SDG 3: Ensure healthy lives and promote well-being for all at all ages
SDG 3 summarised is about “Good health and well-being.” As well as aiming to reduce a number of diseases, it also aims to reduce maternal and child mortality, promote mental health and ensure universal health coverage. For this article, we focus on how AI can be leveraged in healthcare to enable universal health coverage.
Given the complexity and high levels of risk in healthcare, as an industry, we all know it is slower than other industries to adopt digital technologies. We can all relate to the amazing treatment we receive, juxtaposed with time spent in the waiting rooms while we fill out forms at a doctor’s visit, or attempt to locate our child’s vaccination records. We know that technology can be leveraged to improve the overall experience and AI has the potential to play a significant role in enabling universal health coverage.
Here are a few use cases for how AI can enable universal health coverage:
- Enhanced Diagnostics – AI powered systems can quickly analyse medical data such as images, scans and test results more accurately and quickly than humans, potentially leading to earlier, more accurate diagnoses.
- Administrative Efficiency – Automating routine tasks such as billing, and appointment scheduling can help control costs, making healthcare services more affordable.
- Medical Knowledge Management – There is potential on the clinical side to leverage AI enabled virtual reality platforms that enable clinical staff to stay on top of their medical knowledge. On the patient side, access to health information and support can be achieved through AI-enabled healthcare bots and digital assistants.
AI also comes with risks that need to be mitigated:
- Data Privacy & Security – Healthcare AI relies on significant amounts of sensitive, personal patient data, making the risk of data breaches and cybersecurity a top risk. Mitigate this risk by ensuring compliance with data protection regulations (e.g. GDPR, HIPAA) and establish data governance which must include policies on data encryption, access control, data handling, and sharing. (See the article on Data Privacy for more information.)
- Data Quality and Reliability – The quality and reliability of data can vary, leading to inaccurate decisions. Establish robust data validation processes to enable accurate and reliable data for training AI models. Implement processes to identify and rectify errors and inconsistencies in data. Continuously monitor the performance of the AI algorithm to identify and address data-related issues promptly.
- Bias and Fairness – AI algorithms can inherit biases from the data they are trained on, potentially leading to unfair or discriminatory outcomes. Use representative datasets to reduce bias and retrain data to continuously improve. Healthcare professionals will seek to understand the decisions and outcomes, so implement transparency in AI algorithms.
Ultimately, SDG3 aims to enable people everywhere can lead healthy lives and receive healthcare when needed. With the current economic challenges, it is imperative that organisations within the healthcare industry transform their ways of working, to enable people everywhere to access medical care fairly.