Healthcare News & Insights

The hidden value of transparency in master data management

It’s no secret that a robust and reliable master data management (MDM) solution can help a health system or insurance provider make better strategic business decisions. However, those tools are only effective if they adhere to the core tenants of process improvement – self-sufficiency and trust. To achieve these goals, hospitals and insurance providers need an MDM solution with built-in transparency, so their data stewards know exactly what’s going into the business rules and the matching process that will create the single, “golden” record of each patient or member. In this guest post, An-Chan Phung, Chief Innovation Officer of an MDM company, highlights the two most important features of a transparent, customizable MDM platform.

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For hospitals and payors, a multitude of data comes in from many places, and, in the past, it was sufficient to manage this seemingly unrelated data within separate data silos. In hospitals and health systems, this includes electronic medical records, lab data, emergency departments, billing departments, address information, medical history and more. For payors, this includes member claims, provider information, wellness programs, wearable devices, biometric screenings, disease management and chronic care management programs. The problem is, as healthcare has transitioned to shared savings, data stewards have been tasked with transforming disparate and disconnected data into clean, portable information that can be shared with internal and external stakeholders in the community of care. This requires a new data management paradigm – and is one of the reasons why MDM is becoming increasingly important as healthcare evolves.

Adding to the challenge for both health systems and payors is the continuing trend of healthcare consolidation – in the form of smaller hospitals and health plans being acquired by larger organizations, in addition to new, cross-industry mergers like CVS’ proposed acquisition of Aetna announced in December 2017, and the more recently reported talks of Walmart looking to buy Humana. Amazon even announced in January a partnership with Berkshire Hathaway and JP Morgan Chase to “form an independent healthcare company for their employees in the United States.”

As health care becomes ever more complex considering these acquisitions and the current, volatile political climate, both hospitals and payors are facing an environment ripe for disparate, incomplete and duplicate information.

Transparency + empowerment = Trust

Many MDM companies provide the tools for record matching, but few empower their users by offering transparency into what goes into the process, and what steps to take in what order to achieve the desired results. In fact, transparency goes beyond the “what,” taking into account the “why” and “how” of data matching. And make no mistake: in the world of data management, transparency and empowerment are necessary to gain the level of trust and executive buy-in required to implement an MDM solution within a single department, let alone to implement a new data governance process system-wide. Understanding the inner workings of your MDM – exposing the business rules that drive algorithms for data matching, for example – empowers your hospital or payor organization to build complete, 360-degree view records that enable you to accurately identify your patients or members so you can effectively utilize artificial intelligence, perform higher-level analytics, and improve care quality and satisfaction.

Two of the most important features of a transparent, customizable MDM platform include:

  • Customizable fields – With many MDM solutions, you must blindly trust the underlying assumptions and business rules for identifying individual customer records. This includes one-size-fits-all assumptions about the right combinations of names, addresses, partial or complete Social Security numbers and date of birth. If the system picks up various records where enough of these fields are the same, two records are declared a match and they’re combined – end of story. But what if you had the option to add or remove fields, based on the nuances of your particular population, at any given hour of any given day? What if you could pull in allergies, medication lists, and even eye color? Then you could significantly increase the likelihood that these two records are an authentic match. The data can then be analyzed for your particular goals, whether you’re looking for cost savings, better care outcomes, reduced readmission rates or any number of different initiatives.
  • Match analysis reports – In addition to seeing – and controlling – what goes into your particular matches, a robust MDM solution should show you what the end result will look like and offer you the opportunity to adjust. Such a report allows the user to view results, see why the system has matched up particular records, and then accept or deny those records before they’re pulled into one “golden” view of that particular patient or member. Such a report allows the data steward to view what’s happening behind the scenes in a much more transparent way, and reveals potential inconsistencies in the data itself.

For a health plan, for example, such features would give the payor the ability to see data on all members who live within a single household and what the average spend is per household. For a health system, it might mean being able to minimize hospital errors such as duplicate tests, and medication and billing errors.

Transparency can also lead to easier resolution of false positives and false negatives. False positives are records the system has matched that it shouldn’t have, such as twins, or a father and son with the same name, being accidentally matched as part of the same record. False negatives, in contrast, are records the system should have matched that it didn’t, such as when a newly married woman changes her last name or someone moves to a new address, and they were read as separate records when they’re actually the same.

Meaningful insights are only possible with clean data

Healthcare executives often view master data management as simply a tool for managing and cleansing their data. However, it’s only possible to derive meaningful insights when you have clean data that’s useful for practical purposes. Whether that’s for analytics reporting, identifying fraud, service delivery or keeping data up to date, goals are unique from organization to organization and even from day to day. Yet, executives often don’t see the cost of missed opportunities in failing to derive insight from their data when they’re forced to stick to a stringent, one-size-fits-all method for data matching and management.

Transparency in data management puts the hospital or health system in charge of their own data and helps to prepare them for what’s next in healthcare. Creating the data matching, the analysis and the insights you need leads to a process that can survive the test of time, no matter what data, regulatory or technology changes are coming your way. With a robust and customizable master data management platform, healthcare executives can set strategic organizational priorities with confidence, knowing they finally have the necessary control over their data to enable long term success. Master data is important, and it’s difficult to manage or be accountable for master data if it’s not understood or transparent.

An-Chan Phung is the Chief Innovation Officer of VisionWare, a master data management company.

 

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