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Hospital Using AI To Handle Some Tasks Usually Done By Doctors And Nurses

Posted on May 30, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

One of the UK’s biggest facilities has announced plans to delegate some tasks usually performed by doctors and nurses to AI technology. Leaders there say these activities can range from diagnosing cancer to triaging patients.

University College London Hospitals (UCLH) has signed up for a three-year partnership with the Allen Turing Institute designed to bring machine learning to bear on care, a project which could ultimately spark additional AI projects across the entire National Health Service. The NHS is the body which governs all healthcare in the UK’s universal health system.

UCLH is making a big bet on artificial intelligence, investing what UK newspaper The Guardian describes as a “substantial” sum to develop the infrastructure for the effort.

UCLH officials believe — like other health organizations around the world — that machine learning algorithms may someday diagnose disease, identify people at risk for serious illness and more. Examples of related projects abound. Just one case in point is a project begun in 2016 by New York-based Mount Sinai Hospital, which launched an effort using AI to predict which patients might develop congestive heart failure and offer better care to those who have already done so.

Professor Bryan Williams, director of research at University College London Hospitals NHS Foundation Trust, said the move will be a “game changer” which could have a major impact on patient outcomes. “On the NHS, we are nowhere near sophisticated enough,” Williams told The Guardian. “We’re still sending letters out, which is extraordinary.”

UCLH’s first AI effort, which is already underway, is intended to identify patients likely to miss appointments. Using existing data, including demographic factors such as age and address plus outside factors like weather conditions, researchers there have been able to predict with 85% accuracy whether the patient will show up for outpatient visits and MRIs.

Another planned project includes improving the performance of the hospital’s emergency department, which, like many NHS hospitals, isn’t meeting government performance targets such as maximum four-hour wait times. “[This is] an indicator of some of the other things in the entire chain concerning the flow of acute patients in and out of the hospital,” UCLH chief executive Professor Marcel Levi told the newspaper.

The hospital envisions solving its wait-time problem with machine learning. Drawing on data taken from thousands of patients, machine learning algorithm might be able to determine whether a patient with abdominal pain suffers from severe problem like intestinal perforation or a systemic infection, then fast-track those patients. This kind of triage is generally performed by nurses in hospitals around the world.

That being said, the partners agree that machine learning performance must be incredibly accurate before it has any major role in care. At that point, it will be ready to support clinicians, not undercut them. According to Professor Chris Holmes of The Alan Turing Institute, the whole idea is to let doctors do what they do best: “We want to take out the more mundane stuff that’s purely information driven and allow time for things the human expert is best at.”

Will Hospital EMR Prices Ever Fall?

Posted on May 9, 2016 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

In most industries, prices fall as supply rises. Basic economics, right? Well, if that’s true, will the price of EMRs fall as the industry matures?  A recent discussion on LinkedIn demonstrates – as you might expect – that there’s a lot of room for debate on the topic.

Davíð Þórisson, an emergency physician at Landspitali University Hospital in Iceland, kicked things off with this question:

Now that the major workflow has been designed in all major EHR systems available it would seem the biggest part of the hospital needs are addressed. Competition should increase as more vendors catch on… prices surely must go down from here?

Nelson Wong, a senior consultant with Fuji Xerox, responded that price increases are all but inevitable when EMR vendors compete with proprietary technology:

The only way out is a vendor neutral EHR providers to integrate all systems with international standard like HL7.

Zac Whitewood-Moores, a clinical data standards specialist who’s helping to implement SNOMED CT in systems across the NHS in England, noted that EMR vendors currently have little incentive to switch to a cheaper, less-customized EMR model:

Vendors appear reluctant to share work from previous deployments and part of this has to be that the commercial model is built on consultancy, not just licensing of the IT product itself.

But Whitewood-Moores also holds out hope that true data interoperability could do the trick:

When there is more use of SNOMED CT and common interoperability models forced by purchasing goverments/health providers…this may bring down costs if customers are not locked in by their data and the costs of migrating large amounts of it.

And Ryan Pena, social media manager at MentorMate and MobCon, argued that innovation might yet reduce health data management costs:

I think the key with EHRs is to ensure the industry continues to innovate on how information is captured. Perhaps secure automation will drive down this cost as we learn ways to transfer health data from medical grade wearables?

On the other hand, other people who commented felt that even some kind of open source reference EMR wouldn’t do the trick. John Shepard, president and co-founder of HIT software vendor Shepard Health, points out that there’s actually surprisingly little pressure on vendors to lower prices, in part because the market is still evolving:

The cost of EHRs has already gone down but also up. For example, you can buy an EHR out of the box at Costco or utilize one of the open source EHRs for free. However, to get a supported enterprise-level EHR (Epic, McKesson, etc.) then the price is very high and I don’t think it will come down anytime soon…[After all,] the cost of the EHR is not preventing sales because there is minimal change in demand based on increase in cost.

Meanwhile Pim Volkert, terminologies coordinator for Nicitz, the National IT Institute for Healthcare in the Netherlands, shared an interesting view of the future. He seems to suggest that paying more for EMRs may actually be justified as they grow more sophisticated:

EHRs will move more and more into the clinical domains. [They] will become a medical device just like an MRI or DaVinci robot. Development, testing of software and liability insurance fees will increase costs.

Obviously, there’s no way to predict exactly where EMR prices will go, but I’m more on the side of the posters suggesting that enterprise EMRs have nowhere to go but up. I hope I’m wrong!