Learning AI in healthcare

Healthcare global ecosystem today is in a constant flux. New business models and new technologies are emerging. Market needs and patient behaviors are evolving. In the era of AI, human intellectual powers are now simulated, multiplied, and partially substituted by AI-driven digitization.

Data, whether big or smart, does not create any value per se. It is algorithms that have the real value. While data is the gold of the digital era, it is the possibilities of analyzing this data to become usable results that generate the effective value. Algorithms are more important for analyzing this data and the driving force for the digital health. Complex algorithms create sustainable competitive advantage, when applied with right business models.

The healthcare sector is the primary source of the world’s data, generating nearly a third of all the global data. So, hospitals can gain a lot with big data technology. Using big data analytics software and tools, healthcare professionals can extract valuable insights from the information they already possess in various ways, such as examining the patient records, medical imaging to support diagnostics and treatment development, predict potential health hazards by studying the pattern and correlation in health data, shift in epidemiology situations, etc.

Automation and AI have the potential to address some of the new-age problems, such as aging population and expensive elderly care, impact of climate change, shift in lifestyle choices, remote care, personalized care delivery, etc.

Digital health ecosystem is continuously evolving and steadily adopting the new-age technologies, such as machine learning, and generative AI. We are still far from having AI robots having complete complex procedures without human intervention, even in USA, but adaptation of technology for minor aspects of routine tasks will grow in the near future.

Electronic health record (EHR) has been one of the megatrends in healthcare technology for the last few years that serves as a centralized repository for patient data and facilitates the exchange of patient data across various healthcare facilities. This promises data-driven clinical decision-making and improve interoperability for better patient outcomes. Privacy concerns and local government regulations need to be factored in while designing such system in local healthcare ecosystem.

In India, electronic health records (EHR) are not widely implemented, although most hospitals have some type of hospital information system (HIS), and only a few have a basic electronic medical record (EMR) system.

Telehealth and EMR systems together work tremendously well in providing high-quality patient care in a remote environment, including well synchronized insurance information.

Internet of Medical Things (IoMT) and wearable devices are helping healthcare professionals track patients’ health more accurately and build personalized treatment plans. On the patient’s side, using these devices fosters a greater awareness of personal health. With access to real-time data, individuals can better understand their sleep patterns, eating habits, activity levels, and other health-related behaviors.

There are many mobile health (mHealth) apps that are available on major Google Play and AppStore platforms for various applications, such as telehealth, medication management, fitness and wellness, mental health, appointment scheduling, etc.

India has 70-percent import dependency on medical devices. Global big brands are steadily growing their commitment to India market via various local collaborations and global capability centers (GCC).

Imagine a future in which huge database from implants and wearables change the perception of human biology and of how drugs and formulations work, enabling the real-time and personalized treatment for all.

AI can improve the speed and accuracy in use of diagnostics, give practitioners easier and faster access to more patient-oriented knowledge, and enable remote monitoring through self-care.

There are many great initiatives by trade associations and other stakeholders in this digital health journey. But the key challenge for learning and early adoption is how to resist the bias against doing new things, scanning the horizon for growth opportunities, and pushing yourself to acquire radically different capabilities – while still performing your job.

The senior stakeholders, including the government and regulators, consistently encourage the audience to ask and answer a few curious questions about the new topic instead of focusing on and reinforcing initial disinterest in a new subject. That sparks the curiosity for the learners and early adopters. This is a good way to shift the focus from the challenges to the benefits, and increase the aspiration of the stakeholders to do initially unappealing things.

NABH has taken initiative to introduce common vocabulary for standardized business communication, such as RFP, etc.

Many startups in emerging economies are interested in scaling any new AI-powered conceptor solution very fast. Startups/innovators and the practitioners must embrace the importance of AI ethics, or possible algorithm bias, so that any new solution will do no harm before it reaches the patient. AI cannot be treated like a black-box approach in healthcare sector. California in the USA has recently recognized officially the importance of mental privacy in the state law. Neural data needs to be protected. Collaboration and transparency are key ingredients for win-win playbook in digital health sector.

Demand for data scientists will intensify across industries and the competition of the talent will be fierce. Many young computer scientists are excited about the potential growth in ethical adoption of AI and automation in healthcare domain. Developing a flexible and agile organization model to attract and retain such talent will be a key part of the business strategy specifically in emerging economies such as India.

All views and opinions expressed in my article are my own