In Vermont, an Opt-In versus Opt-Out HIE Debate | Rajiv Leventhal | Healthcare Blogs Skip to content Skip to navigation

In Vermont, an Opt-In versus Opt-Out HIE Debate

July 7, 2016
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A July 3 article in VT Digger, an independent organization covering investigative news in the state, revealed a stimulating story about concern regarding health information exchange (HIE) patient privacy. The piece, written by Erin Mansfield, noted that officials from the Vermont Information Technology Leaders (VITL), the publicly-funded Montpelier-based organization that operates the Vermont Health Information Exchange (VHIE), and warehouses patients’ electronic health records (EHRs) across the state, is considering a policy alteration that would change the HIE’s patient consent process from “opt-in” to “opt-out,” despite concerns from the American Civil Liberties Union (ACLU) of Vermont.

Of course, within an HIE, an opt-out policy means that patients must manually remove their records if they choose not to participate in their state’s HIE (the default is for patients’ health data to be included from the start), while under the current opt-in policy in Vermont, patients must give doctors permission to view their records. VITL is required to discuss the policy change with the Office of the Health Care Advocate at Vermont Legal Aid, and the ACLU Union of Vermont, according to the report.

For the patient consent process, there is no single national law, but rather policies vary state by state. According to Office of the National Coordinator for Health Information Technology (ONC) data, as of September 2013, seven states had an opt-in policy, 30 had an opt-out policy, and 13 had either a combination of both, a hybrid model or rules were pending.

In Vermont, with the opt-in policy, about 96 percent of patients still give doctors access to their records, according to Rob Gibson, the vice president of marketing for VITL, per the VT Digger story, and the remaining 4 percent don’t want to share records. But Gibson said his organization is now hearing complaints from doctors who say that the policy causes an administrative burden, the story stated. Mansfield reported, “What we’re starting to hear from people is that having a requirement like we do now, with having everybody being required to opt in creates extra work and administrative burden and sort of limits the flow of information sometimes,” Gibson said.

For provider members of an HIE, not having to ask each patient for consent is undoubtedly a time saver. Doctors also point to the idea that it could be tough to remember which patients opted in or out when they transmit the EHR data to an HIE. On the contrary, Allen Gilbert, executive director of the ACLU of Vermont, told VT Digger that “There is a greater likelihood of informed consent when people have to make a decision to ‘opt in,’ and the ‘opt out’ starts with a default that the person is in, and it requires no fault to have the person essentially enrolled in the system.” He added that with an opt-in policy, patients will be more likely to know what they’re agreeing to.

We should also keep in mind the HIPAA Privacy Rule’s Individual Choice Principle, which states that “Individuals should be provided a reasonable opportunity and capability to make informed decisions about the collection, use, and disclosure of their individually identifiable health information.” To this end, in the VT Digger story, Kristina Choquette, vice president of operations for VITL, recently told state regulators that doctors describe the current opt-in patient consent policy as “HIPAA on steroids.”

For me, a lot of this comes down to patient education. If consumers are informed and educated up front about the policy of the HIE, and where their data might be going, choices will become easier and trust will be gained. According to a 2014 Medical Economics article on patient consent, the Massachusetts eHealth Collaborative (MAeHC) built HIEs in three communities and incorporated an opt-in policy. Front-desk staff in doctors’ offices gave patients brochures about the HIEs and asked them to sign consent forms. More than 90 percent of patients consented, the article stated.

I recently spoke with Christina Galanis, president and CEO of HealthlinkNY, which operates the HIE in the southern tier of New York and the Hudson Valley, who feels that patients need to have the right to choose where their data goes. Galanis said that even if the patient chooses “deny,” that’s something the organization has to comply with. “I have been doing this for nearly 12 years, and I know that you have to go to the patient first, and the patient has to be educated,” she told me. For HealthlinkNY, an opt-in HIE, patients don’t have to say “yes” to the whole HIE, but they can choose which provider organization their data goes to, she added.

According to the ONC’s website, the U.S. Department of Health and Human Services (HHS) does not set out specific steps or requirements for obtaining a patient’s choice whether to participate in an HIE. The agency also doesn’t take a stance on opt-in versus opt-out. However, it says that adequately informing patients of these new models for exchange and giving them the choice whether to participate is one means of ensuring that patients trust these systems. Providers are therefore encouraged to enable patients to make a “meaningful” consent choice rather than an uninformed one, ONC states.  

Clearly, the waters are pretty muddied when it comes to patient consent and HIEs. There are arguments on both sides of the table, but I have to imagine that most logical people would agree on two core points: 1) patients should not be surprised by where their records go and 2) to accomplish the fundamental goal of health information exchange, providers will need to see the information they need to provide the highest quality of care.

And with that, a level of trust between providers and patients must be established. Betty Rambur, a nurse practitioner and member of the Green Mountain Care Board, overseer of VITL, said it perfectly in the VT Digger story: “It is really this complicated balance between providers having the data they need and the people having the privacy that they deserve.” So while patient consent issues might not be totally ironed out in the immediate future, it’s good to know that multiple states are considering the effects of both policies, and most importantly, how they each affect the patient.

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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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