The global pandemic of 2020 and 2021 has taught almost all countries of the world how to react when the healthcare system is under immense strain, almost on the verge of breaking down. The entire world has realized the importance of building a collaborative healthcare system that not only encourages and fosters collaboration among healthcare service providers, professionals, hospitals, and educational institutes but at the same time knows how to manage these systems with ease in the future in terms of crisis. More so in terms of crisis, it becomes important how well we leverage automation, Artificial intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) to facilitate process improvements for providers and patients. As illustrated in the Figure below, a complete patient 360 involves the following services. Now let us see with a few examples given below, how we can help to provide patient care with the use of AI/ML-driven solutions.

Introduction & motivation

We have learned with real experience, that having a patient 360 Insight can aid in building connected personalized systems in the healthcare industry. For example, it has been proven from studies and research that a patient’s detailed health information along with their lifestyle preferences and propensity to avail wellness resources provides doctors and healthcare professionals to study and develop a complete risk profile of the patients and drive opportunities to serve them better. What is most important is to build a Customer 360 Insights that connects fragmented data from any source, namely

• Campaign management,
• Digital health
• EHR (Electronic Health Record)
• CRM (Customer Relationship Management)
• Contact center
• Affiliated clinics
• Customer surveys
• Patient Portal

Data from siloed sources can be synthesized into a consolidated 360 patient view which can be further enriched
with advanced intelligence to produce insights that can be consumed in real-time. Such detailed 360 insights can tie the different industry stakeholders (doctors, providers, clinicians, and administrators), and align them in creating personalized marketing campaigns and communication, providing care management, and wellness services from an industry standpoint. Additionally, it can also discover and manage complex relationships between individuals, households, providers, and services. Hence the primary step to achieving that objective is to build capabilities using Big data components (including Apache Hadoop, Spark, graph, columnar, and in-memory data stores) and AI/ML-based pipelines.

The above figure demonstrates Customer 360 Insights addresses analytical, marketing, and operational initiatives, to:
• Improved identity resolution by matching patients and clinical data to drive HEDIS reporting, population health, and social determinants of health
• Marketing segmentation
• Formulating best actions to retain patients
• Referral capture and in-migration
• Personalized services to provide the best care to patients
• Detect and proactive prevention of fraud, waste, and abuse

1. AI-driven Healthcare workflows

With the increasing adoption of AI, in many countries, 95-97% of health care invoices are digitally generated. Moreover, AI-based digitization plays a key role in discovering errors in treatment, workflow bottlenecks, inefficiencies, etc, through automation to save more time, lower costs and increase accuracy.

2. Medical Records Management

AI-driven Medical Health Record Management has been revolutionized with data compilation, and data management to enable more consistent access. The 360 customer insights enabled by Customer Data Platforms, have been able to drastically reduce 34%-55% of a physician’s schedule, thus boosting physician’s productivity. The application of NLP techniques in clinical documentation, and insurance processing has been a key enabler in not only faster maintaining patient records but been instrumental in saving several million dollars from fraudulent claims. As medical health records need secured data sharing, we are likely to see wide applications of blockchain in this industry in the near future to facilitate the exchange of physical, economic, and content assets on decentralized networks.

Other applications of NLP algorithms include different types of searches in healthcare data, enabling the search of 80 percent of valuable EHR content that’s unstructured, including clinicians’ free-flowing notes, pathology reports, and other documents. Advanced transformer models when put in a real-time medical system can read and interpret patient records and provide insights to physicians who can recommend instant treatment measures.

3. Health Risk Prediction

The healthcare predictive risk analysis has come up with ML-based predictive models that can integrate into a hospital’s or health system’s electronic health record (EHR). The syndromic surveillance technology can scan the EHR for key symptoms, and access the likelihood of COVID-19 or other infectious diseases. This can be then applied to the data to predict which patients may have the disease and determine who’s likely to require hospitalization even before the patient has been admitted to the hospital. Further, hospitals get an opportunity to estimate their capacity in terms of beds, ICUs and other available resources and an attempt to better prepare themselves.

4. 24×7 Virtual Care Assistants

Research has demonstrated positive results with chatbots, and virtual assistants in the medical industry, resulting in reducing $20 Billion annually in interaction with patients to provide effective care to patients by monitoring and answering their queries.

5. Medical Image Analysis

The application of AI has enabled faster analysis of medical images in the order of 1000 times which can better detect cancer, tumors, tissue damage, and genetic diseases that may evade the human eye.

6. Healthcare Process Automation

Advanced research and the use of AI are employing robots to undertake X-Rays, CT scans, and many other tests, which can be later referenced by doctors to generate a precise report.

7. Robotic-Assisted Surgery

Healthcare research is looking to deploy automated assisted robots that can help surgeons during the operation. Research results suggest there will be a 21% of reduction in patient stay when a robot analyzes data from pre-op medical records to guide a surgeon’s instrument during surgery. Further, in this kind of operation, undertaken by robots are capable of providing faster healing to patients as they are minimally invasive.

8. AI Vital Sign Monitoring

With the larger deployment of Iot and IoMD (Internet of Medical Devices) it has become much easier to automate AI Vital Signs Systems for individuals where the Health Data Monitoring Systems at hospitals and nursing homes can continuously sense patients’ vital organs. Such Real-time Vital Signs Monitoring Systems, can collect information from patients anywhere, anytime, and send the information to Medical Drs, Nursing staff, Physicians, and Healthcare Professionals to take immediate action, in case of an emergency. The devices being built on more advanced healthcare with faster processing capacity can raise alarms when vitals show anomalous trends, which can reduce morbidity and mortality rates.

AI Vital Signs Monitoring Systems are equipped to continuous, real-time, high-precision vital signs such as ECG, HR, PAR, SPO2, HBP-LBP-ABP, R-BR, and ST that will be a digital signal and statistically processed, analyzed, detect, correlate, prognosis, and diagnose abnormalities and critical events for cardiovascular, cancer, chronic diseases.

9. Fraud Detection

It has been found that there could be a potential savings of 3-6% savings in healthcare costs. Predictive Analytics are efficient in detecting frauds like illegal medical billing practices when multiple claims are given by providers for the same patient. AI-based fraud algorithms can see scenarios when patients hide their identities or provide false identities to claim reimbursement.

10. Personal Wellness Management

AI has played a great role in shaping physical and mental wellness through the use of wearable healthcare technology, where people have become more proactive to take ownership of their health. The real-time personal health record gets collected using smartwatches, and fitness trackers to get uploaded and stored at the servers. Through this, the system is able to learn heart rate, blood glucose level, blood pressure, and other parameters of an individual and raise an alarm of emergency when the values change beyond a threshold.

11. Future trends in Healthcare Industry

We have seen some use-cases where AI can proactively speed up processing, and provide best-in-class service to patients. However current research techniques suggest AI can be a gamechanger in the healthcare industry where federated hybrid models can be used across hospitals, and medical institutions to support clinicians, doctors, and healthcare professionals to aid in the diagnosis, treatment planning, and identification of risk factors. The next-generation automated services aim to operate at a larger scale and deliver measurable improvements in patient results.

12. Digital Twins

AI aided predictive analytics can predict emergency situations in virtual space and suggest meditative actions before they can be undertaken in a real physical environment. In the healthcare industry such digital replicas of the tools, people, processes, and systems that businesses can lead to the simulation of digital representations of healthcare data, such as hospital environments, lab results, human physiology, etc. through computer models. Digital twins have a significant role to play in :

• Resource Optimization: Digital twin-based simulations using historical and real-time data of hospital operations and surrounding environment (e.g. COVID-19 cases, car crashes, etc.) can facilitate hospital management to detect bed shortages, optimize staff schedules, and help operate rooms. This would, in turn, ensures smooth coordination of processes between the big and small hospital and increases the efficiency of resources and optimize the hospital’s and staff’s performance, while decreasing costs.
• Risk management: Digital twins drive AI/ML-driven intelligent decisions for evaluating patients’ risks health in a complex and sensitive environment so that physicians can drive necessary actions by studying the impact of interventions before trying them on real patients. The predictive risk analysis can also give an estimation of system behavior ranging from change in staff numbers, operation room vacancies, and device maintenance, and thus drive strategic decisions.
• Personalized diagnosis and treatment: Digital twins have further assisted in collecting and utilizing vital data from patients where physicians have been able to track persistent conditions and drive clinical trials and research data at labs
• Drug and Medical Equipment Discovery: Whether research and investigation of new drugs or devices, digital twins have enabled scientists to modify and redesign drugs considering particle size and composition characteristics to improve delivery efficiency. Developers have leveraged digital twins to test the characteristics or uses of a device and make alterations in design or materials. Once devices are tested for success or failure, the modifications in a virtual environment can drastically reduce the costs of failure and increase the safety and reliability of the product during the actual stage of manufacturing

13. Metaverse

The pandemic has seen the birth of new social networks, which accelerated the growth of immersive platforms to resolve the gap between families, friends, communities, and societies. Metaverse 3D translates fundamental dependence and
emotional investment by using 3D avatars of health workers through digital solutions. The avatars are equipped with the necessary functionalities to collaborate with tools such as digital whiteboards and are able to meet face-to-face without any
complex conferencing equipment. Avatars can also simulate realistic consultations, personalized care, treatment, and
diagnosis with real-time data. Metaverse can transform the clinical care space by embedding in immersive experiences recreated from surgery. Surgeons and doctors can receive real-time guidance to perform critical surgeries in the operating theatre that can improve surgical precision and flexibility.

In the future Metaverse will be redefined with AI to impart planning, and medical training with collaborative medical procedures to support accurate clinical decisions, and precise interventions customized for each patient. One example of this is visible from the Beyond Metaverse (https://www.veyondMetaverse.com) which has been successful in creating a future Healthcare Metaverse ecosystem, to nurture an environment of collaborative education. Further AR and VR in clinical environments assist clinicians with flexibility to project medical images, such as CT (Computed Tomography) scans, directly onto the patient and in alignment with the patient’s body, even as the person moves, to provide clinicians with clearer lines of sight into internal anatomy. Apart from improving patient experience, physicians and doctors can provide more accurate diagnoses when they project a map of the patient’s veins on the skin.

AI ethics becoming a predominant part in the evolution of the healthcare industry equally play a dominant role we think of introducing Metaverse in the healthcare domain. For example, if Metaverse requires biometric data then what are the data security, privacy and rights we follow to ensure customer protection. What are the strategies that we follow to ensure informed consent in Metaverse? Moreover, regulations are needed to control the involvement of children in Metaverse so as to mitigate the physical and mental health impacts of Metaverse. Even if the Metaverse becomes an important component of the healthcare industry, we need to be cautious to build equitable, inclusive, and truly decentralized Metaverse systems.

Conclusion

The main objective behind bringing AI/ML into the healthcare industry is to customize services and treatments to individual needs and the service is able to enable family members to find great doctors who can consistently deliver the best value and quality care in their community.

The Metaverse technology can be digitally connected with Digital Twins where they can both explore the power of AI and data. The Metaverse provides a space where it is possible to develop a virtual 3D model, and then use real-world specifications using digital twin technology. We can witness true healthcare digital transformation when we literate the young population so that they understand the risks and actions needed to keep a sound physical and mental health, they are incentivized to learn, about wellness, and are encouraged to educate their peers in a safe social virtual setting. What is needed utmost at this juncture is privacy-aided patient data management that can be collaboratively used by healthcare agencies, hospitals, and providers in the best accurate and precise manner to deal with exigencies. True transformations will open possibilities of shortages of healthcare providers and workers and at the same time will be able to improve community health and drive economic improvements.

References

• https://www.informatica.com/content/dam/informatica-com/en/collateral/solution-brief/customer-360-insights_solution-brief_3828en.pdf
• https://dergipark.org.tr/en/download/article-file/2167692
• https://deloitte.wsj.com/articles/an-ethical-metaverse-takes-trustworthy-ai-01651004946

• https://research.aimultiple.com/digital-twin-healthcare/#:~:text=A%20digital%20twin%20is%20a,through%20computer%20models
• https://www.hcinnovationgroup.com/analytics-ai/artifical-intelligence-machine-learning/article/21223186/beyond-covid19-four-ways-ai-is-shaping-the-future-of-healthcare