For India, a country with over 1.3 billion people and an acute shortage of Medical Staff that includes Doctors, Nurses & ASHA workers. A detailed analyses floated by Indian government’s Niti Aayog places healthcare India acquires only about 50,000 doctors every year that have shortage of qualified healthcare professionals and services like qualified doctors, nurses, technicians and infrastructure: as evidenced in 0.76 doctors and 2.09 nurses per 1,000 population (as compared to WHO recommendations of 1 doctor and 2.5 nurses per 1,000 population respectively) and 1.3 hospital beds per 1,000 population as compared to WHO recommended 3.5 hospital beds per 1,000 population India. So to achieve the above mentioned scenario will acquire a long-term involvement of resources.
To swiftly fill these gap according to us, AI can perform a vital role as immediate extended healthcare support system at non-critical grounds. Thats the reason NITI Ayog and governments bodies sees AI as a novelty that can overcome the above mentioned gaps.
Here are some of the impactful ways that helps in changing the landscape of Indian Healthcare.
Overcoming diagnostic resource flaws in NRHM .
Preventive Healthcare is the future. The overall healthcare industry is one of the largest with a massive patient database in India. It is further expected to reach $6.6 billion by 2021 with the on-going developments in artificial intelligence. By providing relevant predictions to prevent diseases and clinical confidence. According to the Interim Budget of 2019-2020, the government has specifically stressed on creating a strong healthcare delivery system in the country with an ambition to enable people of all strata to avail accessible and affordable medical facilities. In order to fulfill this ambition, the government will be setting up National Center for Artificial Intelligence, along with the development of National AI Portal, to create an ecosystem for the adoption of technologies in order to support the momentum of care.
In simple words, AI portal includes the intervention of device-to-device inputs as well as human inputs to derive the accurate results. In this process of communication, AI has been continuously bridging the gap between healthcare service delivery and patient recovery. Just by uploading the inputs itself into the portal at initial level and can avoid any downfall situation. AI systems can help reduce these errors substantially. A deep learning algorithm could more accurately determine specific threats. And eventually where there are lack of resources as in Limited doctors with limited medical equipments, there such AI portal can cope up the necessities of patients. Mainly the NRHM (National Rural Healthcare management) could get benefited more under this initiative.
Automating Post-Health operational transactions
While no one likes to invest time and money to deal post-health operational things like Insurance Claims, reveal the benefits of government healthcare schemes and non-critical routine check-ups might can be done virtually.
Intelligent Systems can automate Medical/insurance processes with ease of connecting stakeholders. Which impact low or reducing human-generated claiming error, save the time & resources with contribution on Electronic audited digital governance. These analytics will help the citizen to reveal the benefits and government can reduce the bribes and make sure that benefit can reach to relevant beneficiary by removing the mediators.
Applying Computer Vision to analyze patient health data like MRI ,X-Rays.
Patient large Health data like MRI & X-rays have technical advances in combinatorial chemistry, genomics, and proteomics have made available large databases of biological and chemical information that have the potential to dramatically improve our understanding of cancer biology at the molecular level. Such an understanding of cancer biology could have a substantial impact on how we detect, diagnose, and manage cancer cases in the clinical setting of having such acute shortage of Qualified Medical Resources. One of the biggest challenges facing clinical oncologists is how to extract clinically useful knowledge from the overwhelming amount of raw molecular data that are currently available with such short resources.We are focusing on how the exploratory data analysis techniques of machine learning and high‐dimensional visualization can be applied to extract clinically useful knowledge from a heterogeneous assortment of molecular data. After an introductory overview of machine learning and visualization techniques, the two example of recent proprietary algorithmic AI intervention (PURS and RadViz™) that the experts have found to be useful in the exploratory analysis of large biological data sets and reports (X-rays & MRIs). Today we have the use of exploratory analysis techniques on proteomic mass spectroscopy data for the detection of Cancer/Tumor & the diagnostic use of these techniques on gene expression data to differentiate between squamous and adenocarcinoma of the lung. Also they have illustrate the use of such techniques in selecting from a database of chemical compounds those most effective in managing patients with melanoma versus leukemia.
This can help surgeons or cancer specialist to perform better,” commented the chief clinical officer at Sound Physicians, a national practice of 3,000 doctors and medical practitioners offering acute care management says that “We apprehend that a surgeon’s talent, particularly with new or difficult procedures, varies widely, with huge implications for patient outcomes and cost. AI will each cut back that variation, and help all surgeons improve – even the best ones to analyze the accurate data with such inadequate resources. It’s important to leverage that digital feedback.” The result can be safer procedures, more effective results and a better experience for the patient who is being cared for.
As current government initiate Smart Cities, e-governance and health first, we are seeing a large amount of citizen-behaviour data will be generate and that’s the key of personalize citizen solutions. Softvan always contribute in good -cause initiative and that the motivation for us to built citizen-centric solution which can connect the last mile-citizens with necessary Health facility near around with the help of e-Health, Artificial Intelligence, and IOT. Proper AI usage can lead to more streamlined operational activities that improves leading in reducing confusion and improving customer satisfaction.
SOFTVAN has achieved some milestone in bringing “Healthcare-on-demand services” , To gain more perception or discuss it in vastly, feel free to connect us at SOFTVAN. Also if you are willing to enhance your skills more in learning more diversities in Artificial Intelligence solutions & frameworks, we can conduct a Brainstorm session with our domain expert Mr.Shaishav Shah & Mr. Harshal Trivedi.
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Published on September 12, 2019
Mr. Rohan Mishra