Consumer related data is used by many healthcare leaders to predict health outcomes, risks, and healthcare utilization. The ethical norms to use consumer-related data for analytics purposes should be maintained otherwise it will interfere in the privacy and autonomy of the patient, create trust issues in the patient-doctor relationship, and can cause individual issues.
The data used is related to the lifestyle of the consumers, which includes an individual’s purchase transactions, social media presence, internet searches, etc. But this is news to the consumers that the companies which used data for marketing and business purposes are now combining this data with clinical data of the same consumers to make inferences about their health.
The prediction is gone to such level that what consumers buy or want, or their posts on twitter or how many hours they slept as per the wearable’s are deciding their health issues. Dr. Eldesia Granger, a group leader, clinical quality and informatics, at The MITRE Corporation said they recognize that consumer-related data can be used in my productive ways which benefit consumer health, smoothens personalized experience.
Granger further added that consumer-related data use aggravates bias towards health outcomes if structural factors are neglected. For eg. The public institutions and their policies are influencing the consumer-generated data.
The emphasis on this topic that the healthcare industry can be worked on consumer data is a user-driven idea. And, the crucial part here lies in analyzing the data carefully so as the proper user/consumer is given the much-needed care. There are possibilities of users getting marginalized and their information is inaccurate which causes harm to them. So, it is the most sensitive part on the part of analysts to gain proper data. The user should not feel ditched. So to impose trust within them, the consumer should know who is using their data for making out inferences on their health.
MITRE framework create awareness among organization about government policies and regulations. Also, the increase of machine learning use in the healthcare industry pumps up the idea to do it ethically. It acts as a safe buffer between the organization using the data and users.