A large part of my nursing career was spent caring for patients in a critical care or intensive care unit (ICU).
ICUs cater to patients with severe and life-threatening illnesses and injuries, which require constant, close monitoring and support from specialist equipment, medications and clinical staff in order to ensure normal bodily function. Patients need to have their vital signs monitored 24 hours per day and that is why ICUs are required to be operational 24/7. ICUs have a higher staff-to-patient ratio and access to advanced medical resources and equipment that is not routinely available elsewhere in a hospital. This is to ensure that a patient’s physiological data is constantly monitored to assess if there is any clinical deterioration which will trigger appropriate intervention and timely care.
Imagine if your community doctor or nurse could monitor your vital signs and physiological data in the same way. Community clinicians look at snapshots of physiological data taken during your appointment time including heart rate, temperature and blood pressure measurement. Imagine if this data could be recorded in real-time as a continuous stream of physiological data 24/7. This would provide community clinicians with an accurate recording of a patient’s physiological state over time and could be obtained from wearable medical devices.
With access to this real-time data, a system could be implemented to compare results from multiple similar patients, as well use all current clinical guidelines and studies to make clinical suggestions to medical experts. 24/7 monitoring could predict or alert clinicians to the deterioration in a patient’s health just as the monitoring does in a hospital ICU.
For many patients with diseases such as congestive heart failure and Chronic Obstructive Pulmonary Disease (COPD), the processes that lead to severe illness start days before the patient actually becomes acutely ill. Imagine the scenario of an elderly patient who is being monitored 24/7 and living in their own home. Their heart rate rises significantly, but their activity is going down while their breathing rate is going up. This would trigger an alert to the community clinician who can then interpret this data and decide on the best course of action.
There is a transformation underway in healthcare, with the adoption of Clinical Decision Support (CDS). CDS is a health information technology system that is designed to provide clinicians and other health professionals with clinical decision support.
How does it work?
CDS has three core components: Device, Data, and Decisions. Devices are used to monitor and collect health information about a patient. This data is fed into a machine learning algorithm which compares it to the information it has already been ‘taught’. For example, if it detects that a patient’s blood glucose levels are dropping steadily over a period of time, it would have been taught that this means they are becoming at risk of hypoglycemia or low blood sugar which can cause dizziness, confusion, or fainting. This data is then communicated to the clinician or even the patient themselves who can ensure that measures are taken to increase the blood glucose level by eating or drinking fast-acting carbohydrates for example fruit juice.
Device –> Data –> Decision
The rise of machine learning in healthcare has made CDS systems possible. Unless you are in hospital or an ICU, clinicians cannot monitor your vital signs 24/7, but a machine can. Not only can it monitor and respond to certain events like ‘call an ambulance if this heart rate drops below 40bpm’, but it can compare this information to the rest of the database to generate certain insights. In the future we may see machines warning us of dangerous environments due to the number of patients reporting similar symptoms in the same location.
CDS has the potential to provide more appropriate care, reduce medical errors and control healthcare costs. It also highlights the importance of an electronic medical record. This combined knowledge of device, data and decisions, will enhance healthcare globally now and into the future.
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!