M&A and Affected person Information Integrity: An interview with Rachel Podczervinski and Julie Pursley of Harris Information Integrity Options

Merger and acquisition (M&A) exercise continues at a speedy tempo, posing a danger to information integrity. As impacted hospitals and well being programs search to consolidate their operations and applied sciences, duplicate and crossover data surge.  Whereas these errors current quick challenges, the longer-term concern lies in sustaining the accuracy and integrity of affected person information throughout newly merged programs.

We sat down with Harris Information Integrity Options’ govt vice chairman, Rachel Podczervinski, MS, RHIA, and director of trade relations, Julie A. Pursley, MSHI, RHIA, CHDA, FAHIMA, for an in-depth exploration of the obstacles confronting these tasked with sustaining the accuracy of affected person information in a quickly consolidating healthcare atmosphere.

Digital Well being Reporter (EHR): What are the important thing elements of and greatest practices for information conversion planning throughout M&A processes, notably regarding the Grasp Affected person/Individual Index?

A crucial element is the meticulous evaluation and documentation of an MPI’s “present state” and the envisioned “future state.” This includes a radical assessment of database constructions for each current and forthcoming programs, in addition to the evaluation of present and future medical report numbers (MRNs).

Moreover, participating key stakeholders is significant for creating a complete technique that addresses the various wants of the group. Deciding on the appropriate instruments for duplicate and crossover remediation helps guarantee accuracy and integrity all through the MPI administration course of. Clear MPI information extract specs are important for capturing all out there identifiers for every affected person from the system. Lastly, conducting a frequency evaluation on key demographic information fields can uncover patterns and outliers, reveal the construction of MRNs throughout amenities, and spotlight any structural changes wanted for the brand new system.

For testing and validation, confirm the accuracy of the extract by cross-referencing affected person data and conducting focused spot checks. Make sure that accounts marked for retirement are excluded from the extract to forestall duplicates from being created throughout subsequent evaluation. This reduces the workload for well being data groups and maintains information integrity all through the extraction course of.

Develop methods to handle duplicate data, safeguarding information accuracy and integrity.  Set up clear protocols and pointers for resolving duplicates and reconciling crossovers. Lastly, outline a threshold for acceptable error charges and permit adequate time to rectify errors earlier than that threshold is reached.

A number of greatest practices can be utilized to make sure seamless integration:

• Prioritization – consider match standards, resembling weights, to allocate sources effectively for duplicate pair decision.
• Algorithm optimization – collaborate with the technical staff to raised perceive how potential duplicates are recognized and discover alternatives to refine stories.
• Audit MPI information – assess the MPI’s present well being to determine areas for enchancment, whether or not by way of retraining, enhanced processes, or enterprise-wide requirements.
• Determine exterior sources – many instruments may also help design enchancment methods, together with sources from AHIMA element associations, Mission US@ and its companion information, AHIMA’s naming coverage, and so on.
• Contain the registration staff – set up suggestions loops and enhance coaching supplies to strengthen their crucial function in MPI administration and organizational success.

M&A exercise inherently will increase the danger of disrupting the integrity of affected person information as organizations merge disparate programs. Strategic planning and greatest practices that target aligning folks, processes, and know-how can mitigate these dangers and assist navigate the intricacies of pre- and post-merger MPI administration with confidence and effectiveness.

EHR: Why are the Caring Algorithm and the People-in-the-Loop mannequin important facets of a affected person information integrity technique, notably throughout M&A exercise?

Caring Algorithms adhere to an AI governance framework that prioritizes safeguards and promotes moral utilization whereas precisely figuring out people and supporting honest and unbiased identification selections throughout numerous affected person populations. Importantly, Caring Algorithms incorporate a human-in-the-loop assessment mechanism for these matches the place the algorithm shouldn’t be 100% sure. Doing so acknowledges each the constraints of automated algorithms and the potential for automation to influence security and care coordination by introducing gaps in affected person identification.

Ideally, the human-in-the-loop assessment leverages a wide range of instruments past the matching algorithm to validate discrepancies. These embrace guidelines concentrating on particular matching components, information standardization instruments, and third-party sources that offer historic demographics resembling names, addresses, and cellphone numbers from credit score establishments and public utilities.

Harris Information Integrity Options (HDI) not too long ago analyzed 137,080 pairs (two affected person data) of potential duplicates. What we discovered highlights how preliminary selections made by third-party information can change when a human-in-the-loop assessment is integrated into the workflow.

• HDI modified the third-party remediation choice in 9.1% of the pairs.
• Of “sure” selections, 7.2% required modifications, as did 2% of “no” selections.
• Not altering the third-party choice would have created 512 (0.4%) overlays.
• Adjustments from “no” to “sure” concerned 2,490 pairs (1.8%).

These findings clearly point out that the presence of each Caring Algorithms and a human-in-the-loop oversight mechanism is significant to restoring and retaining information integrity earlier than, throughout, and after a merger.

EHR: What function do data know-how professionals play in correctly leveraging AI to resolve duplicate data throughout M&A actions and mitigate its influence on information integrity?

Automation can cut back the necessity for human intervention, however it can not fully exchange it.  With out clear boundaries, governance, and safeguards, AI’s limitations can create gaps that require human assessment and intervention.  Whereas people are answerable for many—however not all—affected person identification errors, they’re important to figuring out, verifying, and correcting them.

AI-enabled applied sciences resembling EMPIs with superior algorithms, biometrics, MLMs, and predictive analytics with augmented information are highly effective however imperfect. They could overlook inconsistencies and can’t make contextual judgments and selections primarily based on nuanced concerns. These require judgment and decision-making, creativity, innovation, and agility, in addition to emotional intelligence and empathy—decidedly human qualities which might be crucial to dealing with the complexity of affected person information.

EHR: How do well being data professionals contribute to navigating complexities resembling particular person matching, error administration, and collaboration with registration companions throughout M&A?

A crucial function of well being data professionals is managing the accuracy and accessibility of affected person information throughout a number of programs, making them very important to efficiently navigating the complexities of sustaining information integrity throughout M&A. Correct affected person identification ensures that well being information seamlessly follows the affected person throughout the continuum of care post-merger. Well being data professionals are answerable for establishing standardized information seize practices and coaching employees to make sure that these requirements are persistently maintained.

In addition they safeguard the continuing integrity of the EMPI, enabling seamless data sharing throughout programs, a functionality that’s particularly crucial for giant, multi-facility well being programs. As consolidation accelerates throughout the healthcare trade, well being data professionals will proceed enjoying a central function in sustaining information integrity and making certain that correct affected person data is accessible every time and wherever it’s wanted throughout the continuum of care.

EHR: The place is the trade with affected person matching IDs? Any motion? Any hope?

Whereas there is no such thing as a federal motion towards implementing a novel affected person identifier (UPI) within the U.S., Congress has launched bipartisan laws with the Affected person Matching and Transparency in Licensed Well being IT (MATCH IT) Act of 2025. This invoice goals to enhance affected person security and privateness by lowering affected person misidentification whereas selling interoperability.

AHIMA up to date and launched the Naming Coverage Framework 2023: Enhancing Individual Matching With Important Demographic Information Components to assist seize standardized information and help in figuring out sufferers in well being IT programs. A nationwide workgroup reconvened this yr to replace the useful resource, offering a one-of-a-kind normal within the trade because of the lack of a nationwide affected person identification and matching technique.

Different initiatives are additionally advancing affected person identification. Affected person ID Now launched a framework for a nationwide technique for efficient affected person identification and matching and continues working to take away legislative limitations that hinder the exploration of a novel affected person identifier. Moreover, Mission US@ printed a technical specification for amassing affected person addresses, supported by a companion information from AHIMA that gives operational steering and greatest practices.

EHR: What are among the issues that encourage you most about the place the trade goes long run, primarily based on what you’re seeing by way of your work?

We’re impressed day by day by the chance to work alongside distinctive well being data professionals, together with these on the HDI staff and inside consumer organizations {and professional} associations. Their dedication to safeguarding information integrity immediately influences the standard of care delivered to our sufferers, our family members, and ourselves.