The Apple ResearchKit and the Economics of Healthcare. Will those that could benefit the most be left out?

This week Apple unveiled some of the key details omitted from their official Apple Watch announcement back in September. In addition to showcasing a beautifully designed watch, it also gave us the first preview of their revolutionary new healthcare software platform called ResearchKit.

Touting how everyone can now do their part to advance medical studies, ResearchKit allows iPhone owners to turn their phones into a powerful medical research tool, which can help healthcare experts battle life-threatening diseases by harnessing the insights of big data analytics.

As Jeff Williams, Senior Vice President of Operations, explained during Monday’s presentation, “one of the biggest challenges researchers have is recruiting.” Apple is looking to address this critical need to make data acquisition a seamless and frictionless task, while using the power of analytics and consumer-driven informatics to drive advancements in healthcare breakthroughs.

Those of us working in the area of chronic disease and population management, can’t help but wonder how accessible this revolutionary innovation for patients across the social economic strata.. Historically patients at the lower end of the socio-economic segment tend to be the groups that are most affected by chronic conditions, and these populations also have high readmission rates. Similarly, studies have also shown that one of the biggest financial burdens these populations face is the cost of addressing the continuity of their care, and the affordability of their prescription medications.

Apple’s cost structure for their new iPhone 6 line ($199 to $649), as well as that of the Apple Watch (ranging anywhere between $349 to $17,000) may very well keep them out of the reach of the millions that would gain the most from the benefits ResearchKit promises.

Moreover, when it comes to data analytics, we know how crucial it is to gather data which is a true composite of our healthcare population spectrum. This is why ReseachKit may be inundated with bias data. As Lisa Schwartz, professor at the Dartmouth Institute for Health Policy and Clinical Practice, told Bloomberg via an email that “Just collecting lots of information about people — who may or may not have a particular disease, and may or may not represent the typical patient — could just add noise and distraction,” and added “Bias times a million is still bias.”

That’s why researches in the area of statistics understand that they must account for skewness and kurtosis of their findings when dealing with biased datasets. And because ResearchKit collects data longitudinally, we wonder whether or not it presents a holistic picture of a large patient population. Will it also measure critically important “hard” outcomes such as survival vs death rates, or will it focus on measuring “softer” outcomes, such as “I’m feeling less chest pain today”? Can this information unlock the potential of geo-medicine studies across the globe, or will it only give us a partial picture.

Nevertheless, the introduction of ResearchKit certainly raises many questions, however, I’m confident it will certainly do wonders for the advancement of medical research and enhancing patients’ lives. As time goes by, we can only hope it can live up to its promise of helping doctors battle diseases and continue to innovate healthcare, especially for those at the lower end of the socio-economic scale.