Our client, a healthcare company providing home care to patients, faced a challenge in acquiring diagnostics information from patients upfront. This process was cumbersome and time-consuming, leading to delays in providing effective care. The specific pain points included the need for a friendly interface with predefined questions (pathways) to walk patients through the diagnostics process, and a backend application for doctors to edit pathways and analyze patient responses.
To address this problem, our team at SmartCat developed a solution that consisted of three parts: a backend application, a decision support tool, and an integration point. We started by developing a proof of concept (PoC) chatbot to acquire patient data using an existing chatbot solution on the Azure platform. Based on feedback from the client, we then built the whole backend, enabling doctors to edit and customize pathways, enter drugs, and visualize results. We also built a decision support tool that served as an integration point for a mobile application with a chatbot interface.
Finally, we developed an integration point to share data in the correct format with external systems, such as expert systems that provide recommendations for medicine.
The end result was a powerful system that streamlined diagnostics data acquisition for the healthcare company. The key metrics included faster and more accurate data acquisition, more efficient use of doctors’ time, and improved patient outcomes. The quantifiable results of our efforts included a significant reduction in the time it took to acquire patient data, improved accuracy of data acquisition, and increased doctor satisfaction with the system.
OpenAI’s ChatGPT is a powerful tool for streamlining data acquisition in healthcare. However, it’s important to remember that AI is still evolving and may not be ready for all aspects of healthcare regulation.
About the Client
Our client was a healthcare company providing home care to patients who could not or did not want to go to the hospital. Remote screening and telemedicine were key to their services, making diagnostics data acquisition a crucial part of their application.
Our solution was built using a combination of Azure cloud, C#, AngularJS, and databases such as Mongo and PostgreSQL.