The ONC Interoperability Vision: An Opinion

The Office of the National Coordinator for Health Information Technology recently issued a “10-year vision” paper on interoperability in the HIT infrastructure[1]. The 10 years are broken up into three different time periods:

•        3-year agenda: Send receive, find & use health information to improve health care quality 

This set of goals is the primary interoperability functionality proposed by the ONC & is focused around the development of “an interoperability roadmap as articulated in HHS Principles and Strategy for Accelerating Health Information Exchange”[2]. This second document emphasizes several tactics for accelerating the use of HIE including: use of DIRECT & development of appropriate Stage 2 & Stage 3 Meaningful Use criteria, developing certification for HIE interoperability & a focus on security & privacy. This is all very well, but there are a number of issues I’d like to point out. First, the emphasis appears to be providing “interoperability” through HIE. the fact is that HIEs are having a hard time developing sustainable business models (regulatory compliance for interoperability is probably not a sustainable model, especially with no Federal money available for it),  & most of them are having trouble exchanging anything but the simplest data. Interoperability is usually provided through standardization of APIs (or other exchange mechanisms) across application boundaries. How does HIE-based interoperability work where healthcare organizations do not participate in an HIE & their EHRs are not interoperable? It seems that broader approach may be necessary.

Second, my personal experience with this approach to interoperability spans about 25 years of system development & includes participation in many standards efforts. Perhaps my most telling experience of defining standards-based interoperability came when I was Digital Equipment’s representative to the OMG effort to define interoperability among (CORBA-based) object systems. The representative from Sun Microsystem, with whom I had been arguing for about 3 months, finally proposed that interoperability would be served if my system sent his system a message & his system sent my system back an error message. This is where I feel we currently are with healthcare information interoperability.

As an example, a paper recently published (3 July 2014) in the Journal of the American Medical Informatics Association[3] looked at interoperability of Stage 2 Meaningful Use certified EHRs as the ability to exchange C-CDA documents (a Stage 2 requirement). In 91 cases, a total of 615 mistakes were found, many of which would have affected the quality of care. These included: incorrect name of medication, incorrect dosage amounts, incorrect dosage units, & incomplete references to narrative text, among others.

Finally, what about improving health care quality. The ONC’s document states: “we will work with federal and state entities to advance payment, policy, and programmatic levers that encourage use of this information in a manner that supports care delivery reform, improves quality, and lowers costs.” This seems appropriately ambiguous & difficult given the current policy & political environment.

•         6-year agenda: Use information to improve health care quality & lower cost

The ONC’s description of this time period describes a large variety of data aggregations becoming available for use by individual providers & healthcare organizations to use analytics to improve quality & lower cost. I think that this is part of the solution for these goals, an important part, but there are other uses of this data than developing new quality measurements & payment models, as important as these might be. This past April, I wrote a post entitled Re-engineering Healthcare: The View from Other Industries (4 April 2014, http://posttechnical.blogspot.com/2014/04/reengineering-healthcare-view-from.html). In this post I emphasized that industries such as Auto Manufacturing, Aerospace & Information had used multisource data aggregations as part of a re-engineering effort that focused on the redesign of workflows & work processes & the more efficient & effective use of R&D resources. This work has yet to be done in any realistic way in healthcare (other the workflow redesign necessary for EHR use, & it’s not clear how efficient & effective that is with respect to outcome improvement or cost reduction), & it will have to be done if quality & cost goals are to be met.

•         10-year agenda: The learning health system.

The ONC states that: “The evolution of standards, policies, and data infrastructure over the next 10 years will enable more standardized data collection, sharing, and aggregation for patient-centered outcomes research. Continuous learning and improvement will be feasible through analysis of aggregated data from a variety of sources.” This is a topic for another post. I spent about ten years working on issues of advanced reasoning & problem solving at Stanford & The Digital Equipment Corporation[4] & I’ll write (real soon now) on what I think a learning healthcare system would be like.

The ONC’s tactics for achieving this vision consists of five building blocks:

1.      Definition of core technical standards & functions
2.      Certification to support adoption & optimization of HIT products & services
3.      Privacy & security protections for health information
4.      Developing a supportive business, clinical, cultural & regulatory environment
5.      Rules of engagement & governance of HIE

Of these tactics, the first, second & fifth are (IMHO) complicated but feasible to achieve. The question is (as evidenced by the D’Amore paper & lots of HIE data), will the development of standards, certification & ROE for HIE actually improve interoperability. The answer is yes, over time – it’s the time element that’s the problem. 2-3 years of development & acclimatization would be annoying, but acceptable (maybe even optimistic). 5-8 years would mean that this approach would not be successful.

Tactic 3, privacy & security concerns are appropriate & inevitable. Balancing these concerns with the need for shared healthcare data for treatment, operational & research purposes has proven difficult & I don’t expect it will get easier as these regulations evolve in the next 2-5 years. One thing about this area is that people’s expectations are being set (& their resistance to various commercial & government tactics) by current practice including the NSA surveillance efforts, security & privacy concerns with current social media like Facebook, etc. & new interaction models on the web such as Snapchat. People are very aware of these issues, but developing healthcare systems that have appropriate function in these areas will take time (3-5 years)

Finally, Tactic 4 – working toward supportive environments… Listing all of the issues involved in this tactic would take pages & pages. Suffice to say that this will never be fully aligned with the ONC’s goals (again IMHO), but over time the edges will get chipped off so that interoperability may be possible.

Time seems to be the common theme here. I expect it will take 3-5 years of real effort to get to the point where these tactics bear fruit. The real question is in this political & cultural environment, do we have 3-5 years to evolve to an interoperable, more cost efficient & clinically effective healthcare system.

Next – What could a “learning healthcare system” look like? & a post on the tension between privacy & usage in healthcare systems

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[1] Connecting Health and Care for the Nation: A 10-Year Vision to Achieve an Interoperable Health IT Infrastructure. ONC. June 2014. http://healthit.gov/sites/default/files/ONC10yearInteroperabilityConceptPaper.pdf, accessed 25 June 2014.

[2] http://www.healthit.gov/sites/default/files/acceleratinghieprinciples_strategy.pdf, accessed 25 June 2014

[3] D’Amore, J.D. et al. 2014. Are Meaningful Use Stage 2 certified EHRs ready for

interoperability? Findings from the SMART C-CDA Collaborative. JAMIA. Published online: 0:1–9. doi:10.1136/amiajnl-2014-002883. Accessed 25 July 2014.

[4] c.f. Hartzband, D.J., L. Holly, and F.J. Maryanski. 1987. The provision of induction in data-model systems: I. Analogy. International Journal of Approximate Reasoning (IJAR) 1(1):1-17. &

Hartzband, D.J. 1987a. The provision of inductive problem solving and (some) analogic learning in model-based systems. Group for Artificial Intelligence and Learning (GRAIL), Knowledge Systems Laboratory. Stanford University. Stanford, CA, USA. 6/87.