AI/ML and Digital Health Infrastructure of India

There have been many technologically-driven advances in medicine and healthcare administration during the previous decade. AI and ML-driven innovations are at the forefront of these shifts. Advances in robotic surgery, heart sound analysis, patient safety, real-time risk assessment, drug manufacturing, etc., will help hospital administrators, medical researchers, and healthcare workers all around the world. Importantly, AI and ML have made it possible to collect, track, mine, and manage data pertaining to a patient's medical history. However, a full-fledged transition to digitised systems is a major hurdle to maximising the benefits of AI and ML in healthcare administration, and this is especially true in India.

?Even the largest hospitals in the country are falling behind the times when it comes to installing even the most fundamental systems for maintaining digital health records. Only about 3% of the country's 10,000+ hospitals have fully electronic medical records, using either a hospital information system (HIS) or an electronic medical record (EMR). A unified, trustworthy, and precise technological answer is urgently required. A system like this has the potential to modernize the healthcare system by making it easier to track and care for patients. The following actions are necessary to close these gaps:

Data digitization is a crucial part of modern healthcare administration. The healthcare system requires an appropriate software architecture to record every patient's information across all domains, including diseases, medications, treatments, and other minuscule data. Because doctors' handwriting might be difficult to decipher, this should also apply to patient prescriptions.

Most data entered in healthcare settings or laboratories is in an unstructured format. Patient demographics such as age, gender, and race are typically included in the context of full sentences and paragraphs. The problem with unstructured data is that traditional statistical methods were created under the false assumption that it will be organized in rows and columns. Natural Language Processing is required to transform the unstructured material into a more manageable organized format. Because of its superior capability for searching, analyzing, and interpreting massive patient database sets, this method is gaining widespread use in healthcare settings around the world.

Health information silos exist between healthcare providers, payers, and patients, making for a fragmented system. For instance, while comprehensive medical records are in place for a patient during their time in the hospital, no such process exists for collecting data on their health status after they have been discharged. When a patient checks into a hospital or other healthcare facility, ideally all of their records, medications, scans, and other relevant data will be readily available. In essence, continuity of care between physicians and their patients should be unbroken. This, however, is not the case because of the inefficiencies of the current system. Medical records and histories are unavailable, decentralised, and fragmented as a result of patients visiting a variety of diagnostic labs, imaging centres, and specialists. In addition, there are a plethora of healthcare-related applications out there, but the vast majority of them are geared toward either providing healthcare or encouraging its increased use. Therefore, a fully integrated digital system to maintain healthcare data is required, and for this, we need holistic or comprehensive care management pathways.

Once these limitations are removed, AI and ML will be able to significantly improve the healthcare management environment. Technology is already assisting hospitals in some developed countries with streamlining operations, managing patient information, scheduling and deploying medical staff, and making the most of available facilities. The medical field is also making use of real-time tools to improve administration.

There will be no revolution in India's adoption of AI and ML until the country's data infrastructure is modernized and brought online. We need to make the most of the potential that AI and ML have to help healthcare providers learn more about their patients' needs and use that knowledge to improve the feedback, guidance, and support they offer for patients to maintain good health.