Case Study

An EHR Solution to Automate Doctor's Notes and Medical Records Summarization

A behavioral healthcare solutions company modernizes its EHR system to automate the process of recording patient-doctor interactions.

A leading provider of behavioral healthcare solutions improves the end-to-end process of doctor’s notes and medical records summarization through automation and analytics to overcome time-intensive, error-prone, and inefficient manual processes.

Industry & Region: Healthcare, USA

Tech Stack & Techniques:
Serverless Compute Service: Azure Function App, Database: Azure SQL , OpenAI: Azure Open AI GPT4.0, Application Performance Monitoring (APM): Azure App Insights, Programming Language: Python, Natural Language Processing (NLP)

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Client Overview
Our client is an EHR service provider empowering payer and provider organizations in behavioral health with modern practice management solutions to improve their process efficiencies, scale, and care outcomes.

Business Problem

Our client approached us seeking a solution that would resolve the various challenges their affiliated behavioral health practices had been facing due to inefficiencies in doctor’s notes recording and summarization processes.
The practices were using a manual process for recording the huge volumes of doctor’s notes that flowed into their EHR system for summarization, which was time-intensive, error-prone, and inefficient.
The purpose of summarization was to condense and capture the essential information from the patient-doctor interaction, however the manual process of recording was not addressing this requirement properly. Doctors had to spend a lot of time going through lengthy notes to understand their patient’s medical history and plan their next course of treatment. And in the process, they were often losing out on the key points that were crucial for accurate diagnosis and treatment. Inaccurate summarization led to misdiagnoses, inappropriate treatment decisions and impact continuity of care.
The inconsistencies in summarizing the doctor’s notes were also raising concerns about billing accuracy, compliance with behavioral health regulations, and other areas of operation.
All this was impacting patient experience and becoming a major roadblock in scaling the business.
Solution Offered
There was a need for a solution that would enable behavioral health provider to improve the efficiency and accuracy of their doctor’s notes recording process, and further help them create accurate summaries of these notes for leveraging across the provider organization.
We started the project by gathering a thorough understanding of how the provider’s existing EHR system was capturing doctor’s notes through its Q&A function and documented the gaps in the existing process. After an in-depth analysis and a series of discussions with the client, the team put together a proof-of-concept of a customized AI/ML solution that could be integrated into the provider’s existing EHR’s Q&A function to improve the process of doctor’s notes summarization end-to-end.
The proposed solution was conceptualized to transform the existing process and accomplish the broader objective of scaling the organization through enhanced patient satisfaction –
  • The solution was capable of ingesting large volumes of detailed conversations between the doctor and the patient from their Q&A sessions.
  • The AI model could deliver clear, concise yet comprehensive summaries of the notes to the user.
  • The summarization of doctor’s notes​ now was much faster, near real-time making the subsequent documentation processes more efficient.
  • The solution could aggregate summaries and analyze the data to identify patterns, trends, and insights for medical research and healthcare planning.
  • The solution enabled the user to create different types of notes like SOAP (Subjective/Objective/Assessment and Plan) Notes.
Value Delivered
  • Automated summarization of doctor’s notes saved time and improved overall efficiency by providing a concise overview of medical records.​
  • The high-quality summaries enabled healthcare professionals to embrace evidence-based treatment approaches and make informed decisions. This improved the quality of care.
  • Further, the aggregated view of all the summarization data provided valuable insights for further research and improvement of treatment quality.
  • The improvement in summarization also enhanced communication and collaboration among team members to identify potential risks in behavioral health care and ensure early intervention.​
  • The EHR solution also enabled sharing of organizational knowledge and preservation for future reference.
  • This summarization enabled doctors to focus on the key action items generated by AI rather than going through the entire notes.

Want to leverage a modern EHR solution to automate the doctor’s notes and medical records summarization processes to deliver better behavioral health care?

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