How Addressing Mental Health Can Improve Chronic Disease Outcomes and Cut Costs

Mental health is one of the largest unmet needs in our country today, representing a significant opportunity for health systems and providers to achieve better health outcomes and reduce the total cost of care. The Centers for Disease Control and Prevention reports that the cost of caring for people with chronic disease and mental health conditions comprises 90 percent of the nation’s $3.8 trillion spend on healthcare. While health systems often invest resources to manage chronic diseases, such as diabetes and heart failure, organizations can further improve chronic disease management by increasing their investment in co-existing mental health disorders.

A strong link connects chronic disease outcomes and mental health disorders, making it difficult to improve chronic disease outcomes without addressing comorbid mental health conditions. The relationship suggests that clinicians who embrace a holistic, patient-centric approach to chronic disease in individuals with comorbid mood disorders can achieve better health outcomes and an overall lower total cost of care.

Mental Health Complexity Can Lead to Worse Chronic Illness Outcomes and Costly Care

A critical step in effective chronic disease management is addressing how a patient’s mental health may impact their chronic condition. For example, effective treatment of Type 2 diabetes must include addressing coexisting mood disorders. Health systems that incorporate mental health treatment into chronic care management programs often see big results in health outcomes and reductions in total cost of care.

A health system could reduce costly emergency department (ED) visits—a common source of care for patients seeking mental health assistance—among patients with heart disease (e.g., congestive heart failure (CHF)) by understanding mental comorbidities. By using data to identify patients with CHF and mental health conditions, clinicians could better engage individuals at increased risk of further mental decline, leading to decreased ED visits and lower costs.

Mental Health Challenges Make Chronic Disease Management Difficult

Given the research highlighting the causal link between mental health and chronic disease, why don’t health systems invest more in the treatment of mental health patients? While the answer is complicated, organizations can consider three primary reasons systems have deprioritized mental health treatment and how these factors impact a system’s approach to mental health.

Lost Revenue

Treating patients with mental health disorders can cost a health system up to twice as much as treating a patient without a mental health disorder. This costly care often results in operating losses due to poor reimbursements from payers as well as a lack of efficient care delivery models from providers.

A Lack of Access

There is a lack of access to mental health tools and clinicians, particularly in rural and poverty-stricken areas. This lack of access worsens already existing healthcare inequities and is a great example of how social determinants, such as mental health, affect health outcomes. Healthcare professionals must rethink their care delivery model, utilizing telemedicine platforms to improve access and data-driven patient engagement tools to drive better overall mental health outcomes.

A Limited View of Mental health

Lastly, the healthcare community often thinks too narrowly about mental health, relegating it to psychiatric conditions only. Healthcare professionals should consider patient decisions, activities, or behaviors that impact overall health as additional aspects of mental health. By more broadly defining the activities that make up mental health, care teams can better link behaviors to physical health in an impactful way.

Four Mental Health-Centered Steps to Effective Chronic Disease Management

Health systems can start the journey to better chronic disease management for patients with associated mental health disorders by following a four-step process:

Step 1: Identify the Patient Population

First, improvement teams should create data-informed patient population definitions. Understanding the patient group’s specific concerns helps the care team design effective interventions. For example, team members can evaluate whether the total cost of care attributable to diabetes is higher in diabetics with poorly managed mood disorders, such as those with higher hemoglobin A1c (HbA1C), compared to those with effective mood disorder management or without mood disorders.

Care teams can define populations according to risk in minutes with a patient stratification tool, such as the Health Catalyst Population Builder: Stratification Module™. After the system defines the patient populations, care teams can identify patients with chronic diseases and risk factors for undiagnosed mental health disorders (e.g., patients with Type 2 diabetes and early signs of a possible mood disorder).

In addition to a stratification tool, health systems need access to traditional data sources, including clinical EMR and claims data, to more rapidly identify at-risk patients within a given population. Early identification of high-risk patients also requires a technology platform that ingests and normalizes data from sources outside of the hospital setting (e.g., ambulatory clinics). An open data management platform and customizable analytics tools aggregate diverse data sources that health systems need to drive better outcomes. This need far exceeds the capabilities of any EHR and requires dedicated data and analytics infrastructure.

Step 2: Identify the Financial Impact

Second, improvement teams can take the claims data of these newly stratified cohorts and see the entire cost of care for specific populations with a tool such as the Health Catalyst Per Member Per Month Analyzer. For example, if 1,000 patients with Type 2 diabetes and a mood disorder are more costly than patients with Type 2 diabetes without a mood disorder, the health system can target the first group with more aggressive monitoring and proactive interventions. Given the possibility of mood disorders in all cohorts of patients, data analytics tools (e.g., the Health Catalyst Population Builder Application) that identify comorbid conditions and help stratify financial and clinical risks across a population are necessary if a system hopes to scale a mental health initiative.

Step 3: Develop a Plan with Experts

After identifying the financial implications of providing care for a specific patient group, the next step is to create a plan. Care team leaders should work with clinical improvement experts with proven track records of delivering success, such as the Heath Catalyst Professional Services business unit.

Team members and experts can work together to develop care processes that target common health barriers for the at-risk patient group. Using the example of patients with Type 2 diabetes and early signs of a possible mood disorder from Step 1, care teams can focus on interventions that promote better mood disorder management. Treating mood disorders can improve a patient’s mental health condition and may lessen the emotional and physical burdens associated with mental illness that interfere with self-care. This self-care improvement can also lead to better chronic disease management, including lowering HbA1C levels for patients with Type 2 diabetes.

Combining data with expertise increases an improvement program’s efficiency. With data highlighting specific improvement opportunities, experts can proactively address early warning signs of mental health conditions and prevent worsening conditions. The benefits include better health for patients and reduced costs for health systems that lead to greater profit margins in a fee-for-service environment and value-based arrangements. In other words, the health and financial benefits exist regardless of payer model.

Step 4: Measure the Impact and Show ROI

Lastly, to earn the ongoing leadership support to sustain mental health improvement initiatives, health systems must measure the impact of their programs’ interventions and demonstrate ROI. Teams must consider how they’ll measure change when beginning any project to ensure the proper protocols are in place.

Throughout the mental health improvement project, leaders can use data visualization tools to track progress and communicate results to the appropriate team members. Visualization tools remove complexity, allowing non-data experts to understand and measure the effectiveness of each mental health intervention in improving chronic disease management.

For example, if care teams implement an additional weekly telehealth visit for patients with high HbA1C, they can create a dashboard to regularly review any change in the HbA1C levels after they implemented the telehealth visits. Seeing results allows team members to monitor patient and intervention progress and generate organizational support for current and future improvement projects.

Addressing Mental Health Can Lead to Optimal Chronic Disease Management

Following the four-step framework for chronic disease and mental health management allows care teams to treat the underlying behavioral factors that may worsen chronic disease and impede effective management. Targeting and improving mental health within chronic disease management can lead to significant improvement, reductions in the total cost of care, and a clear return on the improvement investment.

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:

1. Improving Sepsis Care: Three Paths to Better Outcomes
2. Healthcare Process Improvement: Six Strategies for Organizationwide Transformation
3. Six Ways Health Systems Use Analytics to Improve Patient Safety
4. Using Improvement Science in Healthcare to Create True Change
5. The Top Five Insights into Healthcare Operational Outcomes Improvement

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