https://medurgencyhub.onrender.com/

In the fast-paced world of healthcare, optimizing time is crucial for both patients and medical professionals. This document presents a comprehensive solution aimed at reducing time constraints in medical processes for both patients and hospitals. By utilizing technology and streamlined processes, we aim to enhance patient care, increase efficiency, and improve resource allocation.

Our solution involves the creation of a Generalized Medical Card, securely stored in a manner similar to Digilocker. This GMC will contain confidential patient information, accessible through a QR code. This approach offers the following benefits:

• Time Efficiency: Doctors can quickly access a patient's medical history, eliminating the need for manual data retrieval. This saves precious consultation time and facilitates accurate diagnosis.
• Easy Hospital Resource Access: Patients can seamlessly access their medical records, test results, and prescriptions, allowing for smoother hospital interactions and informed decision-making.
• Legal Compliance: The secure QR-coded GMC ensures data privacy while enabling quick retrieval of medical records for legal purposes.

Incorporating a location-based feature in the GMC app allows patients to find the nearest hospitals. This feature not only saves time during emergencies but also helps patients choose the most convenient healthcare facility.

Implementing an advanced predictive algorithm allows hospitals to estimate the time required for specific procedures or consultations based on historical data. This aids in managing patient flow and scheduling, reducing waiting times, and enhancing patient satisfaction.

Our solution addresses the challenges of accessing and managing medical images like X-rays and MRIs:

• QR Code Integration: By linking medical images to QR codes, medical professionals can swiftly access and review essential diagnostic information, streamlining the diagnostic process.
• Time-Saving Visualization: The integration of quick-view tools for medical images enables rapid analysis and diagnosis, thereby saving time for both radiologists and referring physicians.

Incorporating AI-based predictive modeling allows hospitals to forecast personalized busy months based on historical patient data and trends. This empowers hospitals to allocate resources, staff, and facilities more effectively, ensuring that patient care remains consistent even during peak periods.

By integrating an AI-powered classification model, hospitals can efficiently diagnose pneumonia through X-ray images. This model analyzes entire folders of X-rays and accurately identifies pneumonia cases, saving radiologists' time and improving diagnosis accuracy. This innovation accelerates the diagnostic process and enables medical professionals to focus on more complex cases.

Our dual-sectioned approach focuses on effective resource utilization for critical cases like brain tumors:

• Urgency-Based Resource Allocation: Utilizing urgency scores derived from medical reports, hospitals can allocate resources more efficiently, ensuring that critical cases receive priority attention.
• Personalized Predictive Analysis: By analyzing patient data and historical trends, hospitals can predict the month when a patient might require medical attention. This allows for proactive preparation and smoother resource allocation.

In the ever-evolving healthcare landscape, time is of the essence. Our curated solution addresses the time constraints faced by both patients and hospitals through innovative technology and streamlined processes. By implementing a secure Generalized Medical Card, optimizing hospital resource access, incorporating predictive algorithms for busy month prediction, and integrating AI models for pneumonia classification, we strive to enhance patient care, improve efficiency, and ensure better allocation of critical resources. This approach heralds a new era of time-efficient healthcare services that benefit all stakeholders involved.

Install the given libraries in the requirements.txt in the ml folder and just run both the models named as app.py and visualize_months.py, for app.py use the keras_model.h5 file to access the pre-trained model

Just install the requirements.txt in the different folder and just run main.py!