The future is not a machine treating patients but health professionals using smart algorithms to help make wiser choices.
Identifying the right drug for the right patient at the right time is really hard. That difficulty results in 500,000 deaths worldwide from Adverse Drug Reactions. To identify the right drug options there are 11 different groups of variables that need to be evaluated for each condition; multiply that by five conditions and it gets complex.
Since the evaluative process uses information from published evidence, we created algorithms to do it. Today, we have 32 conditions in our system including genetics in the variable input. The TreatGx algorithms are heuristic models based on what we use to teach pharmacists, medical students, and doctors. The information is taken from guidelines, systematic reviews and clinical trial evidence.
There are four further steps in the development:
1. Machine Learning: Now that we have the algorithms working in clinical practice, we can explore machine learning approaches. We will develop the model from "simple" rules to a system that is even more personalized.
2. Identifying combination options of drugs for a patient with multiple conditions: We will create combinations of drug options for multiple conditions on a smart application that can be used in seconds. A patient with three chronic conditions (11% of all adults) may need 10 different types of medications. There might be more than 100 drug options with thousands of potential combinations but as more data is added to the algorithms, the number of possible drug combinations starts to reduce and may end up being between 5 and 10.
3. Displaying multiple conditions: We are designing the appearance of the system for the future. On the same screen, we will show representations of all the conditions the patient has and the drug combination options for that person. This has to happen in a few seconds during the consultation for the health professional.
4. Develop an app for smartphones: The app will know all your information including genetics and will create safer prescribing more effective treatment options, and better outcomes. Over the Counter medications are important with $6 billion in annual sales in the USA. Unfortunately, these also may result in adverse drug events. Using our software in the pharmacy, the patient will be able to scan the code on the bottle of Ibuprofen that they are considering using to help knee pain, and the software will show them in seconds whether that is safe and effective for them.
Adverse Drug Reactions account for 10% of direct health care costs. This decision support may reduce that by 25% by identifying drug options less likely to result in harm, but also to result in improved compliance and patient satisfaction. The potential cost savings for all healthcare organizations is massive. We cannot continue to rely on physicians or pharmacists trying to analyze 30 variables in a few seconds. TreatGx software algorithms provide the next step to personalized prescribing.
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