Thomas R. Insel, MD

Nearly every psychiatric clinician I know has experienced it: Their patient’s health plan determines—with little justification—that the needed treatment is “not medically necessary.” The clinician is then forced to spend valuable time fighting for coverage of the treatment that is validated by accepted clinical standards of care. Meanwhile, families confront their options: spending life savings, taking on burdensome debt, or going without treatment.

With more than 7.1 million American adults not receiving the mental health treatment they need because of cost,

 ensuring health plans cover the services patients need is essential to maintaining and increasing access to care.

At the root of this issue is who makes the determinations about whether treatment is needed— and what standards should be and must be followed.

The Mental Health Parity and Addiction Equity Act of 2008 established the principle of parity for mental and physical health coverage. Although we have made progress, a significant barrier to achieving parity is insurers’ frequent arbitrary determinations that needed care is not medically necessary.

Yet there has been hope in tackling this issue successfully. In 2019, a federal district court in San Francisco detailed in a more than 100-page ruling how United Behavioral Health (UBH), the nation’s largest insurer of mental health care, used internally developed medical necessity criteria that were inconsistent with generally accepted standards of care to deny mental health and addiction coverage.

 In so doing, the court ruled that UBH had violated its fiduciary duty to plan members under the federal Employee Retirement Income Security Act (ERISA).

After hearing expert clinical testimony, the court described the 8 generally accepted standards of mental health and substance use disorder care. These standards are listed in the 

The 8 Generally Accepted Standards of Mental Health and Substance Use Disorder Care

The court then found that UBH’s medical necessity criteria were inconsistent with each of these standards, resulting in inappropriate coverage denials for more than 50,000 patients.

 Half of those patients were children or adolescents, and the son of 1 of the named plaintiffs died after receiving a denial of coverage.

According to the court, there was clear evidence that UBH used medical necessity determinations to limit the duration of coverage in an arbitrary manner and push patients into inappropriate lower (cheaper) levels of care.

 UBH’s process for developing medical necessity criteria were, the court determined, “infected” by its financial interests.

Importantly, all the experts at trial—including UBH’s own witnesses—agreed that criteria to determine the appropriate level of care that are consistent with generally accepted standards of care already exist.

 These criteria come from nonprofit clinical specialty associations, including the American Society of Addiction Medicine (ASAM), the American Academy of Clinical Psychiatrists, and the American Academy of Child and Adolescent Psychiatry. These associations’ criteria provide holistic assessments of patients’ needs and put patients’ interests first.

In fact, UBH’s own internal clinicians believed that the ASAM criteria were most appropriate for substance use disorder determinations and recommended that UBH switch. This recommendation was vetoed, however, by UBH’s finance department because of uncertainty of the financial impact of the switch.

To fix the violations it found, the court ordered UBH to reprocess denials that were based on its own flawed criteria by using the nonprofit clinical professional associations’ criteria and to use these criteria for mental health and substance use medical necessity determinations going forward in its ERISA plans.

 (UBH’s ERISA plans cover more than 20 million Americans.

 case has been a huge win for millions of Americans across the country seeking treatment for mental health and addiction disorders. The decision also sent a message to insurers that, under ERISA, they must decide whether care is medically necessary based on standards set by clinicians, not based on insurers’ financial self-interest. The case has also spurred state-level action, including new laws in several states, such as California,

 with the aim of preventing insurers from using medical necessity criteria that are inconsistent with generally accepted standards of care as well as to put in place a standardized definition of medical necessity.

 became a huge loss in March 2022, when a 3-judge panel of the federal Ninth Circuit Court of Appeals reversed the 

 It took a single paragraph of a cursory 7-page ruling—in contrast to the comprehensive, 100-page-plus district court decision—for the panel to reverse the district court and allow UBH to use medical necessity criteria that may be inconsistent with generally accepted standards of care.

The appellate panel did not address any of the exhaustive evidence presented at trial or the district court’s findings that UBH’s criteria were consistent with generally accepted standards of care. The panel also ignored UBH’s violations of several states’ laws that are an important part of the case. These state laws required UBH to use the ASAM criteria (or criteria consistent with it) for substance use disorder determinations. The attorneys general of 3 of these states—Illinois, Rhode Island, and Connecticut—filed an amicus brief with the Ninth Circuit asking it to correct the panel’s significant errors.

If the panel’s flawed reversal stands, it will have a ripple effect that undermines the health coverage of the more than 130 million Americans in employer-sponsored ERISA plans. The ruling effectively gives every insurer the green light to use whatever self-serving criteria it wishes when deciding whether to pay for treatment.

Furthermore, the reversal creates more obstacles to individuals who are struggling at a time of unprecedented need. Hardest hit will be those patients in ERISA plans with the least financial resources, who lack the ability to pay out of pocket for care. Already underserved low-income communities, LGBTQ+ communities, and communities of color will once again be disproportionately harmed, reinforcing our deeply inequitable system that rations care based on ability to pay.

 reversal also directly harms clinicians’ ability to properly care for their patients. Clinicians will be forced to spend more time trying to convince insurers that the treatments they select as most appropriate for their patients meet insurers’ self-serving criteria, rather than independent, objective standards. The reversal also is an obstacle to clinicians providing care that is consistent with generally accepted standards. If insurers refuse to cover such care and patients cannot afford appropriate care without coverage, clinicians are often put in an untenable position of negotiating with insurers for coverage for treatment that is not the most appropriate. Our laws should not permit insurers to insert themselves between clinicians and their patients.

With our country’s ongoing mental health and addiction crisis, the consequences of the 

 reversal are too great to ignore. Indeed, this is why several dozen patient and provider groups, including the American Psychiatric Association and the American Medical Association, filed amicus briefs asking the Ninth Circuit to revisit this flawed ruling and grant the rehearing petition.

If insurers are allowed to use whatever criteria they wish, millions of Americans will continue to be subject to arbitrary coverage denials that deny them a path to recovery. The Ninth Circuit must not neglect its duty by allowing insurers not to fulfill theirs.

Should the Ninth Circuit deny a rehearing, the only option will be to push legislation in states and in Congress to require insurers to follow generally accepted standards of care. Because clinicians are on the front lines of helping their patients get the mental health and addiction care they need, I encourage clinicians to join the effort to require insurance companies to make decisions in patients’ best interests. Insurers should be competing on how best to improve their members’ well-being, not on the efficiency of denying coverage.

is a psychiatrist and neuroscientist who has been a national leader in mental health research, policy, and technology. He is the author of 

Healing: Our Path from Mental Illness to Mental Health.

Editor’s note: All information within this article was up to date at the time of print.

1. Adults reporting unmet need for mental health treatment in the past year because of cost. KFF. Accessed August 19, 2022. 

https://www.kff.org/other/state-indicator/adults-reporting-unmet-need-for-mental-health-treatment-in-the-past-year-because-of-cost/

, Case No. 14-cv-02346-JCS (N.D. Cal. Feb. 28, 2019).

, 14-cv-02346-JCS (N.D. Cal. Jan. 5, 2022). 

https://casetext.com/case/david-wit-v-united-behavioral-health

4. California Senate Bill 855, Chapter 151 (2020). 

https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=201920200SB855

, No. 20-17363 (9th Cir. Mar. 22, 2022). 

https://casetext.com/case/wit-v-united-behavioral-health-12

 of Rhode Island, Connecticut, and Illinois. May 16, 2022. 

https://www.thekennedyforum.org/app/uploads/2022/05/2022-5-16-Brief-of-Rhode-Island-Connecticut-and-Illinois-as-Amici-Curiae-in-Support-of-Plaintiffs-Appellees-and-Rehearing-En-Banc.pdf

7. Wit v United Behavioral Health. The Kennedy Forum. Accessed August 19, 2022. 

Articles

"With our country’s ongoing mental health and addiction crisis, the consequences of the Wit v UBH reversal are too great to ignore."

Federal Court Gives Green Light to Deny Coverage

There has been a recent revolution in the development of digital health solutions for various chronic conditions. However, there are relatively few examples of digital health solutions that have been widely adopted in real-world health care settings and are measurably improving patient outcomes or helping health care systems deliver efficient and patient-centric care. From our vantage, there are some fundamental reasons for this “translational gap,” which, if addressed, can go a long way in helping realize the full potential of digital health technologies.

We use mental health/illness as an exemplar because brain-related illnesses will be at the forefront of digital health revolution, as their course can be monitored, outcomes can be measured via digital phenotyping, and interventions can be delivered digitally and remotely. Furthermore, the challenges and opportunities in digital mental health have been further highlighted by the ongoing COVID-19 pandemic, which has added fuel to an already brewing mental health crisis.

Mental Illnesses: The Need and the Opportunity

Mental illnesses represent some of the most prevalent and disabling conditions. Depressive disorders ranked number 4 in age group 10-24 and number 6 in age group 25-49 in the list of leading global causes of level 3 disability-adjusted life-years (DALYS)

 and are associated with a wide range of negative outcomes, including loss of occupational function,

 A key barrier to addressing these challenges stems from inadequate metrics to assess disease status, treatment effectiveness, and patient-relevant outcomes.

Mental illnesses do not have a clear outcome measure such as HbA1C in diabetes; instead, efficacy and outcome measures utilized in clinical practice today are complex and subjective, and are only sporadically collected or recorded in databases. As a result, assessment of quality of mental health care often defaults to the more easily measured process and structure metrics, which are distal from true patient outcomes, and, although they are easier to measure, improvement in these metrics does not always translate to improvement in patient-centric outcomes.

 This hampers outcome-relevant assessment of quality, which in turns hampers creation of value-based incentive structures that would lead to true patient-centric care and direct resources based toward the most impactful interventions (“pay for performance”).

The relative value of patient-generated digital data in driving measurement- and outcome-based care will be disproportionately high in mental health since brain-based disorders manifest with changes in behavior, cognition, and mood that are ripe for digital phenotyping. For example, recent and ongoing digital phenotyping efforts in patients with depression are demonstrating correlations between symptom severity and smartphone and wearable-derived features relevant to daily functioning such as physical activity and sleep as measured by actigraphy,

 sociability as measured by blue-tooth handshakes,

 and anxiety or stress as detected in speech features (natural language processing, or NLP) or physiological read-outs such as Galvanic Skin Response (GSR) and Heart Rate/Heart Rate Variability (HR/HRV). Such digital readouts can be more sensitive to real-time measures of symptom severity, functional impairment, and recovery status than episodic clinical assessments. These digital measures may also serve as proxy measures to assess value and efficacy of clinical care and interventions.

Of course, payers and clinicians will require that evidence for this be generated through long-term digital phenotyping studies that relate digital biomarkers with clinical milestones and cost. This will require long-term adherence to digital phenotyping solutions from the ultimate data source: the patient. Adoption and adherence have been challenges

 that can only be addressed if, regardless of the eventual utility for clinicians and payers, digital phenotyping solutions treat the patient as their first and foremost customer.

Using Patient-Generated Data and Digital Tech for Patient-Centric Care

Utilizing digital health technologies and patient-generated data for enabling outcome-based care hinges on developing a “data partnership” with the patient that is built on principles of trust, mutual benefit, and informed data access and use. This will become more urgent as advances in wearable technology and mobile health provide patients greater control over, access to, and use of their personalized health data. In fact, eventually the boundary between health care data primarily housed in electronic health records (EHRs) and so-called patient-generated health data will blur as agency shifts to the patient and we realize that all data—whether in EHRs or generated by wearable devices—is ultimately patient-generated, and thus, is patient owned and controlled.

Matters of privacy, transparent consenting, and, more broadly, trust are clearly “table stakes” for patient participation in a digital data collection regime. Beyond that, in our experience, conducting long-term multimodal digital phenotyping studies in patients with mental illnesses, 3 factors are most important for patient acceptance, engagement, and long-term adherence. First, any sustained digital phenotyping data collection, in the context of research studies or clinical care, must provide immediate “here and now” value and utility back to the patient, over the friction and burden it creates. This equation can be helped by relying on passive data collection and developing bandwidth-frugal applications with minimal impact on smartphone battery life and other factors important to patients.

Furthermore, the end-user interface and visualizations must be engaging and useful to patients.

 Designing such a solution requires a systematic data-driven understanding of patients’ attitudes, motivations, and barriers toward data collection.

 It is important to follow the common tech industry practice of co-designing solutions in close partnership with end-customers (patients), with iterative learn and improve cycles. We may find ourselves surprised at how often the end-users’/patients’ views on which features are important to them diverge from experts’ preconceived notions.

Second, altruism remains a significant motivator; most patients diagnosed with a serious condition are willing—even eager—for their data to be utilized in research that helps their fellow patients, assuming privacy and security safeguards. This propensity to be part of citizen-science efforts should be leveraged to develop “learning engines” wherein pooled, deidentified digital phenotyping data can be used to solve important problems of broad utility and impact.

Finally, we see physicians’ use of patient-generated data to improve clinical care as crucial motivator for patients to adhere to a digital data regime. In fact, this is perhaps the single most important criterion for patients with serious mental illness who are under the care of a clinician. From the physician’s perspective, the data and visualizations should make the clinic visit more efficient and should integrate readily with their current workflows. Such a system will also improve outcomes by improving patient engagement and enabling more measurement-based care,

 We believe digital health solutions that enhance the patient-physician interface and generate near-term value for both may require more effort for initial uptake, but are most likely to stick than direct-to-patient approaches often utilized by insurance companies and digital health solution providers.

While physicians’ awareness of patient-derived digital data and overall digital platforms was already on the upswing prior to COVID, the pandemic has provided further tailwinds for such solutions.

 The initial deployment of digital phenotyping in clinical care settings will necessarily rely on validated self-reported scales of symptoms, function, and quality of life such as the disease set recommendations by the International Consortium for Health Outcomes and Measurement (ICHOM)

 to implement simple, interpretable flags and known treatment algorithms to aid clinical decision making.

Eventually, growing datasets will enable validation of digital biomarkers and predictive algorithms that are leading indicators of disease burden, health care utilization, crises, relapses, and other cost-driving events that could provide payers with real-time, leading quality metrics that can duly reward risk reduction and preemption. The 

 summarizes the framework of a “learning engine”

 digital health solution that we think can be deployed in mental health to bring together patient-derived digital data and clinical data from EHRs to deliver value to all key stakeholders: patients, physicians, and payers.

Digital Health Solution Framework for Filling the Measurement Gap to Enable Outcome-Based Care in Mental Health

The previously described digital phenotyping framework could become an extension of existing EHRs and clinical decision-enabling software systems, but given the already noted challenges in integrating patient-generated health data (PGHD) into EMRs,

 we expect earlier evolution of stand-alone digital phenotyping solutions that are interoperable with multiple EMR systems.

Collection of longitudinal digital phenotyping data at scale in the real world would necessarily imply reliance on data from patient-owned consumer devices. This will require a concerted effort to develop platform-independent data standards and APIs that can ensure that digital biomarker readouts across different devices are comparable.

Increasingly, data will be analyzed and appropriate features extracted in-situ on the device, which will ease privacy concerns and reduce bandwidth requirements, both of which can severely limit scalability and deployment. We should also note that digital phenotyping solutions that collect continuous data for asynchronous use during patient-physician interaction are distinct from—and in fact, complementary to and synergistic with—synchronous telemedicine solutions that enhance physician reach and accessibility.

In addition to lack of actionable patient-generated outcomes data, true patient-centric and value-based care in mental health is also hindered due to fissures between mental and physical care (ie, a lack of a “whole person” approach). For instance, there is mounting evidence that patients suffering from mental health conditions have markedly poorer outcomes while incurring significantly higher direct medical costs for their physical comorbidities.

 However, such comorbid depression is often undiagnosed or otherwise poorly treated, if at all. Remote assessments and digital diagnostics now offer new scalable ways to detect depression and anxiety and set individuals on a path to formal diagnosis. Digital health solutions can also provide evidence-based tools for self-monitoring, self-care, and management of depression, anxiety, and stress for patients with a primary physical health diagnosis.

Furthermore, comorbid patients can be provided access to high-quality behavioral health care virtually. The virtual behavioral health team can work in partnership with primary/secondary providers treating their chronic physical comorbidities. In this context, use of physician-supervised or autonomous digital therapeutics can also be considered to further address behavioral health co-morbidities in a scalable and effective manner.

The COVID-19 pandemic has illustrated the value of digital technology in health care and has created significant momentum that should be leveraged. Although significant advances have been made, we believe digital solutions can play an even greater role in transforming care. This is particularly true in mental health, where there are inadequate objective measures of disease status or progression. These gaps hinder effective measurement-based patient care as well as an opportunity to better align incentives and scarce resources through more meaningful outcomes-based care. We believe an appropriately designed and safeguarded digital health solution can be used to improve patient care and, at the same time, begin to address critical data gaps needed to enable patient-centric, value-based care.

head of strategy and innovation, Davos Alzheimer’s Collaborative. 

 is the founder and CEO of Holmusk Technologies Inc. 

 is chairman of the board at TheSteinberg Institute. 

 is lead of the Brain Capital Alliance, co-lead of the OECD Neuroscience-inspired Policy Initiative in collaboration with the PRODEO Institute, and senior fellow for Brain Capital with the Meadows Mental Health Policy Institute. 

 is director of the San Bernardino County Department of Behavioral Health. 

 is visiting professor, Oxford University and Duke University, and previously global therapeutic head of neuroscience for Janssen Research & Development, LLC.

 Dr Insel is an advisor to Alto Neuroscience, Cerebral, Compass Pathways, Koa Health, and Valera Health. Drs Narayan and Manji are employees of Jansen Research & Development, LLC, and may hold company stock.

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Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019

 [published correction appears in Lancet. 2020 Nov 14;396(10262):1562]. 

2. Cambridge OR, Knight MJ, Mills N, Baune BT. 

The clinical relationship between cognitive impairment and psychosocial functioning in major depressive disorder: a systematic review

3. Saragoussi D, Christensen MC, Hammer-Helmich L, et al. 

Long-term follow-up on health-related quality of life in major depressive disorder: a 2-year European cohort study

A reassessment of the relationship between depression and all-cause mortality in 3,604,005 participants from 293 studies

5. Kilbourne AM, Beck K, Spaeth-Rublee B, et al. 

Measuring and improving the quality of mental health care: a global perspective

Relationship between major depression symptom severity and sleep collected using a wristband wearable device: multicenter longitudinal observational study

7. Vesel C, Rashidisabet H, Zulueta J, et al. 

Effects of mood and aging on keystroke dynamics metadata and their diurnal patterns in a large open-science sample: a BiAffect iOS study

8. Laiou P, Kaliukhovich DA, Folarin AA, et al. 

The association between home stay and symptom severity in major depressive disorder: preliminary findings from a multicenter observational study using geolocation data from smartphones

Predicting depressive symptom severity through individuals' nearby Bluetooth device count data collected by mobile phones: preliminary longitudinal study

10. 25% of users abandon apps after one use. Upland Software, Inc. Accessed July 20, 2022. 

https://uplandsoftware.com/localytics/resources/blog/21-percent-of-users-abandon-apps-after-one-use/

11. Simblett S, Matcham F, Curtis H, et al. 

Patients' measurement priorities for remote measurement technologies to aid chronic health conditions: qualitative analysis

12. Richardson S, Sinha A, Vahia I, et al. 

13. Simblett S, Greer B, Matcham F, et al. 

Barriers to and facilitators of engagement with remote measurement technology for managing health: systematic review and content analysis of findings

14. Hickie IB, Davenport TA, Burns JM, et al. 

Project Synergy: co-designing technology-enabled solutions for Australian mental health services reform

15. Hong RH, Murphy JK, Michalak EE, et al. 

Implementing measurement-based care for depression: practical solutions for psychiatrists and primary care physicians

Transforming population-based depression care: a quality improvement initiative using remote, centralized care management

17. Andrews JA, Craven MP, Jamnadas-Khoda J, et al. 

Health care professionals' views on using remote measurement technology in managing central nervous system disorders: qualitative interview study

18. Depression & anxiety. ICHOM Connect. Accessed July 20, 2022. 

https://connect.ichom.org/patient-centered-outcome-measures/depression-anxiety/

Moving from 'diagnose and treat' to 'predict and pre-empt' in neuropsychiatric disorders

20. Cresswell K, McKinstry B, Wolters M, et al. 

Five key strategic priorities of integrating patient generated health data into United Kingdom electronic health records

21. Davenport S, Gray TJ, Melek S. How do individuals with behavioral health conditions contribute to physical and total healthcare spending? Milliman, Inc. August 13, 2020. Accessed July 20, 2022. 

https://www.milliman.com/-/media/milliman/pdfs/articles/milliman-high-cost-patient-study-2020.ashx

Considering challenges and opportunities in digital health solutions for mental health care.

Digital Health as an Enabler for Patient-Centric, Outcome-Based Care

[[{"type":"media","view_mode":"media_crop","fid":"43174","attributes":{"alt":"schizophrenia","class":"media-image","height":"167","id":"media_crop_3418870003478","media_crop_h":"0","media_crop_image_style":"-1","media_crop_instance":"4790","media_crop_rotate":"0","media_crop_scale_h":"0","media_crop_scale_w":"0","media_crop_w":"0","media_crop_x":"0","media_crop_y":"0","title":"","typeof":"foaf:Image","width":"138"}}]]As with other chronic health conditions, there is no magic bullet for schizophrenia. At least as important as the search for magic bullets, however, is an increasing focus on early intervention and integrating existing treatments. This year, we’ve had encouraging news about integrated, comprehensive approaches for treating people with first episode psychosis, most recently from a major NIMH initiative, the Recovery After an Initial Schizophrenia Episode, or 

. RAISE looked at coordinated specialty care for first episode psychosis. With coordinated specialty care, the young person experiencing first episode psychosis works with a team of specialists to create a personal treatment plan, combining recovery-oriented psychotherapy, low-dose medication management, family education and support, case management, and work or education support. Coordinated specialty care emphasizes shared decision making, including family members, when possible.

RAISE began as two separate studies back in 2008. Each study looked at a different aspect of coordinated specialty care. One study, the RAISE Implementation and Evaluation Study, focused on the best way for clinics to start using the treatment program. The other project, the RAISE Early Treatment Program (

), studied whether or not the treatment worked better than care typically available in community settings. John M. Kane, MD, of the North Shore-Long Island Jewish Health System and the Zucker Hillside Hospital, led RAISE-ETP and . . . his team released the primary outcomes from this 

The RAISE-ETP research team spent five years testing their coordinated specialty care model, called NAVIGATE, at 34 real-world clinics across the country. They compared their treatment program to typical treatment and found that 223 clients who received the NAVIGATE coordinated specialty care program stayed in treatment longer; experienced greater improvement in their symptoms, interpersonal relationships, and quality of life; and were more involved in work or school compared with 181 clients at the typical-care sites. NAVIGATE clients who had a shorter duration of 

 (the time between the beginning of psychotic symptoms and the beginning of treatment) when they started the study showed greater improvements than those with longer duration of untreated psychosis.

Two other research teams have reported findings in recent months from trials of coordinated care for first episode psychosis. A team at Yale University collaborated with a center run by Connecticut’s mental health agency to test whether a comprehensive first episode psychosis service in the context of a public sector clinic could improve outcomes. After a year, those who received comprehensive care had fewer hospitalizations than those in standard care, were more likely to remain employed, and did better on overall measures of functioning.

 also reported that, in a trial of specialized, intensive treatment, patients receiving the intervention for two years had reduced psychotic and negative symptoms, were more satisfied and adherent with treatment, and were hospitalized less than patients receiving standard care.

 As a result of this study, the treatment has been implemented throughout Denmark.

The information coming from these trials shows that coordinated specialty care is an incremental but positive step in treating first episode psychosis. Coordinated specialty care-and ongoing testing of these approaches, with continuous incorporation of findings into practice-brings us closer to being able to intervene in a way that will enable young people with psychosis to avoid the long-term disability and vulnerability that too many experience. It is encouraging to note that more and more states-32 to date-are adopting coordinated specialty care programs to treat first episode psychosis. Their efforts have been supported by additional funding coming from the 

Community Mental Health Services Block Grant

 program administered by the Substance Abuse and Mental Health Services Administration . Based on the RAISE results, NIMH is launching the 

Early Psychosis Intervention Network (EPINET)

, aimed at creating a network of clinical sites offering evidence-based specialty care to persons experiencing signs of first episode psychosis. Using data sharing agreements, EPINET centers will gather participant-level data which can then drive improvements in clinical practice, a “learning health-care system.”

It should perhaps be no surprise that, given the complexity and variability of schizophrenia, outcomes will be better with treatment that is individualized, multi-faceted, and attuned to the preferences of each patient. While we have a long way to go, these recent reports suggest we’re headed in the right direction.

Dr Insel is the former Director of the National Institute of Mental Health (NIMH). This article, originally published on October 20, 2015, is posted here courtesy of NIMH.

http://www.nimh.nih.gov/about/director/2015/new-hope-for-treating-psychosis.shtml

1 Kane JM, Robinson DG, Schooler NR, et al. Comprehensive Versus Usual Community Care for First Episode Psychosis: Two-Year Outcomes From the NIMH RAISE Early Treatment Program. 

doi: 10.1176/appi.ajp.2015.15050632. Epub 2015 Oct 20.
2 Srihari VH, Tek C, Kucukgoncu S, et al. First-Episode Services for Psychotic Disorders in the U.S. Public Sector: A Pragmatic Randomized Controlled Trial. 

 2015;66:705-712.
3 Nordentoft M, Melau M, Iversen T, et al. From research to practice: how OPUS treatment was accepted and implemented throughout Denmark. 

Results from a major study provide evidence that coordinated specialty care can improve outcomes for first episode psychosis. Dr Insel writes about the RAISE project and other recent studies of coordinated care.

New Hope for Treating Psychosis

The Medici effect encapsulates the benefits of cross-pollination and interaction between individuals and teams from different fields in the pursuit of innovation. It is named after the wealthy Medici family who helped catalyze the Renaissance by bringing together poets, philosophers, scientists, painters, and other artisans to Florence, Italy. We believe the modern Medici effect is convergence science, and it is set to revolutionize health and medicine in the 21st century given the interplays occurring among physical, computer, and life sciences.

Convergence science is defined as the merging of distinct technologies, industries, tools, disciplines, or devices into a unified whole to create new pathways and opportunities. Convergence relies on a new integrated approach to solving problems too complex for any single discipline. Sharp and Langer1 described convergence as the “third revolution” in biomedicine after the development of molecular and cellular biology (the first revolution) and genomics (the second revolution). Based on a study of scientific progress at the Massachusetts Institute of Technology, convergence science was recommended as a way to blend diverse scientific disciplines. More than an interdisciplinary science, convergence integrates distant paradigms, systems, theories, and disciplines with problem-oriented research that crosses boundaries of academic, public, and private spheres.


Oncology may be the current biomedical frontier for convergence science. Indeed, a new academic journal has just been launched: 

 The aim of the journal is to integrate physical science with cancer biology and clinical oncology to advance the understanding and treatment of cancer in patients. The National Cancer Institute’s Center for Strategic Scientific Initiatives supports convergent approaches to cancer innovation. Their mission is to “create and implement exploratory programs focused on the development and integration of advanced technologies, transdisciplinary approaches, infrastructures, and standards to accelerate the creation of publically available, broadly accessible, multidimensional data, knowledge, and tools to empower the entire cancer research continuum for patient benefits.”

This initiative has led to Cancer Nanotechnology Excellence and Integrative Cancer Biology Program Centers to be constructed to support convergent projects. Private companies (eg, IBM and NantHealth) are already developing data intensive systems to integrate, display, and analyze-via machine learning-data from all health providers, genomic and proteomic analyses, imaging, and other medical devices, with the actionable health information available at the point of care, anywhere.

Initiatives supporting convergence science approaches for psychiatry


If convergence is an approach to complexity, then what appears promising for oncology should be even more useful for psychiatry. Indeed, this approach has already been harnessed for neuroscience. A number of large-scale convergence neuroscience initiatives are underway globally, and these initiatives are essential to making progress in neuroscience. These projects bring together researchers from a multitude of disciplines to understand the basic functioning of the human brain, and the dysfunction that occurs during illness.

One such initiative is the Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) initiative funded by public agencies, private companies, and foundations.

 The express aim of this initiative is to develop new tools and technologies that will enable the research community to obtain a dynamic picture of the brain in action. With nearly 100 billion cells making 100 trillion connections, this is no small aim and will simply not be achievable with current tools and disciplinary approaches. One of the express themes of action for the BRAIN initiative is to “cross boundaries in interdisciplinary collaborations.” The BRAIN initiative consists of teams of engineers, nanotechnologists, computational scientists, materials scientists, and neuroscientists to create the next generation of imaging tools or probes for brain activity.

Will convergence become the future of psychiatry?


Arguably, if the brain is so complex as to require a convergent approach, then the study of psychiatry (mind, brain, and behavior) is complexity on a whole different level. Although the challenge is evident, our approaches have been remarkably singular, focusing on the mind, the brain, or behavior in isolation, with too little integration across disciplines and even less assistance from the vast areas of science that are now poised to alter other areas of medicine.

What would a convergence science of mental illness look like?


A convergent approach to mental illness could begin with software engineers, informatics experts, behavioral scientists, and clinicians designing a new generation of devices to provide objective measures to augment patients’ reports of their symptoms. Imagine devices developed to provide continuous assessment of mental state, similar to the glucose or heart rate monitors available today. Passive data such as voice analytics, facial expression monitoring, actigraphy, and engagement of social networks could indicate the onset of depression or mania. Closed loop brain stimulation could detect and correct abnormal neural circuit activity, as is used today for managing epilepsy.

Implanted devices for the management of hallucinations and obsessive thoughts may seem like science fiction, but with the engineering of wireless, miniaturized electrodes, a new era is emerging for interventional neurology as well as psychiatry.

 Envisage creating social prosthetics for children with autism, similar to the cochlear implants used routinely for nerve deafness. While today’s tools such as Google Glass and Siri may not be up to the task, rapid progress in facial processing and computer engineering suggests that software could allow children with “social blindness” to read facial emotions in real time.

The application of convergence to psychiatry can be much more than smart software and social prosthetics. Convergence may include combining the current tools for diagnosis. The neurobiological mechanisms of psychiatric disorders are complex and often involve the interplay of changes in brain structure, function, neurochemistry, and neuropathology. Clark and colleagues5 have outlined the added value of multiple diagnostic modalities used in predicting the risk of transition into first episode psychosis. Karalunas and colleagues

 were able to refine subtyping of childhood ADHD by using biologically based behavioral dimensions (ie, temperament), a novel classification algorithm (ie, community detection analysis), and multiple external validators (ie, resting-state functional magnetic resonance imaging and cardiac measures of respiratory sinus arrhythmia and pre-ejection period). Diniz and colleagues

 have shown how plasma biosignatures and brain pathology relate to resistant cognitive impairment in late-life depression. They utilized comprehensive analysis of blood-based immune proteins, magnetic resonance imaging, and positron emission tomography with machine learning data analytics. While all of these examples draw from multiple data sources, the ultimate promise of convergence is the integration of biological, psychological, sociocultural, and environmental data into a more comprehensive, individualized portrayal of diagnosis (ie, precision medicine).

Early examples of convergence science for psychiatric disorders


Convergence science approaches could also aid in the development of novel treatment strategies for psychiatric disorders allowing interventions to be proactive as well as reactive. There is an increasing interest in electronic mental health interventions given enhanced computing power and the widespread availability and use of smartphones.

 Psychosocial treatments delivered via mobile health (mHealth)-the practice of medicine supported by mobile devices-have the benefit of reach, scalability, affordability, convenience, flexibility, and facilitation by a non-professional workforce. Concerns remain about acceptability to consumers, quality of interaction, and the potential for unsupervised and counterproductive therapy.

Massive open online interventions for mental health is a concept recently defined by Munoz and colleagues

 and describes mental health and substance abuse interventions, scientifically validated and available online to unlimited numbers of consumers. These researchers showed how a free, multifactorial smoking cessation program in English and Spanish could be used by 7607 participants worldwide. Their findings indicate a 50.3% quit rate over 12 months.

The field of socially assistive robotics in mental health, whereby robots assist patients through social interactions (eg, companionship, as therapeutic partner and/or coach), is receiving greater attention, particularly as computing power grows and artificial intelligence systems become more sophisticated, ubiquitous, and useful. A recent review by Rabbitt and colleagues

 has outlined potential benefits of socially assistive robotics (eg, providing therapy and monitoring where there are few mental health providers and reinforcing human-led therapy), as well as potential downsides (eg, poor quality user interface leading to frustration and cost considerations). Even in the near term, the development of monitors for the fidelity of psychosocial interventions could improve the quality of care.

Just as a glucose monitor in a contact lens provides continuous feedback for optimizing diabetes control; engineers, computer scientists, and psychiatrists can develop simple devices to assess the quality of psychiatric treatment as a path to improving outcomes. Examples in psychiatry include software and hardware for smartphones and wearable devices to monitor physical activity, sleep, social interactivity, calorimetry, and emotional tone of voice.

Implications of convergence science for psychiatry


If convergence science principles are part of the future of psychiatry, it may not be too soon to introduce this approach in the teaching of psychiatry. Additional skills may include an enhanced understanding of neuroscience, the “omics” (eg, genomics, proteomics, metabolomics), big data analytics, mHealth, economics, and policy. Yager

 suggests psychiatry training and careers are likely to undergo substantial change in the future to complement projected health burdens and innovations, postulating the development of additional psychiatric specializations in information technology and executive management (ie, combining clinical practice, administration, entrepreneurialism, and health services management).

The training of psychiatrists as clinical neuroscientists has recently been outlined, and indeed there are signs to suggest a resurgent interest in psychiatric neuroscience among medical students given the need for new discoveries and the promise of new research techniques.

 Interesting educational curricula to model include the National Institute of Mental Health (NIMH)-led, 4-day Brain Camp offered in the US to MD-PhD students; the National Neuroscience Curriculum Initiative; and the NIMH-funded, 5-day Summary Research Institute in Geriatric Mental Health.


The Medici effect is upon us in biomedicine, and it’s called convergence science. We believe convergence science and its greater application to psychiatry is important to address the integration of mind, body, and behavior; the urgent need for improved quality and access to clinical care; the high burden of disease; and the promise of new technologies. As the Medici family catalyzed the Renaissance by bringing together poets, philosophers, scientists, and painters and other artisans, we believe a renaissance in psychiatry can be delivered by facilitating interactions, research, and innovations between clinical neuroscientists, molecular biologists, big data scientists, roboticists, imagers, public health experts, economists, and user interface and gamification experts.

Dr Eyre is a PhD student within the discipline of psychiatry at the University of Adelaide in Adelaide, Australia; Dr Lavretsky is Professor in Residence in the department of psychiatry at the Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA; Dr Insel is Director of the National Institute of Mental Health, Bethesda, MD. The authors report no conflicts concerning the subject matter of this article.

 Sharp PA, Langer R. Research agenda: promoting convergence in biomedical science. 

 National Institutes of Health. BRAIN 2025: A Scientific Vision. 

http://www.braininitiative.nih.gov/2025/index.htm

 Williams NR, Taylor JJ, Kerns S, et al. Interventional psychiatry: why now? 

 Clark SR, Schubert KO, Baune BT. Towards indicated prevention of psychosis: using probabilistic assessments of transition risk in psychosis prodrome. 

 Karalunas SL, Fair D, Musser ED, et al. Subtyping attention-deficit/hyperactivity disorder using temperament dimensions: toward biologically based nosologic criteria. 

 Diniz BS, Sibille E, Ding Y, et al. Plasma biosignature and brain pathology related to persistent cognitive impairment in late-life depression. 

 Kazdin AE. Technology-based interventions and reducing the burdens of mental illness: perspectives and comments on the special series. 

 Munoz RF, Bunge EL, Chen K, et al. Massive open online interventions: a novel model for delivering behavioral health services worldwide. 

. 2015. http://cpx.sagepub.com/content/early/2015/05/11/2167702615583840.abstract. Accessed September 29, 2015.


 Rabbitt SM, Kazdin AE, Scassellati B. Integrating socially assistive robotics into mental healthcare interventions: applications and recommendations for expanded use. 

 Yager J. The practice of psychiatry in the 21st century: challenges for psychiatric education. 

 Insel TR. Director’s Blog: The Future of Psychiatry (= Clinical Neuroscience). April 2012. http://www.nimh.nih.gov/about/director/2012/the-future-of-psychiatry-clinical-neuroscience.shtml. Accessed September 29, 2015.


 National Institute of Mental Health (NIMH) Brain Camp. 2015. http://www.nncionline.org/event/national-institute-of-mental-health-nimh-brain-camp/. Accessed September 29, 2015.


The Medici effect is upon us in biomedicine, and it’s called convergence science. 

Thomas R. Insel, MD

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