The Doctor Will See You Now — On Your Cellphone

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Relief may be just a few touchpad swipes away with Doctor on Demand, a new app that lets you arrange video-based doctor visits on the fly, in the style of Uber. The iOS and Andr…

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Digital Health: Apps, Analytics & Agencies

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You CAN step into the realm of digital health and stay on the right side of the law


This is an Introduction to Digital Health presented at the Massachusetts Bar Association “Hot Topics in Healthcare” program on December 10, 2013 by David Harlow.  For more information, see related posts at


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Personalized Medicine: when a computer assists you with your health

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We are all encouraged to live a healthy lifestyle to avoid potentially life-threatening diseases. Exercising and good dietary habits make a big difference in maintaining our health.


However, for some diseases, our cells carry important information that can alter this equation. It is estimated that the cells in our body have about 30 thousand genes. The information they encode tells each cell how to behave within our body.


For example, a particular gene might determine the eye color of a person; another gene might tell a cell that it should become heart tissue; and yet another could be in charge of producing insulin in our body. However, sometimes these genes can be mutated, causing the gene to be either nonfunctional or functioning with a different behavior. These mutations have been found to cause some of the most challenging diseases.



“Personalized Medicine” is a nascent field that tailors diagnosis and treatment to a patient by analyzing their clinical and genomic information. This is where bioinformaticians are assisting clinicians to achieve better diagnosis, treatments and clinical outcomes. Computer algorithms are a crucial part in this process, since human researchers cannot process the vast amount of information and interactions in the genomic data.



Algorithms can take into consideration a wide range of variables, including clinical signs and symptoms, laboratory data, and information from the DNA, such as the functioning of genes. They combine this information from a wide selection of people to come up with a model that can predict reasonably well the presence of a given disease. The rationale for this is to allow computers to ‘learn from past experiences’, and progressively gather data to improve upon their decisions.


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MHealth apps access hidden mobile data to improve patient care

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When mHealth apps access more than just patient-entered data in a mobile device, the mix could provide deeper, more powerful clinical data analytics.


Mobile apps such as Daily Carb, Glucose Buddy, SkinKeeper, Pregnancy Tracker and Fitbit have been popular in the consumer market. These apps are making a difference in patient health, empowering the self-tracking of important health-specific, dietary and fitness data.


Healthcare professionals also are seeing the potential that mHealth apps have in helping patients improve outcomes. Currently apps are designed to specifically capture a limited number of data elements. Patients enter the data points manually, or they are captured through a sensory device such as a glucose level reader. But how far can data mining, social media and patient engagement push the clinical relationship?


An emerging generation of mHealth apps is using more than just patient-entered data to monitor health. For example, collects and analyzes hidden data such as messaging logs onboard a mobile device to help better understand patients. The company’s website explains that the concept is to mine iOS or Android data to monitor user behavior and identify changes to the user’s health — information that can be pushed to a healthcare provider who might need to step in.


There is clearly a significant amount of data analysis and calculation happening in the background of such an app. Enabling these processes requires review of the data to identify any discrepancies. In conjunction with that, the app prompts users with a survey to capture specific data points.


Having sensory data and other insights — such as mood and mental state — can provide a wealth of information for data scientists.

This approach may prove to be a much more comprehensive one.


By monitoring active and passive patient data on a daily basis, both patients and providers can discover significant changes in behaviors and create much closer relationships. Furthermore, it’s likely that social media sites could end up becoming a valid data source for monitoring an individual’s activities and moods.

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