As healthcare institutions scale, they struggle to turn increasingly growing volumes of medical records to their advantage. Doctors, nurses, insurance agents, and medical staffs rely on medical record summaries to make informed decisions and support patients throughout their healthcare journey. Recent advancement in deep learning technologies makes AI medical record summarization an attractive solution for healthcare providers.

The emergence of generative AI technologies primed AI for significant growth in the healthcare industry. According to Statista, the global AI healthcare market will be worth $188 billion by 2030, a 37% increase from its value in 2022. Therefore, building AI-powered solutions, such as medical summarization tools, is a smart move for startups, healthcare institutions, and product managers. 
As the co-founder and tech leader at Uptech, I’ve closely followed the impact of AI in various industries, including healthcare. We’ve also been testing the practical applications of generative AI technologies by building or integrating genAI with clients’ products. For example, we built Hamlet, an app using generative neural networks to summarizes PDFs and text documents.

In this article, I’ll start by covering the general concepts behind an AI medical records summary and then discuss how AI enhances this process. I’ll also explore the challenges and best practices when implementing AI summarization on medical records.

AI helps in many ways with summarizing medical records, but first, it's important to understand the challenges hospitals face and why summarization matters. Then, we’ll look at how AI can make the process easier. 

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What is a Medical Record?

A medical record is a detailed account of a patient's health history. It includes diagnoses, treatments, medications, lab results, and doctors' notes. With these pieces of information, healthcare professionals can understand what's going on with a patient and decide on the best course of care.

Medical records can come in various formats, including handwritten notes, printed reports, electronic health records, and digital images, such as X-rays or CT scans. They are often unique and can be complex, so it takes time and close attention to review and summarize them. That’s why many healthcare providers now use AI medical records summary tools to make the process faster, more efficient, and more accurate.

What is Medical Summarization?

Medical summarization is the process of condensing extensive patient medical records into concise overviews for improved efficiency and decision-making. The goal is to make the information easier to absorb while maintaining accuracy.

Medical records contain the historical prognosis, treatment, lab reports, and other medical notes critical to the patient’s medical interest. In conventional practice, doctors, nurses, and medical staffs take on the administrative tasks to analyze, organize and fill in the missing gap in the patient’s treatment records.

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

• Medical summaries for law firms enable patients to pursue litigation options with a stronger legal foundation. The summaries detailed their medical histories, costs, treatment, and other relevant information that aid their case.
• Insurance firms use medical summaries to assess claims and reimburse patients fairly. In this case, an AI-generated summary provides clear, concise, and objective representations to support the patient’s claim. 
• Medical summaries allow healthcare providers to consolidate siloed patients’ health information in different departments. Medical summarization improves the overall visibility of the patient’s information, making doctors more accurate when rendering prognosis or treatment.

While healthcare providers have strong motivations to summarize patients' data, the process is not straightforward. Medical data are presented in several formats, each regularized by their respective standards. 

• C-CDA, an acronym for Consolidated Clinical Document Architecture, is a popular format that US healthcare providers use to exchange patient information. C-CDA stores healthcare data in XML, providing a patient’s medical history timeline.
• FHIR (Fast Healthcare Interoperability Resources) is a data-sharing standard that promotes clinical interoperability between and amongst medical institutions. It specifies APIs that enable different apps to share medical data reliably across departments.
• HL7 (Health Level 7) is a framework that supports EHR sharing between healthcare providers. It provides messaging formats and protocols that improve efficiency and accuracy for care deliveries.
• SNOMED CT is a comprehensive medical terminology that healthcare providers use to automate healthcare data processing. It allows healthcare systems to maintain consistent definitions, concepts, and entity relationships throughout the data-sharing pipeline.
• ICD (International Classification of Diseases) is a global standard for documenting diseases, injuries, causes of death, and other mortality data with specific codes.

Example of a Medical Record Summary

In everyday practice, healthcare professionals usually document patient details in free-form notes. A summarizer then converts these notes into a clear, organized format that follows standard medical documentation guidelines. The examples below show two common formats used in the field.

Unstructured Medical Notes:

Jane Smith, 52F, presented on 2024-02-25 with lower back pain for three weeks. Pain is dull, worsens with sitting, and improves with movement. Occasional tingling in the right leg; no numbness or weakness. No recent injury or infection. History of osteoarthritis (2018) and mild hypertension. Takes Amlodipine 5 mg daily. No smoking or alcohol use. 

Exam: mild lumbar tenderness, normal reflexes, full range of motion. 

Assessment: Likely mechanical back pain. 

Plan: Physical therapy, stretching, OTC NSAIDs, follow-up in four weeks if no improvement.

Structured Medical Record:

Patient: Jane Smith, 52-year-old female

Symptoms:

• Lower back pain (dull, worsens with sitting)
• Occasional tingling in the right leg

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Medical History:

• Osteoarthritis (2018)
• Mild hypertension

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Lifestyle:

• No smoking or alcohol use

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Medications:

• Amlodipine 5 mg daily

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Physical Exam:

• Mild lumbar tenderness
• Normal reflexes, full range of motion

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Diagnosis:

• Likely mechanical back pain

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Plan:

• Physical therapy and stretching
• OTC NSAIDs as needed
• Follow up in 4 weeks if no improvement

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Why Should Health Providers Summarize Medical Documents 

Medical records in their original form are extensive and useful for specific medical interventions or legal claims. However, much of this data is confined to different systems, which limits its potential to support data-driven decisions. Summarizing the vast amounts of medical data helps streamline the healthcare system in different ways.

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• Improve patient care delivery. Healthcare professionals can provide appropriate treatment more efficiently without spending valuable time sorting through and searching for patient records. With readily available medical summaries, they can complete administrative tasks, such as finalizing lab reports, more quickly and accurately. 
• Consolidate disparate medical data. Medical summarization expands the value of health data collected and stored across different departments.  By extracting key information from multiple medical sources, healthcare providers can gain a comprehensive overview of a patient’s health condition and monitor their progress without repetitive manual intervention. 
• Enable virtual assistant. Summarized patient data also allows healthcare providers to train and deploy intelligent virtual assistants. The virtual assistant can learn from the extracted information to deliver personalized, data-informed patient interactions and support clinical decision-making.
• Supports medical research and education. Summarizing medical records builds up a condensed and organized online knowledge base for medical researchers and students. This streamlined access to key information accelerates learning, supports evidence-based research, and promotes better clinical training outcomes.

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Critical Issues of Summarizing Medical Documents

Summarizing medical documents benefits healthcare providers in many ways, but several factors complicate such efforts.

• Comprehending Biomedical Text. Medical data consists of complex terminologies specific to the healthcare industry. It takes a medically accredited professional, such as a physician, therapist, or pharmacist, to extract and summarize medical text without losing its original meaning.
• Extracting relevant information. Beyond the complexity of language, medical records hold detailed information dictating prognosis, treatment, lab results, and other data specific to a patient’s condition. When summarizing, identifying and extracting only the information relevant to the task is essential, and this can be difficult for individuals without formal training. 
• Producing a comprehensive summary. While medical summaries condense a patient’s medical history, they must detail all the important records. For example, a proper summary should integrate information from admission notes, consent for treatment, lab results, surgical reports, and prescription lists to provide a complete yet concise overview.
• Analyzing patterns and trends. Manual record summarization is tedious and might not be practical for spotting specific patterns or potential correlations within medical data. Human reviewers have limited capacity to gather and process large volumes of information effectively, which can hinder the discovery of meaningful insights.
• Human errors. The accuracy of medical summaries depends on the medical or administrative officers who prepare them. As humans, they may struggle to maintain consistency, leading to occasional gaps or inaccuracies in the final summary.

Where Summarized Medical Records Are Used

An AI medical narrative summary is becoming more common in healthcare. With it, experts can easily interpret this straightforward, easy-to-follow record and apply it in various medical and administrative settings. Let’s take a look at key areas where these summaries can make the biggest impact.

Hospitals and Clinics

In hospitals and clinics, summarized records let doctors, nurses, and administrative staff quickly see the most important patient information. This allows them to make faster and more accurate decisions about diagnosis and treatment. Summaries also help different departments and specialists work together and stay on the same page.

Outpatient and Specialty Care

With an AI medical summary, specialists can instantly review a patient’s past treatments, test results, and medications, then compare them with the person's current findings so they can decide on the best next step in care. Since they no longer need to go through lengthy notes, they can save time and only focus on what matters most.

Medical Research and Clinical Trials

Researchers rely on summarized records to analyze patterns, trends, and outcomes without exposing unnecessary personal details. Because of this, they can easily find patients who fit clinical trial requirements. In addition, summaries help detect broader healthcare trends that can guide new treatments and studies.

Health Insurance Providers

In the insurance sector, a summary of medical records plays a big role in treatment checks and claim evaluations. It reduces paperwork and makes it easier for insurance professionals to catch mistakes or inconsistencies. This leads to faster processing and more accurate decisions for both patients and providers.

Telemedicine and Remote Care

Doctors and care teams use AI medical chronology to access concise patient histories during virtual consultations. This ensures that decisions are based on accurate, up-to-date information. Not only that, but such short write-ups help everyone involved communicate more clearly and reduce the likelihood of repeated or unnecessary tests.

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How AI Medical Documents Summarization Benefits Medical Affairs

Medical Affairs are responsible for propagating medical information amongst clinical practitioners, patients, healthcare providers, and other entities in the industry. This includes generating summaries of medical literature, patient data, and other healthcare documents. Manual summarization cost Medical Affairs time, expenses, and missed opportunities to support patients and medical providers. 

In this sense, generative AI is a potentially transformational technology that benefits the Medical Affairs team greatly. According to McKinsey, using Gen-AI to analyze clinical health records can unlock $1 trillion of opportunities. You wouldn’t want to miss the opportunity that AI presents in advancing medical summarization workflows. 

Here’s why.

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1. Reduce review time

Medical researchers and officers spend several minutes reviewing a page of medical records. With Generative AI, they save up to 90% of their time while accurately extracting essential information. 

2. Accelerate decision-making

Medical Affairs oversee various activities, including clinical trials, creating medical strategies, and disseminating accurate medical information. They can use AI-generated medical records to support critical decisions affecting the outcomes of such activities. 

3. Improve accessibility

With generative AI, Medical Affairs can translate medical content into multiple languages with less effort. This provides the global medical community equal access to medical literature. Moreover, AI can reproduce such content with languages understandable by non-technical audiences. 

4. Detect errors

Generative AI can quickly compare medical records with established ground truth data to spot errors. This prevents erroneous data from being put into circulation, which may affect public safety and patient well-being.

5. Identify patterns and anomalies

Deep learning models that generative AI uses consist of artificial neural networks capable of analyzing complex patterns. Medical teams can use AI to detect anomalies that med workers might overlook in manual reviews.

6.  Save cost

Summarizing AI records help healthcare companies reduce substantial expenses, including physical storage, logistics, and the workforce involved. The Medical Affairs team can also optimize and reallocate their resources to higher-value tasks. 

7. Reduce carbon footprint

Medical companies pursuing environmentally sustainable goals will also benefit from medical records summary services. Instead of storing volumes of paper records, the move shifts their medical knowledge to private cloud storage. 

8. Improve work-life balance

Medical workers burdened with manual summarization tasks will enjoy a welcomed respite when integrating medical summarization with AI. They can divert their effort to supervisory roles and deliver more to the healthcare ecosystem. 

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How to Summarize Medical Records Using AI

Generative AI gives healthcare providers powerful new ways to handle medical data summarization more efficiently. But creating a dependable AI summarizer is more than just choosing a model. It takes a structured, team-based process between the clinic requesting the tool and the developers building it. Here’s what that process usually looks like:

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1. Define objectives together

First, the clinic and development team clarify the goal. Are they focusing on text notes, lab results, or imaging reports? What level of detail do doctors or staff need? Establishing clear goals ensures that the AI tool delivers exactly what the clinic requires.

2. Collect and annotate data

The clinic provides historical records and relevant medical documents, which healthcare professionals review to extract important information. From there, developers turn this data into a structured training set for the AI. Together, both teams guide the model to learn from accurate and useful examples and produce meaningful summaries.

3. Choose and configure the AI model

Based on the type of data they have, developers pick the most appropriate model for the task. It can be a system designed for clinical notes or another for image scans. After the selection process, experts make adjustments to ensure the tool performs correctly and remains easy for clinicians to use.

4. Train and fine-tune the model

Developers train the AI using the labeled data while the clinic regularly reviews the results. This ongoing feedback helps the model adapt to the clinic’s terminology, structure, and reporting style, reducing errors and improving consistency in the generated AI medical records summary.

5. Build the user interface

A practical AI medical summary tool needs an intuitive, user-friendly interface. Developers design dashboards and features for uploading records, viewing summaries, and submitting feedback. This way, clinicians can seamlessly integrate the tool into their daily workflows.

6. Test extensively

Both the clinic and developers test the AI in real-world situations. The clinic checks the summaries for completeness and correctness, while developers look for technical problems, bias, or misinterpretation. Testing multiple times helps ensure the final product works well and is free of errors.

7. Deploy and integrate

Once the AI summarizer is ready, it is added to the clinic’s existing systems. Developers ensure compatibility so that staff can access summaries easily without disrupting daily operations.

8. Monitor, update, and improve

After launch, the clinic and developers keep an eye on performance. They gather feedback to fix issues, improve the model, and update it with new medical standards or types of data. This keeps the AI medical records summary software working well and aligned with the clinic’s needs.

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Challenges of Developing AI-driven Summarizing Medical Documents 

Despite AI's overwhelming benefits, there are concerns when applying AI-powered medical summarization at scale. Moreover, the medical industry is tightly regulated and requires strict compliance by all stakeholders. So, pay attention to these challenges when building AI summarization tools.

Preparing quality training datasets

You must train the underlying deep learning model with high-quality datasets to produce accurate AI summarization tools. Otherwise, the resulting machine-learning model might suffer inaccuracy, bias, underfitting, and other performance issues. Compiling quality datasets for medical AI requires curating and annotating diverse types of sources, including lab results, personal health data, and administrative notes. You might have to commit substantial resources and time to preparing the datasets. 

Choosing the right foundational model

AI summarization apps use NLP and computer vision models to extract data from unstructured resources. There are many such models available from private ML vendors and open-source communities. However, it’s crucial to choose the right model by assessing their performance on existing benchmarks. For example, a language model’s truthfulness score might indicate its likelihood of hallucinating when summarizing unseen data. 

Navigating ethical challenges

Generative AI models aren’t always transparent about how they work, so they can raise questions about security and bias when used to summarize medical records. Healthcare providers must be aware of such risks affecting patient trust and outcomes when medical staffs rely on AI-generated summaries to make decisions. 

Integrating with existing medical systems

When deployed, the AI summarizing app must be able to extract data from existing medical systems. Enabling interoperability amongst disparate medical systems is challenging, as each may function with its own standards and protocols. Moreover, some healthcare providers still operate with legacy paper-based systems, which require the AI summarizer to be fitted with optical character recognition (OCR) technology. 

Ensuring data security, privacy, and legal compliance

Given that AI summarizers process large numbers of sensitive health data, you must ensure their data pipeline is resilient against adversarial threats. Security measures like encryption and access controls help prevent them, but managing multiple data exchange points can be challenging for AI teams. At the same time, legal risks must be considered. Healthcare professionals could rely on AI summaries for diagnosis and treatment, but with the risk of inaccuracies that could seriously impact patient outcomes.

Assessing legal implications

Besides technical complications, developers face legal risks when applying AI summarizers to healthcare facilities. Medical professionals use the AI app to generate summaries, which they later use to diagnose, treat, and support patient recoveries. Any inaccuracies affecting the AI solution could severely impact patient outcomes and put healthcare providers at legal risk. 

Achieving clinical validation

AI-generated medical summaries can provide correct information, but that doesn’t always make them practical. That’s why medical experts review the summaries to ensure they work in real clinical settings. This process also helps healthcare staff feel confident using AI to support patient care.

Scaling across languages and contexts

Medical documents vary by country, language, and healthcare system. Because of these differences, an AI summarizer trained on English records may not work as well with documents in other languages or formats. To address this, the model must be adapted for different languages and local regulations so that the summaries remain correct and consistent worldwide.

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HIPAA Compliance Concerns when Using Gen AI for Summarization 

One of the most straightforward ways to build a medical record AI summarizer is to leverage an existing pre-trained deep learning model. For example, developers can use OpenAI’s APIs to access ChatGPT's functionality. Unfortunately, OpenAI and many genAI providers prohibit using their models for HIPAA-regulated use cases as of 2023. So, this rules out using publicly-available models like ChatGPT unless you can strike up special contractual agreements for HIPAA-enabled data protection. 

Alternatively, you can deploy other open-source large language models (like LLaMA, PaLM, etc) on cloud servers. Doing so gives you greater control and transparency when securing the data the model uses. For example, developers can use HIPAA-aligned services on AWS to store, process, and analyze protected health information.

Even with secure data and cloud infrastructure, developing a HIPAA-compliant summarizer requires coding practices aligned with the policy. For example, your app must automatically log out all users and back up all data.

 Learn more about developing HIPAA-compliant software here.

Choosing AI Tech Stack for Summarizing Medical Documents 

The emergence of deep learning models provides more technological options when building AI summarizers. However, not all summarizing apps need state-of-the-art language models. Depending on their use case, you can create certain AI apps with simpler machine learning algorithms.

I share several types of machine learning models that could form the building blocks of your app.

Deep learning AI models

Deep learning models are neural networks with multiple hidden layers capable of efficiently extracting and analyzing vast information. Large language models like GPT-4, Bard, and LlaMa are exceptional in processing textual information. Meanwhile, generative AI like MidJourney and Dall-E are engineered to understand visual data. 

Neural networks

Basic machine learning models like convolutional neural networks (CNN), recurrent neural networks (RNN), and Bayesian networks can be adapted for NLP tasks, including text mining and establishing semantic relationships. They are also less resource-intensive to train, making them preferable options for some AI apps. 

Image classification models

Models like VGG-16, ResNet50, and Inceptionv3 are variants of convolutional neural networks. CNN uses convolutional layers to extract the finer details in graphical data effectively. Such models enable developers to build AI summarizers capable of segmenting, analyzing, and classifying medical images. 

When choosing AI models, consider their training time, performance, and compliance. Deep learning models take significantly longer time and more computational power to train than simpler algorithms. In most cases, it’s more practical to fine-tune a pre-trained model than training a foundational model from scratch. 

As mentioned, most model providers do not support use cases in healthcare systems. Neither do all models offer similar performance. So, be diligent and choose a model that excels in areas your app requires. Then, train and deploy the model in a secure infrastructure and connect it to a regulatory-compliant app. 

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Practical Tips on Using Generative AI 

Generative AI is still an evolving technology. The black box-like neural networks that deep learning models use make implementing GenAI challenging. Use these tips to reduce risks and challenges affecting the final product.

• Don’t compromise on the training sample’s quality because it affects the model’s performance. When training or fine-tuning a model, use a dataset that resembles what the AI summarizer will process in real life.
• Apply protectionary measures to the data you use for training the AI model. Similarly, ensure the entire data pipeline and infrastructure are secure and HIPAA compliant.
• If you use the summarizer for financial or arithmetic processing, be mindful that language models might produce erroneous calculations. 
• Bias inherited from training samples can negatively affect patient outcomes. Ensure that the model is trained from a diverse and equal representation of the patients to reduce bias.
• Improve the model after deployment by training it with anonymized data generated from actual usages. 

How can Uptech help?

Uptech is an international app development company servicing startups and enterprises worldwide. Our multi-disciplinary team is at the forefront of emerging AI technologies and has integrated genAI into several apps. A notable example is Hamlet, an AI app summarizing PDF documents we built for a US-based company. 

We’re also equally knowledgeable in the medical industry. Our experience in developing healthcare apps is helpful in architecting secure apps that comply with HIPAA and other regulatory acts. The intersecting expertise in AI and healthcare spaces put us in an excellent position to build an AI-powered medical records summarizer for you. 

Through a proven development framework, we balance cost and development time for clients with sensitive budgets and market windows. For example, we can build a PoC for $20K in approximately 2 months. More importantly, we conduct a thorough discovery phase and survey your users to ensure a tight product-market fit.

But don’t take our words for it. Check out what our clients say on Clutch

“Uptech is a great partner for software and web development project”, - Executive, Angler AI.

“They ask questions, provide suggestions, and learn about our business in order to make a proper product.” - Co-Founder, Eatable.

Summary 

Generative AI brings tremendous potential in transforming how healthcare extracts and summarizes information from medical records. It improves patient care delivery by improving efficiency and consolidating fragmented information in medical facilities. Despite that, there exist functional and regulatory challenges that solution providers must tackle when developing AI summarizers. 

Choosing the right AI model, curating quality datasets, and continuous model assessment are crucial to delivering a functional AI app. I’ve shared ways that will help you build an AI medical summarizer. However, practical skillsets, regulatory knowledge, and experience in AI development are indispensable in bringing your ideas to life.

Discover more about building an AI summarizer with Uptech today.