Nearly 1.3 million new peer reviewed papers were published in scientific medical journals and cited in PubMed in 2018 - that is nearly 3,500 new papers every day! More than $100 billion were spent on medical research in the same year. The ICD-9 coding system had ~3,800 procedure codes which expanded to ~71,900 procedure codes when ICD-10PCS was implemented in 2015. All of the above indicate a dramatic and exponential increase in the nature and quantum of knowledge that resides in the healthcare domain.
For the last several decades, billions of dollars are being spent worldwide to digitize healthcare transactions in the form of Health Information Systems (HIS), Electronic Medical Records (EMRs) and various Coding systems that have been developed to facilitate these transactions. These efforts have generated terabytes of transactions data.
However, Doctors, Patients, Payors and Regulators are all struggling because there is now an ocean of data and very few intelligent insights that emerge from this data. Ideally a Doctor should be able to review insights from thousands of similar patients to the individual patient she is treating now and use those insights to benefit her patient in real time. Ideally, a patient should be able to see hard data about her surgeon's clinical experience and medical outcomes for the procedure he is about to perform on her. Ideally, a payor should have hard evidence to support findings of over-utilization or under-utilization of health care resources for specific risk-adjusted diagnoses.
This ideal, of using intelligent insights derived from very large data sets, to benefit the decision making of the doctor, the patient and all other stakeholders in the healthcare delivery system, is what BuddhiMed is striving to achieve.
1. "The complexity of medicine now exceeds the capacity of the human mind. Computers are the solution, but using them to manage this complexity will require fundamental changes in the way we think about thinking, and in the structure of medical education and research": Lost in Thought - the limits of the human mind and the future of medicine; N Engl J Med 2017; 377:1209-121
2. "Gartner defines dark data as “the information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes": Extracting Dark Data