QIPMO services at no charge. All used some level of service during the study, 2020-2022.

METHODS

QM data were drawn from CMS's publicly available website (Refresh April 2023) and NH characteristics data from other public websites. Service intensity was calculated using data from facility contacts (on-site visits, phone calls, texts, emails, webinars). NHs were divided into quartiles based on service intensity.

RESULTS

All groups had different beginning QM scores and improved ending scores. Group 2, moderate resource intensity use, started with "worse" overall score and improved to best performing by the end. Group 4, most resource intensity use, improved least but required highest service intensity.

CONCLUSIONS AND IMPLICATIONS

This longitudinal evaluation of QIPMO, a statewide QI technical assistance and support program, provides evidence of programmatic stimulation of statewide NH quality improvements. It provides insight into intensity of services needed to help facilities improve. Other states should consider QIPMO success and develop their own programs, as recommended by the NASEM report so their NHs can embrace QI and "initiate fundamental change" for better care for our nation's older adults.

Exploring Hospital Transfers for Long-Stay Nursing Home Residents With End-Stage Renal Disease

BACKGROUND

Nursing home residents with end-stage renal disease (ESRD) are an understudied, yet growing population within nursing homes.

PURPOSE

To describe hospital transfers for nursing home residents diagnosed with ESRD and receiving hemodialysis.

METHODS

Data were analyzed for residents with ESRD transferred to the hospital between October 2016 and September 2020 (n = 219). Descriptive statistics, bivariate analyses, logistic regression, and content analysis were used for analysis.

RESULTS

Clinical factors associated with transfers included abnormal vitals, altered mental state, and pain. Other factors included lack of care planning and advance directives, provider communication, resident/family preferences, missing/refusing dialysis, and facility resources. The odds of an observation/emergency department only visit was 2.02 times larger when transferred from the dialysis clinic.

CONCLUSIONS

Advance care planning and coordinated care between nursing home and dialysis clinics are needed along with proactive planning when residents miss dialysis or experience a condition change at the dialysis clinic.

Data-Driven Analytics to Discover APRN's Impact on Nursing Home Hospitalization: Causal Discovery Analysis

OBJECTIVES

Research shows advanced practice registered nurses (APRNs) embedded in nursing homes (NHs) reduce resident hospitalizations. However, the specific APRN activities that reduce hospitalizations have not been adequately investigated. This study aims to identify the causal links between APRN activities and NHs resident hospitalization. The study also examined relationships among other variables, including advanced directives, clinical diagnosis, and length of hospitalization.

DESIGN

Secondary data analysis.

SETTING AND PARTICIPANTS

Residents of NHs participating in the Missouri Quality Initiative for Nursing Homes, 2016-2019.

METHODS

We performed a secondary analysis of data from the Missouri Quality Initiative for Nursing Homes Intervention using causal discovery analysis, a machine learning, data-driven technique to determine causal relationships across data. The resident roster and INTERACT resident hospitalization datasets were combined to create the final dataset. Variables in the analysis model were divided into before and after hospitalization. Expert consensus was used to validate and interpret the outcomes.

RESULTS

The research team analyzed 1161 hospitalization events and their associated NH activities. APRNs evaluated NH residents before a transfer, expedited follow-up nursing assessments, and authorized hospitalization when necessary. No significant causal relationships were found between APRN activities and the clinical diagnosis of a resident. The analysis also showed multifaceted relationships related to having advanced directives and duration of hospitalization.

CONCLUSIONS AND IMPLICATIONS

This study demonstrated the importance of APRNs embedded in NHs to improve resident outcomes. APRNs in NHs can facilitate communication and collaboration among the nursing team, leading to early identification and treatment for resident status changes. APRNs can also initiate more timely transfers by reducing the need for physician authorization. These findings emphasize the crucial role of APRNs in NHs and suggest that budgeting for APRN services may be an effective strategy to reduce hospitalizations. Additional findings regarding advance directives are discussed.

Sharing Lessons From Successes: Long-term Care Facilities That Weathered the Storm of COVID-19 and Staffing Crises

PURPOSE

To identify leadership styles and staffing strategies in Missouri long-term care (LTC) facilities that stood out among their peers as "positive deviants" with regard to COVID-19 infections and staffing shortages.

METHODS

Statewide survey of all LTC facilities to identify exemplar facilities with stable staffing and low rates of COVID-19. Interviews with senior leaders were conducted in 10 facilities in the state to understand the strategies employed that led to these "positive outliers." A result-based educational program was designed to describe their actions and staff reactions.

RESULTS

Exemplar leaders used transformational leadership style. Top reasons for their success were as follows: (1) trusting and supportive staff relationships; (2) positive presence and communication; and (3) use of consistent staffing assignments. Strong statewide participation was noted in the educational programs.

LAYING THE REPORT FOUNDATIONS: A CONCEPTUAL MODEL OF NURSING HOME QUALITY OF CARE

The committee began its work by developing a conceptual model for achieving resident-centered care. The model draws on existing evidence about the multiple dimensions of resident quality of life and the complexity of nursing home care and environments. Dr. Debra Saliba, a professor in UCLA’s Borun Center and Los Angeles VA GRECC with expertise in long-term care policy and delivery, will describe this model and how it guided the committee’s recommendations. She also will discuss how each presentation fits within the model’s domains

LB1532. The Impact of COVID-19 on nursing home residents’ clinical, functional, and psychosocial outcomes

Background

Data on COVID-19 related nursing home infections and mortality accumulated at a rapid pace; yet little is known about the impact of nursing homes’ response to COVID-19 on resident clinical, functional, and psychosocial outcomes.

Methods

We examined aggregated Minimum Data Set (MDS) assessments to describe resident outcomes using an interrupted times series methodology for three timeframes: pre-COVID (1/2019 to 2/2020), pandemic (3/2020–12/2021), and vaccination (1/2021-6/2021). Data included 307,558 federally mandated resident MDS assessments from 60,846 resident in 489 nursing homes in a Mid-Western state. We calculated MDS based quality measures (QM) using definitions available from Centers for Medicare and Medicaid Services. Each QM-based outcome was fit to a logistic regression model using the method of generalized estimating equations.

Results

None of the QMs displayed a statistically significant trend pre-COVID. The prevalence of excessive weight loss and ADL decline increased sharply during the pandemic and reversed that trend with vaccination. Pressure ulcers among high-risk residents followed a similar trend, although pandemic and vaccination-related regression parameters for that QM were only marginally significant (p = .08). Pain worsened during the pandemic and vaccination period approaching significance (p=.07). Antipsychotic medication use worsened in the pandemic (p< .001) and did not improve in the vaccination period. Other QMs including any fall, fall with major injury, and incontinence did not exhibit statistically significant change in trend.

Prevalence Profiles

Circles: Observed proportions, Dashed Line: Model expected value, Solid Lines: 95% confidence limits for expected values

Conclusion

We noted significant changes in QMs for antipsychotic use, ADL loss, and weight loss, with the latter two improving in the vaccination period. Isolation, disease outbreaks, and staffing issues in facilities could have affected these QMs. Data variability may have limited our ability to detect other changes. Antipsychotics may have increased with the need to reduce wandering and other behaviors common in the nursing home population; behaviors high risk for spreading COVID-19. Why antipsychotic use did not improve during the vaccination period is less clear. Data beyond June of 2021 may help clarify the pattern of antipsychotic use.

Disclosures

Lori Popejoy, PhD, RN, FAAN, New Path: Board Member|New Path: Ownership Interest Amy Vogelsmeier, PhD, RN, FAAN, New Path: Board Member|New Path: Ownership Interest Marilyn Rantz, PhD, RN, FAAN, New Path: Board Member|New Path: Ownership Interest.

Impact of Web-Based Cognitive Behavioral Therapy for Insomnia on Stress, Health, Mood, Cognitive, Inflammatory, and Neurodegenerative Outcomes in Rural Dementia Caregivers: Protocol for the NiteCAPP CARES and NiteCAPP SHARES Randomized Controlled Trial

BACKGROUND

Chronic insomnia affects up to 63% of family dementia caregivers. Research suggests that chronic insomnia prompts changes in central stress processing that have downstream negative effects on health and mood, as well as on cognitive, inflammatory, and neurodegenerative functioning. We hypothesize that cognitive behavioral therapy for insomnia (CBT-I) will reverse those downstream effects by improving insomnia and restoring healthy central stress processing. Rural caregivers are particularly vulnerable, but they have limited access to CBT-I; therefore, we developed an accessible digital version using community input (NiteCAPP CARES).

OBJECTIVE

This trial will evaluate the acceptability, feasibility, and short-term and long-term effects of NiteCAPP CARES on the sleep and stress mechanisms underlying poor caregiver health and functioning.

METHODS

Dyads (n=100) consisting of caregivers with chronic insomnia and their coresiding persons with dementia will be recruited from Columbia and surrounding areas in Missouri, United States. Participant dyads will be randomized to 4 weeks (plus 4 bimonthly booster sessions) of NiteCAPP CARES or a web-based sleep hygiene control (NiteCAPP SHARES). Participants will be assessed at baseline, after treatment, and 6- and 12-month follow-ups. The following assessments will be completed by caregivers: 1 week of actigraphy and daily diaries measuring sleep, Insomnia Severity Index, arousal (heart rate variability), inflammation (blood-derived biomarkers: interleukin-6 and C-reactive protein), neurodegeneration (blood-derived biomarkers: plasma amyloid beta [Aβ40 and Aβ42], total tau, and phosphorylated tau [p-tau181 and p-tau217]), cognition (Joggle battery, NIH Toolbox for Assessment of Neurological and Behavioral Function, and Cognitive Failures Questionnaire), stress and burden, health, and mood (depression and anxiety). Persons with dementia will complete 1 week of actigraphy at each time point.

RESULTS

Recruitment procedures started in February 2022. All data are expected to be collected by 2026. Full trial results are planned to be published by 2027. Secondary analyses of baseline data will be subsequently published.

CONCLUSIONS

This randomized controlled trial tests NiteCAPP CARES, a web-based CBT-I for rural caregivers. The knowledge obtained will address not only what outcomes improve but also how and why they improve and for how long, which will help us to modify NiteCAPP CARES to optimize treatment potency and support future pragmatic testing and dissemination.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04896775; https://clinicaltrials.gov/ct2/show/NCT04896775.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/37874.

Repeat hospital transfers among long stay nursing home residents: a mixed methods analysis of age, race, code status and clinical complexity

Background

Nursing home residents are at increased risk for hospital transfers resulting in emergency department visits, observation stays, and hospital admissions; transfers that can also result in adverse resident outcomes. Many nursing home to hospital transfers are potentially avoidable. Residents who experience repeat transfers are particularly vulnerable to adverse outcomes, yet characteristics of nursing home residents who experience repeat transfers are poorly understood. Understanding these characteristics more fully will help identify appropriate intervention efforts needed to reduce repeat transfers.

Methods

This is a mixed-methods study using hospital transfer data, collected between 2017 and 2019, from long-stay nursing home residents residing in 16 Midwestern nursing homes who transferred four or more times within a 12-month timeframe. Data were obtained from an acute care transfer tool used in the Missouri Quality Initiative containing closed- and open-ended questions regarding hospital transfers. The Missouri Quality Initiative was a Centers for Medicare and Medicaid demonstration project focused on reducing avoidable hospital transfers for long stay nursing home residents. The purpose of the analysis presented here is to describe characteristics of residents from that project who experienced repeat transfers including resident age, race, and code status. Clinical, resident/family, and organizational factors that influenced transfers were also described.

Results

Findings indicate that younger residents (less than 65 years of age), those who were full-code status, and those who were Black were statistically more likely to experience repeat transfers. Clinical complexity, resident/family requests to transfer, and lack of nursing home resources to manage complex clinical conditions underlie repeat transfers, many of which were considered potentially avoidable.

Conclusions

Improved nursing home resources are needed to manage complex conditions in the NH and to help residents and families set realistic goals of care and plan for end of life thus reducing potentially avoidable transfers.

Explainable Fall Risk Prediction in Older Adults Using Gait and Geriatric Assessments

Older adults aged 65 and above are at higher risk of falls. Predicting fall risk early can provide caregivers time to provide interventions, which could reduce the risk, potentially avoiding a possible fall. In this paper, we present an analysis of 6-month fall risk prediction in older adults using geriatric assessments, GAITRite measurements, and fall history. The geriatric assessments included were Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Short Form 12 (SF12). These geriatric assessments are collected by staff nurses regularly in senior care facilities. From the GAITRite assessments on the residents, we included the Functional Ambulatory Profile (FAP) scores and gait speed to predict fall risk. We used the SHAP (SHapley Additive exPlanations) approach to explain our model predictions to understand which predictor variables contributed to increase or decrease the fall risk for an individual prediction. In case of a high fall risk prediction, predictor variables that contributed the most to elevate the risk could be further examined by the health providers for more personalized health interventions. We used the geriatric assessments, GAITRite measurements, and fall history data collected from 92 older adult residents (age = 86.2 ± 6.4, female = 57) to train machine learning models to predict 6-month fall risk. Our models predicted a 6-month fall with an AUC of 0.80 (95% CI of 0.76–0.85), sensitivity of 0.82 (95% CI of 0.74–0.89), specificity of 0.72 (95% CI of 0.67–0.76), F1 score of 0.76 (95% CI of 0.72–0.79), and accuracy of 0.75 (95% CI of 0.72–0.79). These results show that our early fall risk prediction method performs well in identifying residents who are at higher fall risk, which offers care providers and family members valuable time to perform preventive actions.

Longitudinal analysis of aging in place at TigerPlace: Resident function and well-being

This paper reports on a longitudinal eight-year analysis (2011-2019) of trajectory of function and well-being residents of TigerPlace Aging in Place (AIP) model of care. Residents were routinely assessed using standard health assessment instruments. Average scores from each measure were examined for changes or trends in resident function; decline over time was calculated. Scores for depression, mental health subscale Short Form Health Survey-12 (SF-12) remained stable over time. Mini Mental State Exam declined to mild dementia range (21-24). Physical measures SF-12 physical health subscale, ADLs, and IADLs declined slightly, while fall risk increased over time. When yearly trends in AIP were modeled with a referent group there was no significant worsening of functioning. The length of stay for TigerPlace residents continued to remain stable at nearly 30 months. Residents maintained function in the environment of their choice longer at cost less than nursing homes, and just above residential care cost.

Value Propositions for Health Information Exchange Toward Improving Nursing Home Hospital Readmission Rates

The importance of health information technology use in nursing home (NH) care delivery is a major topic in research exploring methods to improve resident care. Topics of interest include how technology investments, infrastructure, and work-force development lead to better methods of nursing care delivery and outcomes. Value propositions, including perceived benefits, incentives, and system changes recognized by end-users, are important resources to inform NH leaders, policymakers, and stakeholders about technology. The purpose of the current research was to identify and disseminate value propositions from a community of stakeholders using a health information exchange (HIE). Researchers used a nominal group process, including 49 individual stakeholders participating in a national demonstration project to reduce avoidable hospitalizations in NHs. Stakeholders identified 41 total anticipated changes from using HIE. Ten stakeholder types were perceived to have experienced the highest impact from HIE in areas related to resident admissions, communication, and efficiency of care delivery. [Journal of Gerontological Nursing, 48(1), 15-20.].

A Call to Address RN, Social Work, and Advanced Practice Registered Nurses in Nursing Homes: Solutions From the Missouri Quality Initiative

BACKGROUND

US nursing homes (NHs) have struggled to overcome a historic pandemic that laid bare limitations in the number and clinical expertise of NH staff.

PROBLEM

For nurse staffing, current regulations require only one registered nurse (RN) on duty 8 consecutive hours per day, 7 days per week, and one RN on call when a licensed practical/vocational nurse is on duty. There is no requirement for a degreed or licensed social worker, and advanced practice registered nurses (APRNs) in NHs cannot bill for services.

APPROACH

It is time to establish regulation that mandates a 24-hour, 7-day-a-week, on-site RN presence at a minimum requirement of 1 hour per resident-day that is adjusted upward for greater resident acuity and complexity. Skilled social workers are needed to improve the quality of care, and barriers for APRN billing for services in NHs need to be removed.

CONCLUSIONS

Coupling enhanced RN and social work requirements with access to APRNs can support staff and residents in NHs.

Multiple Hospital Transfers Among MOQI Nursing Home Residents: The Influence of Race

Missouri Quality Initiative (MOQI) was a CMS-funded enhanced care and coordination provider demonstration project (2012-2020) that successfully reduced avoidable hospitalizations and improved nursing home (NH) care quality. Little is known about the influence of race in multiple hospital transfers from NHs. Using a mixed-methods approach we analyzed hospitalization root cause analysis data from 2017-2019 for 1410 residents in 16 MOQI NHs. There were 113 residents who were transferred 609 times. Those with multiple transfers (four or more transfers/year) were compared by race and key characteristics (e.g., code status, diagnosis). A subset of residents with multiple transfers were examined qualitatively to identify and describe key cases. Findings suggest that Black residents have a higher probability for multiple transfers. Findings highlight the need for transfer prevention efforts for Black residents including early assessment and intervention, early/frequent discussion about goals of care, advance directives, resuscitation status, and family/resident understanding of treatment effectiveness.

The Influence of MOQI APRNs on the COVID-19 Response in Nursing Homes

During the COVID-19 pandemic Missouri Quality Initiative APRNs worked in 16 nursing homes (NHs) providing clinical expertise and support. To understand their influence on the NH COVID-19 response, we conducted four group interviews with APRNs from 13 of the 16 NHs. Using thematic analysis, we identified similarities and differences between NH groups and then compared groups by COVID-19 infection rates. Leaders from NHs with high COVID-19 rates were unwilling to report infections and were resistant to resident/staff testing. In contrast, leaders from NHs with low COVID-19 rates were strategic about acquiring supplies, held daily huddles, and initiated CDC recommendations almost immediately. All reported residents lost weight, and experienced mood and physical decline resulting from quarantine/isolation. APRNs worked with providers to identify potentially ill residents/staff, improve isolation/quarantine procedures, manage ill residents, and supported efforts to mitigate viral spread. We will discuss implications for broader infection prevention in NHs.

Social practices of nurse care coordination using sensor technologies - Challenges with an alert system adoption in assisted living communities for older adults

Objectives

From the view of everyday practices and the socio-technical coordination lens, this study aimed to analyz the gap between creators' intention and the users' implementation (mainly nursing staff and social workers) of an alert system in assisted living communities.

Methods

Qualitative methods were employed by way of five user interviews and focus groups with six system developers. Modeling instruments were applied for data collection to analyze the different clinical workflows versus the expectations of the system development team.

Results

Results indicate that the clinical workflow changed over time, which led to a mismatch of nurse care coordination, social practices, and technology use. The results show different mental models of the socio-technical practice. Applying the coordination theory, the following recommendations could be developed to overcome the mismatch. First, it is recommended that nursing staff set goals together. Second, a communication rhythm with the nursing staff and developer teams should be established, with guided questions to facilitate the conversation, to shed light on the different workflows and the difference in social practices when using sensor technologies or alert systems. Third, a checklist for new employees should be created so they know how and on which devices to use the alert system. Fourth, the user experience with the alert system should be improved (e.g., an improved user interface).

Conclusions

This work indicates recommendations to close the mental model gap to overcome the mismatch between optimal use of the alert system and how the nursing staff is actually using it.

Technology for Healthy Independent Living: Creating a Tailored In-Home Sensor System for Older Adults and Family Caregivers

Sensing technologies hold enormous potential for early detection of health changes that can dramatically affect the aging experience. In previous work, we developed a health alert system that captures and analyzes in-home sensor data. The purpose of this research was to collect input from older adults and family members on how the health information generated can best be adapted, such that older adults and family members can better self-manage their health. Five 90-minute focus groups were conducted with 23 older adults (mean age = 80 years; 87% female) and five family members (mean age = 64; 100% female). Participants were asked open-ended questions about the sensor technology and methods for interacting with their health information. Participants provided feedback regarding tailoring the technology, such as delegating access to family and health care providers, receiving health messages and alerts, interpreting health messages, and graphic display options. Participants also noted concerns and future likelihood of technology adoption. [Journal of Gerontological Nursing, 46(7), 35-40.].

Take it to the resident: A model for engaging long-term stay residents in advance care planning

Advance care planning involves a meaningful conversation about residents' end of life goals with the health-care team and documenting these wishes in advance directives; however, these conversations are not taking place early enough or with strategies that allow nursing home residents' preferences to be meaningfully integrated into care plans. This article outlines a new model that nursing home social workers can use to initiate advance care planning discussions called Take it to the Resident. This model was tested with 11 long-term stay nursing home residents. Data were collected through structured memos and field notes. Summative content analysis was utilized to analyze the data. Take it to the Resident facilitated a discussion about advance care planning and allowed residents to consider their wishes before engaging family members. Although discussions took place, some residents were hesitant to document their wishes formally through advance directives. The results of this study support the utility of continued testing of this model. Having empirically supported tools for nursing home social workers is critical to increasing advance care planning conversations and empowering residents to document their wishes.

To Text or Not to Text? That is the Question

Texting is ubiquitous with a text frequency of 145 billion/day worldwide. This paper provides partial results of the national demonstration project called the Missouri Quality Improvement Initiative (MOQI). MOQI goals were to reduce avoidable hospitalizations using APRNs to infuse evidence-based practices, model appropriate decisions and improve communication among workers responsible for nursing home resident care. This is a retrospective content analysis of text messages sent and received via a secure, password protected, encrypted mobile text message platform called Mediprocity. Text messages were created by 15 APRNs and a PhD-RN project supervisor working in 16 nursing homes over 6 months (January 1-June 30 2018). During the 6 months of data collection 8,946 text messages were captured, coded and analyzed. Data included 1,018 sent messages and 7,928 received messages. The most common messages sent (n=324) and received (n=2319) were about patient updates. The second most common texts included messages confirming information (n=1312).

Financial and Work-flow Benefits of Reducing Avoidable Hospitalizations of Nursing Home Residents

OBJECTIVES

1) Explain the financial benefit of potential revenue recapture (PRR) for non-billable days due to hospitalizations of nursing home (NH) residents using a six-year longitudinal analysis of 11 of 16 NHs participating in the Missouri Quality Initiative (MOQI); and 2) Discuss the work-flow benefits of early detection of changes in health status using qualitative data from all MOQI homes.

DESIGN

A CMS funded demonstration project with full-time advanced practice registered nurses (APRN) and operations support team focused on reducing avoidable hospitalizations for long stay NH residents (2012-2020).

SETTING AND PARTICIPANTS

Setting was a sample of 11 of 16 US NHs participating in the CMS project. The NHs ranged in size between 121 and 321 beds located in urban and rural areas in one midwestern geographic region.

METHODS

Financial and occupancy data were analyzed using descriptive methods. Data are readily available from most NH financial systems and include information about short and long stay residents to calculate non-billable days due to hospitalizations. Average hospital transfer rates per 1000 resident days were used. Qualitative data collected in MOQI informed the work-flow benefits analysis.

RESULTS

There was over $2.6 million in actual revenue recapture due to hospitalization of long stay residents in the 11 participating NHs during five years, 2015-2019, with 2014 as baseline; savings to payers was more than $31 million during those same years. The PRR for both short and long stay residents combined totaled $32.5 million for six years (2014-2019); for each NH this ranged from $590,000 to over $5 million. On average, an additional $500,000 of revenue each year per 200 beds could have been recaptured by further reducing hospitalizations. Workflow improved for nurses and nursing assistants using INTERACT and focusing on early detection of health changes.

CONCLUSIONS

Reducing avoidable hospitalizations reduces costs to payers and increases revenue by substantially recapturing revenue lost each day of hospitalization.

IMPLICATIONS

Focusing nursing staff on early illness recognition and management of condition changes within NHs has benefits for residents as the stress of hospital transfer and resulting functional decline is avoided. Nurses and nursing assistants benefit from workflow improvements by focusing on early illness detection, managing most condition changes within NHs. NHs benefit financially from increased revenue by reducing empty bed days.

Advance Directives in the Nursing Home Setting: An Initiative to Increase Completion and Reduce Potentially Avoidable Hospitalizations

Advance directive (AD) completion can improve transitions between hospitals and skilled nursing facilities (SNF's). One Centers for Medicare and Medicaid Services (CMS) Innovations Demonstration Project, The Missouri Quality Initiative (MOQI), focused on improving AD documentation and use in sixteen SNF's. The intervention included education, training, consultation and improvements to discussion process, policy development, increased AD enactment, and increased community education and awareness activities. An analysis was conducted of data collected from annual chart inventories occurring over four years. Using a logistic mixed model, results indicated statistical significance (p < .001) for increased AD documentation. Greatest gains occurred at project mid-point. The relationship between having an AD and occurrence of transfer to a hospital was tested on a sample of 1,563 residents with length of stays more than 30 days. Residents who did not have an AD were 29% more likely to be transferred. A logistic regression was conducted, and the results were statistically significant (p < .02).

Longitudinal Impact of APRNs on Nursing Home Quality Measures in the Missouri Quality Initiative

OBJECTIVES

To measure the impact of advanced practice nurses (APRNs) on quality measures (QM) scores of nursing homes (NHs) in the CMS funded Missouri Quality Initiative (MOQI) that was designed to reduce avoidable hospitalizations of NH residents, improve quality of care, and reduce overall healthcare spending.

DESIGN

A four group comparative analysis of longitudinal data from September 2013 thru December 2019.

SETTING

NHs in the interventions of both Phases 1 (2012-2016) and 2 (2016-2020) of MOQI (n=16) in the St. Louis area; matched comparations in the same counties as MOQI NHs (n=27); selected Phase 2 payment intervention NHs in Missouri (n=24); NHs in the remainder of the state (n=406).

PARTICIPANTS

NHs in Missouri Intervention: Phase 1 of The Missouri Quality Initiative (MOQI), a Centers for Medicare and Medicaid (CMS) Innovations Center funded research initiative, was a multifaceted intervention in NHs in the Midwest, which embedded full-time APRNs in participating NHs to reduce hospitalizations and improve care of NH residents. Phase 2 extended the MOQI intervention in the original intervention NHs and added a CMS designed Payment Intervention; Phase 2 added a second group of NHs to receive the Payment. Intervention Only.

MEASUREMENTS

Eight QMs selected by CMS for the Initiative were falls, pressure ulcers, urinary tract infections, indwelling catheters, restraint use, activities of daily living, weight loss, and antipsychotic medication use. For each of the monthly QMs (2013 thru 2019) an unobserved components model (UCM) was fitted for comparison of groups.

RESULTS

The analysis of QMs reveals that that the MOQI Intervention + Payment group (group with the embedded APRNs) out-performed all comparison groups: matched comparison with neither intervention, Payment Intervention only, and remainder of the state.

CONCLUSION

These results confirm the QM analyses of Phase 1, that MOQI NHs with full-time APRNs are effective to improve quality of care.

Results of the Missouri Quality Initiative in Sustaining Changes in Nursing Home Care: Six-Year Trends of Reducing Hospitalizations of Nursing Home Residents

OBJECTIVES

The purpose of this article is to present six-year findings of the Missouri Quality Initiative (MOQI) to reduce unnecessary hospitalizations for long-stay nursing home residents.

DESIGN

A CMS funded demonstration project analyzed over 6-years using a single group design.

SETTING AND PARTICIPANTS

The setting was 16 Midwestern US nursing homes ranging in size between 121 and 321 beds located in urban and rural areas in one geographic region. The sample of eligible residents averaged from 1819 in 2014 to 1068 in 2019.

MEASURES

Resident data were analyzed using descriptive methods of aggregate facilities' hospital transfer rates per 1000 resident days and changes per year of average hospital transfer rates. Individual facility transfer rates were grouped by level of performance (best, mixed, and low). Leadership turnover and engagement were also described.

INTERVENTION

Full-time advanced practice registered nurses (APRN) and an operations support team focused on reducing unnecessary hospitalizations for long-stay nursing home residents.

RESULTS

Total transfers for 2014-2019 was 6913 and the average transfer rate per 1000 resident days declined from 2.48 in 2014 to a low of 1.89 in 2018 and slightly increased to 1.99 in 2019. Eleven nursing homes achieved sustained improvement, five did not. Differences in leadership turnover and engagement were noted by level of performance; however, three outlier facilities were identified.

CONCLUSIONS/IMPLICATIONS

The MOQI intervention achieved improved outcomes over six-years in the majority of nursing homes in the project. The embedded APRN's daily focus on project goals supported by a multi-disciplinary operations team facilitated success. Facility leadership stability and engagement in the project likely contributed to outcomes. Full-time presence of APRNs coupled with an operations' support team improved nursing homes outcomes, however Medicare currently restricts APRNs hired by nursing homes from billing Medicare for direct care services. This unnecessary restriction of practice discourages nursing homes from hiring APRNs and should be abolished.

Human-centered approaches that integrate sensor technology across the lifespan: Opportunities and challenges

Children, parents, older adults, and caregivers routinely use sensor technology as a source of health information and health monitoring. The purpose of this paper is to describe three exemplars of research that used a human-centered approach to engage participants in the development, design, and usability of interventions that integrate technology to promote health. The exemplars are based on current research studies that integrate sensor technology into pediatric, adult, and older adult populations living with a chronic health condition. Lessons learned and considerations for future studies are discussed. Nurses have successfully implemented interventions that use technology to improve health and detect, prevent, and manage diseases in children, families, individuals and communities. Nurses are key stakeholders to inform clinically relevant health monitoring that can support timely and personalized intervention and recommendations.

Tracking personalized functional health in older adults using geriatric assessments

BACKGROUND

Higher levels of functional health in older adults leads to higher quality of life and improves the ability to age-in-place. Tracking functional health objectively could help clinicians to make decisions for interventions in case of health deterioration. Even though several geriatric assessments capture several aspects of functional health, there is limited research in longitudinally tracking personalized functional health of older adults using a combination of these assessments.

METHODS

We used geriatric assessment data collected from 150 older adults to develop and validate a functional health prediction model based on risks associated with falls, hospitalizations, emergency visits, and death. We used mixed effects logistic regression to construct the model. The geriatric assessments included were Activities of Daily Living (ADL), Instrumental Activities of Daily Living (IADL), Mini-Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Short Form 12 (SF12). Construct validators such as fall risks associated with model predictions, and case studies with functional health trajectories were used to validate the model.

RESULTS

The model is shown to separate samples with and without adverse health event outcomes with an area under the receiver operating characteristic curve (AUC) of > 0.85. The model could predict emergency visit or hospitalization with an AUC of 0.72 (95% CI 0.65-0.79), fall with an AUC of 0.86 (95% CI 0.83-0.89), fall with hospitalization with an AUC of 0.89 (95% CI 0.85-0.92), and mortality with an AUC of 0.93 (95% CI 0.88-0.97). Multiple comparisons of means using Turkey HSD test show that model prediction means for samples with no adverse health events versus samples with fall, hospitalization, and death were statistically significant (p < 0.001). Case studies for individual residents using predicted functional health trajectories show that changes in model predictions over time correspond to critical health changes in older adults.

CONCLUSIONS

The personalized functional health tracking may provide clinicians with a longitudinal view of overall functional health in older adults to help address the early detection of deterioration trends and decide appropriate interventions. It can also help older adults and family members take proactive steps to improve functional health.

THE RELATIONSHIP BETWEEN ADVANCE DIRECTIVE COMPLETION AND POTENTIALLY AVOIDABLE HOSPITALIZATIONS

Advance directive (AD) completion can improve transitions between hospitals and skilled nursing facilities (SNF’s). One CMS Innovations Demonstration Project, The Missouri Quality Initiative (MOQI), focused on improving advance directive documentation and use in sixteen SNF’s. An analysis was conducted of data collected from annual chart inventories occurring over four years. Using a logistic mixed model, results indicated statistical significance (p<0.001) for increased AD documentation. Greatest gains occurred at project mid-point. The relationship between having an advance directive and occurrence of transfer to a hospital was tested on a sample of 1563 residents with length of stays more than 30 days. Residents who did not have an advance directive were more likely to be transferred. A logistic regression was conducted and the results were statistically significant (p<0.02). The MOQI model and initiatives will be explained followed by a discussion of research methodology, data collection, and analyses. Practice implications will be discussed.

Linguistic summarization of in-home sensor data

INTRODUCTION

With the increase in the population of older adults around the world, a significant amount of work has been done on in-home sensor technology to aid the elderly age independently. However, due to the large amounts of data generated by the sensors, it takes a lot of effort and time for the clinicians to makes sense of this data. In this work, we develop a system to help make this data more useful by presenting it in the form of natural language.

METHODS

We start by identifying important attributes in the sensor data that are relevant to the health of the elderly. We then develop algorithms to extract these important health related features from the sensor parameters and summarize them in natural language. We focus on making the natural language summaries to be informative, accurate and concise.

RESULTS

We designed multiple surveys using real and synthetic data to validate the summaries produced by our algorithms. We show that the algorithms produce meaningful results comparable to human subjects. We also implemented our linguistic summarization system to produce summaries of data leading to health alerts derived from the sensor data. The system is running live in 110 apartments currently. By the means of retrospective case studies, we illustrate that the linguistic summaries are able to make the connection between changes in the sensor data and the health of the elderly.

CONCLUSIONS

We present a system that extracts important clinically relevant features from in-home sensor data generated in the apartments of the elderly and summarize those features in natural language. The preliminary testing of our summarization system shows that it has the potential to help the clinicians utilize this data effectively.

Analyzing Hospital Transfers Using INTERACT Acute Care Transfer Tools: Lessons from MOQI

OBJECTIVES

We explored the differences in potentially avoidable/unavoidable hospital transfers in a retrospective analysis of Interventions to Reduce Acute Care Transfers (INTERACT) Acute Transfer Tools (ACTs) completed by advanced practice registered nurses (APRNs) working in the Missouri Quality Improvement (QI) Initiative (MOQI).

DESIGN

Cross-sectional descriptive study of 3996 ACTs for 32.5 calendar months from 2014 to 2016. Univariate analyses examined differences between potentially avoidable vs unavoidable transfers. Multivariate logistic regression analysis of candidate factors identified those contributing to avoidable transfers.

SETTING

Sixteen nursing homes (NHs), ranging from 120 to 321 beds, in urban, metro, and rural communities within 80 miles of a large midwestern city.

PARTICIPANTS

A total of 5168 residents with a median age of 82 years.

MEASUREMENTS

Data from 3946 MOQI-adapted ACTs.

RESULTS

A total of 54% of hospital transfers were identified as avoidable. QI opportunities related to avoidable transfers were earlier detection of new signs/symptoms (odds ratio [OR] = 2.35; 95% confidence interval [CI] = 1.61-3.42; P < .001); discussions of resident/family preference (OR = 2.12; 95% CI = 1.38-3.25; P < .001); advance directive/hospice care (OR = 2.25; 95% CI = 1.33-3.82; P = .003); better communication about condition (OR = 4.93; 95% CI = 3.17-7.68; P < .001); and condition could have been managed in the NH (OR = 16.63; 95% CI = 10.9-25.37; P < .001). Three factors related to unavoidable transfers were bleeding (OR = .59; 95% CI = .46-.77; P < .001), nausea/vomiting (OR = .7; 95% CI = .54-.91; P = .007), and resident/family preference for hospitalization (OR = .79; 95% CI = .68-.93; P = .003).

CONCLUSION

Reducing avoidable hospital transfers in NHs requires challenging assumptions about what is avoidable so QI efforts can be directed to improving NH capacity to manage ill residents. The APRNs served as the onsite coaches in the use and adoption of INTERACT. Changes in health policy would provide a revenue stream to support APRN presence in NH, a role that is critical to improving resident outcomes by increasing staff capacity to identify illness and guide system change. J Am Geriatr Soc 67:1953-1959, 2019.

An observational study of antipsychotic medication use among long-stay nursing home residents without qualifying diagnoses

WHAT IS KNOWN ON THE SUBJECT?: In the United States, 15.5% of nursing home residents without qualifying diagnoses of schizophrenia, Huntington's' Disease, and/or Tourette Syndrome receive antipsychotic medications. Antipsychotic medications are used off-label (i.e., used in a manner the United States Food and Drug Administration's packaging insert does not specify) to treat neuropsychiatric symptoms, often before attempting nonpharmacologic interventions, despite evidence that this drug class is associated with significant adverse events including death. Less than optimal staffing resources and lack of access to geropsychiatric specialists are barriers to reducing antipsychotic use. WHAT THE PAPER ADDS TO EXISTING KNOWLEDGE?: Antipsychotic use occurred in 11.6% of nursing home residents without qualifying or potentially qualifying diagnoses (bipolar disorder and psychotic disorder); antipsychotic use was more prevalent in residents with a dementia diagnosis than those without. One additional registered nurse hour per resident day could reduce the odds of antipsychotic use by 52% and 56% for residents with and without a dementia diagnosis respectively. WHAT ARE THE IMPLICATIONS FOR PRACTICE?: Given the influence of total staffing and professional staff mix on risk of antipsychotic use, nursing home administrators may want to consider aspects of facility operation that impact antipsychotic use. More stringent Unites States' survey and certification standards for dementia care implemented in 2017 demand proactive person-centered care that promotes maximal well-being and functioning without risk of harm from inappropriate psychoactive medications. Mental health nurses have requisite training to provide expert person-centered care to nursing home residents with mental illness and geropsychiatric disorders.

ABSTRACT

Introduction Antipsychotic use in nursing homes varies widely across the United States; inadequate staffing, skill mix, and geropsychiatric training impede sustained improvement. Aim This study identified risk factors of antipsychotic use in long-stay residents lacking qualifying or potentially qualifying diagnoses. Method This secondary analysis used 2015 Minimum Data Set and cost report data from 458 Missouri nursing homes. The full sample (N = 29,679) was split into two subsamples: residents with (N = 15,114) and without (N = 14,565) a dementia diagnosis. Separate logistic regression models were run. Results Almost 15% of the dementia subsample and 8.4% of the nondementia subsample received an antipsychotic medication in the past week. Post-traumatic stress disorder, psychosis indicators, behavioral symptoms, anxiety medication with and without anxiety diagnosis, depression medication with and without depression diagnosis, and nurse staffing were among the strongest predictors of antipsychotic use in both subsamples. Simulation analyses showed decreased odds of receiving an antipsychotic in both subsamples when registered nurse hours matched the national average. Discussion Matching nurse staffing mix to the national average may improve antipsychotic use in nursing homes. Implications Knowledge of antipsychotic use risk factors use can inform care planning and staff education to minimize use of these medications in all but severe cases.

Monitoring the Relative Blood Pressure Using a Hydraulic Bed Sensor System

We propose a nonwearable hydraulic bed sensor system that is placed underneath the mattress to estimate the relative systolic blood pressure of a subject, which only differs from the actual blood pressure by a scaling and an offset factor. Two types of features are proposed to obtain the relative blood pressure, one based on the strength and the other on the morphology of the bed sensor ballistocardiogram pulses. The relative blood pressure is related to the actual by a scale and an offset factor that can be obtained through calibration. The proposed system is able to extract the relative blood pressure more accurately with a less sophisticated sensor system compared to those from the literature. We tested the system using a dataset collected from 48 subjects right after active exercises. Comparison with the ground truth obtained from the blood pressure cuff validates the promising performance of the proposed system, where the mean correlation between the estimate and the ground truth is near to 90% for the strength feature and 83% for the morphology feature.

Involvement of Advanced Practice Nurse in the Management of Geriatric Conditions: Examples from Different Countries

The increasing demand for healthcare services is placing great strain on healthcare systems throughout the world. Although the older population is increasing worldwide, there is a marked deficit in the number of persons trained in geriatrics. It is now recognized that early detection and treatment of geriatric conditions (e.g., frailty, sarcopenia, falls, anorexia of aging, and cognitive decline) will delay or avert the development of disability. At the same time, recent years have seen an increased interest and use of advanced practice nurses (APN). Models of best practices of supervision and collaboration have been promulgated by many organizations. APN's roles and scope of practice have been expanded in many countries and the quality and cost-effectiveness of healthcare systems have improved. Nevertheless, in older people, evidence of advanced practice roles remains scattered, and there is little synthesis of evidence, and therefore it is not easy to visualize the different practice models and their components. The aim of this paper is to explain the need for advanced practice nurses to manage geriatric conditions.

Testing non-wearable fall detection methods in the homes of older adults

In this paper, we describe two longitudinal studies in which fall detection sensor technology was tested in the homes of older adults. The first study tested Doppler radar, a two-webcam system, and a depth camera system in ten apartments for two years. This continuous data collection allowed us to investigate the real-world setting of target users and compare the advantages and limitations of each sensor modality. Based on this study, the depth camera was chosen for a current ongoing study in which depth camera systems have been installed in 94 additional older adult apartments. We include a discussion of the different sensor systems, the pros and cons of each, and results of the fall detection and false alarms in the older adult homes.

Randomized Trial of Intelligent Sensor System for Early Illness Alerts in Senior Housing

OBJECTIVES

Measure the clinical effectiveness and cost effectiveness of using sensor data from an environmentally embedded sensor system for early illness recognition. This sensor system has demonstrated in pilot studies to detect changes in function and in chronic diseases or acute illnesses on average 10 days to 2 weeks before usual assessment methods or self-reports of illness.

DESIGN

Prospective intervention study in 13 assisted living (AL) communities of 171 residents randomly assigned to intervention (n=86) or comparison group (n=85) receiving usual care.

METHODS

Intervention participants lived with the sensor system an average of one year.

MEASUREMENTS

Continuous data collected 24 hours/7 days a week from motion sensors to measure overall activity, an under mattress bed sensor to capture respiration, pulse, and restlessness as people sleep, and a gait sensor that continuously measures gait speed, stride length and time, and automatically assess for increasing fall risk as the person walks around the apartment. Continuously running computer algorithms are applied to the sensor data and send health alerts to staff when there are changes in sensor data patterns.

RESULTS

The randomized comparison group functionally declined more rapidly than the intervention group. Walking speed and several measures from GaitRite, velocity, step length left and right, stride length left and right, and the fall risk measure of functional ambulation profile (FAP) all had clinically significant changes. The walking speed increase (worse) and velocity decline (worse) of 0.073 m/s for comparison group exceeded 0.05 m/s, a value considered to be a minimum clinically important difference. No differences were measured in health care costs.

CONCLUSIONS

These findings demonstrate that sensor data with health alerts and fall alerts sent to AL nursing staff can be an effective strategy to detect and intervene in early signs of illness or functional decline.

Assessment of Fall Characteristics From Depth Sensor Videos

Falls are a major source of death and disability in older adults; little data, however, are available about the etiology of falls in community-dwelling older adults. Sensor systems installed in independent and assisted living residences of 105 older adults participating in an ongoing technology study were programmed to record live videos of probable fall events. Sixty-four fall video segments from 19 individuals were viewed and rated using the Falls Video Assessment Questionnaire. Raters identified that 56% (n = 36) of falls were due to an incorrect shift of body weight and 27% (n = 17) from losing support of an external object, such as an unlocked wheelchair or rolling walker. In 60% of falls, mobility aids were in the room or in use at the time of the fall. Use of environmentally embedded sensors provides a mechanism for real-time fall detection and, ultimately, may supply information to clinicians for fall prevention interventions. [Journal of Gerontological Nursing, 43(7), 13-19.].

Exploring Health Information Exchange Implementation Using Qualitative Assessments of Nursing Home Leaders

OBJECTIVES

Limited research exists on nursing home information technologies, such as health information exchange (HIE) systems. Capturing the experiences of early HIE adopters provides vital information about how these systems are used. In this study, we conduct a secondary analysis of qualitative data captured during interviews with 15 nursing home leaders representing 14 nursing homes in the midwestern United States that are part of the Missouri Quality Improvement Initiative (MOQI) national demonstration project.

METHODS

The interviews were conducted as part of an external evaluation of the HIE vendor contracting with the MOQI initiative with the purpose of understanding the challenges and successes of HIE implementation, with a particular focus on Direct HIE services.

RESULTS

Emerging themes included (1) incorporating HIE into existing work processes, (2) participation inside and outside the facility, (3) appropriate training and retraining, (4) getting others to use the HIE, (5) getting the HIE operational, and 6) putting policies for technology into place.

DISCUSSION

Three essential areas should be considered for nursing homes considering HIE adoption: readiness to adopt technology, availability of technology resources, and matching of new clinical workflows.

Robust heartbeat detection from in-home ballistocardiogram signals of older adults using a bed sensor

We propose a simple and robust method to detect heartbeats using the ballistocardiogram (BCG) signal that is produced by a hydraulic bed sensor placed under the mattress. The proposed method is found beneficial especially when the BCG signal does not display consistent J-peaks, which can often be the case for overnight, in-home monitoring, especially with frail seniors. Heartbeat detection is based on the short-time energy of the BCG signal. Compared with previous methods that rely on the J-peaks observed from the BCG amplitude, we are able to achieve considerable improvement even when significant distortions are present. Test results are included for different BCG waveform patterns from older adults.

Exploring the feasibility and acceptability of sensor monitoring of gait and falls in the homes of persons with multiple sclerosis

Gait parameters variability and falls are problems for persons with MS and have not been adequately captured in the home. Our goal was to explore the feasibility and acceptability of monitoring of gait and falls in the homes of persons with MS over a period of 30 days. To test the feasibility of measuring gait and falls for 30days in the home of persons with MS, spatiotemporal gait parameters stride length, stride time, and gait speed were compared. A 3D infrared depth imaging system has been developed to objectively measure gait and falls in the home environment. Participants also completed a 16-foot GaitRite electronic pathway walk to validate spatiotemporal parameters of gait (gait speed (cm/s), stride length (cm), and gait cycle time(s)) during the timed 25 foot walking test (T25FWT). We also documented barriers to feasibility of installing the in-home sensors for these participants. The results of the study suggest that the Kinect sensor may be used as an alternative device to measure gait for persons with MS, depending on the desired accuracy level. Ultimately, using in-home sensors to analyze gait parameters in real time is feasible and could lead to better analysis of gait in persons with MS.

Using Embedded Sensors in Independent Living to Predict Gait Changes and Falls

This study explored using Big Data, totaling 66 terabytes over 10 years, captured from sensor systems installed in independent living apartments to predict falls from pre-fall changes in residents' Kinect-recorded gait parameters. Over a period of 3 to 48 months, we analyzed gait parameters continuously collected for residents who actually fell ( n = 13) and those who did not fall ( n = 10). We analyzed associations between participants' fall events ( n = 69) and pre-fall changes in in-home gait speed and stride length ( n = 2,070). Preliminary results indicate that a cumulative change in speed over time is associated with the probability of a fall ( p < .0001). The odds of a resident falling within 3 weeks after a cumulative change of 2.54 cm/s is 4.22 times the odds of a resident falling within 3 weeks after no change in in-home gait speed. Results demonstrate using sensors to measure in-home gait parameters associated with the occurrence of future falls.