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Home / Healthcare & Medicine / AI & Robotics / AI and Big Data in Global Health Improvement / Evaluating Large-Scale Clinical Information Systems

Evaluating Large-Scale Clinical Information Systems

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14.4
So this is my centre and we have a particular focus on evaluating large-scale clinical information systems. We’re interested to know when we implement electronic health record systems and decision support. Does it actually make a difference to patient care at the end of the day? How does it impact clinicians’ work? And sometimes, how does it make things worse, not always better? So how can we design better systems? So Jack has given us a beautiful introduction around medication safety. So clearly it is one of our biggest international public health safety problems, and as Jack described, WHO has identified this as their latest patient safety challenge. It costs a lot of money.
63.5
And really what we’ve understood about medication errors is there are multiple factors involved. It’s the people factors, it’s not having the right people, it’s having tired healthcare staff, but also it’s about not having the right systems in place. And errors can occur at all the different stages of medication delivery. So it is a very ambitious goal that is set for all of us to try to tackle this problem. What we do know from our evidence so far is that when, sorry, when hospitals introduced electronic medication management systems in hospitals, they are very effective, probably more effective than any other intervention that we have tried today.
111.5
When we started to introduce these systems into Australia, we undertook some of the first studies
117.6
to try to answer the question: do these systems reduce medication errors? And so we had two of our major teaching hospitals, each of them had implemented a different commercial system, so some of you will be familiar with the Cerner system from the US, and another one of our hospitals introduced (iSoft) MedChart, and we wanted to understand through a control design whether errors changed once we introduced a system. And very briefly what we found was that we reviewed patient records, we reviewed over 3000 patient records, identified over 12,000 prescribing errors in those records, but we found that for all the wards that implemented this system, they reduce their prescribing errors by over 50% regardless of the system that they used.
166.6
We looked at the most serious errors because obviously they’re the ones that we particularly want to target, and we reduced those by around 44%. So we went from a rate of 25 serious prescribing error admission per hundred admissions to 14, so still quite a way to go, and this is where we are looking for decision support to help us get there. We also looked at medication administration errors, and this is an area that is not as well studied because you can’t identify administration errors by looking at patient records, you have to watch them happening.
206.9
So in this part of the study, what we did was we followed over 200 nurses, and we recorded exactly what drug they gave to patients as they did it. So here you can see one of our researchers, and we’ve developed software to collect this data, and they recorded what drug was administered, what dose, what rate, etc. And then we took that observational data, and we compared it with patient’s medication chart to say did the patient get the drug that was ordered on the chart, and that was the way in which we identified errors.
241.3
We found following the introduction of the electronic medication system that there was a significant reduction in administration errors, but certainly, it wasn’t as big as that that we saw for prescribing errors which we would expect because these systems are primarily designed for reducing prescribing errors. But we did see that the most severe administration errors declined, so before the system went in, around four percent of all drug administration errors were rated as serious, and that halved to just under two percent. So we use this data to say, well, are these systems actually cost-effective because they are extremely expensive to implement, and internationally, they’ve been very few cost-effectiveness studies; literally, they’ve been about five in the world at this time.
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So here what we did was we took our data, and we were able to estimate that on average, we released around sixty Australian dollars, which is
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sounds much more impressive in Taiwanese dollars: of twelve hundred and fifty-seven per admission. On a cardiology ward, we’ll be looking at saving, reducing by eighty adverse drug events, and so if you extrapolated from an entire Hospital, you’d be looking at releasing around fifty six million Taiwanese dollars.

Dr. Johanna Westbrook will introduce you to how large-scale clinical information systems work on hospitals.

A clinical decision support system (CDSS) is a health information technology system that is designed to provide physicians and other health professionals with clinical decision support (CDS). In other words, it’s assistance with clinical decision-making tasks.

When a hospital implements electronic health record systems, what is their decision support? Several topics were discussed: How does it change patient care? How does it impact clinicians’ work? How can the researcher design better systems?

Different experiments were held to see if there is a decline on a medicine administration error. In the video, Dr. Johanna reports these results. In one example, when hospitals introduced electronic medication management systems in hospitals, these systems are very effective. Prescribing errors are reduced by over 50%. This is a great discovering.

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AI and Big Data in Global Health Improvement

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