Public Health Informatics is the science and the art of taking raw data and turning them into useful information for health policies and programs. It takes all those data out there and turns them into knowledge of how people can live healthier lives. But how does this process work? My name is Jessica Hill, and I work at the Public Health Informatics Institute in Atlanta, Georgia. This podcast is my quest to learn about informatics and how it’s made people’s lives better. How has it made my life better? And really, why does it matter? So, I’m ready. Inform Me, Informatics.
Hi, this is Jessica Hill. Public health practitioners often talk about policy as an important tool for improving health outcomes. For example, smoke free policies impact the places where people can and cannot use tobacco, and they have impacted exposure to secondhand smoke and smoking rates more generally. But how do policies impact health information systems and informatics practice? To answer some of these questions, I spoke with Megan Douglas from the Morehouse School of Medicine about her work at the Intersection of Health Policy and Health technology.
I was really excited to talk to Megan. She works at the Morehouse School of Medicine’s National Centre for Primary Care and is the Associate Project Director for Health Information Technology Policy within the Transdisciplinary Collaborative Centre for Health Disparities Research. Much of Megan’s work focuses on how Health Information Technology Policy can support improved health outcomes and can advance health equity. We started our conversation with Megan explaining a little bit about how she became involved in this work.
I am an attorney by training. And before going to law school, had done some research at Emory doing some basic science work, and thought I was going to get my PhD in neuroscience. And I worked with enough students doing that and did enough of that research that I realized I was actually more interested in how I research and how the evidence impacts people. So, that drove me to look at law school and health care in particular.
So, I went to law school at Georgia State University. They have a really strong health care law program. And had a few experiences there as a law student that really honed in my interest in policy and public health. So as a student, I did an internship with a medical legal partnership, which is really where attorneys partner with physicians to address some of the non-medical related issues that their patients might have.
So, this partnership was between the Georgia State College of Law and Children’s Healthcare of Atlanta. And we would work with patients who maybe, for example, had come to the hospital for asthma, and kept coming back to the hospital for asthma, and the physician realized that that child had mold growing in the house. So, they’re able to work with the attorneys in that medical legal partnership to address the mold issues in the house and then hopefully prevent the recurrence of that child coming into the emergency department for their asthma. So, that experience really introduced me to the issues of policy and how by addressing health at a policy level, we can really have an impact on populations as a whole.
So, then how did you become involved in research specific to like health information technology?
An opportunity came up through the Transdisciplinary Collaborative Centre for Health Disparities Research. One of the sub-projects focused on studying how health information technology policy impacts health disparities. You know, that was because of the policy aspect of that, and I also knew how much health information technology was changing the delivery of health care and how physicians practice medicine, how patients engage with medicine, that it was a really interesting area for me to explore further.
So, I’m really interested to kind of dive in and learn more about how health information systems really can either influence or perpetuate disparities in some ways, or be able to be a tool to help address health disparities. But I feel like some people might think, well, the data are the data and they say what they say, so how does the information system interact in that?
Sure, that’s a really great perspective and great question. A great way to ask that question, too. Because I strongly believe in the data, and that’s one of the things that can sometimes drive me nuts on a daily basis, how much I want to rely on the data and how much I want that to drive my decisions, and try to be very cognizant of making sure that the data is driving the decisions.
But to your question about the data is the data, it comes down to how we are analyzing that data and how we’re breaking up that data and what we’re looking at. So, if we are looking at a certain health outcome across the general population, we may never see that that outcome differs by race, ethnicity, gender, sexual orientation, gender identity, all of these different factors that we do know, because we look at the data that way, there are differences.
And especially when we’re talking about these minority populations, sometimes they make up such a small percentage of the population, that doesn’t mean that their outcomes are less important. But from the data and from the analysis perspective, we may think we’re doing a really good job because population level is showing that we’re improving outcomes. But then when we stratify by those different factors, we see what we’re doing really well maybe for this group of people, but we’re not doing so well for this group of people, and what interventions can we put in place to improve the outcomes for this group? So, just making sure that we’re not allowing the overall large groups to mask some of those disparities that we see within the subgroups.
Could you give me a specific example how that functions?
Well, one of the projects that we worked on when I first started here was looking at some of the policies around how health information technology is being adopted by healthcare providers. A study was done showing that among Asians in California that rates of colorectal screening varied across racial subgroups. The disparities were seen in Chinese, Korean and Vietnamese individuals compared to whites, but there was no disparity seen in other Asian subgroups.
So, in that instance, the more effective intervention to reduce the disparity would be to target the Chinese, Korean and Vietnamese patients where the disparity was seen, rather than an intervention to all of, you know, the whole Asian population within that clinical setting. So, that’s really where the granular race and ethnicity data provides the more meaningful information that we can target those disparities.
And information systems come into play with that intervention in the being able to use clinical decision support. So, within the physician’s electronic health record, the patient may have self-identified as being Chinese. And for that intervention, when that patient, either when that patient is with the physician, the physician might get a reminder, make sure that the patient has had their colorectal screening or have a discussion with that patient about why this is important if they haven’t had it.
Or even from a practice level, they could use the data in the electronic health records to create a list of all the patients who are behind on their colorectal cancer screening. And they can then again stratify by those subcategories that we know there are disparities within and those that are not, so that then they have better information to use at that clinical level to identify the population that they want to reach out to.
OK, so then how does national policy really impact and connect with the data available to providers at the point of care? Enter the HITECH Acts, whose full name is the Health Information technology for Economic and Clinical Health Act. This is federal policy, so there’s going to be a lot of words and a lot of names. This is part of the American Recovery and Reinvestment Act of 2009. That act included various measures that focused on modernizing the country’s health infrastructure, and HITECH was one of those measures.
HITECH has a lot of parts, but I’m going to explain some of the key concepts that most closely relate to some of the work that Megan is going to talk about. Number one, HITECH is the policy that led to what are called “Meaningful Use” requirements. “Meaningful Use” for electronic health records means that the records need to be able to send and receive data electronically, and to be able to do so in a way that will improve care.
So, number two, how will a provider know if their electronic health record meets those requirements? Well, they need to use a certified product. How do you know if your product is certified? Well, that comes from another federal body called the Office of the National Coordinator for Health IT or ONC. So, HITECH is saying “meaningful use” needs to happen, and the ONC is certifying products or electronic health records that can help you meet those requirements.
So, why would providers go through this? Why would they use certified electronic health records? Ah, because the Centre for Medicare and Medicaid Services or CMS has an incentive program. Which means that the providers and hospitals get paid more if they use electronic health records that are certified in helping them fulfil “meaningful use.” Because of this federal legislation, there would now be standards about what data were and were not required in order for electronic health records to be certified.
Megan was the lead author of a 2015 paper in the American Journal of Public Health that looked at what demographic data were required in the different phases of HITECH. This paper was called “Missed Policy Opportunities to Advance Health Equity by Recording Demographic Data and Electronic Health Records.” Check it out.
Why is all this important? Because what demographic data fields are required to be in an EHR and what data providers are required to collect both impact the information we have about the health of patients and communities. Such information is foundational to being able to identify and then to address health disparities. In this next part, Megan will explain her team’s work on that 2015 paper and what they found.
So, we looked at the rulemaking process. So, the agencies CMS and ONC put out this proposed rule. And then that’s open for public comment for usually about 60 days, anyone can comment. A lot of times it’s, you know, those stakeholders that are directly impacted by the policy, and they’ll tell the agency in formal or very informal ways how the policy and how the proposed rule will impact them. And they’ll make suggestions about how the proposed rule could be better, what parts of the rule are going to really negatively impact them, and what parts of the rule are positive and should definitely be kept in that rule. And then after that comment period closes, the agency takes all of those comments into consideration and puts out a final rule.
So, what we did was look at the proposed rule and compared that to the final rule with regard to this demographic data that physicians were required to collect. So, we found that in the first stage of rulemaking, and this was for “meaningful use” phase one, that the five basic categories of race, two categories of ethnicity, preferred language, and gender were required to be collected. Now, during that comment process, a lot of people proposed granular race and ethnicity, and let’s get to some of those subgroups that we know there are disparities.
People were also proposing collecting sexual orientation and gender identity, knowing that there are disparities related to those populations as well. And people with disabilities, identifying more functional status questions rather than focusing on the medical issues for people with disabilities. What we saw after stage one was that very few of those changes were actually adopted, there was no granular race and ethnicity, no sexual orientation and gender identity, no disability. They did a little bit of work around preferred language, but that was about it.
And so, we also looked at stage two. And again, kind of saw a very similar theme where they propose some pretty basic categories. And they even proposed some of those expanded categories that I mentioned, but those did not make it into the final rule again. So, at that point that’s when we did the research and published the paper. And soon after the paper was published, the proposed rule for stage three came out. So, at that point, again, we saw a lot of these categories in the proposed rule, but this time, we had the evidence behind us showing that it had been proposed before and hadn’t made it into the final rule. So, we were able to submit our own public comment, and really use the evidence and use the data around the health disparities to include in that public comment.
And we also had talked to a lot of healthcare providers who already collected that data but didn’t have the fields in their electronic health records to record it. So, that really highlighted that gap between what the technology vendors were required to do and what the physicians were required to do. So, even if the technology vendors provided the fields within their products, then those physicians who wanted to collect that data could record it and use it in their EHRs.
So, we also worked with all of those partners that we have, and there were a lot of different organizations who had been recommending this and we’re on board to continue including those provisions in their public comments. So, we did all of that work. And when the final rule came out, and that was in October of 2015, a lot of the changes had been adopted.
So, the race and ethnicity categories were expanded even beyond the subcategories that we had been talking about. They actually adopted a CDC standard that provides many, many, many options, more options than an individual healthcare provider would really be able to handle. But that at least provides them the flexibility to decide which categories are most important to the populations that they serve.
They also included sexual orientation and gender identity. So, now electronic health records will at least have those fields, so physicians that want to collect that about their patients and want to, you know, and recognize the disparities within those populations and do something to address them will have the fields to do so. Another category is social, psychological and behavioral health data. We know how much the social determinants of health impact health disparities, and things like housing, things like exposure to violence, food availability and access.
Those are all issues that impact health, and a physician would be able to make better decisions and engage their patients being informed of those issues. And now there will be fields within the electronic health record to collect some of that data too. So, it was really exciting to see the policy advance in that way and understanding just how much of an impact this can have for health equity.
I mean, at one point “meaningful use” and the standards and certification program had different requirements for demographic data. Is that right? Did I understand that correctly?
Well, so for stage one and stage two of “meaningful use,” the two programs really mirrored each other. So, you know, we didn’t get into a whole lot about how all of the financial and resource decisions came into developing the technologies and the EHR technologies. But really, from that policy perspective, the agencies didn’t want to put too many requirements on the technology vendors that went beyond what the “meaningful use” program was going to require of the physicians.
So, you know, from that perspective, it made sense that the two programs evolved as kind of mirror images of each other. But then, as we talked about, also that there were a lot of things that physicians wanted to be able to do with the technology and data that they wanted to be able to record in that technology. And clinical needs vary so much from setting to setting and provider to provider, that finally with the stage three “meaningful use” in the 2015 standards and certification criteria, that was really the first time that we saw a difference in the two programs. And the requirements for the EHR technology went beyond what the requirements were for the physicians, but really allowed that flexibility of now, physicians, you can collect some of this data that you didn’t have any fields in your EHR to do so before.
I see. OK, so maybe previously, if physicians were keeping paper records, they may have had the ability to record a lot more of the demographic data, but now, they’re really streamlined into just filling in those fields in the EHR. And so, after around three, they were able to have more information that was in that EHR.
Yes, that is correct. And not only would physicians collect this on paper, those physicians that did have their electronic health records that didn’t have the fields would collect some of these pieces of data that we’re talking about in the nutrition field, or in the allergies field, even though that wasn’t what the data was about. But they would try to leverage the technology so that it’s still in the electronic health record in some way. But sexual orientation in the allergies field, yeah.
That’s a data scientist nightmare.
So, you know, that was an intrinsic challenge, an intrinsic issue with the technology that hopefully has…it hasn’t been solved, but it has definitely been improved with this latest round of rules.
Even when policies are a certain way, people kind of build their own workarounds, and so in some ways the policy needs to catch up with how people were using it.
Of course, most of the time policy has to catch up with what’s actually happening on the ground. And that’s the challenge with policy.
But that’s so interesting, because I would often think about policy as kind of bringing people together and trying to set that vision for how things go. So, it’s always both at the same time.
It is. It is.
Now, that’s so interesting.
And yeah, I mean, policy in many ways is visionary, but usually those visions are based on kind of the best performers who are already there, and how do we get the rest of the country, you know, to follow within that vision?
And so, what are some of the questions we could answer now that we wouldn’t have been able to before?
I think the information systems in healthcare really automate the process. You know, a lot of the practices that we talk about for actually addressing disparities don’t necessarily require the information systems, but it’s really that automation of the process and making manual tasks much easier and equitably implemented.
So, you know, one example that we have is around preferred language. So, that’s not something that necessarily started with electronic health records. Healthcare providers identified that, obviously, if they speak a different language from their patient and the only information and education materials they have for their patient is in English, that that patient is not going to understand what they need to do and what they can do to improve their health.
So, preferred language is one of the categories, though, that is collected in electronic health records. And now with automating that process, we have hospitals and healthcare organizations that will run a report of the patients that are going to be coming into the clinic over the next week, and their language needs. And then that allows them to prepare in advance all of the materials that all those different patients and the different languages that they may be using can be met. So, they can have all of the educational materials in whatever different languages their patients are going to have.
So, just that automation of that process has been able to improve the efficiency of providing those services to those patients. And then I mentioned the clinical decision support, as well. So, you know, for example, with lesbian women who are less likely to receive mammograms and pap smears and some of those preventive screenings, the clinical decision support can provide the reminder to the physician to make sure that the patient has received those screenings, that if the patient is hesitant, or you know, has objections to receiving some of those services, that that’s an opportunity for the physician to engage with that patient and have the conversation around why they’re important. And identifying as a lesbian woman, we know that there’s a disparity there. And we’re seeing higher rates of cancer and worse outcomes in some cases, so, you know, just want to make sure that you’re receiving the healthcare services that you need.
And that’s, again, kind of the automation of what before would be intrinsic, if there was a provider who, you know, was very culturally competent in those matters and really prioritized making sure that those conversations were had, then, you know, that would happen. But a lot of providers are incredibly busy and not able to see all of these different factors for every patient that they see. So, just providing that automation and flagging and some of the support around those clinical decisions.
And hearing you talk about how different aspects of social determinants of health are recorded in the EHR, it codified that those factors are a part of health, so it’s not something outside of health, or running parallel to health, which is often how in my experience, it kind of feels like something that is slightly outside of, you know, clinical care or an individual patient’s experience. So, this is like literally putting it inside what we are calling the health sphere. And that sounds just so exciting.
It is. And it is, it’s a huge change. And those silos of public health and healthcare are being broken down. And that realization that they don’t live independently of each other, that both community and different patient factors and the health care system have to work together, and the public health. You know, public health in many ways can be kind of the conduit between the two. You know, public health for so long has done a really great job of collecting the data and collecting meaningful data. And that’s what even the, you know, the granular race and ethnicity came from CDC, and those are standards that they use regularly in their national surveys.
So, you know, public health, having that experience and having the knowledge and the methodologies to really do a good job with the data are skills that clinicians who may not be trained in those methodologies can use. And that’s a partnership that we are seeing more and more of, and I think will be incentivized more and more. And we’re even seeing some of that incentive, those incentives in reimbursement, and some of these policies that are really trying to break down those silos, so it is, it’s exciting.
I wonder if we could go then to like how you define informatics?
Sure. This is an entertaining conversation to me, because when I started talking to you about the podcast, informatics is just not a term that I really use on a day-to-day basis with the work that I do. But then we started talking about it and even started talking about the definition a little bit, and that made me start thinking about how much even though I don’t use the term on a regular basis, it runs throughout everything that I do.
So, for me, informatics is really about making data actionable. And that is the biggest thing we work with, so many physicians who are, you know, again, kind of struggling with that adoption of the technology. And, you know, one of their biggest complaints is, “I have too much data, I don’t know what to do with it, and it doesn’t mean anything to me the way that it is.” So, I think this is really an opportunity for some of those entrepreneurs and those that do have those skills in public health to develop some of those tools that put the dashboard in front of the physician with the data that they need to make that clinical decision that is going to improve the health for that patient that’s in front of them.
And I know that’s a big leap from where we are right now, but that’s where we will be and that’s when health information technology will really have been maximized. You know, health IT is the tool to help us improve health outcomes and advance health equity.
Of course, having the fields for that demographic data in the EHR is one thing, and the quality of the data entered into those fields is another. But hey, that’s another discussion for another day. Thanks again to Megan Douglas from the Morehouse School of Medicine for discussing her work with us. If listeners want to learn more about the Transdisciplinary Collaborative Centre for Health Disparities Research and the work that they do, check out their website at healthpolicymatters.org. That’s healthpolicymatters, all one word, .org.
And of course, you can always visit phii.org to learn more about the Public Health Informatics Institute and all of our work, Inform Me, Informatics is a project of the Public Health Informatics Institute and the Informatics Academy. Our theme music is called Carnivale Intrigue, and was composed by Kevin McLeod. Finally, many thanks to our production team, especially Piper Hale, our producer, editor and “meaningful use” expert extraordinaire. I’m Jessica Hill, and you’ve been informed.
Many thanks to our production team, especially Piper Hare—Hare—
You know what, I’m changing my name. I like it better.