As we accelerate our predictive patient flow and outcomes journey we look towards advancing our technology and the insights our customers get from their data. One of the most spoken about trends in the healthcare industry in the last 5 years is the growing prominence of AI and its potential applications in both patient care and in healthcare research. So much so that startups who were focused on AI in healthcare saw funding hit a new high in Q2’18, making healthcare the largest industry for AI interest.
This interest continues to accelerate in 2019, highlighted by the growing investment from big tech companies in exploring the application of AI in health. Noticeably Google’s DeepMind is extending their interest and development of using AI in diagnosing eye diseases with plans to start clinical trials later this year. If successful it will see machines reading retinal scans with more accuracy than an experienced junior doctor. The implications of this would be huge if proven to be successful, as it could result in the tech being extended to other diseases and we could potentially see unrestricted use cases. Apple is another big tech company pushing their interest in AI by providing medical researchers and developers with their ML frameworks, giving researches the tools to advance health technologies that allow users to manage their health better.
At Draper & Dash we understand the potential applications of ML and AI and have seen first-hand the benefits it has brought our partner organisations and thus have started to adapt it in more of our modules. So far, we have integrated machine learning in our complex patients with long lengths of stay solution, commonly referred to as our Stranded and Super Stranded Module. The solution analyses an enormous number of complex variables, we look at patient’s profiles and admission conditions and more with the new integration giving us the ability to accurately class patient’s likelihood of having long lengths of stay in hospitals. As part of our predictive patient flow offering, the module allows clinicians to see which factors are contributing to the high risk and intervene early, pushing predictive analytics a step further. As part of our ongoing investment in this area, specifically as it relates to patient flow and outcomes, we have focused on rapidly releasing advance algorithms relating to patient safety, mortality and outcomes, patient’s medicines and outcomes.
One of the complaints opponents of AI have is that the quality and breadth of data in most cases is not adequate for ML to be precise, a valid point of course as if you put trivial data in you get trivial results out. However, in most cases this can be avoided by working with organisations and setting expectations right. Fortunately, we have been able to put systems in place that allow Draper & Dash to work with our partner organisations to ensure the quality and breadth of their data is to a standard that allows for meaningful results. This coupled with adequate training and our invite only AI and ML Healthcare focus groups, we have seen a shift in mindset and an increase in interest from organisations wanting to have AI and ML integrated in their dashboards and clinical systems.
This year we plan to be pushing even harder in our AI integrations and are excited to see how this helps our partner organisations drive new analytics and even superior insights.