The healthcare challenges in India are unique, complex, and growing. The imbalance between the already inadequate healthcare workforce and patients is widening. There is a need for 2.3 million doctors by 2030 but only 50,000 doctors graduate every year. A significant portion of the increasing (15% inflation) hospital costs,is financed by loans and the sale of assets. This is pushing thousands of people below the poverty line every year.To add to these complexities access to care is uneven and inadequate,and there is an increasing incidence of chronic diseases in younger people. These challenges are spiraling out of control and need to be addressed.
Private hospitals cater to around 70% of the patients in the country. This is not going to change radically in the foreseeable future. Private hospitals now have an obligation to support Ayushman Bharat patients at rates that are 30-70% lower than the current rates being charged for similar treatments. With a 25% allocation of capacity to Ayushman Bharat patients, private hospitals would need to become 25-50% more productive to even maintain their current revenue levels. This is exerting immense pressure on the health system.
To address these challenges conventional approaches alone will not suffice. New innovative approaches leveraging technology are required. Artificial Intelligence (AI) can play a pivotal role in addressing these challenges.
AI has the potential to transform healthcare delivery in India by improving care outcomes, patient experience, and access to care. Care can move from being curative to being preventive. Healthcare providers can improve productivity and the efficiency of care delivery, thereby enabling them to deliver quality care to a larger population. Healthcare practitioners will be able to reduce burnout and spend more quality time on patient care.
There are a few areas under AI such as Machine learning (ML), Natural Language Processing (NLP), and Deep Learning (DL) which have the potential to make a significant impact while addressing the challenges being faced by the healthcare system in the country. These need to be further evaluated and prioritized for implementation.
Improving Operational efficiency
Optimizing material procurement and consumption by driving commercial effectiveness, standardizing clinical pathways and treatment guidelines for homogeneous patient pools. This can help in driving down material costs which contribute to 25-35% of hospital expenses.
Improving Manpower productivity which contributes to 20-25% of hospital costs by aligning staffing dynamically with work-load patterns, reducing non-value adding activities, and reducing wasteful movements through layout optimization and process change.
Improving Facility utilization by standardizing clinical pathways and predicting the length of stay of patients.
Optimizing Clinical workflows
Improving productivity by streamlining clinical workflows. Automating and prioritizing routine tasks can help in reducing wasted time moving between disparate tasks. For example, radiologists can benefit if X-rays are automatically prioritized based on the probability of incidence of an abnormality.
Improving accuracy by ensuring that the correct information is accessible and is separated from non-useful information. An example is automatically pulling up the relevant prior clinical studies can help radiologists save time and improve accuracy.
Optimizing and improving care delivery by using predictive and clinical tools to forecast issues such as no-shows or patients lost to follow-up for further action. This can help reduce the impact on scheduling and resource allocation.
Decision support in clinical operations management will help in streamlining care delivery and operations. Can help for example in predicting and proactively addressing machine failures and recognizing free or slow machines for rerouting patients.
Diagnosis and decision support
Automating image analysis and diagnosis from digital pathology and radiology images (MRI, CT, Ultrasound, and X-ray). This can highlight regions of interest on a scan to help improve efficiency and reduce human error. Some examples are the diagnosis of cancer, tuberculosis, etc.
Identifying patient risk by analyzing historical patient data. This can provide real-time support to clinicians to identify at-risk patients for further action and also in reducing re-admissions.
Supporting decision making in telehealth and the last mile delivery of primary care. This could include screening, diagnosis, monitoring, and evaluation.
New Patient interfaces
NLP-based virtual personal assistants can help in appointment scheduling, monitoring, understanding the needs, and assisting prospective and discharged patients in the absence of clinical personnel. Virtual assistants capturing data through a voice interface can help in elderly care.
Despite the compelling benefits, AI adoption by the healthcare industry in India has been lagging behind several countries.This needs to change. All stakeholders including the government, providers, startups, health tech multinationals, and healthcare professionals have a role to play in this journey.
- Global health tech multinationals are leveraging the local AI workforce for developing healthcare solutions for the global markets. Steps need to be taken by the government to have a mechanism in place to motivate these health tech companies to make India the AI innovation bed for building healthcare solutions to address local challenges and then adapting these for the global markets. Government policy and accelerating the pace of digitization in the country will be key enablers in making this happen.
- Healthcare startups(~200) in the country need to have a better understanding of the workflows and the clinical pathways in private hospitals. Understanding the global opportunity for the local problem being addressed can help them with larger addressable and profitable markets. This can help them scale and get the requisite financial backing.
- There is a lack of understanding and skepticism among healthcare practitioners on the power of AI. To convince healthcare providers of the benefits of using AI it is required to have a clear AI strategy, and get quick wins to get buy-in.
AI-powered solutions have made small steps towards addressing the challenges being faced by the Indian healthcare industry. To make a meaningful impact several key issues need to be addressed in the coming years.
AI could play a pivotal role in defining how the Indian healthcare industry will function in the future, by improving productivity, augmenting clinical resources, and ensuring improved patient outcomes. This is not just an opportunity to help India achieve the goal of universal healthcare but also an opportunity for India to become an AI powerhouse for the rest of the world.