Artificial intelligence is poised to become a transformational force in healthcare. How will providers and patients benefit from the impact of AI-driven tools?
Major changes are coming to the healthcare industry. There are almost endless possibilities to use technology to deliver more effective, efficient and impactful interventions in the right time for a patient's care, including radiology, risk assessment, chronic diseases, and cancer.
Patients are demanding more from their providers and increasing data volumes continue to drive up the demand for care. Artificial intelligence is poised as the engine driving improvements across the care continuum.
AI has many advantages over traditional analytics and clinical decision making techniques. As they interact with training data, learning algorithms can become more precise and exact, which allows humans to gain unparalleled insights into diagnosis, care processes, treatment variability and patient outcomes.
Partners Healthcare presented the 2018 World Medical Innovation Forum on artificial intelligence (WMIF). Leading researchers and clinicians highlighted the 12 technologies and areas in the healthcare industry most likely to benefit from artificial intelligence over the next decade.
Each member of the "Disruptive Dozen", has the potential to provide significant benefits to patients and have the potential for wide commercial success. Anne Kiblanksi (MD, Chief Academic Officer at Partners Healthcare) and Gregg Meyer (MD, Chief Clinical Officer) are co-chairs of WMIF.
The moderators Keith Dreyer (DO), Chief Data Science Officer at Partners, and Katherine Andriole (D.O., Director of Research Strategy and Operations, Massachusetts General Hospital (MGH), compiled the top 12 ways artificial Intelligence will revolutionize healthcare delivery and science with the assistance of experts from Partners Healthcare.
UNIFYING MIND & MACHINE THROUGH THE BRAIN-COMPUTER INTERFACES
Although computers are not new in communication, creating interfaces between technology, the human mind, and technology is cutting-edge research that could have significant applications for some patients.
Some patients may lose their ability to communicate, move and interact with others and their environment due to neurological diseases or trauma to the nervous systems. Artificial intelligence and brain-computer interfaces (BCIs), which are backed up by artificial intelligence, could help restore these fundamental experiences for those who have lost them forever.
Leigh Hochberg, MD, PhD is the Director of the Center for Neurotechnology and Neurorecovery (MGH).
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"By using an artificial intelligence and a BCI, we can decode neural activates associated to the intended movement of one’s hand and should be able allow that person communicate in the same way many people have communicated with each other at least five times during the morning using ubiquitous communication technology such as a tablet computer or a phone."
Patients with ALS, strokes or locked-in syndrome could see a dramatic improvement in their quality of life. This is also true for the 500,000 worldwide people who suffer from spinal cord injuries each year.
DEVELOPING THE NEXT GENERATION of RADIOLOGY TOOLS
Non-invasive radiological images are obtained using CT scanners, MRI machines, and xrays. However, many diagnostic procedures still depend on biopsies of physical tissue. These can pose risks and even infection.
Experts predict that artificial intelligence will allow the next generation radiology tools to be more precise and detailed than tissue samples, which could replace the need for them in certain cases.
"We want to bring together diagnostic imaging with the surgeon, interventional radiologist, and the pathologist," stated Alexandra Golby MD, Director Image-Guided Neurosurgery (BWH). It is difficult to bring together different teams and achieve common goals.
"If imaging is to provide us with information from tissue samples, we will need to be able achieve very close registration to ensure that the ground truth of any given pixel can be known."
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This quest could allow clinicians to gain a better understanding of how tumors behave in general, and not just a specific segment.
Providers might also be better able to define the severity of cancer and target treatment more effectively.
Artificial intelligence is helping "virtual biopsy" and advance radiomics. This innovative field focuses on using image-based algorithms to identify the phenotypes of and genetic characteristics of cancerous cells.
EXPANDING ACCESS to CARE IN UNDERSERVED AND DEVELOPING REGIONS
Access to life-saving healthcare in developing countries can be severely limited by a shortage of qualified healthcare providers such as radiologists and ultrasound technicians.
The session highlighted that more radiologists work in Boston's half-dozen hospitals along Longwood Avenue than in any other part of West Africa.
Artificial intelligence could be used to reduce the impact of this severe shortage in qualified clinical staff, by taking over certain diagnostic duties normally assigned to humans.
AI imaging tools, for example, can screen chest radiographs for signs and symptoms of tuberculosis. They often achieve a level comparable to human accuracy. This ability could be made available through an app that is accessible to healthcare providers in low-resource locations, which would reduce the need for a trained diagnostic radioologist on-site.
Jayashree Kalpathy–Cramer, PhD Assistant in Neuroscience at MGH, Associate Professor of Radiology, HMS, stated, "The potential for the tech to increase accessibility to healthcare is enormous."
Developers of algorithm must remember that different ethnicities or residents from different areas may have unique physiologies that can influence the presentation and spread of disease.
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She said that the course of a disease, and the population affected by it, may be very different in India from the US.
"While we are developing these algorithms, we must ensure that the data includes a variety of diseases and populations. We can't base an algorithm on one population and expect it will work on all.
REDUCING BURDENS IN ELECTRONIC HEALTH RECORD USE
EHRs played a key role in the industry's move towards digitalization. However, the transition has presented many problems such as cognitive overload, excessive documentation, and user burnout.
EHR developers now use artificial intelligence for intuitive interfaces and to automate routine processes that can take up so much time.
According to Adam Landman, MD Vice President and Chief Information Officer at Brigham Health, the majority of users spend their time on order entry and clinical documentation.
While voice recognition and dictation can improve clinical documentation, natural languages processing (NLP), tools may not be reaching the right level.
Landman stated that he thinks we might need to be bolder and look at video recording clinical encounters, much like body cameras worn by police officers. "And then, you can use AI or machine learning to index these videos for future information retrieval.
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"And just as in the home, Siri and Alexa are used, so will the future bring virtual assistants to bedside for clinicians, to use with embedded intelligence to order entry."
Artificial intelligence could also be used to process routine requests, such as medication refills or result notifications. Landman said that artificial intelligence may be able to help prioritize tasks that really require attention from the clinician, which will make it easier for users and their teams to complete their to-do list.
CONTAINING RISKS OF ANTIBIOTIC RESISTANCE
Overuse of antibiotics can lead to superbugs, which are a threat to the health of people all over the globe. Multi-drug resistant organisms (MDR) can cause havoc in hospitals and take thousands of lives each year.
C. Difficult alone is responsible for $5 billion annually in healthcare costs and has claimed more than 30,000 lives.
Identifying infection patterns can be done with electronic health records. This will highlight patients who are at greatest risk and help them to identify the source of their symptoms. These analytics can be improved by using machine learning and AI tools to generate faster and more accurate alerts for healthcare professionals.
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Erica Shenoy (MD, PhD), Associate Chief of MGH's Infection Control Unit, said that AI tools could live up to expectations for antibiotic resistance and infection control.
"If they don’t, that’s a real failure on all our parts." Hospitals with mountains of EHR data that they are not using to their full potential and industry that is not creating faster, smarter clinical trial design and EHRs that create these data to not use them...that would be failure."
CREATING MORE PRECISE ANALYTICS FOR PHOTOLOGY IMAGES
Jeffrey Golden, MD, Chair of Department of Pathology at BWH, and Professor of Pathology at HMS, states that pathologists are one of the most important sources of diagnostic data for providers in all areas of care delivery.
He said that seventy percent of healthcare decisions are based upon a pathology report. "Somewhere between 70% and 75% of the data in an EHR come from a pathology report. The sooner we can get the correct diagnosis, the more accurate we will be. This is what digital pathology, and AI can do.
Providers can identify subtleties that might be missed by the human eye with analytics that drill down to the pixel level for large digital images.
Golden stated, "We are now able to do a better job of assessing if a cancer will progress quickly or slowly. This could affect how patients will be treated. Based on an algorithm and not clinical staging or the histopathologic grades. This is a significant advance.
He said that artificial intelligence can also increase productivity by identifying key features in slides, before a human clinician reviews and evaluates the data.
AI can screen through slides and direct you to the right thing so that we can evaluate what's important. This increases efficiency and the value of each case's time spent by the pathologist.
References: https://healthitanalytics.com/news/top-12-ways-artificial-intelligence-will-impact-healthcare
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