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Data, analytics and emerging technology already play a central role in improving care for people with multiple chronic conditions (MCCs). Now, health plans and providers are poised to use these tools to identify people sooner on the path to becoming polychronic — patients who have three or more chronic conditions, with at least one impacting their quality of life. 

Early intervention can lead to improved quality of life and reduced future health care costs.

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A burden on individuals and the health system

A chronic condition is a condition or disease that’s long lasting. Chronic conditions include diabetes, cardiovascular disease, cancer, musculoskeletal conditions, chronic kidney disease and gastrointestinal disease. Several of these conditions top the list of leading causes of death and disability in the U.S.¹

Chronic conditions are considered complex when they affect more than one of the body’s systems. Individuals with three or more chronic conditions are said to be polychronic.

The rate of polychronic patients is climbing. In fact, the number of people with three or more chronic conditions is expected to reach 83.4 million by 2030 — that’s up from 30.8 million in 2015.²

Health care spending in the U.S. is driven in large part by care for people with complex or multiple chronic conditions. By some estimates, spending on an individual with multiple complex conditions is eight times more than that spent on a healthy individual.³

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Early identification key to care

Proactively identifying individuals at high risk for chronic conditions is key to providing them with the services and targeted interventions they may need. As a first step, it’s worth asking: How do individuals develop MCCs?

Family history and genetic makeup certainly play a role, but many chronic conditions can also be linked to lifestyle choices and behavior. Tobacco use and lack of physical activity, for example, have been tied to diabetes, cardiovascular disease and cancer.⁴

Lifestyle choices and behaviors are considered social determinants of health (SDOH). Also included in SDOH — and linked to a person’s overall health — are socioeconomic factors like education level, employment and housing stability, and environmental factors like access to healthy food, access to public parks or walkways and air quality.

Risks associated with these conditions can compound over time. For example, an individual may be more predisposed to asthma and type II diabetes if they live in an area without a nearby grocery store, poor air quality and few public green spaces to exercise in.⁵

To find people on the path to MCCs, we can bring together currently available data to predict who is likely to develop more acute forms of disease.

Artificial intelligence (AI) is one way to make these important predictions. First, data scientists train AI models to detect patients with a specific chronic condition, like diabetes or heart disease. AI models do this by analyzing historical data and finding patterns between electronic health record (EHR) data, health plan claims and other relevant datasets like patient-generated health data (PGHD) or SDOH. These models incorporate natural language processing (NLP) and machine learning (ML) techniques to extract and ingest relevant information.

Once AI models prove their ability to accurately identify people with a chronic condition, they can be fed new information to make predictions about other individuals at risk of developing chronic disease months or even years into the future. They do this by looking for common traits and data signals among people who have been diagnosed in the past.

Adding SDOH data to disease progression models can give an even clearer picture of population and individual-level risk for developing MCCs. When models are trained to derive information from data points that influence health — like food insecurity and poor environmental conditions — predictions become even more accurate.

There is a virtuous circle to these efforts. As models churn through more data and learn, their precision and accuracy increase. Then, when a patient is flagged as at-risk for a condition and is later actually diagnosed, that finding feeds back into the models, creating stronger predictive capabilities that benefit future identification of patients.

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Predictive modeling's virtuous circle

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How tech can improve care for the polychronic

Early identification of individuals at risk for MCCs provides the knowledge needed to launch disease prevention efforts and better manage care. Steps can be taken by employers, health plans, life sciences organizations, state government agencies and health care providers to help ensure success.

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  • Employers can offer employees resources to help them understand the benefits available to them and make healthier lifestyle choices. Tech-enabled benefits, like online wellness programs with incentives tied to things like physical activity, can also help employees as they work to manage chronic diseases. Internet-connected wearable devices can help monitor engagement and adherence.

    Employers can also leverage predictive analytics to increase engagement in the benefits services they offer. AI can help segment their population by the individual’s level of interaction with the health system and personal health status, and then predict his or her likelihood to engage in programs and which outreach method (mail, email, etc.) they’re likely to respond to. 

    Advocacy services, nurse lines and tobacco cessation programs are also good examples of benefits aimed at preventing or managing chronic disease. 

     

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  • Mobile health technologies and care management apps can offer individuals important information and feedback about their health habits. Watches and other wearable devices, for example, allow individuals to monitor their vital signs and health progress. 

    Remote patient monitoring — like Vivify Health’s platform — offers health care providers a way to connect with patients from afar, monitor specific health measurements, and intervene at the right time to keep them on track toward their goals.

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  • Providers spend a lot of time managing care for people with MCCs. Adding telemedicine visits — whether by phone or video — to chronic care management could help with treatment adherence and prevent avoidable hospital admissions.

    Telemedicine also offers providers another direct interaction with patients through which they can document hierarchical condition categories (HCCs) used to calculate risk adjustment factor (RAF) scores. To achieve appropriate reimbursement from government payers, health plans need to document their members’ HCCs at least once annually. Accurately documenting the health status of members with MCCs is a critical step towards achieving financial targets. 

    As the polychronic population grows, the demand for this low-acuity care interaction will increase. Digital health solutions, like telemedicine, offer a great way to expand access to meet the growing demand. 

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Guidance for stakeholders across health care

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  • As this population grows, the need for communication between teams of specialists will become even more critical. Most peer-reviewed research focuses on treatment of a single chronic disease, not the confluence of multiple comorbidities.⁶ Thus, there are not many protocols to help guide care decisions across multiple stakeholders. 

    Developing a whole-person care plan is a necessary step towards avoiding readmissions and minimizing the prevalence of super-utilizers. Data visibility is a key enabler of that plan.

    Your IT strategy also needs to account for a larger population that may benefit from telehealth offerings, in particular telemedicine visits and remote patient monitoring capabilities. 

     

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  • Identifying members at risk for MCCs earlier means health plans can connect those members with services that can mitigate or even reverse an individual’s progression towards higher disease burden. 

    Proper care coordination for the polychronic population is critical. A tech-enabled platform with smart workflows to keep all parties informed and automatically trigger follow-up tasks can greatly assist care managers as the number of members they work with increases. 

    Real-time knowledge of a member’s full health status can also help avoid adverse drug events that could result from the multiple prescriptions a person with MCCs may be taking. 

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  • Statistically, 66% of individuals with MCCs are of working age.⁷ Employers who self-fund health coverage for their employees have a clear financial incentive to reduce the prevalence of MCCs, and benefit design can have a big impact. Using technology that can track fitness incentives or connect employees with care resources virtually could help many individuals avoid developing multiple chronic conditions. 

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  • As the incidence of MCCs rises and more Americans deal with more chronic conditions, pharmaceutical therapies and medical devices will play a pivotal role in both reducing the trend and maintaining individuals’ health. Understanding the implications of polypharmacy — the simultaneous use of multiple drugs by a single patient — will help life sciences organizations better anticipate market trends and potentially uncover new ways to fulfill patient needs.

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  • More than 74 million Americans benefit from Medicaid programs. They represent a heterogenous spectrum of polychronic needs, from those who are dually eligible for Medicare and Medicaid, individuals with disabilities, those with chronic, high-cost needs, and low-income pregnant women. 

    Medicaid also provides services for those with complex conditions that include a broad range of medical, behavioral and psycho-social needs. As a major part of state budgets, Medicaid, while very efficient and effective, is under constant strain due to populations shifts, public health crisis or recession. 

    Analytics and the ability to predict the ongoing needs and associated costs for those in the program are critical to making sure the programs can continue to serve and support citizens, especially when crises strike.

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  • The number of American citizens living with polychronic disease is on the rise and is generating a rapidly increasing economic burden on all federal health care systems. Although the problem is complex, we can enable federal health agencies to proactively mitigate the impact through the use of prevention, early detection and data analytics. 

    The Optum approach with federal health agencies — including active duty military and veterans — enables a better understanding of the unique characteristics of each population, so that they can better the health and well-being of those they serve.

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A collective call to action

No single therapy or treatment can help cure polychronic patients. Likewise, no single actor in health care can stem the tide of individuals with MCCs that our health system will need to manage over the next decade. Millions of people are on track for a diminished quality of life, and astronomical costs are on the horizon.

For these reasons, stakeholders from all corners of health care have a vested interest in identifying these individuals earlier, when interventions can prevent the complications that arise from MCCs instead of mitigating the damage caused by them.

With new technologies and predictive analytics, we have the tools in our arsenal to make these trends more manageable. It’s on us to put them to good use in time to make a difference.

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Omar Baker, MD, FAAP

Executive Vice President and Chief Medical Officer, OptumHealth

Dr. Omar Baker is the executive vice president, strategic initiatives and innovation for OptumHealth, and chief medical officer, health services. He provides insight and guidance on a broad range of strategic, operational and financial initiatives. This includes leading the care delivery component of the Optum enterprise strategy, accelerating payer contracting for commercial and Medicaid risk, and driving the Optum patient experience strategy.

Dr. Baker is board certified in pediatrics and is a Fellow of the American Academy of Pediatrics. He received his undergraduate and medical degrees from The George Washington University and completed his residency in pediatrics at NYU Medical Center.

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  1. Centers for Disease Control and Prevention. About Chronic Disease. 2019. Available at: https://www.cdc.gov/chronicdisease/about/index.htm. Accessed September 29, 2020.
  2. Hales, et al. Prevalence of Obesity Among Adults and Youth: United States, 2015–2016. NCHS Data Brief No. 288, October 2017.
  3. PriceWaterhouseCoopers. Medical cost trend: Behind the numbers 2020. Available at: https://heatinformatics.com/sites/default/files/images-videosFileContent/pwc-hri-behind-the-numbers-2020.pdf, Accessed September 29, 2020.
  4. Centers for Disease Control and Prevention. Tobacco Use. 2020. Available at: https://www.cdc.gov/chronicdisease/resources/publications/factsheets/tobacco.htm. Accessed September 29, 2020. 
  5. Thornton, et al. “Evaluating Strategies for Reducing Health Disparities By Addressing The Social Determinants Of Health.” Health Affairs 2016. Available at https://www.healthaffairs.org/doi/10.1377/hlthaff.2015.1357, accessed November 24, 2020.
  6. Vogeli, et al. “Multiple Chronic Conditions: Prevalence, Health Consequences, and Implications for Quality, Care Management and Costs.” Journal of General Internal Medicine 2007. Available at https://link.springer.com/content/pdf/10.1007/s11606-007-0322-1.pdf accessed October 13, 2020.
  7. Optum e-book. “The Rising Tide of Multiple Chronic Conditions.” Available at https://www.optum.com/content/dam/optum3/optum/en/resources/ebooks/optum-polychronic-insights.pdf accessed October 13, 2020.

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