Healthcare Data and the Fuzzy Matching Challenge

In today’s data driven world, it is often necessary to link people from one database to another. The challenge is especially important in the healthcare field. Our mobile society has changed the delivery of healthcare and many people choose to get their healthcare delivered from a multitude of providers, usually dictated by their work and travel schedules. Long gone are the days of an individual or family having one physician and one healthcare facility as their lone source of treatment. Physicians are shopped and chosen by referral and online reviews.

Fuzzy Matching and Healthcare Data

Healthcare facilities deliver more services in outpatient clinics and attract patients with unique options and special pricing. Patients now may have a roster of providers for their care and utilize each one. This brings special challenges to the surface when that patient has a significant health event and the previous treatment profiles could be crucial to a physician dealing with their current situation. This lack of coordinated information could delay or prevent information being available to an attending physician, thus delaying accurate diagnosis and treatment in a timely fashion.

 

Healthcare is constrained in this manner by two major considerations. The first challenge is patient privacy. Providers are restricted or limited in sharing patient information without proper releases. The second challenge is that healthcare networks don’t talk with each other, especially if they are competing providers. Those two major factors present significant factors in timely and accurate diagnosis and treatment. To further complicate an already difficult situation, standardization in data entry and coding are difficult to achieve, even in the most ideal situations. Fuzzy matching techniques help to automate the process of linking people based on the closeness when unique identifiers don’t exist. Aside from basic data quality issues, it is entirely possible for patients to have identical data in multiple databases and still not be the same patient. Using proper fuzzy matching techniques, there is the ability to automate the process of closeness when unique identifiers don’t exist.

 

Having an accurate and efficient system for fuzzy matching data is crucial to resolving the challenges of healthcare data. It is crucial that organization and providers continue to address these issues, to ensure the highest quality and timely delivery of care to the patient. It is possible to provide patient privacy and quality care across a multitude of provider networks. The future of healthcare and patients demand the attention.

Data Ladder is the premier partner for healthcare and healthcare research facilities to provide the appropriate solutions to their challenges with data quality, record linkage, and fuzzy matching. In fact, an independent study was done by an independent organization. The study found that Data Ladder’s DataMatch software outperformed several major providers such as IBM and SAS.

Speed, accuracy and value are just some of the qualities you get when utilizing DataMatch from Data Ladder.