Tiered Care, Technology, and the Future of Mental Health

welcome to Mental Health Evolution, a podcast about what's changing in mental health and why it matters. I'm your host, Rachel Harrison, inviting you into honest conversations with people from all perspectives in the field. Clinicians, tech founders, investors, insurance companies, and all the folks in between. Let's explore what's working, what's not, and what's next.

Welcome back, everyone, to the Mental Health Evolution podcast, where we explore changes happening in the mental health industry and discuss why they matter. Although we do often have guests, today's episode is a solo discussion. I want to share some observations based on a research article that I read, as well as some additional articles and questions that have been coming up for me as I reach more research, talk with clinicians, and watch how mental health care is being delivered in real time. What I think is interesting is there is very clearly a couple of different sides of the story here. There are people who are thinking about how technology can take over mental health, right? How do we not even need a person to do this anymore? And then there are others who are thinking about how could technology even replace anything remotely? And so for those people, you know, they're thinking technology should be avoided at all costs. We don't want that. We don't want anything related to that. And then there's the middle. Then there's how do we integrate these two things? How do we look at that? And that's kind of what I want to talk to you about today, is the middle. So if you are on either side of this, you may not completely agree with a lot of things. You may even have questions about what I say. And this is more about asking questions. I'm not predicting the future, I'm not taking a hard stance, but exploring a model that is already emerging. It's right out there in the research. I'll show you the articles that it's in and one that blends technology, human care, and escalation across different levels of need and thinking through what that could mean for clients, clinicians and organizations and businesses involved in care. So let's get started with some of these articles so that you have an idea of where we are launching from. This first one is the research article I was talking to you about and referencing already in the intro, and it's called the Evolving Field of Digital Mental Health. And this peer review article outlines how AI and digital tools are currently being used across multiple stages of mental health care, from prevention to post treatment, monitoring. And this is from PMC published this. And I am actually going to look at these different stages and, and some of the things that are happening in each stage of treatment. So we're kind of using this article as a way to sort of structure what our conversation is going to be about. The next article is from the apa and if you haven't noticed a trend, I just want to give out a shout out to the apa, the American Psychological Association. In so many of our episodes, we are using things that they are producing and writing because they are standing out right now as a body that is actually providing guidance and publishing guidance on all kinds of things. But this article in particular says health advisory on AI, Chatbots and wellness apps. And this article looks at where AI based tools may be helpful and where limitations, risks and ethical concerns remain. I really appreciate that they are coming from a clinical standpoint and looking at what could be useful and also what we need to be aware of. And then this third article is the first published chatbot trial from Dartmouth. And I think we talked about this in another episode, but it yielded some positive results. So this is looking at a therapy chatbot and showing research shows that there are some improvements. And as we all know, this field is a field that is based on research. All clinicians want to do things that are shown to be effective, that are evidence based. So I think we have to look at it when we have some of these, and I think we have to look at it critically, we have to ask the question of, okay, great, it showed a benefit and are there any holes in this research? What do we need to be concerned about? So looking at it from all angles, and I really encourage you to do that for yourselves. The next article is called AI is providing emotional Support for Employees. But is it a valuable tool or a privacy threat? And this explores a lot of the workplace use of AI support tools and the tension between access, effectiveness and privacy. And I think privacy is something that. Well, I was having a conversation last night with another clinician and they were saying, listen, nobody cares about privacy. Nobody cares that tech companies sell people's data as a means to make more money. Nobody cares. I don't know. Is that true? I care. I think it's important. But I think it is possible that we're pretty desensitized to people selling our data. We have data breaches where our data is out there in who knows where. And I think it's become kind of commonplace. So maybe people don't care, maybe they do, but there are some things that we need to think about when it comes to privacy. And in the clinical mental health field, privacy has always been a number one concern. It's our ethics. Confidentiality is number one. The first thing that a clinician says to a client is that everything we talk about is confidential with these exceptions. The exceptions are regarding safety, child abuse, things like that that are reportable, and that's about it. Everything else is by our ethics supposed to remain confidential. So it's an interesting question, is that shifting? Are those ethics shifting? Maybe, maybe not. The next article is called AI Mental Health Tools Breakthrough or Band Aid. Again examining whether these tools are meaningfully expanding access or are they a risk at becoming substitutes for care when these systems are under strained. I think, you know, it's also affordability piece if, if someone, if a child has free access to AI to talk about their thoughts and feelings, or let's use an adult, an employee, is that going to be where they turn rather than someone who is trained and licensed and following a set of ethics? I think those are really good questions to ask. And lastly, from Clinical judgment to Machine learning, this article looks at how AI is beginning to influence clinical decision making and what that may mean for professional roles. And that's from the Society for Psychotherapy. So a really interesting perspective of where AI is supporting clinical judgment or rethinking how we're doing decision making with AI. So let's dig into some of these articles and some of the distinct stages of treatment. So across many of the recent articles on digital mental health and AI, I do see this theme that breaks into these distinct stages of treatment with each different treatment and goals. So for example, one of the supporting articles I outlined, the very first one up there, the PMC article, it talks about several different stages. So I'm going to list those stages and then I'd like to kind of break into what each one would potentially utilize for technology. So the first is pre treatment and screening. This is interesting because if you think about different things like pre treatment, to me pre treatment and screening may be different or maybe the same. It sort of indicates that this is somebody who is interested in getting treatment. They've identified that they want treatment. And this stage may be the stage where they are just thinking about what can support them until they get to see the clinician what kind of data is helpful for them to gather before the clinician sees them. And a lot of these things really can be utilizing technology. So things like self referral tools, symptom checkers, triage systems, risk screening, think about the apps that do things like that already. Think about the benefits for a client. Maybe logging their mood even before they come into therapy, maybe trying out a few different skills, or maybe a mindfulness app as they are waiting to get into care. These are things such that could be used with technology. I think also about there are lots of online ways to do evaluations, screening for depression, screening for anxiety. All of that could be done before treatment starts. Potentially there are some risks to consider, like what if somebody has a negative reaction to those screenings? What if they think oh my gosh, this means I'm depressed and that makes me even more depressed or suicidal. I mean, there are some safety risks and things to consider here for sure, but making that process more automated, utilizing technology could be really useful both for clinicians and for clients. The next step that is outlined here is treatment, meaning the actual treatment with a licensed mental health clinician. And how does that use technology? The biggest piece I see here, there are a couple things. There are certainly tools out there about recording sessions and doing notes for you that has become pretty popular in the industry. They are also things like what about pharmacology? How do we integrate AI with pharmacology? How do we do combined treatment approaches? How does decision or support decision making tools or support help? So I personally still don't love the idea of recording a session. I think notes are treatment planning and conceptualization and I think there are also privacy concerns to consider. But despite AI writing our note for us or creating a treatment plan for us, what if it can help us think through case conceptualization? What if it can find nuances? What if it can track treatment goals in more detail and perhaps aid us in a more robust conceptualization? This would have to be specifically created with models in mind that include like the DSM and other knowledge accessible to make sound clinical suggestions. And I think the key here would be utilizing any of those support systems would have to be evaluated and reviewed by a human trained, licensed clinician before moving in any particular direction. One of the things I fear the most with AI tools that are presented to help clinical ease, which great clinical ease is great. And if we ever lose the part where a clinician reviews the notes, reviews the treatment plan, reviews the diagnoses, reviews the suggestions, I think we're going to see a lot of problems. I think the reality is there might be some of the legwork that is done for us, but if we don't review it as a clinician ourselves, then we are not and make edits, make adjustments, we are not serving our clients very well, so those are a few of the pieces potentially with the treatment phase now. Also in this article they talked about post treatment and monitoring. So there's again some interesting ideas here like AI tools used for follow up care symptom monitoring, risk assessment, relapse prevention or treatment adjustment after formal therapy ends. So this would be considering it a phase where formal therapy is no longer happening. But what if there's a check in? What if there could be an assessment automatically given three months after treatment to see where somebody is to make suggestions for what might be supportive if they are struggling after three months, three months after treatment or if they are doing well. You know, kind of looking at those pieces could be a way that technology could assist. The caveat here for me is that any kind of safety access is a must have. There must be a way for any technology system to access a live person who can help if there's a safety emergency. Anytime we are asking technology to ask someone questions about their mental health, that safety planning piece I believe absolutely needs to be in place. That is one of the biggest gaps that I see currently. And then we have general support and prevention as a way a place where technology is being used. This is already pretty commonplace I would say. These would be standalone tools that are designed to maintain well being, reduce stress or present mental health problems in a non clinical or community population. And similar to the pre treatment, these already exist, right? They're being used. Think of apps that help people rate their mood every day. Think of apps that help people with sleep hygiene. Think of apps that help people do mindfulness or meditation every day. These are things that all support wellness and they are standalone in their just providing a service, a supportiveness to wellness. And the last area that the article highlighted was clinical education using AI tools to train, assess or upskill mental health professionals, clinical students or educators. And I think this is an interesting question to ask ourselves. How can we train clinicians using technology? Perhaps practice sessions where AI is our client or perhaps we can have a database for clinical education that allows clinical clinicians to access it when they need to. If I need to ask about a particular symptom of OCD, for example, and what the best method is to treat that. Is there an education tool that technology can give me? I can of course Google it. I could ask ChatGPT about it. Do I get accurate enough information? I think it that would need to be the piece that we are really making sure that it has sound clinical education, not just something that is searched on Google. That would maybe need to be a Diff a big difference. It kind of makes me think about the idea that if any of you are part of like a membership group, for example, we have the EMDR circle, that is a database and training videos is all about EMDR to help clinicians when they're having stuck points or questions and to be available 24,7. But this might be a step beyond that of taking that kind of sound clinical data and having it accessible with a question, right? With one question and then maybe a video comes up for you and it's like this is a verified, clinical, appropriate technique that you could use in this situation. I don't know. Interesting to think through and looking at all these stages together. I think it's possible that we're moving toward a tier or stepped model of mental health care. In fact, I know that we have a guest coming up that's going to be talking a little bit about this. But in this kind of model, many people might first encounter support through these low intensity tools. Thinking about the basic ones, I talked about apps for sleep, stress, meditation, those kind of things. And if those aren't sufficient for someone, then they could escalate. Maybe it escalates to coaching, maybe it escalates to group therapy, then it escalates to individual therapy, then to specialized care, and when necessary to outpatient or inpatient or excuse me, and when necessary it escalates to inpatient or higher levels of care. I think this model serves to improve access. If you think about it, it's a lot of what happens in an emergency room. There's a triage, there are levels of care, getting people to the right care at the right time. It could certainly help with provider shortages as well. It's already implemented in some places and like I said, we're going to talk to an insurance provider that utilizes that especially for their employee offerings. But I think it's interesting what it could mean for different stakeholders in the ecosystem here. So for clients, there are clear benefits to having earlier access and fewer barriers to entry. They might not be waiting for an appointment or a person. They may have some tools that can help them early on. At the same time, there's a risk of bottlenecks if pathways are higher to higher levels of care are unclear or delayed or unsafe, underfunded. For clinicians, this raises important questions about professional roles and identity. Where do generalists fit in to a system where technology may address some of the lower level needs? If you are a clinician that specializes in teaching clients how to manage stress, can this be done with an app or a chatbot, what skills are still necessary for that in person? Relationship based therapist? And what specialties are needed as the tiered approach happens? As higher levels of care are needed for businesses and systems, I think technology companies may benefit from being embedded earlier in the care process. Traditional providers may see changes or declines in referrals if some needs are resolved through technology. At the same time, specialized services could see an increase in demand, potentially even a surge if escalation pathways are well designed and functioning as intended. So overall, this episode aims to just begin to spark the ideas, the questions about these emerging models. They are all over the research. If you dig into these articles, you're going to see this is how a lot of tech companies, this is how a lot of treatment providers, this is how a lot of university systems are starting to conceptualize what care would look like. It's reshaping clinical work, potentially referral patterns and the mental health care ecosystem as a whole. So I wanted to present these really as a starting point for you to think about in whichever part of this ecosystem you're in. And what does this mean for you if you start thinking about this differently than maybe the vantage point that you're sitting in today? As always, thank you for listening to today's episode and I hope that exploring this developmental model in mental health care raises some questions for you and we'd love to hear from you always. So take a look at those articles, let us know what you think and we will be back next week. Thanks for listening. Bye for now.