Artificial Intelligence for EHR

Solution

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.

Solution

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.

Solution

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.

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.

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.

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.

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.

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.