Building a Product Recommender Application for Improved Prescription Practices


In today’s healthcare industry, providing accurate and efficient treatment to patients is of utmost importance. However, doctors and clinicians often face challenges in prescribing the most appropriate medications and services for various patient concerns. This case study focuses on developing a Product Recommender application to address these challenges and enhance the prescription practices of doctors, ultimately improving patient care and increasing revenue for the organization.

Client Requirement:

The client, a large healthcare organization, identified several key challenges in their current prescription practices. Doctors and clinicians often overlook commonly co-occurring products and services, resulting in suboptimal treatment for patients. The burden on doctors to remember all the appropriate medications and services for each concern has increased, leading to missed product prescriptions. These missed prescriptions not only impact patient care but also result in revenue loss for the client. Additionally, doctors lack a tool to update their prescription patterns based on the latest trends in treatments and medical innovations.


To address the client’s requirements, we proposed the following solution:

  1. Embedding Concerns and Patient Demographics: We implemented a Deep Learning model using Keras and TensorFlow to efficiently embed patient concerns and demographics into a latent space. This approach allows for a better representation of patient data and enables effective recommendations.


  1. Distributed Framework Implementation: To enhance speed and scalability, we adapted the Deep Learning model to a distributed framework such as PySpark/Spark. This enables efficient processing of a large volume of data, improving the overall performance of the application.
  2. High Availability Database: The embedding results obtained from the Deep Learning model are stored in a low-latency and high-availability database, such as Cosmos DB. This ensures quick access to the embedded data, enabling fast retrieval for online processing.
  3. Fast Product Recommender Algorithm: We developed a Python-based product recommender algorithm that leverages the stored embedding. This algorithm utilizes the embedded data to recommend the most appropriate products and services based on a given patient’s concerns. The algorithm is optimized for efficiency and accuracy.
  4. REST API Interface: To facilitate easy integration and usage, we containerized the online application code and provided a REST API interface. This allows doctors and clinicians to access the Product Recommender application seamlessly and retrieve personalized recommendations.


The implementation of the Product Recommender application yielded significant benefits for the client:

  1. Improved Patient Care: By prescribing the most appropriate products and services, doctors can now provide enhanced care to their patients. The application takes into account commonly co-occurring items, reducing the chances of suboptimal treatment.
  2. Up-to-date Prescription Practices: Doctors and clinicians can now update their prescription patterns based on what others in the field are recommending. The application provides insights into the latest trends in treatments and medical innovations, enabling practitioners to stay informed and make informed decisions.
  3. Standardized Prescription Practices: The Product Recommender application promotes more standardized and consistent prescription practices across the organization. This ensures that patients receive consistent treatment regardless of the doctor they consult.
  4. Increased Efficiency and Productivity: With the application handling the task of recommending products and services, doctors can focus more on patient care. This leads to increased efficiency and productivity, as doctors can dedicate their time and expertise to directly interacting with patients.
  5. Reduced Missed Medications and Increased Revenue: The Product Recommender application significantly reduces the number of missed medications and services, leading to increased revenue. By accurately recommending the appropriate products, the organization minimizes revenue loss due to oversight or under-prescription.

In conclusion, the implementation of the Product Recommender application successfully addressed the client’s challenges and achieved their business objectives.

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