This is a mental health (MH) data dashboard working with all electronic health records (EHRs), and includes population health monitoring with measured care driving improved outcomes, patient engagement and reduced costs. The thrust is to include MH risk measures as part of the EHR. Prompts show on the doctor’s "desktop" if a patient is "at risk" for MH conditions, eliciting a discussion with the patient comparable to conversations in physical treatments, e.g. blood sugar abnormalities. These scores help doctors and patients plan and monitor progress. For instance, in M3’s work with LabCorp, patients complete the M3 checklist through physician’s Lab Corp order. The M3 score is then available to all clinicians to identify and manage MH issues. The Dashboard shows care gaps, reduces costs, prompts providers for reimbursement and de-stigmatizes MH problems.
BH disorders are a leading chronic condition and the only health condition with no common metric expressing severity or complexity or a way to monitor progress, improving outcomes. This void is what the M3 Scattergood Population Dashboard seeks to fill, combining access to MH data including individual risk scores for anxiety, depression, bipolar and PTSD or alcohol use, and then place the person on the clinical map for treatment. We are piloting this concept in primary care in Federally Qualified Health Centers (FQHCs) and in managed care settings, creating paths addressing MH disorders like other chronic conditions. This approach de-stigmatizes MH assessments and treatments while integrating physical and MH care and access to BH treatment in primary care.
The US hallmark of BH care quality in the past decade was the review of psychotropic medicines. Prescribing has doubled with little improvement in mortality or disability. This shifts the concern away from access to providers, to quality. Further the majority of MH issues are detected and treated in primary care consequently quality efforts should start at this level. Examples of improved quality, prompted by the Dashboard, include people who may be depressed or have bipolar disorder and are not screened/identified; and individuals with anxiety that may do better with CBT in place of or with medication. These are two areas where assessing individuals multi-dimensionally versus a siloed diagnostic process provides the opportunity to improve quality and reduce costs. The MH Dashboard provides the starting point for assessing these issues.
Costs for MH care can be looked at the individual (productivity and success), or at the system level; where the savings are mostly in comorbid disease costs, e.g. costs of managing chronic conditions (COPD, asthma, diabetes) double with a comorbid untreated MH condition. These additional costs produce no benefit for patients, increase costs and slow the system. The good news is the system is already clogged so the extra weight of treatment will be a reallocation of care. For example M3 collaborated with Suburban Hospital (Maryland) including mental wellbeing in treatment algorithms; reducing readmissions by 52% and savings of about $1,800 per bed per night.
Our Dashboard enables widely accepted points: BH care is broader than just depression and people do not live in a silo of one condition but often have additional symptoms. It is imperative to look beyond depression to help individuals with early detection and monitoring to lessen severity and reduce negative progress. M3 works to apply both of these factors in a way that allows MH to leverage chronic disease management combined with scientific evidence to change existing approaches and enable scalability of current treatments and processes familiar to physicians and patients. M3’s MH dashboard data from FQHCs point to the benefits of improved patient outcomes.
Existing screening principles are leveraged and used in numerous settings. Adding predictive metrics to programs work. Two examples: SBIRT and IMPACT Model. Both leverage staff so more skilled providers can treat more difficult diagnoses. M3’s leads through inserting better “science” raising the tide on existing efforts without having to start anew.
Several payment models contribute to sustainability. In bundled models, savings are seen from using technology for screening, scoring and documentation. Fee for service, representing 95% of health costs has different sustainability measures. FFS, using CPT codes, supports continued payment for specific services and therefore are continually sustainable. Newer CPT Codes pay for interpretation of psychodiagnostic assessments that are also on the dashboard. Clinicians receive MH data at the point of care, including gaps in care analysis and receive payment for this work, e.g. code 96103. While there is MH parity, the majority of clinics care for less fortunate populations, receive a fixed payment and no additional funds for incremental care.
There is a shortage MH data at all levels. Data is the heart of M3, allowing any practice, small practices to large systems, to have common nomenclature allowing clinical observations, treatment paths, algorithms and measureable data to drive consistent observations and treatment paradigms throughout networks. The data obtained are the same regardless of setting and consequently are highly replicable while using information at patient/symptom level standardizing and scaling practices.
The attached includes meaningful clinical outcome measures (HgbA1c, lipid profile, blood pressure, BMI, etc.), social measures (occupational and housing statuses, 12 step meeting attendance, etc.) and utilization (primary care, MH and emergency department visits, hospital days, etc.). These outcome measures are part of a patient’s whole health and produce savings comprising ROI, fueling uptake of management of MH problems in all settings. This application creates dashboards demonstrating savings and support MH parity, aligning treatment with payment for complex patients who have diverse needs met through resource provision while receiving payment. These savings are reinforced by improved patient outcomes.