In an interview with The Arab Hospital magazine, Akram Sami, General Manager of UAE and Kuwait, Cerner Middle East and Africa, and Dr Mohamed AlRayyes, the Senior Physician Executive, Cerner Middle East and Africa, talk about the significance of artificial intelligence adoptions and data-driven innovations in the health care industry today.
Can you explain the growing importance of utilizing data, analytics and artificial intelligence in the future of health care and proactively keeping our communities healthy – including population health management and chronic disease management?
Akram Sami: Health care analytics is growing in importance with increased competition, growing regulatory complexity and other innovations ranging from precision medicine to value-based care to population health management. Today, health care industry stakeholders trust that a robust and meaningful analytics platform holds the potential power to transform a health care organization and differentiate it from the competition.
As more and more data become available from sources – electronic health records (EHR), insurance claims, wearable medical devices, social media, the patients themselves and more – analytics can increasingly help detect patterns in information. This delivers actionable insights to predict and conceive alternatives that might not otherwise be obvious. At Cerner, we continue to work with our clients to deliver meaningful analytics across all points of care, and we assist them in workflow improvements through data and analytics to drive cost and waste out while continually improve on quality and safety. Additionally, Cerner provides our clients with actionable insights for the population, risk management and chronic disease management programs.
Artificial intelligence (AI) has come a long way and can help manage and analyze data, make decisions, and conduct conversations. As such, it is destined to drastically transform the health care industry and change clinicians' roles and everyday practices. According to the results of a survey by Stanford Medicine and The Harris Poll1, 62% of a physician’s time with a patient is spent in the EHR. AI has the potential to take over the time-consuming task of data input so that clinicians can focus on providing the highest quality of care to patients. The technology can also be used to improve physicians’ workflows while simultaneously contributing to burnout relief and prevention.
For example, envision a world where a provider can walk into an exam room, have a conversation with their patient, complete a physical exam, discuss treatment options, and then walk out of the room with all discrete clinical data documented, orders placed, notes completed and charges captured. At Cerner, this vision is becoming a reality – microphone arrays, sensors, video and other inputs are being designed to observe patient-provider interactions, transcribe those encounters and correlate them with sensor data. Key clinical concepts extracted from the transcripts and non-verbal cues derived from sensor data can provide supplemental information to aid in speaker identification and non-verbal communication.
In the next few years, AI will rapidly transform the health care industry and the way clinicians interact with EHRs. Clinicians’ perceptions of EHRs will also transition as the technology becomes an invaluable tool that empowers them to practice with confidence, and establish and maintain meaningful relationships with the patients they serve.
What is the role of data governance and security when seeking data-driven innovation?
Akram Sami: To survive in today's digital health care environment, the quality, accuracy and completeness of an organization's data are essential. As the health care industry increasingly focuses on data analytics, the quality and longevity of the data are of utmost importance to cultivate an information-rich organization. Creating this information-rich culture requires robust data governance and security strategies, ensuring that data assets are cleansed, managed, stored, retrieved, maintained, utilized, shared and protected throughout the enterprise.
Data must be governed to ensure everyone is playing on the same field. In health care, there are so many interrelated data points, but the way people look at these data points from different views can cause varying interpretations that can lead to misunderstandings, inaccurate decisions or distrust of data. A robust data governance program includes not only providing definitions, but also diving down to the data level – locating the source or sources, its table and field locations, acceptable values for the data and abbreviations or alternative names.
Data security strategy is of utmost importance as well and can’t be underestimated or neglected. In 2019, health care data breaches tripled, impacting 41 million records, compared to the previous year’s 15 million patient records, according to the Protenus Breach Barometer2.
With such a growing trend and with what is at stake in terms of patient privacy, organizational credibility and financial ramifications, it is essential health care systems put in place measures to secure health care data. National guidelines and legislation are essential in order to set the foundation for all health systems to comply and abide by. With an increase in the mobility and access to data through interoperability and automation to support improved patient outcomes and population health, stringent technical guidelines and national privacy and security protocols need to be established. Many organizations, such as the US National Institute of Standards and Technology and ISO, have provided guidance required to establish these protocols.
What is the current state of data sharing practices and the ‘fluidity’ of health care data in the Middle East? And what is their impact on generating actionable insights for the industry?
Dr Mohamed AlRayyes: This is a very important question, indeed. We have come a long way over the past decade in digitalizing the regional health care industry, and the overall progress is certainly positive. However, there remains to be many data-sharing barriers and silos in the region that exist on every vertical and horizontal level. Inter-departmental in individual organizations, inter-organizational across local health care markets, and inter-industrial within the geographic boundaries of countries.
Allow me to give you a few examples of that. On one level, within even the most digitalized health care organizations, you still find the disconnect among the various clinical, operational and financial databases. Thus, the organizational decision-making process is based on fragmented pieces of the puzzle that lead to incoherent solutions to their problems. On a second level, you find patients having their clinical data dispersed across various health care organizations, each of which holds just a cross-section of the patient’s true clinical picture, without strong health information exchange protocols across the various organizations. On a third level, you find the disconnect to be very prominent between the health of the populations and the care they receive. The health of the population is not only determined by the care provided within the four walls of hospitals and clinics, but also by their social determinants of health, lifestyle and daily habits that impact the quality of their health far more than any single encounter with a clinician. Where does that type of data sit right now? It’s in our wearables, it’s on our smartphones, it’s in our bank accounts and spending behavior, it’s even in our social network interactions. It is everywhere but the medical records. Although privacy might be a concern for some, the technology industry is already providing us with the tools to anonymize personal data for strategic decision-makers, and the protection mechanisms for individuals to keep others from accessing their data while they reap the benefits of personalized insights.
And, as you may know, there are many restrictions right now on the flow of data across the geopolitical borders between countries in the region. I am not particularly referring to the sharing of health care data among different countries, which can open up a new array of opportunities, but that is an argument for a different time. What I am referring to is the flow of data to countries where certain technologies are available for more advanced processing and back, while maintaining the security and privacy of processed data. Where you find such practices accepted as a standard in other regional industries, like banking, we are still facing strong resistance in health care.
For us to generate actionable insights for the health care industry, we need to develop the right policy and technology ecosystems to break all these silos. After all, the quality of any artificial intelligence or analytics-generated insight is determined by the quality of data – big or small – that goes in. The higher the quality of the data, both in scale and scope, the more comprehensive our views, and thus our decisions, will be.
What are your views on the adoption of artificial intelligence innovations in the regional health care industry? And are we seeing higher adoption levels amidst the COVID-19 pandemic?
Dr Mohamed AlRayyes: Various countries in the region, like the Kingdom of Saudi Arabia and the United Arab Emirates, are positioning AI at the center of their future visions and their policy development – that is surely a step in the right direction. However, a level of skepticism remains in the region around the robustness and accuracy of the tools and their output for health care. This skepticism will fade away, sooner or later, because the evidence is coming, and coming fast, from all around the world. We have a choice here in the region whether to wait for the evidence to grow more solid or contribute to its generation.
The COVID-19 pandemic made it easier for several healthcare and research organizations in the Middle East to choose the latter option. We are starting to see their innovative projects that use AI to answer important questions raised by the pandemic. From machine learning models that can predict the probability of a COVID-19 patient requiring intensive care admission to models that stratify the population into priority groups for vaccination – even to models that try to predict the possible upcoming waves of the infection in certain cities or districts; the experiments are surfacing from many countries in the region. Nonetheless, we still find these projects to be done on small scales and struggling to be further validated on larger, more representative datasets. Hence, making the data sharing and accessibility issue even more crucial to resolving.
These and other AI projects and machine learning models must originate from the region and must be validated within. After all, we cannot borrow models that were developed in other regions and apply them in a plug-and-play fashion to our populations. It is through identifying, embracing, enabling and disseminating these small pockets of innovation that our health care industry can prosper in the Information Age 2.0. And it is the responsibility of everyone in the industry to take the initiative to support the ’new normal’.