Catering to Patients: How Data Can Cut Patient Costs
In virtually every medical bill that’s ever been mailed, there is a breakdown of charges. Fees for one’s hospital stay, charges for tests and medicines, and possibly even bills from different departments. Because of the way healthcare entities are set up, it’s virtually impossible to send a single bill without showing where each dollar is due. Though it may take more time to decipher, the current set up protects both the service providers and their patients.
However, that doesn’t mean the fees themselves can’t be reduced. With big data prediction and analyzing, services (and therefore their fees) can be greatly reduced. By having a better idea as to what will happen – symptoms, diseases, treatment options, etc. – doctors can use fewer resources to pinpoint each issue.
One of the most effective ways to reduce patient costs is that of preventative care. This can come in the form of patient education, or by using data to determine who is more likely to develop which symptoms. Ages, past health, demographics, and more will also work to make these predictions more accurate. Obviously, the more tests and doctor time that’s needed to diagnose a patient, the higher the bill.
Predictive diagnosis also comes in lieu of the upcoming healthcare reform (PPACA), which is meant to encourage doctor collaboration and reduce patient fees … for both individuals and government-funded accounts. By incorporating software that performs more efficiently than humans ever could, more results are had for less money.
Over time, it’s projected that these perks will only grow. As more data becomes available, as well as the ways in which it’s analyzed, the journey to reduce patient fees becomes a steady, streamlined process.
To learn more about big data and what it can do, click the tabs above.
