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TDWI Upside - Where Data Means Business

Executive Q&A: Transforming Data Management at a Healthcare Organization

The need for more effective data analytics across the healthcare industry is rising. Upside spoke with Jenny Hyun at physician-owned healthcare delivery organization Vituity about their use of master data to manage recent growth.

Today, organizations across the healthcare industry are working to improve their use of data and analytics. Upside recently spoke with Vituity's director of enterprise data analytics, Jenny Hyun, to discuss how the company's rapid growth required them to rethink their enterprise data strategy and pursue a successful digital transformation.

Upside: Tell me a little bit about Vituity and the business drivers that caused the company to pursue a digital transformation.

For Further Reading:

Best Practices to Modernize Your Data Management

What Healthcare IT Leaders Need to Know about Digital Transformation

How to Avoid Inefficiencies and Engender Trust in a Data-Driven Enterprise

Jenny Hyun: Vituity is a physician-led and -owned partnership that combines clinical excellence with business acumen to help healthcare organizations raise the standard of patient care and improve their performance metrics. The largest practice line is emergency medicine, but our clinical footprint has expanded rapidly over the last decade to include additional practice lines such as hospital medicine, critical care, anesthesiology, acute psychiatry, and neurology.

Vituity has grown so rapidly over the past few years that it now encompasses over 450 practices that care for 8 million patients annually. With this growing footprint came a substantial amount of clinical, medical billing, and other data flowing into our enterprise systems from multiple data sources that then had to be properly validated and merged.

Dealing with these siloed data sources resulted in poor visibility, operational errors, inefficiencies, and unnecessary redundant effort on the part of our team -- all of which could hinder our growth and ultimately affect our operational efficiency and patient experience.

Not to say our source system data was bad -- only that it had been created in multiple systems each with different rules and requirements. To drive our digital transformation efforts, we realized we needed to impose some data governance and quality rules so we could ultimately have a single, integrated source of truth for reporting, decision making, and operational efficiency.

What were some of the options Vituity considered?

We looked at a few approaches to solve our data silo problem. First, we considered each of our existing applications as its own source of master data, but we quickly found out that these systems could not manage data originated from, and stored in, the other systems.

We also tried deploying a custom-coded solution to merge all this data into a custom-built data warehouse, but this required a lot of effort, was difficult to maintain, and provided no visibility into matching or survivorship rules. Ultimately, we decided to move forward with a dedicated master data management (MDM) platform from Profisee to accommodate multiple key master data domains and the data interactions around them. More than just implementing a technical solution, we found out we needed to develop improved processes around data governance and quickly realized that the MDM program could help us with that as well.

Can you speak to the types of data you're managing with MDM?

We started with our provider data, which includes the doctors and advanced providers (nurse practitioners and physician assistants) maintained in our systems. This was helpful because it allowed us to start with a single domain and use case to build a framework and take a quick win before getting buy-in for additional domains.

Once we proved we could collect and collate provider data from multiple sources and consolidate it based on matching and data quality rules to create a single source of truth, we quickly moved on to other domains such as facility/location, which includes important identifiers for each contracted location (site identifiers, contract names, etc.), and patients, which includes all patient data for billing (insurance, addresses, guarantor information, etc.).

For Further Reading:

Best Practices to Modernize Your Data Management

What Healthcare IT Leaders Need to Know about Digital Transformation

How to Avoid Inefficiencies and Engender Trust in a Data-Driven Enterprise

Next, we plan on adding payor contracts to standardize the names and contract details with the private insurance and public payors who reimburse us for the care we provide, followed by reference data. This last domain will involve mastering and managing the data used across our data types and include CPT codes, ICD-9/10 codes, CCS diagnostics codes, and others that we use to organize our clinical and billing data.

Because we quickly recognized that we would need to master multiple data domains to handle our companywide business requirements, we limited our search to only those MDM vendors that offered multidomain functionality.

What were some of the business questions you needed consistent data to answer and solve?

As we started to analyze the scale of our enterprise data and the various source systems we would be working with, we realized that the problems we were trying to solve involved multiple data types. For example, when we looked at potential inefficiencies in our billing process, it involved harmonizing data on our providers, including their credentialing, as well as our locations, where simple spelling errors such as abbreviating St. Luke's when the managed care organization that processes our invoices writes it out as S-A-I-N-T could prove detrimental and cause delays.

Beyond billing and scheduling, we needed to arm our clinical partners with the information they needed to care for our patients. Cleaning and consolidating this data -- and making it available to all our locations -- was critical for tracking patients throughout their entire continuum of care and their experience with Vituity. We needed to have clean, reliable data to provide clinical and operational insights to our clinical partners to ultimately improve processes and patient outcomes.

Overall, what were you able to accomplish with the data governance and MDM effort?

Our initial goal was to improve the consistency, accessibility, and usability of our enterprise data. Thankfully, we approached the initiative by thinking about our issues holistically across data types and source systems. As a result, we were able to include all our critical domains in our governance and MDM programs and execute a phased delivery plan, knowing we could always add additional use cases as we matured and received additional buy-in from leadership.

Some of our immediate benefits fall into two categories -- the first being improved reporting, metrics, and insights. Without consistent trusted data, we couldn't create meaningful reports or get insight into how our business was running, from utilization to profitability. Now that we know our data has been matched, deduplicated, and enriched, we know we can trust the critical insights around effective healthcare delivery and clinical-quality metrics.

The second benefit was better operational efficiency. Synchronized, trusted data leads to reduced data entry, errors, and duplicative work -- and that impacts all areas of the business, from accurate credentialing to patient billing. This well-managed data also allows us to identify future data quality or governance issues and then implement necessary changes quickly and efficiently.

However, one of the more remarkable benefits beyond the tangible operational improvements has been our ability to build a stronger data culture throughout the company. Once we started this journey, other business owners started coming to us and saying, "Hey! We know you have a ready technology solution for this, so help us with this problem."

It really led to a grassroots adoption of data governance because others saw the benefits -- as opposed to it being a top-down directive. We're in a much better place now as we explore additional use cases and domains, and I'm excited for what's next.

[Editor's note: Jenny Hyun is the director of enterprise analytics at Vituity (a nationwide, multispecialty partnership of physicians, advanced providers, and industry professionals) where she works across cross-functional teams and oversees the production of analytics and operational tools, including taking user requirements with clinical subject matter experts, establishing technical and functional parameters with developers and releasing to end users.]

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