I couldn’t attend the session today on StealthVest–and small surprise. Who wouldn’t want to come see an Arduino-based garment that can hold numerous health-monitoring devices in a way that is supposed to feel like a completely normal piece of clothing? As with many events at the HIMSS conference, which has registered over 35,000 people (at least four thousand more than last year), the StealthVest presentation drew an overflow crowd.
StealthVest sounds incredibly cool (and I may have another chance to report on it Thursday), but when I gave up on getting into the talk I walked downstairs to a session that sounds kind of boring but may actually be more significant: Practical Application of Control Theory to Improve Capacity in a Clinical Setting.
The speakers on this session, from Banner Gateway Medical Center in Gilbert, Arizona, laid out a fairly standard use of analytics to predict when the hospital units are likely to exceed their capacity, and then to reschedule patients and provider schedules to smooth out the curve. The basic idea comes from chemical engineering, and requires them to monitor all the factors that lead patients to come in to the hospital and that determine how long they stay. Queuing theory can show when things are likely to get tight. Hospitals care a lot about these workflow issues, as Fred Trotter and David Uhlman discuss in the O’Reilly book Beyond Meaningful Use, and they have a real effect on patient care too.
The reason I find this topic interesting is that capacity planning leads fairly quickly to visible cost savings. So hospitals are likely to do it. Furthermore, once they go down the path of collecting long-term data and crunching it, they may extend the practice to clinical decision support, public health reporting, and other things that can make a big difference to patient care.
A few stats about data in U.S. health care
Do we need a big push to do such things? We sure do, and that’s why meaningful use was introduced into HITECH sections of the American Recovery and Reinvestment Act. HHS released mounds of government health data on Health.data.gov hoping to serve a similar purpose. Let’s just take a look at how far the United States is from using its health data effectively.
Last November, a CompTIA survey (reported by Health Care IT News) found that only 28% of providers have comprehensive EHRs in use, and another 17% have partial implementations. One has to remember that even a “comprehensive” EHR is unlikely to support the sophisticated data mining, information exchange, and process improvement that will eventually lead to lower costs and better care.
According to a recent Beacon Partners survey (PDF), half of the responding institutions have not yet set up an infrastructure for pursuing health information exchange, although 70% consider it a priority. The main problem, according to a HIMSS survey, is budget: HIEs are shockingly expensive. There’s more to this story, which I reported on from a recent conference in Massachusetts.
Stats like these have to be considered when HIMSS board chair, Charlene S. Underwood, extolled the organization’s achievements in the morning keynote. HIMSS has promoted good causes, but only recently has it addressed cost, interoperability, and open source issues that can allow health IT to break out of the elite of institutions large or sophisticated enough to adopt the right practices.
As signs of change, I am particularly happy to hear of HIMSS’s new collaboration with Open Health Tools and their acquisition of the mHealth summit. These should guide the health care field toward more patient engagement and adaptable computer systems. HIEs are another area crying out for change.
An HIE optimist
With the flaccid figures for HIE adoption in mind, I met Charles Parisot, chair of Interoperability Standards and Testing Manager for EHRA, which is HIMSS’s Electronic Health Records Association. The biggest EHR vendors and HIEs come together in this association, and Parisot was just stoked with positive stories about their advances.
His take on the cost of HIEs is that most of them just do it in a brute force manner that doesn’t work. They actually copy the data from each institution into a central database, which is hard to manage from many standpoints. The HIEs that have done it right (notably in New York state and parts of Tennessee) are sleek and low-cost. The solution involves:
Keeping the data at the health care providers, and storing in the HIE only some glue data that associates the patient and the type of data to the provider.
Keeping all metadata about formats out to the HIE, so that new formats, new codes, and new types of data can easily be introduced into the system without recoding the HIE.
Breaking information exchange down into constituent parts–the data itself, the exchange protocols, identification, standards for encryption and integrity, etc.–and finding standard solutions for each of these.
So EHRA has developed profiles (also known by its ONC term, implementation specifications) that indicate which standard is used for each part of the data exchange. Metadata can be stored in the core HL7 document, the Clinical Document Architecture, and differences between implementations of HL7 documents by different vendors can also be documented.
A view of different architectures in their approach can be found in an EHRA white paper, Supporting a Robust Health Information Exchange Strategy with a Pragmatic Transport Framework. As testament to their success, Parisot claimed that the interoperability lab (a huge part of the exhibit hall floor space, and a popular destination for attendees) could set up the software connecting all the vendors’ and HIEs’ systems in one hour.
I asked him about the simple email solution promised by the government’s Direct project, and whether that may be the path forward for small, cash-strapped providers. He accepted that Direct is part of the solution, but warned that it doesn’t make things so simple. Unless two providers have a pre-existing relationship, they need to be part of a directory or even a set of federated directories, and assure their identities through digital signatures.
And what if a large hospital receives hundreds of email messages a day from various doctors who don’t even know to whom their patients are being referred? Parisot says metadata must accompany any communications–and he’s found that it’s more effective for institutions to pull the data they want than for referring physicians to push it.
Intelligence for hospitals
Finally, Parisot told me EHRA has developed standards for submitting data to EHRs from 350 types of devices, and have 50 manufacturers working on devices with these standards. I visited a booth of iSirona as an example. They accept basic monitoring data such as pulses from different systems that use different formats, and translate over 50 items of information into a simple text format that they transmit to an EHR. They also add networking to devices that communicate only over cables. Outlying values can be rejected by a person monitoring the data. The vendor pointed out that format translation will be necessary for some time to come, because neither vendors nor hospitals will replace their devices simply to implement a new data transfer protocol.
For more about devices, I dropped by one of the most entertaining parts of the conference, the Intelligent Hospital Pavilion. Here, after a badge scan, you are somberly led through a series of locked doors into simulated hospital rooms where you get to watch actors in nursing outfits work with lifesize dolls and check innumerable monitors. I think the information overload is barely ameliorated and may be worsened by the arrays of constantly updated screens.
But the background presentation is persuasive: by using attaching RFIDs and all sorts of other devices to everything from people to equipment, and basically making the hospital more like a factory, providers can radically speed up responses in emergency situations and reduce errors. Some devices use the ISM “junk” band, whereas more critical ones use dedicated spectrum. Redundancy is built in throughout the background servers.
Waiting for the main event
The US health care field held their breaths most of last week, waiting for Stage 2 meaningful use guidelines from HHS. The announcement never came, nor did it come this morning as many people had hoped. Because meaningful use is the major theme of HIMSS, and many sessions were planned on helping providers move to Stage 2, the delay in the announcement put the conference in an awkward position.
HIMSS is also nonplussed over a delay in another initiative, the adoption of a new standard in the classification of disease and procedures. ICD-10 is actually pretty old, having been standardized in the 1980s, and the U.S. lags decades behind other countries in adopting it. Advantages touted for ICD-10 are:
It incorporates newer discoveries in medicine than the dominant standard in the U.S., ICD-9, and therefore permits better disease tracking and treatment.
Additionally, it’s much more detailed than ICD-9 (with an order of magnitude more classifications). This allows the recording of more information but complicates the job of classifying a patient correctly.
ICD-10 is rather controversial. Some people would prefer to base clinical decisions on SNOMED, a standard described in the Beyond Meaningful Use book mentioned earlier. Ultimately, doctors lobbied hard against the HHS timeline for adopting ICD-10 because providers are so busy with meaningful use. (But of course, the goals of adopting meaningful use are closely tied to the goals of adopting ICD-10.) It was the pushback from these institutions that led HHS to accede and announce a delay. HIMSS and many of its members were disappointed by the delay.
In addition, there is an upcoming standard, ICD-11, whose sandal some say ICD-10 is not even worthy to lace. A strong suggestion that the industry just move to ICD-11 was aired in Government Health IT, and the possibility was raised in Health Care IT News as well. In addition reflecting the newest knowledge about disease, ICD-11 is praised for its interaction with SNOMED and its use of Semantic Web technology.
That last point makes me a bit worried. The Semantic Web has not been widely adopted, and if people in the health IT field think ICD-10 is complex, how are they going to deal with drawing up and following relationships through OWL? I plan to learn more about ICD-11 at the conference.