Why We Must Measure Disease Burden to Close Gaps in Care Delivery

The COVID-19 pandemic has held a magnifying glass to many significant infrastructure weaknesses in the U.S. healthcare system. Inefficient supply chains, capacity issues and high rates of comorbidities, such as diabetes and heart disease, have all exacerbated this health crisis.

Yet, there is reason for hope. The skill and courage of our healthcare providers, Herculean efforts by life sciences companies to fast-track new treatments and vaccines, and a massive push by public health officials to create stop-gap solutions will all be crucial as we battle this pandemic. 

Unfortunately, gaps in data remain a singularly difficult obstacle to tackling COVID-19, or any disease.

Long before COVID-19 was even on the radar, the U.S. had the highest disease burden among other comparable nations, driven largely by the growing prevalence of chronic diseases (e.g., obesity, dyslipidemia, diabetes, hypertension, to name a few), which cause seven out of every 10 deaths in the U.S. By 2030, researchers project an estimated 83.4 million people in the U.S. will have three or more chronic diseases. 

Yet, the lion’s share of these chronic diseases are preventable. The World Health Organization estimates that 80% of heart disease and type-2 diabetes and 40% of cancers could be prevented through earlier intervention and better lifestyle behaviors. Therein lies the challenge – and the opportunity. There is a problem in our country that’s killing millions and causing runaway spending that accounts for nearly one-fifth of our total economic output – and most of it is preventable.

Meanwhile, acute conditions like heart attacks, broken bones and infections face their own challenges. Healthcare providers are overloaded with information between scientific journals and electronic medical records. And yet they often lack an accurate medical history. This makes it difficult to reach optimal therapeutic choices despite a new proliferation of sophisticated and increasingly personalized options. It is a nearly impossible challenge to predict future patient outcomes when you’re missing the history on that patient’s journey and disease progression over time. 

Many have tried to address these challenges with short cuts, point solutions, and analytics that seek to spot at-risk populations sooner, trigger earlier interventions, and tailor evidence-based treatment. But they’ve all been built on incomplete data infrastructures that fail to provide enough detail to be effective.

Why? In a word: fragmentation. 

The healthcare industry is siloed into discreet fiefdoms, each with its own rules, legacy ways of doing things and incentive structures with objectives and incentives that are wholly misaligned. Providers, payers, life sciences companies, and the government are all producing petabytes of patient data every day, but storing it differently, omitting or suppressing important contextual information, and limiting accessibility to other constituencies.

All of these issues, which have been on display prominently throughout the COVID-19 crisis, are central to getting to the root of chronic disease and developing evidence-based treatments for illnesses ranging from cancer to infectious disease. However, because the healthcare system was designed to address the volume of treatments over the value of outcomes, few healthcare stakeholders were incentivized or empowered to focus on connecting the dots to get to the ground truth.

More recently, as the healthcare industry has started to adopt value-based care, which bases payment incentives on healthcare outcomes, providers, health plans and population health authorities have sharpened their focus on taking a comprehensive, evidence-based approach to disease treatment and prevention. This includes steps like identifying the clinical, demographic, and social factors that may put a patient at greater risk for costly and debilitating complications. 

By leveraging the power of AI, along with strategic partnerships with health plans, providers, life sciences companies, and the government, it is possible to address both systemic and technological hurdles to create a comprehensive understanding of individual patient journeys at scale. This is a foundation for ensuring the right interventions that will improve patient outcomes over time. But doing that requires more than just collecting and aggregating data. To obtain value from the trillions of new explicit and implicit data points that the healthcare system generates daily requires a framework that provides context, ensures fidelity, and produces clarity of action.

At Komodo Health, we have a plan to do just that, and we’ve outlined it in our whitepaper, Connecting the Dots to Reduce Disease Burden in the U.S. To read our full insights, click here.