Healthcare costs continue to grow year after year with no slowdown in sight. These costs can be exacerbated by unnecessary tests, misdiagnoses, readmissions and other inefficiencies in the healthcare system. As a worker in healthcare, I witness firsthand so many opportunities for improvement through technology.
As a rule, when a patient is admitted to a hospital with a new health related problem, they can often stay in the hospital for days while being worked up, costing thousands of dollars a day while going through test after test, including invasive diagnostic tests and non-invasive imaging. They can be trialed on several different therapies targeting all differential diagnoses until the patient responds to one, thus narrowing the diagnosis. They can be given suboptimal therapies or ones that simply do not work. That is when clinical decision support systems can truly save the day and possibly the patient.
What if a patient could simply enter their symptomatology, their age, demographic, gender and routine blood work and be returned a list of potential diagnoses with percentiles of likelihood. The physician could then step through the diagnostic plan for the most likely diagnosis. Then after rule out of the first diagnosis, conduct a diagnostic of the second, and so forth. The predictive value of historic patient data would give a highly accurate list of potential diagnoses to start from. Starting with a rule out of the most likely diagnosis instead of conducting a wide battery of tests targeted at several possible diagnoses would lessen inpatient time and subsequent costs.
But why stop at bloodwork? We now have the ability to collect genomes and map them to patients. We have the ability to predict how a patient will respond to certain therapies based on their genetic makeup. This is a very powerful predictor in terms of healthcare treatment. It will reduce the instances of failed therapy and help optimize and potentiate the therapeutics given. This type of personalized medicine will be a gamechanger in terms of cost.
Finally, lack of access also drives up cost. If clinical decision support systems are available to Physician Assistants and Nurse Practitioners in areas commonly staffed by Physicians, we could increase the availability of treatment to communities with little access to healthcare. By treating patients early in their disease process or patients who would otherwise seek primary care through the Emergency Department, the cost of healthcare would be greatly reduced.
Tech is in it’s infancy when it comes to healthcare. It will not be the money made that attracts the healthcare community and insurance companies to big tech, but the money saved. Once tech really takes a foothold, clinical decision support systems will be like the autopilot of a commercial airplane. No clinician will be without it. Errors and deaths will be reduced. And medicine, like air flight, will be a much more failsafe and consistent undertaking.
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