The ever-blooming Indian medical industry is not far behind in incorporating digital technology for its operations. According to a 15-country Future Health Index (FHI) report published in 2019 by Royal Philips – a global leader in health technology, India is steadily gaining momentum in utilizing digital health technology at its best. Around 76% healthcare professionals across the country, are using Digital Health Records (DHR) in their practice. 

Let’s see how technological advancements like AI and Machine Learning are helping revolutionize health records, along with a few pitfalls associated with them. 

Impacts of healthcare digitization 

The constantly growing medical knowledge-base and digitization of healthcare system have revolutionized the way knowledge is utilized and implemented in making clinical decisions. 

As of now, clinical research data forms the basis of evidence-based medicine, still it takes around 17 years for only 14% of latest scientific discoveries to see the light of the day. If it remains rooted to the digital library, knowledge would never actualize into clinical care. 

Therefore, as the digitization of health records produce more amount of data pertaining to patient health status and healthcare delivery, both AI and Machine Learning can convert “real world data” into “real world evidence” thereby enhancing collective knowledge of the existing medical community. 

Although the healthcare professionals’ knowledge repository is considered as the backbone for improving practice, it can also be quite challenging – especially when it comes to making life and death decisions of a patient lying unconsciously in an operation theatre.

Real-time digital healthcare 

In a world of limited time and resources, the implementation of real-world evidence and Clinical Decision Support (CDS) tools not only reduces costs but also keeps inefficiencies at bay – right from the decision to admit to decision for treatment. 

Any medical expert would be delighted to work with CDS as it also provides the use of patient-specific healthcare data for optimizing care decisions, personalized care giving, and improving precision care delivery. By implementing machine learning and AI, we can have better prescriptive and predictive capabilities to deliver specialized care for patients. It can also reduce unintended variations and improve the data quality fed into the system. 

Every good attracts bad

Just like its brighter side, digital healthcare system also has some of its own downsides, or dangers, which need to be dealt with effectively. Let’s have a quick glance at them:

  • Worldwide people go for wearable technology like Apple Watches, Fitbit, etc.  but they are not well-aware of what exactly must be done with the tons of data being collected with these internet-enabled devices. It becomes important for the medical industry professionals to develop a set of “community standards” to handle the data collected from wearables, AI algorithms, and other technologies.
  • AI just acts as an assistant in diagnosis and healthcare operations, but it cannot replace the medical experts. In the long run, there are little to no chances of technology to take over knowledge and expertise of the doctors.

Although digital healthcare setup has some pitfalls, due to its existence we can have a democratized system of healthcare. Healthcare providers should commit to digital transformation in order to enable a knowledge-powered system that provides better outcomes to their patients.

 

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