MPI Clean Up Across Hospitals

Last month we covered hospital merger and acquisition activity and IT implications. One consideration reviewed was the importance of MPI clean up around M&A and EHR integration. 

The sheer complexity of migrating millions of patient records from multiple EHRs into a new system should be a system-wide priority. A master patient index (MPI) clean up strategy to tackle all disparate systems and duplicate patient information across facilities is crucial. 

Intellis’ long-time partner T3K’s Brad Beavais has decades of experience with Epic installations and integrations. Here Brad shares his thoughts on MPI conversion.

Are there any misconceptions about MPI conversion?

Brad Beauvais: A lot of stakeholders outside of HIM don’t understand data that comes over in MPI conversion. But in fact, it’s the lifeblood of an entire patient population. If you need to manage a patient population, you need a good foundation. We can’t build on it, it needs to start off clean. If patient information is bad, we won’t know if a prior diagnosis is good or bad. For instance, if a system is treating 5-10M patients for pre congestive heart failure and 10% of patients have bad data, that’s 1M patients with bad information. Aside from credibility issues, there are a cascading set of events that have to be cleaned up as a result of bad data. However, if MPI is done correctly from the start, you mitigate and avoid several risks – reputational, organizational, and financial. A lot of people don’t think about it. Physician workflow is most important. If you don’t take those things into account, they will haunt you. 

We couldn’t agree with Brad more. MPI conversion and cleanup are ground zero to ensure a successful EHR implementation or upgrade. Here are three tips to ensure data integrity.

Preserve data integrity with MPI execution in three areas: 

  1. Resolve enterprise overlaps and same-source duplicates: This should be done in patient populations of each legacy system. Next, deploy referential matching technology to identify enterprise overlaps and match same-source facility duplicates. Finally, the resulting data should be used to verify each matched case, merge the records, and address the impacts on downstream systems. 
  2. Determine surviving records/targets: A surviving record/target is the designated “source of truth” into which all data for a patient is merged. First, consider leveraging a partner to build a customized merging algorithm using health system-defined parameters. The algorithm is based on a weighted record hierarchy. Factors used to determine the algorithm can be number of visits, most recent visit dates, oldest registration dates, and records that exist in specific downstream systems. Then, a highly trained team should use the algorithm’s specific enterprise and facility rules to determine target records.  
  3. Merge downstream systems: This effort should involve a detailed decision flow process to ensure that all downstream systems are accounted for and appropriately matched with the EHR and EMPI. Project leaders should consider each facility, each ancillary system within that facility, and whether the downstream system overlaps across multiple facilities within the enterprise. 

When done right, MPI clean up enhances patient identity integrity, care delivery, and revenue cycle activities. If you’ve got a project on the horizon or simply want to learn more, visit https://intellisiq.com/

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