Across the healthcare industry, artificial intelligence (AI) is making significant strides in streamlining administrative tasks and supporting clinical decision-making and care. But for providers serving clients with intellectual and developmental disabilities (IDD), including autism spectrum disorders (ASD), there’s still confusion about the feasibility and safety of the application of AI in the IDD space.
Core Solutions recently hosted an AI executive roundtable, “Shaping the Future of IDD and ASD Services,” to provide clarity and achieve mutual understanding around AI’s capabilities and limitations and to discuss current and future AI use cases in IDD care. Here are key takeaways from this highly engaging and informative meeting.
Leaders across IDD healthcare facilities aspire to use AI in ways that will deliver efficiencies while transforming their operations and care delivery. They have many ideas for leveraging AI to address unique provider needs and better serve their IDD population, but internal and external pressures on both providers and payers are not making adoption of these solutions easy.
In recent years, IDD healthcare facilities have felt the strain of urgent problems from many sides. A staffing crisis has left resources thin, and care delivery has been slowed down even further by a lack of provider training and language barriers for staff who speak English as a second language.
Anticipated Medicaid cuts and the possible cancellation of the 90% federal match rates for the Affordable Care Act (ACA) expansion left payers, providers, and states worried about the level of care quality that can be provided, loss of access to care for large swaths of the IDD population, and funding and reimbursement for IDD healthcare services. Concerns about changes to work requirements for people with disabilities also threaten care access and client volumes, while potential stoppage of IDD waivers by the Centers for Medicare & Medicaid Services (CMS) and a return to block granting sow concern as well.
The implementation of performance-based contracting has proven a challenge — notably in Pennsylvania, where three different rate levels were put into place, but no provider has yet to receive the most enhanced rate. Funding for family support models is also woefully inadequate.
What’s more, there’s belief that waiting list issues and a “silver wave” of retirees will add pressure on Medicaid funding, while pushes for universal electronic health record (EHR) adoption are piling stress on to providers who may not have the resources to ensure the software can be purchased and used effectively.
In these uncertain times, IDD AI adoption may seem like a low priority — but the efficiency it creates for back-end operations and the care delivery areas it can support, like AI in clinical decision-making and routing for care, are hot topics among leaders in the space.
Given the demands on their time, IDD leaders are focusing on AI use cases with the highest potential impact. Roundtable attendees discussed several use cases they’d like to see in their practices.
In recent years, DSP turnover rates have hovered around 50%. A 2023 survey suggested that leaders focus on recognition, education and skills development, and better supervisor relationships to improve retention. To gain back time for learning and development and the direct client care that they most enjoy, IDD providers are eyeing the use of AI in clinical decision-making and other areas, including scheduling, note-taking, and reporting. Some AI technologies have already been implemented but aren’t well-integrated into existing systems, said one roundtable participant.
Several attendees stressed the importance of delivering better care and outcomes with the help of IDD AI, including finding ways to share and learn from the immense amount of data and insights that machine learning-backed solutions can gather. One possibility discussed was comparing similar client profiles and identifying which treatment approach had better results for the client. This focus on quality over quantity of services aligns well with a move into value-based care and payment, noted an attendee.
When provider notes are inaccurate or don't comply with regulations and standards, opportunities are missed for billing revenue. This challenge is particularly complex for IDD providers, who often meet and speak with multiple people simultaneously rather than holding 1:1 client sessions. Ambient listening and documentation AI can simplify these complex encounters by allowing hands-free recording, which frees up providers to engage more actively in the moment, and which can then summarize notes, highlight likely symptoms, and recommend treatments based on the discussion.
With no clear end to the DSP staffing crisis in sight, an effective use case for IDD AI could be more personalized, AI-generated care planning that provides clients with education and resources for living an independent life, noted one participant. Tools for AI in clinical decision-making are already supporting treatment plan generation in multiple areas of healthcare, and machine learning could easily help create learning paths that empower IDD clients and better engage them in their journeys.
The possible benefits of IDD AI are impressive, and tools already used in other healthcare fields have shown that attaining goals like the above quickly is not just possible, but likely, given the speed with which AI learns.
Still, as a relatively unknown commodity in IDD healthcare, AI has raised many questions among leaders hesitant to commit to a purchase and implementation of AI solutions. Roundtable participants reviewed the biggest obstacles standing in the way of the fair and safe use of AI and steps to take to safeguard their facilities and client information from potentially harmful data collection and sharing.
Large language models (LLMs) are the engine driving AI data outputs, but despite their processing power, they have multiple weaknesses, shared Core Solutions President Ravi Ganesan. These include:
There are further ethical considerations that providers must take into account to protect their facilities and IDD population, Ganesan added, like the level of autonomy IDD AI is given, patient awareness of when AI is used, and accountability for AI-driven decisions.
While these risks might seem daunting, they can be mitigated, Ganesan shared, through human intervention and transparency.
Unlike other industries, in clinical settings, AI can’t be granted full autonomy, said Ganesan. Although administrative duties may be mostly automated, the stakes are too high to not have a human being involved in decision-making that impacts care — nor in reporting that can influence industry-specific approvals, like the ability to bill for services covered by IDD waivers.
It’s also essential to have a real person review how the AI they’re using was built, since clinical AI platforms will often start with a foundational model like OpenAI or Meta and then be trained with additional data. Reputable vendors should be able to produce a model card, Ganesan said, which shares this information and acts as a tool to make a more informed buying decision. Also, as more payers use AI to approve or deny claims, providers should ask how the AI is making approval determinations to ascertain if the data it’s fed is biased toward denials.
When using AI in direct client care, providers should engage in good clinical practice and be open and transparent, getting informed consent and explaining what information is being collected and how it will be used.
With these guardrails in place, IDD providers are more likely to have the confidence and certainty needed to pursue and adopt AI tools that deliver on all of their goals.
In the past few years, Core Solutions developed multiple behavioral health AI solutions, including “Core Clinician Assist” tools that surface hard-to-identify symptoms, provide health-related social needs (HRSN) information at the point of care, identify anomalies in operations and care delivery, and enhance revenue cycle management. Core’s Cx360 GO mobile application combines several functionalities, including transcribing provider-client sessions anywhere, summarizing notes, and providing insights on client symptoms and treatment options.
These are just some of the tools that are strengthening operational efficiency by reducing the time it takes for behavioral health staff to complete monotonous administrative tasks and helping to improve care and outcomes by leveraging AI in clinical decision-making. Their applications across IDD and ASD care are sure to give a head start to leaders in addressing their own urgent needs.
Existing Behavioral Health models are already functional with Core Solutions. If you would like to see them in action, contact us today.