One would be forgiven to think that a physician or administrator just finished listening to Billie Holiday’s rendition of Gloomy Sunday when the discussion of the current state of EMR interoperability comes up.
Many physicians, and not necessarily incorrectly, attribute some of the record-high level of physician burnout to EMRs and the myriad of issues that surround the products; likewise, administrators are often willing to express their frustration with growing IT budgets, increased stakeholder demands for interconnectivity, and a sometimes significant gap between promised product features and delivered results.
These issues are not, however, limited to the C-suite and physicians’ lounge; rather, interoperability problems make it much more difficult to migrate away from fee-for-service to value-based payment programs, it makes it much harder to increase patient satisfaction, it makes it harder to meaningfully implement PCMHs, and it makes it extremely difficult to implement extensive process improvement strategies such as LEAN to redesign healthcare.
Private and public payers as well are impacted by interoperability difficulties. Many health plans have alternative payment models such as accountable care organizations or bundled episode payments that they would like to see further implemented and refined.
Unfortunately, however, data exchange from both EMRs and health plan data needs to occur with a high degree of accuracy and at a reasonable cost to allow for the process improvement needed for alternative payment models to flourish and potentially further the Triple Aim.
Patients as well suffer from interoperability deficiencies. Everyone who has had to act as a caregiver for a child, parent, or other relative is aware of the frequency with which one must retell the patient’s story, correct outdated medication profiles, and shuffle papers in and out of a binder all to ensure that the loved one is provided with high quality care.
Meaningful Use, the growth of HIEs, and commitments from vendors were supposed to eliminate that. Patients, correctly, are frustrated that the credit bureaus can regurgitate their life’s story on-demand; however, the healthcare system – where errors could mean serious harm or death – is still reliant on scanned documents, fax machines, and repetitive patient/caregiver interviews.
The Legal/Regulatory Hurdles
While not alone among industries in living in an environment of dual federal and state regulations, EMR interoperability is uniquely challenged by it.
While HIPAA and other Federal regulations set a baseline for data sharing – e.g., HIPAA’s treatment, payment, and operations – states were free to establish stricter regulations to govern data sharing.
At a simple level this could just govern what methodology of consent is used in a state – if a state decided to set one for the jurisdiction as a whole. Some states have what is commonly termed as an informed opt-out which means that under many circumstances, a patient’s data can be shared without explicit consent although they must be given the option to opt-out.
Other jurisdictions are opt-in based which means that under most circumstances, a patient’s explicit authorization is required before data can be shared. These frameworks are further complicated by some regulations that govern unique forms of health information – e.g., abortions, HIV testing/status, mental health information, and substance abuse data.
Vendors must contend with this complex regulatory framework when developing interoperability solutions. In some cases, they must have the capability to opt a patient out of data sharing, and in other instances, they must have the capacity to opt patients in.
Further complexity must be introduced if EMRs must contend with substance abuse data regulated by SAMHSA. It is not just consent itself that must be programmatically added and maintained by EMRs, vendors may also need to invest resources in developing methods to tag data as being subject to additional consent requirements.
Outside of the EMR, integration engines and consent management systems must also then have accurate processes in place to properly adjudicate consent and process records appropriately. As identity theft and other cyber crimes continue to cause anxiety among the public, there will likely also be additional pressures to empower patients to exercise more volition in the exchange of their healthcare data.
Solutions may be driven down to the patient portal level or, possibly, new applications or systems will need to be developed to address this challenge.
Patient matching is a problem that lurks behind almost every interoperability or integration initiative – especially projects involving multiple entities.
Health Systems likely have a master person indexing (MPI) system that can be referenced; whereas, disparate entities likely have no common source of truth for identifying users.
That said, even health systems have matching issues. One relatively recent study found that eight to twelve percent of the records in any given health system are duplicates.
Another study found that in four percent of cases where duplicate records exists, patient care was negatively impacted.
The United States lacks a national identifier and that is likely to continue given the significant influence of concerned privacy and civil libertarian groups in the country; without such an identifier, matching depends on a variety of demographic data points that must, to varying degrees, be synchronized between organizations for reliable and consistent matches to occur.
In addition to cleaner, more consistent data, existing matching algorithms must be refined, and new ones may possibly need to be developed. EMRs and other healthcare software systems must then either make use of robust master person indexing systems or implement their own within their system to allow for the matching to occur.
Without strong and reliable matching processes in place, regardless of the protocols and clinical data that vendors allow to be exchange easily from their system, meaningful interoperability will not occur because data quality errors will negate any of the benefits.
EMRs require that clinic or hospital personnel regularly review and manually certain sets of records due to deficient matching algorithms and data quality issues.
Systems must also have mechanisms in place to mitigate problems caused by human error such as transposing characters, the use of shortened first names, and inconsistent use of hyphens in hyphenated last names.
Additionally, external data sources may also need to be more frequently accessed to verify demographic data to ensure consistent matching. This is already done with some MPI systems, and it is a frequent occurrence outside of healthcare in personal financial areas such as when generating a person’s credit report from one of the credit bureaus.
Information blocking is when the exchange of healthcare information is unreasonably constrained. That is not to say that whenever access to information is disallowed that information blocking has occurred; for example, there may be perfectly good security reasons to disallow some integrations if a product has known security concerns, or there may be state-level regulations that make certain forms of data sharing exceedingly difficult.
The kind of information blocking maligned is more deliberate, and it is driven by incentive models the EMR vendors exist within and by the business interests of various healthcare organizations – e.g., competing healthcare systems. Misunderstandings of HIPAA’s privacy requirements sometimes also contribute to information blocking.
The Federal Government chose to implement a largely, albeit regulated, market-based framework for pressure the healthcare industry to adopt information technology solutions at a widespread level, and the single biggest actors in this market are EMR vendors and EMR users.
Through Meaningful Use certification, the Federal Government provided some emphasis on data exchange; nevertheless, there were other priorities. EMR vendors, therefore, worked to meet the Meaningful Use requirements and didn’t emphasize easy data exchange as there is the potential that it would make it easier for providers to leave their platform and a large percentage of EMR vendors’ revenue comes from license fees.
The market rules set by the government coupled with the incentive structure EMR vendors exist within creates a system that doesn’t necessarily encourage significant interoperability and data exchange.
Furthermore, there have been significant complaints from providers that interface costs are too high and make meaningful, consistent interoperability prohibitively costly for providers and health systems to implement.
Coupling the EMR vendor market incentive structure with anti-competitive behavior by some providers and health systems has created a structure where the contributions from healthcare IT in general and EMRs specifically are well below their potential.
Where to go from here?
Not everything is bleak. On all these fronts, there has either been significant progress already or initiatives that seek to work towards a solution.
In the legal/regulatory area, SAMHSA has taken steps to streamline patient consent for the healthcare data – notably substance abuse – that they regulate.
The ONC has also sponsored a competition to determine the best patient matching algorithms. EMR vendors have also committed to work against information blocking and facilitate interoperability and the ONC has begun to study market transparency in the health IT industry.
More significantly, in the CURES Act, the Federal Government has instructed its enforcement agencies to crackdown on information blocking and to assess significant fines for such behavior.
The CURES Act also created a structure to force EMR vendors to create functioning APIs or Application Programming Interfaces – to allow for further interoperability. As the push for interoperability and its benefits become more concrete for providers and health systems, there may be additional discussions about better standardizing data collection to allow for enhanced patient matching algorithms to work and about prioritizing interoperability when pressuring vendors to add new features.
The main actors that have – sometimes deliberately, sometimes inadvertently – led to the current lackluster state of EMR interoperability have appeared to learn lessons from the decisions that led to the current state of affairs and have, when able, taken steps to remedy them in the future.