Consumer Analytics for Carolinas HealthCare System
CHS invested in DA to quickly build its data analytics capacity and ultimately track quality assurance and shift organizational attention to value-based care and patient outcomes. CHS had been forced to reconsider its care delivery strategies because of extensive changes in the US healthcare market at the time, focusing on value-added patient care service delivery and outcomes (Quelch & Rodriguez, 2015). An example includes a shift to a “fee-for-value” from a “fee-for-service” model (Quelch & Rodriguez, 2015, p. 3). Fee-for-value is more focused on data-driven best practices in the treatment of patients (Shrank et al., 2021). The change came about after the passage of the Affordable Care Act (ACA), forcing CHS to prioritize quality outcomes consistent with the new model.
DA has achieved three main objectives by November 2014, which form the basis on which its successes should be evaluated. First, it successfully accumulated and managed massive quantities of data after developing a robust data governance mechanism. Effective data governance can speed up a health practitioner’s evolution to value-based patient care (Shrank et al., 2021). Second, it helped CHS shift from an anecdotal to an evidence-based patient care practice. In addition to offering tools to enable CHS-affiliated hospitals to provide patients with the best possible healthcare, DA would develop analytical tools for evidence-based population health management, personalized patient care, and time series modeling (Quelch & Rodriguez, 2015). Lastly, it successfully created an enterprise data warehouse (EDW), allowing DA to independently analyze massive healthcare data sets. It is estimated that the data amounted to 1.5 petabytes of data, which had more than quadrupled in size by 2015 (Quelch & Rodriguez, 2015). With the help of EDW, DA was able to build models with a lot of different patient variables in them.
The most significant challenge was the excessively high demand for DA’s services beyond its operating capacity. Internal demand for DA’s services could easily outstrip its existing capacity. In 2014, DA was working on over a dozen risk prediction models and was frequently overwhelmed with requests from CHS (Quelch & Rodriguez, 2015). Managing DA’s internal clients, whose need for analytics soon surpassed the team’s capability, was a major challenge for Dulin.
The other challenge was justifying the cost of the investment in DA, as CHS management was looking into commercial opportunities that could turn DA into a profit center. DA functioned separately from the rest of CHS’s operations, yet it was still operated as a cost center (Quelch & Rodriguez, 2015). This forced Dulin and his team to come up with a business strategy for DA that demonstrated its long-term return on investment (ROI) and prioritized initiatives of strategic relevance to the CHS.
IBM and partner hospital groups seem to be in the best position to provide integrated data management. This is important even as DA seeks outside expertise to increase the scope and quality of integrated data. Indeed, the Data Alliance Collaborative (DAC) was created in 2013 by CHS, four hospital groups, and IBM with the purpose of building scalable data models that can improve public health (Quelch & Rodriguez, 2015). IBM could build a data architecture that connects people’s medical data, insurance claims, and payment information.
Apple and Google are also in the best position to provide integrated data management. According to Dulin, firms like Apple and Google have entered the healthcare space, releasing features in their new mobile operating systems that gather and track data from various consumers (Quelch & Rodriguez, 2015). Partnerships with companies like these could help DA better analyze and get patient demographic and location data.
Quelch, J., & Rodriguez, M. (2015). Carolinas healthcare system: Consumer analytics. HBS No. 9-515-060. Harvard Business School Publishing.
Shrank, W., DeParle, N., Gottlieb, S., Jain, S., Orszag, P., Powers, B., & Wilensky, G. (2021). Health costs and financing: challenges and strategies for a new administration. Health Affairs, 40(2), Web.