Challenges AI is Facing for Advancing Patient Safety in Healthcare, Health News, ET HealthWorld
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 enhancing medical facilities for treatment and disease diagnosis. The growing trend towards digitalization in healthcare, coupled with increased government funding and improved access to healthcare data, is a clear indication of the transformative shift taking place in the industry.
To fully leverage the potential of this transformation, the delivery of healthcare in India must be based on the convergence of information technology and healthcare. The COVID-19 pandemic has presented a plethora of difficulties, but it has also forced the healthcare sector to adapt and innovate in order to manage the disease and bridge the gap between technology and traditional healthcare delivery methods, ultimately improving patient safety and care. According to NASSCOM, by 2025, data and AI are projected to boost India’s GDP by US$25 to US$30 billion. However, in order to fully improve patient safety in Indian healthcare, there are a number of obstacles that AI must overcome. With the healthcare industry becoming more data-driven and technology-dependent, it is vital that the industry addresses the below listed challenges:
Digitising the Data: The digitization of data is a fundamental aspect of healthcare management. In order to effectively manage patient information, a comprehensive software configuration is necessary to store and organize data pertaining to various categories, such as disease types, prescriptions, treatments, and other relevant details pertaining to patients’ health. Of particular importance is the digitization of doctor’s prescriptions, as the interpretation of handwriting can often pose significant challenges.
Structure the Unstructured Data: In healthcare settings, such as hospitals and laboratories, unstructured data is commonly encountered. This type of data often includes demographic information, such as age and gender, that is recorded in narrative form rather than in structured, numerical format. Traditional statistical methods, which are predicated on the assumption of structured data, are thus ill-suited 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 searching, analysis, and interpretation of large patient datasets. This approach is increasingly being utilized in the healthcare industry globally, as it allows for more comprehensive and accurate analysis of patient information.
Disconnected Nature of Healthcare Data: The fragmented nature of the current healthcare system poses a significant challenge in terms of data management. Healthcare providers, payers, and patients often operate independently of one another, resulting in a lack of standardization in data collection methods. This can be seen in the case of medical records, which are comprehensive while a patient is in the hospital, but lack a standardized method for monitoring patient health after discharge. Ideally, all of a patient’s medical records, information about their medications, scan results, and other relevant data should be easily accessible and centralized, allowing for seamless continuity of care.
However, due to the shortcomings of the current system, medical histories and records remain offline, decentralized, and dispersed, with patients visiting multiple diagnostic labs, imaging centers, and specialist doctors. Even though there are a plethora of healthcare-related applications available in the market, the majority of them focus on promoting increased use of services, rather than connecting fragmented data and constructing an integrated digital system for healthcare data management. To address this issue, holistic or comprehensive care management pathways are required to connect all of this fragmented data and construct a completely 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, a consequence of geographical barriers. Artificial Intelligence (AI)-powered healthcare solutions have the potential to bridge this gap and improve access to specialized care for this population. However, the implementation of these solutions may be hindered by a number of factors, including limited access to the internet and power shortages. These issues must be addressed in order to fully realize the potential of AI-driven healthcare solutions in increasing access to specialized 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 the necessary steps are taken to establish the foundation for the effective utilization of AI and machine learning in healthcare by addressing the deficiencies in data digitization, in order to fully tap into 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 patient needs, enabling them to offer more personalized feedback, direction, and support for maintaining good health. It is crucial that we take the necessary steps to set the groundwork for the effective use of AI and machine learning in healthcare, in order to improve patient safety and care.
Dr Suman Katragadda, CEO & Founder, Heaps Health
