How analytics could defray the immense financial impact of mental illness


The physical and social costs of untreated mental illness are significant and have been discussed in detail in previous posts. Now let’s talk about the immense financial costs, then I’ll wrap up the series with a conclusion. The financial costs cover a broad cross-section of society, including government services and the private sector. Here are some startling numbers from the American Hospital Association:

  • Mental health care services in 2008 were almost $60 billion
  • Each year US business concedes almost $22 billion in lost/partially lost work days
  • Canadian research looking at acute care hospital readmissions within a year after discharge found those with mental illness were readmitted 37% and those without mental illness 27%
  • Monthly healthcare costs for patients with chronic medical conditions and depression are 65% higher than patients with chronic medical conditions and no depression

And the National Alliance on Mental Illness states:

  • 6 million Emergency Room in visits in 2007 were mental illness related
  • Incarcerated mentally ill cost an estimated $9 Billion per year
  • Medicaid is the single largest payer of mental health services

Many of the numbers above are staggering, as are the costs associated with treatment. Again, from NAMI:

  • Average per year cost of mental health treatment for an Adult - $1,551
    • Average per year cost of incarceration of an adult – Federal: $28,893.40, Community: $26,163., according to the Bureau of Prisons
  • Each $1 in mental health treatment = $3.68 savings in hospitalizations/criminal activity
  • 69% of adults return to employment following treatment
  • 50,000+ private industry jobs are from Community mental health and substance abuse agencies
  • Physical health services for Medicaid beneficiaries with mental illness is 32% more than those without[1]

What makes this issue of particular urgency is that overall public spending on mental health services dropped $4.35 billion from 2009-2012[2]. While funding is starting to be slowly restored across the nation it will take some time, perhaps several years, before we are back to prior levels of funding. During this time it is imperative to continue to investigate ways in which the cost effects of mental health can be better contained. Applying advanced data management and analytic technology to currently available data can help both providers and system managers better understand cost-effective, evidence-based treatment programs, social service programs/services, payment reform, and much more. In fact, data from many of the systems discussed in my post on quality of care can prove to be equally valuable for financial cost analysis and enable change to payment and service delivery.

For instance, providers could analyze and forecast costs for mental health services provided to Medicaid/Medicare/Commercial Payer (via APCD and/or other mental health public data initiatives) populations. This would be useful for:

  • Comparing cost effective evidence-based mental health treatments in private/public settings
  • Better understanding mental health-related “super utilizers” (e.g. double digit ER visits per year)
  • Better understanding of dual eligible population and cost attribution
  • Analyzing cost effectiveness of social service programs on mental health
  • Analyzing geographic/demographic/provider cost variation
  • Better understanding Medicaid managed care costs
  • Better understanding cost data for rate review activities
  • Detecting potential cases of fraud/waste/abuse

The benefits of integrating those systems also include:

  • Promoting payment models which encourage collaboration and quality of care such as Episodes of Care
  • Comparing benefits of various incentive-based payment models
  • Calculating payments that cover all treatment in a specific Episode of Care, including acute or ambulatory settings
  • Attributing clinical services associated with a specific Episode of Care to appropriate clinician
  • Calculating and understanding avoidable complications associated with an Episode of Care


The impact of mental illness on individuals and our society as a whole is both costly and tragic. Given the limited resources available to address these needs, it is essential that we have a data driven approach to support the allocation of these resources.   Medicaid (the largest payer for mental health services), provides a fertile ground for the use of advance data analytics as a system management tool.

The Center for Medicaid Services has a strong push towards integrated eligibility (which would include many other social services key to supporting those who are untreated), quality improvement and co-ordination of care, payment reform and modernization initiatives. As a result, Medicaid is well positioned to drive change in the mental health arena. With Medicaid enhancements there is also great potential for a trickle-down effect to the rest of the health system as the mentally ill who are in Medicaid could receive a more effective model of treatment, based on analytic support.

This group may transition eventually into commercial healthcare having the benefit of more established and effective treatment plans. As we have seen, the use of advanced data management and analytic/visualization technology can be a unique driver in enabling mental health treatment stakeholders to effectively identify mental health risk factors, model the right blend of effective services to stimulate positive change, determine efficacy of new evidence-based treatments, identify future trends, analyze costs and new ways to improve access to care, enhance co-ordination of care efforts and more. Ultimately, we can achieve change in these areas and positively influence physical, mental and social outcomes with the expanded use of data management and analytics.

[1] Medicaid Institute at United Hospital Fund, “New York Medicaid Beneficiaries with Mental Health and Substance Abuse Conditions” (2011)

[2] National Alliance on Mental Illness, “Medicaid Expansion and Mental Health Care” (May 2013)



About Author

Jeremy Racine

Healthcare Strategy Consultant

Jeremy draws on more than 20 years of experience in data science to evangelize the benefits of advanced analytics – including AI – in health care. He helps lead SAS health care initiatives for data, AI and analytics, ensuring solutions align with health care market needs. He's passionate about applying analytics to health care modernization and executing new strategies within complex global health systems. Jeremy's work focuses on the essential interdependencies between healthcare policy, programs, and providers, payers and patients.

1 Comment

  1. Jeremy,
    Good article and a step in the right direction through Education. The more this issue is exposed to the masses the better off we all will be down the road. With our limited public resources we must get agencies to collaborate, share client information and analyze what works best for the client in the long term, not just their current crisis. Preventative measures can strengthen our resources.

    Best Regards,
    Bruce Baker

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