A study conducted in hospitals by the General Medical Council, UK and published by BBC, found out there are errors in the prescriptions done by doctors.* Among those wrong prescriptions, some of the errors are lethal. To reduce such mistakes, an AI-based matching algorithm can be used to suggest drugs to patients based on the patient profile(pre-existing condition). You might be hesitant to implement AI as a traditional development cycle of an AI-based matching algorithm takes almost 4 to 12 months to create such matching algorithms. However, Caboom can reduce such a traditional development cycle by one-fourth to one-twelfth. Caboom’s short development cycle includes several iterations to achieve high accuracy.
Emma is a patient of hypertension. During the cold, she has a nose blockage problem(decongestants). Pseudoephedrine is a popular drug both by doctors and over-the-counter. Pseudoephedrine is great for clearing up the congestion of bad colds, however, it shrinks the blood vessels, they raise the blood pressure. This is dangerous to patients with hypertension like Emma. Phenylephrine can be an alternative drug for Emma based on her pre-existing condition.
According to a report published by Pharma Publishing Media Europe, around 1 in 10 hospital prescriptions contain errors. A study done by General Medical Council(GMC) in 2009, UK found that out of 124,260 around 9% prescriptions have errors such as omitting drugs, incorrect doses, patient allergies not taken into account, and so on. Out of 9% of the error, 2% contain potentially lethal instruction.
There is a serious discrepancy between the doses given to the patients on doctors recommendation and the clinical pharmacists medical dose reaction study. In a conventional way, communication between the doctors and clinic pharmacists can minimize this kind of error. The sophisticated way to tackle this problem would be using Artificial Intelligence(AI) based solutions. AI can enable doctors to match the patient condition profile based on demographic information, allergies and disease diagnosis with the drug profile, based on drug reaction on drugs, food, allergies and disease and the adverse impact. AI solutions will remarkably reduce the errors.
Caboom is a platform that enables the state of the art recommendation, where it can recommend the drug based on the personal profile to reduce the adverse impact on patients.
Building an AI-based matching algorithm is about working on heavy math, finding patterns hidden in data, working with all the caveats, and power technology to meticulously combine everything to your use-cases. At Caboom, this complex and time-consuming task is automated with just a few clicks without hiring an in-house data science team in your organization. Caboom’s objectives are:
In a drugs-patient profile matching solution, the Caboom platform can replace the traditional development cycle to provide AI-based recommendation solutions at low cost and reduce time without compromising accuracy.
If you are a product manager, product owner or in a hospital management team without an AI/ML job title with a team of software developers looking to develop an AI-enabled recommendation system, then Caboom is the right platform to start. You can focus on your service, Caboom will take care of your AI model and its deployment for you.
* Article published by BBC