Knowledge is power – with these lines, Angelina Jolie-Pitt concluded her article in The New York Times dated 24th March 2015. Angelina Jolie is considered as among the most attractive women in world cinema. In the last couple of years, she has undergone multiple surgeries to remove female organs in her body i.e. breasts and ovaries. Now the question is: why has Ms. Jolie taken such a drastic decision? And also, how with the advent of data science and healthcare analytics will decisions like these be part of everybody’s life?
To answer the first question, Ms. Jolie has a long family history of cancers. She has lost her mother, grandmother, and aunt to breast and ovarian cancers. Her genetic profiling through a simple blood test revealed that she has a mutation in the BRCA1 gene. All this information collectively puts her at a high risk of 87% chance for breast cancer and 50% chance of ovarian cancer.
To explain the mathematical meaning of 87% chances of breast cancer, if one creates 100 exact replicas (read clones) of Angelina Jolie then 87 of them will have breast cancer within their human lifetime. Now, for Ms. Jolie, she is not sure which of these 100 replicas she belongs to hence a prudent choice for her was to evaluate options to reduce her chances of acquiring cancer. The unfortunate part about current medical science, which we will discuss in some detail later in this article, is that she had very few options to reduce her chances of cancer. In the same effort, she had a double mastectomy, a breast removal surgery, two years ago. Additionally, a few weeks ago she had surgical removal of her ovaries and fallopian tubes.
You may want to read an earlier post on Healthcare analytics on YOU CANalytics.
Healthcare Analytics and Breast Cancer
I heard of Tamoxifen a little over 5 years ago when my close cousin’s wife was diagnosed with breast cancer. That was a difficult period for the whole family and the most difficult for the patient where she underwent a major surgery followed by chemotherapy and radiation therapy. To everybody’s relief, she has recovered and is doing well now. She is on a daily dose of Tamoxifen started immediately after her initial treatment 5 years ago.
Tamoxifen is an oral medication to prevent recurrence of cancer. Before I come back to Tamoxifen, let’s quickly look at some descriptive statistics on breast cancer. Every year close to 2 million people, mostly women, are newly diagnosed with breast cancer around the globe. The 5-year survival rate for a patient with stage 4 cancer is as low as 22%. This puts breast cancer as the primary cause of death among all the cancers for women. Healthcare analytics is a heart-numbing experience for its practitioners since the data is not about fraud or default but human death.
Personalized Medicine – Direction for Healthcare Analytics
Back to Tamoxifen which has close to 80% success rate to prevent recurrence of cancer. Let me suggest a way to read this statistic: of the 100 women on Tamoxifen, after being diagnosed with breast cancer, 80 will find it effective while remaining 20 will find it completely ineffective similar to popping a placebo (sugar cubes). The reason is that every human body is different and we all react to medicines differently. Something that works for me may not work for you at all since we have a different genetic make-up. This highlights the need of personalized medicine for effective treatment of diseases.
Personalized medicine is not a new concept. Much traditional alternative healthcare practices like Homoeopathy, Naturopathy, and Ayurvedic medicine work on the principles of personalized medicines. However, personalized medication is a time-consuming affair for both doctors and patients.
Healthcare analytics and big data offer a quick and viable solution to make medicines personalized. The rate at which data is generated and analytics are possible we are soon reaching a place in human history where medicine will be specific to an individual’s body type. Tamoxifen has close to 80% success rate, however, there are other medicines that have never made it to drug stores because they have much lower success rate say 10%. This means that these medicines can work wonders for only 10% of the population but will be ineffective for the remaining 90%. If we can identify the right body-type for these medicines which are not even in the market, we can effectively use them for that 10 % of patients and save many more human lives.
This is a great news for clinical research, pharma companies, and patients. If the companies identify the right body-type for each medicine and package these personalized medicines for a small 5-10% of the population then the research and expenses behind this medicine won’t go wasted. Additionally, a small portion of the patient population will get benefited by a specific personalized medicine.
Preventive Medicine – Direction for Healthcare Analytics
Healthcare analytics is offering viable and cost effective solutions to predict the likelihood of a person to acquire diseases like cancer, diabetes, Crohn’s disease (read an earlier article) etc. Let’s come back to Angelina Jolie and her statement: knowledge is power. Is knowledge really power? I think knowledge can be extremely agonizing if one doesn’t know how to act on that knowledge and prevent an unwanted event. Imagine the mental state of a person who knows she is likely to acquire a horrible disease in the next year but doesn’t know how to prevent it. Doomsday prophecy without an instrument to prevent it is a source of unwanted anxiety.
One of the biggest shortcomings of our current medical practices and healthcare system is that we work on post facto diagnosis or treating the symptoms. However, the new wave of healthcare analytics and data science will soon take us to the place where we will predict diseases (ex-ante). Now, current healthcare practices of extinguishing the fire won’t work as well in the new era of fire prediction and prevention. Proactive / preventive medicine, I believe, will be the new direction for pharma companies and healthcare professionals. Angelina Jolie had to resort to an extreme measure to reduce her chances of cancer however I wish there were less drastic and painful medications available for Ms. Jolie to achieve the same results.
Data is a manifestation of life – nothing makes this point more than analysis of healthcare data. There are always instances while analyzing patient data that some records cease to exist on a time scale. This kind of termination of information in any other scenario like banking or retail has little personal significance for analysts. However, for healthcare, it is hard for analysts not to take a moment and remember the life that was captured in this data. Apparently, nothing makes life more life than the absence of it (death).