The Relationship Between Manpower Allocation and Nursing Home Patient Quality of Care
Writer: Wee Siang Tay
Date: Fall 2016
Citation: Tay, W. S. & Jarrín, O. (2016). The Relationship Between Manpower Allocation and Nursing Home Patient Quality of Care. Rutgers Research Review, 1(2).
My name is Wee Siang Tay and I am a Class of 2019 international student from Singapore in the School of Arts and Sciences Honors Program. I am majoring in Economics and currently work with Dr. Olga Jarrín. Previously, I worked in a frontline clinical setting at a hospital and I served in the military. I was injured during my days of service, but I experienced genuine warmth from nurses who took care of me when I was a warded patient. My experiences with healthcare are in both as a provider and patient. The relief and gratitude I felt from nurses fuel my impetus to select the field of nursing to do research.
Currently, an estimated “half of all US nursing homes have insufficient staffing” (Harrington et al., 2016). Hence, my primary goal is to determine if an optimal pattern of staffing for nursing homes is available using publicly available data. My secondary goal is to explore and evaluate public policy strategies, using staffing results I will have obtained from my primary goal. This project uses datasets from publicly available sources such as the Center for Medicare and Medicaid Services (CMS) Nursing Home Compare dataset, which includes 15 thousand nursing homes.
Dr. Jarrín leads our interdisciplinary research team, which is composed of students majoring in Public Health, Industrial Engineering, Economics, Biology, and Nursing. We tackle different contextual issues that are critical for Home Health and Nursing Home operations and outcomes. Dr. Jarrín’s research is focused on understanding how to best redesign the longterm health care delivery system for frail and chronically ill community-dwelling older adults on a national level. I hope that my research will pinpoint areas of concern regarding staffing levels, which might one day benefit all current and potential nursing home patients.
Picture your grandmother, lying in a nursing home. She requires assistance in getting to the bathroom. She calls for help. The nearest available nurse is busy attending to another patient. She becomes anxious. There is a shortage of nurses.
Insufficient levels of staffing in nursing homes are a widespread issue throughout the United States that contributes to poor quality of care for nursing home residents (Harrington et al., 2016). An example of inadequate staffing would be the low Certified Nursing Assistant (CNA) hours per resident day (HPRD) ratio. CNA‘s main role is to provide basic care to patients, as well as assist them in daily activities they may have trouble with. Their responsibilities are assisting residents with bathing, grooming, toileting, and feeding assistance. Currently, CNA‘s staffing falls below 2.08 HPRD or ratios of about 10 to 11 residents to one CNA during the labor-intensive day and evening shifts. (Harrington et al., 2016).
The consequence of inadequate levels of staffing in nursing homes is disastrous. A recent study conducted by the U.S. Office of the Inspector General found that 33 percent of Medicare nursing home residents experienced adverse events, resulting in harm or death during the first 35 days of a post-acute skilled nursing stay (Harrington et al., 2016). In addition, 60 percent of adverse events were due to substandard treatment, insufficient monitoring, and failures or delays in treatment by nursing staff and other staffs. In total, this cost Medicare 2.8 billion dollars (Harrington et al., 2016).
However, higher levels of staffing and acuity-based staffing (i.e. number of nurses on shift per patient’s needs) are associated with better nursing home outcomes including fewer residences experiencing unexpected weight loss and fewer feeding tubes needed. This suggests an improvement in patient quality outcomes with better Registered Nurse staffing levels. (Gray-Siracusa, 2005).
Our approach is first to determine the current levels of staffing in nursing homes using data from Nursing Home Compare. These data sets will be mapped out on Watson Analytics for trend analysis. Then, we will compare the current levels of staffing to current state minimum levels of staffing (Harrington, 2008). Next, we will use staffing results and trends to explore and evaluate public policy strategies. For example, we will look at how and why nursing homes are penalized through payment denials.
The team’s overall methodology will be to first review literature regarding nursing home variables. Next, we will compile a unified table of data obtained from multiple sources including Nursing Home Compare that will be processed and uploaded to the IBM Watson Analytics software. This data will be used to discover patterns and explain trends and relationships between variables. Trifecta Data Wrangler and SAS will be used to assist us in refining and linking different datasets together.
It is important that we understand how different (but related) variables in our datasets were created for us to select the correct variable to represent what we study. For example, we were surprised to discover the different ways that staffing data are presented in the consumer view of Nursing Home Compare database. It is designed to allow patients, providers, and the public to compare the quality of nursing homes on a variety of metrics. This data are presented in two ways, which may be misleading to consumers. First, there is a star rating, which is based on a complex formula accounting for the clinical severity and level of care required by residents in nursing homes. Second, presented in hours and minutes, there is detailed information about the total staff time per patient day, and breakdown by provider type including Registered Nurses, Licensed Professional Nurses, CNAs, and physical therapists (a formula of staff to patients not adjusted for how much care residents require). For our study, we will use detailed and acuity-adjusted staffing data to calculate the Nursing Home Compare star ratings for safe and adequate staffing. Acuity-adjusted staffing data account for clinical severity and level of care required by patients.
Our project is still in its initial phases. Over time, we expect to discover more results and uncover meaningful patterns and trends that will inform policy makers and nursing home executives. A hypothesized trend that we expect to see from our primary goal will be regarding staffing trends; when staffing levels are stagnant / below average, a penalty might be incurred by that nursing home, which signals for an increase in staffing levels. After some time, staffing levels may stagnate or fall, which will lead the nursing home to incur another penalty. Hence, my primary goal of determining an optimal staffing level would help nursing homes save money and avoid penalties, which would lead to my secondary goal to analyze policies regarding penalties. Possible changes in the future may include initiatives that encourage/incentivize operating procedures of nursing homes. This encourages nursing homes to deliver better quality standards of care to their patients.
- Gray-Siracusa, K. (2005). Acuity-based staffing in long term care: Does it influence quality?. UMC Digital Archive.
- Harrington, C. (2008). Nursing home staffing standards in state statutes and regulations. UCSF 2007 Survey of Nursing Home Staffing Standards.
- Harrington, C., Schnelle, J. F., McGregor, M., & Simmons, S. F. (2016). The Need for Higher Minimum Staffing Standards in US Nursing Homes. Health Services Insights, 9, 13.