Objective:
In today’s world, customers expect personalized and intelligent products that meet their unique needs. This expectation is not limited to consumer products but extends to the healthcare sector as well. Medeva, a leading provider of healthcare technology solutions, has embraced this trend and designed their EHR platform to provide personalized recommendations to clinicians at the point of care using AI and ML algorithms.
The EHR platform’s personalized recommendation system is a game-changer in the healthcare industry, making it easier for clinicians to capture data quickly and efficiently, both within and between various stages of consultation, such as presenting complaints, medication, and diagnosis, etc… In this blog post, we will explore the approach used by Medeva to develop this recommendation system and how it works.
Approach
Medeva has developed a pipeline that combines three different techniques: multi-class classification, Topic Models, SVD (a decomposition algorithm), and market basket analysis (association analysis). Currently, the pipeline is based on descriptive models because of the cold start. However, once enough data is gathered, Medeva plans to transition to more advanced predictive models such as ANN.
How it works
The recommendation system begins by taking one or more previous sections of data as input. Based on this data, the system uses multi-class classification/SVD algorithms to predict the recommendations for the next section. Once the clinician chooses a recommendation from that section, the system applies a within-section model (market basket analysis) to provide more precise recommendations.
After the within-section recommendations are completed, the data is sent back to the input of the previous sections, and the next section’s recommendations are predicted based on the updated input data. This process continues until all the recommendations for the entire consultation are provided.
The Benefits
The benefits of the EHR platform’s personalized recommendation system are numerous. Clinicians can access personalized/custom recommendations at the point of care, making the data capture process more efficient and streamlined. The system also reduces the chances of errors as it leverages AI and ML algorithms to provide recommendations.
The system’s ability to learn and adapt based on clinician feedback means that the recommendations become more accurate over time. Additionally, the platform’s use of market basket analysis ensures that the recommendations are relevant to the patient’s unique needs and clinical history.
Conclusion
Medeva’s EHR platform is a leading example of how AI and ML algorithms are transforming the healthcare industry. The platform’s personalized recommendation system improves the data capture process and reduces the chances of errors, ultimately resulting in better patient outcomes. As Medeva continues to gather more data, the platform’s predictive models will become even more accurate, making it an indispensable tool for clinicians.
