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AI-Powered Medical Form Automation for Reducing After-Hours Work

AI-Powered Medical Form Automation for Reducing After-Hours Work cover image

Why should physicians sacrifice their sleep for admin tasks? Let's rethink the “pajama time” problem with AI. Pajama time refers to healthcare professionals, particularly physicians, who need to complete administrative tasks and update electronic health records (EHR) outside of regular hours, often from home. This growing issue significantly contributes to high clinician burnout and job dissatisfaction.

In this article, we’ll take a closer look at the impacts of pajama time as well as the practical AI solutions that can minimize these challenges for healthcare professionals and organizations, like intelligent medical form automation and EHR automation.

The Impact of the Pajama Time Problem on Healthcare

Complex EHR systems, significant regulatory and compliance requirements, and growing tasks like completing insurance paperwork, coordinating care, and quality reporting consume valuable time. Clinicians fill out over 100 million forms annually across various care settings, a burden that directly impacts their ability to focus on what matters most — caring for their patients.

Physician Burnout

As a result, pajama time not only impacts medical staff but also lowers healthcare quality and raises financial risks for organizations.

Consequences of the pajama time problem include:

  • Burnout Among Physicians
    Pajama time in healthcare, where physicians spend an additional 7.9 hours a week on administrative tasks after regular work hours, is a major contributor to burnout and exacerbates the clinician shortage. With physicians already averaging 59 hours of work per week, this burden highlights the urgent need for systemic reforms to reduce administrative tasks, allowing doctors to focus more on patient care.
  • Document Errors
    Rushed administrative work performed after-hours increases the risk of errors in medical documentation, including inaccurate patient records or misfiled information. These errors can result in serious consequences, like patients being treated for conditions they don’t have, misfiled medical histories, or the wrong records being given to the wrong person, compromising the quality of care.
  • Economic Consequences: Mismanagement of Resources and Service Quality Decline The mismanagement of physician workload due to after-hours administrative tasks distorts the true picture of provider capacity and efficiency. This can result in underestimating staffing needs and misallocating resources, which not only leads to financial losses but also hampers service delivery. Additionally, the time spent on EHRs and administrative tasks reduces physicians' ability to focus on direct patient care, lowering service quality, patient satisfaction, and patient retention, ultimately impacting revenue negatively.

Reducing Pajama Time and Work Outside of Work Hours With AI

Healthcare institutions can effectively reduce the documentation burden on physicians by implementing intelligent automation in healthcare, such as AI-powered clinical forms and ambient listening technologies, into their workflows. While 83% of physicians agree that AI has the potential to alleviate administrative tasks, a systematic approach is essential to fully address the pajama time problem and ensure that the technology delivers tangible benefits.

The process of transforming data flow from AI-Powered clinical forms into an EHR system can be broken down into the following key stages:

data flow from AI-Powered clinical forms into an EHR system

Stage 1: Data Intake and Preprocessing

AI-driven ambient listening technology allows provider-patient conversations to be transformed into clinical notes that can be seamlessly integrated into EHRs. For example, the Mayo Clinic uses this technology in clinical voice applications, allowing physicians to focus on patient care rather than paperwork.

In just minutes, the AI is able to create a concise, bulleted note summarizing the patient's medical and social history, exam findings, diagnosis, and treatment plan. Physicians need only to review and approve the note, which significantly reduces the manual documentation burden.

Before, one of the challenges was to capture data accurately despite background noises and language contractions in the natural flow of conversations. Ambient listening technology helps overcome this challenge by using natural language processing (NLP) and machine learning to accurately capture conversations. It also ensures patient privacy.

At Akvelon, we have the expertise to implement ambient listening technology and a history of success in the development and integration of speech-to-text solutions in various projects, demonstrating high accuracy and compliance with privacy standards like HIPAA and GDPR. For instance, our AI translation service for medical consultations enables real-time voice capture and translation during video calls, converting spoken language to text and producing speech or subtitles. This facilitates seamless communication between patients and interpreters that specialize in medical terminology.

Additionally, Akvelon created an interactive voice-controlled assistant for ad-hoc whiteboarding sessions. Utilizing automatic speech recognition (ASR), our solution enables users to manage auto-capturing intervals, trigger snapshots, and send enhanced whiteboard images via email. This is all completed through voice commands, greatly improving efficiency and reducing manual effort.

Stage 2: Data Processing and Enhancement

Once data is collected, it needs to be efficiently processed and structured. AI solutions can help with organizing data, filtering relevant information, and populating clinical forms with minimal manual input. Through NLP and predictive analytics, AI-powered clinical forms can auto-populate fields, suggest diagnoses, and flag inconsistencies—streamlining workflows and reducing the time physicians spend on documentation. Unlike traditional forms, AI-driven solutions adapt to individual preferences, improving both accuracy and efficiency.

Beyond daily clinical workflows, AI also has the potential to indirectly reduce pajama time by enhancing broader healthcare data processing and enabling large-scale data analysis. Akvelon's PHI de-identification solution is a good example of how AI can bring instant value to medical institutions by securely processing large datasets. Our solution enables secure and efficient processing of various types of data: tabular data, text, images. This frees up valuable time for healthcare providers, while ensuring they maintain compliance with data protection regulations. 

By speeding up the analysis of anonymized data, this type of AI solution allows healthcare institutions to gain valuable insights, optimize EHR systems, and improve clinical workflows — ultimately contributing to long-term reductions in administrative burdens. For more insights on how AI can enhance data protection and compliance, explore our article on Balancing Innovation and PII Protection.

Additionally, Akvelon’s AI tools are designed with compliance at their core, adhering to HIPAA, GDPR, and other regulatory standards. With features such as robust encryption and secure processing of sensitive information, our tools safeguard data integrity while maintaining operational efficiency.

Stage 3: EHR Integration

Seamlessly transferring data from AI-powered clinical forms into an EHR system is essential for maintaining workflow efficiency. Many systems face challenges with interoperability, as well as ensuring that data is transferred securely without errors or loss.

To address these challenges, custom APIs and integration protocols can be implemented to enable smooth, secure, and efficient data transfers into existing EHR systems. Based on Akvelon's experience, these solutions help reduce the risk of disruptions while transferring data in real-time, preserving data integrity, and ensuring compliance with privacy regulations like HIPAA and GDPR.

Akvelon's proprietary AI tools allow us to add even more value to these integration efforts. For example, thorough testing of the APIs is essential to ensure secure and error-free data transfers while reducing expenses, and streamlining software development and testing flows. Our AI API Testing Tool saves up to 80% of our test engineers' time by generating test scripts 6 times faster, ensuring complete test coverage. This is just an example of how software development teams can benefit from AI daily, doing more with less effort.

Stage 4: Provider Review and Finalization

Even after documentation has been automated, healthcare providers still need to ensure that all information is accurate, as this step is critical for maintaining clinical quality and compliance.

Drawing from Akvelon’s experience with AI-driven documentation, these solutions help streamline the process by organizing data into clear and concise notes. Physicians can quickly verify, adjust, and approve the documentation, ensuring accuracy while significantly reducing time spent on manual data entry.

AI can also significantly improve how healthcare providers access and manage data. For example, Akvelon’s expertise in developing LLM-powered search tools enables quick and intuitive access to authorized patient records. This technology enhances operational efficiency by reducing the time physicians and staff spend searching for critical information, allowing for more streamlined workflows.

These are just a few examples of how AI can automate document-related tasks and save physicians valuable time. Beyond documentation, AI also plays a role in data visualization, medical image analysis, and data-driven predictions. As AI capabilities continue to evolve, so too will its applications and benefits in healthcare and healthtech.

AI benefits for healthcare

AI Adoption Tips

With years of experience in applying AI and ML in software solutions, Akvelon's AI adoption team provides practical guidance for healthcare organizations looking to reduce administrative overload and improve staff work-life balance. Below, we provide essential AI adoption tips to help organizations realize the benefits of AI while maintaining the highest standards of information security, safety, and ethics.

Step 1: Choose the Right AI Model and Deployment Strategy
Companies need to carefully select AI models that are well-suited to their specific healthcare applications. They need to consider factors like accuracy, explainability, and compatibility with existing systems. In cases when healthcare providers need extra control over data, they can leverage solutions based on self-hosted AI to maintain sensitive data within the organization rather than use third-party services. 

Step 2: Implement Robust Security Measures
When it comes to AI, healthcare organizations and software vendors need to prioritize security from the outset. This includes rigorous security checks, encryption, and continuous monitoring to protect against data breaches. Given the sensitive nature of healthcare data, it’s extremely important to ensure compliance with all regulatory privacy requirements.

Step 3: Conduct Thorough Model Tuning and Validation
AI models should be rigorously tested and tuned before full-scale deployment. For example, Akvelon’s AI adoption team validates models with real-world data, tunes them to achieve the expected level of accuracy in their performance, and monitors their performance to catch any deviations or biases early and eliminate them.

Step 4: Establish Continuous Human Oversight
Even after AI is implemented successfully, it’s important to maintain human oversight, especially in critical areas like healthcare documentation. Automated outputs should be reviewed by qualified professionals before being finalized to ensure accuracy and maintain accountability.

Step 5: Focus on Incremental Implementation and Iteration
Companies that want to test the AI waters first need to start with small, manageable AI projects that can be scaled as confidence in the technology grows. This approach allows for iterative improvements and reduces the risk of large-scale failures during initial implementation.

General advice: Partner with Proven AI Experts
Collaborate with AI software development vendors who have a history of successful AI implementations in healthcare. Their experience can provide invaluable insights into best practices of secure AI adoption, potential pitfalls, and industry-specific considerations. This collaboration is crucial given the complex and rapidly evolving nature of AI in healthcare, where expertise can significantly mitigate risks and enhance outcomes.

These principles and tips help ensure that AI adoption in healthcare is both ethically grounded and technically sound, minimizing risks while maximizing benefits.

Conclusion

The pajama time problem remains a significant challenge in healthcare, impacting clinician well-being and patient care quality. However, responsible AI adoption suggests a multi-faceted solution, addressing issues on various levels, from automated information gathering and summarization to PII de-identification.

By strategically partnering with Akvelon — a leading provider of AI solutions and services in Healthcare and implementing these technologies, healthcare institutions can significantly reduce after-hours documentation, lower attrition rates, enhance care quality, and optimize costs.

Akvelon's team can guide your organization through this transformative journey of AI adoption, including its integration with existing systems, employees' onboarding with AI, and software maintenance. As we move forward, the strategic integration of AI will help you reclaim your clinicians' personal time to improve their work-life balance, streamline healthcare delivery, and reduce administrative expenses.