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Blog
Three Themes to Keep in Mind When Looking for Matching Solutions
Don't overlook these important ideas in your search
IQVIA Technology Solutions
Jul 14, 2020
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  • Three Themes to Keep in Mind When Looking for Matching Solutions

In our first healthcare data matching blog, Three Data Matching Challenges in Healthcare , we talked about how healthcare provider (HCP) and healthcare organization (HCO) data matching are particularly challenging when a unique identifier is not available. We discussed how problems with data cleanliness and completeness can arise, the ambiguity of data leading to false positives, and the lack of appropriate tools to help matching in this specific context.

In this blog, let’s explore some of the most common themes that are overlooked when considering matching solution.

Can the matching tool accept messy data?

When the data you need to integrate originates from another department or from a system that was not intended for matching, a cleaning and reformatting data process may be needed. Ideally, the matching tool is built with data preparation (data staging) features that can be customized and automated. These pre-processing features need to include:

  • The ability to remove or rearrange information within and between fields in a record. For example, splitting first name and last name from one field into two separate fields in order to match their corresponding fields in the reference data.

In addition, data staging/pre-processing features should be built with appropriate rules and understanding of the reference and context of the input data.

How well does the tool deal with healthcare data complexities?

Without a good knowledge of the healthcare landscape and lexicon, matching exercises can lead to all sorts of ambiguous results. When considering a matching tool, you should ask:

  • Can the tool logically display multiple addresses that can be associated with an individual? For example, a doctor who practices in multiple locations or a pharmacist who works in more than one pharmacy. Can it then complete an HCP match to an individual person, regardless of location affiliation?
  • Can the tool be configured to understand the relevance of an individual field or group of fields (within the healthcare context) to judge if a match is weak or strong? For example, different address fields, combined, define a specific geographic location. If you matched on postal code, then added on scores for the other address fields, you overweight the score for the match.
  • Does the tool include measures to reduce false positives? For example, value-based weight scaling which not only considers the match but assigns a score based on the relative frequency of the matched value.
  • Does the tool handle matching entries in multiple languages? In Canada, organization names, addresses, and any labels can be in either official language. For example, St-John boulevard is the same as Boulevard St-Jean.

Was the tool designed with healthcare data knowledge as part of its DNA?

Choosing a tool that has not been designed with the main goal of matching healthcare data is like choosing a fork to eat soup . Here are some questions to keep in mind when choosing a solution for your organization:

  • Does the tool’s interface take into consideration healthcare business processes? For example, a workflow process that can assign the decision on the validity of a match to the correct individual (i.e. a field person or external validation services).
  • Can I see all the record attributes including affiliations, non-primary fields (e.g. other specialties and interests, graduation data, etc.), and anything that adds to the identification of an individual or organization, even if they are not necessarily fields used for matching?
  • Healthcare data is being updated constantly, so does the tool automatically sync all reference changes to help keep the data current?

Some other useful features, although not necessarily healthcare specific, include:

  • The ability to enter a value for an input field to search for a match without changing the original field value. For example, to correct an obvious spelling mistake.
  • The ability for the tool to remember past matches so that the same record does not have to be re-evaluated each time, saving time and effort.

Unique approach to overcome healthcare data matching challenges

When we worked with clients on data management projects, we found clients struggling with these same issues. In order to help our clients and our own teams, we developed a tool that could be automated to handle the complexities of healthcare specific data. We created this tool to handle ad hoc -- or on-demand -- matching requests. We architected this tool to be quick, easy to use, intuitive, and to not require programming knowledge or extensive training. We ensured the tool will keep track of the records matched and allow a data steward to review ambiguous records. And most importantly, considering our small and mid -sized clients, we built a tool that did not require a huge investment

IQVIA's On-demand Matching offers a nimble, affordable and healthcare-specific solution to solve your data matching challenges.

To get more information on our unique approach to overcome healthcare data matching challenges, view the On-demand Matching video available on our website now or contact Canadainfo@iqvia.com.

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