When it comes to a symptom checker, people searching online for the right diagnosis can be hard to find. Now Google and Microsoft are working on ways to improve things. A good example of why this comes about is mentioned below…
Jurcik, a 31-year-old human resources professional at Boeing, exercised regularly and was in good shape. She felt a sharp pain in her side and back but didn’t think too much about it. She thought it was probably a strained muscle from the workouts but the pain got worse, and by early February she could barely stand up. “I had the absolute worst pain in my life” she said. “I couldn’t stand up straight.”
Like most people, Jurcik Googled her symptoms. She typed “upper left abdominal pain” into the search engine. “I learned all about gall stones, and ulcers and gas pain” she said. It became so painful that she called her mother who is a nurse, who urged her to go to the emergency room. She was eventually diagnosed with pancreatic cancer. The doctor said, the good news is you’re going to be okay, but the bad news is you’re going to die before you turn 38 if you don’t have it taken out.
Jurcik feels lucky she caught her cancer when she did. But she still feels that the notion of searching for an online symptom checker did not serve her well. Nowhere in the search results and articles did it say a tumor may have caused the pain.
With the continuous amounts of information available online, more than half of people in North America look up health information on the net alone; and more than a third try to diagnose themselves or others with what they discover, according to the Pew Research Center. Yet other studies have found that much of the information found online is incorrect or out of date. Harvard researchers analyzed 23 online symptom checkers and found that they produced an accurate diagnosis as the first result just 34% of the time.
Another problem is that it can be difficult for people without a healthcare background to distinguish between multiple conditions with similar symptoms. Because of this, tech giants including Microsoft and Google are looking for ways to improve the power of health search tools.
Symptom Checker Search Made Better
In June, Google announced it was partnering with Harvard Medical School and Mayo Clinic, to launch a symptom search feature to the tech giants capabilities. “Health content on the web can be difficult to navigate, and tends to lead people from mild symptoms to scary and unlikely conditions, which can cause unnecessary anxiety and stress” says Google product manager Veronica Pinchin in a statement.
The symptom search feature will give you an overview description, along with information on self-treatment options and what might warrant a doctors visit. Google creates its list of symptoms by looking for health conditions mentioned in web results, and then checking them against high-quality medical information collected from doctors.
Microsoft researchers have continually been using search to test predictive algorithms. With millions of patients making many millions of health-related searches with similar terms, huge troves of powerful data are being uncovered and created. Researchers are using these pools of big data, to mine for information in search of new tools to help find ways to screen and identify disease and other health risks at an earlier state.
“Its not uncommon for patients to jump to the conclusion that they have a life threatening illness from a common symptom. ” Eric Horvitz, a technical fellow and managing director at Microsoft Research, calls this phenomenon cyberchondria. Humans generally have a poor ability to understand the probability of events, and websites are fairly poor at communicating them in the right methods. To make things worse, search tends to push the scary rare disease higher, and as a result you’re much more likely to think you have a rare disease based on the symptoms.
Speaking from the International Conference on Machine Learning in New York, Horvitz explained that he wants search engines to realize when someone is using it as a diagnostic tool, so that it can then through probability, hone in on and explain the most likely conditions.
Horvitz began his work at Stanford University as a medical student in the 1980s with a deep interest in the foundations of thinking. But his interest in nervous systems gave way to an interest in artificial intelligence. At Microsoft, he uses computers to find patterns in data people unwittingly provide through search and other data symptom checker sources, such as large-scale electronic health records.
His latest study was unfortunately inspired by means of a loss. A close friend called him, told him he had this weird itching all over his body, and that he had some yellow in his eyes. Having studied medicine, Horvitz knew that these could be symptoms of pancreatic cancer, and told his friend to talk to a doctor about these symptoms. He was soon diagnosed afterwards with advanced pancreatic cancer.
Horvitz began to think about how people tend to whisper all sorts of concerns into web searches. “People don’t talk about dark urine, or strange back pains, or losing weight for no reason in public” he said. “If you had access to millions of search records, can you use machine learning to identify patterns? ” He found that you can, and in a study published in early June, Horvitz and his colleagues identified queries that provided strong evidence of a recent diagnosis of pancreatic cancer.
They used machine learning to identify searches by the same group months earlier, by combining patterns of symptoms used in searches, and other information seen in the logs over time. They found they could predict significant fractions of those searchers with pancreatic cancer based on their earlier searches. These results suggest that predictive modeling may be able to help screen for diseases early enough to improve outcomes, and not just for pancreatic cancer. Horvitz and his colleagues have also used search and social media data sources to identify pregnant women at risk of postpartum depression before they give birth, and to predict a likely stage of breast cancer.
Horvitz and Russ Altman, a doctor and director of the Biomedical Informatics Training Program at Stanford, note that these studies are promising but exploratory, and that the methods need to be validated in clinical trials. The Googles Flu Trends tool, introduced to wide acclaim in 2008, looked like a promising epidemiological method to predict the spread of seasonal influenza. But it failed to predict the spread of flu in 2013, and was discontinued from that point on.
“We should be thinking about how to bring this data to patients” says Altman. Horvitz’s team is exploring how symptom checker technology can be used to do valuable screening, while protecting the users private health information. “We could build filters, or auto pattern recognizer’s from this large-scale anonymized data, that feeds into apps for your smartphone that would work in complete privacy” Horvitz suggests.
Such tools might eventually work in tandem with electronic medical records behind a secure wall, and incorporate the biological data people collect from their own personal health devices, like a Fitbit for example, to fine tune and personalize the analytical power. Perhaps by combining the data with that from electronic medical records, or genetic testing, this technology can learn more and more about an individual and provide targeted health information to each person and healthcare provider.
There are very real privacy and ethical concerns. Lee Tien, an attorney for the Electronic Frontier Foundation, says that he is worried about personal biomedical data being opened up for this kind of research. Big data about rocks or stars or the moon is just ethically different from big data that comes from, and is thus about, people in general. The human biographical aspect of data is effaced by just calling it data. He suggests we think carefully before weakening any privacy protections in search of an uncertain benefit.
“Its unethical to not do everything we can with our resources” Horvitz says. Altman agrees, “I believe it would be a tragedy if the privacy people who are for putting everything in a lock box win this argument. It would slow down medical discovery substantially.”
Some tools are already in development. Prescription drug plans use big data to predict which patients are likely to skip medications, and alert them when its time to take a pill, or other forms of a medical solution. Hospitals are developing ways to predict which patients are most likely to be readmitted, to direct resources to prevent those poor outcomes. Epidemiologists are using social network data to track food born illnesses and other infections.
Jurcik and her story welcomes the pancreatic cancer finding. Now 35 and working in human resources at Providence Health and Services, she has become a volunteer for the Pancreatic Cancer Action Network to get the word out for early detection. She says because this cancer has many unrelated early symptoms, and strikes so quickly, any tool that helps people connect the dots earlier might be lifesaving. “I don’t think there’s anything better to do as patients, than to come in to the doctor knowing what questions to ask” she says.