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Enabling Healthcare with Technology: PWC Report
Enabling Healthcare with Technology: PWC Report

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Introduction
AI is becoming an important part of the Indian healthcare system, and COVID-19 has played an important role in its progress. Today, we highlight the points from an article by Price Waterhouse Coopers International Limited (PwC India) and The Bengal Chamber of Commerce and Industry (BCCI). They bring to our attention how AI can change the current status of the Indian healthcare system and its challenges.

Healthcare AI can improve preventive care, disease detection, and prediction, devise treatment plans, and deliver effective and appropriate care, thus benefitting patients and healthcare providers. However, 70% of healthcare facilities are in urban areas, whereas only 30% are rural ones. There needs to be a greater distribution of healthcare in India, which can be achieved with AI. However, AI in the Indian healthcare system faces challenges such as unintended bias, data privacy and security, and increased unemployment.

The government of India has developed several acts and regulations to promote the progress of AI in healthcare.

Transpiring 4 themes of AI in healthcare

Patient centricity:

  1. Patient centricity prioritizes patient needs for better care delivery.
  2. Growing complexity in disease management demands personalized approaches.
  3. Emphasis on adherence to both medication and non-medication treatments.
  4. Technology plays a critical role, necessitating security and governance for ethical data use.

Care delivery: 

  1. Healthcare providers are loaded with several administrative tasks that require no skill and can be automated. All this extra time could instead be utilised for interactions with the patients reducing the scarcity of providers overall.
  2. AI models with deep learning technology can be used for automated work, recognizing human errors and suggesting corrective measures to further improve patient care. 
  3. AI can help deliver preventive, curative, and palliative care to the patient effectively. 
  4. AI-based algorithms can expedite the process of detection and disease prevention which are beyond the scope of a human's ability.
  5. Post-diagnosis treatment implementation is a time-consuming process. AI can help identify the patient's need for a particular treatment and ensure its compliance.
  6. It can identify which patients require palliative care and provide real-time updates to the physicians and the patient's family enabling them to deliver patient-centered care.

Disease surveillance:

  • The discovery of a new disease requires processing information from various sources, validated, processed, and broadcasted in a short period. 
  • AI helps reduce this time by processing large amounts of data in a short time.
  • It can detect real-time anomalies in the patient's condition through wearable sensors and send the appropriate notifications to the physicians. This can improve the follow-up studies of a disease as well.

Research and development:

  •  Every study and discovery requires a huge amount of data which would require a large manpower to process making it time-consuming and costly. For example, recruiting people for clinical trials is the most vital and tough step in drug discovery. People do not know which clinical trial to participate in. 
  • AI-based selection of clinical trial participants.
  • Use of global datasets to provide a global overview to the cohort.

 

Primary survey report

  • 97% of people think that adopting AI in healthcare will give them a better understanding of their condition.
  • 88% of people believe that these AI models would aid providers with quick data-driven decisions.
  • 95% of people presented their interest in a system where AI would accurately predict their medical condition.
  • 78% of people would want to discuss the AI-based results with their physicians. 
  • More than 60% population is willing to share their medical data to create a centralized repository.

 

Components of AI implementation

Implementation of AI in healthcare focuses on 6 dimensions which together will create a healthy and mature healthcare ecosystem. 

  1. Organization, culture, and talent:Implementing AI in skilled tasks to enhance efficiency operated by skilled AI teams 
  2. Process and Integration: Integration of AI in clinical decision-making after it has matured with the help of data from clinical trials.
  3. Cybersecurity and privacy: Ethical AI practices are established to prevent the misuse of sensitive information. 
  4. Business decisions and analysis: AI models are expanded to handle multiple tasks. 
  5. Data and information: The quality of data is continuously monitored and improved for the maturation of AI models. 
  6. Technology and infrastructure: AI-implemented processes are integrated with advanced technologies to increase efficiency.


Each of these components uses AI in 5 steps. First being descriptive and the last being the cognitive stage. The descriptive stage uses AI for nonspecific purposes and automated work mainly to reduce human errors. The cognitive stage on the other hand uses AI for very complex, clinical decision-making tasks.

 

Challenges in Deploying AI in India

  1. Fear of unemployment: The perception of losing out on manual jobs to AI is an important reason for challenging its adoption
  2. Lack of skilled workforce: Operating AI models and systems with sensitive health information requires skilled and trained professionals. 
  3. Cybersecurity: The healthcare industry is very prone to cyber threats due to the nature of health information stored. Appropriate measures need to be taken to prevent the misuse of the same. 
  4. Lack of structured data: Over the years, India has relied on manual documentation. Collecting, storing, and processing such unstructured data could lead to misinterpretation and misdiagnosis. 
  5. Lack of proper policies and regulations: Although the Indian government has implemented various policies for the development of the use of AI in healthcare, there is still a lack of an efficient and satisfactory framework.
  6. Low Intensity of AI Research: AI in healthcare can be used for research and epidemiological purposes also by processing a large amount of data in a short period. However, India currently stands 19th in terms of research. There is a significant lack of good-quality research due to a lack of data.

 

Conclusion

The introduction of AI in healthcare has become a necessity in India because of the increasing burden of disease and limited resources. AI can transform patient healthcare experience, increase the efficacy of clinical decision-making, and accelerate disease surveillance. 

AI in healthcare is seeing constant progress with new technologies being implemented across domains. There is a constant increase in the accumulated medical information and it is practically very difficult for human manpower to process without making mistakes. Therefore, AI-based processing has become important and easy, especially with the advent of the 5G network. It has improved the accessibility of the internet across the country and made the processing of information faster. Additionally, cloud computing has brought about a great change. Large amounts of data can now be stored and exchanged continuously with low expenditure. 

The Indian government has implemented strategies and policies to encourage the development of AI in healthcare. An indicative plan for the adoption of technology given by the PwC and BCC highlights four stages- Strategize, Design, Implement, Operate, and Monitor. It provides a pathway for the healthcare industry to implement AI. 

 


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