Recently there has been a lot of discussion about “Big Data” in the media. Information is collected every time a person accesses the health care system. Some of this data is being used to determine rates for everything from insurance premiums to determining whether a patient would be a good candidate for a certain kind of medical treatment. Discuss the dilemma of medical data privacy v potential benefits of mining large clinical research repositories. Provide examples to support your position.
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PLEASE REPLY TO THIS STUDENT : Big data analytics in healthcare, while potentially overwhelming from the perspective of data volume, diversity and speed of change, could, if synthesized appropriately could allow for significant healthcare insights, innovations and improvements in outcomes for patients. Contrasting with the benefits of mining healthcare databases are ethical concerns with data security and privacy of healthcare information.
While drilling into each benefit of big data analytics in healthcare is beyond the scope of this post, a high-level summary of benefits is cited in Table 1. An example that exemplifies how big data is being used in today’s healthcare sector is the alliance between Fitbit and United Healthcare, an HMO in the United States which discounts healthcare insurance for clients who can demonstrate active lifestyles through collection of data via Fitbit (“NEJM”, 2019). In another example, the Mayo clinic is using big data to identify patients who could benefit from earlier intervention though mining medical data to find patients with multiple medical conditions (“NEJM”, 2019) In the Mayo clinic example, the net effect is ensuring better care and preventing admissions thus saving money.
Table 1: Benefits of Big Data Analytics in Healthcare
Benefits
Benefits
Predictive Analytics for preventative medicine
Supporting Clinical Decision Making
Earlier Detection of Disease
Monitoring Adverse Events
Precision Medicine (tailoring medical management)
Medical Research
Informing Management and Policy
Adverse Event Monitoring
Population Health (epidemiological models)
Automated analysis of diagnostic information
Pivoting to the issue of privacy of information within the context of big data, significant hurdles are only now becoming more broadly appreciated. Privacy is a big category and there are differing opinions depending on perspective (individual, institution, company and government), and then comes the ethical debate as to good of society versus good of the individual.
I think the key issue is that the field of big data in health is evolving so quickly that privacy legislation often is reactive, and this somehow needs to change. This is eluded to in a paper by Ienca and colleagues (2019) where they speak to the issue of big data studies and challenges to ethics review committee’s (ERC). In the paper they cite issues with collection with data from sources like Twitter and Facebook which for clinical trials is not possible under tradition guidelines such as informed consent, minimal risk and fair subject selection (Ienca et al., 2018). This is exemplified by Google’s “Project Nightingale” whereby it has been reported that personal health information is being shared between an HMO (Ascension) without adequate informed consent with the goal of mining the data to provide Ascension’s customers with, “ individualized treatment plans, tests and procedures”(Barber, 2019).
In the end, we are in a period where information availability mined ethically and appropriately will lead to an evolution in personalized care and health insights, but along with this rapid path is a true need to question and debate what privacy and security measures are necessary to guard patients.
Q2
Why is it important to incorporate a theory or model related to change when implementing practice changes? Does the benefit of incorporating a change model outweigh the time and effort it took to include it?
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