As accountable care organizations (ACOs) and pay-for-performance reimbursement models take on greater prominence, the ability to leverage clinical data is emerging as a key tool in reducing costs and improving patient outcomes. While both clinical and claims data have valid uses in the industry, robust and complete clinical data can paint a fuller picture of the health of a given patient population.
The challenge lies in accessing and analyzing clinical data. Although it can help to improve patient outcomes — and outcomes-based reimbursement — clinical data is more difficult to obtain. Still, those organizations that take steps to leverage clinical data will be poised for greater success than those that rely on claims data alone.
The strengths and weaknesses of claims data
Claims data will remain a key part of revenue cycle management processes, despite the fact that it cannot provide the same level of detail about a patient’s true health status that clinical data can. Claims data will show practices the kinds of services their patients are receiving throughout the continuum of care and will also reflect the costs of care to payers.
However, claims data falls short in three important ways. First — and most significantly — is that it does not measure patient outcomes. For example, claims data might reveal that an A1c lab test was ordered for a diabetic patient, but it will not reveal the results of the test. With healthcare’s growing emphasis on pay-for-performance, the ability to monitor outcomes must play a key role in reimbursement.
The second problem is the significant time lags incurred because health insurance claims often take months to complete. In many ways, using claims data is like looking in the rearview mirror while driving a car instead of out the front window to determine how best to proceed. Imagine the “accidents” that could occur.
Finally, claims data is biased. Those who generate claims want to maximize billing, yet often must work within the confines of bundled and non-covered services. As a result, claims data often provides a skewed vision of actual clinical status. Take, for example, a patient with obesity who visits their physician for high blood pressure. Since there is no code for obesity, the claims data will not provide a full view of the patient’s health. The bottom line is that claims data is not designed for clinical purposes, so it cannot serve as an effective driver for healthcare transformation.
Clinical data: A better solution
A more effective solution for driving costs down and improving patient care lies in clinical data from the electronic health record (EHR). This is true for three reasons:
1) EHR data captures all outcomes from an individual patient’s treatment.
2) EHR data comes directly from a provider’s system and can be used to drive clinical decision-making in real time. For example, a practice can use its EHR to create proactive outreach or “in-reach” programs for patients who need care for specific conditions. This would be very hard — or impossible — to do with reports based on claims data that is months old.
3) Clinical data is more useful for improving treatment across patient populations. By leveraging data directly from the EHR, providers can use indisputable evidence to change the behavior of individual patients and groups.
Of course, there are challenges to leveraging clinical data. The existence of paper-based charts makes extracting timely information extremely difficult. Even when EHRs are used, data often is not entered or stored consistently, so normalization is required to achieve effective and meaningful use of the data across patient populations. Finally, EHR records can be incomplete so gaps in information would need to be filled with data from other sources.
Meaningful Use regulations aim to overcome some of these challenges by bringing more structure to data capture, which in turn makes normalization easier. In addition, technologies now exist that can help practices reap the benefits of clinical data content more easily.
Technology solutions can be used to aggregate and analyze data across multiple providers or across multiple practices in larger entities, such as ACOs. Scalable and cost-effective solutions allow physicians to continue using their EHRs just as they always have, but are able to normalize the data for meaningful use. They are especially valuable for enhancing outreach efforts to certain patient populations, or for improving in-reach efforts in which patients are offered specific preventive screenings or other services when they present for a scheduled appointment. If augmented by information and cost information from claims data, practices can gain greater insight about their population’s health and the financial impact of reoccurring care on their business. In addition, clinical data can drive performance enhancement by providing the benchmarking data to work in tandem with incentives.
The saying is true: You can only improve what you measure. Ultimately, solutions that make it easier to leverage clinical data provide invaluable insights into the most effective treatments. Thus, they are destined to improve both patient care and the bottom line.
Jacob “Kobi” Margolin is the chief executive officer of Clinigence, a clinical business intelligence technology provider.