The ONC has proposed a
ten-year vision[1]
for interoperability in healthcare information technology that divides this
time into three periods. Years 1-3 are devoted to achieving technical
interoperability & sharing of healthcare information; while years 4-6 focus
on using this shared information to improve quality & lower cost. Years
7-10 are labeled the “learning health system” & described as “Individuals, care providers, public health (officials) and
researchers contribute information and learn from information shared across the
health IT ecosystem, with rapid advancement in methods for deriving meaning
from data without sharing PHI.[2]”
What “learn” might mean in this context is an interesting question… First,
let’s look at what the ONC appears to mean to by it, & then we’ll look more
broadly.
The ONC lists a number of characteristics of
the healthcare system during this timeframe (2021-2024) in no particular order:
- Enhanced healthcare information contribution & sharing across clinical (provider & patient), public health & research areas
- More functional technical tools available to apply to this data- search & visualization are examples
- General availability of “patient-centered” outcomes research results
- Continuous learning through predictive & retrospective analysis of aggregated data
- Availability of patient-specific clinical decision support taking into account the patient’s genetic profile, clinical history, local public health trends & relevant socio-cultural trends (social determinants)
- Improved public health surveillance integrated with point-of-care decision support
This is actually quite a good list, but much of it is either
already available or will be available in the near future (18-24 months). Let’s
review where (I think) we are with this list, & then let’s explore some of
the possibilities for a learning healthcare system in the ONC’s 10-year
timeframe. The issue is, as William Gibson famously observed, “The future is
already here, it’s just not evenly distributed”[3]
- Point 1 - Enhanced information contribution & sharing across different healthcare contexts, is what the ONC’s years 1-3 are about. A high level of data interoperability will allow contribution of information from a variety of sources, including patients, for a variety of purposes. Interoperability is a frustrating issue today, as many vendors can’t effectively share data across their own product lines. Hopefully this will change in the next three years. We have achieved high levels of data interoperability in other industries, & notwithstanding that many people working in healthcare believe its data to be substantially more complex & sensitive to error than data in, say… banking or aerospace design, I think that with a pragmatic approach, not just to standards & certification, but also to vendor architecture, API development & in the field data sharing, that we can achieve appropriate levels of interoperability in this timeframe.
- Point 2 - We have many advanced functional tools available today; we’re just not using them. This is often because HIT vendors are loath to integrate their systems (practice management, EHR, lab reporting etc.) with external tools, but prefer to develop tools themselves. This doesn’t always work for several reasons: the vendor may not have the necessary skill &/or resources to develop such tools, the vendor’s business model may not include such development, the vendor may have allocated this development to partners who have their own agenda & business model(s) & many other factors. There is another much larger issue; most of these HIT systems are architected on an enterprise model that is not as scalable or flexible as contemporary designs. As HIT products migrate to contemporary infrastructure (Hadoop, NoSQL etc.), interoperability & integration will become possible at larger & larger scale.
- Point 3 – The American Health Information Management Association provides a good introduction to the variety of healthcare research data already available[4]. In addition the Agency for Healthcare Research & Quality (AHRQ, HHS) & the Healthcare Information Management Systems Society (HIMSS) both make a good deal of data available. The Centers for Medicare & Medicaid has also recently made a substantial amount of claims & provider payment data available[5]. This trend will continue, especially as large healthcare organizations begin making public the results of analyses of ultra-large data sets (see immediately below).
- Points 4-5 – These points are linked, especially at the point-of-care. Continuous learning, in this context, is the ability to develop new knowledge & strategies for using that knowledge based on an understanding of current & previous results & information. Many systems currently perform retrospective (& in some cases predictive) analysis of large amounts of healthcare data to determine patterns in both clinical & operational areas for healthcare organizations (Point 4). When this type of analysis is done based on specific patient characteristics at the point-of-care, diagnosis & treatment planning can be based on the empirical data & learning is brought forward with each analysis (Point 5). Examples include:
o
Mayo
Clinic - AWARE “bedside consulting” system (5M patient records over 15
years)
o
Beth Israel
Deaconess Medical Center (Boston) – Clinical Query system (2.2M patient
records)
o
Kaiser
Permanente – Natural language query system (9.1M patient records over 10
years)
o
Partners
Healthcare (MA) – Queriable Inference Patient Dossier
o
IBM/Wellpoint
– “Dr. Watson”, deep understanding system applied to healthcare information
(cancer diagnosis)
- Point 6 – Systems today used analysis of regional to hyperlocal trends in disease patterns to characterize the public health context of specific locations. These analytic results can be combined with point-of-care recommendation systems to improve diagnosis & treatment. An example would be Google Flu Trends although there are many apps such as Healthify[6] that provide hyperlocal services recommendations based on EHR encounter information.
To summarize: current & near future HIT systems can
provide appropriate levels of data interoperability, new architectures &
tools are already making HIT & the analysis of HIT data much more scalable
& performant, large amounts of research data is already available, even to
patient & consumers, ultra-large scale pattern matching in healthcare data
sets can provide the basis for both continuous learning by systems & their
human users, this learning is already being applied to point-of-care
recommendation systems that draw from millions of patient records & finally
current & near future HIT systems are reporting large amounts of public
health data which is being analyzed to provide better understanding of large
scale health phenomenon & eventually integrated with point-of-care
recommendation systems.
OK – so what isn’t being done? & What could be done? One
major thing is that the more information you include in these analyses, the
better the results are, so a broader range of inputs should be included. Such
information streams as public social media, data on social determinants, even
online & conventional shopping data can be important in understanding a
person’s health profile. A recent story in Bloomberg Business Week[7]
described the use of credit card purchasing data to supplement providers
information about patient behavior – are you actually picking up your
prescriptions, buying a lot of junk food, shopping at Big & Tall etc.
Marketers use this kind of data routinely in other industries, so why not in
healthcare[8]?
There are almost an infinite number of information sources that could be used
productively, once the sociocultural issues are understood & ameliorated.
There are also new kinds of analysis that are being
developed. An example would be work at Oxford University[9]
where an algorithm analyzes ordinary photographs & can predict genetic
anomalies & diseases. Hundreds or more of such new uses of information are
being developed & will be available (& more evenly distributed) in the
near future.
But what about learning, I hear you say… A recent issue of Health Affairs was devoted to the theme
of “big data”. One of the articles reviewed work on a learning health system,
talked about impediments & made some predictions[10].
This work used the following definition of a rapid learning healthcare system, “a health system that learns as quickly as
possible about the best treatment for each patient—and delivers it. This kind
of system draws on a much faster knowledge production process: from discovery
science, to new therapies and clinical science that can inform personalized
medical care, to better-informed physicians and patients.” This idea of a rapid
learning health system was first proposed in 2007[11], &
the Rapid Learning Project & others have done a good deal of work, mostly
workshops & policy papers. As we have seen, however, this vision of deep
analytics applied at the point-of-care to diagnosis & treatment of
individual patients is already in place in a number of settings. This is a lot,
but a learning healthcare system has to be more than this.
As already stated,
learning can be thought of as “the ability to develop new knowledge
& strategies for using that knowledge based on an understanding of current
& previous results & information”. This ability is continuous &
ongoing, the implication for a healthcare system is that whenever an actor
(provider, patient, caregiver &c.) is using a part of the system, the
system is moderating the user’s context (usage) & anticipates what
information & analysis may be relevant. The system may then give the user
the opportunity to request this information; which can be diagnosis, treatment
suggestions, data on treatment, analysis of alternatives, public health
implications, information & recommendations on amelioration of social
determinants & many other possibilities. In order to do this, the system
would have to have access to a great many data sources as well as have deep
understanding, hypothesis testing & recommendation capability (in the Dr.
Watson mode) & an interface that allowed substantial interaction with the
user in a manner that was non-threatening & productive. In addition, the
system would serve as an information source & liaison for public health
& social systems as well as healthcare systems at other organizations (that
the user might be associated with). It might communicate with the user through
a variety of devices & in a context (app or portal) that they were used to.
We’re obviously not there yet.
Can we get there? I believe that we can, but we have to
focus. The we, here, is not only the producers of software & systems, but
providers, patients, caregivers & healthcare organizations (if corporations
can be people, so can healthcare organizations)[12]. Here is my (partial) list of what’s important:
- Facilitate real interoperability for healthcare systems – The development & adoption of standards does not automatically convey interoperability[13]. A lot of really hard work has to be done to ensure that even things like standard documents (like C-CDA) can be assimilated by multiple system & that the data, once imported, makes sense. This could easily take more than the three years the ONC has allowed.
- Develop learning in the healthcare context – Learning is not just analyzing ultra-large information “lakes” to do pattern matching & make diagnosis & treatment recommendations. It is creating new knowledge & new strategies for developing & using knowledge. In this sense, it is more like IBM’s Watson, that attempts a semantic understanding of material & then forms & tests hypotheses to answer questions about that material than it is like most of the point-of-care recommendation systems currently in use or under development. These systems do some form of pattern matching to an initial set of data about a patient, & if their information source is large enough, may discern patterns that can be translated into recommendations with a very high “probability” of relevance (if not correctness, based on the analyzed data).
An aside is relevant here. The current point-of-care systems
we are talking about are not conventional rule-based systems. They do not have
domain-specific heuristics about cancer diagnosis & therapy (as an example).
The heuristics that they have are about semantic normalization, general pattern
matching, visualization etc. They operate by taking input on a patient’s condition
& comparing that to (potentially) millions of patient records to determine
what the most effective diagnoses & treatment plans have been for those
specific inputs. Earlier “expert” systems operated quite differently by taking
the input on patient condition & executing a set (sometimes as large a set
as 10s-of-thousands) of domain-specific rules. These systems often had
relatively high percentages of effectiveness – Mycin,[14]
an expert system (with approximately 600 rules) that made recommendations for
treatment of bacterial infections, developed at Stanford University in the
1970s, had an effectiveness of 69% which was higher than that of medical
experts surveyed. Current point-of care systems have an effectiveness of (close
to) 100% relative to their information base. This is a quite different kind of
effectiveness than that of a rule-based system (& discussion of the causes
of this difference are beyond the scope of this current blog).
Learning in healthcare systems won’t come about by itself.
It will have to be facilitated by government-public-private partnerships &
specifically funded. Real prototypes & production systems will have to be
subsidized & deployed for testing & feedback. A project similar to that
which produced the NwHIN (originally NHIN) needs to be planned & quickly
started so that working groups can begin describing the functionality of
healthcare learning & companies can be selected to begin prototyping.
Standards will not be as important initially in this effort (as they were in
NHIN development) as innovation will be more important. The companies should
not just include the usual suspects (IBM, Google, Microsoft, etc.) although
they are important, but should also include some smaller organizations with
different ideas that may (or may not) be layered on the infrastructure provided
by their larger brethren.
A learning healthcare system is a great goal, but it won’t
happen without a lot of support (funding) & leadership. Let’s start now.
[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] ONC. 2014. P.8
[3] William Gibson, interview in
The Economist, 4 December 2003.
[4]http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_050345.hcsp?dDocName=bok1_050345
[5] http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/Medicare-Provider-Charge-Data/
[6] https://www.healthify.us/en
[7] http://www.businessweek.com/articles/2014-07-03/hospitals-are-mining-patients-credit-card-data-to-predict-who-will-get-sick?utm_content=buffer7874a&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
[8] Privacy issues are the first
reason to think twice about it, but we have already ceded our privacy when
Amazon or Google makes purchasing suggestions for us.
[9] Ferry, Q. et al. 2014. Diagnostically relevant
facial gestalt information from ordinary photos - See more at:
http://elifesciences.org/content/3/e02020#sthash.c8S7Tm7d.dpuf
[10] Etherege, L.M. 2014. Rapid
Learning:A Breakthough Agenda. Health
Affairs. vol. 33 no. 7 1155-1162. July 2014.
[11] Etheredge LM.A rapid-learning health system. Health Aff
(Millwood). 2007;26(2):w107–18. DOI: 10.1377/hlthaff.26.2.w107.
[12] The doctrine of corporations
as people has been established in the U.S. as early as 1819 (Dartmouth College
vs. Woodward, 17 U.S. 518 (1819) & as recently as Burwell vs. Hobby Lobby
(573 U.S. ___ 2014)
[13] As I stated in my last post,
during the development of interoperability standards for CORBA, a rep from one
of the other vendors (I was representing the Digital Equipment Corporation)
told me he would be compliant if my system sent his system a message & his
system sent my system back an error message!
