Healthcare has an information problem, and the cure is data analytics. Each year, the U.S. healthcare system produces 1.2 billion clinical care documents, and the data in the documents is not used as fodder for more informed clinical decision-making.

While we are collecting more data than ever before about patients and the workings of the healthcare system, we are not using data and analytics to improve how care is delivered. This is largely because the healthcare industry has been based on a fee-for-service payment model, and what that model asks of health IT is an adjudicated claims stream to state what services were provided that required payment.

The move to value-based care is putting a premium on moving beyond simply collecting transactional data and toward using all types of patient data to improve care processes and treatment. There is huge potential for big data analytics to lead to higher quality care at lower costs, but it requires giving care teams the ability to see a patient’s complete care profile so they can use it in meaningful ways.

Less than 10 years ago, the majority of healthcare documents were paper-based. In early 2009, the shift to digital records accelerated after the passing of the HITECH Act, which provided financial support to hospitals and doctors for the adoption of Electronic Health Record systems (EHRs).

Now, the EHR adoption rate for hospitals is around 95 percent, and physicians are not far behind at 75 percent. However, while this rapid EHR adoption has the potential to ignite transformation in healthcare, it’s not all sunshine and rainbows.

The healthcare industry is struggling to come to grips with the fact that it’s no longer a people-and-paper enabled industry, but rather a people-and-information technology enabled industry. Clinicians have been told they must use software that, to them, seems to only impose social and time costs without markedly improving care.

Also, the technology and tools have not had time to accommodate the richness of clinical data. For example, 80 percent of the data in EHR clinical records is unstructured and thus difficult to access outside of the clinical setting for which the EHR systems were developed.

The HITECH act incentivized the adoption of IT systems in healthcare, but those were primarily point-of-sales systems whose purpose was to facilitate the business side of healthcare. The Meaningful Use rules written into the HITECH legislation were intended to assure that the new EHRs being deployed had tangible benefits to patients and healthcare outcomes. However, these rules have been somewhat contentious, not in the least because the EHR systems we have deployed were not originally developed to support shared models of care and value-based medicine.

Under the Medicare Access and Chip Reauthorization Act, CMS is applying new metrics that would effectively eliminate the Meaningful Use program as a stand-alone entity for office-based physicians and other eligible clinicians. The proposed rule would replace Meaningful Use with a simplified program under which those professionals will receive incentives for reporting progress using IT to support care.

However, while the recently proposed rules have made significant progress toward making the new framework less rigid and more valuable, the issue remains that we will be adapting systems that are designed for a pay-per-service model rather than an actual model of a patient’s state.

At their core, EHRs were architected in a fee-for-service environment to improve clinician workflows and optimize billing. To accomplish that goal, they don’t require complete knowledge of patients. However, in a value-based environment, complete knowledge of a patient's health is critical.

The reality is that EHRs contain information that primarily is needed to run a healthcare business—transactional events and claims stream data and, if we are lucky, some diagnostic codes that accurately represent the patient’s current state.

So it should come as no surprise that EHR systems have to walk a fine line. If EHRs stray too far from the status quo of supplying the claims streams that have been the financial lifeblood for healthcare organizations, then provider organizations that use them have a problem.

Simply put, the competing financial and enterprise management needs currently trump the needs of data scientists, and this must change for healthcare to evolve.

EHR adoption has multiplied the number of data points available in healthcare, and this material should be contributing to better patient care and more informed clinical decision making. But there are significant hurdles to be overcome for health IT to deliver on its promise. A shift in mentality toward EHRs and developing systems that cater to clinical, and not just administrative needs, are two of the most important changes.

The ability to combine data from EHRs with clinical data from hospital notes, insurance claim data, pharmacy data, laboratory tests, patients themselves and more sources will lead to better care outcomes. For example, with a system that combines both clinical and transactional data, healthcare providers will be able to understand patterns to improve care, by analyzing patient profiles to identify segments that will benefit most from preventative care and scaling disease profiling to uncover protocols that deliver the best value. Most importantly, healthcare organizations will have more incentive to do so.

As they stand now, transaction-based EHRs encourage a fee-for-service system, and at its core, a fee-for-service system is not focused on improving outcomes and patient health. However, if EHRs can combine data from financial transactions with clinical data, then they will play a key role in transforming incentives and transitioning to a value-based care system.

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