Google outlines its plans to harness AI in healthcare
Recently rebranding its Google Research division to Google AI, at Google’s 2018 I/O, festival, the technology giant has revealed its ongoing plans to harness artificial intelligence and machine learning across a multitude of consumer technologies. However, health technology is gaining traction, and the company continues to grow apace in this space.
Recently partnering with Fitbit, the company will seek to use its cloud healthcare API, founded on Fast Health Interoperability Resources (FHIR) to connect user data with electronic medical records (EMR).
Following on from Fitbit’s recent acquisition of Twine Health (now rebranded as the Fitbit Health Platform), the collaboration will also open doors for the duo to provide solutions to those with chronic, long-term health solutions.
“At Google, our vision is to transform the way health information is organised and made useful,” explained Gregory Moore MD, PhD, Vice President, Healthcare, Google Cloud.
With this in mind, the company has unveiled its new consumer health apps and new smartwatch, all which will successfully integrate with healthcare organisations EMR systems.
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"Artificial intelligence and machine learning are not just technologies of the future. I can assure you that the best artificial intelligence and machine learning in the world is already in your pockets," noted Greg Corrado, Principal Scientist and Director of Augmented Intelligence Research at Google.
In a recent blog, the company has explained how it is working to recognise patterns or “signals” in EMR’s, utilising AI to drive data-driven health outcomes. However, this has remained a complex feat. Working in partnership with a number of healthcare providers, such as UC San Francisco, Stanford Medicine and the University of Chicago, Google has sought to implement deep learning models to make essential predictions regarding patients in hospital through de-identified electronic medical records.
“For each prediction, a deep learning model reads all the data-points from earliest to most recent and then learns which data helps predict the outcome. Since there are thousands of data points involved, we had to develop some new types of deep learning modelling approaches based on recurrent neural networks (RNNs) and feedforward networks. We engineered a computer system to render predictions without hand-crafting a new dataset for each task, in a scalable manner,” Google explained.
Additionally, by utilising the area-under-the-receiver-operator curve to measure the accuracy of its findings, Google has distinguished those with particular traits against those which don’t. However, the company has been keen to stress that such technology is not set to replace medical professionals, but rather enhance the patient experience.
“The model is more like a good listener rather than a master diagnostician,” it adds.
How UiPath robots are helping with the NHS backlog
The COVID-19 pandemic has caused many hospitals to have logistical nightmares, as backlogs of surgeries built up as a result of cancellations. The BMJ has estimated it will take the UK's National Health Service (NHS) a year and a half to recover.
However software robots can help, by automating computer-based processes such as replenishing inventory, managing patient bookings, and digitising patient files. Mark O’Connor, Public Sector Director for Ireland at UiPath, tells us how they deployed robots at Mater Hospital in Dublin, saving clinicians valuable time.
When Did Mater Hospital implement the software robots - was it specifically to address the challenges of the pandemic?
The need for automation at Mater Hospital pre-existed the pandemic but it was the onset of COVID-19 that got the team to turn to the technology and start introducing software robots into the workflow of doctors and nurses.
The pandemic placed an increased administrative strain on the Infection Prevention and Control (IPC) department at Mater Hospital in Dublin. To combat the problem and ensure that nurses could spend more time with their patients and less time on admin, the IPC deployed its first software robots in March 2020.
The IPC at Mater plans to continue using robots to manage data around drug resistant microbes such as MRSA once the COVID-19 crisis subsides.
What tasks do they perform?
In the IPC at Mater Hospital, software robots have taken the task of reporting COVID-19 test results. Pre-automation, the process created during the 2003 SARS outbreak required a clinician to log into the laboratory system, extract a disease code and then manually enter the results into a data platform. This was hugely time consuming, taking up to three hours of a nurse’s day.
UiPath software robots are now responsible for this task. They process the data in a fraction of the time, distributing patient results in minutes and consequently freeing up to 18 hours of each IPC nurse’s time each week, and up to 936 hours over the course of a year. As a result, the healthcare professionals can spend more time caring for their patients and less time on repetitive tasks and admin work.
Is there any possibility of error with software robots, compared to humans?
By nature, humans are prone to make mistakes, especially when working under pressure, under strict deadlines and while handling a large volume of data while performing repetitive tasks.
Once taught the process, software robots, on the other hand, will follow the same steps every time without the risk of the inevitable human error. Simply speaking, robots can perform data-intensive tasks more quickly and accurately than humans can.
Which members of staff benefit the most, and what can they do with the time saved?
In the case of Mater Hospital, the IPC unit has adopted a robot for every nurse approach. This means that every nurse in the department has access to a robot to help reduce the burden of their admin work. Rather than spending time entering test results, they can focus on the work that requires their human ingenuity, empathy and skill – taking care of their patients.
In other sectors, the story is no different. Every job will have some repetitive nature to it. Whether that be a finance department processing thousands of invoices a day or simply having to send one daily email. If a task is repetitive and data-intensive, the chances are that a software robot can help. Just like with the nurses in the IPC, these employees can then focus on handling exceptions and on work that requires decision making or creativity - the work that people enjoy doing.
How can software robots most benefit healthcare providers both during a pandemic and beyond?
When the COVID-19 outbreak hit, software robots were deployed to lessen the administrative strain healthcare professionals were facing and give them more time to care for an increased number of patients. With hospitals around the world at capacity, every moment with a patient counted.
Now, the NHS and other healthcare providers face a huge backlog of routine surgeries and procedures following cancellations during the pandemic. In the UK alone, 5 million people are waiting for treatment and it’s estimated that this could cause 6,400 excess deaths by the end of next year if the problem isn’t rectified.
Many healthcare organisations have now acquired the skills needed to deploy automation, therefore it will be easier for them to build more robots to respond to the backlog going forwards. Software robots that had been processing registrations at COVID test sites, for example, could now be taught how to schedule procedures, process patient details or even manage procurement and recruitment to help streamline the processes associated with the backlog. The possibilities are vast.
The technology, however, should not be considered a short-term, tactical and reactive solution that can be deployed in times of crisis. Automation has the power to solve systematic problems that healthcare providers face year-round. Hospital managers should consider the wider challenge of dealing with endless repetitive work that saps the energy of professionals and turns attention away from patient care and discuss how investing in a long-term automation project could help alleviate these issues.
How widely adopted is this technology in healthcare at the moment?
Automation was being used in healthcare around the world before the pandemic, but the COVID-19 outbreak has certainly accelerated the trend.
Automation’s reach is wide. From the NHS Shared Business Service in the UK to the Cleveland Clinic in the US and healthcare organisations in the likes of Norway, India and Canada, we see a huge range of healthcare providers deploying automation technology.
Many healthcare providers, however, are still in the early stages of their journeys or are just discovering automation’s potential because of the pandemic. I expect to see the deployment of software robots in healthcare grow over the coming years as its benefits continue to be realised globally.
How do you see this technology evolving in the future?
If one thing is certain, it’s that the technology will continue to evolve and grow over time – and I believe there will come a point in time when all processes that can be automated, will be automated. This is known as the fully automated enterprise.
By joining all automation projects into one enterprise-wide effort, the healthcare industry can tap into the full benefits of the technology. This will involve software robots becoming increasingly intelligent in order to reach and improve more processes. Integrating the capabilities of Artificial Intelligence and Machine Learning into automation, for example, will allow providers to reach non-rule-based processes too.
We are already seeing steps towards this being taken by NHS Shared Business Service, for example. The organisation, which provides non-clinical services to around two-thirds of all NHS provider trusts and every clinical commissioning organisation in the UK, is working to create an entire eco-system of robots. It believes that no automation should be looked at in isolation, but rather the technology should stretch across departments and functions. As such, inefficiencies in the care pathway can be significantly reduced, saving healthcare providers a substantial amount of time and money.