Kno2 was recently published in Health Data Management with our five best practices for patient matching. The article begins by explaining that one of the great consequences of COVID-19 is a rapid shift to digital enablement. Healthcare providers had no choice but to move to contactless admissions and new patient onboarding, try to support e-case reporting, and look for ways to electronically share patient information — all in a rising virtual health environment.

“These new digital requirements, combined with staffing challenges, demand the need for more efficient processes. Interoperability and the ability to share critical patient data cannot be overlooked as an essential way to optimize the front office. Data sharing is then elevated when patient matching best practices are implemented.”

The article explains that while it may not be intuitive to providers, patient matching is really the first line of defense when it comes to interoperability.

There isn’t a magic cure

There are many supposed cures to the common patient record mismatch on the market. Examples provided include Enterprise Master Patient Index (EMPI), Record Location Service (RLS) and the idea of a National Patient Identifier. “The reality is while all of these solutions seem like magic bullets, they are not. Any would be a tremendous step towards interoperability, but providers need to pump the breaks before going down these rabbit holes.”

Slow your roll and implement these 5 patient matching best practices

Recognizing that patient matching will always be an imperfect science is critical.  We recommend the following best practices be implemented:

  1.  Train staff to be the frontline defenders of interoperability: Train your people and make sure they understand why accuracy in patient information is so critically important. Equip them with knowledge because algorithms only go so far.
  2. This magic question will improve your accuracy: Confirm accuracy of existing information by asking one simple question every time the patient enters your door: is your patient information still current and correct?
  3. Revisit and revise: Many approach patient matching implementations with a “set it and forget it mentality.” There is no solution in existence that magically fixes interoperability. If insufficient data and processes go into it, poor results will ensue. Thus, it is essential to revisit your algorithm to ensure it optimizes your patient matching automation.
  4. Stop before you start from scratch: Many providers feel they need to start from scratch, but before you pay a developer to build your algorithm, evaluate current technology. Many EHRs come with patient matching capabilities “out of the box” that you can simply enable and configure.
  5. Implement a process for unmatched records: Again, many EHRs come with capabilities that already have an exceptions report that lists any electronic patient document that could not be matched automatically. Ensure your staff knows to run this report and that accountability is assigned to team members to work it on a daily basis.

Read the full article

Author:  Matt Becker

This topic was also published by HIT Consultants and Health Tech HotSpot: