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Nearly one million people have died since 1999 from a drug overdose, and 75% of overdose deaths in 2020 involved an opioid — a tragic statistic that has increased eightfold since 1999.
Compounding the problem? Almost one-fifth of the country has a diagnosed anxiety disorder — and this can start as early as puberty. Chronic anxiety can lead to worsened mental and physical conditions such as depression, substance abuse, chronic pain, poor quality of life, and suicide, impacting the nation’s already strained healthcare system.
Let’s not sugar-coat it: We are in crisis. And current treatment models for substance use disorders (SUD) and mental health aren’t working.
Unlocking the right data — at the individual patient and population levels — is critical to reversing this crisis.
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How can data help?
Healthcare data is complex, fragmented, and often incomplete. In behavioral health, the data reality is most dire.
Patient data on mental health and substance abuse is often kept separate from other medical records due to a lack of interoperability, regulatory constraints and underinvestment in health IT. But, if providers could easily and securely access reliable behavioral health data, they could develop more effective treatment plans based on a patient’s complete history.
This concept sounds simple, but it’s more complex in practice.
Picture this: A patient with a history of opioid addiction is brought into the ER after a car accident. The attending doctor prescribes an opioid for pain management without knowing the patient’s record. The patient unknowingly takes it and subsequently relapses.
What are the roadblocks to data integration and insight for healthcare providers?
The healthcare industry continues to struggle with a lack of mental health and substance abuse data. The reasons for this gap stem from many ingrained and institutionalized realities.
For starters, the healthcare industry and SUD and mental health treatment providers in particular are late to adopt new technologies and processes.
Conspicuously excluded from the incentives provided by the Health Information Technology for Economic and Clinical Health Act (HITECH) of 2009, behavioral health has been left behind in technology access and funding.
The persistence of the problem is evidenced by the drumbeat of reform from policy centers and industry organizations. In a June 2022 report from Medicaid and CHIP Payment and Access Commission (MACPAC), extending funding through state Medicaid programs to drive health IT adoption was explicitly cited as a critical step to addressing gaps in access, outcomes data, and oversight.
Behavioral healthcare’s current fee-for-service payment model also financially rewards doctors and hospitals for the volume and cost of services they provide — not the quality of outcomes. Until a significant shift toward value-based care models, even the most impactful technology could be a hard sell.
Compliance, industry regulations, privacy concerns consistent roadblocks
The Code of Federal Regulations (CFR) Title 42, Part 2 aims to protect the confidentiality of addiction treatment records of any person who has sought treatment for or has been diagnosed with addiction at a federally assisted program by restricting the information that can be shared across providers.
Part 2 has been a central area of confusion and contention, especially in the shadow of the opioid crisis. Recent changes helped alleviate some of the stress created by Part 2, but questions and challenges around consent management and data segmentation remain.
The good news is that we have a template for the path forward. Pediatrics faced the same general challenge years ago. Through grassroots engagement and provider input, carve-outs for pediatric data and restrictions have been successfully implemented and operationalized.
Process lag for updating records results in “bad data”
Since many facilities still store records via paper, the time required to update records and enter data electronically is substantial. Moreover, most healthcare data is incomplete and often lacks crucial connective tissue to tie it together for analysis and outcome improvement. To make data useful, these providers must use an advanced data approach that can consume, connect, and clean data from multiple sources across time.
Disparate and legacy systems remain a lasting hurdle
Many care-critical applications and technologies remain married to purpose-built bespoke infrastructure that is difficult and costly to bring up to date. And these systems are often siloed, limiting care teams’ ability to develop comprehensive insights.
Empowering patient care through data
There are a number of processes and data best practices that facilities and providers can use to build a strong data foundation and better collect, share, and analyze data.
Data depth and accuracy
Advanced data science algorithms that account for missing data can correct systematic errors in SUD and mental health data. Further, data sets like the social vulnerability index can enrich patient-level information. Robust analytics and better patient outcomes can then follow from these improvements in the integrity and quality of data.
Data insights and trends
Artificial intelligence (AI) and machine learning (ML) can comb through datasets to find meaningful patterns and insights. By leveraging these powerful technological capabilities and innovations, behavioral health providers can more quickly and effectively make up for incomplete values and standardize disorganized data.
What insights and value providers can derive from data has increased even when data is less precise. A good analogy is in gasoline production. Dynamic tools and technology can quickly transform crude data into a refined product usable for predictive and even prescriptive analytics.
Simply put, SUD and behavioral health facilities, often at much earlier stages of data maturity than their traditional healthcare counterparts, stand to benefit the most, curating the data they currently have in actionable ways.
By future casting through dynamic modeling, providers can identify people at risk of developing a mental health condition and offer preventative care before a disease progresses, stopping a disease before it even starts. Calls for prevention efforts are increasing as recognition of the prevalence and cost of behavioral health conditions rises.
Across SUD and behavioral treatment providers, predictive analytics could give insight into which behavior patterns indicate a disorder or if a patient is at risk of relapse. The infrastructure for predictive capabilities already exists — patient engagement solutions, electronic surveys and analytics. Industrializing, standardizing, and making these capabilities available to all providers will augment individual patient care and the industry’s alignment to measurement-based care.
What is the future of data-driven SUD and mental health information?
The increasing availability of data brings a substantial opportunity to help advance patient care, especially when it comes to mental health and substance abuse. Identifying patients who are predisposed, have early warning signs, are currently battling addiction, or are in treatment, will be critical to the future of holistic health care.
SUD and behavioral, as well as traditional healthcare providers, can leverage data science, predictive algorithms, and other technology-driven approaches to build more competent and effective treatment models from the start. And providers can empower ongoing operational and clinical improvements by harnessing the power of their data assets and the proven advanced analytic capabilities that are already solving some of the most complex patient and population health challenges.
Richard Daley is CEO of Sunwave Health
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