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The prime focus in almost every field of work is data, acting as the key component in serving the overall purpose. Scientist, physician or researchers are constantly finding ways and means to collect data for the higher purpose or achieving goals that are entitled to conducting research, trials, and population tests for people registered for healthcare stake. As easy and organized this may sound, running an entire mulch-billion dollar industry on the disposal of such data increases the importance of data and its acquisition is concerned.
Research Kit comes off as a path breaking initiative by Apple on 9th March, 2015 to ease the process of data collection with accuracy and convenience. Though the launch of this particular application might have not been promoted adequately, the need for the same is reaching sky high capacities.
In the most primary sense, ResearchKit is a well developed open framework to make collection of data required in the scope of medical research in a simple way. However the data is limited in access as the collection can only happen via hardware, sensors and phones with a supportive software in the features. The ResearchKit is also efficient enough to sensor movements, take measurements while collecting data for key studies.
The scope of this application can be extended as a recruitment channel to place study participants. And the dynamic attribute to this invention is the two way process that allows participants to share valuable data to medical researchers through their personal devices.
The argument on how a similar app can be developed to carry out the same purpose as against the ResearchKit may seem valid. But the application under scrutiny imbibes simpler techniques for accumulation of population data collection. And the way this is achieved is by delivering to software developers a set of pre-built, customizable modules that cuts down on the task from equation while building high quality clinical research apps.
The focus of the developers is entirely placed on the intricate needs and requirements of the researchers. The system designed with an open source framework, qualifies them to adhere to the development of the community through updated and improved modules.
ResearchKits to begin with were introduced under three modules of clinical research in the form of surveys, informed consent, and active tasks. These three methods will be elaborated in the text to follow:
In the traditional framework of questioning research participants direct questions required ample undertaking and involvement of engaging with them over phone, emails, mailers, and in person. This was definitely costly and time consuming, for the kind of clinical research the professionals were approaching. On the contrary with the ResearchKit in place researchers can ask questions to the participants on an authentic and accepted platform with a built-in survey framework. The settings relevant to factors like geographical locations, age group and other concentrated groups can be adjusted in this open framework.
The nature of data collected is highly confidential and sensitive in clinical research. And in order to carry out such research, the respondents or participants are entitled to know everything about the information they are sharing to the processing and the nature of its purpose. Hence under the ResearchKit’s module of informed agreement, lets researchers to design the content flow of any study with respect to the participants. The researches have a window of opportunity to design and include tests to ensure participants comply with the survey needs. And the main feature of this application includes obtaining signatures of the participants under the informed agreement and deliver them as signed PDF’s to a server, email address or to the app itself.
As for many studies, researches require data that may not be accessible from the participants end. In order to place this problem the original innovator of this open framework application introduced five active task modules to initiate gathering of the data categories. The categories like Motor activities, fitness, cognition and voice, allowing researchers to bring together a variety of tests to get good quality data from the respondents.
After all of the facts and figures established in the above paragraphs, it might seem as the ultimate solution to a data collection method. But there are some limitations that might contradict the feasibility and accessibility. The foremost problem is that it does not give developers the “automatic compliance” feature as against thinking through HIPAA requirements and research guidelines to be adhered to. Secondly there is no secure establishment of communication between the app and the backend server; being the developer’s sole responsibility. The app in itself does not have a set standard norm or format for data serialization.
The main issue is the idea of a ResearchKit and not being officially released to carry out the complex task of data collection in medical research. The app is still under scrutiny and tested on all grounds by researchers. When the framework is released there will be an instant switch by the medical community in acquiring data, however the major question is how well will the app be adopted by the common audience. This is going to help a lot of iOS app developers to explore opportunities in health & fitness sector.