In the past decade, no technology has generated more buzz than artificial intelligence. Speculation surrounding its ability to add value to the health care space has dominated conversations at every level, sparking debate over how—and at times even if—artificial intelligence should be incorporated into the business strategy of health care organizations.
What artificial intelligence means
The term artificial intelligence was first coined in the 1950s and it has been used to refer to multiple types of applications, ranging from the simple to the complex, where each application of artificial intelligence technology offers differing tradeoffs in performance, transparency, speed, and training data requirements. At its core, artificial intelligence “makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks,” according to SAS, an analytics and business intelligence provider. Most applications of artificial intelligence today leverage deep learning and natural language processing, which allow computers to be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.
Machine learning capabilities, such as natural language processing, are the “most mature and widely adopted parts of [artificial intelligence] today,” according to Ahmed El Adl, Ph.D., a technology thought leader focused on the advancement of artificial intelligence and its adoption in industries. Indeed, natural language processing now powers many applications and products that have had a positive impact on both businesses and individuals on a personal level.
Artificial intelligence in health care has quickly become a powerful tool for clinicians across the health care ecosystem. It has detected care gaps, made predictions for adverse events, and augmented clinical decision-making. Artificial intelligence has become a transformational force, paving the way for more efficient care delivery and creating space for innovation and growth.
There’s no question that artificial intelligence tools are shifting how patients and providers search for and use data. Advancements in big data analytics in health care stemming from interoperability and artificial intelligence technologies are rapidly evolving, and health plans, providers, and other organizations across the industry are heralding the potential of artificial intelligence to improve the user experience and overcome clinical and financial challenges.
But before any industry—especially one that has a direct impact on the lives of individuals—jumps full throttle into the world of artificial intelligence, it must carefully consider the benefits and consequences. Many within and outside of the health care industry have debated at length how artificial intelligence can change health care, and with that debate has come no shortage of misunderstandings and misperceptions.
Myth #1 | Artificial intelligence functions just like the human brain
One of the biggest misperceptions today regarding the use of artificial intelligence in health care is that the technology is similar to or functions like an actual human brain. Artificial intelligence is certainly capable of identifying information it has been engineered to recognize (e.g., image recognition performed by artificial intelligence technology is often more accurate than what a human can achieve). However, in general, artificial intelligence is very skilled at solving one task at a time—one that it has been specifically trained to complete. If any of the circumstances surrounding the task shift, the technology will fail. The artificial intelligence system that has been trained to identify gaps in care not indicated in the patient’s record is not the same application that can identify patients at risk of experiencing falls. Artificial intelligence systems are incredibly specialized and are far from modeling full human intelligence.
Myth #2 | Artificial intelligence technology is capable of learning on its own
Another myth that permeates the conversation around artificial intelligence in health care and raises concerns is that intelligent, artificial intelligence-powered machines can learn on their own—a misperception that stems from the idea that a finished machine learning product possesses the ability to independently learn. While artificial intelligence technology has both the capability to learn—either through online learning or batch training—and correct itself based on information provided to and feedback derived from the system to improve accuracy, all of this requires a significant amount of human decision-making and brainpower. Humans use data from clinical activities to train and continually update machine learning software to support learning in artificial intelligence-powered machines—pushing the integration of new knowledge and data into the next learning cycle.
Myth #3 | Artificial intelligence is primed to take health care jobs
There is some concern from health care professionals, such as nurses, that artificial intelligence-powered machines will replace them in the near future. Machines, however, lack characteristics like empathy, creativity, judgement, and critical thinking. In aspects of patient care where human compassion is essential, artificial intelligence-powered machines or solutions won’t be a replacement because they lack emotional intelligence, which is critical to providing holistic, patient-centered care. Certainly, the use of artificial intelligence in health care will evolve to minimize the need for humans to take on repetitive tasks and some basic tasks, such as appointment scheduling, but the health care industry is moving forward with the focus on patient-centric care—something that cannot be accomplished without the decision-making skill of humans.
Artificial intelligence in health care: Where we are and where we’re headed
Opinions about how artificial intelligence can and should be leveraged in health care are as varied as the applications of the technology itself, but it is impossible to deny apparent benefits of artificial intelligence within the health care industry. Artificial intelligence can use algorithms to “learn” features from a large volume of health care data, and then use the obtained insights to guide clinical decision-making, according to a report by the center of Stroke and Vascular Neurology (SVN). This learned behavior can streamline the clinical record review process—a significant advantage for organizations that choose to implement artificial intelligence can benefit from right now.
Applications of natural language processing in health care—powered by machine learning can be used to evaluate clinical information found within patient medical records with greater efficiency and completeness over the traditional human clinical review approach, delivering greater care and bottom-line savings in the process. And with 75 percent of clinical data found in unstructured form and more than 100 million clinical record reviews performed each year, natural language processing in health care can accelerate and automate this process to deliver rapid insight into clinical quality, risk score accuracy, disease outcomes, and cost.
Artificial intelligence technology has also proven itself useful in other areas of health care—for instance, in assisting physicians with patient care by providing up-to-date medical information from journals, textbooks, and clinical practices to inform proper patient care. The fact that these patient-focused applications of artificial intelligence is now available should come as welcome news for patients, physicians, and even hospitals, as any technology designed to improve patient care is certainly worth exploring.
Artificial intelligence solutions hold promise for closing care gaps and advancing patient-centered care
Although there remain significant misunderstandings about what artificial intelligence is and how organizations across the health care ecosystem can reap its benefits, there are plenty of examples of how artificial intelligence can be applied successfully.
In the health care market today, the payer space presents the lowest barrier opportunity for artificial intelligence applications to meaningfully improve operational efficiencies and economic performance at a massive scale. Approaches in machine learning and neural networks can further bolster gap detection by permitting more variables to be accounted for, expanding beyond diagnosis codes to procedure codes, drug codes, and much more. Thousands of codes can easily be used across any health care policy model existing today. This information can also be used to assess overall patient risk.
From identifying the patients and providers most at risk for gaps to providing financial estimates to locating patients within a membership who are over a specified risk threshold, artificial intelligence is perfectly aligned to help move the needle on providing patient-centered care. Solutions leveraging artificial intelligence, when combined with deep learning algorithms, are helping clinicians provide more—and better—care. In 2018, Apple launched Health Records, a personal health record feature that aims to combine patients’ existing health care-related data found on their personal Apple devices with data from electronic health records from hospitals they have visited.
Two other initiatives launched last year, MyHealthEData, a government-led initiative to give people greater control over their medical data and Medicare’s Blue Button 2.0, a secure way for Medicare patients to access and share their personal health data, paved the way for innovative applications of artificial intelligence in health care.
The future of artificial intelligence in health care is now
Applications of artificial intelligence like machine learning have already been incorporated into our society. Today, it is a part of everyday life, and we often interact with artificial intelligence-powered technologies in ways we don’t even realize. Scheduling a doctor’s appointment, for example, is now often an automated process that allows one to make an appointment.
Once an idea that could possibly be applicable in the future, artificial intelligence in health care is here now, and like any other breakthrough technology, it isn’t likely to slow down. Organizations that choose to eschew artificial intelligence technology from their business strategy are putting themselves at a competitive disadvantage.
As more health care organizations adopt artificial intelligence tools, the amount of data powering the industry will grow exponentially—to the benefit of the industry but more specifically to patients. Now is the time to strategically incorporate artificial intelligence into workflows and collaborate with business and technology leaders to drive transformative and relevant innovation powered by applications of artificial intelligence.
About the author
William Kinsman manages Inovalon’s artificial intelligence team and the implementation of their products. In his first year at Inovalon, Kinsman has made it his goal to bring clinicians and this technology together by leading the development of the advanced natural language processing algorithms presently in Inovalon’s product offerings.
He collaborates with professors and doctorial candidates from the University of Maryland and the New Jersey Institute of Technology to bridge the gap in health care data extraction and develop never seen approaches in patient-centric health care, with a focus on patient gap detection and intervention optimization.
Kinsman has nearly a decade of experience in the construction of machine learning algorithms. Prior to joining Inovalon, he led the development of several novel language processing algorithms as a contractor at the National Security Agency, but also has previous work experience as a developer at a high frequency trading firm and as an engineer at NASA. He received his bachelor’s degree from Clarkson University in mechanical engineering prior to years of research in electrical materials research at Penn State University.
Inovalon is a leading provider of cloud-based platforms empowering data-driven health care. Through the Inovalon ONE® Platform, Inovalon brings to the marketplace a national-scale capability to interconnect with the health care ecosystem, aggregate and analyze data in real-time, and empower the application of resulting insights to drive meaningful impact at the point of care. Leveraging its platform, unparalleled proprietary data sets, and industry-leading subject matter expertise, Inovalon enables better care, efficiency, and financial performance across the health care ecosystem. From health plans and provider organizations, to pharmaceutical, medical device, and diagnostics companies, Inovalon’s unique achievement of value is delivered through the effective progression of “Turning Data into Insight, and Insight into Action®.” Supporting thousands of clients, including 24 of the top 25 U.S. health plans and 22 of the top 25 global pharma companies, Inovalon’s technology platforms and analytics are informed by data pertaining to more than 972,000 physicians, 531,000 clinical facilities, 271 million Americans, and 45 billion medical events. For more information, visit www.inovalon.com.