The Future Health Index Report: Issues Arising
Infrastructural challenges like internet speed are critical factors. For example, 28% of Australians live in rural areas, with 40% of them having poor quality internet speed unfit for a video consultation. So, there is a need to address the quality of infrastructure so as to increase access. Excellent outcomes can be achieved by patients not having to travel vast distances to access healthcare or by doctor’s intervention at the right time.
Three-quarters of surveyed health leaders believe that predictive analytics and predictive health technologies can increase healthcare equality. Essentially, data can be collected and consolidated in usable formats to create insights. For example, various datasets can be collected from people in rural and remote locations and then, over time, benchmarked with other datasets from unidentified or de-identified patients. This can be used to predict the healthcare needs of people and help to prevent waste, e.g. preventing the unnecessary use of doctors’ time on patients who do not need it.
With this technology, patients in remote locations may no longer need to drive long distances just to visit a doctor. Digital technology can be used to measure and predict what could go wrong with the patient in such a situation.
All of these have a lot to do with AI. The datasets are built, and then algorithms are used to predict the actual outcomes. Furthermore, more than 40% of healthcare professionals are beginning to invest in AI because of the benefits they see in it, like predicting patients’ outcomes, improving clinical diagnosis, and confidence in the diagnosis. It also has upsides in clinical settings and operational settings in terms of managing resources, bottlenecks in hospital flows, etc. A study from the Grattan institute says that the use of more resources can be optimised with the proper management of bottlenecks. AI and algorithms take away the guesswork from questions like who should go into an ICU, at what point should they be discharged from an ICU into step-down care, at what point should they be discharged and allowed to go home, etc. Instead of making judgments by mere observations, vital signs could be collected, and algorithms used to measure them against other datasets, which could produce insights to reach a more informed decision.