Peeling the Layers of the Onion Around HIE Policy, with Julia Adler-Milstein, Ph.D. | Healthcare Informatics Magazine | Health IT | Information Technology Skip to content Skip to navigation

Peeling the Layers of the Onion Around HIE Policy, with Julia Adler-Milstein, Ph.D.

August 1, 2016
by Mark Hagland
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Dr. Adler-Milstein offers a healthcare policy researcher’s perspectives on HIE development

Earlier this month, HCI Editor-in-Chief Mark Hagland wrote a blog analyzing two articles about health information exchange (HIE) that had appeared in the July issue of Health Affairs. The two articles are entitled “The Number of Health Information Exchange Efforts Is Declining, Leaving The Viability Of Broad Clinical Data Exchange Uncertain,” authored by Julia Adler-Milstein, Sunny C. Lin, and Ashish K. Jha; and “Engagement In Hospital Health Information Exchange Is Associated With Vendor Marketplace Dominance,” by Jordan Everson and Julia Adler-Milstein.

As referenced in the blog, the way in which HIE is described in the first article is as a fairly fragile phenomenon in the wake of the loss of most federal and state funding in the past two years—something that has been widely known—with a strong need for operating HIEs to prove strong market value (though that term is not used) in order to survive. Meanwhile, the second article looks at the issue of intense market dominance by a few large electronic health record (EHR) vendors, and its strong impact on how HIE is pursued in markets in which a single vendor dominates. The authors of the second article did make it clear that the impact of market dominance on the part of the Verona, Wis.-based Epic Systems Corporation is quite different from that of market dominance on the part of any of the other largest EHR vendors—the Kansas City-based Cerner Corporation, the Chicago-based Allscripts, or the Westwood, Mass.-based Meditech.

As a follow-up to the publication of the blog, Julia Adler-Milstein, Ph.D., the co-author of both articles, spoke recently with Hagland recently, to discuss some of the implications of the two articles at this moment in the ongoing evolution of the HIE phenomenon. Dr. Milstein is an associate professor at the University of Michigan, with appointments in both the School of Information and the School of Public Health. in Below are excerpts from that recent interview.

Reading both Health Affairs articles carefully, I came to the unmistakable conclusion that there is no ideal “silver bullet”-type solution for fixing all the challenges facing the HIE phenomenon. Whatever we do, we’re trying to make the best of what was not an optimal seedbed for HIE development to begin with, correct?

Yes, I definitely agree. Now, if I put on my policy hat, from a policy perspective, I would ask, what was the market likely to under-invest in? And it was pretty clear from the start that HIE was not something that the market felt it needed to invest in. So it seems like that is the place where we should have started doing the most policymaking around. We started doing the most policymaking around EHR, but it really should have been the opposite. I think it should have been much more on interoperability and not as much around EHR adoption. We put so many eggs in the EHR adoption basket, and so few into interoperability, so now we’re having to catch up.


Julia Adler-Milstein, Ph.D.

And because of the diversity of HIEs, it makes it that much more difficult to give good policy guidance?

Yes. I think running an HIE is one of the most difficult things to do. The governance, the technology, the business processes, and the workflows, are all too diverse. And the problem is not well-defined. Are we really envisioning that every piece of data can be shared seamlessly with every other piece of data? We’ve never defined the endpoint. I don’t think HIEs were ever going to get to that seamless interoperability. But there remains that fundamental problem of what we want to get to at the end of the day. We let ourselves think that we’d get there by having everyone share everything with everyone. But that was never feasible. But we never had the discipline to say, OK, what is feasible, and where can we reach consensus?

If you were the federal HIE fairy, and you could just wave a wand, what ideal things would you make possible, from a policy standpoint?

What I struggle with most is what lever we press. Do we just hold providers accountable for certain things, require some basics, and pay for what we care about and pay for that? We haven’t sorted it out. So maybe we should essentially hold providers accountable for cost and quality measures that they cannot do if they don’t share information across the care continuum. That holds appeal, because we’re paying for what we care about, and providers would put pressure on vendors. If you said, we won’t pay for readmissions, period, that would force hospitals and long-term care facilities to coordinate in a way they’d never done before, and that would force interoperability. That’s not feasible, but is tempting. The other alternative is to put providers and vendors on the hook for whether data is moving. And we could do a lot more to make sure providers are sharing data. Decertify systems, take money away from providers in the absence of interoperability and data-sharing. I truly believe that if Epic and Cerner believed they wouldn’t be in business in a year, that they’d figure this out.

Might it be feasible or worthwhile for HHS to force EHR vendors to take very specific steps?

We don’t have a fully specified approach [on a healthcare policy level]. A lot of it comes down to, when you’re implementing standards, to interpret those. Right now, vendors have no incentive to work together to implement standards. So in concept, yes, you could say that they would be much more motivated to work in lockstep with an implementation guide. So I think that could happen. I think in industries were standards adherence is essential to staying in business, they’ve figured out how to make it happen.

And Epic has been motivated to share information among Epic customers. But why have they not been motivated to share with non-Epic customers? We have not set up requirements for them to share with non-Epic customers. And we shouldn’t criticize them for sharing among their own customers. And the question is not, how do we bring them down, but how do we bring others up to their level?

So I understood from your article that Epic being dominant in certain markets is discouraging non-Epic customers from creating health information exchange. Did I understand that correctly?

It’s a complicated thing, but when Epic is not the dominant vendor, the greater the dominance of that vendor, the better off everyone is in the market in terms of developing HIE. If I’m in a Cerner-dominated market, it’s easier for me to set up an HIE when the majority of the market is any one vendor, except for Epic. And my guess is that that has to do with the fact that Epic is not playing well with others. And so when they’re the biggest, whether at 30 or 80 percent, it is having an effect.

Is Epic having a deleterious effect nationwide, on non-private HIE development, because of its dominance in some healthcare markets?

It seems to be so, though perhaps there are other explanations. That is what you hear. It appears to discourage organizations. We’re even seeing it here at U of Michigan Health System. They’re having to fight the fight every day. It’s almost as though CareEverywhere puts up a contrast to others who would have to set something up on their own. You’re going to have to pay for something that you have to set up separately. There are so many pressures that lead people to say, I can’t justify paying to participate in a third-party HIE. That is real. But you only see those when you’re in the decision-making meetings of healthcare organizations…

Would you agree that if it were true, that that offers a policy conundrum?

I agree with you, yes, it would. At the end of the day, I’d like a provider to be able to share patient data appropriately. There are several ways to go about this. The question is, do you come down harder on the providers or vendors, or both? And you think about CommonWell and others… they see the problem. But CommonWell has been around for a while, but what’s actually changed on the ground today, is not obvious. So how do we move these issues up the priority list? I don’t know the answers, at the end of the day. But if you do pressure vendors, it will have a bigger impact. The provider market is much more fragmented. So if you could figure out a way to make the six biggest vendors share information with each other, you’d have a huge impact on the market. And it would also give leading vendors an advantage. And ONC [the federal Office of the National Coordinator for Health IT] is very hesitant to do that; they don’t want to pick winners and losers. You could clearly make a difference by going to a select set of vendors, but that would be problematic in that sense.

Meanwhile, we haven’t done a good job of making sure that data flow is patient-centric. And here in Ann Arbor, the big University of Michigan Health System has Epic, and the smaller system down the road has Cerner, and patients are going between them a lot. But if we could do a better job of measuring the problems inherent in markets where the two big, dominant health systems are not sharing data, that might be helpful. So I think we also have to decide which kinds of problems we want to solve. It’s very different, too, to get smaller practices connected into the major health systems in their communities, versus getting the “big dogs” to play together. Those sound like different solutions to different policy problems.

What do you think will happen in the next few years?

That’s a good question. I’m not involved in FHIR [the Fast Healthcare Interoperability Resources standard], but those involved in it do believe it will be a game-changer. I hope that that is true. I don’t feel that I really understand enough about it. I never think that the standards are the hard part, I think it’s the implementation and the governance; and if you reduce the technology barriers enough, that could possibly move the market. Part of me feels like things won’t be that different three years from now, but part of me believes it will be better, once we’re able to extract data from EHRs and move it around. I really don’t know.

What do you think of the vision of the future some have put forward involved with the sharing of very small pieces of discrete data, rather than the sharing of entire CCDs [continuity of care documents]?

It seems like we do need to move forward on atomic data-sharing. The CCD sharing has clearly not been working very well. And moving forward in more of a use case-based way, which FHIR supports—I think we need to try that. There is a risk of perhaps going to extremes. And I haven’t pondered that scenario that much. But clearly, some of the information in CCDs is not being used. So I would say we need to try that approach; I think we should go in that direction. I have not heard anything that suggests that we shouldn’t go in that direction.

What should CIOs and CMIOs be thinking about now, and knowing right now?

One of the things I’ve tried to bring clarity to is the distinction between perverse and weak incentives. A perverse incentive is the idea that perhaps Epic and other vendors might feel better off by not sharing information. That’s very different from a weak incentive, where I’m interested in sharing data, but progress is slow because it’s a hard problem. So being able to be clear about where stakeholders stand on whether specific incentives are perverse or only weak; and then creating clarity on priorities. There are so many challenges in healthcare today, there’s so much to do, and it makes it hard to make meaningful progress. We see it manifested in so many different areas, in the struggle to create and sustain health information organizations; but you make fast progress when it’s at the top of your priority list.

So I would say that, for your audience, a key message is that they need to decide whether this [HIE development] is high on their priority list. CIOs and CMIOs may believe that this is an important priority, but is it one of their top priorities? They need to know whether it is a top priority for their organization. And to go to Epic and say, we need X next year, it takes strong organizational muscle. So it’s not so much a message to CIOs, but it would be a helpful thing. I think it’s hard to pursue the right priority actions, without knowing what the most important points of friction are for CIOs and CMIOs. And the reason we haven’t made as much progress as we could, is that people aren’t openly saying that this hasn’t been a priority. I think, in general, being able to discuss and raise these issues, is important. And forums in which there can be more communication and collaboration around this, will be helpful. There are so many people trying to independently pursue this, but it’s a problem that by definition requires a lot of multi-stakeholder participation. Everyone keeps talking about the same problems, but why aren’t we getting together to discuss these issues?

 

 

 

 


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Across the Care Continuum, Improving Patient Matching Capabilities Has Become Paramount

December 13, 2018
by Rajiv Leventhal, Managing Editor
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Combining its internally developed “common key” service with a big data MPI platform, MiHIN is uniquely identifying patients at a much higher rate than ever before

Patient matching—the ability to accurately link all of a patient’s health data within and across health systems—has been a challenge for the healthcare industry for decades, and the lack of a nationwide patient matching strategy remains one of the largest unresolved issues in the safe and secure electronic exchange of medical data.

In a 2014 report about the issue, the Office of the National Coordinator for Health Information Technology (ONC) found the best error rate among healthcare providers is around 7 percent, though the error rate is typically closer to 10 to 20 percent within healthcare entities, and this rises to 50 to 60 percent when entities exchange information with each other.

And for health information exchanges (HIEs), the issue is often compounded as they specifically face a significant challenge with matching and linking patient identities because of the diversity and independence of the institutions they serve.

To this end, the Michigan Health Information Network (MiHIN)—a state-designated entity that facilitates information sharing among the state's numerous HIEs and hundreds of thousands of providers—has embarked on a major project: standardizing patient matching across all these different entities in the state.

Specifically, MiHIN officials said in an August announcement that the HIN uses the big data MPI (master patient index) from California-based clinical data exchange company 4medica, along with a private patient attribute or “common key” that links individuals and their health information across systems and organizations. The combined solution enables MiHIN to match patient identities in real time, officials noted.

Shelley Mannino-Marosi, MiHIN's senior director of state and national programs, says in a recent interview that in the early stages, MiHIN’s leaders were entirely determined to only aim for exact matching. “At MiHIN, and in Michigan, our goal is to get the right information about the right patient to the right care team member or provider at the point of care in near real time,” she says.

To help reach that goal, Mannino-Marosi points to one of MiHIN’s core services, its active care relationship service, or ACRS, which enables organizations to submit data files which record the care team relationships attributing a particular patient with health professionals at that organization. As Mannino-Marosi explains, this service enables a provider or payer to “declare” a patient, so that if something happens to him or her, such as a hospital admission, ACRS can be searched to see what providers or health plans have declared that relationship with the patient, leading to that stakeholder getting a notification.

Early on in the process, MiHIN’s staff would search the service and compare the demographics on an incoming ADT (admission, discharge, transfer) notification with the data that was stored in ACRS. “And we would look for an exact match only. We were very cautious in wanting to be extremely accurate and ensure we weren’t sending the incorrect information to the wrong place,” Mannino-Marosi says.

But over time, MiHIN’s leaders realized that there was a need to be more probabilistic. For example, Mannino-Marosi offers that if a patient’s first name is Bill and his doctor has already declared a relationship with this person as William, with the same date of birth, address and social security number, a computer can easily determine that Bill and William are in fact the same person. But with MiHIN’s early-stage patient matching system, the stakeholder who declared that patient would not receive this alert since the demographic information was not an exact match.

So what MiHIN did next was develop its in-house common key service that assigns a “key” or “ID” attribute to each patient. This information was funneled to the centralized MPI from 4medica, and then the common key, or attribute, is given back to the participating organization so that it could be stored in its system to add to any subsequent information, or to strengthen the matching that’s being done in ACRS. Essentially, explains Mannino-Marosi, “the common key becomes a shared attribute that MiHIN’s participating organizations could use to talk to the HIN or to one another.”

When used in action, 4medica’s big data MPI and the common key service can now determine with certainty that even if there’s just a bit of a difference in demographic information, the inbound ADT message is in fact talking about the same patient that the doctor cares about, Mannino-Marosi attests. Gregg Church, president of 4medica, adds, “It’s a guaranteed match even though the demographic change is in there, as we know it’s the same person based on everything that’s being looked at and scored against on the historical information.”

Notably, Mannino-Marosi contends that the common key service acts as a “gatekeeper” to the MPI and won’t automatically add information for patients when they don’t meet a certain threshold for the quality of the data. She explains that if the common key service queries the MPI and knows with certainty that a given patient is in the system, that unique attribute can be used. And at the same time, if the service knows that the person is brand new, he or she is assigned a new key and added to the MPI.

But where things can get tricky is the “middle zone” where it’s too close to call and the computer, as configured, isn’t 100 percent confident. “In that case, we will not assign a common key and will [instead] provide an error message back to the organization,” says Mannino-Marosi. That way, the cleanup of the data is also shared across the state, so the burden of data stewardship is spread out and everyone benefits from the data cleansing activities—as opposed to the person being added to the MPI and potentially pulling redundant information, she adds.

When it comes to evaluating the results of this system, Mannino-Marosi points out that some patient matching companies will boast about 100 percent or near 100 percent matching, but the criteria they are using to match is a grey area. For MiHIN, she notes, one key measure is how many unique identities they have been able to assess in Michigan. “Over a year-and-a-half, we have been able to uniquely identify nearly 8 million of the 9 million Michigan residents, and that number is of course growing,” she says.

Beyond that, another massively important metric to measure is if MiHIN can send more information to more care team members as a result of the service. “If I have three members of our care team but just one is notified, that doesn’t achieve our mission to ensure we’re getting the right information where it needs to go so that a singular patient’s care can be coordinated cost effectively and quickly,” Mannino-Marosi says. But by using the common key service with the 4Medica MPI, MiHIN has been able to send out well over a million more ADT notifications per week. “That’s very significant,” she asserts.

It’s important to note that the common key service from MiHIN is unique to Michigan, and other organizations that do not develop their own unique identifiers are using patient matching solutions—such as 4medica’s big data MPI—in a more conventional manner.

One such entity is the Nebraska Health Information Initiative (NeHII), a statewide HIE that is using the solution to assign unique patient identifiers to patient identities and to match incoming patient data to the right master patient record. According to Jamie Bland, NeHII’s CEO, because her HIE is a qualified clinical data registry for quality reporting purposes, it needed a robust solution that would be able to match patients across various complex data sets. The goal at NeHII, Bland says, “is to grow a true population health repository that matches patients. We can do better population health management and that starts with the identity matching aspect of data management.”

Currently, NeHII collects data from nearly 70 different hospital sources, and more than 200 sources in total across the state and region, says Bland. And because different hospital electronic health records (EHRs) have different matching algorithms for how they match patients, the level of complexity meant that the HIE needed to prioritize identity matching as it continued to grow in the diversity of data it was collecting.  

Bland says that the idea is to marry clinical data, along with claims data and PDMP (prescription drug monitoring program) data so that a population health utility is created. “That’s the last mile of interoperability,” she says. “We are here as a public service to assist in improving identity matching across the state so that we can build a population health infrastructure to make that a solid suite of applications that’s available through the HIE.”


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Becoming a Data-Driven Ecosystem: How San Diego County is Moving the Needle

December 11, 2018
by Rajiv Leventhal, Managing Editor
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Collaboration around information sharing and integration is serving as the foundation for a person-centered healthcare environment in San Diego

“Living well" would be an accurate way to describe the experiences of tourists who visit San Diego, often for its miles of white-sand beaches and amazing weather. But behind the scenes, too, city healthcare leaders have been working hard on their own version of living well.

Indeed, a strategic vision known as “Live Well San Diego”—the city’s 10-year plan to focus on building better health, living safely and thriving—has provided a foundational base for how healthcare in San Diego should be imagined. Essentially, the strategy aligns the efforts of individuals, organizations and government to help all 3.3 million San Diego County residents live well, the region’s health officials say.

As Nick Yphantides, M.D., the chief medical officer for San Diego County’s medical care services division, puts it in a recent sit-down interview with Healthcare Informatics, “It’s not just about healthcare delivery, but it’s about the context and environment in which that delivery occurs.” Expanding on that, Yphantides notes that the key components for Live Well San Diego are indeed health, safety, and thriving, and within these larger buckets are critical care considerations such as: economic development, vitality, social economic factors, social determinants of health, preparedness and security, and finally, being proactive in one’s care.

So far, through the Live Well San Diego initiative, the city has created more than 8,000 healthcare jobs over a five-year span and more than 1.2 million square feet of additional hospital space, according to a 2017 report on Southern California’s growing healthcare industry.

From here, the attention has turned to improving the data sharing infrastructure in the city, a significant patient care challenge that is not unique to San Diego, but nonetheless critical to the evolution of any healthcare market that is progressing toward a value-based care future. To this end, toward the end of 2016, ConnectWellSD, a county-wide effort to put Live Well San Diego into action, was launched with the aim to improve access to county health services, serving as a “one-stop-shop” for customer navigation. Officials note that while still in the early stages of development, ConnectWellSD will implement new technologies that will allow users to perform functions such as looking up a customer file, making and managing referrals, or sharing case notes.

Carrie Hoff, ConnectWellSD’s deputy director, says the impetus behind the web portal’s launching was the need to pull disparate data together to have a fuller view of how the individual is being serviced, in compliance with privacy and confidentiality. “Rounding up that picture sets ourselves up to collaborate across disciplines in a more streamlined way,” Hoff says.

Moving forward, with the ultimate goal of “whole-person centricity” in mind, San Diego health officials envision a future in which ConnectWellSD, along with San Diego Health Connect (SDHC)—the metro area’s regional health information exchange (HIE)—and the area’s “2-1-1 agency,” which houses San Diego’s local community information exchange (CIE), all work in cohesion to create a longitudinal record that promotes a proactive, holistic, person-centered system of care.

Yet as it stands today, “From a data ecosystem perspective, San Diego is still a work in progress,” Yphantides acknowledges. “But we’re looking to really be a data-driven, quantified, and outcome-based environment,” he says.

To this end, SDHC is an information-sharing network that’s widely considering one of the most advanced in the country. Once federally funded, SDHC is now sustained by its hospital and other patient care organization participants, and according to a recent newsletter, in total, the HIE has contracted with 19 hospitals, 17 FQHCs (federally qualified health centers), three health plans and two public health agencies.

The regional HIE was shown to prove its value during last year’s tragic hepatitis A outbreak in San Diego County amongst the homeless population that resulted in 592 public health cases and 20 deaths spanning over a period of a little less than one year. In an interview with Healthcare Informatics Editor-in-Chief Mark Hagland late last year, Dan Chavez, executive director of SDHC, noted that the broad reach of his HIE turned out to be quite helpful during this public health crisis.

Drilling down, per Hagland’s report, “Chavez is able to boast that 99 percent of the patients living in San Diego and next-door Imperial Counties have their patient records entered into San Diego Health Connect’s core data repository, which is facilitating 20 million messages a month, encompassing everything from ADT alerts to full C-CDA (consolidated clinical documentation architecture) transfer.”

According to Chavez, “With regard to hep A, we’ve done a wonderful job with public health reporting. I venture to say that in every one of those cases, that information was passed back and forth through the HIE, all automated, with no human intervention. As soon as we had any information through a diagnosis, we registered the case with public health, with no human intervention whatsoever. And people have no idea how important the HIE is, in that. What would that outbreak be, without HIE?”

To this point, Yphantides adds that to him, the hepatitis A crisis was actually not as much about an infectious outbreak as much as it was “inadequate access, the hygienic environment, and not having a roof over your head.” Chavez would certainly agree with Yphantides, as he noted in Hagland’s 2017 article, “We’re going through a hepatitis A outbreak, and we’re coming together to solve that. We have the fourth-largest homeless population in the U.S.—about 10,000 people—and this [crisis] is largely a result of that. We’re working hard on homelessness, and this involves the entire community.”

Indeed, while administering tens of thousands of hepatitis A vaccines—which are 90 percent effective at preventing infection—turned out to be a crucial factor in stopping the outbreak, there were plenty of other steps taken by public health officials related to the challenges described above. Per a February report in the San Diego Union-Tribune, some of these actions included “installing hand-washing stations and portable toilets in locations where the homeless congregate and regularly washing city sidewalks with a bleach solution to help make conditions more sanitary for those living on the streets.” What’s more, Family Health Centers of San Diego employees “often accompanied other workers out into the field and even used gift cards, at one point, to persuade people to get vaccinated,” according to the Union-Tribune report.

Yphantides notes that the crisis required coordinated efforts between the state, the city, and various other municipalities, crediting San Diego County for its innovative outreach efforts which he calls the “Uberization of public health,” where instead of expecting people to come to healthcare facilities, “we would come to them.” He adds that “hep A is so easily transmissible, and it would have been convenient to say that it’s a homeless issue, but based on how easily it is transmitted, it could have become a broader general population factor for us.”

Other Regional Considerations

Beyond the problem of homelessness in San Diego, which Jennifer Tuteur, M.D., the county’s deputy chief medical officer, medical care services division, attributes to an array of factors, some unique to the region, and others not: from the warm year-round weather; to the many different people who live in vastly different areas, ranging from tents to canyons to beaches and elsewhere; and to the urbanization of downtown and the building of new stadiums; there are plenty of other market challenges that healthcare leaders must find innovative solutions to.

For instance, says Yphantides, relative to some parts of the U.S., although California has made great strides in expanding insurance coverage, due to the Affordable Care Act—which lowered the state’s uninsured rate to between 5 and 7 percent—there are still core challenges in regard to access. “We’re still dealing with a fragmented system; like many parts of the U.S, we are siloed and not an optimally coordinated system, especially when it comes to ongoing challenges related to behavioral health,” he says, specifically noting issues around data sharing, the disparity of platforms, a lack of clarity from a policy perspective, and guidance on patient consent.

To this end, San Diego County leaders are looking to bridge the gap between those siloes while also looking to bridge the gap between the healthcare delivery system, having realized how important the broader ecosystem is, Yphantides adds. “But what does that look like in terms of integrating the social determinants of health? Who will be financing it, and who will be responsible for it? You have a tremendous number of payers who all have a slice of the pie,” he says.

Speaking more to the behavioral health challenges in the region, Yphantides says there are “real issues related to both psychiatric and substance abuse.” And perhaps somewhat unique to California, due to the cost of living, “we have tremendous challenges in relation to the workforce. So being able to find adequate behavioral health specialists at all levels—not just psychiatrists—is a big issue.”

What’s more, while Yphantides acknowledges that every state probably has a similar gripe, when looking at state reimbursement rates for MediCal, the state’s Medicaid program, California ranks somewhere between 48th and 50th in terms of compensation for Medicaid care. Put all together, given the challenges related to Medicaid compensation, policy, data sharing, workforce and cost of living issues, “it all adds up with access challenges that are less than ideal,” he attests.

In the end, those interviewed for this story all attest that one of the unique regional characteristics that separates San Diego from many other regions is the constant desire to collaborate, both at an individual level and an inter-organization level. Tuteur offers that San Diego residents will often change jobs or positions, but are not very likely to leave the city outright. “That means that a lot of us have worked together, and as new people come in, that’s another thing that builds our collaboration. I may have worn [a few different] hats, but that commitment to serving the community no matter what hat we wear couldn’t be stated enough in San Diego.”

And that level of collaboration extends to the patient care organization level as well, with initiatives such as Accountable Communities for Health and Be There San Diego serving as examples of how providers on the ground—despite sometimes being in fierce competition with one another—are working to better the health of their community. “Coopetition—a hybrid being cooperation and competition—describes our environment eloquently,” says Yphantides.

Learn more about San Diego healthcare at the Southern California Health IT Summit, presented by Healthcare Informatics, slated for April 23-24, 2019 at the InterContinental San Diego.

 

 

 


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Hawaii’s HIE Leveraging Technology to Improve Patient Identification

November 8, 2018
by Heather Landi, Associate Editor
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Hawaii Health Information Exchange (HHIE), Hawaii’s state-designated HIE, is taking action to improve patient identification and the accuracy of provider data for enhanced care coordination across the state.

HHIE is working with Pasadena, Calif.-based NextGate to implement an enterprise cloud-based master patient index and provider registry software to create a sustainable statewide system of accurate patient and provider data by resolving duplicate and incomplete records.

HHIE was established in 2006 to improve statewide healthcare delivery through seamless, safe and effective health information exchange. The HIE covers more than 1.2 million patients and has more than 450 participants including Castle Medical Center, Hawaii Pacific Health, The Queen's Medical Center, and the state’s largest insurance provider, HMSA.

“Accurate, comprehensive data that flows freely across boundaries is a catalyst for informed, life-saving decision making, effective care management, and a seamless patient and provider experience,” Francis Chan, CEO of HHIE, said in a statement.

Chan adds that the technology updates will help to ensure providers have “timely and reliable access to data to deliver the high-quality level of care every patient deserves.” “We are building a scalable, trusted information network that will positively influence the health and well-being of our communities,” Chan said.

“The partnership will enable HHIE to develop internal support tools to create accurate, efficient patient identity and provider relationships to those patients to support focused coordinated care,” Ben Tutor, information technology manager of HHIE, said in a statement.

Cross-system interoperability is critical to the success of HHIE’s Health eNet Community Health Record (CHR), which has more than 1,200 users and 470 participating physician practices, pharmacies, payers and large healthcare providers that contribute to over 20 million health records statewide. Deployment of the EMPI’s Patient Matching as a Service (PMaaS) solution will support HHIE’s vision of a fully integrated, coordinated delivery network by establishing positive patient identification at every point across the continuum for a complete picture of one’s health, according to HHIE leaders.

By ensuring that each individual has only one record, participants of HHIE will be able to map a patient’s entire care journey for informed decision-making and population health management, HHIE leader say.

The provider registry will synchronize and reconcile provider data across clinical, financial and credentialing systems to enable an accurate directory and referral network of providers. Using a single provider ID, the registry aggregates and maintains up-to-date information about individual providers and provider groups, such as specialties, locations, insurance options, hospital privileges, spoken languages, and practice hours. Providers can also easily identify who else is on their patient’s care team as well as what other clinicians should receive test results, lab reports and other treatment summaries.


 

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