Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
How Generative AI Helps Predictive AI
 Originally published in Forbes, August 21, 2024 This is the...
4 Ways Machine Learning Can Perpetuate Injustice and What to Do About It
 Originally published in Built In, July 12, 2024 When ML...
The Great AI Myth: These 3 Misconceptions Fuel It
 Originally published in Forbes, July 29, 2024 The hottest thing...
Where FICO Gets Its Data for Screening Two-Thirds of All Card Transactions
 Originally published in The European Business Review, March 21,...
SHARE THIS:

6 years ago
The Future of EHRs, Big Data, and Patient Privacy

 

Originally published in insideBIGDATA, October 26, 2018

For today’s leading deep learning methods and technology, attend the conference and training workshops at Predictive Analytics World for Healthcare, June 16-19, 2019 in Las Vegas.

Moving patient data online has been a great boon for the practice of medicine. Patient records, formerly pieces of paper in a folder, are transitioning to electronic health records, or EHRs. While this has done wonders for transferring records between offices and aiding in connecting technology like wearables and providing big data for machine learning, the quantity also raises questions of patient privacy and data security.

Volume of Data

The start of this story is in the volume of data. Even back in 2011, Duquesne University estimated there to be 150 exabytes of healthcare data collected. In 2015, they noted, 83 percent of doctors had adopted electronic records. This, on the surface, sounds fantastic. All that data, which has only increased since then, can be used for predictive analytics.

Big Data

Thanks to the amount of data collected, predictive analytics is possible. This helps healthcare professionals make smarter decisions regarding patient care. A computer is able to catch symptoms that are comorbid of other problems that a specialized doctor would miss.

An added bonus to being able to make better decisions thanks to big data is that it has also created new roles such as the nurse informaticist. These nurses care for patients and affect healthcare policy. The key, however, is they use big data to drive these decisions. For example, they might be able to predict staffing needs based on historical data, so that they have enough nurses to manage patients. Before the adoption of EHRs and machine learning, the technology that makes this job possible, and thus those decisions, simply did not exist.

Privacy and Security

On the flip side, the massive amount of data means security needs to be kept to a high standard, or data breaches could reveal patient data. In the first quarter of 2018 alone, nearly 1.13 million records were exposed due to a data breach. Then, in July, more than 2 million more records were exposed. Over the past 8 years, more than 176 million records were breached.

Worse, in 2016, the WannaCry ransomware locked out hospitals from EHRs, effectively stopping all patient care due to lack of access to patient charts. The hospitals were forced to pay a ransom or let their patients suffer from lack of treatment.

Continue reading this article here.

About the Author

Avery Phillips is a freelance human based out of the beautiful Treasure Valley. She loves all things in nature, especially humans. Leave a comment down below or tweet her @a_taylorian with any questions or comments.

 

4 thoughts on “The Future of EHRs, Big Data, and Patient Privacy

  1. Pingback: Ciencia de datos en la industria del cuidado de la salud

Leave a Reply