Inspired & Developed accordance with HIMSS.org
The primary issue is the fragmented nature of patient records within traditional healthcare systems. These records are stored in disparate formats across various departments or facilities, which hinders comprehensive analysis and decision-making. Additionally, medical data is voluminous and heterogeneous, originating from diverse sources like electronic health records (EHRs), medical imaging, and clinical notes. This poses challenges in integration, preprocessing, and ensuring data accuracy for analysis. Furthermore, administrative and security concerns arise regarding managing access to sensitive health records while ensuring authentication, authorization, and regulatory compliance. Unified Electronic Health Record (EHR) System: Implement a centralized EHR system that integrates patient records from different departments and facilities. This system should support standardized formats and protocols for data exchange to ensure compatibility and interoperability. Integrate image analysis algorithms to analyze medical imaging data, such as X-rays, MRIs, and CT scans. This enables automated diagnosis, anomaly detection, and treatment planning based on imaging findings.
EHRM [ Electronic Health Record Management ] introduces a centralized platform for analyzing patient records, offering insights into billing amounts, demographics, prevalent diagnoses, medical conditions, consulted doctors, admission types, and medication usage. The system gathers, integrates, and preprocesses medical data, ensuring accuracy and reliability for analysis. Leveraging predictive analysis, the system identifies trends within patient data, while prescriptive analysis provides actionable insights for personalized care and resource allocation. The EHRM Admin is a Django based secure site administration panel facilitating authentication, authorization, and management of user groups and individual users. It also provides functionality for managing health records, allowing admin users to add and modify health record entries efficiently. Clinical Support System offers a comprehensive suite of healthcare solutions including predictive analysis, informatics, differential diagnosis support ( DDx ), image analysis, speciality analysis, and medical note classification, facilitating enhanced decision-making and patient care in medical settings.
The maturity models have been developed by students of Information Technology to bonafide that the work of:
| Name | |
|---|---|
| Jeyaseelan J | jeyaseelanj2003@gmail.com |
| Kameshwaran T | thangarajkamesh123@gmail.com |
| Prithiv Sakthi U R | prithivsakthi676@gmail.com |
| Shivaraj A | shivarajlingam@gmail.com |
Github : Branching Directory to StreamLit Streamlit : Community Cloud [ Web-app Deployment ]. Django Administrator Panel : Security, Data Handling. Data Spell : JetBrains IDE [ Personalize source code Editor ] Hugging Face : For Model Deployment. Interfacing : Streamlit, Gradio, Docker Blank
System RAM : 6GB & above (Preferred) System Storage : 520GB (Minimum) RAM Speed : 3800 MHz (Better) System Power Delivery : 65 W (Minimum)
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