In today’s era of rapid technological advancement, healthcare systems are increasingly leveraging data analytics platforms integrated with artificial intelligence (AI) and machine learning (ML) technologies to revolutionise patient care and operational efficiency. By amalgamating vast amounts of patient data from electronic health records (EHRs) and remote monitoring devices in cloud environments, healthcare organisations are poised to achieve significant cost reductions and improved patient outcomes.
Healthcare providers possess a wealth of patient data sourced from EHRs and remote monitoring technologies. However, translating this abundance of information into actionable insights has historically been challenging. According to industry experts at .conf24, Splunk’s annual conference, the convergence of AI and ML presents a transformative opportunity for the healthcare sector. Brett Roberts from Splunk emphasised AI and machine learning can greatly benefit the healthcare industry by addressing challenges and enhancing the quality of care.
The Power of Integration: Splunk and AWS in Action
During a live demonstration at .conf24, Brett Roberts and Alan Peaty showcased how integrating Splunk’s data analytics capabilities with Amazon Web Services (AWS) can unlock powerful insights for healthcare providers. They demonstrated importing simulated data from wearable patient devices into AWS, integrating it seamlessly with data from EHRs and other sources using AWS Glue. Utilising Amazon SageMaker Canvas, a no-code ML interface, they analysed 183,000 patient records to uncover correlations between wearable data and hospital readmission rates.
The results were immediate and enlightening, as age and device uptime significantly influenced patient readmissions. This insight enables healthcare providers to personalise patient care and intervene proactively.
Transforming Healthcare Delivery: Predictive Analytics in Practice
Reducing patient readmissions is a critical goal for hospitals, not only to enhance patient care but also to meet financial incentives tied to performance metrics like those under the Hospital Readmissions Reduction Programme. By deploying predictive models, healthcare providers can identify patients at high risk of readmission and intervene pre-emptively. Alan Peaty illustrated this with an example involving an elderly patient whose low device usage predicted a high likelihood of readmission. Such predictive capabilities empower healthcare organisations to tailor interventions and improve patient outcomes.
Brett Roberts underscored the collaborative nature of developing and deploying ML models in healthcare. He emphasised the crucial role of clinicians in ensuring the accuracy and relevance of predictive analytics. In cases impacting patient outcomes, collaboration between data scientists and domain experts is essential. Including clinicians in the model design and decision-making processes enhances accuracy and relevance.
Integrating data analytics platforms with AI and ML technologies represents a paradigm shift in healthcare delivery. Healthcare organisations can optimise care delivery, reduce costs, and ultimately improve patient outcomes by harnessing the power of data to drive actionable insights and predictive models. As these technologies continue to evolve, their potential to revolutionise healthcare remains vast, promising a future where data-driven decisions enhance the quality and efficiency of patient care worldwide.
Source: HealthTech Magazine
Image Credit: iStock
