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

Mental health is one of thelargest unmet needs在今天的我国，这是卫生系统和提供者实现更好的卫生结果和降低护理总成本的一个重要机会。TheCenters for Disease Control and Preventionreports that the cost of caring for people with chronic disease and mental health conditions comprises90 percentof 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.

Astrong linkconnects 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 ofType 2 diabetesmust 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 patientsseeking mental health通过了解精神合并症，在心脏病(如充血性心力衰竭(CHF))患者中提供援助。通过使用数据来识别CHF患者和精神健康状况，临床医生可以更好地处理那些精神进一步衰退风险增加的个体，从而减少急诊次数和降低费用。

Mental Health Challenges Make Chronic Disease Management Difficult

既然研究强调了精神健康和慢性疾病之间的因果关系，为什么卫生系统不加大对精神健康患者的治疗投入呢?虽然答案很复杂，但组织可以考虑三个主要原因，即系统将精神健康治疗置于次要地位，以及这些因素如何影响系统对精神健康的方法。

Lost Revenue

Treating patients with mental health disorders can cost a health system up totwice as muchas 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 CatalystPopulation 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).

除了分层工具之外，卫生系统还需要利用包括临床EMR和索赔数据在内的传统数据源，以更快地识别特定人群中的高危患者。对高危患者的早期识别还需要一个技术平台，该平台可以吸收来自医院外部环境(如门诊)的数据并使其正常化。世界杯厄瓜多尔vs塞内加尔波胆预测开放的数据管理平台和可定制的分析工具汇集了卫生系世界杯厄瓜多尔vs塞内加尔波胆预测统推动更好结果所需的各种数据源。这种需求远远超过了任何EHR的能力，需要专用的数据和分析基础设施。

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 CatalystPer 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 CatalystPopulation 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 CatalystProfessional Servicesbusiness 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

最后，为了获得持续的领导支持以维持心理健康改善举措，卫生系统必须衡量其规划干预措施的影响并展示投资回报率。团队必须考虑在开始任何项目时如何度量变更，以确保适当的协议就位。

Throughout the mental health improvement project, leaders can usedata visualization toolsto 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.

例如，如果护理团队对糖化血红蛋白高的患者实施额外的每周远程医疗访问，他们可以创建一个仪表板，定期审查实施远程医疗访问后糖化血红蛋白水平的任何变化。看到结果可以让团队成员监控患者和干预的进展，并对当前和未来的改进项目产生组织支持。

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

你想了解更多关于这个话题吗?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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