of people with
die by suicide
of people with
at least once
a year is the cost of
on the U.S.
Consequences of multiple schizophrenia episodes and poor prediction relapse solutions
In a given year, community mental health facilities treat an estimated average of 1,500,000 persons with the diagnosis. Post-hospitalization treatment in the US is primarily through over-burdened community mental health care organizations which, despite best practices in behavioral cognitive therapy, lack effective means to predict when a person with schizophrenia will relapse, requiring crisis intervention and rehospitalization.
With each hospitalization due to psychotic relapse, the affected person experiences additional deterioration, either slight or great, in behavioral functioning. In contrast with important advancements in pharmaceutics to stabilize persons with serious mental illness, clinical management (symptom tracking, flagging anomalies, alerting to crises, etc.) to identify imminent relapse of affected persons, especially for those in community mental health clinics and outpatient facilities, lacks tools to adequately identify incipient relapse
Innovating detection tools to predict relapse risk in schizophrenia patients
Mental Health Metrics is developing tablet- and smartphone-based tools that directly address and moot the limitations associated with behavioral cognitive therapy for schizophrenia. By providing clear warnings of impending crisis, our tools will significantly reduce both the number of relapses and the costs of hospitalization associated with relapse.
Preliminary evaluation of the Mental Health Metrics approach correctly identified 100% (7/7) of imminent relapses compared with 14% (1/7) by best clinical practice.
Provide better service to individuals through early assessment
Enables community mental health professionals to provide better service to individuals with schizophrenia through early assessment of potential relapse.
Directly address the limitations associated with behavioral cognitive therapy
Overcomes the temporal and professionally time-intensive limitation by digitally capturing a self-report of Birchwood Early Signs and Symptoms (BESS) survey by persons with schizophrenia on their current state-of-being.
Address patient non-compliance and non-adherence issues
Offers a small stipend for weekly BESS survey completion and uses machine learning techniques on the accumulating database to identify a shortened survey that maintains the same reliability.
crisis costs by 50%
Lessens the burden on resources for other mental health services.
management of patients
This supports clinician efforts to achieve better outcomes and transition patient from relapse mode to recovery mode.
Predict relapse at a
90%+ reliability rate
The high level of conﬁdence in the
sensitivity and speciﬁcity rates
informs clinical judgement.
"While over a longitudinal period, community based mental health professionals may get better at identifying early warning indicators for certain individuals, it is just as likely that the first solid indication of serious decompensation, all too often, occurs the same day as the need for hospitalization or other similar clinical intervention. It is most difficult to have any real useful/reliable/predictive way of knowing when specific individuals are likely to or already are experiencing significant reduced levels of functioning and now are at heightened risk. The trick is to see this kind of change early enough to be able to make more modest interventions, alas, that remains highly elusive for far too many people. But that is exactly what MHM’s system does. It is also noteworthy that a significant number of inmates in local jails and state prisons have ongoing serious mental health illnesses and the same problems exist and MHM can address these also."