How many steps does ResearchKit need to subvert the medical industry?

This Apple spring conference was robbed by the new MacBook and Apple Watch. When everyone was discussing who would buy the 12,600-meter local table, they ignored the invisible and intangible. Something - ResearchKit. In fact, ResearchKit, which is known as a "medical consultant", is not simple. It is even thought to be or will change the entire medical industry and even have a profound impact on human life.

How many steps does ResearchKit need to subvert the medical industry?

Simply put, ResearchKit is Apple's software infrastructure for medical researchers. Researchers can design a variety of medical apps based on this open source architecture to collect health data for a variety of patients. In this respect, it is very likely to subvert traditional medical research methods. In addition, ResearchKit really launched the mobile medical mechanism, and the core role in this process is "medical researchers", through the mobile app can really solve patient-related problems or propose treatment plans, and even develop new drugs, This will touch the “pain point” of the mobile medical industry, which is more professional and mobile than the mobile medical platform that only provides participation.

In addition, with the integration of software and hardware such as Apple and the strong control of the industry chain, the prospect of ResearchKit will be limitless. However, when it comes to changing the entire medical industry, ResearchKit still needs to face several problems.

Problem 1: Sample bias and hardware acquisition affect data accuracy

Apple COO Jeff Williams explained some of the dilemmas of medical research at the press conference: At present, medical research still has several limitations. First, participants are difficult to recruit, medical research often requires a large number of volunteers to participate; second, survey data is subjective; third is Data is released very frequently.

Collecting patient data is one of the biggest problems in medical research. For example, when an expert studies the mechanism of heart disease formation, he hopes to obtain the largest case data. Traditional medical research projects usually take thousands of years to collect thousands of samples. In the case, the project is very difficult and the number of cases is scarce.

Based on this breakthrough, Apple's ResearchKit idea is: There are already 700 million iPhones sold worldwide, and each iPhone is a smart hardware that can record user health information. Why not use this huge user group to let patients and researchers work together. Participate in medical research? Indeed, through ResearchKit, thousands of Apple phones can be turned into tools for medical research. With this tool, medical researchers can quickly and extensively collect patient data from around the world. ResearchKit provides medical researchers with a new access to patient data. However, the data obtained through this channel will be affected by two aspects in terms of accuracy.

On the one hand, there is a sample bias in the data collected by ResearchKit. Because the iPhone is positioned in the high-end market and is expensive, its user base is relatively young and affluent. The data obtained through ResearchKit may come from a group with higher income, and economic factors such as income are also very healthy. For example, poor economic conditions make the incidence of heart disease higher. Most of the volunteers who are willing to participate in ResearchKit research are those who take their health problems seriously. These people will be more active in improving their health, so research based on ResearchKit is more inclined to obtain data of such people.

On the other hand, medical research requires very accurate data collection, and ResearchKit combines software and hardware sensors for data collection, which poses a challenge to the accuracy of data collection for smart terminal devices such as iPhone or Apple Watch. In other words, all experts who want to rely on Apple's mobile phone to obtain large-scale research data must pay attention to the accuracy of the data.

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