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Apr 02, 2019

Artificial Intelligence is the great healthcare equalizer

Estimated reading time: 7-9 minutes

Next-generation technologies such as artificial intelligence, machine learning and blockchain will democratize healthcare and provide access to care in any setting

One of the biggest limitations I see in the current global healthcare system is the degree of health inequality. Differences in healthcare accessibility and levels of clinical expertise and performance are staggering, depending on the region and care setting. Today, a woman being screened for breast cancer in a rural environment has limited chances of finding disease early compared to a woman going to an urban breast cancer center that performs more than 300 screenings a day. I believe that next-generation technologies such as artificial intelligence (AI), machine learning and blockchain will change this by democratizing healthcare and providing access to care in any setting -- opening up possibilities for patients and providers alike.

 

The gaps we see today in life expectancy, socio-economic conditions and access to care, along with the huge variations in standardized care, are not limited to emerging countries like India and China. They exist as much when comparing rural America with a metropolitan city like Boston or San Francisco. As trained clinicians become a diminishing and unsustainable resource, virtual care, connected health systems and powerful data analytics offer the promise of a universal standard of patient care.

AI is equalizing healthcare

I believe that Philips and other health tech companies hold a key to this future as we develop ways we can use artificial intelligence and machine learning to advance precision diagnosis. With this technology, we can help eliminate operator error and inconsistency and provide physicians with tools to help find disease as early as possible. 

 

The market for AI in healthcare is expected to surpass $36 billion by 2025[1]. Hospitals and providers are seeing a vast range of potential applications, from improving workflow and connectivity to enhancing image analysis and patient segmentation.

The Three As of AI in healthcare

 

Accessibility 

Today, more than 150 countries have some form of healthcare system that provides coverage to at least 90 percent of their citizens. [2] Yet, variations in access to basic and specialist care are vast. As the industry shifts to value-based care, we need to bring healthcare closer to patients and make sure those who have imaging performed in rural areas can benefit from expert, “virtual” analysis independent of where the radiologist is located geographically.

Virtual radiology is a game-changer in equalizing access to care, and AI is the technology powering it – ensuring connectivity, reducing human error and enhancing quality.

Rob Cascella

Chief Business Leader, Precision Diagnostics

Access to high-quality analysis from any setting

Receiving high-quality diagnostic images for review anywhere in the world, for example, is possible due to virtual radiology as AI algorithms can be supported in the cloud, speeding workflows and rapidly consolidating data. Virtual radiology is a game-changer in equalizing access to care, and AI is the technology powering it – ensuring connectivity, reducing human error and enhancing quality. 

 

AI is also a precursor to adaptive intelligence – where the system gets smarter and learns by use and codifying existing knowledge and past diagnoses to better understand patients and diseases. This allows real-time guidance to be given to patients or clinicians so they can catch signs of medical emergency. Last year, in a major breakthrough, AI showed how it can help identify patients at highest risk for developing sepsis[3], which often requires intensive analysis of highly complex data sets.

 

In North America and Europe, a ‘hub and spoke’ model for healthcare systems is emerging, through which care is delivered primarily through ambulatory channels and outpatient clinics with a focus on better prevention and out-of-hospital care, reserving hospital visits for complex care cases. AI can collate data from the clinics to a centralized war room, connect remote doctors with expertise from hospitals and determine the right location of clinics and service mix based on community needs.

 

AI is already making headway in enabling access. U.S. start-up InfiniteMD provides second-opinion video consultations to patients around the world, who may not otherwise have access due to financial or geographical constraints. Building on that, they are developing an algorithm for cancer patients that would aid in treatment decision-making and connect them with global treatment options or clinical trials. [4]

Learn more about Philips adaptive intelligence - applying AI in a meaningful way to improve people's lives.

Affordability

Patchy healthcare coverage means that patients often must dig into their own pockets for treatment. In the U.S., one in four American families turn down necessary medical care due to cost.[4] These high costs of care are often related to the burden of excessive administration costs.

 

AI can significantly cut down these costs. Machine learning can find patterns in patient admission and discharges to determine which patient categories tend to overstay in hospitals -- a major expense for providers -- and reduce patients’ stays accordingly. The same algorithms can see patients at risk of readmission, so they can remain under close monitoring. AI can also enable streamlining of processes and creation of more user-friendly workflows, reducing staff time spent on tasks so the time can be used elsewhere. By powering virtual chatbots, AI eliminates unnecessary in-person doctor visits and readmissions, which can potentially save billions of dollars annually. 

 

Accuracy

The use of AI to predict diseases is in its early stages, but the technology is already being used to treat patients with more precision. 

 

As more healthcare providers invest in population health management, AI is helping drive more accurate risk stratification. By finding patterns based on large subsets of data, AI lends greater precision to segmenting patients based on risk levels and identifying a proactive course of action to treat those deemed high-risk patients. 

 

In the case of imaging, machine learning and AI can help rule out false negatives – a valuable aid to clinicians. The UK government recently announced new medical technology centers[5] that will use AI to aid in disease diagnosis. One such center, the London Medical Imaging and Artificial Intelligence Centre for Value-Based Healthcare, will apply AI to detect anomalies in scans, helping with earlier detection. Intelligent information will set new precedents when it comes to diagnosis accuracy. The healthcare industry has the capabilities to do this today and are creating the proof points to broaden its use.

 

While precision medicine is in varying stages of maturity across markets, I believe AI will serve as a springboard for its uptake. No one can deny the benefit of combining different information sets to create a single point of view for more accurate patient diagnosis. Technology will make disease assessment and interpretation even more sophisticated, so we can find the right treatment for the right person at the right time.

AI is crucial to the future of healthcare

The future of healthcare rests on AI 

As with any new technology, AI comes with questions about safety, privacy and possible repercussions of combining machine systems with care delivered by humans.  Adoption won’t be rapid, and there will be periods of testing to determine exactly what the technology can do and how efficiently and safely it can do it. 

 

But I believe if you look ahead to the next five years, the course of healthcare will shift drastically –driven by economic necessity – towards standardized care regardless the setting, more accessible care, virtual solutions and experts who can provide consultation on cases around the world. At the heart of this paradigm shift will be AI.

 

[1] Tractica Report - Artificial Intelligence for Healthcare Applications

[2] 2018 STC Health Index

[3] Health IT Analytics article

[4] BostInno article 

[5] Juniper Research: https://www.iot-now.com/2017/10/13/69037-ai-powered-chatbots-drive-dramatic-cost-savings-healthcare-saving-3-6-billion-2022/

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Rob Cascella

Rob Cascella

CEO, Diagnosis & Treatment, Philips

Robert A. Cascella leads Philips’ multi-billion-dollar cluster of imaging businesses, including Diagnostic Imaging (DI), Interventional Devices and Therapies and Ultrasound (US).

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