Advancing the Evidence-Base for Infection Control and Prevention in the Home Health Care Setting
The Visiting Nurse Service of New York (VNSNY) established the Center for Home Care Policy & Research in 1993. The Center’s mission has been to conduct research that advances knowledge promoting the delivery of high-quality, cost-effective care in the home and community. From the beginning, our goal has been to provide useful and timely information to key decision makers – within the VNSNY and outside – on issues significant to the delivery and financing of home- and community-based health care.
A Vision for the Future
VNSNY and the Center were ahead of the curve in the home health industry in transforming data into information to inform action. In 2000, the Centers for Medicare & Medicaid Services (CMS) began requiring all CMS certified home health care agencies to start collecting data through the standardized Outcome Assessment and Information Set (OASIS). VNSNY set up processes to not only meet the requirement of collecting information and transmitting data to CMS, we allocated resources to build a data infrastructure to support practice and research.
For the past two decades, this infrastructure has provided VNSNY investigators and staff with the ability to create dashboards and a reporting system to sustain a robust quality improvement program. The infrastructure has also allowed Center investigators to generate generalizable knowledge about home health care to inform evidence-based practices and promote quality care management.
Over the years as more resources were allocated for informatics, analytics, and research, two major divisions within the VNSNY emerged which focus on data and analytics. The VNSNY Center for Home Care Policy & Research, which remains the only research center embedded within a home health care organization, is now largely externally-funded (through grants with the National Institutes of Health, the Agency for Healthcare Research and Quality and other agency/foundation support) and focuses on creating generalizable knowledge for the home health industry and policy makers. The VNSNY Business Intelligence and Analytics Department (which includes Analytics, Business Intelligence, and Data Science groups) primarily focuses on internal reporting for quality improvement, decision support, program evaluation and development, and strategic planning.
Learning from Evidence to Improve Practice and Care
In 2007, the Institute of Medicine (now the National Academy of Medicine) promoted the term and concept of the Learning Healthcare System (LHS).1 A LHS is defined as a model that ‘generates and applies the best evidence for the collaborative healthcare choices of each patient and provider; drives the process of discovery as a natural outgrowth of patient care; and ensures innovation, quality, safety and value in healthcare.’
Andy Bindman, the Director of the Agency for Healthcare Research and Quality, outlined the following qualities for a successful Learning Health System2:
Through historic investments in research and data science, and resulting advances in innovations that inform quality care, VNSNY operationalizes the qualities of a Learning Health System. For example, a major initiative over many years is research and evaluation focused on infection control and prevention. Some of these efforts have been internally funded while others have been supported through Federal grants (Grant: R01HS024723 from the Agency for Healthcare Research and Quality (J. Shang, Principal Investigator); Grant: R01NR16014 from the National Institute of Nursing Research (K.Bowles/C.Murtaugh, Principal Investigators).
Practical Applications Guide Real-Time Response
Cultivating a Learning Health System requires sustained focus and effort. VNSNY has an ongoing commitment to working effectively integrating research and practice for this purpose. Front-line clinicians have been involved in the development, usability and evaluation of dashboards for several years, (https://pubmed.ncbi.nlm.nih.gov/30394879/), and are now becoming more involved in informing our research questions as well as the translation of evidence for the VNSNY setting.
Advances in electronic health records and information technologies are providing easier, quicker and less costly ways to capture and analyze health data for knowledge building and quality improvement efforts. Heightened attention has been given to infection control and prevention over the past few months due to the coronavirus pandemic. VNSNY staff are actively using the LHS concept to guide innovations and to develop a framework that strengthens clinician, operational and research/data science collaboration during the pandemic and beyond.
1 Institute of Medicine. The Learning Healthcare System: Workshop Summary. Olsen L, Aisner D, McGinnis JM (Eds). National Academies Press, Washington, DC, USA, DOI: 10.17226/11903 (2007).
2 Supporting Learning Health Systems. Content last reviewed November 2016. Agency for Healthcare Research and Quality, Rockville, MD. https://www.ahrq.gov/news/blog/ahrqviews/supporting-learning-health-systems.html, accessed 8/28/20.
Select VNSNY findings/initiatives informing implementation of evidence-based practice and optimized care at VNSNY and across the home health industry:
Patient predictive risk modeling to identify those at the start of home health care who are at heightened risk of an infection-related emergent care event.
In a collaborative effort with investigators from Columbia University School of Nursing, VNSNY research and data science staff, used clinical data from 112,788 home health care admissions to develop and test a predictive risk model. The model identified over 30 factors associated with the risk of an infection-related hospitalization or emergency department visit. The discussion on how best to integrate the infection risk prediction model to help focus tailored intervention to those at the highest levels of infection risk is underway.
How nurses independently identify patients at infection risk and strategies they use to reduce risk.
Fifty home health nurses were interviewed to learn how home health nurses identify and manage infection risk. Using qualitative analysis, the nurse interviews identified older age and select clinical conditions, along with patient knowledge deficits and environmental issues as factors that place a patient at higher risk for infection. They also reported using patient and caregiver education, heightened protective care management behavior, and plan of care adjustment to address the risk.
Hand hygiene practices and the factors related to adherence and the increased need for hand hygiene at a given visit.
An evaluation of hand hygiene practices was completed through observation of 50 nurses during 400 patient care visits. The study looks at nurse, patient and environmental factors related to the number of times hand hygiene was indicated during a given visit and factors related to adherence. Findings have been used to inform education and quality improvement efforts at VNSNY. An article describing this study with recommendations for promoting evidence-based practice is pending publication.
Article in press: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7490582/
What impacts compliance with infection control practices more: knowledge or attitude?
Through a survey of 359 nurses from two home health agencies, it was found that knowledge of recommended infection control was high. A multivariate mixed regression analysis demonstrated that attitudes toward infection control were associated with reported compliance, while knowledge was not. This finding suggests that improvement efforts should focus on strategies to alter perception about infection risk and other attitudinal factors as opposed to a need to increase knowledge.
Innovative COVID-19 (and other infectious disease) contact tracing app for home- and community- based providers.
The VNSNY Data Science Team developed VisitContactTrace, a new contact-tracing tool to help home- and community-based health care providers limit the spread of the coronavirus and other infectious diseases. The tool identifies individuals who may have been exposed and the people they came into contact with. VNSNY began using the tool early in the coronavirus outbreak and now the tool is available for no cost through an open source statistical R software program. Providers can install and run their own contact-tracing models by visiting GitHub.
Learning more about the high-risk population of sepsis survivors who get transitioned to home health. The evaluation of a national home health sample of 165,228, found that most of the patients being admitted to home health with sepsis had severe sepsis with organ dysfunction (80.7%). Twelve key variables were identified that raise the relative 7-day readmission risk by as much as 60%. Early and more targeted interventions with patients who have any of these variables may reduce readmission risk.
Timely visits by home health and outpatient providers is associated with reduced re-hospitalization for sepsis survivors.
Investigators at the Center for Home Care Policy & Research and collaborators evaluated clinical provider visit patterns using a sample of 170,571 home health care admissions to determine what patterns of service use were most effective to improve the outcomes of sepsis survivors discharged to home health care. In that comparative effectiveness study, they found 30-day rehospitalization rates were 7 percentage points lower (a 41% relative reduction) when sepsis survivors received a HHC nursing visit within 2 days of hospital discharge, at least 1 more visit the first week, and an outpatient provider follow-up visit by 7 days compared to those without timely follow-up.