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URMC / Education / Graduate Education / URBest Blog / November 2018 / Another Type of Story Teller – The Quantitative Researcher

Another Type of Story Teller – The Quantitative Researcher

Career Story by Nan Tracy Zheng, PhD, Senior Manager and Research Analyst at RTI International

In my third year of graduate study, I decided to focus my research on nursing home care. Most researchers who do quantitative research of nursing home care in the US use Minimum Data Set (MDS) - a standard data collection instrument with more than 500 items that all nursing homes serving Medicare and Medicaid beneficiaries are required to use for collecting and submitting residents’ health and treatment information. Although it’s called the Minimum Data Set, it is nowhere near small. As I joined my advisor, Dr. Helena Temkin-Greener, on a project focused on the quality of care for nursing homes and started thinking about my dissertation in the same area, she advised me that “from now on, you are going to eat, drink, and breath your data”.

And I did. For the next three years, I spent most of my working days managing, analyzing and interpreting the MDS. I was, and still am, amazed by how much data can tell me about a person’s care experience. I would feel sad when I read a person’s MDS long data stream which told me they were very frail and vulnerable, frequented the hospital, and eventually never returned to the nursing home – which meant they likely died in the hospital.

After graduation, I joined RTI International, an independent, nonprofit research institute dedicated to improving the human condition. I worked on a project that develops and implements nursing home quality measures for the Centers for Medicare & Medicaid Services (CMS). My graduate work, specifically my experience with the MDS, prepared me well for this project. I was able to lead the development of a quality measure to assess antipsychotic medication use in nursing homes soon after I started. Many nursing homes use antipsychotic medications to “control” their residents, although these medications are fatally dangerous if used without proper clinical indications. The MDS data shines light on whether or not a resident is on antipsychotic medications and if they have the diagnoses that would justify the prescription. Our team then used this information to develop a quality measure that informed both CMS and nursing home themselves on the number of residents who potentially inappropriately received these dangerous medications. Within a year, following the inception of our new quality measure, the use of antipsychotic medications dropped by 15% nationally and has continued to drop ever since. Seeing how data can be used to not only tell stories about care, but also improve care reinforced my passion as a quantitative researcher.  

In my work now, I use a number of national data sets and apply a similar approach: analyze care utilization patterns and outcomes portrayed by the data and identify gaps in quality of care. The luxury of spending time during graduate school to learn the MDS inside and out not only taught me how to use MDS, but it also gave me the skills and confidence I needed to learn other data sets quickly. I am happy about my career choice as a quantitative researcher and am excited to continue my journey in using the power of data to improve quality of care for the elderly.

Please join me to learn more about my career path in data science and public health November 2 at 11 am in the Anderson Room (G-8534) in the School of Medicine and Dentistry. If you are interested in a small group lunch that will be catered at noon, please RSVP to join us.

Tracey Baas | 10/19/2018

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