Share with your friends


Analytics Magazine

Healthcare Analytics: Algorithm is the new doctor and data is the new drug

March/April 2014

Rajib GhoshBy Rajib Ghosh

Two years ago, Vinod Khosla, the luminary venture capitalist and the co-founder of Sun Microsystems, shook the technology and the medical communities with his highly talked about article, “Do We Need Doctors Or Algorithms?” In the article Khosla argued that given the level of service that we seek and eventually receive from 80 percent of physicians, we might be better off receiving that care from a computer with sophisticated algorithms. Khosla fondly named that system “Dr. Algorithm” or “Dr. A,” for short.

Later in his follow-up talks, including the recently concluded Rock Health CEO Summit in San Francisco, he ignited the debate further by saying that 80 percent of physicians in the United States can be replaced with machines, and that day is not very far away. The medical community responded with the argument that healthcare is not about technology – it is about the intersection of technology, science and human emotions, along with the therapeutic touches and listening abilities of a doctor. David Liu, M.D., did a balanced rebuttal in The Healthcare Blog.

As healthcare analytics continues to evolve in 2014, let’s pause for a few moments and think about the debate at hand. There are some big ideas embedded in it that we as data scientists and big data technologists need to consider seriously. If Khosla is right in his prediction that clinical data analytics will usher in a new era in U.S. healthcare – a sea change that will transform healthcare like never before – what Khosla is actually predicting is that healthcare in the future will essentially become a data game! Data is the new drug!

In theory, the more data available, the more precise the diagnosis, and the efficacy of the treatment will also improve, which, in Khosla’s words, is far less complex than the problem of autonomous driving. With the deluge of data coming from multiple sources, such as wearable and ambient sensors, gene sequencing and digitized encounters, diagnosing a problem in the human body will become a matter of pattern recognition. There could be billions of possibilities, but searching a large set of possibilities with sophisticated algorithms, image processing, machine learning and artificial intelligence is what machines do well. Machines are doing it now and in real time!

Humans create algorithms tapping into their own “knowledge” of today. Machines take that knowledge and develop new knowledge based on the emerging patterns in the data. That’s the whole premise of IBM’s (Dr.) Watson, which is using cancer knowledge created by oncologists plus related data to fine-tune cancer treatments for patients [1]. So why do we need doctors to tell us what ails us when machines are capable of doing this?

Can Machines Really Replace Physicians?

Physician with patient
Will data, analytics and computers replace physicians? Probably not, but they can help improve healthcare by augmenting human capabilities.

The 2010 National Ambulatory Care Survey reveals that out of 1 billion physician office visits, the average number of visits per person is approximately four per year. The most common reason for the visit is for a cough, and the most commonly diagnosed condition is “essential hypertension” [2]. Algorithms are available today for diagnosing hypertension. But legal liability and regulatory hurdles play a big role in preventing software developers from declaring a confirmed diagnosis.

Decision support software, therefore, seeks confirmation from the clinicians. Machines also don’t have access to the huge data in healthcare that is needed to generate the desired precision in diagnosis. Genomic data is sporadic, and the majority of the clinical encounter data is still not digitized. Further complicating matters, when electronic data is available, the absence of data liquidity and interoperability within and among healthcare organizations makes it harder to get a holistic view of any patient. IBM’s Watson, therefore, is not only just an “advisor” it is an incredibly expensive “advisor” that takes too long (18 to 24 months) to understand how care pathways work [3].

The key here is that physicians have to let the machines learn from their decisions or mistakes, and as IBM is finding out, that is non-trivial. How do you scale when every project is custom built, takes a long time to complete and yet you are at the mercy of the physicians who fear that they are training their replacement? Moreover, even when personal medical data is available patients are concerned that seamless data flow among healthcare stakeholders will destroy their privacy and make them more vulnerable to insurance payers and employers. Not an easy problem – is it?

Developing algorithms and technology for the purpose of replacing physicians is the wrong premise to begin with. Having said that, I have to admit that the future of medicine will no doubt embrace a larger role of data and analytics. The barriers that face Dr. Watson today will eventually come down. Business models will emerge. Privacy will be addressed through legislation. Treatments will be personalized in real time. But human beings are social animals – we want to hear from other humans that no matter what the current situation is, we will be OK! A sick patient wants to go back home with assurance from a human minus the “confidence levels of 90 percent.”

Armed with the data and algorithms, doctors of the future will be able to triage patients far more effectively and preemptively, spend more time with those that they need to see, and be the listener, healer and collaborator that a patient expects. This is how Dr. A will help to augment, not replace, the human capabilities to take care of an increasingly aging population that will continue to live longer.

Rajib Ghosh ( is an independent consultant and business advisor with 20 years of technology experience in various industry verticals where he had senior level management roles in software engineering, program management, product management and business and strategy development. Ghosh spent a decade in the U.S. healthcare industry as part of a global ecosystem of medical device manufacturers, medical software companies and telehealth and telemedicine solution providers. He’s held senior positions at Hill-Rom, Solta Medical and Bosch Healthcare. His recent work interest includes public health and the field of IT-enabled sustainable healthcare delivery in the United States as well as emerging nations. Follow Ghosh on twitter @ghosh_r.


1. Laura Nathan-Garner, “The future of cancer treatment and research: What IBM Watson means for our patients,”
2., “Ambulatory Care Use and Physician Visits,”
3. Spencer E. Ante, “IBM Struggles to Turn Watson Computer Into Big Business,”

business analytics news and articles



Fighting terrorists online: Identifying extremists before they post content

New research has found a way to identify extremists, such as those associated with the terrorist group ISIS, by monitoring their social media accounts, and can identify them even before they post threatening content. The research, “Finding Extremists in Online Social Networks,” which was recently published in the INFORMS journal Operations Research, was conducted by Tauhid Zaman of the MIT, Lt. Col. Christopher E. Marks of the U.S. Army and Jytte Klausen of Brandeis University. Read more →

Syrian conflict yields model for attrition dynamics in multilateral war

Based on their study of the Syrian Civil War that’s been raging since 2011, three researchers created a predictive model for multilateral war called the Lanchester multiduel. Unless there is a player so strong it can guarantee a win regardless of what others do, the likely outcome of multilateral war is a gradual stalemate that culminates in the mutual annihilation of all players, according to the model. Read more →

SAS, Samford University team up to generate sports analytics talent

Sports teams try to squeeze out every last bit of talent to gain a competitive advantage on the field. That’s also true in college athletic departments and professional team offices, where entire departments devoted to analyzing data hunt for sports analytics experts that can give them an edge in a game, in the stands and beyond. To create this talent, analytics company SAS will collaborate with the Samford University Center for Sports Analytics to support teaching, learning and research in all areas where analytics affects sports, including fan engagement, sponsorship, player tracking, sports medicine, sports media and operations. Read more →



INFORMS Annual Meeting
Nov. 4-7, 2018, Phoenix

Winter Simulation Conference
Dec. 9-12, 2018, Gothenburg, Sweden


Making Data Science Pay
Oct. 29 -30, 12 p.m.-5 p.m.

Applied AI & Machine Learning | Comprehensive
Starts Oct. 29, 2018 (live online)

The Analytics Clinic
Citizen Data Scientists | Why Not DIY AI?
Nov. 8, 2018, 11 a.m. – 12:30 p.m.

Advancing the Analytics-Driven Organization
Jan. 28–31, 2019, 1 p.m.– 5 p.m. (live online)


CAP® Exam computer-based testing sites are available in 700 locations worldwide. Take the exam close to home and on your schedule:

For more information, go to