Artificial Intelligence for EHR
Use Cases, Costs, Challenges
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
When embedded in EHR, AI analyzes health records to improve diagnostic accuracy and predict complications. Quality-of-life AI features (NLP, image recognition, smart input suggestions) enhance operational efficiency and streamline patient records management for physicians and nurses.
Market Overview and Benefits
The healthcare AI market was estimated at $5 billion in 2022 and is projected to reach $70 billion by 2032 at a CAGR of 29%. The need to improve complex and inefficient EHR workflows and get valuable insights from historical patient data drives the demand for AI-powered EHRs.
How does AI improve EHR?
Speeds up patient record management thanks to convenience features (e.g., speech recognition, AI suggestions for data entry).
Lifts the load of administrative tasks off physicians and nurses by automating routine processes (e.g., appointment scheduling, clinical documentation), leaving more time for direct patient care.
Improves patient care quality and consistency thanks to more cohesive records, AI-enhanced treatment planning, and more.
Provides diagnostic assistance with an accuracy of up to 98.7%.
Use Cases of AI for EHR
Patient records management
AI optimizes patient data entry and helps physicians get relevant patient data fast while avoiding costly medical errors. As a result, medical staff becomes more efficient, and the costs of patient data management decrease.
With AI-enabled EHRs, physicians can get valuable diagnostic insights based on the listed symptoms, medical images, lab test results, and more. Smart diagnostic suggestions help promptly identify diseases, side effects, and factors that influence health conditions and assign the right treatment.
Patient treatment personalization
AI algorithms help identify patterns in the symptoms and test results and predict patient outcomes. Based on this data, the system can suggest individual treatment plan adjustments to ensure state-of-the-art patient care.
Looking for something special?
Our team is ready to develop a tailored EHR system and build custom medical AI models to help you tackle your unique EHR challenges.
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.
Features 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.
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.
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.
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.
Tech Stack to Employ AI for EHR
With 18 years of experience in healthcare IT, ScienceSoft recommends using the following technologies for AI-enabled EHR software 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 34 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.
Challenge #1: Biased data sets
The data sets used to train ML-based treatment or diagnostics modules may be biased (e.g., in favor of certain demographics or conditions) and deliver inaccurate results.
Domain-proficient data scientists must ensure the quality of the data sets used to train an ML model for EHR. If you’re using ready-made data sets for medical AI training, data scientists should run multiple tests to check whether an ML model returns reliable results. If high-quality data sets are unavailable, data scientists create them using anonymized patient data.
To increase the accuracy of diagnostic or prescriptive AI tools, ScienceSoft’s data engineers offer two practical solutions. The first one is to aim for the diversity of the data set: ideally, it should closely match the composition of the society in which you intend to use the ML engine. Alternatively, you can define a narrow demographic group from the start (e.g., geriatric patients in Florida) and compose a smaller data set that’s fully representative of the target group.
Challenge #2: Security risks
Technology-intensive, highly interconnected EHR systems pose high data security risks.
From the very start of its development, an AI EHR requires an elaborate cybersecurity strategy. To mitigate the risks, ScienceSoft builds EHR software with secure architectures, considers regulatory requirements from day one, follows secure development practices, and conducts regular security testing.
Challenge #3: Adding AI into existing EHR
Introducing AI functionality into existing EHRs may affect their performance or lead to business disruptions.
Some healthcare organizations are satisfied with their current EHR but want to leverage AI functionality to improve care efficiency. If you’re using a market-available EHR product, a good strategy is to create a separate AI software module (e.g., for NLP-powered data input) and integrate it with the off-the-shelf EHR in use. In the case of custom-built EHRs, ScienceSoft starts with assessing the EHR architecture. If it’s flexible enough, we develop additional AI functionality for the solution. If our engineers see that the additions will deteriorate EHR performance, we recommend EHR re-architecting before adding AI functionality.
How Much Does a Custom AI EHR Cost?
Key cost factors
- The intended scope and complexity of AI functionality.
- The required accuracy of ML algorithms.
- The data quality thresholds, number of data sources, and the volume of data to be stored and processed.
- The number and complexity of integrations with other solutions (e.g., patient portals).
- Security, UI, and UX requirements.
- Compliance-associated costs (e.g., for FDA registration of SaMD functionality).
- Cloud services (e.g., ML tools), infrastructure, maintenance, and support costs.
5 Questions about AI EHR Development, Answered by ScienceSoft
EHR development is a challenging endeavor, and we know these questions may keep you up at night:
Do I need deep AI knowledge to build an AI-powered EHR?
No, ScienceSoft’s healthcare IT consultants will help you shape a winning EHR concept and a fitting AI feature set based on their 9-20 years of experience in the industry. During the development, we’ll create sufficient documentation for AI EHR for easier certification, maintenance, support, and updates. If needed, ScienceSoft is ready to create training materials and conduct knowledge transfer to your IT team.
I’ll need two separate teams, one for EHR development and one for AI, won’t I?
Definitely no. ScienceSoft takes over the whole EHR delivery process – from design and development to AI model training, software certification, and launch. We have 34 years of experience in AI and over 18 years in healthcare software development, so your project is safe with us.
Will the development take forever?
It usually takes around 2–6 months to deliver the first version of EHR software. Then, we will gradually evolve the system using our mature Agile approach, established CI/CD and DevOps practices, and a feasible share of testing automation to ensure frequent and stable updates. When your AI EHR is market-ready, we will start the certification process (it may differ based on your target market) and launch the software.
Can I develop an AI EHR that will communicate with other IT systems without any issues?
Sure, it’s possible! Leveraging 18 years in healthcare IT and expertise in HL7, FHIR, ICD-10, CPT, and XDS/XDS-I standards, our team designs EHR integrations at the very beginning of the project and can build custom APIs to facilitate smooth and secure PHI exchange.
Won’t AI complicate regulatory compliance?
Yes, AI components, especially if they’re used for diagnostic assistance, complicate things and may be harder to get relevant approvals for. But even the most complex software is still code when it comes down to it. We know how to build transparent and compliant EHR from the ground up and prepare for certification ahead of time. Being ISO 9001, ISO 27001, and ISO 13485 certified, ScienceSoft follows industry best practices in software quality and security, including for SaMD development. Our in-house consultants proficient in HIPAA, GDPR, ONC, FDA, MDR, MACRA, MIPS, CEHRT, SAFER, and SAMHSA, are involved in the process every step of the way, participating in software design, QA, and pre-market submission.
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.
ScienceSoft as a Reliable AI and Healthcare Software Development Partner
President & CEO
bioAffinity Technologies hired ScienceSoft to develop automated data analysis software for detection of lung cancer using flow cytometry. Our project required a large amount of industry-specific methodology and algorithms, which the team handled well.
Prof. Dr. Department of Biomedical Science
ScienceSoft proved to have vast expertise related to the development of desktop software connected to a data analytics platform. They bring top-quality talents and deep knowledge of IT technologies and approaches in accordance with ISO 13485 and IEC 62304 standards.
We worked with ScienceSoft on the modernization of our telehealth web portal and a cross-platform patient app. They created comprehensive project documentation, feature lists, and worked out thorough recommendations to improve the stability and performance.
ScienceSoft’s team undertook the development of our AI-based software product from scratch. […] They delivered software in time and with the required quality. The team regularly shared detailed project updates and recommendations on how certain functions could be implemented in a more efficient way.
Invention Machine Corporation
ScienceSoft has delivered cutting-edge solutions to complex problems bringing in innovative ideas and developments. ScienceSoft is dedicated to handling any problem that occurs as a result of hardware or software issues; simply put, they will go the extra mile to support their customers regardless of the time of day these issues arise.
A global IT consulting and software development company, ScienceSoft was founded in 1989 and worked with AI ever since. Now, with a team of seasoned healthcare IT consultants and developers, ScienceSoft designs and develops EHR solutions with best-in-class advanced features.