Gains in interoperability, pharmacogenomics, and mobile monitoring tools will accelerate the ability to help physicians deliver more data-driven decisions.
For quite some time, the ability of healthcare enterprises to capture and store data has greatly outstripped their ability to create meaningful information from it. And much of that insight has been retrospective in nature. However, recent gains in interoperability and technologies such as in-memory computing and machine learning tools are able to dramatically change the equation, creating insight that can lead to broad impact across the enterprise at the point-of-decision.
Continued reimbursement pressure along with the inevitable shift of payments from volume to value have created keen interest in most health systems. Data is increasingly being viewed as an asset and the ability to leverage it for process improvement a competitive advantage. Healthcare providers are learning to better leverage evidence-based medicine.
However, most of care delivered does not have strong clinical evidence supporting a best practice. As a result, "standards of practice" can vary from region to region, hospital to hospital and many times between providers at the same institution. Clinical practice in these cases is a composite of what individual providers have learned during training and honed based on individual experience over time. While better than nothing, this approach fails to leverage the "organizational memory" from results of many similar patients treated across the organization over time.
The shift to leveraging outcomes to drive process improvement across practice units will continue and accelerate in 2017, as data driven decision-making will help healthcare providers to improve quality, while often reducing costs at the same time.
Unlocking the Value of Data
Technology solutions powered by rich datasets and high-performance, in-memory computing capabilities are available now and already in use. With these types of tools, healthcare organizations and research centers can integrate terabytes of existing health data, spread across scores of databases whose content and quality can vary widely.
As amassed healthcare data continues to grow exponentially, researchers have the opportunity to study illnesses with greater depth and precision. They can leverage insights to treat patients with greater precision. This capability is power within an institution, but becomes turbo-charged when we begin to link together the vast amount of health data globally.
Pharmacogenomics
Another area primed for accelerating change in 2017 is pharmaceuticals. Doctors have known for as long as they've been prescribing medicines that drug affects often vary by patient. Pharmacogenomics, the study of how genetic variation contributes to an individual's response to drugs, is an example of how genomics are increasingly relevant in clinical decision making. Researchers have already identified a few hundred genes that are related to drug metabolism, and are continuing to identify more.
The Clinical Pharmacogenetics Implementation Consortium (CPIC) has released prescribing guidelines for individuals expressing certain genetic variation. With a relatively inexpensive genome-based drug metabolism test (from $200-500), a doctor can determine the rate at which an individual metabolizes specific classes of drugs, including drugs used in HIV treatment as well as cancer drugs.
With these developments in testing, doctors no longer have to use a one-size-fits-all approach in medication. Doctors can more optimally prescribe medications and/or doses based on patients-specific factors including age, gender, ethnicity and genomic factors. Tailored drug treatment plans also help reduce wasted resources and adverse events, allowing physicians to become more efficient and additionally serve more patients.
Improved Population Health Management
Population health management may be where analytics bring the broadest rewards. Chronic diseases now affect almost half of all Americans, approximately 133 million in the US. By 2020, that number is projected to grow to an estimated 157 million, with 81 million having multiple conditions.* And the problems of chronic disease are global: About 347 million people worldwide have diabetes, according to WHO, and the cost of treating diabetes is estimated to be $500 billion worldwide.
Chronic diseases pose a huge problem to the business community, as they undermine productivity and account for more than 85 percent of health care costs in the US. Chronic diseases are one of the biggest reasons that premiums for employer-sponsored healthcare have increased by 123 percent since 2000.
In addition to classic sources of data, mobile apps and home biometric devices represent another growing source of insight that can help with chronic disease management. Some methods collect data directly, while others capture it indirectly, all with goal of assisting providing insight and improving the patients course. The apps can monitor adherence to drug and treatment regimens and detect trends that lead to both individual and population level wellness benefits.
The Journey to the Data-Driven Enterprise
2017 will represent an important acceleration on the way to a more data-driven delivery of healthcare. The core ingredients of big data sets, improving interoperability, and mature analytic tools are largely in place. But more importantly, provider organizations have an accelerating appetite to leverage these tools to fundamentally transform how care is delivered. This shift from intuition to data-driven decision-making and proactive management of chronic illnesses throughout the continuum of care will not only improve care and lower costs, but also be more satisfying and empowering to healthcare providers.
*National Health Council http://www.nationalhealthcouncil.org/sites/default/files/AboutChronicDisease.pdf
** Kaiser Foundation, http://kff.org/private-insurance/issue-brief/workplace-wellness-programs-characteristics-and-requirements/
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