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Traditionally, consumers did not have easy access to their medical information. However, the widespread adoption of wearable fitness trackers and connected devices has enabled anybody to become an active participant in their own health monitoring.
Apple HealthKit, Google Fit, and Samsung’s S Health have expanded on this behaviour by providing a centralized repository for users to store health data collected from third-party devices like smart scales, step trackers, and blood pressure monitors.
These platforms reorganize all of the data and present it in intuitive and useful ways. Apple is piloting a program with HealthKit where, with a user’s permission, the data can be exported directly to their electronic medical records, giving their doctors direct access to their day-to-day wellbeing.
This self-collected data, though far from conclusive, can serve as a conduit between doctor and patient, helping prevent unnecessary trips to urgent care when a quick reassurance from a medical professional is all that’s needed. While advancements in the field of telemedicine have helped make this a reality, Chinese search giant Baidu has gone a step further and created the equivalent of Siri for medical diagnostic suggestions.
The AskADoctor app is a deep-learning system that uses natural language, letting users simply speak or type their symptoms (e.g. joint pain, sore throat, or itchy skin) into their mobile device to get a possible diagnosis. The app also gives users the odds that the diagnosis is correct and connects them to nearby medical professionals should they need further medical attention.
As a byproduct of this decentralized and increasingly digital system, valuable data exhaust is generated. While consumers are able to learn more about what is causing their symptoms, researchers can analyze larger patterns in the data.
Google Flu Trends was one of the first experiments in linking search data to predict the spread of diseases. The program used the insight that as people get sick, they search their symptoms online. The initiative demonstrated the new value proposition for connected healthcare: individuals receive personal insights on their condition, while adding to the collective knowledge shared by society.
Unfortunately, despite everyone’s love for the idea, the service consistently overestimated the number of flu cases.
Though Google Flu Trends was always intended as a "complementary signal" rather than a stand-alone forecasting tool, last year the tech giant began working with the Centers for Disease Control and Prevention (CDC) to improve its flu-tracking model by integrating it with traditional data. In August 2015, Google closed down the website and handed over the data to the CDC, as well as to several universities that specialize in tracking the spread of infectious diseases.
Access to Google’s real-time, anonymous search results made it possible to predict flu patterns with relative accuracy; as smartphones get faster and continue to integrate more advanced sensors, researchers now have a direct link to personal data that goes well beyond the number of steps a person has taken.
Using a phone’s gyroscope, accelerometer, microphone, and GPS, researchers can glean accurate insights about a patient, such as their gait, motor impairment, general fitness, speech, and memory. The biggest development here has been Apple’s ResearchKit, which effectively turns smartphones into medical diagnostic tools.
ResearchKit enables iPhone owners to voluntarily join medical research studies and share their data. So far, it has been used to study breast cancer, cardiovascular disease, asthma, diabetes, and Parkinson’s disease.
This type of information is highly sought after by companies doing medical research. Some companies, such as Genentech, have even formed deals with social networks like PatientsLikeMe to understand patients’ real-world experience with treatments for various diseases. The partnership enables Genentech to "subscribe" to all the patient data generated on the social network.
As with ResearchKit, the hope is that the information shared by people living with chronic disease will help researchers identify unmet needs and generate medical evidence.
As our increased connectivity contributes to even larger sets of self-generated data exhaust, Silicon Valley, built upon the principles of Moore’s Law, hopes to use its computing muscle to process all this self-collected information—thrusting itself into the health sphere. Unsurprisingly, this cycle is virtuous: technology companies create products that generate tremendous amounts of data, and that data is now fueling exponential advancements in medical research.
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