My healthcare career originated in nursing school. Although I ended up shifting gears to an accounting and information systems program during college, what I learned during my time in nursing school gave me a crucial foundation for understanding clinical language. My ability to translate in both clinical and technical terms served me well during my first job working for a managed care organization where I was introduced to the field of analytics.
In that role, I found that my technical background supported the unique ability to act as a translator between our technical and executive teams. I eventually transitioned into work with clients and strategic accounts, helping our Administrative Services Only (ASO) customers better understand how they were spending their healthcare dollars. We routinely provided clients with cost-trending reports to help quantify their healthcare expenditures, and there was a mounting collective interest in finding ways to do more with that data.
Could the data help us determine whether any healthcare episodes were preventable? If we pulled enough information together on high-cost, high-utilization areas, could we identify lead-up events that we could use to predict future instances? Using the data, could we develop initiatives designed to manage healthcare resources more effectively?
My work evolved from data gathering to analyzing that information to better understand where our pressing issues were and what we could do to improve outcomes and bend the cost curve. I reached into the medical management side of the house—disease management, case management and utilization management—to begin weaving together a broader picture of what was going on.
We used data to assess ER utilization trends and identified geographic clusters of patients using the ER for non-emergent care. We began exploring ways that social factors might be impacting health outcomes in those areas—low socioeconomic status (SES) areas where patients couldn’t afford to miss time away from work but had limited access to providers with after-hours services. This insight enabled us to tailor engagement opportunities to better support the needs of those patient populations.
Over the next several years, my career segued from simple analytics into data science. In 2004,I joined the leadership team that was responsible for the build of an enterprise data warehouse to streamline the arduous process of centralizing patient data assets—health data, claims, social determinants of health (SDOH), consumer preferences. This program supported the build-out to establish a system for patient risk-scoring and ensure all departments understood how to access pertinent information. I was responsible for the team that established a business intelligence (BI) competency center.
Working on this project gave me great insight into how different areas within the organization—medical management, patient engagement, customer service, technology teams—can work together to make a more significant impact. With that broader insight, we began humanizing analytics—tailoring outreach models to drive more effective engagement based on what we learned about different patient cohorts.
I’ve spent the last year and a half exploring ways to close critical care gaps, including cancer screenings using advanced analytics and psychographic segmentation to identify at-risk patients, tailor communications and care settings, and increase patient engagement. I’m thrilled that my career has brought me to a place where I can promote change in such relevant areas.
We’ve only started scratching the surface of SDOH’s impact on healthcare. While consumerism is still a relatively new concept in our industry, the more we can meet patients where they are through offerings such as retail and home screenings, the more we’ll be able to drive positive, substantial change. My advice to anyone interested in entering the skyrocketing field of clinical analytics and informatics is to listen and be persistent. Listen to what patients and providers are really asking for. Look at it from all the angles and consider all the variables. Stay curious.
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