Healthcare providers find AI-based medical record summarization attractive. Medical professionals rely on concise medical record summaries throughout the healthcare journey to facilitate informed decision-making.

The emergence of generative AI has opened the gates of automating the generation of medical records summaries. 

As per Statista reports, the global AI healthcare market is expected to experience significant growth, with a remarkable 37% increase from 2022, reaching $188 billion by 2030.

Hence, creating AI-driven medical summarization tools is a wise step for healthcare institutions and product managers.

In this article, we will elaborate on the benefits and challenges of using AI for medical record summarization.

What is medical record Summarization?

Summarized medical records benefit several parties when rendering services to patients.

Medical records are a compilation of past diagnoses, lab reports, medical notes, and treatments received. It is used to analyze and fill the missing gap in patient’s treatment records. 

Summarized medical records benefit several parties. Let me phase out some examples. 

• Insurance companies utilize medical summaries to evaluate claims and ensure equitable reimbursement for patients. Here, an AI-generated summary offers a precise, concise, and impartial portrayal to substantiate the patient’s claim.

• Law firms use medical summaries to empower patients in pursuing legal action with a more robust legal basis. These summaries provide comprehensive insights into their medical histories, expenses, treatments, and other pertinent details that strengthen their case.

• Medical summaries help healthcare providers combine patient data from various departments. This consolidation improves overall visibility for more accurate prognosis and treatment.

Why to Summarize Medical Records? 

Summarizing medical records saves time for doctors, providing a clear view of a patient’s health.

Law firms benefit, too, with organized case records for stronger legal arguments. It cuts review time by 25-40% by consolidating data from different systems into one folder.

Summarizing medical records streamlines the process of documenting events in chronological order and offers an overview of multiple medical records.

Here are some roles that benefit from reading and analyzing summarized medical records since their job responsibilities involve reviewing medical records and assisting clients.

• Healthcare providers
• Superior Technology Litigation service providers
• Insurance and law firms
• Medical record management companies
• Medico-legal consultants
• Private corporations
• Peer-reviewers
• Social security disability
• Workman’s compensation settlements

Challenges in Medical Records Summarization Using AI

Accurate & Completeness

The process of summarizing medical records involves compressing a significant volume of data into a brief format. If not done meticulously, this procedure may result in the exclusion of vital details or the risk of inaccuracies. It’s essential to guarantee that the condensed information provides an accurate representation of the patient’s medical history, diagnoses, treatments, and other relevant data.

Complexity & Technical Language

Medical documents frequently include intricate medical language and specialized terms that might be perplexing for those without medical expertise. Simplifying medical records into a format that individuals with no medical background can grasp presents challenges. Achieving a balance between offering pertinent information while steering clear of overwhelming technical jargon is essential.

Addressing AI Hallucination

When employing generative AI for medical record summarization, it’s crucial to bear in mind that AI can be susceptible to creating fictional content. In simpler terms, the AI might generate an inaccurate yet believable narrative. The Venture7 team takes extensive measures to prevent any such AI-generated inaccuracies.

Preparing Quality Training Datasets

It’s essential to train the deep learning model with top-notch datasets to create precise AI summarization tools. Without this, the machine learning model will encounter problems like inaccuracy, bias, or underfitting. This task will be resource-intensive and time-consuming.

Selecting the Right Foundation Model

AI summarization apps utilize NLP and computer vision models to gather information from messy sources. You can find various models from different places, like private ML companies and open-source groups. But it’s important to pick the right one by checking how well they perform on standard tests. For instance, you might look at a language model’s accuracy rating to see how often it might make up information when summarizing new data.

Data Security & Privacy

AI summarizers handle vast amounts of sensitive health data. Ensuring their data system can withstand security threats is crucial. This means using encryption and access authorization to protect patent privacy. However, securing many data exchange points can be challenging for AI developers.  

Integrating with Current Healthcare Systems

When deploying the AI summarizing solution, it needs to connect with medical systems. Making different medical systems work together can be hard since each one uses its own rules and methods.

HIPPA Compliance Concerns

One of the simplest methods to create a medical record AI summarizer is to use a pre-trained deep learning model that already exists.

As an example, developers have the option to utilize OpenAI’s APIs to tap into ChatGPT’s capabilities. However, it’s worth noting that, as of 2023, OpenAI and several other generative AI providers don’t allow the use of their models for scenarios regulated by HIPAA.

So, this rules out using publicly available models like ChatGPT. 

Alternatively, you have the option to set up different open-source, extensive language models (such as LLaMA, PaLM, etc.) on cloud servers. This approach provides you with increased control and transparency in terms of data security for the model.

For example, developers can use HIPAA-aligned services on AWS to secure, store, process, and analyze protected health information.

Even with a secure data environment and cloud infrastructure, creating a summarizer that complies with HIPAA necessitates adhering to specific coding practices. For instance, your application must ensure the automatic logout of all users and the regular backup of all data.

What constitutes the AI technology stack for a medical records summarization tool?

Many model providers do not endorse healthcare system applications, and model performances can vary. It’s essential to carefully select a model that aligns with your app’s specific needs. Afterwards, train and deploy the model within a secure infrastructure and integrate it with a compliant application, adhering to regulations.

Deep Learning AI Models

Deep learning models are complex neural networks with multiple hidden layers, adept at efficiently extracting and analyzing extensive data. Large language models such as GPT-4, Bard, and LlaMa excel at handling textual information. On the other hand, generative AI models like MidJourney and Dall-E are specifically designed to comprehend and work with visual data.

Neural Networks

Fundamental machine learning models such as convolutional neural networks (CNN), recurrent neural networks (RNN), and Bayesian networks can be modified for natural language processing (NLP) functions. This includes activities like text mining and creating semantic connections. Moreover, these models are less demanding in terms of resources for training, rendering them a favourable choice for certain AI applications.

Image Classification Models 

Models like VGG-16, ResNet50, and Inceptionv3 are types of neural networks. They’re really good at understanding and working with pictures. These models help make AI systems that can break down and categorize medical images.

Guidance for Utilizing Generative AI

Generative AI is a technology that’s continuously developing. The neural networks in deep learning models, which function somewhat like black boxes, can present challenges when implementing Generative AI.

Here are some tips that can help you overcome challenges and reduce their impact:

• Ensure you maintain the quality of your training data, as it significantly impacts the model’s performance. While training or refining a model, use a dataset that closely mimics what the AI summarizer will encounter in real-world scenarios.

• If you employ the summarizer for financial or mathematical tasks, be aware that language models can generate incorrect calculations.

• Biases that come from the training data can harm patient outcomes. Make certain that the model is trained using a diverse and balanced representation of patients to minimize bias.
• Implement safeguards for the data you utilize in training the AI model. Also, guarantee the overall data process and system are secure and adhere to HIPAA regulations.

How Can Venture7 Help? 

Venture7 is an international custom healthcare software development company serving enterprises in the USA. 

Our team has extensive experience in both emerging AI technologies and the healthcare industry. This knowledge is instrumental in designing secure apps that comply with regulatory acts such as HIPAA.

The combination of our expertise in AI and healthcare has positioned us perfectly to create an AI-powered summarizer for medical records.

Our development framework delivers cost-effective solutions for budget-conscious clients.

Summary

Generative AI holds great promise for revolutionizing how healthcare extracts and condenses data from medical records, enhancing patient care by streamlining processes and consolidating fragmented information in healthcare settings. Salesforce consulting can play a pivotal role in helping healthcare organizations integrate AI-driven solutions into their existing systems. However, there are practical and regulatory hurdles that developers, alongside consulting experts, must overcome when creating AI summarization solutions.

Selecting the appropriate AI model, crafting high-quality datasets, and ongoing model evaluation is vital for creating a working AI application. Yet, practical expertise, regulatory awareness, and experience in AI development are essential in turning your concepts into reality.

Discover more about building healthcare apps with Venture7 today.