Valued at $22.45 billion in 2023, the healthcare AI market is expected to grow to $208.2 billion by 2030, with a compound annual growth rate (CAGR) of 36.4%. This rapid expansion is fuelled by the growing need to streamline complex and inefficient electronic health record (EHR) workflows and extract meaningful insights from historical patient data through AI for EHR automation.
The Health Information Technology for Economic and Clinical Health Act in the United States started incentivizing hospitals, and physician practices to use EHRs as early as 2009. Even though the intention behind this was to improve care quality and efficiency, there was an inadvertent fallout to this. As EHR workflow often prioritizes billing and administrative tasks over clinical decision-making and care delivery, clinicians started spending a disproportionate amount of their workday on EHR and desk work. This not only disrupts patient-clinician relationships, but also leads to inefficiencies, and increases cognitive load.
Also, higher than usual automated messages in EHR inboxes leads to clinicians experiencing a higher probability of burnout. According to an article dated September 11, 2017 by the American Medical Association (AMA), primary care physicians spent over 50% of their workdays on their EHRs, which averaged about 4.5 hours per day in the clinic, and 1.5 hours after hours each day at home. Physicians have reported that this disproportionate time spent documenting impede their interactions with patients, contributing to higher levels of stress and job dissatisfaction.
One of the major reasons for medical errors is the lack of complete and accurate medical information for physicians while making clinical decisions. The Institute of Medicine in the United States had earlier released a report that highlights medical errors as a serious phenomenon. It further points out that more than 250,000 deaths annually in the United States (US) are due to medical errors, making it the third leading cause of death after cancer and heart diseases.
Health care is a complex industry shaped by a wide range of challenges and stakeholders, including rising costs, restricted access to services, and physician burnout. However, artificial intelligence or AI for healthcare is beginning to transform the field. Its applications span diagnostic medicine, drug development, robotic surgery, personalized treatments, documentation efficiency, and optimizing physician workflows. While obstacles remain, AI holds significant promise to improve patient care, increase system efficiency, and drive better health outcomes across the board.
The benefits of AI-powered EHR systems for physicians and hospitals are many. These systems help physicians ensure the right kind of care for their patients by having easy and timely access to the required health information. To mine the required data from the immense medical information contained in EHRs is not an easy task but the effective deployment of AI can assist doctors in saving time for this process.
Benefits of AI-powered EHR systems for Physicians
Let’s first take a look at the key benefits of AI-driven EHR automation:
- Features like speech recognition, AI suggestions for data entry etc. speed up patient record management. This results in medical staff becoming more efficient, and the costs of patient data management decrease.
- Physicians can get easy access to valuable diagnostic insights based on the listed symptoms, medical image analysis, lab test results, and more using AI enabled EHR. Also, smart diagnostic suggestions help promptly identify diseases, side effects, factors that influence health condition, and assign the right treatment.
- More cohesive records, AI-enhanced treatment planning, and more improves patient care quality and consistency.
- Healthcare professionals get more time for direct patient care as AI-based EHR automation lifts the load of routine administrative tasks like appointment scheduling, clinical documentation etc.
Other Major Benefits:
Streamlining Documentation: AI-driven documentation tools can help alleviate documentation-related burnout, implement strategies such as allocating time for more meaningful work and improving time management. These tools can reduce physicians’ documentation time by as much as 70% by transcribing patient conversations, updating electronic health records (EHRs), and managing data for prescriptions and orders. Reducing physician burnout with AI in EHR workflows gives them more time to focus on providing direct patient care.
Text-to-Speech Functionality: The text-to-speech feature can be used for writing prescriptions and clinical notes; it can also help medical staff double-check the entered data and eliminate errors.
Clinical Data Extraction: AI-powered optical character recognition (OCR) fetches data from unstructured transcripts such as clinical notes and printed health records, which the system processes, adds to the right databases and maps to ICD-10 and CPT codes.
Natural Language Processing (NLP): Clinicians can create patient records simply by dictating relevant information, while AI-powered natural language processing (NLP) converts their speech into structured clinical notes—saving physicians valuable time.
Physician Decision Support: During the course of treatment, AI functionality can help personalize patient treatment by offering medication alternatives considering allergies (if needed), calculate medication dosage, or suggest additional tests if a patient has other health risks.
Built-in AI Suggestions: This feature helps physicians by offering autocomplete suggestions for clinical terms and codes, and can autofill certain patient details. It also boosts efficiency by showing AI-powered insights—like recent lab results or allergies—on a convenient side panel.
Improving Diagnostic Accuracy: AI-powered EHR software color-codes lab test results, interprets medical images, defines potential diagnoses, and warns physicians about high-risk conditions (e.g. heart failure, diabetic coma).
Billing: AI-based solutions for medical billing and coding can deal with structured and unstructured care data, and also help identify the right billing details, generate complete billing reports, and handle tasks like patient eligibility checks and insurance verification.
Telemedicine Appointments: EHRs with built-in telemedicine support can help schedule follow-up appointments based on doctor’s notes and automatically send visit reminders to the patient’s app.
Challenges Involved
Though there are numerous benefits outweighing the challenges posed by AI-powered EHR, it’s worth looking into some of the most common ones:
Data with inherent bias: Machine learning-based treatment and diagnostic models can be affected by biased datasets—often skewed toward certain demographics or conditions—which may result in inaccurate or unreliable outcomes. To reduce this risk, it’s essential to use datasets that reflect the diversity of the population where the model will be deployed. Alternatively, you can define a specific target group from the outset (e.g. geriatric patients in Texas) and build a smaller, but fully representative dataset tailored to that demographic.
Data Anonymization: Anonymizing data used in the training and operation of AI systems is a vital measure for reducing privacy risks. By stripping away or masking personal identifiers, data can be used to train AI models without exposing individual identities. This enables researchers and developers to harness large datasets for advancing AI technologies while maintaining privacy protection. In addition to supporting compliance with privacy regulations, data anonymization also plays a key role in building public trust in the use of AI within healthcare.
Security Risks: Given that healthcare data contains highly sensitive personal information, data privacy concerns in AI-powered healthcare systems is on the rise. This challenge is intensified by AI’s reliance on large volumes of data for training and optimal performance, which, if not properly managed, can increase the risk of data breaches. To address these risks, the healthcare industry must prioritize several key areas such as encryption, access control etc.
High Cost: Implementing AI in healthcare comes with significant costs, including the purchase of advanced software, hardware upgrades to meet high computational demands, and the recruitment of specialized personnel to oversee AI operations. While these investments can lead to substantial long-term savings and increased efficiency, the initial financial burden remains a major hurdle—especially for smaller healthcare practices operating within tight budget constraints.
Complexity of Integrating with Existing EHR: Challenges of integrating AI into legacy EHR systems pose a major concern. Extensive technical adjustments may be necessary. Many existing infrastructures lack the capability to support the fast data processing and storage requirements of AI applications. As a result, healthcare providers may experience considerable disruptions during the integration process, potentially impacting the continuity of patient care.
Why Collaborate with DeepKnit AI for Automating EHR?
If you’re thinking of building a smart EHR solution or integrating your legacy EHR system with an advanced AI-powered one, consider partnering with our company, which has expertise and a proven track record in the field.
Key Features of DK AI:
- Comprehensive Summary Builder
Interactively explore key text and visual elements of documents, page by page, and effortlessly create a complete medical summary that captures every critical detail.
- Document Comparison
Need to compare two documents or notes? DeepKnit’s summary review makes it easy—offering clear, in-depth insights into each document for better understanding and analysis.
- Custom/Pre-defined Templates
Choose from a pre-built summary template or create a personalized medical summary tailored to your specific requirements.
- Instruct DK
Get customized instructions to improve data, and provide specific instructions you have for individual case files.
- Advanced Data Processing
DeepKnit AI specializes in eliminating duplicate information, adding hyperlinks and page stamps, and creating clear summaries and timelines—making medical records more organized and easier to understand.
- Smart Search & Save
Quickly locate key details in the summary using this AI model’s advanced OCR search. Our model scans Electronic Health Records (EHR) and retrieves relevant information instantly.
- Intelligent Data Extraction
Our model is well-trained in healthcare domains such as anatomy, physiology, pathology, ICD codes and CPT codes that helps it extract pertinent information from any document.
- Advanced Filtering
Filtering your search by narrowing the medical summary to document type, Subjective, Objective, Assessment, and Plan (SOAP) categories, or a specified date range is easy.
- Interactive Timeline Summary
Gain instant, comprehensive insight into a patient’s medical history with the interactive timeline, which arranges key assessments, examinations, and injury events in chronological order.
- Built-in Annotator
Easily highlight crucial details by annotating directly on medical records. Your notes are saved automatically as the medical summary updates with real-time insights.
- Flexible File Import/Export
You can easily upload medical records through the this innovative platform, connect via API, or use FTP for larger batches. Download your results anytime in PDF or Word format.
- Quantum Guideline Management
Easily search for and extract specific details like surgeries, injury locations, related events, and key notes. Important data—like lab results or SOFA scores—is automatically highlighted or organized separately for quick access.
Conclusion
While AI holds significant potential to revolutionize clinical documentation by automating repetitive tasks, optimizing workflows, generating deeper insights, and helping to reduce physician burnout, implementing them thoughtfully and ethically can serve as a smart assistant—freeing up physicians to focus more on building patient relationships and delivering quality care.
As healthcare systems embrace AI, it’s essential to collaborate with a partner who knows to balance innovation with accountability, ensuring that patient experience remains a top priority. The future of AI in clinical documentation lies in enhancing efficiency, improving accuracy, and supporting personalized care through intelligent, automated data processing.
Ready to enhance your EHR capabilities? Partner with DeepKnit AI to introduce cutting-edge healthcare solutions to your organization.
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