The article discusses how Generative AI (GenAI) can transform Indian healthcare by enhancing information retrieval, standardising medical data across languages, and reducing clinical errors.

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The healthcare industry in India is about to experience a digital revolution, with vast amounts of patient data being generated and stored electronically. While this wealth of information has the potential to greatly improve patient care, it also presents significant challenges for clinicians who must efficiently navigate and extract relevant data from complex EHRs, medical reports, and research databases.

Information retrieval in healthcare

Effective information retrieval is crucial for clinicians to make informed decisions and provide high-quality patient care. GenAI systems improve this process by:

1. Natural Language Processing (NLP)

Example: An AI assistant at AIIMS could understand and interpret complex medical queries posed in Hindi or English, allowing clinicians to search for information using conversational phrases rather than specific keywords or codes.

2. Contextual understanding

Example: At a rural health centre in Bhagalpur, Bihar, an AI system could analyse the context of a query within a patient's overall health profile, ensuring that retrieved information is relevant to the specific case at hand, considering factors like local disease prevalence, season, culture, food habits and available resources.

3. Multi-source integration

Example: A GenAI system at a tertiary care hospital in Bangalore could simultaneously search and synthesise information from various sources, including the hospital's EHR system, Indian Council of Medical Research (ICMR) databases, and international clinical guidelines, providing a comprehensive overview of relevant data.

4. Summarisation

How it works:

Table: Key benefits of GenAI in information retrieval

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Standardisation of medical information

One of the major challenges in healthcare is the lack of standardisation in how medical information is recorded and reported. GenAI can contribute to standardisation efforts, especially in the Indian context, which encompasses multilingual and multicultural by:

A. Terminology mapping

Example: Consider a case where a patient's record from a rural clinic in Tamil Nadu describes a condition as "மாரைடMЦ" (heart attack in Tamil). The AI system can automatically map this to the standardised term "Myocardial Infarction" in English and assign the appropriate ICD-10 code (I21). This ensures that the patient's condition is understood accurately across different healthcare providers, regardless of language barriers or local terminology variations.

B. Structured data extraction

Example: A GenAI model can analyse a handwritten discharge summary from a district hospital in Uttar Pradesh. It can extract key information such as diagnosis, prescribed medications, and follow-up instructions, and organise this into a structured format compatible with electronic health record (EHR) systems. This not only improves data quality but also facilitates easier sharing of patient information between primary health centres and tertiary care hospitals.

C. Template generation

Example: An AI assistant could generate a standardised template for a COVID-19 screening report, taking into account guidelines from the Indian Council of Medical Research (ICMR). This template would include fields for patient demographics, symptoms, travel history, comorbidities, and test results. By using such AI-generated templates, even small clinics in remote areas can ensure they're collecting all necessary information in a format that's compatible with national health databases.

D. Cross-lingual standardisation

Example: Think of a multi-state telemedicine platform connecting doctors and patients across India. A GenAI system could provide real-time translation of medical terms and concepts between languages like Hindi, Bengali, Telugu, and English. For instance, it could accurately translate and standardise descriptions of ayurvedic treatments or local herbs used in traditional medicine, ensuring that this information is correctly interpreted and recorded in the patient's medical history, regardless of the healthcare provider's linguistic background.

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Standardisation flow

Error reduction through AI assistance

By improving information retrieval and standardisation, GenAI can significantly contribute to error reduction in clinical practice. Key areas of impact include:

1. Diagnostic support

AI systems can quickly retrieve and present relevant patient history, lab results, and similar case studies, reducing the likelihood of missed or delayed diagnoses. For instance, when dealing with complex cases like tuberculosis, which is prevalent, AI can swiftly retrieve and present relevant patient history, previous X-rays, and similar case studies from the hospital's database.

2. Medication management

In India where polypharmacy is common, AI tools are proving invaluable. For example, at Apollo Hospitals, AI systems flag potential drug interactions or contraindications based on a patient's comprehensive medical history. This is particularly helpful in preventing medication errors for elderly patients who often consult multiple specialists and may be on several medications.

3. Clinical decision support

By providing evidence-based recommendations and alerting clinicians to potential risks or oversights, AI assistants can enhance clinical decision-making. For instance, in managing tropical diseases like dengue, which sees seasonal outbreaks in cities like Mumbai and Delhi, AI tools can alert clinicians to potential risks or oversights based on the latest treatment guidelines and local epidemiological data.

4. Documentation accuracy

In many Indian hospitals, especially in tier-2 and tier-3 cities where resources are often stretched thin, GenAI can assist in creating more accurate and complete medical documentation.

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Potential error reduction with AI implementation

Figure 1: Annual population incidence of serious misdiagnosis-related harms from vascular events, infections, cancers and all non-Big Three others The estimated total annual US incidence for serious harms (combining "Big Three' harms with other non-"Big Three' harms) is 795 000 (probabilistic plausible range (PPR) 598000-1023000) Whiskers denote PPRs from the Monte Carlo analysis

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Key benefits

Disclaimer- The views and opinions expressed in this article are those of the author and do not necessarily reflect the official policy or position of M3 India.

About the author of this article: Mr Rohit Kumar has done a Master's from IIT Kharagpur and is a Co-Founder of Rapida.AI also Founder and CEO of Neeba Health, Bengaluru, Karnataka.