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A Mature API for an Electronic Health Record: the OpenMRS Process

Posted on August 14, 2015 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

By some measures, OpenMRS may be the most successful of the open source EHRs, widely deployed around the world. It also has a long experience with its API, which has been developed and refined over the last several years. I talked to OpenMRS developer Wyclif Luyima recently and looked at OpenMRS’s REST API documentation to see what the API offers.
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Apervita Creates Health Analytics for the Millions

Posted on January 9, 2015 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

Health officials are constantly talking up the importance of clinical decision support, more popularly known now as evidence-based medicine. We’re owning up to the awkward little fact–which really should embarrass nobody–that most doctors lack expertise on many of the conditions they encounter and can’t read the thousands of relevant studies published each year. New heuristics are developed all the time for things such as predicting cardiac arrest or preventing readmissions after surgery. But most never make their way into the clinic.

Let’s look at what has to happen before doctors and patients can benefit from a discovery:

  1. The researcher has to write a paper with enough detail to create a working program from the heuristic, and has to publish the paper, probably in an obscure journal.

  2. A clinician or administrator has to find the article and line up staff to write and thoroughly test a program.

  3. If the program is to be used outside the hospital where it was created, it has to be disseminated. The hospital is unlikely to have an organization set up to package and market the program. Even if it is simply put out for free use, other institutions have to learn about it and compile it to work on their systems, in order for it to spread widely. Neither the researcher nor the hospital is likely to be compensated for the development of the program.

  4. The program has to be integrated into the doctor’s workflow, by being put on a menu or generating an alert.

Evidence-based medicine, therefore, is failing to tap a lot of resources that could save lives. A commonly cited observation is that research findings take 17 years to go into widespread practice. That’s 17 years of unnecessary and costly suffering.

I have often advocated for better integration of analytics into everyday medical practice, and I found a company called Apervita (originally named Pervasive Health) that jumps off in the right direction. Apervita, which announced a Series A round of funding on January 7, also has potential users outside of clinical settings. Pharma companies can use it to track adverse drug events, while payers can use it to predict fraud and risks to patients. There is not much public health data in the platform yet, but they’re working on it. For instance, Leapfrog group has published hospital safety info through their platform, and Diameter Health provides an all-cause 30-day readmissions prediction for all non-maternal, non-pediatric hospitalizations.

Here’s how the sequence of events I laid out before would go using Apervita:

  1. The researcher implements her algorithm in Python, chosen because Python is easy for non-programmers to learn and is consequently one of the most popular programming languages, particularly in the sciences. Apervita adds functions to Python to make it easy, such as RangeCompute or tables to let you compute with coefficients, and presents these through an IDE.

  2. The researcher creates an analytic on the Apervita platform that describes and publishes the analytic, along with payment terms. Thus, the researcher derives some income from the research and has more motivation to offer the analytic publicly. Conversely, the provider pays only for usage of the analytic, and does not have to license or implement a new software package.

  3. Clinicians search for relevant analytics and upload data to generate reports at a patient or population level. Data in popular formats such as Excel or comma-separated value (CSV) files can be uploaded manually, while programmers can automate data exchange through a RESTful web service, which is currently the most popular way of exchanging data between cooperating programs. Rick Halton, co-founder and Chief Marketing Officer of Apervita, said they are working on support for HL7’s CCD, and are interested in Blue Button+ button, although they are not ready yet to support it.

  4. Clinicians can also make the results easy to consume through personalized dashboards (web pages showing visualizations and current information) or by triggering alerts. A typical dashboard for a hospital administrator might show a graphical thermometer indicating safety rankings at the hospital, along with numbers indicating safety grades. Each department or user could create a dashboard showing exactly what a clinician cares about at the moment–a patient assessment during an admission, or statistics needed for surgical pre-op, for instance.

  5. Apervita builds in version control, and can automatically update user sites with corrections or new versions.

I got a demo of Apervita and found the administration pretty complex, but this seems to be a result of its focus on security and the many options it offers large enterprises to break staff into groups or teams. The bottom line is that Apervita compresses the difficult processes required to turn research into practice and offers them as steps performed through a Web interface or easy programming. Apervita claims to have shown that one intern can create as many as 50 health analytics in one week on their platform, working just from the articles in journals and web resources.

The platform encrypts web requests and is HIPAA-compliant. It can be displayed off-platform, and has been integrated with at least one EHR (OpenMRS).

Always attuned to the technical difficulties of data use, I asked Halton how the users of Apervita analytics could make sure their data formats and types match the formats and types defined by the people who created the analytics. Halton said that the key was the recognition of different ontolgies, and the ability to translate between them using easy-to-create “codesets.”

An ontology is, in general, a way of representing data and the relationships between pieces of data. SNOMED and ICD are examples of common ontologies in health care. An even simpler ontology might simply be a statement that units of a particular data field are measured in milliliters. Whether simple or complex, standard or custom-built, the ontology is specified by the creator of an analytic. If the user has data in a different ontology, a codeset can translate between the two.

As an example of Apervita’s use, a forward prediction algorithm developed by Dr. Dana Edelson and others from the University of Chicago Medical Center can predict cardiac arrests better than the commonly used VitalPAC Early Warning Score (ViEWS) or Modified Early Warning Score (MEWS). Developed from a dataset of over 250,000 patient admissions across five hospitals, “eCART” (electronic Cardiac Arrest Triage) can identify high-risk hospital ward patients and improve ICU triage decisions, often as much as 48 hours in advance.

The new funding will allow Apervita to make their interface even easier for end-users, and to solicit algorithms from leading researchers such as the Mayo Clinic.

Halton heralds Apervita as a “community” for health care analytics for authors and providers. Not only can the creators of analytics share them, but providers can create dashboards or other tools of value to a wide range of colleagues, and share them. I believe that tools like Apervita can bridge the gap between the rare well-funded health clinic with the resources to develop tools, and the thousands of scattered institutions struggling to get the information that will provide better care.

Communities Help Open Source Electronic Health Records Thrive (Part 3 of 3: Project Round-up)

Posted on December 16, 2014 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

This series examines the importance of community and what steps are being taken by open source projects in health IT to create communities around their projects. My previous posting covered VistA and its custodial organization, OSEHRA. The last article in this series covers some important projects in open source with very different approaches to building community.

In addition to VistA, the electronic health record with the most success in building community is OpenMRS, using a unique approach. The project has an unusual genesis. They didn’t come out of a technology center such as Silicon Valley, or a center of health research such as my own Boston. Instead, they were inspired by the Regenstrief Institute at Indiana University.

Getting only a small amount of attention in the United States, OpenMRS proved quite valuable in the developing regions of Africa, especially Rwanda. The U.S. developers realized right away that, for their software to be useful in cultures so far from Indiana, it would have to be understood and fully embraced by local experts.

Indeed, a number of accomplished software developers can be found in Rwanda and surrounding countries. The challenge to OpenMRS was to attract them to the goal of improving health care and to make work on OpenMRS easy.

OpenMRS not only trained developers in African countries to understand and adapt their software to local conditions, but mentored them into becoming trainers for other developers. The initial project to train Rwandan developers thus evolved, the local developers becoming competent to train others in neighboring countries.

In this way, the OpenMRS developers back in the U.S. opened up the project in a unique way to people on other continents. To be sure, the developers had a practical end: they knew they could not provide support to every site that wanted to install OpenMRS, or adapt it to local needs. But they ultimately created a new, intensely committed, international community around OpenMRS. Regular conferences bring together OpenMRS developers from far-flung regions.

The SMART platform is not an EHR itself, but an application programming interface (API) that its developers are asking EHR vendors to adopt. The pay-off for adoption will be that all compliant EHRs can interoperate, and a software developer can write a single app that runs on all of them. SMART was developed at Harvard Medical School with support from ONC. It now runs on top of FHIR, an HL7 project to provide a modern API giving access to all EHR data.

EHRs are not by any means the only community-building efforts in open source health IT. Another significant player is Open Health Tools, which came into being in recognition of the creative work being done by research firms, university professors, and others in various health IT areas.

OHT brings together a wide range of developers to build software for research, clinical work, and other health-related projects. It’s remarkably diverse, providing a meeting place for all projects interested in making health care technology work better. Although they have had problems finding financial support, they now solicit dues from interested projects and seem poised to grow.

For a while, OHT had grand visions of recruiting their members to contribute to a unified “framework” on which other software developers could build applications. This proved to be a bit too big a bite to chew, given the wide range of activities that go on in health IT. But OHT still encourages members to find common ground and make use of each other’s advances.

Aaron Seib, CEO of Open Health Tools, listed the main goals OHT has for its member developers: making communities discoverable, making their licensing intelligible, and addressing the intellectual property barriers that can constrain a project’s adoption. OHT also helps establish trust and connect the dots among the community members to multiply effects across member communities. Roger Maduro of Open Health News writes that OHT has played a critical role in building the open health ecosystem, including the VistA community.

Many other institutions also sense a need for community. A few years ago I spoke with John Speakman, who was working for the National Cancer Institute at the time. They had developed some software that was very popular among developers, but no one made any contributions back to the common software base, and the NCI wanted the users of the software to start taking responsibility for the tools on which they depended. He took on the task of building community, but left when he realized it was not going to take hold.

Among the problems was the well-known dependence of government agencies such as the NCI on contractors. Speakman points to an organizational and cultural gap between “the big Beltway Bandit companies (who will never use the code themselves to do biomedical science) over academic groups engaged in biomedicine.” He also thinks the NCI intervened too much in community activities, instead of letting community members work out disagreements on their own. “If the government is going to invest in the seeding of open source communities,” he says, “it has to (a) focus on releasing the data and see what folks do with it, and (b) use as light a hold as possible on how the communities run themselves.”

Athenahealth stands out among EHR vendors with its More Disruption Please program. There it is building an ecoystem of third-party tools that its customers can use as part of its cloud-based service. This goal is similar to that of the open source SMART platform, which is trying to get EHR vendors and other data stores to adopt a common API and thus make themselves more open to software developers.

Openness and community go together. Although the health IT field is slow to adopt both practices, some projects could be entering into a virtuous cycle where open source developers learn to appreciate the value of their communities, which in turn reward the most open projects with greater success.

Open Source Electronic Health Records: Will They Support Clinical Data Needs of the Future? (Part 2 of 2)

Posted on November 18, 2014 I Written By

Andy Oram is an editor at O'Reilly Media, a highly respected book publisher and technology information provider. An employee of the company since 1992, Andy currently specializes in open source, software engineering, and health IT, but his editorial output has ranged from a legal guide covering intellectual property to a graphic novel about teenage hackers. His articles have appeared often on EMR & EHR and other blogs in the health IT space. Andy also writes often for O'Reilly's Radar site (http://oreilly.com/) and other publications on policy issues related to the Internet and on trends affecting technical innovation and its effects on society. Print publications where his work has appeared include The Economist, Communications of the ACM, Copyright World, the Journal of Information Technology & Politics, Vanguardia Dossier, and Internet Law and Business. Conferences where he has presented talks include O'Reilly's Open Source Convention, FISL (Brazil), FOSDEM, and DebConf.

The first part of this article provided a view of the current data needs in health care and asked whether open source electronic health records could solve those needs. I’ll pick up here with a look at how some open source products deal with the two main requirements I identified: interoperability and analytics.

Interoperability, in health care as in other areas of software, is supported better by open source products than by proprietary ones. The problem with interoperability is that it takes two to tango, and as long as standards remain in a fuzzy state, no one can promise in isolation to be interoperable.

The established standard for exchanging data is the C-CDA, but a careful examination of real-life C-CDA documents showed numerous incompatibilities, some left open by the ambiguous definition of the standard and others introduced by flawed implementations. Blue Button, invented by the Department of Veterans Affairs, is a simpler standard with much promise, but is also imperfectly specified.

Deanne Clark, vxVistA Program Manager at DSS, Inc., told me that VistA supports the C-CDA. The open source Mirth HIE software, which I have covered before, is used by vxVistA, OpenVista (the MedSphere VistA offering), and Tolven. Proprietary health exchange products are also used by many VistA customers.

Things may get better if vendors adopt an emerging HL7 standard called FHIR, as I suggested in an earlier article, which may also enable the incorporation of patient-generated data into EHRs. OpenMRS is one open source EHR that has started work on FHIR support.

Tolven illustrates how open source enables interoperability. According to lead developer Tom Jones, Tolven was always designed around care coordination, which is not the focus of proprietary EHRs. He sees no distinction between electronic health records and health information exchange (HIE), which most of the health IT field views as separate functions and products.

From its very start in 2006, Tolven was designed around helping to form a caring community. This proved useful four years later with the release of Meaningful Use requirements, which featured interoperability. APIs allow the easy development of third-party applications. Tovlen was also designed with the rights of the patient to control information flow in mind, although not all implementations respect this decision by putting data directly in the hands of the patient.

In addition to formats that other EHRs can recognize, data exchange is necessary for interoperability. One solution is an API such as FHIR. Another is a protocol for sending and receiving documents. Direct is the leading standard, and has been embraced by open source projects such as OpenEMR.

The second requirement I looked at, support for analytics, is best met by opening a platform to third parties. This assumes interoperability. To combine analytics from different organizations, a program must be able to access data through application programming interfaces (APIs). The open API is the natural complement of open source, handing power over data to outsiders who write programs accessing that data. (Normal access precautions can still be preserved through security keys.)

VistA appears to be the EHR with the most support for analytics, at least in the open source space. Edmund Billings, MD, CMO of MedSphere, pointed out that VistA’s internal interfaces (known as remote procedure calls, a slightly old-fashioned but common computer term for distributed programming) are totally exposed to other developers because the code is open source. VistA’s remote procedure calls are the basis for numerous current projects to create APIs for various languages. Some are RESTful, which supports the most popular current form of distributed programming, while others support older standards widely known as service-oriented architectures (SOA).

An example of the innovation provided by this software evolution is the mobile apps being built by Agilex on VistA. Seong K. Mun, President and CEO of OSEHRA, says that it now supports hundreds of mobile apps.

MedSphere builds commercial applications that plug into its version of Vista. These include multidisciplinary treatment planning tools, flow sheets, and mobile rounding tools so doctor can access information on the floor. MedSphere is also working with analytic groups to access both structured and unstructured information from the EHR.

DSS also adds value to VistA. Clark said that VistA’s native tools are useful for basic statistics, such as how many progress notes have not been signed in a timely fashion. An SQL interface has been in VistA for a long time, DSS’s enhancements include a graphical interface, a hook for Jaspersoft, which is an open source business intelligence tool, and a real-time search tool that spiders through text data throughout all elements of a patient’s chart and brings to the surface conditions that might otherwise be overlooked.

MedSphere and DSS also joined the historical OSEHRA effort to unify the code base across all VistA offerings, from both Veterans Affairs and commercial vendors. MedSphere has added major contributions to Fileman, a central part of VistA. DSS has contributed all its VistA changes to OSEHRA, including the search tool mentioned earlier.

OpenMRS contributor Suranga Kasthurirathne told me that an OpenMRS module exposes its data to DHIS 2, an open source analytics tool supporting visualizations and other powerful features.

I would suggest to the developers of open source health tools that they increase their emphasis on the information tools that industry observers predict are going to be central to healthcare. An open architecture can make it easy to solicit community contributions, and the advances made in these areas can be selling points along with the low cost and easy customizability of the software.