You are not wide of the mark when you say that “screwed-up, mismanaged, automated processes for medical data could potentially produce one of the biggest large-scale trainwrecks ever”.

In industries as diverse as telecommunications, financial services, government services, healthcare, and education (to name but a few), in every country of the world, crummy quality information is costing money and often lives. Throwing more automation at bad processes and crummy data just leads to faster arrival at a trainwreck scenario.

Your story just has so many specific examples of the common types of error that it was too good a write up to miss out on.

If what you have uncovered about Up-coders is true, then it is a classic example of the objectives for which information is captured and the purposes for which it will be used being at odds with the strategic and tactical intent of the insurance company – and it is a recipe for an IQTrainwreck and potential litigation in an e-Patient record scenario where a doctor misdiagnoses or mis-medicates based on intentionally inaccurate information.

See this article from the IAIDQ and this article as well for some insight into the legal issues that arise in Information Quality (the latter article is restricted to members of the IAIDQ at the moment but I can email you a copy if you are interested).

The International Association for Information & Data Quality is an all-volunteer non-profit dedicated to raising awareness of these types of issues, and promoting the development of a strong professional approach to tackling these issues. Your offer of input/support for research is a generous and intriguing one as we do have research partnerships with a number of Universities in the US and elsewhere. I’ll talk to our partners there and see if we can’t put something together on this.

Okay, you’re the expert(s) – is it overreaching to assert that screwed-up, mismanaged, automated processes for medical data could potentially produce one of the biggest large-scale trainwrecks ever?

Count me as an ally in whatever research you want to do. This isn’t my day job, obviously (I’m “just a patient”) but we did dig some more. Interesting example: the armpit cyst item was submitted for billing during an abdominal ultrasound. Hmmmm.

I hear tell that “upcoding” is a common (and widely known) practice – people called “coders” sit in hospital basements combing through records of your visit and picking out keywords that justify billing for the highest-priced item on the insurance company’s reimbursement menu. Aside from the ethical issues, you and I know the implications of then reading back that data as if it were an earnest attempt to express reality.

I’ve now gotten invited into some policy discussions and I’m arguing that “IT grown-ups” should scrutinize any plans to go large on EMRs…. I’ll subscribe here; keep in touch.

I mean, this is personal: we’re talking about the quality of YOUR data a few years from now, YOUR mom’s, YOUR kid’s, whoever it might be.

A related story: The Data Model That Nearly Killed Me.

Thanks for the link to the Consumerist article. 80% of hospital bills having errors counts as an IQTrainwreck in its own right, so we’ll be investigating that story further.

http://consumerist.com/5254208/80-of-medical-bills-have-errors

considering that billing data is being relied on for providing details because it is considered to be more complete.