Analytics Stories: A case study from UPMC
As I have shared within the posts of this series, businesses are using analytics to improve their internal and external facing business processes and to strengthen their “right to win” within the markets that they operate. Like healthcare institutions across the country, UPMC is striving to improve its quality of care and business profitability. One educational healthcare CEO put it to me this way–“if we can improve our quality of service, we can reduce costs while we increase our pricing power”. In UPMC’s case, they believe that the vast majority of their costs are in a fraction of their patients, but they want to prove this with real data and then use this information drive their go forward business strategies.
Getting more predictive to improved outcomes and reduce cost
Armed with this knowledge, UPMC’s leadership wanted to use advanced analytic and predictive modeling to improve clinical and financial decision making. And taking this action was seen as producing better patient outcomes and reducing costs. A focus area for analysis involved creating “longitudinal records” for the complete cost of providing particular types of care. For those that aren’t versed in time series analysis, longitudinal analysis uses a series of observations obtained from many respondents over time to derive a relevant business insight. When I was also involved in healthcare, I used this type of analysis to interrelate employee and patient engagement results versus healthcare outcomes. In UPMC’s case, they wanted to use this type of analysis to understand for example the total end to end cost of a spinal surgery. UPMC wanted to look beyond the cost of surgery and account for the pre-surgery care and recovery-related costs. However, to do this for the entire hospital meant that it needed to bring together data from hundreds of sources across UPMC and outside entities, including labs and pharmacies. However, by having this information, UPMC’s leadership saw the potential to create an accurate and comprehensive view which could be used to benchmark future procedures. Additionally, UPMC saw the potential to automate the creation of patient problem lists or examine clinical practice variations. But like the other case studies that we have reviewed, these steps required trustworthy and authoritative data to be accessed with agility and ease.
UPMC’s starts with a large, multiyear investment
In October 2012, UPMC made a $100 million investment to establish an enterprise analytics initiative to bring together for the first time, clinical, financial, administrative, genomic and other information together in one place. Tom Davenport, the author of Competing on Analytics, suggests in his writing that establishing an enterprise analytics capability represents a major step forward because it allows enterprises to answer the big questions, to better tie strategy and analytics, and to finally rationalize applications interconnect and business intelligence spending. As UPMC put its plan together, it realized that it needed to impact more than 1200 applications. As well it realized that it needed one system manage with data integration, master data management, and eventually complex event processing capabilities. At the same time, it created the people side of things by creating a governance team to manage data integrity improvements, ensuring that trusted data populates enterprise analytics and provides transparency into data integrity challenges. One of UPMC’s goals was to provide self-service capabilities. According to Terri Mikol, a project leader, “We can’t have people coming to IT for every information request. We’re never going to cure cancer that way.” Here is an example of the promise that occurred within the first eight months of this project. Researchers were able to integrate—for the first time ever– clinical and genomic information on 140 patients previously treated for breast cancer. Traditionally, these data have resided in separate information systems, making it difficult—if not impossible—to integrate and analyze dozens of variables. The researchers found intriguing molecular differences in the makeup of pre-menopausal vs. post-menopausal breast cancer, findings which will be further explored. For UPMC, this initial cancer insight is just the starting point of their efforts to mine massive amounts of data in the pursuit of smarter medicines.
Building the UPMC Enterprise Analytics Capability
To create their enterprise analytics platform, UPMC determined it was critical to establish “a single, unified platform for data integration, data governance, and master data management,” according to Terri Mikol. The solution required a number of key building blocks. The first was data integration to collect and cleanses data from hundreds of sources and organizes them into repositories that would enable fast, easy analysis and reporting by and for end users.
Specifically, the UPMC enterprise analytics capability pulls clinical and operational data from a broad range of sources, including systems for managing hospital admissions, emergency room operations, patient claims, health plans, electronic health records, as well as external databases that hold registries of genomic and epidemiological data needed for crafting personalized and translational medicine therapies. UPMC has integrated quality checked source data in accordance with industry-standard healthcare information models. This effort included putting together capabilities around data integration, data quality and master data management to manage transformations and enforce consistent definitions of patients, providers, facilities and medical terminology.
As said, the cleansed and harmonized data is organized into specialized genomics databases, multidimensional warehouses, and data marts. The approach makes use of traditional data warehousing approaches as well as big data capabilities to handle unstructured data and natural language processing. UPMC has also deployed analytical tools that allow end users to exploit the data enabled from the Enterprise Analytics platform. The tools drive everything from predictive analytics, cohort tracking, and business and compliance reporting. And UPMC did not stop here. If their data had value then it needed to be secured. UPMC created data audits and data governance practices. As well, they implemented a dynamic data masking solution ensures data security and privacy.
As I have discussed, many firms are pushing point silo solutions into their environments, but as UPMC shows this limits their ability to ask the bigger business questions or in UPMC’s case to discover things that can change people’s live. Analytics are more and more a business enabler if they are organized as an enterprise analytics capability. As well, I have come to believe that analytics have become foundational capability to all firms’ right to win. It informs a coherent set of capabilities and establishes a firm’s go forward right to win. For this, UPMC is a shining example of getting things right.
Author Twitter: @MylesSuer