by Dr. Suman Katragadda
The implementation of the PM Ayushman Bharat Health Infrastructure Mission by the National Health Authority (NHA) is set to address deficiencies in the sector, by expanding pandemic research centers and improving medical facilities for the treatment and diagnosis of disease. The growing trend towards digitization in health carecoupled with increased government funding and better access to healthcare data, it is a clear indication of the transformative change taking place in the industry.
To fully harness the potential of this transformation, healthcare delivery in India must be based on the convergence of information technology and healthcare. The COVID-19 pandemic has presented a host of challenges, but it has also forced the healthcare sector to adapt and innovate to control the disease and bridge the gap between technology and traditional methods of healthcare delivery, improving in ultimately patient safety and care. According nasscom, by 2025, data and artificial intelligence are projected to increase India’s GDP by US$25-30 billion. However, to fully improve patient safety in Indian healthcare, there are a number of hurdles that AI must overcome. As the healthcare industry becomes more data-driven and reliant on technology, it is vital that the industry address the challenges listed below:
Digitization of data: The digitization of data is a fundamental aspect in health management. To effectively manage patient information, a comprehensive software setup is needed to store and organize data belonging to various categories, such as types of diseases, prescriptions, treatments, and other relevant details related to the health of patients. Of particular importance is the digitization of medical prescriptions, as the interpretation of handwriting can often pose significant challenges.
Structure unstructured data: In healthcare settings, such as hospitals and laboratories, unstructured data is commonly found. This type of data often includes demographic information, such as age and gender, which is recorded in narrative rather than structured form, number format. Traditional statistical methods, which are based on the assumption of structured data, are not suitable for the analysis of unstructured data. To overcome this limitation, Natural Language Processing (NLP) techniques are employed to convert unstructured data into structured formats, enabling more efficient search, analysis, and interpretation of large patient data sets. This approach is being used more and more in the healthcare industry globally, as it allows for a more complete and accurate analysis of patient information.
Unconnected Nature of Healthcare Data: The fragmented nature of today’s healthcare system poses a significant challenge in terms of data management. Health care providers, payers, and patients often operate independently, resulting in a lack of standardization in data collection methods. This can be seen in the case of medical records, which are complete while a patient is in the hospital, but lack a standardized method for monitoring the patient’s health after discharge. Ideally, all of a patient’s medical records, medication information, scan results, and other relevant data should be easily accessible and centralized, allowing for seamless continuity of care.
However, due to shortcomings in the current system, medical records and registries remain offline, decentralized, and dispersed, with patients visiting multiple diagnostic laboratories, imaging centers, and medical specialists. Although there are a large number of healthcare related applications available in the market, most of them are focused on promoting more usage of services, rather than connecting fragmented data and building an integrated digital system for data management. medical attention. To address this problem, holistic or comprehensive care management pathways are required to connect all these fragmented data and build a fully integrated digital system for healthcare data management.
Limited access to specialized care: India’s vast rural and remote population often lacks access to specialized medical treatment, due to geographical barriers. Artificial intelligence (AI)-powered healthcare solutions have the potential to close this gap and improve access to specialty care for this population. However, the implementation of these solutions can be hampered by a number of factors, including limited internet access and power shortages. These issues must be addressed to fully harness the potential of AI-powered healthcare solutions to increase access to specialty care for underserved populations.
In conclusion, artificial intelligence (AI) and machine learning have the potential to revolutionize the healthcare management ecosystem in India. It is imperative that steps are taken to lay the foundation for the effective use of AI and machine learning in healthcare by addressing gaps in data digitization, in order to fully realize the potential of these technologies. and improve patient safety and care. By ensuring the digitization of data and the structure of unstructured data, we can pave the way for the implementation of AI and machine learning in healthcare. These technologies have the potential to provide healthcare professionals with a deeper understanding of the needs of patients, enabling them to offer more personalized feedback, guidance and support to maintain good health. It is crucial that we take the necessary steps to lay the foundation for the effective use of AI and machine learning in healthcare to improve safety and patient care.
Dr. Suman Katragadda, CEO and Founder, Heaps Health
(DISCLAIMER: The views expressed are solely those of the author and are not necessarily endorsed by ETHealthworld. ETHealthworld.com shall not be liable for any damage caused to any person or organization directly or indirectly.)
