Electronic Health Reporter

Merger and acquisition (M&A) activity continues at a rapid pace, posing a risk to data integrity. As impacted hospitals and health systems seek to consolidate their operations and technologies, duplicate and crossover records surge.  While these errors present immediate challenges, the longer-term concern lies in maintaining the accuracy and integrity of patient data across newly merged systems.

We sat down with Harris Data Integrity Solutions’ executive vice president, Rachel Podczervinski, MS, RHIA, and director of industry relations, Julie A. Pursley, MSHI, RHIA, CHDA, FAHIMA, for an in-depth exploration of the obstacles confronting those tasked with maintaining the accuracy of patient data in a rapidly consolidating healthcare environment.

Electronic Health Reporter (EHR): What are the key components of and best practices for data conversion planning during M&A processes, particularly concerning the Master Patient/Person Index?

A critical component is the meticulous analysis and documentation of an MPI’s “current state” and the envisioned “future state.” This involves a thorough review of database structures for both existing and forthcoming systems, as well as the assessment of current and future medical record numbers (MRNs).

Additionally, engaging key stakeholders is vital for developing a comprehensive strategy that addresses the diverse needs of the organization. Selecting the right tools for duplicate and crossover remediation helps ensure accuracy and integrity throughout the MPI management process. Clear MPI data extract specifications are essential for capturing all available identifiers for each patient from the system. Finally, conducting a frequency analysis on key demographic data fields can uncover patterns and outliers, reveal the structure of MRNs across facilities, and highlight any structural adjustments needed for the new system.

For testing and validation, verify the accuracy of the extract by cross-referencing patient information and conducting targeted spot checks. Ensure that accounts marked for retirement are excluded from the extract to prevent duplicates from being created during subsequent analysis. This reduces the workload for health information teams and maintains data integrity throughout the extraction process.

Develop strategies to manage duplicate records, safeguarding data accuracy and integrity.  Establish clear protocols and guidelines for resolving duplicates and reconciling crossovers. Finally, define a threshold for acceptable error rates and allow sufficient time to rectify errors before that threshold is reached.

Several best practices can be used to ensure seamless integration:

• Prioritization – evaluate match criteria, such as weights, to allocate resources efficiently for duplicate pair resolution.
• Algorithm optimization – collaborate with the technical team to better understand how potential duplicates are identified and explore opportunities to refine reports.
• Audit MPI data – assess the MPI’s current health to identify areas for improvement, whether through retraining, enhanced processes, or enterprise-wide standards.
• Identify external resources – many tools can help design improvement strategies, including resources from AHIMA component associations, Project US@ and its companion guide, AHIMA’s naming policy, etc.
• Involve the registration team – establish feedback loops and improve training materials to reinforce their critical role in MPI management and organizational success.

M&A activity inherently increases the risk of disrupting the integrity of patient data as organizations merge disparate systems. Strategic planning and best practices that focus on aligning people, processes, and technology can mitigate these risks and help navigate the intricacies of pre- and post-merger MPI management with confidence and effectiveness.

EHR: Why are the Caring Algorithm and the Humans-in-the-Loop model essential aspects of a patient data integrity strategy, particularly during M&A activity?

Caring Algorithms adhere to an AI governance framework that prioritizes safeguards and promotes ethical usage while accurately identifying individuals and supporting fair and unbiased identity decisions across diverse patient populations. Importantly, Caring Algorithms incorporate a human-in-the-loop review mechanism for those matches where the algorithm is not 100% certain. Doing so acknowledges both the limitations of automated algorithms and the potential for automation to impact safety and care coordination by introducing gaps in patient identification.

Ideally, the human-in-the-loop review leverages a variety of tools beyond the matching algorithm to validate discrepancies. These include rules targeting specific matching elements, data standardization tools, and third-party resources that supply historical demographics such as names, addresses, and phone numbers from credit institutions and public utilities.

Harris Data Integrity Solutions (HDI) recently analyzed 137,080 pairs (two patient records) of potential duplicates. What we found highlights how initial decisions made by third-party data can change when a human-in-the-loop review is incorporated into the workflow.

• HDI changed the third-party remediation decision in 9.1% of the pairs.
• Of “yes” decisions, 7.2% required changes, as did 2% of “no” decisions.
• Not changing the third-party decision would have created 512 (0.4%) overlays.
• Changes from “no” to “yes” involved 2,490 pairs (1.8%).

These findings clearly indicate that the presence of both Caring Algorithms and a human-in-the-loop oversight mechanism is vital to restoring and retaining data integrity before, during, and after a merger.

EHR: What role do information technology professionals play in properly leveraging AI to resolve duplicate records during M&A activities and mitigate its impact on data integrity?

Automation can reduce the need for human intervention, but it cannot completely replace it.  Without clear boundaries, governance, and safeguards, AI’s limitations can create gaps that require human review and intervention.  While humans are responsible for many—but not all—patient identification errors, they are essential to identifying, verifying, and correcting them.

AI-enabled technologies such as EMPIs with advanced algorithms, biometrics, MLMs, and predictive analytics with augmented data are powerful but imperfect. They may overlook inconsistencies and cannot make contextual judgments and decisions based on nuanced considerations. These require judgment and decision-making, creativity, innovation, and agility, as well as emotional intelligence and empathy—decidedly human qualities that are critical to handling the complexity of patient data.

EHR: How do health information professionals contribute to navigating complexities such as person matching, error management, and collaboration with registration partners during M&A?

A critical role of health information professionals is managing the accuracy and accessibility of patient data across multiple systems, making them vital to successfully navigating the complexities of maintaining data integrity during M&A. Accurate patient identification ensures that health data seamlessly follows the patient across the continuum of care post-merger. Health information professionals are responsible for establishing standardized data capture practices and training staff to ensure that these standards are consistently maintained.

They also safeguard the ongoing integrity of the EMPI, enabling seamless information sharing across systems, a capability that is especially critical for large, multi-facility health systems. As consolidation accelerates across the healthcare industry, health information professionals will continue playing a central role in maintaining data integrity and ensuring that accurate patient information is available whenever and wherever it’s needed across the continuum of care.

EHR: Where is the industry with patient matching IDs? Any movement? Any hope?

While there is no federal movement toward implementing a unique patient identifier (UPI) in the U.S., Congress has introduced bipartisan legislation with the Patient Matching and Transparency in Certified Health IT (MATCH IT) Act of 2025. This bill aims to improve patient safety and privacy by decreasing patient misidentification while promoting interoperability.

AHIMA updated and launched the Naming Policy Framework 2023: Enhancing Person Matching With Essential Demographic Data Elements to help capture standardized data and assist in identifying patients in health IT systems. A national workgroup reconvened this year to update the resource, providing a one-of-a-kind standard in the industry due to the lack of a national patient identification and matching strategy.

Other initiatives are also advancing patient identification. Patient ID Now released a framework for a national strategy for effective patient identification and matching and continues working to remove legislative barriers that hinder the exploration of a unique patient identifier. Additionally, Project US@ published a technical specification for collecting patient addresses, supported by a companion guide from AHIMA that provides operational guidance and best practices.

EHR: What are some of the things that inspire you most about where the industry is going long term, based on what you’re seeing through your work?

We are inspired every day by the opportunity to work alongside exceptional health information professionals, including those on the HDI team and within client organizations and professional associations. Their dedication to safeguarding data integrity directly influences the quality of care delivered to our patients, our loved ones, and ourselves.