There is no higher importance in managing customer information than when making decisions on healthcare. While most markets are busy striving for a “single customer view” to improve customer service KPIs or marketing campaign results, healthcare organizations must focus on establishing a “single patient view”, making sure a full patient history is attached to a single, correct contact. 

Unlike traditional CRM solutions, healthcare data is inherently disparate and is managed by a wide variety of patient systems that, in addition to collecting and managing contact data, also tracks thousands of patient datapoints, such as electronic health records, insurance coverage, provider names, prescriptions and more. 

Needless to say, establishing the relationship between patients and their healthcare providers, insurers, brokers, pharmacies, and even the grouping of families and couples together is a significant challenge. Then there’s the challenges with maiden/married last names, migration of individuals between family units and insurance plans, keying errors at the point-of-entry, or even deliberate attempts by consumers to defraud the healthcare system.

In many cases, the single patient view can be handled through unique identifiers, such as those for group health plans or for individuals within their provider network. But while patient ID numbers seem like standard identifiers, they can differ between suppliers, and patients don’t always refer to it as their first method of identification. This is where accuracy and access to other collected data points such as social security number, birth date, and addressing information become critical. 

While healthcare organizations attempt to leverage this complex array of ever-changing datapoints, the healthcare data quality paradigm is shifting once again. For example, The Patient Protection and Affordable Care Act (PPACA) means that healthcare organizations will now have to deal with more data, from more sources and face tougher regulations on how to manage and maintain that data. The ObamaCare Health Insurance Exchange Pool means that more Americans can potentially benefit from health insurance coverage, increasing the number with coverage by around 30 million. Through these new initiatives, consumers will also have a greater choice for both coverage and services  – all further distributing the data that desperately needs to be linked.

With such inherent change, healthcare practitioners are challenged with how to effectively service patients at the point-of-care. Here are just some of the new dynamics that healthcare companies need to account for:

  • Addition of new patients into a system without prior medical coverage or records
  • Frequent movement of consumers between healthcare plans under the choice offered by the affordable care scheme
  • Increased mobility of individuals through healthcare systems as they consume different vendors and services

This increased transactional activity means that healthcare data managers must go beyond the existing efforts of simply linking internal data and start to look beyond. Data must now be shared across both internal and external systems and investment in the right technology to facilitate the security and speed of that process is critical. Granted, this will be a significant challenge given the fact that many organizations have several proprietary systems, contract requirements, and privacy concerns but oddly enough, this begins with best practices in managing contact data effectively.

For healthcare customers, a hybrid approach of solutions such as matchit for Microsoft SQL Server and findIT S2  can deal with the backend data matching, validation and merging of complex records so users can quickly and accurately identify patient records with minimal keystrokes. This complementary approach gives a huge return on investment allowing clinical end-users to focus on the task at hand, rather than repeatedly dealing with data issues.

Like healthcare, data quality is both preventative and curative. Curative measures include triage on existing poor quality data, and investigating the latent symptoms of unidentified relationships in the data. The preventative measures are to introduce a regimen of using DQ tools to accurately capture new information at the point-of-entry efficiently and to help identify existing customers quickly and accurately. When it comes to healthcare data quality, improving and streamlining data capture while tapping into various, fast-moving databases is necessary to give physicians and service providers access to the kind of data that can change a life.