How Big Data is Revolutionizing the Healthcare Field

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By: 321

January 25, 2019

At 321 the Agency, healthcare marketing is one of the pillar services of our organization. We’re excited to share insight into healthcare marketing, data, and tech through our healthcare series. 
Big data’s role in revolutionizing the way businesses make strategic decisions isn’t exactly news. Everything from banking, to retail, and even construction processes have become increasingly more defined by big data. 
However, one of the industries that is just hitting the top of the big data iceberg is healthcare. The digitization of healthcare information has made a remarkable impact on the industry and is projected to continue transforming healthcare as researchers learn how to harness its value. 
By leveraging big data, healthcare providers can improve patient outcomes, reduce costs, and even broaden their patient base.  

BIG DATA AND PREDICTIVE HEALTH ANALYTICS

 
Electronic health records, or EHRs, are what make it possible for physicians and hospital systems to collect data from thousands of patients every year. This data is then stored in databases and synthesized using algorithms from which analysts have the ability to extract and study valuable information. 
According to a study done by Health IT on the rise of EHR use amongst hospitals, the adoption of a Basic EHR system increased nine-fold from 2008 to 2015.
Hospital Adoption of Basic or Comprehensive EHR Systems from 2008 to 2015.

SOURCE: dashboard.healthit.gov.
The wide adoption of EHR has allowed for big data to aid healthcare providers in cultivating a data-driven, evidence-based process in which their schooling, experience, and research isn’t the only thing being taken into account when predicting the best course of action to treat a patient — past treatment data patterns are being considered as well.
EHR has streamlined the handoff of patient care to other medical providers, significantly reducing avoidable medical errors.

SOURCE: https://www.nejm.org/doi/full/10.1056/NEJMsa1405556
Evidence-based care lowers trial-and-error methods and replaces them with comprehensive treatments that take into account what has worked on other patients exhibiting the same symptom progression.
Big data has gone so far as to also have opened the door for researchers to discover new medications, alternative treatments, seemingly unrelated symptoms, and unknown side effects. 
Pathologists and postdoctoral scholars Avantika Lal, PhD, Daniele Ramazzoti, PhD, and Arend Sidow, PhD, proved how valuable this method can be when they devised a computer algorithm named Cancer Integration via Multikernel Learning, or CIMLR, that combines genetic data about specific cancers and divides them into groups by their common biological traits.
By using CIMLR, Lal, Ramazzoti, and Sidow were able to identify the genomic subtypes for 36 types of cancer, allowing them to better predict the chances of survival or relapse that a patient with one of the types of cancer has. 
Health analytics allow physicians to bridge gaps in performance in a timely and cost-effective matter, allowing providers to diagnose and treat illness with a higher success rate than ever before, which isn’t just good for the patient’s health — it’s good for their wallet too. 
A study done by McKinsey found that providers are making the change from paying physicians on a fee-for-service basis, where a physician’s compensation directly correlates to how many patients they see, to a system where their treatments’ success dictates their pay. 
By incentivizing physicians to focus on improving the success of their treatments, physicians are more likely to exchange patient data, patient outcomes are enhanced, and patients spend less money on healthcare treatments. 
 

THE DATA INTEGRATION CHALLENGE

 
If sharing data is the key to providing better care and lowering healthcare costs what has been stopping physicians from collectively reaping the benefits of big data all this time?
For starters, most patient data is scattered throughout a wide array of databases, and because patient health data is so sensitive, the success of securely sharing data between databases is highly reliant on cybersecurity. 
However, because of their proven applications in the medical world, big data and healthcare analytics will hopefully inspire a higher prioritization in cybersecurity investment throughout the field in the years to come.

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