7 Big Data Steps in Health Science


Every year we go to our doctor to get checked out. And being really busy, our doctor has only minutes to complete an examination and do a preemptive strike on any health ailments that we personally may be facing in the next few years. But did you know… to not miss anything our doctor is getting help from Big Data? With the rising costs from R&D and us getting longer life-spans, Big Data is becoming more entrenched and more crucial to reducing the investment needed to keep us healthy. But, how does Big Data actually do this?

From a bird’s eye view Big Data encompasses seven steps. Understanding these steps will help us better leverage Big Data. Not only that, we’ll feel more comfortable sharing our health information and be better prepared to place stricter regulations on which of our health data should be collected and when. Let’s take a look at these seven steps in detail.

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  1. Sources.The sources for health data can be our bodies, where we are, what we’re doing, and what we eat. This includes if we’re sitting or standing, exercising, our Google searches, our pulse rate, blood pressure, cholesterol, and blood sugar.
  2. Collection.With the use of sensors, implants, and our mobile devices, our health data is collected once a second, once an hour, or even just once a day. Our data isn’t collected all in one shot. We’re being monitored. Our data is continuously being collected and recollected at the right volume. The right volume depends on the individual, the ailment we have, and our physical behaviors and culture.
  3. Model, Algorithms, Artificial Intelligence.Our health data is fed to fit the model, a representation of how health experts understand health. The model is supported by algorithms and artificial intelligence (AI) that uses automation to process, predict, and forecast our health. The model may contain health scenarios such as a viral outbreaks, the spread of cancer, or reactions to a drug or surgical treatment. The model was constructed and refined by medical experts, behavior experts, statisticians, and data scientists… all analyzing past health data on events and people just like us who had similar, if not the same, diagnosis. After algorithms find where we fit in the model, algorithms share our forecast data with medical experts.
  4. Options.From our forecast data the medical experts determine the options available. If there is an advanced AI, the AI may order or rank options according to an estimated effectiveness. If there is an even more advanced AI involved, the AI may recommend the medical experts to select specific options, decisions, and methods of treatment.
  5. Decision.From the options determined the medical experts select the most appropriate option for our specific case. To make a good decision the experts need to accurately interpret the results from the model, algorithms, and AI. This means the medical experts need to understand Big Data, how Big Data works, and Big Data’s short comings.
  6. Methods.After completing a formal approval to meet ethics and regulatory compliance, to keep us healthy the needed approaches, treatments, and regiments are carried out. If there is an advanced AI involved, the majority of the manual efforts, such as orders, scheduling, and communiqués may be automated.
  7. Measurements.During and after our treatment, measurements are collected to anticipate how well we’ll respond to our treatment in the short and long term and forecast any negative scenarios we may encounter. Our new health data from our actual health and from these predictions, forecasts, and measurements are then feed back into the model to add to the model’s accuracy for forecasting future patients, future events, and our future treatments.[/message][su_spacer]

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From mapping out what our bodies look like when we’re well, to how our bodies degrade when we’re sick, to how disease spreads in our bodies, to how diseases spread on a global scale. But Big Data is just another tool. And like any tool, we must rely heavily on the knowledge and training of our tool user, our health practitioner, to keep us all healthy.

Health Science must be trained in Data Science.

Resources that inspired this article…


Chris Pehura
Chris Pehura
DATA-centric Executive Management. Chris is a management consultant with a data emphasis helping Fortune 100/1000 companies strategically evolve and reinvent their businesses to maximize their revenue growth. Through realignment, to overhauls, to rebuilding things from the top down and ground up, he integrates and solidifies leaders, strategies, and solutions into all aspects of the organization. Strategies and solutions leverage Big Data, MDM, business intelligence, competitive intelligence, data science, chief data officers, and data offices. Chris is a coach, trainer, and the voice for how data is the new capital that drives, multiplies, and maximizes revenue growth.

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  1. To add. The work environments in Canada and the USA are designed for doctors to specialize and push through as many patients as possible. It’s becoming harder to give quality care in a timely manner. Big Data can help shape the org structure for health care providers and guide behaviors of doctors to research more. It’s worrisome that patients are better researchers than their own doctors. Doctors should acknowledge this and take advantage of it.

  2. I’m an advocate of keeping good records so there is information available to base diagnosis and prognosis of health issues. What I hope doesn’t happen is that doctors gravitate more toward collective big data than on the individual records of an individual. I like how you ended your article. “Big Data is just another tool. And like any tool, we must rely heavily on the knowledge and training of our tool user, our health practitioner, to keep us all healthy.” I think it’s going to be a discerning medical practitioner that sees that data is a tool, but is not a substitute for personal, in-depth examination and dialog with the patient.

    • I’ve already seen people process out the typical things that make a doctor a good doctor. This is primarily done to reduce costs (without considering quality). Many are supporting their poor processes with mentoring… but poltical friction can easily put a stop to that. To make a doctor great technology, solutions, and people need to support the principles and spirit of the process for cost effective quality care.