The Dangers of Data Lock-In: Complexity

The complexities surrounding health data management are becoming increasingly apparent. As enterprise health data engagements expand in size and sophistication, integrating hospital systems and regional health initiatives often presents significant challenges. While the ambition to create cost-effective, enterprise-wide, and cross-regional benefits for patients is noble, it is frequently hindered by the intricate web of legacy data systems and vendor-specific silos that dominate most healthcare organizations.

Data lock-in occurs when organizations become dependent on specific technologies or vendors, making migrating to newer, more efficient and feature-rich systems more difficult. As hospitals strive to migrate data from older, fragmented, or proprietary systems into more modern repositories, they typically encounter many challenges. The desire to consolidate and improve patient data accessibility is met with the harsh reality that extracting, harmonizing, and verifying data can feel overwhelming. Stakeholders may push projects forward, yet without effective solution architecting, they often must delay critical advancements, deferring essential changes in hopes of a simpler method becoming available in the future.

The challenges of health data lock-in can be distilled into three primary issues: the "how," "where," and "who" of data access, location, and source of truth, respectively. In an enterprise healthcare environment with multiple independent systems, inconsistencies in patient identifiers pose a significant hurdle. This is especially true when the enterprise level may be state or provincewide, nationwide, or even across international borders. Different hospitals may utilize varying formats for patient identifiers, differing body part designators, and study descriptors, leading to confusion when linking records across systems. This lack of standardization can result in orphan studies and duplicate data, ultimately leading to delays or outright cancellations of upgrade and replacement projects.

Determining where the definitive source of truth for each patient dataset is crucial. For example, suppose one facility's radiology system contains the medical images for a patient study, but annotations and reports are created during a multi-disciplinary team (MDT) meeting. In this case, these new objects may not make their way back to the originating system or to a linked regional vendor neutral archive (VNA). The situation becomes even more concerning when some systems are not receiving demographic updates, such as via an HL7 feed, leaving stale data inside some systems but not others.

As the number of imaging and ancillary studies within enterprises runs into tens or hundreds of millions, the alignment and correction of these discrepancies becomes arduous and can lead to decisions to "park" unclean data outside of standard retrieval workflow. The absence of a clear source of truth within these parked "dirty" studies can lead to an eroded trust in the entire data management process.

Understanding where the underlying raw data resides is essential for successful extraction and integration. Each study—whether an MR scan, digital pathology slide, mammogram, ECG, or lab report—may have its own repository, often within a siloed system such as a Picture Archiving and Communication System (PACS), Anatomical Pathology Laboratory System (APLIS), Laboratory Information Management System (LIMS), Radiology Information System (RIS) or Vendor Neutral Archive (VNA). The complexities of accessing and managing this raw data can create significant barriers to successful data integration and movement to new solutions.

With the advent of the VNA, one might assume that issues surrounding the source of truth would dissipate. However, this is not always true. VNAs promise to streamline data storage and access, but without proper architecture and methods for data synchronization with originating systems, they can inadvertently add to the confusion. Standards like DICOM Image Object Change Management (IOCM) and HL7 demographics update messages are essential for ensuring data remains accurate and up to date. Yet, without these protocols adequately implemented, the complexity only intensifies.

Each PACS, VNA, or LIMS creates its own database, leading to an internalized copy of the patient demographics, possibly superseding the underlying stored files, which are usually not updated after being stored within their respective storage silos. As discrepancies arise, healthcare providers may feel overwhelmed by the lack of simple methods for data correction. This complexity contributes to data lock-in, as organizations remain tethered to their existing systems, hesitant to pursue more modern solutions that promise improved efficiency and outcomes.

To combat the dangers of health data lock-in, healthcare organizations must prioritize several strategies. These include having a complete and comprehensive understanding of all components within their enterprise and cloud environments. Furthermore, this means knowing who and what device are responsible for each point at which patient data is stored and ensuring that access to those sources (whether databases, storage devices, or cloud installations) has not been exclusively handed over to vendors without procedures and contractual methods for gaining access when upgrading or leaving a vendor. In addition, implementing robust data governance frameworks that define the source of truth for patient datasets is essential, leveraging standards like IOCM and HL7 for ongoing data synchronization. Lastly, equipping staff with the tools to navigate these complex systems is crucial.

As healthcare organizations continue to pursue the integration of their systems for better patient outcomes and cost savings, they must confront the dangers of health data lock-in resulting from the complexity of their disparate systems. By addressing the triad of challenges—how (data access), where (data location), and who (data source of truth)—healthcare leaders can pave the way for a more efficient, interoperable future. The journey may be complex, but with commitment and strategic action, it is possible to break free from the shackles of outdated systems and embrace a modernized approach to health data management.