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Behavioral Health News Spotlight on Excellence – An Interview with Carol Cassell and Nadeem Ramjan from Advanced Health Network & Recovery Health Solutions


David Minot, Executive Director of Mental Health News Education, the non-profit organization that publishes Behavioral Health News, interviews Carol Cassell, Executive Director, and Nadeem Ramjan, Director of Data Strategy & Analytics at Advanced Health Network & Recovery Health Solutions (AHN|RHS). Carol and Nadeem detail their work to help behavioral health providers understand the value of a data-informed culture and its positive impact on the entire care continuum.

Interview Transcript

David Minot: Hello, and welcome to the Behavioral Health News Spotlight on Excellence series, where we feature exceptional leaders and innovative healthcare solutions that are raising the standards of care in the behavioral health community. My name is David Minot and I am the Executive Director of Mental Health News Education, the non-profit organization that publishes Behavioral Health News and Autism Spectrum News. Our mission is devoted to improving the lives of individuals living with mental illness, substance use disorder, and autism while also supporting their families and the professional communities that serve them by providing a trusted source of science-based education, information, advocacy, and quality resources in the community.

Today, we are speaking with Carol Cassell, Executive Director, and Nadeem Ramjan, Director of Data Strategy & Analytics at Advanced Health Network & Recovery Health Solutions, otherwise known as AHN|RHS, operating in affiliation. AHN & RHS work together with the shared goal of improving the health and wellness of individuals experiencing behavioral health disorders, often further complicated by medical and social determinants of health issues. AHN|RHS is a network comprised of almost 50 New York State OMH & OASAS licensed agencies providing comprehensive community-based services to individuals and their families experiencing behavioral health issues throughout the five boroughs of NYC and Nassau and Suffolk counties in Long Island.

Working as a team, Carol and Nadeem have been busy gathering quality metrics data from their member agencies and have worked to put this data in a format that is actionable and visualized so that providers can easily determine where there are opportunities for improvement and where they are doing well. With their impressive visual reports, they are helping providers understand the value of a data-informed culture and its positive impact on the entire care continuum.

Carol and Nadeem, thanks so much for being here today.

Carol Cassell: We’re happy to be here. And I look forward to sharing our learning as we’re learning on this journey, and hopefully can share that with others as we go forward here.

David: Let’s start out by telling us about the Advanced Health Network and Recovery Health Solutions and the services that are provided.

Carol: Our providers represent both mental health and substance use inpatient detox/rehab, residential, outpatient, and recovery programs across the five boroughs of New York City and all of Long Island with multiple locations including close to 50 providers and over 200 locations with several upstate serving about 50,000 patients annually. Where we’ve been focused on, we often hear people say, “Data is the asset of the future.” But what does that mean?

Because we all collect a lot of data, whether we collect it through our EMRs, assessments, and care plans. But how do we take that data and really transform it into a way that it becomes actionable and measurable to support what we do every day. The competitive advantage that we’re looking for is to get paid for the incredible work that our providers provide every single day. It’s an investment. If the data is an asset, an asset has to be managed and invested in and there’s investment through cultural change – a cultural change being with people, processes, workflows, and technology itself. It starts with (and we sometimes take this for granted) what data we collect, where we collect it, how we collect it, and then how we summarize it in a way that we can then publish it and have it visually actionable so that decision making can occur.

As we’ve worked with some of our providers, we’ve discussed how we make the data we have collected visually useful and actionable at an executive level, at a management level, and at a staff level. It’s frustrating to staff when they’re collecting data and they don’t have an appreciation for how their data is really informing care coordination plans, quality improvement efforts and the like. That investment is a significant one that we’ve been supporting our providers in.

The other piece that we are working on as a data-informed culture is to bring together medical data, mental health data, substance use disorder data, and social determinants of health data in a way that we can package it, visualize it, and utilize it to inform our quality improvement efforts, our care coordination activities, our contracting, and our grants. We’re fortunate that we’re emerging with all this data but making it actionable is where we’re focused. We have built a robust business intelligence platform that we’ll share more with you – Nadeem has been leading that effort .

We’ve also learned from the healthcare community as a whole that a referral management solution would be helpful. Too often emergency rooms, hospitals, physicians in the community might identify the need but they don’t know where and how to refer. And so, we have built a platform that supports that data flow. The other piece not to be taken lightly is addressing consent management because in this space of mental health and substance use, while we all are familiar with HIPAA, there are additional consent requirements because of the nature of the data that we have worked on to help our providers best manage. And then all of that, moving towards management of grants, bundled payments, incentive programs, and visualizing the data. So, we’re working with our providers on quality measures, performance improvement, ad hoc reporting, care-coordination, bundled payments, and clinical viewers and we look forward to sharing some of that with you.

David: Why should providers support the transformation to a data-informed culture?

Carol: It’s their future that is going to be predicated on making informed decisions focused on certain population cohorts. So, if we build the data warehouse by bringing in all of this data from these multiple sources, we’ve been able to define population cohorts in terms of those who have complex needs – complex needs being a population cohort where they have a medical condition combined with mental health, substance use disorder, housing or employment concerns that requires a different level of care management/care coordination than someone who might just present with a substance use disorder or alcoholism. In building this data warehouse, we brought in all this data; we’ve collected it, we’ve normalized, and we’ve standardized it so that we can, through actual data analytics, look at retroactive practices – what their performance is and meeting HEDIS metrics, what their performances in 30 day, 60 day, and 90 day engagement periods.

We’ve also started to develop comparative analytics so that if you and I are both in the same space, how is my performance vs. your performance and what shared learning can come from those best practices that we’re deploying is measured in the analytics. We are now also starting to move into predictive analytics because where we can intervene earlier by engaging individuals where they’re not presenting repeatedly at the emergency room – that’s where we want to get. We really want to focus more on the prevention in the recovery along with just treatment. Again, we’re looking at how do we visualize it from looking at cohorts; provider cohorts, population cohorts, whether it’s individuals who have come out of the justice system or the veteran’s cohort. Because how we engage with these populations are different and they should be because we want to be patient-centered and person-focused. And then, again, supporting the providers in how we optimize their EMRs in the PSYCKES database that New York State provides to our mental health and substance use providers. I’m very passionate about that. In my background, I’ve worked with implementing EMRs in hospitals, for primary care physicians, and for the behavioral health community. It’s one thing to implement it but it’s another step to optimize all the functionality and capabilities that exist in that EMR. So, as we’re helping our providers and building that data from culture, we’re actually helping them optimize the investments that they’ve made in their technology platforms and also helping them on their consent-management.

David: Can you provide some details on what AHN|RHS is doing to support its behavioral health providers?

Nadeem Ramjan: We’re all quite familiar with the standard HEDIS metrics. What we’ve tried to do is to peel back the layers and say, “How can we identify why patients are not adhering on certain metrics?” We want to peel back the layers to get to actionable data. But in doing so, we want to be able to leverage the existing data collection tools. Sometimes you find our new project and then we say, “Let’s build a survey tool to get that data” or we take all types of approaches which can create a lot of administrative burden. So, one of the strategies we have employed is to look at existing data collection tools and squeeze as much value out of them as possible. On the right, here are some metrics we’ve built internally, which start to tell the true story of which patients we need to focus our attention on. For example, we have an AMA report where we’re able to track patients that don’t complete treatment and we look at patients who are staying less than 30 days, shown in red. We take this information, we drill down with the providers, and we look for the commonalities among the patients that are not staying the minimum 90 days as recommended by various programs. And then lastly, we built an entire suite of SDOH measures. One of them is here, where we look at patients who, when they come in for treatment, they’re unemployed, and by the time they leave, they have some level of employment. So, in doing this, we’re able to look at the non-traditional data, but utilize the existing data sources that are there to again build deeper insights into why we’re not able to move the needle on certain metrics.

Carol: We have another example to share, which builds upon the PSYCKES reporting that the state has built the infrastructure for and our providers are working with.

Nadeem: So here we show an example where we have some data that is perhaps dated; it’s not as real time as we’d like it to be. However, it doesn’t mean we can’t utilize it. And so, what we have here is an example of a trended report where we’ve trended about two and a half years’ worth of data. And it tells a really strong story where, if the provider, for example, is not improving on a measure over a period of two years, then definitely they need to look at some new methods, some new engagement techniques to employ with their populations.

We trend data over time to show how providers are doing and we also compare them against their peers. We do some ranking reports where we rank our mental health outpatient providers against each other, our SUD providers, those that have those that are Certified Community Behavioral Health Clinics (CCBHCs) those that are inpatient. And we also rank them with respect to how they’re doing on measures, compared to their peers, and we do it in a blinded way so there’s no feelings of highlighting people. What this data shows is that if you are, for example, “the worst” among your peers, what often is the story is that you are dealing with a far more complex population. So that also helps them as they engage with different grant applications and MCOs to say, “Look, I’m not able to perform as well as my peers because I’m dealing with a much more complex population set. What can I do here? And what help can you offer me, so I can improve the care that’s being delivered.”

Carol: Our next example is with population health.

Nadeem: We try to empower providers so that we’re giving them data at the PHI level, at the patient level, building registries and so forth. But then we also do work at a population health level, and we span such a wide range and have so many locations and providers. So, we pulled together demographic data, medical data, mental health, substance use, and SDOH. We pulled together all this data so we can tell the full story of what’s going on with the patient. We look at clusters. You can see over here, we have some examples of some geo mapping we’ve done. This is really trying to get into this space that is getting more attention now, which is to be more neighborhood-focused.

A lot of grants that are coming out now ask you to focus on a collection of contiguous zip codes in a specific region. We drill down on that and we say, “These folks have high rates of diabetes, they also have high unemployment rates, and they also suffer from depression.” So, we’re able to combine all this data so we understand the population. And then on the lower right, we try to make sure that we’re also being culturally sensitive. We look at data, both public and internal, to see what the ancestry of the folks in those regions is so that, again, we’re being culturally sensitive. This is an example where we tried to pull all the data together in one place and put it together in one presentation so that, as we’re applying for grants or for targeting populations, we see all the factors that are affecting people in a specific region. But certainly, a patient in the South Bronx would have a different set of needs then one perhaps in Western Suffolk County. And that’s exactly what we find as we do these types of analyses.

Carol: For the next example on where we are utilizing these analytics, we are pleased to share that we have initiated a pilot project with HealthFirst called “Walk With Me.” This is a medication-assisted treatment (MAT) program. We spent about three years developing a clinical model and we have built a program that embraces a patient-centered approach that captures data for what’s termed as wraparound or add-on services; services that are not billable today but are a means of having better engagement of the individual in the program. And so, it’s a pilot program to test how this bundled payment approach supports a clinical model, all supported by data; data that gets captured, gets gathered, and summarized. We are just now rolling it out. We anticipate that in about a year, we’ll have sufficient data we can learn from so that we can then modify this program as we go forward.

Nadeem: To summarize the work we do, in terms of supporting our providers, we’ve spent a significant amount of time on consent-management. And one of our guiding principles we have is trying to make sure we find solutions that don’t create administrative burden for both our providers as well as the patient. And so, we’ve leveraged existing consents that have been well-vetted within New York State and we’ve trained our providers, through a series of webinars, on how to implement those consents while also helping them build scripts so they can clearly communicate what the patient is consenting to. That’s been a great success, I would say. There’s no conversation we have about improving care that doesn’t involve consent. So that’s something that you really have to have a clear strategy and focus around if you want to help improve patient care and break down barriers with data silos.

In terms of quality management and reporting, we show some examples of some reports that we’ve created at the population health level as well as at the provider level. We share performance reporting with our providers on a quarterly basis so they can look at how they’re doing and if they’re adhering to goals with respect to different grants that they’re a part of. For data visualization, similarly, we leverage existing tools that are available at the state level and create visualizations on an ad hoc basis to again support the management of various grants.

We look to optimize their use of the EMRs across our IP. We have 15 different EMRs and we work with all of our providers to help them make sure that they’re making use of the data that is there. This leads to a lot of shared learnings because as we become experts in different EMRs, we’re able to show our providers how you can use these two or three reports and basically answer 80% of the questions you have. And again, as we also have this wide network of MH and SUD, we are able to come together, form workgroups around specific initiatives using learnings from specific providers and then sharing it across the board – so everyone in the IPA benefits.

Lastly, we are working towards this goal, which is a goal for everyone in data and in healthcare, to break down silos into interoperability. We have strategic partnerships with specific vendors that we’ve hired in the IPA that will support interoperability so that if a patient moves from the outpatient setting to the residential setting, the outpatient provider can see which meds their patients are receiving. So, we break down those barriers, again leveraging our vendor relationships and strategic partnerships so that we can have data flowing between providers even if they have different EMRs. I think that’s really critical because, while it’s easy to focus on data visualization, sometimes providers just want a simple, one line statement in their EMR: “Did the patient pick up their meds?” or “What meds were they prescribed?” We support that as well to have better patient care, break down silos, and hopefully see better outcomes for our patients.

David: You are certainly doing a lot to support for providers, they are lucky to have you! What have you learned throughout this journey?

Carol: That’s a great question because it is a journey. And as I shared in my opening comments, we all think that we have data and that data is just magically going to transform itself into these reporting capabilities that’s going to help us in our day to day management and our strategic leadership but it’s not that easy. What we’ve learned, or what we’ve confirmed, is that the build of a data warehouse is a journey. All this data coming in needs to be normalized and standardized and it takes time to do that.

We’ve also learned that in the build of the data warehouse, not everyone captures data in the same way. Also, how you capture data and how I capture data can look very different. I might ask questions very thoroughly and capture a lot of data points and my colleagues may not. So, the data capture is an operational investment that really requires orientation and education of the workforce in terms of why it’s important and how it’s going to be used.

And then there is an investment in the infrastructure itself. There is a lot of data that gets captured. And what we’ve learned, and what we’ve had to do through our shared learning with our providers, is to prioritize and focus on what matters. What we don’t want to do, as the cliche goes, is to be managed by the data but rather manage the data so that we are prioritizing and focusing on those key data points and data elements that, when made actionable, will help contribute to improvement in care, improvement in quality, and will support the ongoing funding that’s needed to make the investments in the interventions, the workflows, the processes, the resourcing, and the technology that’s required. In the end, what we’re about is improving the lives of those who are receiving mental health and substance use services. That’s the summary – it’s a journey. It’s an investment in people, technology, workflow, and infrastructure. It is a change of culture to be data-informed and data-driven where we manage the data going forward.

Nadeem: I think you said it well that it’s really a journey. And we’re so happy with the partnerships we have with our providers. We always try to meet them wherever they are and remain focused on what we can achieve today rather than focusing on something that is going to take years and years. Taking a phased approach is critical when it comes to data. We’ve been able to lay that out through all the collaboration we have with our providers, making sure we’re listening to what they’re saying and providing them with the greatest value for the data. We make sure that we’re not just producing data for the sake of dashboards but making data that’s actionable, relevant, and again, barrier free where we’re not creating a burden for them. We’re doing whatever we can today and then laying out a path for the future for things that are much needed like interoperability.

Carol: I would add to that the way we put data visualizations together is that we take it out, we’ll get feedback and we get their input in terms of what metrics matter or what data matters. It is a collaborative, shared learning and development culture that we are building. And because of that, it really comes down to trust. There are many questions about the data and if it can be trusted. That trust comes from the engagement with the leadership of the organization, getting their input and their feedback on what’s important and how we can make it available. It comes from the workforce, those who are collecting the data, and learning how their data is being used to management. Trust in the data and in a data-informed, culturally-driven organization. It’s making data a part of almost every meeting; how it’s being used and worked with. And then the processes; how is the data captured and at what level of detail is it captured? And then, as those processes are defined, it’s looking at the technology. It’s the enabling technology that supports the capture of the right data at the right time by those who are skilled to capture the data so that it can then be gathered, summarized, and visualized in a way that leadership can lead the culture of the organization to be data informed and data driven and demonstrate that to those external partners that we are the quality driven provider – in our case the AHN|RHS network – that services want to be provided and be received from.

David: This has been very informative, and I had no idea that so much is being done with data to help these provider agencies improve their services and improve outcomes. It’s wonderful what you guys are doing at AHN|RHS. Thank you both so much for your time today to share your impressive work with the Behavioral Health News readership!

Carol: And thank you for the opportunity of sharing this story because data is something that we all need to make an investment in, learn from, grow with, and share with each other. So, we thank you for the opportunity to share our story.

For more information about the work Carol and Nadeem are doing at Advanced Health Network and Recovery Health Solutions, please visit and stay tuned for our next installment of the Behavioral Health News Spotlight on Excellence Series.

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