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Your Healthcare Data Has Immense Value, and it can Earn You Money!

In the era of lawsuits over copyright infringements of intellectual property, one related question is – can there be a copyright over one’s health data? In other words, who has the ownership over the data related to one’s health? Is it the doctor who generates such data? Is it the hospital or clinic where this data is generated? Is it the laboratory which performs various tests on the different samples procured from the patient and generates reports?

The correct answer may surprise you. The sole owner of all health-related data generated from a patient is the patient himself/herself. And the patient also has the right to use this health data to whatever purpose he/she deems fit.

Now that the ownership of the health-related data is clarified, the next question is – does such data carry any value? Data pertaining to my health is definitely important to me, but why would it be of any interest to an unrelated third party? What value does it generate in the real world?

The fact is that all health-related data carries enormous value, more than you had ever imagined. To understand the reason why health-related data has such immense value, we need to understand what has influenced decision-making by doctors in disease situations with development in medical field.

In the beginning days of modern medicine, doctors took treatment decisions based on either their own past experiences, or those of their peers and seniors. With time, it was realized that this approach was inadequate, and gradually doctors started asking for evidence proving that a particular intervention worked on a large number of individuals, rather than relying on anecdotes or experiences.

For generating such an evidence that a medicine worked, pharmaceutical companies were asked to test the medicine on consenting and willing patients. These patients would be recruited into the experiments using strict inclusion and exclusion criteria, and would be randomly divided into two groups. One group of patients would receive the standard medicine used for treating a disease, and the other group would receive the new drug. At the end of a fixed amount of time, doctors would measure if there was any change in the response of the health of patients in both the groups. Only after such a clinical trial demonstrated that a new medicine works against a disease, would the authorities grant marketing permission.

Even though data generated by means of such an experiment is very useful, this approach has a major disadvantage. Generally, the recruitment criteria for clinical trials are strict, and patients with serious disease, or comorbidities, or patients at the extremes of ages, are excluded from participating in such clinical trials. For example, a clinical trial about a new medicine to treat hypertension may exclude patients with severe diabetes and elderly patients as a part of its protocol. However, when the same medicine is marketed, obviously the treating doctor cannot exclude diabetic patients or elderly patients, because a large proportion of hypertensive patients have diabetes and are elderly.

The realisation that the portfolio of patients in the experimental setting might not accurately resemble the portfolio of patients in the real-world lead to the birth of a new concept called Real World Evidence (RWE). In RWE, data is taken from real life where patients are given medicines as per their need and indications, and data is collected from them and studied. Many authorities nowadays routinely ask for RWE data in addition to experimental data as a part of the regulatory cycle.

However, it is not easy to collect RWE data. While there are structured mechanisms and dedicated personnel to collect experimental data, the RWE data is not so lucky. As an example, try to answer this simple question: can you produce the results of the laboratory tests that you undertook a year ago as a part of routine investigations? Or, can you enumerate the details of treatment given by your physician six months ago when you consulted him/her for a fever? You might have preserved all those details for a few weeks or so, before discarding it thinking that it was not necessary. What if you are told that some pharmaceutical companies immensely value all such healthcare-related data generated in real world?

Now you might be thinking, how good it would be if there was a mechanism or method to securely store all your health-related data and share it with interested parties and at the same time earn money from it. This is exactly what is meant by data monetization: letting your health-related data earn money. And as for the ‘mechanism’ part, the good news is that there is an app – KYT or Know Your Treatment – which does all these things, and more! The KYT is the first healthcare data monetization platform in India, brain child of Dr. Amit Dang (Pharmaceutical Physician), Ms. Dimple Arora (Data Scientist) and Dr. Pawan Rane (Head and Neck Oncosurgeon), who took two years to culminate this unique concept. The app further helps you to store all your health-related data, including your lab tests, prescriptions, doctor consultations, hospital visit details, and much more in a timeline manner, which is easy to access.

The KYT app is now available for free download from Play Store. For more details visit

About the Author

Dr. Amit Dang is the Founder & CEO of MarksMan Healthcare Communications, a data science company providing HEOR, Medical Affairs and Data Analytics services to pharmaceutical and medical device companies, based out of Hyderabad. He did his Doctor of Medicine in Pharmacology from Goa Medical College. He is the convener of Indian Medical Affairs Summit (INMAS) and Indian Patient Access Summit (iPAS). He is also the President of ISPOR Mumbai – India Chapter. He has recently founded a patient centric mobile app named KYT or Know Your Treatment, which is India’s first healthcare data monetisation and digital therapeutics platform, that incentivizes patients, who maintain their health records and take their prescribed treatment on time.