Since the introduction of the HITECH Act, there has been a fast-paced shift to incorporate technology into healthcare. With a focus on improving patient outcomes and establishing true interoperability as the Act originally intended, we have fallen quite short of that mark. However, better utilization of data from our systems can help with business decisions, patient care, and yes, even interoperability.
In a recent review from NTT Data Services, they noted how healthcare data is growing exponentially. According to Forbes, the “yield is something on the order of 750 quadrillion bytes every day – or some 30% of the world’s data production.” Think about that. Nearly 1/3 of the world’s DAILY data production comes from the Healthcare industry.
With that volume of data, challenges arise. From interoperability, data quality issues and security, many issues can make even the thought of trying to manage data too overwhelming to pursue. The best recommendation is to find flexible AND scalable data solutions. What is the importance of scalability? If you have a single practice with no intention of adding locations, then probably not very important. However, if your practice has any intention of growth, be it mergers, multiple locations, etc. scalability is critical. It does you no good to invest in a data solution that can’t integrate data at an Enterprise level.
Today’s healthcare data is necessary and should be viewed as an asset, rather than a hindrance. You’ve been collecting healthcare and insurance payment data for years – now is the time to put it to use.
Beyond the value of the revenue cycle data you can extract from your EMR’s, the clinical data is likewise as valuable. It can move us from a “treat the issue” mentality to a “prevent the issue” mentality. How can this happen? Let’s look at an example from the report:
Joe has asthma and had 3 visits to the ER in 2017. Joe is prescribed both rescue and maintenance inhalers, however, he still has multiple visits to the ER in 2018. Through data points like prescription fills, claims data (including diagnosis codes) and geographical or regional information, you are able to discover that Joe’s asthma is triggered by dust storms. His inhaler prescriptions have lapsed, and he does not readily have transportation to get to the doctor. Also, a dust storm is predicted just a few days away.
With these types of data points, an alert could be triggered to call in some new inhalers for Joe and to warn him about the pending dust storm to take whatever respiratory precautions he can.
Make your goal this year to start looking into ways to make this data work for you, rather than you continuing to work for it.