Sponsored Content | Case Study | Artificial Intelligence | June 05, 2018

Discover the Benefits of AI in Healthcare

Vinay Vaidya, Chief Medical Information Officer at Phoenix Children’s Hospital

Vinay Vaidya, Chief Medical Information Officer at Phoenix Children’s Hospital

AI offers a unique opportunity to make sense out of clinical data to enable fully integrated healthcare that is more predictive and precise.

AI offers a unique opportunity to make sense out of clinical data to enable fully integrated healthcare that is more predictive and precise.

While AI has been around for decades, most healthcare organizations are still at the very beginning of their AI journeys.

While AI has been around for decades, most healthcare organizations are still at the very beginning of their AI journeys.

The power to predict a cardiac arrest, support a clinical diagnosis or nudge a provider when it is time to issue medication — for many people artificial intelligence (AI) in healthcare represents a great new frontier. In addition to providing new applications improving delivery of care, AI is expected to bring improvements to hospital operations and to a range of clinical specialties.

With increased computing power, new storage and devices, the amount of healthcare data captured inside a hospital today has far outpaced our ability to analyze it. There is a growing concern that only a fraction of this data is being used to improve the quality and efficiency of care.

Technology’s predictive abilities promise new applications that will one day transform health systems, although artificial intelligence could also be seen as simply speeding up the way physicians have always worked.

“Using large data sets to gather insights isn’t an abrupt, sudden discovery,” said Vinay Vaidya, M.D., Phoenix Children’s Hospital chief medical information officer. “Humans have been doing this for a long time. Looking at data, physicians were very painstakingly taking notes, collecting all the observations to uncover patterns long before the rise of AI. A perfect example is the cholera outbreak in Soho, London, in 1854.”

 

Connecting Each Piece of Data

At the end of August 1854 London’s third big cholera outbreak was beginning. It was thought to have been passed between people by “miasma” or through the air, a theory supported by leading physicians at the time.

However, an alternative theory had been published a few years earlier by a local physician named John Snow who suggested that rather than being airborne, cholera was passed through water supply. One month into the outbreak and the disease led to more than 50 cases.1 During the next four days another 400 came to light.

Snow set about proving this theory by meticulously mapping each known case of cholera in Soho, discovering that rather than being an airborne disease, it had originated from a contaminated water supply in a local street. He had the handle of the water pump removed, and cases of cholera immediately began to diminish.

“Even in those days, we did something that is very similar to what we do with deep learning or artificial intelligence today, which is to take all the data that is available and applying the principles of analysis to come up with a solution,” said Vaidya.

 

Clinical and Productivity Benefits of AI

Over the past decade, the growth of computational power has led to a massive increase in the amount and granularity of stored digital medical and healthcare data. The ability of AI to quickly analyze large volumes of this data and create meaningful — and actionable — insights will have profound effects on how healthcare is delivered and received.

“Information in medicine is expanding at a pace at which the human mind cannot keep it in one single view or frame, so we do need the power of machines to support us,” said Vaidya. “Today we have computers which can bring data together, mine it and identify patterns. We use algorithms that are able to identify infections, or those patients who are going to have cardiac arrests.”

Another great promise of AI is in its ability to bring new efficiencies to hospital workflows including planning, procedure times or selecting the right exam for the right patient, which will enhance care delivery and reduce treatment costs.2

The ability of AI to sift through large amounts of data can help hospital administrators optimize performance and improve the use of existing resources, generating time and cost savings. This could vastly increase overall hospital productivity.

 

Human and Machine, Working Together

As much as Vaidya sees potential for AI in healthcare, he believes that any application of AI has to start with a proper understanding of clinical and hospital workflows. AI should simplify and speed up workflows, supporting the needs of the healthcare provider.

“Rather than replacing human clinical judgment, artificial intelligence will augment the clinical acumen to scales that we may not imagine today,” said Vaidya. “It is the hybrid, the human and the machine together, that is the most powerful.

“It is no different than any other field where pieces of information have come together to produce a jigsaw,” he concluded. “We are going to evaluate [artificial intelligence] like everything else. We let the technology proceed, we see what advances it brings.”

 

References

1. BBC, “In the time of cholera,” www.philips.com/nobounds

2. Frost & Sullivan, www.philips.com/nobounds

 

Case study supplied by Philips Healthcare.

Related Content

Machine Learning IDs Markers to Help Predict Alzheimer's

Neurologists use structural and diffusion magnetic resonance imaging (MRI) to identify changes in brain tissue (both gray and white matter) that are characteristic of Alzheimer's disease and other forms of dementia. The MRI images are analyzed using morphometry and tractography techniques, which detect changes in the shape and dimensions of the brain and in the tissue microstructure, respectively. In this example, the images show the normal brain of an elderly patient. Image courtesy of Jiook Cha.

News | Neuro Imaging | September 20, 2018
New research has shown a combination of two different modes of magnetic resonance imaging (MRI), computer-based...
LVivo EF Cardiac Tool Now Available for GE Vscan Extend Handheld Mobile Ultrasound
Technology | Cardiovascular Ultrasound | September 19, 2018
DiA Imaging Analysis Ltd. (DiA), a provider of artificial intelligence (AI)-powered ultrasound analysis tools,...
Exact Imaging Partners to Improve Prostate Cancer Detection With Artificial Intelligence
News | Prostate Cancer | September 19, 2018
Exact Imaging, makers of the ExactVu micro-ultrasound platform, has partnered with U.K.-based Cambridge Consultants to...
SimonMed Deploys ClearRead CT Enterprise Wide
News | Computer-Aided Detection Software | September 17, 2018
September 17, 2018 — National outpatient physician radiology group SimonMed Imaging has selected Riverain Technologie
Acuson Sequoia
News | Ultrasound Imaging | September 12, 2018
Siemens Healthineers announced the first global installation of its newest ultrasound system, the...
Veye Chest version 2
News | Lung Cancer | September 11, 2018
Aidence, an Amsterdam-based medical AI company, announced that Veye Chest version 2, a class IIa medical device, has
Sponsored Content | Case Study | Information Technology | September 07, 2018
Established in 1970, Sovah Health – Martinsville, Va., resides in the foothills of the beautiful Blue Ridge Mountains...
Sponsored Content | Case Study | Information Technology | September 07, 2018
One of the Northeast’s major teaching hospitals is an international leader in virtually every area of medicine. It has...
Feature | Population Health | September 07, 2018 | By Jeff Zagoudis
Over the last several years in the U.S., healthcare providers have been trying to shift their focus to more preventive...
The CT scanner might not come with protocols that are adequate for each hospital situation, so at Phoenix Children’s Hospital they designed their own protocols, said Dianna Bardo, M.D., director of body MR and co-director of the 3D Innovation Lab at Phoenix Children’s.

The CT scanner might not come with protocols that are adequate for each hospital situation, so at Phoenix Children’s Hospital they designed their own protocols, said Dianna Bardo, M.D., director of body MR and co-director of the 3D Innovation Lab at Phoenix Children’s.

Sponsored Content | Case Study | Radiation Dose Management | September 07, 2018
Radiation dose management is central to child patient safety. Medical imaging plays an increasing role in the accurate...