Ticking all the boxes for a health care upgrade at Strata Rx
Healthcare will be the next area to take advantage of newer types of databases. At O’Reilly’s recent Strata Rx conference, one of the main issues that was disucssed included how the medical field can use databases, including Neo4j offered by Neo Technology!
Here’s what we all know: that a data-rich health care future is coming our way. And what it will look like, in large outlines. Health care reformers have learned that no single practice will improve the system. All of the following, which were discussed at O’Reilly’s recent Strata Rx conference, must fall in place.
Data in practice
You can learn a lot by focusing in microscopic fashion on one use of data, so Jo Prichard’s talk on fraud detection taught several principles that can be applied more broadly. He revealed how LexisNexis Risk Solutions identified fraud among Medicaid recipients using graph search, a fairly recent innovation that traces the chains of relationships among people or things. LexisNexis used an SQL database, but there are graph databases that represent such relationships directly, such as the Neo4j used by Fred Trotter to store referral information among doctors.
Medicaid cheaters tend to request small amounts of money that don’t draw attention, but derive value by spreading out lots of fraud among people they know and trust. LexisNexis checked for evidence of ties among Medicaid recipients, including shared houses and shared businesses.
Similarly, they could uncover doctors who were illegally prescribing controlled substances by looking at the people the doctors seemed to be connected to personally.
Accountable Care Organizations (ACOs) have insatiable appetites for data (although I don’t know whether their managers understand yet how fundamental data is to their operations). Many people take the term ACO generically to mean a health provider who has to show that their treatments are state of the art and are having a positive effect. But officially, the ACO is a regulatory category created by Centers for Medicare & Medicaid Services (CMS). As summarized by Michael Gleeson, ACOs use data to find at-risk patients and make sure they come in for treatment, measure their own success at restoring patients to health, and do other performance improvements. As I mentioned earlier, ACOs have to choose exactly the right treatment, not too little and not too much. They can’t cut off patients arbitrarily, like earlier managed care plans did.
As examples of performance improvement, Gleeson mentioned tracking the length of the doctor’s workday to determine stress levels, and to measure the time the doctor spends using an EHR to indicate where the EHR is inefficient.
He emphasized the advantage that data gives large organizations. Small ones will be able to draw fewer conclusions from data, and therefore can’t use it to improve care. Combining payer data with provider data (that is, insurers along with clinics and hospitals) is also valuable.
Finally, Gleeson lamented that Health Information Exchanges (HIEs) don’t have better data than payers, possibly because the standard data collected by doctors and transmitted to HIEs don’t have the types of data ACOs need.