4 May 2021

New DoH data matching powers deny 20% of practices ePIP

KnowCents MBS MyHealthRecord Practice Management

If having to upload shared health summaries and the My Health Record seems an annoying waste of your time, your practice manager probably won’t agree any more.

1106 practices which were paid ePiP last quarter have been denied the payment this quarter after DoH data matched records with the Australian Digital Health Agency (ADHA) and found that these practices had “not met the shared health summary upload requirements for a payment quarter under the Practice Incentives Program eHealth Incentive (ePIP)”.

According to the DoH, all practices were sent an initial compliance letter, outlining an exemption process, and so far the department has granted 80 exemptions.

If we assume 1000 practices don’t end up exempt, then you can do some back of envelope calculations here to arrive at an average amount those 1,000 practices may have lost in revenue. We get $6250 per practice for that quarter . Of course that is going to be lower and higher depending on FTE numbers in each practice, but, ouch.

The department has provided quite a bit of information for those practices which have missed out this quarter on what to do to make sure they get in line for next quarter, so the problem is likely to go away over time for those practices affected. If you’re interested you can start HERE.

We have put some further questions to the department asking it to detail some of the reasons practices had not met “the requirements”.

It doesn’t seem to be that they failed to upload the minimum number of summaries required as the system rejects a practice straight away if the required number of the summaries aren’t uploaded.

It seems like it is something the department has managed to audit within the summary data itself. It should be interesting to find out what constitutes some of the fails. Are some of the summaries dummy summaries or automated summaries which aren’t loading real or meaningful data?

We’ll get back to you if and when we find out.

Probably the more important message for practice managers in this episode is that Big Brother has officially arrived in the form of genuine data matching to reveal inconsistencies in practice management, claiming and funding.

The legislation to clear the way for such sophisticated and automated prying was passed nearly two years ago, with not a great deal of fuss, at that time.

The even bigger warning in terms of data matching starting up is probably for individual GPs because the more sophisticated the department gets in matching data between the various arms of government in health – MBS, PBS, ADHA et al. – the more they don’t need to rely on random and small scale auditing. Even Robodebt type guessing via analytics, which they’ve used a bit in the past to generate nudge letters, will be surpassed in accuracy and performance.

Now they have actual individualised data which is synchronising to confirm compliance. But they have that data on scale and it’s automated.

I’ve always been anxious that one day it will be legislated in my state that the local road authority (the RTA) will have permission to take a satellite feed from my car GPS, so they can constantly match my speed with the speed limit, and automatically send me a notice (in my case many notices) on my every speeding indiscretion. The technology is available, they just haven’t got around to the legislation part yet.

In a manner they have the legislation part in place for this sort of monitoring in healthcare payment compliance now.

You can’t say it isn’t fair. It would be like me saying it isn’t fair to fine me for speeding by using a system which far more accurately identifies my many speeding indiscretions.

But it feels pretty unfair.

The question I’d have is: will data matching like this actually change the behaviour of doctors for the greater good of the system and patient care, or is it just going to lessen overall GP income and make the profession a little bit more downtrodden? How will that help?

In terms of Medicare claiming, it’s a big reason for the DoH to get together with the medical colleges and make the whole interpretation of MBS billing far less a mystery for doctors by developing a commonly agreed upon set of standards from which their members can operate.

Currently many GPs are struggling to find the line which will optimise their income but remains safely within the intent of billing that the MBS has for all the items on its schedule. It’s a harder problem than simply data matching because there are elements of clinical discretion in determining how and when an item should be billed. The clinical discretion part should be in the hands of the colleges, not the DoH.

If data matching does become a common part of compliance then a set of standards developed and maintained by the medical colleges could do a lot to help define better clinical context for the department and the MBS on how such compliance could be applied for better healthcare, and not just for saving money.

Perhaps there is a way that both GPs and the department can benefit in the long run.

If you’re interested in just how much data matching capability the DoH has at its disposal and hasn’t unleashed yet, it’s quite a bit.

In essence, the bigger story here isn’t that 1,000 practices got caught out not doing their ePiP correctly in an early run of DoH data matching. It’s that the start of the age of data matching for auditing of GP payments has likely begun.

You can read about all the powers that the DoH has to data match between various health bodies and use it to audit going forward HERE (if you want to wreck your afternoon).

Oh, one thing that the DoH and the ADHA may not have considered in this whole new set up just yet.

If 20% of practices haven’t been filling out their health summaries properly, does that mean that at least 20% of all the summaries in the My Health Record to date are garbage?

And might this mean that the My Health Record is at least 20% – or probably a lot more – less useful in terms of health summaries than everyone has been thinking, and maybe even, unsafe to use in some circumstances, as a result?

Data matching might not just end up causing nightmares for doctors.

It’s a can of worms for everyone usually.

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