Diagnostics
team is made up of Cooper and three other analysts. “The actual technical implementation really didn’t take that long – just a few months – but what we took our time on was making sure that we ran it extensively in the background to check the variables and to ensure the nurses weren’t going to be overwhelmed by the number of alerts.” She stresses the importance of not only data availability, but also the need to use it effectively. “It’s a fine line when you are dealing with so much data – it’s precision medicine at that point,” she says. “It’s like dumping everything into a bucket and seeing what’s what. There are probably other variables we could go back and add – like alert status and SOFA [sequential organ failure assessment] scores to evaluate a patient’s deterioration, but for the moment our system is working. If it ceases to work, we can go back and add other variables to the mix.”
Wealth of opportunities While Augusta Health isn’t the only institution using AI and machine learning to fight sepsis, it is one of the smaller operations – and Cooper is adamant that data analysis technology can be implemented by health providers of all sizes. Cooper says Augusta’s findings have already been shared with other hospitals in Virginia – both large and small – with a view to ensuring more lives are saved. “There’s no reason why other small community hospitals like ours cannot use a system like this,” she says. “You’re selecting key data elements during an initial analysis and then pulling the latest instances of those vitals – respiration, pulse, temperature – for every patient and evaluating that based on set parameters. It’s a very simple process. “I think my advice would be: don’t let the data intimidate you because it’s not overly difficult once you break it apart. There is so much opportunity to improve the outcome for patients and to improve the environments that our physicians and nurses work in.”
Data engagement is also vital – and something that has been extremely positive at Augusta Health. Cooper’s team has had input from across the hospital, from pharmacists and emergency room nurses to management. Clinical and administrative staff recognise that AI-led systems can not only improve patient outcomes but also free up valuable time and expertise. That buy-in has been crucial. “As the data team is non-clinical, we need that input to guide us,” says Cooper, who at the time of writing had turned her attention to Covid projections. She says data is vital to modern and efficient healthcare systems, but AI and machine learning are gradually removing layers of manual functions to make more efficient use of staff time and assist with faster, more accurate record keeping and diagnosis.
Practical Patient Care /
www.practical-patient-care.com
“There is definitely a lot of opportunity, but one of the biggest problems in terms of getting systems like this fully automated is our documentation,” Cooper says. “The data needs to be stored in discrete data elements and not free text. When it’s stored in discrete data elements, a date, for example, looks like a date and is evaluated as a date.” Without discrete data elements, Cooper says she’s seen some “very messy data”. As clean and consistent as the data they’re fed might be, all AI-based systems must come with clear caveats. “We make sure that when we push out data to users that this is not a replacement for good clinical judgement,” she says, stressing that the system acts as another diagnostic tool – it does not replace or replicate nurse or physician decision making.
Augusta Health monitors temperature, heart rate, respiratory rate and white blood cell count to identify patients at risk of sepsis.
“Don’t let the data intimidate you because it’s not overly difficult once you break it apart. There is so much opportunity to improve the outcome for patients and to improve the environments that our physicians and nurses work in.”
Cooper says diagnostic AI algorithms like the ones devised for sepsis could be adapted across other disease states, potentially making a whole raft of conditions easier to identify and treat. Indeed, the system could also be designed to identify disease states that aren’t even on a clinical team’s radar. If you looked at diabetes, Cooper says, and used the information from lab results in combination with patient obesity levels and other social determinants, then you could identify which patients are at a high risk for the disease. The algorithm could even be adapted not to predict disease state, but the likelihood of readmission to the hospital. “It’s really not rocket science”, says Cooper.
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