نهجنا في رسم خرائط الأنظمة المدعومة بالذكاء الاصطناعي من HL7 إلى بيانات FHIR

In the medical field, diverse data formats often present a significant challenge for healthcare organisations. Data from one device may not correspond seamlessly with target instances, leading to a need for data mapping. Usually, this is a manual, time-consuming process. We decided to take on an initiative that automates this job, focusing on mapping data between the HL7 standard and FHIR. This article explores the hypothesis that artificial intelligence (AI) can effectively support the process.

حول معايير بيانات HL7 وFHIR

HL7، أو المستوى السابع للصحة, is a set of international standards for exchanging, integrating, sharing, and retrieving electronic health data. It primarily operates at the seventh level of the OSI (Open Systems Interconnection) model, focusing on application-level communication in healthcare systems. HL7 is widely used for clinical and administrative data exchange in information systems such as Electronic Health Records (EHR), hospital management systems, and other tools used by doctors, nurses, and hospital administration to exchange patient information efficiently and securely, ensuring proper patient care.

FHIR (موارد قابلية التشغيل البيني السريعة في الرعاية الصحية) is a standard developed by HL7 International. It leverages web technologies and data formats like JSON and XML to facilitate efficient data interchange in the healthcare industry. FHIR is part of the HL7 standards family but differs significantly from its predecessors, such as HL7 versions 2 and 3, in its approach and ease of implementation. FHIR facilitates the integration of different healthcare systems, offering a more accessible and flexible way to exchange patient information between healthcare providers.

The key differences between HL7 and FHIR lie in their data formats, structure, and interoperability challenges due to these differences. Thus, to use data that is in different standards in one system, it is necessary to unify it, and this process is called data mapping.

ما هو تعيين البيانات في الرعاية الصحية؟

افترض أن أحداً جهاز طبي collects and stores a patient’s data in a database. The patient goes to a hospital, and the staff needs their previous health results. A doctor or a nurse sends a query via an application, but the hospital system operates in the FHIR standard and the medical device from before stores data in the HL7 format. To make use of the results of the examination taken earlier, the data has to be “translated” from one format to another so the hospital system can understand it.

يؤدي تعيين البيانات إلىقابلية التشغيل البيني للبيانات الطبية; it’s a process of matching fields from one database or dataset to another, creating a link between the elements. It’s a crucial step in data integration and migration projects, ensuring that data from one format is accurately translated and transferred to the target system.

Usually, data mapping in the healthcare sector is conducted manually, which limits real-time data access possibilities. This method involves domain experts who analyse and align data elements from one standard to the other. This approach, while thorough, significantly slows down the data exchange process and burdens healthcare IT professionals.

دراسة حالة لنظام رسم خرائط البيانات المدعوم بالذكاء الاصطناعي

The healthcare data mapping system we developed responds to the need for automation in the patient data exchange process. By automating data mapping from one system to the other, as an example HL7 to FHIR, the system aims to shorten the time needed to transform the data, thus relieving people involved in the process.

تم تنفيذ المشروع كجزء من برنامج البحث والتطوير الخاص بـ Spyrosoft، Innovation Lab. واستجابةً لاحتياجات السوق، قررنا تنفيذ الذكاء الاصطناعي كمكوّن أساسي في نظام لرسم خرائط بيانات الرعاية الصحية.

المتخصصون المشاركون في المبادرة

جمع هذا المشروع فريقاً من الخبراء من ثلاثة مجالات:

• خبير المجال – شخص يتمتع بمعرفة معمّقة بالمعايير الطبية. وقد شارك لتقديم التوجيه للمطورين وإجراء فحوصات الجودة على النتائج؛
• مطورو البرمجيات الذين بنوا التطبيق وضمنوا فعاليته. كانوا أيضًا مسؤولين عن اختبار الحل وتحسينه لتلبية متطلبات المشروع؛
• AI specialists who supported the developers in critical areas such as learning and model fine-tuning. They ensured the developers had the necessary assistance to deliver the best possible outcome.

التقنية المستخدمة لتبادل بيانات الرعاية الصحية

The system’s backend is developed using C# in the .NET environment and features an integrated ChatGPT model. We chose to use a commercial AI model rather than build one from scratch because it had already implemented both HL7 and FHIR standards. Using a commercial model complies with medical regulations since our system only processes data structures and schemes, not patient data. The ChatGPT feature is hosted in the cloud, and training was conducted there, too.

تدريب نموذج الذكاء الاصطناعي

We chose to work on observational data such as blood test results, blood pressure, or weight. There is also coded information on the type of examination, when the test was taken, and if the data is still valid. Many of those labels differ between the HL7 and FHIR standards.

We generated data that complies with both formats to train the model. While training, we observed that when asked the same question many times, the model generated different answers. At first, programmers were feeding the model all the information in one query, and that was the moment AI specialists came with help, introducing the development team to fine-tuning. Fine-tuning is about building the context and working on the results for longer, pointing out good answers and wrong answers and providing examples of correct responses rather than sending single, complex queries.

التحقق من صحة البيانات

For now, the system generates several data mapping proposals, and the user chooses the one that best meets their expectations. The user checks if the structures seem valid and manually completes the mapping in areas that were too complex for the model to handle.

خطط التطوير المستقبلية

The next idea is to use AI to validate data that the previous model had come up with. Now, the model gives three or four different results, and a person has to choose one. This model would pass all mapping proposals through a validator, refer them to standards, find errors, correct them, and pass again.

نتائج التنفيذ محدثة

Currently, at about 70% operational level, the system has demonstrated that AI can significantly speed up the medical data mapping process. So far, the model has only been trained on artificial data; feeding it the target information (e.g., client’s medical data) will enable it to learn even better and increase its efficiency level.

For us, the most significant outcome of the initiative is the knowledge and experience we gained. It’s not only limited to mapping data from HL7 to FHIR, as it’s possible to build an AI-powered system that works with other input and output data.

استفد من قوة الذكاء الاصطناعي في الرعاية الصحية

سيقدم لك خبراؤنا المشورة حول كيفية تطبيق الذكاء الاصطناعي في حالة عملك المحددة. اعرف المزيد عن خدمات الرعاية الصحية وعلوم الحياة أو تواصل معنا عبر النموذج أدناه.