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Researchers Work to Define, Harmonize, Share EHR Phenotypes

September 7, 2016
by David Raths
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NIH Research Collaboratory group seeks to ease use of EHR data to identify populations for research
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As an early step in the development of a learning health system, the National Institutes for Health (NIH) is sponsoring large-scale pragmatic clinical trial demonstration projects that rely heavily on EHR data from multiple health systems. In order to promote transparency, reuse and data quality, informatics researchers and data analysts are working to identify best practices and advocate for cultural and policy changes related to using EHRs to identify populations for research.

Rachel Richesson, Ph.D., M.P.H., associate professor of informatics in the Duke University School of Nursing, recently gave an online presentation to describe the work of the NIH Research Collaboratory’s Phenotypes, Data Standards, and Data Quality Core group.

First Richesson described the clinical information system landscape that researchers face. “There is little that is standardized in terms of data representation in EHRs today,” she said. And what appears to be standard is not always so. Each health system has multiple sources of ICD-9 and ICD-10 codes, lab values, and medication data.

Also, EHRs have no standard representation or approach for phenotype definition — that is, a way to define populations with certain conditions such as chronic pain or uncontrolled diabetes.

Additionally challenging is that multi-site pragmatic clinical trials pull information from many ancillary systems as well as the EHRs into a single research database to support the study. A common process used in data warehouses is extract, transform and load (ETL). This has to happen for each organization contributing data to the trial, and there are many sources of error that can be introduced or sources can be missed completely. One trial studying colon cancer has had trouble identifying colonoscopies done outside the health center because they are embedded in PDF or narrative reports but not coded in data.


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Besides co-leading the Phenotyping, Data Standards, and Data Quality Core, Richesson is also the co-lead of the Rare Diseases Task Force for the national distributed Patient Centered Outcomes Research Network (PCORnet), specifically promoting standardized EHR-based condition definitions (“computable phenotypes”) for rare diseases, and helping to develop a national research infrastructure that can support observational and interventional research for various types of conditions. Before joining the Duke faculty in 2011, Richesson spent seven years as at the University of South Florida College of Medicine directing strategy for the identification and implementation of data standards for a variety of multi-national multi-site clinical research and epidemiological studies housed within the USF Department of Pediatrics, including the NIH Rare Diseases Clinical Research Network (RDCRN) and The Environmental Determinants of Diabetes in the Young (TEDDY) study.

In her recent presentation, she gave a few examples of the use of EHRs in the Collaboratory trials:

• The Collaborative Care for Chronic Pain in Primary Care (PPACT) needs to identify patients with chronic pain for the intervention. This is done in different EHR systems using a number of “phenotypes” for inclusion – e.g., neck pain, fibromyalgia, arthritis, or long-term opioid use. Harmonizing that data has proven challenging.  “They have had to monitor large groups of codes that represent these conditions, particularly after the change to ICD-10 to make sure there were no changes in coding behavior,” she said.

• The Strategies and Opportunities to Stop Colorectal Cancer (STOP CRC) trial needs to continually identify screenings for colorectal cancer from each site, so it must maintain a master list of codes (CPT and local codes) related to fecal immunochemical test orders across multiple organizations.

 • The Trauma Survivors Outcomes and Support (TSOS) trial needs to screen patients for PTSD on Emergency Department admission. Yet the wide variety of clinical information systems used in the 24 sites’ emergency departments have varying ability to screen for substance-related disorders and mental health. (Richesson’s Ph.D. dissertation from the University of Texas Health Sciences Center in Houston involved the integration of heterogeneous data from multiple emergency departments.)

“These examples give an idea of how crucial EHR data is to the functioning of these trials and underscore the need to be active and iteratively reach out to IT staff to understand what data it collects and work flow,” Richesson said. “That is a universal experience of the projects in the Collaboratory.”

Transparency and Reproducibility of Pragmatic Clinical Trials

Ultimately, these clinical trial demonstration projects are going to be reporting their results in journals and describing the characteristics of the patients in the intervention and control groups. They will need to point to definitions for diabetes, hypertension, etc. Today there is a wide variety of phenotype definitions based on lab codes, medication codes, or any combination of the two.

“There are huge variety in how conditions could be defined using EHR data,” Richesson explained. “To support transparency and reproducibility in these pragmatic clinical trials, we want to be able to allow readers and consumers to identify what was the definition and how data was obtained and used. Having an explicit phenotype definition would certainly be useful in this area. Our Core has been working toward explicit reporting in these trials.”

Because there is a need for transparent reporting, the Collaboratory’s Phenotypes, Data Standards, and Data Quality Core group has put together a list of the elements it believes should be included in the reporting of pragmatic trials. Those elements should be available for public comment in the next month or so, she said. The group also believes that there needs to be a repository — perhaps at the National Library of Medicine — where researchers could put detailed specifications on how they collected data and defined each clinical phenotype (EHR-based condition definition) used. They also strongly recommend doing a data quality assessment, including a description of different data sources or processes used at different sites.

The goal, Richesson said, is to come up with a limited number of validated and sound definitions for clinical populations in pragmatic clinical trials. “Our approach has really changed over the last couple of years,” she said. “When we first started, we were thinking we would list the definitions and post them as standards: This is how you will define chronic pain, suicide attempts, etc.,” she explained. “Now we see this as one step in a bigger process to facilitate thoughtful use of different definitions. The idea is that we post what we have, and then we provide justification and guidance. We can describe the purpose of phenotype definition and have information describing the researchers’ thinking. We could point to other repositories hosting phenotype definitions. We could maintain this information and keep it updated and support dialog,” she added.

 “Our Core is trying to provide easy access to definitions As we move toward learning healthcare systems, we really want to reduce the number of definitions out there and reduce unnecessary variation across phenotype definitions,” Richesson said, adding that research and clinical use cases should move toward using the same definitions. “The idea of evidence-based practice brings us to the conclusion that for health services research and comparative effectiveness research, using these same phenotype definitions used in research is a goal we should ultimately move toward. We want to be able to identify equivalent populations as we implement best practices and evaluate how an implementation is performing in real life.”




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NCQA Moves Into the Population Health Sphere With Two New Programs

December 10, 2018
by Mark Hagland, Editor-in-Chief
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The NCQA announced on Monday that it was expanding its reach to encompass the measurement of population health management programs

The NCQA (National Committee for Quality Assurance), the Washington, D.C.-based not-for-profit organization best known for its managed health plan quality measurement work, announced on Dec. 10 that it was expanding its reach to encompass the population health movement, through two new programs. In a press release released on Monday afternoon, the NCQA announced that, “As part of its mission to improve the quality of health care, the National Committee for Quality Assurance (NCQA) is launching two new programs. Population Health Program Accreditation assesses how an organization applies population health concepts to programs for a defined population. Population Health Management Prevalidation reviews health IT solutions to determine their ability to support population health management functions.”

“The Population Health Management Programs suite moves us into greater alignment with the focus on person-centered population health management,” said Margaret E. O’Kane, NCQA’s president, in a statement in the press release. “Not only does it add value to existing quality improvement efforts, it also demonstrates an organization’s highest level of commitment to improving the quality of care that meets people’s needs.”

As the press release noted, “The Population Health Program Accreditation standards provide a framework for organizations to align with evidence-based care, become more efficient and better at managing complex needs. This helps keep individuals healthier by controlling risks and preventing unnecessary costs. The program evaluates organizations in: data integration; population assessment; population segmentation; targeted interventions; practitioner support; measurement and quality improvement.”

Further, the press release notes that organizations that apply for accreditation can “improve person-centered care… improve operational efficiency… support contracting needs… [and] provide added value.”

Meanwhile, “Population Health Management Prevalidation evaluates health IT systems and identifies functionality that supports or meets NCQA standards for population health management. Prevalidation increases a program’s value to NCQA-Accredited organizations and assures current and potential customers that health IT solutions support their goals. The program evaluates solutions on up to four areas: data integration; population assessment; segmentation; case management systems.”



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At the D.C. Department of Health Care Finance, Digging into Data Issues to Collaborate Across Healthcare

November 22, 2018
by Mark Hagland, Editor-in-Chief
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The D.C. Department of Health Care of Finance’s Kerda DeHaan shares her perspectives on data management for healthcare collaboration

Collaboration is taking place more and more across different types of healthcare entities these days—not only between hospitals and health insurers, for example, but also very much between local government entities on the one hand, and both providers (hospitals and physicians) and managed Medicaid plans, as well.

Among those government agencies moving forward to engage more fully with providers and provider organizations is the District of Columbia Department of Health Care Finance (DHCF), which is working across numerous lines in order to improve both the care management and cost profiles of care delivery for Medicaid recipients in Washington, D.C.

The work that Kerda DeHaan, a management analyst with the D.C. Department of Health Care, is helping to lead with colleagues in her area is ongoing, and involves multiple elements, including data management, project management, and health information exchange. DeHaan spoke recently with Healthcare Informatics Editor-in-Chief Mark Hagland regarding this ongoing work. Below are excerpts from that interview.

You’re involved in a number of data management-related types of work right now, correct?

Yes. Among other things, we’re in the midst of building our Medicaid data warehouse; we’ve been going through the independent validation and verification (IVV) process with CMS [the federal Centers for Medicare and Medicaid Services]. We’ve been working with HealthEC, incorporating all of our Medicaid claims data into their platform. So we are creating endless reports.


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Kerda DeHaan

We track utilization, cost, we track on the managed health plan side the capitation payments we pay them versus MLR [medical loss ratio data]; our fraud and abuse team has been making great use of it. They’ve identified $8 million in costs from beneficiaries no longer in the District of Columbia, but who’ve remained on our rolls. And for the reconciliation of our payments, we can use the data warehouse for our payments. Previously, we’d have to get a report from the MMIS [Medicaid management information system] vendor, in order to [match and verify data]. With HealthEC, we’ve got a 3D analytics platform that we’re using, and we’ve saved money in identifying the beneficiaries who should not be on the rolls, and improved the time it takes for us to process payments, and we can now more closely track MCO [managed care organization] payments—the capitation payments.

That involves a very high volume of healthcare payments, correct?

Yes. For every beneficiary, we pay the managed care organizations a certain amount of money every month to handle the care for that beneficiary. We’ve got 190,000 people covered. And the MCOs report to us what the provider payments were, on a monthly basis. Now we can track better what the MCOs are spending to pay the providers. The dashboard makes it much easier to track those payments. It’s improved our overall functioning.

We have over 250,000 between managed care and FFS. Managed care 190,000, FFS, around 60,000. We also manage the Alliance population—that’s another program that the district has for individuals who are legal non-citizen residents.

What are the underlying functional challenges in this area of data management?

Before we’d implemented the data warehouse, we had to rely on our data analysis and research division to run all the reports for us. We’d have to put in a data request and hope for results within a week. This allows anyone in the agency to run their own reports and get access to data. And they’re really backed up: they do both internal and external data reports. And so you could be waiting for a while, especially during the time of the year when we have budget questions; and anything the director might want would be their top priority.

So now, the concern is, having everyone understand what they’re seeing, and looking at the data in the same way, and standardizing what they’re meaning; before, we couldn’t even get access.

Has budget been an issue?

So far, budget has not been an issue; I know the warehouse cost more than originally anticipated; but we haven’t had any constraints so far.

What are the lessons learned so far in going through a process like this?

One big lesson was that, in the beginning, we didn’t really understand the scope of what really needed to happen. So it was underfunded initially just because there wasn’t a clear understanding of how to accomplish this project. So the first lesson would be, to do more analysis upfront, to really understand the requirements. But in a lot of cases, we feel the pressure to move ahead.

Second, you really need strong project management from the outset. There was a time when we didn’t have the appropriate resources applied to this. And, just as when you’re building a house, one thing needs to happen before another, we were trying to do too many things simultaneously at the time.

Ultimately, where is this going for your organization in the next few years?

What we’re hoping is that this would be incorporated into our health information exchange. We have a separate project for that, utilizing the claims data in our warehouse to share it with providers. We’d like to improve on that, so there’s sharing between what’s in the electronic health record, and claims. So there’s an effort to access the EHR [electronic health record] data, especially from the FQHCs [federally qualified health centers] that we work closely with, and expanding out from there. The data warehouse is quite capable of ingesting that information. Some paperwork has to be worked through, to facilitate that. And then, ultimately, helping providers see their own performance. So as we move towards more value-based arrangements—and we already have P4P with some of the MCOs, FQHCs, and nursing homes—they’ll be able to track their own performance, and see what we’re seeing, all in real time. So that’s the long-term goal.

With regard to pulling EHR information from the FQHCs, have there been some process issues involved?

Yes, absolutely. There have been quite a few process issues in general, and sometimes, it comes down to other organizations requiring us to help them procure whatever systems they might need to connect to us, which we’re not against doing, but those things take time. And then there’s the ownership piece: can we trust the data? But for the most part, especially with the FHQCs and some of our sister agencies, we’re getting to the point where everyone sees it as a win-wing, and there’s enough of a consensus in order to move forward.

What might CIOs and CMIOs think about, around all this, especially around the potential for collaboration with government agencies like yours?

Ideally, we’d like for hospitals to partner with us and our managed care organizations in solving some of these issues in healthcare, including the cost of emergency department care, and so on. That would be the biggest thing. Right now, and this is not a secret, a couple of our hospital systems in the District are hoping to hold out for better contracts with our managed care organizations, and 80 percent of our beneficiaries are served by those MCOs. So we’d like to understand that we’re trying to help folks who need care, and not focus so much on the revenues involved. We’re over 96-percent insured now in the District. So there’s probably enough to go around, so we’d love for them to move forward with us collaboratively. And we have to ponder whether we should encourage the development and participation in ACOs, including among our FQHCs. Things have to be seen as helping our beneficiaries.

What does the future of data management for population health and care management, look like to you, in the next several years?

For us in the District, the future is going to be not only a robust warehouse that includes claims information, vital records information, and EHR data, but also, more connectivity with our community partners, and forming more of a robust referral network, so that if one agency sees someone who has a problem, say, with housing, they can immediately send the referral, seamlessly through the system, to get care. We’re looking at it as very inter-connected. You can develop a pretty good snapshot, based on a variety of sources.

The social determinants of health are clearly a big element in all this; and you’re already focused on those, obviously.

Yes, we are very focused on those; we’re just very limited in terms of our access to that data. We’re working with our human services and public health agencies, to improve access. And I should mention a big initiative within the Department of Health Care Finance: we have two health home programs, one for people with serious mental illness issues, the other with chronic conditions. The Department of Behavioral Health manages the first, and the Department of Health Care Finance, my agency, DC Medicaid, manages the second. You have to have three or more chronic conditions in order to qualify.

We have partnerships with 12 providers, in those, mostly FQHCs, a few community providers, and a couple of hospital systems. We’ve been using another module from HealthEC for those programs. We need to get permission to have external users to come in; but at that point, they’d be able to capture a lot of the social determinants as well. We feel we’re a bit closer to the providers, in that sense, since they work closely with the beneficiaries. And we’ve got a technical assistance grant to help them understand how to incorporate this kind of care management into their practice, to move into a value-based planning mode. That’s a big effort. We’re just now developing our performance measures on that, to see how we’ve been doing. It’s been live for about a year. It’s called MyHealth GPS, Guiding Patients to Services. And we’re using the HealthEC Care Manager Module, which we call the Care Coordination Navigation Program; it’s a case management system. Also, we do plan to expand that to incorporate medication therapy management. We have a pharmacist on board who will be using part of that care management module to manage his side of things.



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