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Precision Medicine Pinpoints Diagnosis, Care Options

By Matthew Zubiller, for HealthLeaders Media  
   January 19, 2011

Physicians often review 10 to 15 variables to diagnose a single patient – a physical exam, lab values, and patient history, to name a few. Precision medicine has enormous potential for eliminating the costly trial-and-error that is intrinsic to medical practice. Plans must be prepared to offer the right test, at the right time and in the right location – all within coverage policies and based on data and evidence. As advanced diagnostics and targeted treatments evolve over the next few years, a physician soon may be faced with more than 1,000 diagnosis variables.

This stream of complex data will include genetic-level mutations as well as other characteristics that, in innumerable and unique combinations, drive how a specific patient will respond to a specific treatment. While the volume of data physicians need to synthesize today can be overwhelming, the future will bring astronomical growth in that data as we move forward in the age of precision medicine.

With the tremendous growth in advanced diagnostics, health plans need to enable an automated approach to evidence-based decision support at the point of care, so as to optimize the use of precision medicine as a catalyst to improve care and reduce cost. The challenge is to do so while not promoting overutilization of advanced diagnostics themselves.

The term "precision medicine," popularized by business strategist Clayton Christensen, describes a step forward in the art of medicine. The ability to test for genetic predisposition to a disease (or its recurrence) is the aspect that garners the most public attention. But more important for health plans is the much larger category of pharmacogenomic tests. These tests pinpoint a patient's diagnosis and determine which drug is the best option, which has a far more immediate impact on costs and care.

Sub-typing a patient's LDL particles for their size and density, for example, can lead to prescribing the right statin on the first try – ideally reducing the risk of a heart attack. With expensive oncology treatments, stakes may be even higher. Consider the breast cancer patient whose doctor is ordering chemotherapy. A $300 test to determine the levels of HER-2 protein in a breast tumor can rule out the use of a $40,000 chemotherapy regimen that will not work on that patient.

Another example is a test that suggests how quickly a patient will metabolize the drug Coumadin, prescribed to prevent blood clot formation. Determining the optimal dose of Coumadin early on can save multiple tests, office visits, and adverse events.

While precision medicine can help rule out unnecessary, expensive treatments, the tests themselves can be ordered incorrectly. In just a few years, molecular diagnostics has become a $6.2 billion industry that is growing at 21% annually. As with any high-growth medical cost, health plans should stay on top of molecular diagnostic utilization to ensure its appropriateness. However, plans are often unable to measure their own spend on advanced diagnostics, due to today's limited coding scheme.

Just a couple dozen codes now exist, primarily for test methods rather than the specific tests themselves. These codes are applied to more than 2,000 different genetic tests. This creates great confusion and, if plans cannot measure utilization, then how can they effectively manage it?

Some healthcare organizations, including our own, are working to establish a rational and transparent coding scheme. Identifying which test is performed, by whom, and at which lab would help to connect patients, providers, insurers and labs with all the information they need to ensure the right decision making in a more efficient manner. Indeed diagnostics represent an opportunity for labs and plans to collaborate to reduce the cost of care.

Prospectively managing the network of labs selected within the clinician's workflow is another important step. According to research at McKesson, more than 20% of the costs of molecular diagnostics can be attributed to out-of-network or "non-par" labs at a cost that is double to quadruple that of in-network labs. With the high proliferation of these non-par labs, health plans are having difficulty managing and contracting with them to support all appropriate testing in-network.

The administrative burden created by the enormous range of tests can be relieved by providing transparent information to all parties – most importantly at the point of care, where test orders are initiated and fulfilled. The best tools help the physician decide on the most appropriate test; they also inform the plan whether to authorize the test and the treatment. Ideally, a lab that performs the test would be identified and informed that the test has been authorized. A process that might otherwise consume hours of manual back-and-forth work could be automated – saving not only valuable administrative time, but also bringing better, faster patient care.

As health plans prepare for precision medicine, an effective program takes a comprehensive, systematic approach that includes measuring, defining and managing advanced diagnostics over time and rewarding best-practice use. This should be done in collaboration with their lab partners and includes:

  • Measure: Looking at test volumes and costs retrospectively is one means of measuring utilization. Equally important is notification at the point of care, wherein providers and labs have a mechanism to notify the health plan prior to performing a specific test. Doing so helps the plan identify outliers early and provides information to the plan as to what labs have the capabilities to perform which tests, while reducing the burden of requiring authorization for everything.
  • Define: 80% of spending on genetic testing is concentrated across roughly 2,500 tests. It makes sense to focus intervention initially on managing the highest-cost tests. Plans should define medical, benefit, network and payment policies for these tests, using the best evidence as it becomes established. Feasibility of preauthorization can then be determined through the measurement step above.
  • Manage: The most effective programs will integrate clinical and financial policy in real time within the ordering workflows of physicians and labs. It is now possible to provide real-time, fully automated molecular diagnostics decision support at the point of care, with appropriateness review driven by evidence-based criteria. A fully automated decision support system not only drives those activities that typically are done manually, such as notification and preauthorization, but also guides and incentivizes physicians to work with in-network labs. Intervention needs to be exception based – by managing those instances that require review and simply measuring the rest – the system becomes much more efficient and effective.
  • Reward: Medical practices and labs that consistently meet utilization criteria can be made exempt from arduous authorization and approval processes and only need to submit a notification. They can also participate in pay-for-performance programs, risk sharing, alternative quality contracts, or receive other incentives to drive optimal ordering.

Much of the $2 trillion spent annually within today's healthcare system is spent on inappropriate care and administration. If an accurate diagnosis can be reached faster, the inappropriate care that comes along with trial and error will diminish. Automating much of the decision-making process can further lower administrative costs. We can attack that 50% by connecting all involved parties with timely and useful information. Health plans can prepare for the shift toward ever-more personalized medicine by combining evidence-based content, decision-support technology (ideally at the point of care), prospective, exception-based authorization technology and analytic services.

The role of managed care in precision medicine is not to reduce the use of all tests. Rather it is to optimize the use of diagnostics, taking into consideration the total cost of care. Plans would not want to deny a $200 test that could reveal that Patient X will not respond to an $80,000 drug – and a patient certainly does not want to waste valuable time on a treatment that could do more harm than good. A successful program makes all information available and transparent to all parties to speed optimal decision-making. It also gathers a rich, data set that can be further extended and drive the analytics for future planning.

Medical science is rapidly developing better genetic-level tools to accurately diagnose and treat disease. Used well, the new paradigm of precision medicine has great potential for eliminating the trial and error that is one of the greatest drains on the healthcare economy. 


Matthew Zubiller is vice president of advanced diagnostic management at McKesson Health Solutions. He can be reached via email at matthew.zubiller@mckesson.com  

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