In the world of medicine, trial and error is largely the norm today. Doctors make a "most likely" diagnosis consistent with symptoms and prescribe treatment accordingly -- treatment that might include drugs, devices or surgery. If the treatment doesn't work, the doctor most likely alters dosage or prescribes something else. This iterative cycle is repeated until the diagnosis and treatment present the desired clinical outcome.

The bad news is that this paradigm has reached a point of diminishing returns, as evidenced by the fact that most drugs prescribed in the United States today are effective in fewer than 60 percent of treated patients. The good news is that new technology could transmute trial-and-error medicine, replacing it with an evidence-driven paradigm—one where each patient receives care, medication and treatment predicated on his or her unique genomic profile.

Precision medicine is essentially the ability to tailor treatments, as well as prevention strategies, to the unique characteristics of each person. The closest real-world analogy to this process would be a recruitment system that matches a person’s job to his or her education, experience and skill sets as laid out in a profile or resume to ensure the best fit for the job.

The first step of precision medicine is mapping a person’s genomic profile. Then, by analyzing and correlating vast pools of genomic data, researchers and doctors can identify variances and biomarkers for potential diseases that will shape what treatment is best, assuring the best therapeutic outcome with minimal adverse effects. This type of treatment is made possibly by genomic profiling platforms, electronic health records (EHRs), analytics tools for self-service data discovery, visual and predictive analytics with machine learning, and other new tools.

In recent years, precision medicine has attracted significant hype and sometimes pessimism from a number of quarters (including, perhaps unsurprisingly, the pharma industry). It's sometimes perceived as a grandiose vision that is way out there and not yet ready for real-world patients and consumers. But considering we can already move beyond a real-world analogy to real-world examples, it's clear that precision medicine holds promise and is poised to continue replacing the old-school trial-and-error approach.

One of the preeminent areas of application of precision medicine for personalized healthcare delivery pertains to the diagnosis and treatment of cancer. When it comes to cancer, personalization can take several different forms:

  • Looking at genomic profiles to determine if patients have certain genetic mutations that could put them at higher risk for developing cancer, and whether they can handle specific drugs and treatment protocols.
  • Testing a patient’s tumor or cancer to figure out the best treatment protocols that will deliver the optimal outcomes.

One real-world example involves breast cancer treatment. Certain kinds of breast cancer are not candidates for chemotherapy alone. But Herceptin, a monoclonal antibody delivered by Genentech, has been found to be particularly efficacious as a first-line treatment with chemotherapy for aggressive forms of breast cancer in women whose tumors have an overabundance of HER2 -- a protein that promotes cell growth.

Herceptin has been found to reduce the likelihood of cancer spreading to other parts of the body in such patients by a remarkable 53 percent, compared with traditional chemotherapy alone. This is compelling from both an outcomes perspective and a cost-benefit perspective. The tests to detect whether a breast cancer patient has an overabundance of HER2 protein (and thereby is a candidate for Herceptin) cost only $400. The test potentially saves thousands of dollars by preventing the cancer in HER2 patients from spreading to other parts of the body and by not treating HER2-negative patients with a drug that won’t do anything.

In essence, this kind of precision medicine means the "mass customization” or the “targeted treatment” of cancer.

So how does precision medicine fit in with population health management? One of the fundamental tenets of healthcare reform and the Affordable Care Act (ACA) has been the transformation of the economically unsustainable fee-for-service model into a pay-for-performance paradigm that has far reaching implications for the healthcare delivery system in the U.S. Key to this paradigm shift has been the advent of the Accountable Care Organization (ACO) responsible for ensuring population health management.

According to the Centers for Medicare and Medicaid Services, ACOs are groups of doctors, hospitals and other healthcare providers that collaborate voluntarily to deliver coordinated high quality care to their Medicare patients. It can also be defined as a set of healthcare providers—including primary care physicians, specialists, and hospitals—that accept collective accountability for the cost and quality of care delivered to a population of patients. This, in essence, is the concept of population health management.

From a healthcare provider perspective, population health management can also be defined as an overarching set of capabilities which support the delivery of and adherence to evidence-based and integrated clinical care activities tailored to individual patients and populations

Here is the current model for a risk based approach to Population Health Management being embraced by most industry leaders today, illustrated in the figure seen earlier in this column.

STRATEGY: Population Health Segmentation (PHS) and Community Health Assessment (CHA) is critical to assess the state of health of the population being served. Community Health Assessment (CHA) that is also referred to as ‘Community Health Needs Assessment (CHNA) refers to the process of community engagement, collection, analysis and interpretation of data on health determinants and health outcomes, health disparities, and identification of resources to fulfill these needs and ensure superior patient and population health outcomes. The CDC has identified and articulated 42 metrics for health determinants and health outcomes that if measured and analyzed, will provide healthcare providers with an accurate blueprint of the health of the population being served. These can then be leveraged to segment the population based on risk and cost to serve, to drive a pragmatic PHM strategy to deliver the highest quality of care cost effectively while managing risk.

TACTICAL: Patient Risk Stratification (PRS) using a risk-based management approach: is arguably, one of the most challenging aspects of PHM, demanding sophisticated machine learning, advanced predictive analytics software leveraging complex models to predict risk not only at an aggregate population level, but also at a discrete patient level. Leveraging solutions like JVION, industry leaders are stratifying their patients based on 30 day re-admission rate risk, risk of overshooting their length of stay (LOS) and other key performance indicators (KPIs) into high risk patients (multi-morbid, catastrophic conditions like heart attacks, heart failure and cancer), medium risk (chronic conditions like diabetes, arthiritis, Alzheimers, Parkinsons et al) and low risk (preventable conditions).

OPERATIONAL: Appropriate risk based treatment and case management as shown in the figure (below) involving care coordination, intervention by nurse case managers, and a care ecosystem comprising family, friends and social workers for the highest risk patients. For medium risk patients, this would involve enrolling them into a health plan funded wellness and disease management program for managing chronic conditions like diabetes to ensure that these can be managed well without exacerbation. Treatment of low risk patients would be ‘business as usual’ with primary care and preventive services.

To further enhance this risk-based approach, what if we can capture the genomic profiles of children and their parents at birth, or shortly thereafter? Imagine the impact of including the complete genomic profile and clinical analysis as part of every patient’s electronic health record that provides the basis for personalized healthcare treatment across the life-time of the patient.

The benefits for patients by way of superior patient outcomes, and for physicians and clinicians to move from trial-and-error medicine to evidence-based care, would be measurable and life changing. Equally compelling for pharma and medical devices companies would be the ability to target and recruit patients with exotic diseases meeting multiple qualification criteria, as the basis for higher efficacy and efficiencies with clinical trials and biomarker, drug and devices development.

While not deployable at scale today, the promise and efficacy of precision Medicine, demonstrated by visionary leaders like the Inova Translational Medicine Institute, makes this a provocative, disruptive and yet pragmatic innovative paradigm to improve outcomes based on personalized healthcare treatment, whose time has come.

Register or login for access to this item and much more

All Health Data Management content is archived after seven days.

Community members receive:
  • All recent and archived articles
  • Conference offers and updates
  • A full menu of enewsletter options
  • Web seminars, white papers, ebooks

Don't have an account? Register for Free Unlimited Access