Medical records, especially those of patients with extensive or complex treatment histories, can quickly balloon to hundreds of pages. Trying to parse through these records when recommending care—or even just when creating clean claims for reimbursement—takes time that could be better spent serving patients. Using AI can summarize records quickly, giving your teams the information they need to take action with the click of a button.
What Is Summarization of Medical Records?
A medical record summary is exactly what it sounds like: a short rundown of a patient’s medical records. Summaries can be created for entire records or specific portions that contain helpful insights for medical staff.
AI summarization is relatively new but has a tremendous impact. Summarizing with AI programs harnesses the capabilities of different models, like large language models (LLMs), which can process extremely large amounts of information and deliver relevant, actionable points based on the parameters set in the program.
Summarizing Medical Records Is Strategic for Healthcare Organizations
Summarizing medical records makes them more accessible for patients (who can view them in electronic health records), providers creating treatment plans, and office staff who bill, schedule, and create insurance claims. Rather than starting a task and wading through hundreds of pages, looking for the small bits of information needed for processing, or trying to create a timeline to match care to charges, a summarized record provides the necessary information quickly.
Using AI to create summaries of medical records greatly reduces the administrative burden on healthcare staff, helping improve their overall efficiency. This ultimately leads to better patient care, as providers have more time to focus on treating patients instead of spending hours combing through medical records.
Human + AI Processes Reduce Error
AI-generated summaries can also reduce documentation and treatment errors. Information overload is a quick way for people to burn out, overlook important details, or make their summaries too complicated for the intended purpose. In a recent study, LLMs performed at least as well as physicians 81% of the time (and outperformed them 36% of the time) when summarizing radiology reports, patient questions, progress notes, and doctor-patient dialogue.
But providers and staff should still review AI outputs. Human-in-the-loop processes, which bring humans and AI programs together at different stages, are more trustworthy than just relying on AI to summarize records. AI programs can “hallucinate” and generate outputs that are not true, and sometimes, they do not write with enough substance to fully capture the intention of the record. Working with programs that use restricted data sets—like just the medical records under review—can limit errors, and setting goals and parameters for the output can guide AI programs toward better, more consistent summaries.
Where Can Record Summaries Fit in Your Workflows?
Realistically, you could fit AI summaries in most places in your workflows. Take, for example, a few ways behavioral health providers can save time by turning to AI for help:
- Biopsychosocial assessments – Summarizing the data collected in a biopsychosocial assessment brings lots of data into one clear, actionable picture.
- History and physical – Having a full patient history presented in a logical format with the context of a current physical saves time and makes it easier to connect care dots.
- Group and individual therapy notes – Spend less time on notes and get more time with patients. AI-generated summaries can create a quick note that has room for personalization in individual therapy sessions.
- Medication reconciliation – Ensure that new prescriptions are safe based on a patient’s current and past medications. AI summaries can also help with understanding drug interactions.
- Form filling – Once you have the right information, you’ll probably need it on a form. You can create an efficient workflow by having AI summaries available for quick referral and easy sharing.
- Revenue cycle management – With a quick summary, teams can identify and resolve coding inconsistencies and justify billing for specific services. AI summaries also help with audits by providing a clear, concise summary of a patient’s medical history and treatment plan.
Before, during, and after treatment, AI medical record summaries give staff time back in their day and provide an extra layer of accuracy to patient care. There is a learning curve, though, and building proficiency and comfortability with AI tools will take time.
Implementation Challenges of AI Record Summaries
While there are plenty of opportunities to fold AI into clinical workflows, there are plenty of challenges it will bring, too.
Extra Work
AI models that are more intrusive than intuitive can reduce efficiency instead of improving it. When additional clicks, reviews, or training periods are required, adoption and trust in the new technology will suffer.
Data Restriction
Privacy and accuracy concerns are some of the most serious when adopting an AI program for medical records review. Practices must ensure that all data that is reviewed by an AI remains completely private. Plus, while AI tools do need to be trained on large datasets in order to be more functional, the data they interact with to create accurate summaries must be limited.
Training
There’s a learning curve when adopting AI. Providers and support staff need to learn how to interpret and use the information provided, as well as how to use AI tools themselves for tasks such as training models or setting parameters. All staff that uses AI will still be very involved in the review, and will need to be able to understand and improve the quality of AI outputs.
Practical Tips for Using GenAI for Summarizing Medical Notes
- Practice – Using an AI program for the first time, especially in a healthcare setting, can be stressful. Give teams time to practice using the program and become comfortable with its features.
- Training – There should be a consensus on how AI outputs will be used so that all staff members are aware of what is expected and can work together to ensure consistent results.
- Communication – Keep communication lines open when implementing AI. Encourage feedback from all team members and allow for adjustments to be made as needed. You might find that the first workflow you create isn’t the best one for what your staff needs.
- Learn to restrict data – AI models perform best when they have particular parameters set to guide their outputs. You’ll need to look at what information your team needs from medical records and then determine how to store that data in a way that’s both secure and easy for AI models to access.
- Keep humans in the loop – There won’t be a time when staff members are completely removed from the process. Humans provide essential context that AI alone can’t replicate, and all generations will need to be reviewed for accuracy.
AI Can Be at Your Fingertips Today
AI isn’t the future; it’s here now. If you’ve been looking for a way to alleviate staffing shortages and employee burnout while improving efficiency, summarizing records with AI is a logical place to start. Sunwave’s revolutionary AI healthcare platform, Mental-Health Artificial Reasoning Agent (MARA), is built right into our HIPAA-compliant platform—no need to export records to summarize or work with them.
Automate outreach efforts in the CRM module, summarize biopsychosocials, history/physicals, and therapy notes within the EMR, and get forms filled faster with MARA. Better yet, it’s designed to work right out of the box—no training, no lengthy setup, no complicated systems to navigate. With just the click of a button, MARA will transform the way you work. You don’t need any additional equipment or technical expertise. Schedule a demo online now.
