The benefits of CDS are evident. Humans, including trained clinicians, are poor performers when it comes to assessing many data dimensions at the same time: we do not integrate data well and tend to interpret negatively. On the other hand, in their current form, machines are still poor in interpreting contextual situations and dealing with uncertain situations, and therefore dependent on access to high quality and quantity of data. Humans make errors, and so will the machines. In general, if identified early, a good clinician can redress a mistake. Current, non-AI based CDS do not have the capacity to predict intervention outcomes; this is the main reason these systems operate in an open loop where clinicians are the central part of the loop.
More accurate and safer
While in the past many systems and approaches have been explored in an academic environment, their adoption into medical practice has been very slow. The main reason being that neither health systems nor humans were ready for it. After all, like other medical devices, CDS need an appropriate design: they need to fulfill the high medical standards and performance requirement, fit into local cultural, ethical, regulatory, and organisational settings, be cost effective, and have a sustainable business model. This will also hold true for the new AI-based generation of CDS which may indeed have the capacity to deliver more accurate diagnoses than humans.
The inherent goal of automation in health care, however, is more profound: to improve the quality and safety of services. Medicine has already an established safety culture and is consequently ready for better and safer systems. If we can demonstrate that automated systems consistently increase patient safety, then any discussion of if and when CDS should be used to replace doctors is unnecessary.
No personalized medicine without AI
The harsh reality is that without CDS and intelligent agents, our health system will simply grind to a halt. The staff in intensive care units are already overwhelmed with biosignals to interpret and alarms to silence; very soon, this burden will reach other specialist and general practitioners who will be bombarded with data from wearables, genetic tests and other biomarker results that must all be integrated into a diagnosis, and treatment decisions made within 10 to 30 minutes of meeting a patient. We can’t miss this opportunity for automation if we want to make personalised medicine a reality. This data will have to be processed, classified, and analysed by automated algorithms.
And this leaves the big questions, which are not mine to answer at this time, but for public debate. In the future, who will have the final word in a clinical decision delivered by a computer? Will the all-knowing machine be closing the loop, or will the wise clinician still have a role to play? Will the insurance companies or the hosting hospital be the ones to configure the algorithms, and will the patient still have a say?
