Most healthcare organizations have already managed the transition from paper to electronic medical records. It was time-consuming, challenging and nerve-wracking. But with the first-generation of EMRs approaching the end of their usefulness, with mergers and acquisitions requiring the adoption of new systems (most incompatible with the legacy systems), with the advent of new government standards, more rigorous legal requirements and higher expectations associated with “value-based care” and overall “population health,” the previous transition may seem almost quaint in its simplicity. Migrating health data from older EMRs to more comprehensive EMRs is a seemingly unending challenge for which there are no clear guidelines, well-accepted studies or consensual standards to facilitate the ease of change.
To try and make sense of this, healthcare organizations will sometimes simplify the question by making it a binary choice: abstraction of the data or full-scale digital conversion (data migration)?
Data conversion moves information existing in one format and exports it into another format, usually changing the way the information is presented without misinterpreting the practical meaning it had in the earlier format. The primary goal is mapping source data elements to their matches in the target system. This can be accomplished by a 1:1 match between dictionaries or data sets so that converted data appears nearly identical in the target systems. This is critical for the clinical impact and workflow integration required to support a discrete clinical data conversion.
But the underlying data models used by EMRs differ greatly from one another, and it’s not a simple matter of export/import. Instead, it’s a true ETL process – extract, transform, load.
Healthcare data abstraction, on the other hand, entails the manual review of the same data stored in the legacy system, sifting through it and determining which data is essential and which is not (“stare and compare”). In practice, unfortunately, abstraction is highly susceptible to human error, and the fidelity of the data abstracted may be suspect.
Generally, there is an inverse relationship between data migration and abstraction: data that is electronically converted does not need to be abstracted. But abstraction can serve as a substitute for programmatic data migration, often augmenting data migration efforts with specific data elements that are not part of the electronic conversion.
Even with a comprehensive electronic data conversion, clinicians frequently insist on having additional data abstracted manually into the new EHR because it can be needed to influence medical decision-making at the point of care or to trigger patient safety alerts such as active or standing orders, allergies, medication, immunization, scheduled appointments, problem lists, and patient histories.
For example, oncology clinics often need a manual abstraction of chemotherapy plans, while OB practices will have unique needs based on the kind of pregnancy care they provide. These data elements can be manually collected and transcribed into discrete fields in the new EHR to ensure availability in the system at go-live.
In short, one of the biggest challenges is defining a standard set of data that can be abstracted organization-wide while also ensuring that the abstraction plan addresses specialties with unique needs outside the standard set of data.
Consequently, we often must often weigh questions about the merits of abstracting data versus the value of a full-scale conversion. And the answer is never a simple choice of one over the other. Every organization has different needs and different timelines. Every organization has different business needs. And the choice between abstraction and conversion ought to focus on the cost and time-intensive nature of abstraction before adopting alternatives. Will it be possible for the organization to achieve its goals economically and on schedule?
Meanwhile, managing expectations with stakeholders is critical. Organizations need to carefully strike a balance between clinicians who want their patients’ entire record and the cost and time involved in manual chart abstraction. The amount and type of information that is abstracted depends on the scope and extent of the organization’s electronic data conversion efforts. The scope of the planning process may also involve identifying key paper-based clinical documents that will be scanned into the new system.
While manual data abstraction generally begins prior to go-live, with as much of the high priority data identified by the organization entered into the system as early as possible, it frequently becomes a routine part of patient care well after go-live as well. For example, organizations might use manual data abstraction to “prep” the charts populated through data migration for new patients, or to add outside information to an existing patient’s record.
Often, the combination of both data migration and abstraction represents the best approach. Most organizations will find it unfeasible to electronically migrate or convert all legacy data into the new EHR – particularly if the data is not stored in a standardized format. As previously mentioned, abstraction can be leveraged to supplement gaps in electronic data conversion due to data accessibility, accuracy, fidelity, or cost-effectiveness.
As with any large movement of data in healthcare IT, accuracy and integrity of the medical record is the essential pillar of a successful operation. We urge ample commitment of preparation and time well in advance of the conversion go-live. Business needs, especially those regarding staffing and time management, are paramount, particularly if abstraction is being considered as both an adjunct or standalone solution. Ultimately, it is imperative that organizations proactively and accurately forecast how much time and money they can commit to data migration. Those estimates may turn out to be the ultimate value when considering conversion and abstraction for data migrations.
About Justin Campell
Justin Campbell is the Vice President at Galen Healthcare, a professional services and solutions company providing IT consulting services including strategy, optimization, data migration, project management, and interoperability for specialty practices, hospitals, health information exchanges, health systems, and integrated delivery networks.