This appeared last week:
How the ACT is eliminating hospital errors
By Justin Hendry on Jan 22, 2018 6:00AM
New systems cross-reference patient with process.
ACT Health has embarked on a clinical systems transformation that will see patients electronically cross-referenced with pathology orders and medication at their bedside in an effort to eliminate errors at the point of care.
The territory's health directorate has completely overhauled how it interacts with patients to banish the paper-based records and processes that are the traditional causes of mixed up blood samples and medications in hospitals.
Despite having made a significant effort to strengthen these processes in recent years, ACT Health wasn't getting the reduction in avoidable errors it wanted.
So it starting looking at electronic tools to eliminate transcription mistakes.
Chief information officer Peter O'Halloran and team decided to take the bold step of equipping Health's electronic record systems with identification standards.
Patient wristbands and staff ID cards were upgraded to include GS1 compliant barcodes, as were clinical note labels and specimen labels.
It required modifications to eight separate IT systems provided by different vendors to get them to accept the GS1 barcodes.
Now, when a clinician takes a blood sample, they are required scan both their own and the patient's barcodes at the outset, before the specimen label can be scanned and printed. A computer on wheels kitted out with the barcode scanners is used by the patient's bedside.
It means a pathology specimen label can only be printed in the presence of the patient, reducing the risk that the label will be incorrect or misapplied.
"Electronic ordering and collection has eliminated paper order readability and transcription incidents," O'Halloran said.
More here:
While hardly earthshattering it is these sort of initiatives that incrementally improve the outcomes provided by the health system and overall can make hospitals safer places to the cared for within.
More of this sort of initiatives and less crusading against the fax machine makes pretty good sense to me – not that we should not be able to both walk and chew gum!
David.
The headline "How the ACT is eliminating hospital errors"
ReplyDeleteshould actually be more like "How the ACT Health department is trying to reduce some hospital errors but doesn't list the unwanted risks".
Great work. These errors are not insignificant. If you extrapolate the little data we have on errors in diagnosis from the US it amounts to 7 times our road toll from avoidable errors. Identification is a big part of it!
ReplyDeleteI agree this is a great outcome. It may prove to be that one small step that opens up a larger set of much needed outcomes. I recall one of AMT original goals was along these lines.
ReplyDeleteOne wonders how much a distraction the PCEHR/MyHR has been over the past seven years and if it has helped or hindered progress
"...decided to take the bold step of equipping Health's electronic record systems with identification standards."
ReplyDeleteWhat?
It is a "bold step" to properly identify electronic records? And to make sure separate systems can use match identifiers? In any other industry this would be simply good design.
The rest of the article is a good example of introducing sensible processes, although I note that the Blood Bank has been following this sort of standard for decades. But I hadn't realised that the interoperability between health systems was such that correlation and cross-referencing required modification to the vendor offerings.
I choked on the eight separate IT systems. Which, in the greater scheme of things, is probably at the low end of complexity.
ReplyDeleteA pretty good article on the Strava story.
A challenging feature of machine learning is that exactly how a given system works is opaque. Nobody — not even those who have access to the code and data — can tell what piece of data came together with what other piece of data to result in the finding the program made. This further undermines the notion of informed consent, as we do not know which data results in what privacy consequences. What we do know is that these algorithms work better the more data they have. This creates an incentive for companies to collect and store as much data as possible, and to bury the privacy ramifications, either in legalese or by playing dumb and being vague.