Artificial Intelligence for EHR
Use Cases, Tech Stack, Costs
Having worked with AI technology since 1989, ScienceSoft develops robust EHR and EMR software that employs artificial intelligence for data-driven care.
The Essence of AI Technology in EHR
Used for optimizing patient records management, diagnostics, and care delivery, AI helps lift the load of administrative tasks off physicians, improves care quality and consistency, and offers diagnostic accuracy of up to 98.7%.
Artificial Intelligence for EHR: Market Overview
The healthcare AI market was estimated at $5 billion in 2022 and projected to reach $70 billion by 2032 at a CAGR of 29%. The demand for AI-powered EHRs is incentivized by the need to get valuable insights from stored data and dissatisfaction with complex and inefficient EHRs.
Use Cases of AI for EHR
Patient records management
AI optimizes patient data entry and helps physicians get relevant patient data with little to no search time while avoiding costly medical errors. As a result, medical staff becomes more efficient, and the costs of patient data management decrease.
Diagnostics support
With AI-enabled EHRs, physicians can get valuable diagnostic insights based on the listed symptoms, medical image analysis, lab test results, and more. Smart diagnostic suggestions help promptly identify diseases, side effects, factors that influence health condition, and assign the right treatment.
Patient treatment personalization
AI algorithms within the EHR help identify patterns in the symptoms and test results and predict patient outcomes. Based on this data, the system can suggest individual treatment procedure adjustments to ensure state-of-the-art patient care.
Robust Architecture for an AI-Enabled EHR System
The essential elements of an EHR system are medical staff interface, admin interface, patient record storage, and a terminology service. An AI engine is used to power the software with clinical insights, suggestions from patient health history, and more.
Based on hands-on experience in designing AI-powered EHR software, ScienceSoft's architects recommend starting with the following architecture and adjusting it to the project specifics.
I always recommend integrating an EHR with your revenue cycle management system, practice management software, HIE software, a CRM, medical imaging software, a laboratory information system, a patient portal, or a telehealth app.
It is especially important when you build an AI engine since it requires access to the most recent and relevant data from the connected systems to make correct decisions regarding patient care, diagnostics, follow-up examinations, and more.
Core Functionality for Advanced AI-Powered EHR Software
No two custom EHRs are the same. However, our healthcare IT experts gathered a comprehensive list of the most commonly requested EHR features that can be adjusted to your unique needs.
Natural language processing (NLP)
Medical staff can create patient records by simply dictating the relevant information. AI-powered NLP will interpret the voice and compose clinical notes to help save physicians’ time.
Clinical data extraction
Using AI-based optical character recognition, EHR extracts the data from unstructured text (e.g., clinical notes, printed health records). Then, the system can process the data, add it to the relevant databases, and link it to ICD-10, SNOMED CT codes.
Text-to-speech functionality
A built-in AI assistant can voice the relevant patient records to save a physician’s time on patient health data search. Using text-to-speech for prescriptions and clinical notes may also help medical staff double-check the entered data and eliminate errors.
Built-in AI suggestions
When managing patient records in EHR, physicians can get autocomplete suggestions for clinical terms and codes, autofill certain patient information, etc. To increase physicians’ efficiency, AI suggestions from a patient’s medical history (e.g., recent lab tests, allergies) are displayed on a side panel.
Diagnostic functionality
Leveraging AI algorithms and comprehensive patient data, EHR software color-codes lab test results, interprets medical images, defines potential diagnoses, and can warn physicians about high-risk conditions (e.g., heart failure, diabetic coma).
Physician decision support
During care planning, AI functionality can help personalize patient treatment (e.g., offer medication alternatives considering allergies), calculate medication dosage, or suggest additional tests if a patient has other health risks.
Telemedicine appointment planning
In telemedicine-integrated EHRs, AI can assist in scheduling follow-up appointments based on the doctor’s notes and send notifications of the upcoming visits to the patient app.
Billing
Dealing with structured and unstructured care data, AI helps define relevant billing information and create comprehensive billing reports, as well as conduct patient eligibility check and insurance verification.
Haven’t Found What You Need?
Let’s discuss your unique project! ScienceSoft is ready to design a fully tailored feature set for an AI-based EHR system in compliance with HIPAA, FDA, CEHRT, SAFER, MACRA, etc.
Technology Elements
With 17 years of experience in healthcare IT, ScienceSoft recommends using the following technologies for AI-enabled EHR software development:
Cloud data storage and databases
AWS
Azure
Google Cloud Platform
Others
Machine learning platforms and services
AWS
Azure
Google Cloud Platform
Machine learning frameworks and libraries
Frameworks
Libraries
Machine learning algorithms
Supervised learning
Unsupervised learning
Reinforcement learning
Neural networks, including deep learning
Neural networks
Convolutional and recurrent neural networks (LSTM, GRU, etc.)
Autoencoders (VAE, DAE, SAE, etc.)
Generative adversarial networks (GANs)
Deep Q-Network (DQN)
Feedforward Neural Network
Radial basis function network
Modular neural network
Analytics of cardiac data
AWS
Azure
Others
Backend development
Frontend development
Languages
JavaScript frameworks
Mobile development
How to Tackle Challenges of AI Implementation for EHR
With AI-based EHRs, healthcare organizations can significantly improve the performance of the medical staff and reduce care costs while improving the value of care. Relying on 33 years of experience in AI implementation, we know the benefits always come with technology challenges. Here, we list the most common of them and offer the solutions that proved to be efficient in practice.
How Much Does a Custom AI-Based EHR Cost?
On average, the development costs of custom EHR software with AI features start from $400,000 – $450,000 and depend on the following factors:
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Take a First Step Toward Your AI-Powered EHR with Experts
With over 100+ successful IT projects for the healthcare industry, ScienceSoft implements cutting-edge EHR software with AI capabilities for records management, diagnostics, and treatment.
AI-powered EHR consulting
Want to create a truly convenient EHR with advanced features? ScienceSoft’s AI and healthcare IT experts will guide you through the project planning and software design stages to help avoid any risks and speed up the development.
AI-powered EHR development or evolution
ScienceSoft’s team is ready to take charge of the entire development project and deliver a turnkey AI-powered EHR with all the needed integrations. With our decades-long experience and KPI-based approach, you will get a robust EHR solution fast.
ScienceSoft as a Reliable Healthcare Software Development Partner
About ScienceSoft
A US-headquartered global IT consulting and software development company, ScienceSoft was founded in 1989 and worked with AI ever since. Now, leveraging 17 years of experience in the healthcare IT domain, ScienceSoft designs, delivers, and enhances EHR and EMR solutions with best-in-class advanced features.