With the dramatic increase in the application of AI over gargantuan heaps of healthcare data, stringent measures for data governance have become even more indispensable in the interest of patient safety.
Data is everywhere and is being produced all the time. Most of the routine tasks we perform, from making a simple phone call to doing the grocery, results in some form of data. The $3.7 trillion healthcare industry of the United States is no different. From appointment scheduling to meet the practitioner, from generating lab reports to prescribing medicines, everything creates data. And considering that, just within the US, over 20 million patients visit their practitioners every day, the data generated by these meetings is enormous. Moreover, considering that this data holds patients’ confidential details, makes the safeguarding of this data imperative. However, if all the necessary protocols of data safety are honored, the opportunities for bringing innovation in healthcare by the application of AI are limitless.
Artificial intelligence refers to the inculcation of ‘intelligence’ into machines, making them ‘smart’. This is done by feeding the computers with a certain set of logics, which allows them to learn (i.e., machine learning) and then apply their learned knowledge in the given practical scenario. By AI, a machine scans large volumes of data, and in doing so, the machine tries to figure out specific correlations and patterns within that data. As humans, what we exhibit is termed as natural intelligence and it is far more superior than artificial intelligence. However, even in its nascent stage, artificial intelligence has shown us its glimpses of brilliance and has given us the impression that it holds immense potential for the welfare of mankind.
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It was only a matter of time when AI had to make its way into healthcare – and it proved to be an instant success. With the advent of automation and AI, data management has become pretty simplified and systematic. The demands of the current healthcare system of the US demand that several people should have access to a patient’s data: provider, front desk staff, pharmacist, lab technicians, and third-party data collectors. As they say, a chain is only as strong as its weakest link; therefore, any lapse in data governance from any component of this chain can have dire consequences for the entire healthcare sector.
The purpose of data governance is to monitor the data lifecycle – how the data is entered, processed, transferred and stored. It looks after data security, data loss protection, data integrity, data integration, and data completeness.
Data governance becomes extremely important when artificial intelligence comes into play. Artificial intelligence is meant to manipulate data in multiple ways and hence, develop complex neural networks. These neural networks are what the system actually ‘learns’. These neural networks can be a little tricky to handle, since AI is still pretty much in its nascent stage, and makes it likelier for hackers to exploit these weak links to obtain large amounts of sensitive information. To make data governance in healthcare a standardized process, there are certain rules and regulations in place.
One of the most important ones is the HIPPA Privacy Rule (Health Insurance Portability and Accountability Act), which was signed in 1996 by Bill Clinton, the 42nd President of the United States. The purpose of enacting HIPPA was to issue a set of necessary provisions regarding patient privacy and protecting healthcare data. Although HIPPA was implemented way before the advent of modern-day AI, it did help in bringing some uniformity in managing the healthcare information. In 2009 the HITECH Act (The Health Information Technology for Economic and Clinical Health) was enforced, which was supposed to strengthen the provisions previously given by the HIPPA Privacy Rule. Outside of the US, European Union has strong privacy laws, known as GDPR (General Data Protection Regulation), which aims to inform users as to how is their data stored and manipulated and what are their rights over their data. All these measures attempt to restore faith in the public that their privacy will not be compromised at any cost.
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According to Experian Information Solutions, 95% of the C-level executives believe that data is an integral part of their business strategies and 89% of the C-level executives believe that inaccurate data is undermining their ability to offer prime customer experience. Hence, all kinds of industries feel the need of using some form of AI to help them with their analytics. Integrating AI with business practices can boost the productivity of any firm by several folds and opens up new avenues of R&D in that particular industry. Healthcare experts have realized this and are working day and night to integrate AI to medical practices, in order to improve the efficiency of medical treatments and reducing the net cost of the healthcare system. However, with AI, data governance becomes extremely important. This is because, if AI ‘goes wild’ or comes under the influence of hackers, it can trigger a chain reaction that would create havoc and may collapse the entire healthcare system. Therefore, within the domain of healthcare, data governance in AI is imperative, in the best interests of everyone.
Author Bio:
This article is written by Saad Mubashar. He is a Healthcare Analyst and content writer in an EHR company, which helps physicians in switching EHR vendors according to their needs. In his free time, Saad likes to read different historical novels, swimming, traveling and cooking.
