June 3, 2024

Hawaii Behavioral Health Dashboard: Defining the Magnitude of the Behavioral Health Problem

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- And so I'm really excited, Dr. Baker, thank you, thank you for connecting all of us. And so we are lucky today to be hearing about a data dashboard that's been created there in Hawaii for behavioral health and social determinants of health, and so we have Doctors Becker and Dr. Cleveland here. Dr. Becker is a PhD in Health Economics, did biomedical research as her postdoc, and specializes in addiction research, and is a principal investigator on this project they're gonna be speaking about. And then Dr. Cleveland has a PhD in Microbiology, emphasis in bioinformatics, and a Cyber Infrastructure Researcher and Affiliate Researcher at the Hawaii Data Science Institute. And so really excited to hear your presentation today, and so the general plan for everybody on here, it's gonna be about a 20-minute presentation and then 10 minutes for us to do Q&A, so I'm gonna hand it over to Doctors Becker and Cleveland then.

- Hello, everyone. Thank you for having us. Again, yeah, my name's Sean Cleveland, I'm Associate Director of Cyber Infrastructure for the Information Technology Services Group at the University of Hawaii system, so it's the full 10 campuses.

- Hi, I'm Treena Becker, and we're working on this behavioral health dashboard and it mainly focuses on substance use disorder and mental health, and this came about because of funding from CDC OD2A, which is Overdose Data To Action.

- So yeah, as we get started here, wanted to highlight, this wasn't just work from me and Treena, there was a group of folks that contributed to this work, so there were all the folks in the Department of Health that did this, Amy Curtis, who's the CDC Grant PI, that's part of the OD2A, and Treena's the University of Hawaii PI on this, and then the University of Hawaii team that does a lot of the technical work that's part of my team here with Jared McLean, Jennifer Geis, these folks here, so big thanks to all of these folks that made this possible. So the topics that we'll cover is, I'll just briefly touch on OD2A as Treena talked about to give a little background, our approach to the dashboard, the current sort of dashboards that we're sort of offering, the current data sets, a walkthrough of just a couple pieces of the dashboard for you, some of our feature plans, and then Treena's gonna talk a little bit about the social determinants of health related to the data dashboard here. So as Treena mentioned, Overdose to Action, this OD2A is this CDC cooperative agreement aimed at expanding this sort of surveillance in public health, or higher quality, more timely, more comprehensive data collection for drug-related things, as well as using these things to drive prevention strategies. And so these pieces of data and things like this are part of what's feeding into this dashboard, and this dashboard is one of the focuses of trying to disseminate this out and drive, I think some of these prevention strategies and policies and communicate out this information. So our approach to this dashboard was we wanted to make something that was clean and usable, we wanted to make this web content accessibility guideline AA standard applicable, so making sure that the information was presented clearly and accessibly, we wanted something that was maintainable, so we built this on the Department of Health infrastructure 'cause they have long-term plans to do other things with this data, we built it on top of a CMS that folks who are not technical can interact with, and do low-code, no-code stuff, as well as Power Bi, which is a lot of the epidemiologists in the DOH, they work a lot with Power Bi, and so we are doing all of these things so that it's a maintainable infrastructure. And then we went with a beta rollout this last October, so Gmail was in beta for 10 years, we figured, you know, might as well roll this out in beta, and let folks know that we're actively developing on this, they can actively provide feedback, and make requests and things like that. So that was our approach. Current dashboards that we have within the system, currently there's kind of four areas here, so substance use, since this is OD2A-driven, substance use was a big thing, so dashboard's relating to overdoses, hospitalizations within the state, then having mental health dashboards here, so looking at emergency room discharge information with mental health diagnoses, then looking at the co-occurrence of substance use and mental health diagnoses within hospitalizations and other Department of Health services, and then finally looking at crisis care information, so looking at 911 calls, crisis phone line calls, and what that sort of tracking that sort of information. So current data sets that were covered that are presented within these dashboards is, as we mentioned, the Emergency Department discharges, so this is called Laulima, there's also what's called the SUDORS dataset, so this is the State Unintentional Drug Overdose Reporting System, currently this only covers Oahu County, that we're confident about related to this, so we're gonna be replacing this with another data set from CDC Wonders data here in the next few weeks, then there's also, as I mentioned, this Crisis Calls resource, so this is this Hawaii Coordinated Access Resource Entry System, so this is the 911 and other crisis online information, and then we have some other Behavioral Health Service Administration information from the Hawaii Department of Health, so Alcohol and Drug Division, Adult Mental Health Division, and then the Child and Adolescent Mental Health Division and some of their Developmental Disabilities Division information there. Now what I'll do is I'll kind of just give a brief kind of walkthrough of the current dashboard, so I'll look at the sort of overview landing page, and what that's all about, looking at the mental health dashboards, and then a little bit about that. So this is the landing page for the dashboard, we base, you know, like as I said, it's Beta, so we call it right out there, we try let people know that this is substance use, mental health, and crisis care-related information. We do provide a walkthrough here for folks if they've never seen this, you know, what's this all about, how do you interact with the dashboards to ask questions that you may want to answer. This is kind of the main overview dashboard pane here that has some of the key indicators that the Department of Health wanted to highlight as in some of the overdose deaths related to substance use, looking at, say, methamphetamine or cocaine, or opioid overdoses, so some of the things that a lot of the other stakeholders are very interested in, so just bringing that information right to the front page, looking at discharge information related to substances, and substance use, as well as co-occurring, and then a few things related to mental health, and then the sort of the CARES information, like the crisis information. So the way these different cards are sort of broken down into is they have that indicator, they have a little trend line here for what things were for a specific time, so depending on what the number of years that we have for the different data sets, so either 2020 to 2021, or 2018 to 2022, in some cases, kind of that little trend line, and then what kind of the total is for the indicator for that time span, and then we use these little icons to help indicate, you know, if this is substance, if it's mental health, a little visual indicators, and then we have these icons people can click to get more information about, you know, one of these specific indicators. This is the mental health dashboard. So to start, we cover a couple different things, but we'll take a look here at the Emergency Department discharges dashboard, so all of our dashboards have a similar format, we went with putting the most important information that we want people to take away initially, like in the top left corner here, since that's kind of how most folks read, so this is the total for discharges related to mental health disorders for the time span of 2018 to 2021 in here, and so we track these different mental health diagnoses in this dashboard, and so we report those, and those total up to this number, the 64,000 number here. All of the different tables and graphs are interactive within this dashboard, so you can click on say the year 2021, and that will filter down all of the other pieces of information to 2021, so when we did that, we see that our mood affective disorder, so things like depression for diagnoses are in like 4,900 for the year 2021, if we wanted to say, "in 2021, how many females had mood effective disorder diagnoses in their Emergency Department discharge information?", we see that was 2,176. And if we wanted to see folks that were, say, in the 65 to 70 age group that were female, we can hold down Control, click that, and then, so we're filtering by 2021 female in this age range here, and we can say if we wanna see what the mood affective disorder is, it's 131 for 65 to 70-year-old females in the year 2021, we had 131 diagnoses. So that's sort of how these dashboards kind of works, so you can kind of continue to sort of drill down by just adding on additional filters by, you know, interacting here with the visuals. We sort of went with our charts initially, since a large majority of folks only understand a few different visualization charts, so we try to make it very simple for folks to use, in addition to the mental health diagnoses here, I did mention that we have a co-occurring, so we could also cross-filter with substance use diagnoses in here if we wanted to, to look at, say, methamphetamine use across the different four years, we could also look at 2021, so looking at, say, mood effective disorder with a secondary diagnosis of methamphetamine use would show us 1,025 different folks in that group. So that's just a brief introduction to this dashboard here related to the Emergency Department discharge information. So if we look at the data here, so the data that's directly related to that dashboard, this Laulima Data Alliance here, all of those different mental health diagnoses groupings that we saw are based on the ICD-10 codes, and this is kind of talks a little bit about that dataset and how those are grouped and derived. And we do that for all the different data sets that we present within the dashboard. So we go back here to the mental health in addition to discharge, we also have information about adolescent, child-adolescent mental health division dashboards, and who's using those services, as well as who's using Adult Mental Health Division services and you know, where they're accessing those services and things like that, and the same thing from the Alcohol and Drug Abuse Division. So that is a brief introduction to sort of the dashboard there. Future plans are gonna be adding more data sets, and updates to this dashboard, we'll be looking at adding things that are more on the weekly and daily timescale for updates, as we go forward, right now we've taken a lot of legacy information, and look at things that update, you know, on a monthly or yearly time span, but we're looking at a little more fine grain with that. We'll be having more stakeholder meetings like input and feedback on the design related to this, and we also are working with some external folks on user experience and accessibility pieces, so all those things will be integrated into future work. This is a URL for the dashboard for folks that are interested, and we'll share this out after the presentation, and so with that, I will hand it over to Treena, to talk about social determinants.

- Thanks, Sean. So Sean showed you that he demonstrated, gave you a quick walkthrough of the data dashboard, and now I'm gonna tie this back to the social determinants of health. The most important contribution is of data dashboards like these is disaggregation of data into clusters, or categories of factors, or variables of interest, and data disaggregation helps us to identify or pinpoint these health disparities. It gets us looking at social determinants of health, and by that, the definition is non-medical factors that influence health outcomes, where our patients or people were born, they grow up, play, live, work, and age. Now, previously in the data dashboard, you saw more data about discharges after an ED visit because of an opioid use disorder, it could have been for methamphetamine or for cocaine. But future plans for this data dashboard is to then add additional variables of interest related to the social determinants of health. Next slide. So coming back to this behavioral health dashboard as an example, in the future, we will be adding a data set that contains patients getting substance use disorder treatment in Hawaii state. Now this data set tells us patient demographics, and Sean also had some patient demographics on the dashboard just now, gender, age range, and county. But we can go beyond that, we can drill in, get granular, and that's where we get to find out more about the social determinants of health. So within this substance use disorder treatment dataset, what else do we know? We'll also know sources of patient referral, such as the patient's self-referred. So this then, if you think about it, could be a patient, who has realized that, "You know what? I admit it, I'm an addict and I need help", and so then there's self-referral into treatment. Then there's also, in the data, we know if the patient has been court ordered into treatment, and this is then the patient is criminal justice involved, and this is related, right? As a condition for release, just even getting information about the source of referral tells us a lot about where this patient is coming from, and not just that the patient you know, is going into treatment, and so then we can work backwards to find out more, you know, how did things fall apart for this patient. Within the same dataset, we also know the number of substances used, it could be up to three substances. So there's very detailed information, starting with single substance, plus substance number two, plus substance number three. And so then this enables us to ask questions about medical implications of single substance use versus polysubstance use, because there's these interactions, we can see in the dataset, and this is just not in Hawaii, but across the mainland too, so all states, alcohol tends to be the first substance. There's that joke that it used to be that marijuana was the gateway drug, well it isn't, it's alcohol, 'cause alcohol is a substance too, but there's a tendency for people not to think about alcohol that way, 'cause alcohol is legal, and available everywhere. So then what are the medical implications, and then this is for people within the healthcare system, right in the emergency room, and in hospitals, it's one of the medical implications for the liver, heart, and respiratory system. And this is the information that we're getting, right? That is if we take these bullet points above here about the non-medical factors, and we work backwards, it helps us a lot with messaging. Don't do drugs, do less drugs. So if we're gonna have this kind of messaging or comms, right? So prevention strategies, public service announcements, this data set that will be joining the dashboard will tell us about race and ethnic minority groups, because especially for Hawaii State, which is the largest race-ethnic minority group state in the country, we are at 72% of race-ethnic minority groups, also tells us then about people, about patients, who are immigrants, and they're first generation, and this then also gives us clues about whether they have health insurance coverage, and the extent of that health insurance coverage, and also, of course, language, but it's not just language, the culture, their are cultural views of substances. So for example, Tongan immigrants, kava clubs, there's a thing called kava clubs. So the men go hang out in kava clubs, and then they party on, and it's socially acceptable. So kava clubs are great. So it's not substance use. And then for immigrants, recent immigrants from Laos view opium as very normal use, using opium for everything, from, you know, having, you know, if they have a headache, and they're in pain, instead of Tylenol, it's opium. We can take all this, right? And work backwards to figure out how did this patient get there, how did this patient go into treatment, and then what can we do to just help out before we get them into treatment, and hopefully, you know, they'll be in recovery, and we'll be there for them, using all this information about their lives, and to get them on the road, right? To like living healthy.

- Oh, Doctors Becker and Cleveland, thank you so much. This is, it really, it highlights the ... I mean for the Health Innovations Technology Committee, right? The use of technology and data in a useful way, and it's impressive to me. So I know that there's probably others on this call that are much more deep into data, and the cyber infrastructure, so I'm sure they may have some questions too, so please feel free, anybody who wants to drop in the chat or just unmute yourself.

- It's Mark. Hawaii has a lot of methamphetamine, and one of the issues related to meth is heart failure. And until recently, I didn't use an ICD-10 diagnosis for methamphetamine use disorder when I would see our kind of recurrent heart failure patients, and I'm just wondering if you know if that's getting captured somehow, or are those patients kind of escape data?

- So Mark, CDC is working on it right now, and finalizing the ICD 10 codes, or fine-tuning it for methamphetamines, because the complications with ICD-10 codes and methamphetamines is that it falls in with a whole bunch of amphetamine-type stimulants. So they're coding everything now, and they're being more detailed, because nationally there is an uptick. So not just in Hawaii, in the rest of the country, methamphetamine use is spiking, it's going up for everyone, I'm not sure if you know this, but there are, since I think the year 2000s, three waves of the opioid crisis. So we are now entering the fourth wave, so the previous wave was prescription opioids, and hopefully, you know, we knocked that wave off, right? You know, by bankrupting the Sacklers, Purdue, and everybody else, it ain't over, the bloodshed ain't over. So I think there's ... So methamphetamine is a part of the next wave nationally together with opioids, so it's polysubstance use, and I think that you will be seeing more of these cases nationally. Sean, there's a question for you about crisis care.

- So right now the crisis care basically is just relating to the crisis call tracking information, there, I think, probably will be some other data sets that will be be added by DOH related to crisis care, like looking at like beds in different communities for crisis, I think, intervention and things like that, but right now, it's tracking the crisis hotlines, and things like that, and some of those responses. So right now it's just reporting, I think we're just tracking the dates and the call numbers related to that information within the dashboard, but there's gonna be I think some more granular related information potentially some maybe what some of the ... This is currently what we're looking at for some of the crisis line information, just tracking, and seeing volume between the different crisis lines, and looking at when they occur, and so some of this I think is related to you can look at trends within, you know, weekends and weekdays and seeing that oftentimes, you know, middle-end of the week there's a lot more crisis, you know, call lines than there is on the weekends oddly enough, so that's some of the information we're currently tracking with that.

- Question for you from Emily here, "Who are your target audiences? I mean I know you have the funding, and so that's definitely a stakeholder, but who are some of those target audiences that you really hope who may not have thought to go to this dashboard?"

- One of the, right? Target audience is the Native Hawaiian community. There is a disproportionate number of Native Hawaiians who are incarcerated, and also the data is showing that the Native Hawaiian community tends to be more on meth than the other race and ethnic minority group.

- Thank you. Other questions from the group? Hope I'm seeing John Manning, "Are there any plans to extend to other social determinants of health domains, homelessness, food, housing insecurity, and then are working connected with Gravity Project?"

- Yes. Within Hawaii State is judiciary, they would like to track more data about the homeless, because we have, unfortunately, also a disproportionate number of the homeless, who are arrested, and then they cycle in and out of the system, and so we would rather that doesn't happen, for sure about housing and the homeless.

- Thank you.

- Yeah.

- Any -- Oh yeah, go ahead, Sean.

- Yeah, I don't think we're doing anything with Gravity Project to my knowledge.

- [John] This was John. Thank you for answering, they are actively developing a lot of the codes that you would need as you're trying to track a lot of those metrics. So food insecurity, housing, homelessness, transportation, and employment status. There's a technical version of the Gravity, and then there's also the standards development, like the terminology work stream, and so if there's any interest in actually getting more involved in having the data, so that you can then be able to track this more easily from an ED setting, that may be helpful.

- All right. Well, thank you, Treena and Sean, really appreciate you taking the time, one to build this, two to make it look so nice, and three to come to speak to us today about it, so we can provide your contact information to our committee if anybody has any other questions, or thoughts, or any other suggestions too. So thank you very much.

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