The Xconomy Healthcare + A.I. Northwest Conference at Cambia Grove featured speakers and panels discussing the opportunities and prospects for applying machine learning and artificial intelligence to find solutions for health care. The consensus was that we are no longer held back by a lack of technological understanding and ability. A.I. and M.L. models can be learned at a large scale by harnessing the power of the cloud and advances in data science. According to the panelists, today’s challenges to incorporating A.I. into healthcare include abundant, but inadequate data and resistance from health systems and providers.
Many researchers have found insufficient data to be an unexpected challenge. As keynote speaker Peter Lee of Microsoft Research pointed out, the more data we have, the better our machine learned models can be. He used an analogy to a speech identifier trained on multiple languages such that the model predicted English better after learning French to illustrate that improvements can be made with large sets of unstructured data. Unfortunately, because we are not capturing enough of the right kind of data for researchers, much patient data is getting lost in the “health data funnel” due to PHI and quality concerns. Lee called for more data sharing and data transparency at every level.
Physician researchers on multiple panels were concerned about a lack of suitable data. Soheil Meshinchi, a pediatric oncologist from Fred Hutchinson Cancer Research Center, is engaged in collecting data specific to children. He discussed his research on Acute Myeloid Leukemia on the panel titled, ‘Will A.I. Help Discover and Personalize the Next Breakthrough Therapy?’. While there is a large body of research on AML in adults, he has found that the disease behaves much differently at a genomic level in children. He also expressed distrust in some published research because studies are rarely reproduced and often a researcher who presents results contrary to existing research faces headwinds at journals who are reticent to publish “negative data”. His focus at this point is gathering as much data as he can.
Matthew Thompson, a physician researcher at the University of Washington School of Medicine, argued on the “Innovations for the Over-Worked Physician” panel that technology has made patient interaction demonstrably worse, but that these problems can and should be solved innovatively with artificial intelligence. His specific complaints include both inputting and extracting data from health system EHRs, as well as an overall glut of raw patient data, often generated by the patient himself, and far too much published research for clinicians to digest.
Both keynote speakers, Microsoft’s Lee and Oren Etzioni of the Allen Institute for Artificial Intelligence, referenced the large numbers of research papers published every year. According to Etzioni, the number of scientific papers published has doubled every nine years since World War II. Lee referenced a statistic that 4000 studies on precision cancer treatments are published each year. They are both relying on innovative machine reading techniques to analyze and categorize research papers to make them more available to physicians (and other scientists). Dr. Etzioni’s team has developed SemanticScholar.org to combat the common challenges facing those who look for research papers. He aims to reduce the number of citations they must follow while also identifying the most relevant and up-to-date research available. One of the advantages of taking this approach to patient data
is that scientific texts have no PHI concerns. Lee’s team is marrying patient data and machine reading to match potential research subjects with appropriate NIH studies.
Dr. Thompson was concerned that too much data is presented to the medical staff and very few of the “predictive rules” used by ER personnel are both ‘accurate and safe’. When reviewing patient outcomes and observations to predict the severity of an infection, he found that patients or their caregivers would provide ample information, but often clinicians would disregard certain details as noise because they were atypical symptoms. The amount of data that providers have to observe for a patient is massive, but machine learned models may be utilized to distill that data into the most relevant and actionable signals.
Before data is gathered and interpreted, it must be collected. Like Dr. Thompson, Harjinder Sandhu of Saykara sees ponderous, physician-driven data entry via EHR as significant barrier to efficient data collection. Sandhu notes that healthcare is the only industry where the highest-paid teammember is performing this onerous task and his company is using artificial intelligence to ease that burden on the physician.
Once patient data has been aggregated and processed into models, the challenge is getting the information in front of providers. This requires buy-in from the health system, physician, and, occasionally, the patient and his caregivers. Mary Haggard of Providence Health and Services spoke on the “Tech Entrepreneurs Journey into Healthcare” panel and stated that the biggest problem for entrepreneurs is defining the correct problem to solve. During the “Investment Perspective” panel, Matt Holman of Echo Health Ventures recommended tech startups emphasize an understanding of the context of the problem within a health system.
One of the most important and difficult hurdles for health technology companies is working into clinical workflow. Mike McSherry from Xealth has found that physician champions who know how they want to use technology help with integrating into a health system or physicians group. Lynn McGrath of Eigen Healthcare believes physicians want their data to be defined, quick to assess, condensed, and actionable, while Shelly Fitz points out that providers are not used to all the data they are receiving and they don’t yet know how to use it all. These are all issues that can and will be solved as healthcare technology continues to become more intelligent.
As Wellpepper’s CTO, Mike Van Snellenberg pointed out, health systems and doctors are resistant to “shiny new things”, for good reason. When approaching a health system, in addition to engaging the administration, clinicians need to understand why the machine learned model is recommending a given course of treatment. After integration, patients will also want to understand why a given course of treatment is being recommended. Applying artificial intelligence solutions to medicine must take into account the human element, as well.
The exciting possibilities of artificial intelligence and machine learning are hindered more by human constraints in health systems and data collection than by available technology. “Patients are throwing off all kinds of data when they’re not in the clinic,” according to our CTO. Wellpepper’s tools for capturing patient-generated data provide a pathway for providers to access actionable analysis.