Impact of COVID-19 on India’s healthcare industry - Part 2


In Part 1 of the topic "Impact of COVID-19 on India’s healthcare industry", we explored two of the top four anticipated trends as a consequence of digitization and COVID-19. In this blog, let's continue the discussion and explore the remaining two trends on Electronic Health Records (EHR) and Artificial Intelligence (AI).

Note: This article uses the term “provider” or “healthcare provider” which includes an individual health professional (doctor, nurse practitioner) or a health facility organization (hospital, clinic) licensed to provide health care diagnosis and treatment services.

#3: Electronic Health Record

Electronic Health Record (EHR) is an electronic version of a patient’s medical history stored and maintained by a doctor or multiple doctors (and possibly multiple hospitals) over time. An EHR provides a holistic, long-term view of a patient’s medical conditions; this helps providers to gain a better understanding of a patient's history to provide a well-informed, more coordinated and efficient treatment. The lack of digitization and subsidized health insurance coverage are among the biggest challenges in India’s healthcare system; EHR helps to solve both.


The adoption of EHR has been historically low in India. However, there is tremendous growth of smart ‘consumers’ in India and a strong impetus on digital health laid by the Government of India through large multi-year schemes such as National Health Stack (NHS). Announced by the Prime Minister of India, Narendra Modi on August 15, 2020, the NHS is aimed to be a holistic health platform for the country resting on two important pillars: National Health Registry (digital records of all healthcare providers) and Personal Health Record (digital record of every citizen). The term Personal Health Record (PHR) being used in this scenario is synonymous with the widely used term EHR.


Advantages of EHR:


  • EHRs maintain a complete view of patient’s medical history and help doctors to traverse the history in a quick, efficient and reliable manner. This decreases average time spent per patient by a doctor while improving the diagnosis and treatment.

  • EHRs allow for safe data sharing among different specialties in a hospital and with the patient. This saves enormous time for a patient in need of urgent care.

  • Availability of health data will help Research departments of hospitals and universities in performing data analytics to improve treatment, conduct more research studies, reduce time for drug discovery and reduce operational costs.

  • Availability of digital health and demographics data can also assist Central and State Governments to identify ways of reducing cost of medical services and health insurance thereby making healthcare accessible to more citizens of the nation.


Challenges:


India is years behind in EHR adoption compared to many Western nations. However, we also know that these nations have failed in protecting EHR data multiple times. In India. Though we hope to achieve interoperability and easy data transfer across hospitals, the lack of adequate privacy laws and a suitable authority to regulate the digital health phenomena raises multiple questions not only in the minds of patients and consumers but also in the minds of healthcare players who want to monetize a share of this emerging trend.


Some of the other challenges in creating and maintaining EHR:

  • Safety and privacy of personal and health data

  • Changing mindset of patients and providers alike to switch to a digital format and maintain reliable data

  • Making digital systems easy to use for all demographics based on geography, age, digital literacy, access to mobile communication, socio-economic conditions 

  • Creating digital support systems in multiple regional languages and ensuring consistency 

  • Training and skills required to operate digital systems

 

#4: Artificial Intelligence

Artificial Intelligence (AI) is taking every industry by storm. AI has already disrupted industries such as financial services, gaming, robotics, manufacturing, customer service and so on. AI in Healthcare is no more in research papers, Healthcare is the industry being disrupted by AI right now! AI solutions applied to healthcare are disrupting all players including the Governments, Payers (health insurance companies), Providers, Suppliers (pharma, medical devices) and Consumers.


Growth opportunities and common use cases:


  • Precision medicine: This approach takes into account an individual’s genes, environment and lifestyle choices while prescribing a treatment plan. Deep Learning models to diagnose diseases from radiology images, Genome Sequencing for early detection of cancer - these are some of the examples where AI models are helping reduce manual tasks with good accuracy.

  • Population health management: This is the aggregation of health data of various patients segmented by a widely accepted set of parameters providing actionable insights to providers for improving clinical outcomes while reducing costs. Few examples include: Chronic care management; Early detection of risk-prone population for diseases such as cancer, Alzheimer's; Processing insurance claims to identify financial spending patterns.

  • Drug discovery & research: Drug discovery is a time consuming and expensive process. AI helps in reducing manual tasks, improving accuracy, reducing time of drug discovery process. AI technologies are used in many ways: NLP to extract information from large repositories; High-Content Screening; Clinical trials and imaging analysis. 

  • Predictive insight and Risk analytics: AI solutions are helpful in administrative tasks of reducing billing and claims fraud, maintaining regulatory policies in billing codes. Also, AI helps in predicting hospital readmission rates, patient inflow, ER occupancy rates and staffing needs. Such predictive analysis prevents overwhelming the providers and helps to reduce costs while improving quality of care.

  • Mental health: Early detection of mental health issues can improve chances of successfully treating a patient. AI models can detect neuroimaging biomarkers that make diagnosis and treatment more precise. 

  • NLP and Conversational bots: NLP models and chatbots heavily reduce the burden on healthcare employees by automating tasks such as extracting key phrases (billing codes or medication names) from large unstructured text files, appointment scheduling, conducting initial diagnosis, answering FAQs, reminding to take medicines and so on.

Challenges:

  • Ever increasing threat of cyber attacks on personal health data

  • Privacy and security of patient’s personal, health and financial data 

  • Nascent stage of many use cases resulting in AI model’s lower accuracy and higher bias

  • Skills and resources gap in data science, AI, healthcare sectors


I hope you found this two-part series on trends in healthcare insightful. Please comment below your suggestions on topics you would like to learn more about the healthtech industry.

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