
The federal program recently joined with six private institutions, including Harvard Teaching Hospitals and Stanford Medical Center, as part of the Undiagnosed Disease Network (UDN), which links databases and lets would-be patients apply for admission online rather than having to ship paper records. "This allows all of the sites to really work together to diagnose the patients," says Anastasia Wise, an epidemiologist and co-coordinator of the UDN. Such pooling of resources is part of a bigger trend linking up medical databases around the world to mine them for insights.
Since its founding in 2008, the original Federal program has been able to diagnose about 25% of the 800 people—from the U.S. and other countries—who have been accepted for exams. That modest diagnosis rate belies other benefits. "Sometimes [the] information that has come out of the clinical evaluation has led to a treatment option for the condition that has helped the patient, even without a diagnosis," says Wise.
"We understand that patients can have a difficult time finding someone who can understand their condition," says Wise. Applying to UDN does require a doctor's letter describing the patient’s medical history and symptoms, but that’s a standard procedure that any doctor can do. "Typically, patients can find someone who can write that letter for them, even if it’s not their primary care physician," says Wise.
Those who are accepted into the program undergo a customized week of examinations by anyone who might be able to help. "It’s really unique to get that many specialists to see a patient at once," says Wise. Most of the patients also receive genetic sequencing.
Collecting and comparing all this patient data is key for understanding new diseases. If two or more patients share outward symptoms, what clinicians call phenotypes, and genetic variations, or genotypes, doctors might be able to make a cause-and-effect link. As the databases grow, UDN will have more opportunities to put the robots to work, using the artificial intelligence of machine learning. Programs sort through massive amounts of data to flag similarities that a human doesn't have the capability to process. "Machine learning may be used to help determine how patterns of patient symptoms, genetic variants found from the sequence, and phenotypes seen in model organisms relate to help with diagnosing patients," Wise wrote in an email.
All the UDN sites have agreed to share patient data, which includes the names of the patients. "This is really rather unique," says Wise. It’s not unusual for medical research institutions to share anonymous data to study diseases, but you can’t treat someone if you don’t know who they are. Patients in UDN sign a consent form allowing their personal info to be available to all the researchers and clinicians in the network, combining all the institutions into a giant virtual hospital.
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"So you can have two patients, but you might not have put in the exact same terms to describe their symptoms," says Wise. "But you can tell that those two symptoms are similar to each other because they’re related to a higher-level term that’s more general." PhenoTips also has a freeform field; if variations of a new term keep popping up, a standard version can be added to the database. All members of UDN use PhenoTips, as do other organizations that can all mine the PhenomeCentral database for similar patients.
While everyone wants a quick answer, the pooling of ever more medical data and the development of smarter AI raises the hope that answers will eventually come. "Sometimes it can still take years before a diagnosis is found," Wise says. "Because it might be that data will be reanalyzed later with a new technique that comes along, and that might be the piece that finds a solution."

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