Mohana chandran, Head IT, Maharashtra, Apollo Hospitals, in an exclusive interview with CIOTechOutlook, shares his views on how the innovations in AI and machine learning transformed diagnostic accuracy, the impact of EHR on improving care coordination, the implications of using AR and VR for surgical procedures, and more. He is a seasoned healthcare IT professional and consultant, with over 19 years of extensive and rich experience driving digital transformation and operational excellence within the healthcare sector. 

How have recent innovations in AI and machine learning transformed diagnostic accuracy and treatment personalization in healthcare?

Many areas come under innovation, and predictive analytics is one of those that help in personalized treatment and diagnostic accuracy. Predictive analytics works with a more extensive set of data that we collect from a patient database. With that, we can collect multiple pieces of information and predict any disease risk or its progression. Hence, we could provide risk scores and earlier interventions by evaluating electronic health records, genetic data, and lifestyle factors risk to avoid any complex conditions.    

The other innovative area is diagnostic accuracy. Multiple diagnostic methodologies occur in hospitals, mainly radiology images and radiological investigations. So, we have numerous imaging like x-rays, MRIs and CT scans. Besides, specific algorithms involving deep learning can analyze any of these medical images to discover any anomalies that human eyes can miss. In addition, we have personalized treatment plans; it is like gathering multiple information such as genetic information, medical history and response to the prior treatments. Having data, we will have specific algorithms that analyze data and, from all these data and clinical trials, assist in recommending the most effective therapies.

Eventually, clinical decision support will help medical professionals by providing evidence-based recommendations. Hence, we may have larger clinical trials happening across the globe, a common repository of this data and diagnosis and treatment planning for various clinical conditions. These clinical decision support systems will analyze this data in the larger area of medical literature to suggest optimistic diagnostic tests and therapeutic options.

Telemedicine bridges geographic gaps. In what ways can telemedicine platforms improve access to healthcare services for rural and underserved populations?

Telemedicine eliminates the travel needs of patients, as in rural areas, we may have multiple patients who have to travel for their treatment. Telemedicine will reduce the cost and need for travel. Hence, by enabling universal consultations, patients can receive treatment advice without extensive travel. The other is the increasing availability of specialists. In some specific cases, like intensivists or any other specialist doctors, we may have limited availability across rural areas, and telemedicine can increase the availability of specialists for any people sitting in rural areas, which is another advantage. The next one is the continuity of care.

For instance, we may have a chronic disease that must be monitored regularly and consult a doctor at specific intervals. Telemedicine enables the platform to avoid in-person doctor visits for complex and continuous continuity of care treatments. Hence, they can monitor the clinical condition frequently and maintain consistent treatment, reducing the risk of complications to the treatment protocol.

Emergency care is another advantage. There are instances where we may need emergency medication facilities in rural areas. By telemedicine, patients can see doctors and get expert advice in an emergency. They will be able to get treated in the facilities available in the rural areas—finally reducing healthcare disparities. With telemedicine, we can always access medical services and expert advice, and the disparity between urban and rural areas will be ruled out so that underserved populations can receive adequate care and access to quality healthcare.

EHRs centralize patient information, what impact does the integration of EHR have on improving care coordination and reducing medical errors?

EHR collates the patient's information into an electronic health record, including its medical history, investigation results, prescriptions and treatment plans. The details are collated into a single entity to which the patient has access, and that comes under improved care coordination pertaining to centralized information access. The second point towards improved care coordination would be enhanced communication. We have a repository of information with us, but if we cannot share it with multiple people who need the information, it is not adequate. Hence, patients must always carry records and will only have some from the start.

Communication technologies and tools will allow healthcare providers to share information more effectively with the consulting doctors, which can be in the form of securing messages and shared information. The other is integrated care plans. EHR can facilitate creating and managing integrated care plans that can be shared among consultants, specialists, primary health providers and other healthcare professionals. This will allow coordination of complex care across multiple providers, ensuring the patient gets a complete treatment approach so that nothing is missed. So, these are the crucial points that add value to improved care coordination.

Now, focusing on reducing medical errors, one is accurate and complete records. Most often, we may not have complete patient records, which leads to inadequate information since the patient may be considering the last consultation record. Still, if we check the past records, the patient may be unable to provide that information. Hence, one EHR can collect all the information from starting if the patient is aligned with this process, and the records will be completed so that the healthcare provider doesn't miss any records.

The other is decision support tools. Many EHR systems integrate clinical decision support system tools so that we get proper alerts or reminders for allergies, drug interactions or deviations from best practices. These tools will enable healthcare providers to eliminate prescription errors, administration errors and clinical decisions. The final one is audit trails or accountability. While maintaining or sharing data and witnessing the information across multiple platforms, accountability is essential, considering patient privacies. Those who accessed or edited the records and any information are captured in the system, contributing to fewer medical errors.

AR and VR offer immersive training experiences and simulation for surgeons, what are the implications of using AR and VR for medical training and surgical procedures?

In medical training, augmented reality (AR) and virtual reality (VR) play a significant role in enhanced learning experiences, and we can have extensive training in AR and VR. These will create realistic environments where medical students can practice surgical techniques and procedures. Hence, this will enable people to develop skills without the risk of association with real-time surgeries. So, there may be complex surgeries that have been simulated and will be able to be practiced. VR can provide detailed 3D visualizations of human anatomy, allowing trainees to understand complex surgeries from various angles.   

And simulation of rare and complex cases as some of the complex cases we may not get frequently for somebody to get trained on. So, if we have a simulation of complex cases without a real-time case, they can get the information to practice this. The other is remote learning opportunities. For instance, If we want to have virtual classrooms, we can do this with real-time experience of complex or real-time surgeries with multiple locations. People can participate in these training sessions, and they will be able to get the information correctly. 

In the case of surgical procedures, we have enhanced surgical positions. Augmented visualization will give us anatomical landmarks and the surgeon's view of a procedure. This real-time guidance enhances precision and helps navigate through complex anatomical structures. Pre-operative planning is another aspect, and Surgical simulation can do well in planning surgeries. Hence, we can simulate the process that we have to do on a real-time complex case. 

Risk assessment is one of the crucial points, as multiple surgeries may have complexities. VR and AR can predict the risk associated with any surgery, and respective measures can be taken to eliminate that risk in real-time surgery. Also, any consultations of collaboration is an aspect of this, like remote assistance—eventually, the patient safety and outcomes. We can ensure that we have minimal errors in patient safety and outcomes and can have an enhanced decision-making capability while planning for surgery.

Innovations enable more precise diagnostics and treatment plans, how are innovations in healthcare IT driving the future of precision medicine and population health management?

Precision medicine is a significant aspect of data integration and interoperability. Interoperability will allow patients and healthcare professionals to go through multiple systems seamlessly and provide transparent data exchange between numerous systems, and machine learning can help in personalized treatment recommendations and drug discovery and development for more precision of any diagnosis and treatment. Precision diagnostic is another aspect like enhanced diagnostic tools and innovative technologies such as AI-powered imaging and biomarkers can enable more accurate and early detection of diseases.

Moreover, risk elimination is present in precision diagnostics, which uses predictive analytics to eliminate patients based on their risk levels, allowing them to have more precise screening. Population health management is another major aspect of big data analytics. Healthcare IT systems collect data from large populations to identify health trends, disparities, and risk factors. This analysis supports public health initiatives and the development of targeted interventions.

Also, machine learning models predict future health trends and potential outbreaks, enabling proactive management of population health and resource allocation, which is one aspect of population health management. The other is integrated care models, which are like coordinated care IT systems. They facilitate the integration of care across different providers and settings, ensuring that patient information is shared and utilized effectively. This coordinated approach improves care continuity and outcomes.

The other point is remote monitoring and telehealth, which manages chronic diseases and health-related aspects. These technologies enable continuous tracking of patients with chronic conditions to support early intervention and personalized management. Telehealth expands access to care for underserved populations, improving overall health outcomes and reducing healthcare disparities. Health information exchanges are the final aspect of population data management, and they are like efficient data sharing. Health information exchange facilitates efficient data sharing between different organizations and systems. Eventually, population-level insights and health information exchange provide valuable data for analyzing health trends and managing population health initiatives by aggregating and analyzing information from multiple sources.