Fall 2023

Inside Informatics: Effective IT and HIM Collaboration
By Richard Amelio and Todd Goughnour, RHIA, MBA
For The Record
Vol. 35 No. 4 P. 8

It Drives Success During Major System Change

Even the most prepared organizations encounter data integrity challenges following large IT change events. Whether facing conversion to a new EHR or upgrading to a new revenue cycle platform, cross-functional management is your first step for better data accuracy and long-term system success during technology shifts.

Data integrity challenges in health care are long standing and well documented. For decades, health IT and HIM professionals have struggled to ensure patient data are secure, complete, and accurate. Inspiring full confidence in the accuracy of every patient’s health information is health care data’s nirvana. And it was the promise of many EHRs. However, optimal data integrity is not our current state.

According to an early 2023 survey by Clinical Architecture, a health care IT solutions company, 69% of respondents said their organization’s data quality was either mixed or poor. The majority of those surveyed also reported that their organizations had limited time and resources to keep data clean.

While there are many reasons for inaccurate clinical data, missteps during major IT system change events remain a root cause for concern. Major system change events occur when organizations implement a new EHR, install significant upgrades to an existing revenue cycle platform, acquire new facilities, or retire a legacy application. During these events, organizations must build an effective data strategy, develop a rigorous best practice plan, correctly migrate information from legacy systems, and then validate transferred data within the new platform. Failure to do so places data integrity at risk and may compromise quality patient care.

This article provides an overview of common data integrity challenges experienced during major IT system change events. It also lays out effective best practice processes, procedures, and cross-functional collaboration to achieve an optimal state of quality data in health care.

Common Data Integrity Challenges During Major EHR Change
Significant data issues arise when organizations transition to new IT systems. This is a well-known reality in health care for several reasons. The following are the top five to consider:

• Millions of terabytes of data are transferred from old to new systems.

• Differences between the source system and new system capabilities affect the transfer of data.

• Duplication of data is common even within a single system. The problem is exacerbated when multiple systems are involved.

• Crossmatch analysis often reveals up to 25% duplication rates. Some duplicates may auto-resolve, but millions may remain.

• Clinical terminology and vernaculars vary greatly, causing a multitude of data inconsistencies.

Given these challenges and many more potential risks, it’s essential to plan early and build a strong IT and HIM team. By combining IT and HIM experience during system transitions, organizations improve their chances of achieving optimal data integrity and quality.

Advice for Your Cross-Functional Collaboration Team
It’s rare that a hospital or health system has enough in-house expertise to implement a new EHR or revenue cycle system without some level of external support. These change events are extensive, multiyear undertakings that require hundreds of resources. Therefore, specific effort should be made to increase team efficiency and make the most of all expert resources deployed.

Following are six questions to ask from both the IT and HIM perspective when building a cross-functional collaboration team:

• What systems have you worked with during prior data transfer projects—in both transferring inbound data and sharing outbound data?

• Do you have experience with master patient index remediation across systems?

• Do you have experience with all the other data that we plan to convert during this project, and can you help confirm their accuracy before conversion?

• Are you skilled at manual data abstraction in those cases where automated data transfer is not an option?

• What is your experience with document naming conventions to establish a go-forward record of truth in the new EHR or revenue cycle platform?

• What are your skills in managing data error fallout following new system conversion? This includes resolving duplication of records, manually converting information, and ensuring data accuracy from day one in the new system.

Furthermore, each team member contributes significantly to the outcome of the whole. For example, IT professionals approach major system change from a different perspective than do HIM experts. IT teams work to prove efficiency and reduce implementation costs, while HIM experts focus on data quality and integrity.

HIM Professionals Focus on Data Integrity for the Long Run
Since the entire organization will work based on new EHR information, an upfront focus on data quality and integrity is essential for long-term success. Short-term decisions made by IT or finance to cut implementation costs may jeopardize data quality for years ahead.

We have personally witnessed situations where millions of patient records are loaded into a new EHR without HIM oversight or regard for initial data quality. In these cases, data errors eventually occur, creating negative impact on end-user satisfaction and patient safety. And since errors can now be copied, pasted, shared, and duplicated within milliseconds through health information exchange, data cleanup becomes a herculean task.

Two essential best practices yield significant improvement in long-term data accuracy. First, transfer the most recent patient information and then conduct quality assurance reviews after data are loaded into the new system.

Use the patient’s most recent data. Pulling data from multiple clinics that have served a patient as opposed to information only from the patient’s most recent visit creates a multitude of downstream errors.

Conduct quality assurance. Validate data in the new system through a quality assurance process. This step is often ignored, leading to duplication of erroneous data and costly cleanup.

There’s not a single path for major EHR change events that suits everyone. Each organization must determine its comfort with the associated risks. However, as records are fully digitized, and HIM roles shift to technology-based functions, the case for including these professionals in IT transitions becomes clear to ensure data integrity in go-forward EHRs or revenue cycle systems.

Better Together for the Journey Ahead
Stronger collaboration between HIM and IT supports better data integrity for the future. As data continue to be shared and exchanged through health information exchanges, Qualified Health Information Networks, and other initiatives, a stronger focus on getting accurate information into the EHR from the start is essential. Using these practices also strongholds your organization’s cash flow during revenue cycle system upgrades and conversions to new platforms.

Prepare for the future by understanding new technologies and how they impact data integrity. Also, stay atop new industry initiatives and mandates for data exchange. By continually learning and sharing with each other, HIM and IT professionals can successfully collaborate to improve the accuracy of health care data for all.

— Rich Amelio is an IT executive with more than 15 years of experience. Prior to joining e4health in 2017, he developed his knowledge through various IT management and leadership roles in the greater Philadelphia area, most notably at Einstein Healthcare Network and Universal Health Services. Amelio can be reached at ramelio@e4.health.

— Todd Goughnour, RHIA, MBA, is an accomplished and proven health IT and HIM professional with a multitude of successful projects completed in a variety of scales and settings. He manages more than 200 staff members across 20 to 30 different clients and new IT system engagements. He can be reached at tgoughnour@e4.health.

 

Six Practical Ways HIM and IT Work Together During IT Change
• Develop new algorithms to identify low and high thresholds for duplicates.

• Establish automated workflows for HIM experts to review potential duplicates and
avoid “auto merging” of records.

• Use data analytics to qualify the percentage of data that is accurate vs inaccurate as a system go-live metric.

• Build a process for manually transferring any data that cannot be converted electronically.

• Incorporate Lean methodologies to optimize efficiency across the team.

• Tap clinicians as needed to verify decisions made by HIM and IT on clinical data discrepancies.