Economics of AI, Design Thinking, and Data Science for Smart Healthcare


Health systems are multi- faceted and continually changing across a variety of contexts and health service levels. For example one of the critical challenges of the resource deficient public health infrastructures worldwide is the spread of the communicable diseases.

As seen during the outbreaks of the fatal communicable diseases like Severe Acute Respiratory Syndrome (SARS) in 2003, H1N1 in 2009, the Zika virus in 2016, Ebola and Middle East respiratory syndrome (MERS) in 2014, and the Nipah virus in 2018, infectious diseases can spread rapidly within the countries as well as across the national borders.

Artificial intelligence (AI) has been making its way into the healthcare sector, presenting a variety of possibilities in disease diagnosis, treatment, and prevention. The adoption of artificial intelligence in the healthcare sector is growing substantially. One of the other major problems plaguing India’s premier public hospitals is overcrowding. It takes months for even those with terminal diseases like cancer to get started with their treatment.

The presence of AI would allow individuals to easily access and secure patient’s medical data, then understand and timely analyse their illnesses. Successful implementation of AI can provide major relief to these issues.


The most critical, vital and fundamental part for initialising Artificial Intelligence for the CEO of a company is to start thinking as a data scientist. This activity in itself is contradictory to the thought process of a doctor or a medical professional, therefore it is the primary requirement to employ an analytical data expert who is the blend of a mathematician, a computer scientist and a trend-spotter who has the curiosity to explore the problem that needs to be solved so as to make the organisation AI-enabled. The basic requirement to successfully implement AI for a smarter healthcare is the availability of adequate digitised data.

The primary objective in the health sector is the successful diagnosis of the ailment in which AI can play a significant role. For the analytics of the symptoms, the primary data is the medical record that the patients present in the first level of data documentation.

For a developing and an aspiring country like India where we like to heighten our reach to the world, simultaneously inwardly looking into the challenges of Indian medical system, it is very important and critical that the medical records of the patients are digitised.

Towards this the baby steps have been taken by the MeitY – Ministry of Electronics and Information Technology. However, MeitY is facing serious challenges in its efforts to adopt an Electronic Health Records (EHR) system—a digital database of every Indian’s medical record that can be accessed by all doctors and hospitals.

The major challenges to cope up with include the infrastructure creation, standards and inter- operability, research and development as well as the legal and policy framework which forms the foundation for mandating it for governance. And lack of these fundamental infrastructures would make it nearly impossible for a developing nation to establish AI in its true sense. There is a need to start a debate on the aspects of the department of involvement of AI in various health segments. It need not necessarily mean that all segments require the same level of AI infusion as the certain critical ones need.

The field of predictive analytics holds increasing promise for helping clinicians diagnose and treat patients. Machine-learning models can be trained to find patterns in patient data to aid in sepsis care, design safer chemotherapy procedures, and predict a patient’s risk of having cancer. The accuracy of decision making is vital in the health as it builds the patient doctor trust which forms the foundation cornerstone for any segment in health sector to flourish. There is a need to cautiously examine that the infusion of AI in the segment shouldn’t downwardly change this agreement.


AI is tightly linked with a very strong health sector business analytics which in turn has strong linkage’s with the big data generation and big data analytics in the sector. It is strongly suggested based on our evaluation that this realisation need to be market driven for health sector to flourish, as it has been seen in most of the government participations, after a quantum of load the services are not able to cope up with the load. AI eventually will settle down in providing healthcare to all financial segments of Indian citizens as it will optimise the cost of evaluated medical tests, cut expensive surgery’s and will eventually balance out the demand and supply chain. This technology will act as a catalyst for optimisation of process which otherwise would have taken many years to achieve. Implementation of AI will also lead to economic advantage in overall hospital care, clinical research, drug development and even in the insurance cause economic benefits. AI applications are revolutionising how the health sector works to reduce spending and improve patient outcomes. Health sector is the only sector where all 34 disruptive technology’s needs to be synchronised to achieve the eventual goal. Few of these technologies meet the governmental legal and policy framework whereas most of them are self-sustaining in the business environment.


The AI has started taking the centre stage due to benefits being visible in the public sectors, its positive effect on the population and the advances that would visibly reduce the data privacy issues in the medical electronic records. Still, the mind-melding requires political and governmental support.

The AI data monetisation would become a vast business opportunity. It will form a critical pillar towards the structured AI in the medical field. If we take the history as the guiding principal, the monetisation of medical big data can safely assume free medical scanning capabilities available to the patients just to have generated databases. It is expected that this industry towards which the seeds have just been sown, will form a vibrant system in another 2-3 years. Those days are now near when any government hospitals would have counters that will provide your complete health records, which will form the fodder for AI diagnostics machinery.

In 5-6 years, AI may assist doctors by providing assured diagnosis to patients. In 7 -8 years we will have alexia like devices which will assist and advice based on the health and photo inputs about the impending disease which may affect its users. The ecosystem need for AI driven e-health is vast and complex due to the trust element inside it, between the doctors and the patients. The three key areas of investment for AI in healthcare would include:

• Digitisation: Utilisation of AI to reduce the cost and time and hence increase the efficiency

• Engagement: Improvement of patients/consumers interaction with healthcare providers, systems and services.

• Diagnostics: Development of efficient tools/products/services for timely and accurate diagnoses and health advices.


There is an increasing need for healthcare establishments to implement solutions that effectively improvise treatment outcomes, manage rising costs and navigate through the demands confronting the sprawling healthcare system. Some of the potential applications and advantages of AI in health sector include:-


Many a times due to situations like calamities, overcrowding, or difficult geography the access to a physician becomes nearly impossible. At these conditions an applications can give a primary medical consultation based on the personal medical history and common medical information. The users can feed their symptoms into the application, the algorithm then using the speech recognition can compare against a database of illnesses to give the primary consultation.


The first step for effective implementation of AI in healthcare is compiling and analysing information (like medical records and other past history). Data management would be the most widely used application of artificial intelligence and digital automation as it would reduce the time consumed in the mundane works. Robots/machines would accumulate, store, and trace data to provide faster as well as more reliable access.


As AI systems would have the capability to learn from previous cases and store knowledge, also access stored knowledge from anywhere around the globe. It would play a very significant role in diagnostics.

It is scientifically proven that artificial neural networks can diagnose some other diseases includes eye problems, malignant melanoma etc in fast & accurate manner.

In the medical field, Cancer is one of the major challenges in which early detection plays an important role. AI has the potential to diagnose diseases and illnesses through deep learning. For example, many medical facilities are shifting to Digital Breast Tomosynthesis (DBT) technology solutions as preferred method for screening and diagnostic mammography in order to detect and diagnose women with early-stage breast cancer.


Analysing lab reports, X-Rays, CT scans, data entry, and other routine tasks can all be done faster and more accurately without any biases by the trained machines. Cardiology and radiology are two disciplines where the amount of data to analyse can be vast, time consuming and also time critical. Proficient utilisation of AI would facilitate doctors to spare more of their time in the patient interactions/ treatments.


Development of pharmaceuticals through clinical trials can take more than a decade and cost billions of dollars. And if the whole process of research and development is assisted by an AI system it can make the process faster and cheaper. For example, during the Ebola virus spread, an AI programme was used to scan existing medicines that could be redesigned to fight the disease. The programme was able to identify two medications that could reduce Ebola infectivity in one day.


Genetics and genomics look for mutations and links to disease from the information in DNA. If a database of family genetic history of any hereditary disease is created and digitised accordingly, then using the appropriate AI algorithm, early diagnosis and appropriate treatments of ailments like cancer, physiological and vascular diseases can be done.


The growth of computational power has led to a substantial increase in the amount and granularity of stored digital medical and healthcare data. The ability of AI to quickly analyse huge volumes of this data and create meaningful and actionable insights will have weighty effects on how healthcare is delivered and received. AI can be utilised to scrutinise the data to highlight mistakes in treatments, workflow inefficiencies, billing errors, hospital administration, and unnecessary patient hospitalisations and hence increasing the efficiency of the healthcare system analysis. It can also, help doctors to work more reasonable hours by optimising shift scheduling with data and measuring physician satisfaction to further improve the system.


AI may be most effective at reducing human error. Humans after analysing the routine scenario many times get biased and results to an error at the diagnosis phase. Even due to any emotional trauma or stress on the doctor’s side might be a reason for the error in the diagnosis and the treatment. AI could be the best assistant as it would monitor the whole procedure and greatly decrease stressful situations.


Humans are complex bio-chemical devices due to our complex evolution from a single cellular organism to a human machine over a billion years, the complexity is unimaginable. A complex AI with advance computing power will also take at least one generation to understand complete molecular level of this bio-chemical machine.

The AI has started taking baby steps by understanding the interdependence of various artworks of famous artists. These types of algorithms will also form the eventual symphony in understanding the human bio-chemical device. The policy making bodies need to understand, the effort towards these researches in the present context will have negligible benefits but will eventually make an exponential effect on eradication of diseases at genetical and molecular level with maximum economic benefits.

From hospital care to clinical research, drug development and insurance, AI applications would revolutionise how the health sector works to reduce spending and improve patient outcomes. These benefits will accrue incrementally, from automated operations, precision surgery, and preventive intervention. Healthcare providers need to have confidence over the algorithms to use them, and that often means they want to see clinical validation of it. Many will continue to be skeptical about adopting AI tools until a large body of proof verifies their outcomes.

There is reluctance on the patient side, too: Around one-fourth of the consumers surveyed by one of the leading IT multinational said that they would not use AI-powered health services and cited concerns about the technology as their reasoning, from not understanding enough about how AI works to worrying that the technology might not understand them.

To overcome the fears and concerns associated with AI, it’s imperative that providers work to implement these new, innovative technologies effectively by first carefully considering and researching the right solution. Then, providers should invest time considering and understanding how their system is capturing and collecting data in order to analyse it and check for errors.

With artificial intelligence, the patient can get doctor assistance without visiting hospitals/ clinics which results in cost-cutting. AI assistants provide online care & assist patients to add their data more frequently via online medical records etc. The ability of AI to sift through large amounts of data can help hospital administrators optimise performance and improve the use of existing resources as well as saving cost and time.

The algorithms are able to ingest years of electronic health record data and apply data science and AI to learn how best to manage expensive constrained resources infusion chairs, operating rooms, imaging equipment, inpatient beds and more to improve patient access, decrease wait times and reduce healthcare delivery costs.

Physicians and healthcare organisations are facing big challenges nowadays. Physician breakdown is increasing and the repercussions are expensive. Rather than replacing human clinical judgement, artificial intelligence will augment the clinical intelligence to scale that we may not imagine today and economically benefiting the patients as well as the health sector which is the prime requirement of a developing country like India.

(The article has been written by Diana Yohannan, AI and Big data Analyst, Technology Enthusiast, and Pratyush Mathur , Technology  and Business Analytics enthusiast, Narsee Monjee Institute of Management Studies. Views expressed are a personal opinion.)

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