The most exciting part is that the technology available today is already so advanced that it can keep learning and refining to improve accuracy over time. It won’t ever replace humans in healthcare. Far from it, in fact. The effective and responsible use of AI will always require a combination of data science and medical knowledge, to ensure that the increasing amounts of data available are always interpreted within a real-world context. But for the first time ever, it is possible to apply the knowledge of the world’s best doctors on a global scale for faster and more accurate diagnoses.
Another huge benefit is the ability to build a detailed, single patient view that marries all the healthcare data ever collected on a person, from their vital signs to any ailment they have suffered. The immediate recall of a patient’s entire medical history and the ability to overlay and analyze multiple datasets simultaneously make it easier to spot any anomalies, compared to the capacity of one single doctor.
This is not only a big leap forward in the treatment of illnesses that have higher survival rates when caught earlier, such as cancer. It also makes it easier for healthcare systems to shift their focus and resources from cure to prevention. This will be fundamental to winning the war on lifestyle-related conditions such as heart disease and diabetes, which are a growing threat in ASEAN. Diabetes affects an estimated 96 million people in South-East Asia, according to the World Health Organization (WHO).
The opportunities that this presents for developing nations in particular are huge. For a start, the ability to speed up diagnosis and automate elements of care delivery is a boon for countries that have long suffered from skill and equipment shortages. This is overdue in South-East Asia, which has the largest needs-based shortage of health workers in the world.
By facilitating routine and straightforward tasks, AI frees up precious human resources and medical equipment to focus on more complex cases where they can make a bigger difference. For example, a new system that we have introduced to a hospital in Malaysia automates the process of measuring patients’ vital signs, so that care teams can be alerted sooner to any changes in a person’s condition.
Global trials of this technology show that it has been able to reduce unexpected cardiac arrests and shorten the length of hospital stays, while at the same time freeing up staff to focus on patient care rather than monitoring. For example, within a year of Lakeland Health implementing patient monitoring technology in the US, they were able to reduce the number of cardiac and respiratory arrests by approximately 56%.
AI-powered telemedicine and the ability to equip patients to help prevent and self-manage certain chronic conditions is also a means of improving standards of health in remote communities that have been under-served for a long time. Increased mobile ownership means people can easily collect and monitor their own health data in real-time. This will play a big part in shifting healthcare from the hospital to home.
Finally, AI has the potential to tackle the spread of infectious diseases, helping countries to predict and manage outbreaks better through data modelling and predictive analytics. However, some challenges remain to be addressed before this technology can reach its full potential.
Are we nearly there yet?
Digitizing the region’s health records is one of the biggest tasks. The majority of the world’s patient data is currently “unstructured”, meaning it’s in a format that healthcare IT systems cannot use. This problem is by no means unique to developing nations, but it is exacerbated in countries where patient records are often written and not well-maintained. The breakneck growth in mobile ownership in ASEAN could become part of the solution, as it presents a potential means for digital data collection at scale. But developing nations also need to invest in digital infrastructure and a common, secure system for storing patient data to enable AI to fulfil its potential.
Another ongoing challenge is the shortage of skills. Although AI presents a viable and much-needed relief to doctor shortages, it introduces another human capital demand that must be addressed – that of having medical professionals trained in data science and AI so they can interpret the data. Investment in these specialist skillsets has to become a priority for medical education and training, in order to future-proof healthcare systems in the developing world.
Finally, AI and automation can only go so far in improving health outcomes. If treatment options themselves aren’t equally advanced, then improving the speed and accuracy of diagnosis is unlikely to make a big difference. Healthcare systems need to make sure that the resources freed up by AI are redeployed in the best possible way to make the highest impact on patient outcomes.
Looking to the future
There is no denying that AI holds a lot of promise for ASEAN’s developing countries. Not in some far-off, sci-fi future, either – all of these applications are possible now. The pace of development is also helping to drive down the cost of scaling this technology, making it an increasingly viable option.
If ASEAN’s developing countries can digitize their health records, upskill in data science and be smart about redeploying resources to where they can make the biggest difference, I am hopeful that the region’s healthcare provision will improve significantly within the next five years, if not sooner.
There are still some hurdles to jump. But for the first time, universal access to healthcare is truly within our reach.
Source: World Economic Forum