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Readers Write: The Journey from Population Health Management to Precision Medicine

April 20, 2016 Readers Write 1 Comment

The Journey from Population Health Management to Precision Medicine
By David Bennett

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Imagine a world where individuals receive custom-tailored healthcare. Patients are at the center of their own care, making key decisions themselves. They are supported by research and education, and their information is shared easily between caregivers and clinicians. Preventive care is more effective than ever, and medical interventions occur in record time.

With precision medicine, this world is not just within reach — it’s already happening.

Precision medicine (also known as personalized medicine) is the next step in population health management, transforming healthcare from being about many, to focusing on one.

Population health serves as the “who” to identify cohorts of patients that are at risk and require attention. Precision medicine is the “what,” providing caregivers with the specific information they need to create effective prevention and treatment plans that are customized for each individual.

Having the largest variety of data sets possible optimizes therapeutic tracking of each patient’s care plan to make and refine diagnoses. This sets the stage to pursue the most personalized therapy possible by detecting patterns in clinical assessments, behavior, and outcomes.

Data is essential, but it’s only useful if you have the ability to make big data small in order to personalize care. Today’s technology platforms can do just that, by capturing vast amounts of health data and applying real-time analytics that provide information and tools that help healthcare professionals and health insurers make more effective, individualized treatment decisions.

Using this information to engage patients and guide care management makes the journey from population health management to precision medicine that much easier, paving the way for an era of truly personalized medicine that prevents the deterioration of health.

The timing couldn’t be better for precision medicine’s heyday, and here’s why: one-size care does not fit all.

Many factors are converging to make the adoption of precision medicine a reality:

  • A growing number of EMRs, EHRs, and HIEs are being connected and cover a significant number of individuals.
  • Patients are more interested in participating in their care, especially when they get access to their own data. There are myriad devices on the market today that are relevant — from wearable devices that measure activity and sleep quality, to wireless scales that integrate with smartphone apps, to medical devices that send alerts (such as pacemakers and insulin level trackers). The data from these devices contribute to a robust longitudinal patient record. The interactive nature of the technology is also an excellent way to engage patients.
  • MHealth advances allow us to easily capture consumer data using cellphone technology and monitoring patients remotely with telehealth and virtual consultations.
  • Ability to see which inherited genetic variation within families contributes both directly and indirectly to disease development. We can now adjust care plans when genetic mutations occur as a reaction to the treatment in place.

If we look at healthcare outcomes in the United States, it’s clear that we need to anticipate patients’ needs with evidence and knowledge-based solutions. Only then will we will be able to identify a patient’s susceptibility to disease, predict how the patient will respond to a particular therapy, and identify the best treatment options for optimal outcomes. Precision medicine will get us there.

Precision medicine is about aggregating all forms of relevant data to enable different types of real-time data explorations. More concretely, specific areas of medicine are expected to make use of new sources of evidence, and the data types they leverage vary based on medical specialty. A good example would be the difference between the data sets used by oncologists versus immunologists.

There are two critical types of data explorations that both need a very large number of data sets to bring results:

  • Medical research with scientific modeling. Precision medicine can be leveraged to advance the ways in which large data sets are collected and analyzed, which will lead to better ways and new approaches to managing disease.
  • Clinical applications. Treatment plans and decisions can be greatly improved by identifying individuals at higher risk of disease, dependent on the prevalence and heritability of the disease. We call this cognitive support at the point of impact. To support this, more control is needed in real time over macro variables: genomics, proteomics, metabolism, medication, exercise, diet, stress, environmental exposure, social, etc. Precision medicine provides a platform that has an extensive number of data sets with the ability to easily create custom data sets to capture these types of variables.

Precision medicine not only means care tailored to the individual, it also brings to the healthcare industry the visibility on variability and the speed necessary to act expediently on findings to prevent the deterioration of health. Not only does this enhance patients’ lives, it saves healthcare dollars and prevents waste.

Tailoring deliverables to the needs of individuals is nothing new, at least in other fields such as banking and retail. Pioneers in these industries have leveraged open-source technology on a solid data foundation to meet their markets’ challenges.

Surely we can do the same in healthcare, where it’s literally a matter of life and death. That’s why so many of us are working on a daily basis to accelerate the science behind precision medicine and to encourage its adoption. Precision medicine is nothing short of revolutionary, and together, we can all make it a reality.

David Bennett is executive vice president of product and strategy at Orion Health of Auckland, New Zealand.

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Currently there is "1 comment" on this Article:

  1. Good discussion, well articulated. Our startup has spent the last 2 years building this system leveraging predictive analytics and sophisticated patient engagement models.







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