BENJAMIN Franklin once said, “An ounce of prevention is worth a pound of cure”, an advice that has anticipated hundreds of years of healthcare best practices.
Spotting and preventing medical problems early on is far cheaper and more efficient than catching them late. Artificial intelligence in healthcare is helping to fill in the gaps by diligently mining as much data as possible and making helpful suggestions that can lead to potential medical problems being identified earlier.
Globally the healthcare sector is experiencing staff shortages as well as pressure on costs. In Southeast Asia, cost and quality are the two key concerns for the health care providers. With medical costs increasing due to demographic change and the rise in chronic disease population, governments are starting to evaluate the true cost effectiveness of medical treatments.
Malaysia is no exception. According to Deloitte’s Voice of Asia report (2017), Malaysia’s population is expected to grow by more than 75 per cent from 19 million as recently as a quarter of a century ago to nearly 34 million in 2022. Meanwhile, the population aged 65 and above is projected to more than triple over that time, from 700,000 to more than 2.5 million, accounting for 7.5 per cent of the total population.
One of the most promising approaches to the global healthcare challenge is the application of technology, and a key element of this approach is artificial intelligence. The processes of healthcare generate huge quantities of data that, when correctly analysed, can help treat patients faster and more effectively, and also avoid errors.
Detecting patterns in data can be a tedious process for which machines are better suited, particularly when there are lots of variables or scenarios to reference. Artificial intelligence in healthcare can help by surfacing signals that well-meaning physicians may otherwise miss.
AI does this by processing vast amounts of historical data using statistical models that identify patterns. It can then process new data against the historical model to look for similarities that may not be immediately evident to a human physician. Hospitals might experiment with this technology to scan medical images and evaluate possible diagnoses based on a large selection of similar pictures.
Around the world, AI solutions are being developed to help with everything from oncological scan analyses to the predictive capability of alerting nurses to a patient’s risk of falling.
Earlier this year, Malaysia introduced the Stethee Pro, the world’s first stethoscope with artificial intelligence (AI) capabilities built in. It allows users to listen to patients’ hearts and lungs with sophisticated amplification and filtering technology. The recordings can then be transmitted to a smart device, such as a smartphone or a tablet, via Bluetooth, and analysed to build a personal biometric signature for each patient to detect the presence of heart or lung diseases.
The benefits of AI are clearly visible in the areas of healthcare where doctors and patients meet. However, machine-learning algorithms also have a part to play at other stages along the healthcare value chain, as they help hospitals improve their processes behind the scenes.
For example, in some hospitals, data about treatment times is being gathered and processed to help predict how long a particular surgical procedure will take, or how long an imaging device may be in use. This helps to avoid waste and improve patient outcomes by developing treatment schedules that maximise the use of scarce human talent and technology resources.
None of these advances would be possible without data: today’s machine-learning systems depend on it. The existence of huge volumes of historical and real-time data holds out the promise of more accurate AI models and better preventative care. The challenge for hospitals lies in processing that data, which is often delivered in high volume thanks to high-resolution medical imaging, and deriving meaningful insights from it. This is the fundamental challenge that the healthcare industry must overcome before all the promises of AI can be realised.
From responding to outbreaks of new diseases, to managing increasing numbers of elderly patients, to improving operational efficiency, AI can provide healthcare professionals with exceptional support — so long as there is a solid bedrock of data management.
Many hospitals around the world are already using tools that help manage these huge volumes of data as well as controlling access to them, as they begin to take advantage of the benefits AI brings to medical care. It is essential to put in place effective platforms to securely manage medical data today, in order to create a solid and reliable foundation for AI developments in the future.
Tomorrow’s doctors will be more informed, more confident, and far less stressed, thanks to a digital ecosystem that supports their decision-making processes and reduces the probability of error.
Kamal Brar is Vice-President and General Manager Hortonworks, Asia Pacific, a data software company