As global populations increase, so too do the capacity requirements for public and private health facilities, and with this comes an exponential increase in the amount of data being modified, stored and transmitted daily.

There are few working environments where clear and efficient communication is more crucial than in healthcare organisations especially in hospitals. For clinicians to make informed decisions, it’s essential that they have access to the most up-to-date, precise and relevant information at the point-of-care. A major technological breakthrough being made in this field is using machine learning to optimise the diagnostic process. This will help to assist and streamline the patient experience from the hospital admission, through to being discharged and their on-going care in the community.

For example, in case of a non-acute hospitalisation, a patient may be led through a questionnaire while awaiting their consultation. The questionnaire would comprise of a few diagnostic questions pertaining to any symptoms they may be exhibiting. Their responses can then be fed through a machine learning model using a combined database of the most up-to-date clinical knowledge available. There is a wealth of information to consider: the patient’s electronic medical record, the medical research relevant to the patient’s symptoms, previous similar diagnoses and treatments plus the information from the risk assessment questionnaire. 

The machine learning model uses an algorithm to understand the data it has been given and recognise patterns. This high-powered computing uses insights from machine learning – a type of artificial intelligence that enables computers to find hidden insights without being programmed. Algorithms are able to scan vast data sets and identify indivisualised and precise treatment plans. After a patient consultation, a clinician would then have the option to consult their clinics aggregated data from the machine learning model to help verify their own findings and decide on a diagnosis and treatment plan.

The exciting part about this is the speed and efficiency with which a computer could sift through data. Removing the time-consuming administrative tasks would allow clinicians more time to spend with their patients.

This type of clinical decision support (CDS) is already being implemented – albeit currently in a limited capacity – in organisations all over the world and the results so far have been encouraging. 

With a staggering amount of information being processed through CDS software, indexing and filters are necessary to ensure a user would only get what they’re looking for, but a major upside to data aggregation is the ability to cross-reference information in a totally neutral fashion. This is key to helping create a complete picture of a patient and it could also mitigate possible cognitive biases. 

Depending on what type of medical practitioner a patient is with, the initial suppositions could look nothing alike. A neurosurgeon and a cardiologist, for example, may have completely different first impressions about a patient reporting a recurrent ache in their leg. A CDS system could be programmed to display the most pertinent information first; a family history of heart disease for a cardiologist, perhaps a recent MRI scan for a neurologist. After checking their own input against the database recommendations, a clinician can delve into cross-referenced data and compare their diagnosis with those from other specialists to verify – or in some cases challenge – those initial impressions. From there AI can solicit users feedback to record what information proved most useful, improving future responses and helping determine relevance of secondary patient data. In this way, the system learns to curate evidence-based information to clinical professionals, in real time and at the point-of-care.

CDS technology is still in its infancy at this stage and it will still be a long time before diagnostic and triage processes can even come close to being fully automated. The primary goal of artificial intelligence in medicine is ultimately to work in concert with medical staff, not to replace them, so people shouldn’t be holding their breath for a robotic doctor any time soon. For now, AI will serve as an invaluable tool for helping clinicians make the most recommended, precise and timely decisions that they possibly can. 

CDS has enormous benefits that optimise every aspect of healthcare (i.e. the individual patient, the acute care setting in a hospital and the health of the national population), but the challenge is to make the CDS implementation easier to attain its full potential.

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To learn more about Clinical Decision Support systems, the drivers and challenges involved and the basic CDS functionality required to start making positive change in the healthcare ecosystem, download the white paper now!