AI-powered real-time patient risk scoring system for predictive health management

Abstract

An AI-powered real-time patient risk assessment system (100) for predictive health management, comprising: (a) a data acquisition and integration module configured to collect and standardise patient data from multiple clinical sources, including electronic health records, monitoring devices and laboratory systems; b) a feature extraction and data processing module for pre-processing the collected data and extracting clinically relevant features; (c) a machine learning-based risk prediction module configured to generate real-time risk scores based on the extracted features using trained AI models; (d) a clinical decision support and alerting module configured to trigger alerts and suggest interventions based on risk thresholds; (e) a visualisation and dashboard module for displaying patient risk scores and trends via an interactive interface; and f) a feedback learning and model update module configured to incorporate clinical feedback and retrain the AI models to improve prediction accuracy over time.

Description

[0001] The present invention relates to the field of healthcare analytics and artificial intelligence. More specifically, it relates to a real-time patient risk scoring system powered by AI for predictive healthcare management. This system enables the early identification of at-risk patients through continuous analysis of clinical data to support timely and personalized medical interventions.

[0002] In modern healthcare systems, one of the greatest challenges is the timely identification of patients at risk of clinical deterioration, complications, or adverse events. Traditional risk assessment methods often rely on static scoring models or manual assessments that may not capture changes in a patient's condition in real time. This delay in detection can lead to delayed interventions, increased morbidity, and higher healthcare costs.

[0003] Furthermore, given the exponential growth of electronic health records (EHRs) and patient data from diverse sources—such as vital signs, lab results, and clinical notes—healthcare providers face significant challenges in quickly and accurately integrating this information. Current systems are unable to continuously and effectively process and interpret this multidimensional data, leading to missed patterns or warning signs that could signal an impending deterioration in health.

[0004] To address these problems, the invention introduces an AI-powered real-time patient risk scoring system that integrates and analyzes continuous streams of clinical data using advanced machine learning algorithms. This system dynamically assesses patient risk levels, prioritizes care based on predictive insights, and supports clinical decision-making to improve patient outcomes, optimize resource utilization, and enable proactive health management.

[0005] One objective of this disclosure is to enable early detection of deterioration in a patient's condition through real-time risk assessment.

[0006] Another objective of this disclosure is seamless integration into existing hospital systems using standard protocols.

[0007] Another objective of this disclosure is to reduce clinical workload by automating risk assessment and alert generation.

[0008] Another objective of this disclosure is to improve patient outcomes through timely and personalized interventions.

[0009] Another objective of this disclosure is to continuously update and learn from new clinical data to improve accuracy.

[0010] Another objective of this disclosure is to provide intuitive dashboards for efficient patient monitoring and triage.

[0011] Another objective of this disclosure is to improve resource allocation by proactively identifying high-risk cases.

[0012] Another objective of the present disclosure is to support scalable deployment across different medical facilities and departments.

[0013] Further objects and advantages of the present disclosure will become apparent from the following description, which is not intended to limit the scope of the present disclosure.

[0014] The present invention generally relates to an AI-based system for predicting health risks for patients in real time through continuous analysis of clinical data.

[0015] It improves proactive health management by identifying potential adverse events before they occur.

[0016] In one embodiment of the present invention, the key component is the data acquisition and integration module, which collects and standardizes real-time data from various sources such as EHRs, monitors, and laboratory systems. This ensures an accurate and timely flow of information.

[0017] Another embodiment of the invention is the feature extraction and data processing module, which transforms raw patient data into clinically meaningful features. It applies advanced processing techniques to improve the relevance and quality of model inputs.

[0018] Another embodiment of the invention is the machine learning-based risk prediction module, which uses AI algorithms such as neural networks to assess the probability of clinical deterioration.

[0019] Another embodiment of the invention is the clinical decision support and alerts module, which responds to risk assessments and generates actionable alerts for medical personnel. It prioritizes high-risk cases and suggests appropriate actions.

[0020] Another embodiment of the invention is the visualization and dashboard module, which provides interactive, real-time displays of patient risk levels and trends. It includes tools such as risk maps and progress timelines.

[0021] Another embodiment of the invention is the feedback learning and model update module, which ensures that the AI models evolve over time. It incorporates real-world results and user feedback to retrain and refine the predictive models.

[0022] Another embodiment of the invention is that the invention enables predictive, personalized healthcare by using AI for real-time risk assessment. This reduces interventional delays, optimizes hospital resources, and improves patient safety.

[0023] The present invention relates to an AI-powered real-time patient risk scoring system for predictive healthcare management, designed as a modular framework where each module performs a critical function in the end-to-end risk prediction and decision support pipeline. The system works seamlessly with hospital information systems and electronic health records (EHRs) to deliver dynamic, data-driven insights for clinicians. Data acquisition and integration module:

[0024] This module is responsible for collecting and aggregating real-time patient data from various sources such as electronic health records, bedside monitors, portable devices, laboratory systems, and nursing notes. It standardizes and preprocesses the data to eliminate inconsistencies, missing values, and noise, ensuring high-quality inputs for subsequent processing. The module supports secure APIs and HL7 / FHIR interfaces for seamless integration with the hospital's IT infrastructure. Feature extraction and data processing module:

[0025] Once the data is ingested, this module extracts relevant clinical features such as heart rate variability, oxygen saturation trends, laboratory test histories, and medication responses. It applies data normalization, transformation, and dimensionality reduction techniques to prepare the input features for machine learning models. Temporal and contextual patterns are also inferred to improve predictive performance. Machine learning-based risk prediction module:

[0026] This core module uses advanced AI algorithms such as ensemble learning, deep neural networks, or recurrent models (e.g., LSTM) to predict patient risk scores. It continuously evaluates incoming data and updates risk predictions in real time. The models are trained on historical patient records with labeled outcomes to predict the probability of events such as sepsis, cardiac arrest, or ICU transfer within a specific time window. Clinical decision support and alerts module:

[0027] Based on the generated risk assessments, this module triggers actionable alerts and recommendations for the healthcare team. It uses threshold-based or risk-tiered logic to prioritize patients who require immediate attention. The alerts are contextualized with supporting explanations, potential diagnoses, and suggestions for next steps, allowing clinicians to make timely and informed decisions. Visualization and dashboard module:

[0028] This module provides intuitive and interactive dashboards for healthcare providers. It displays real-time patient risk trajectories, trend analysis, heat maps of deteriorating patients, and predictive outcomes. The user interface is designed for both bedside clinicians and hospital administrators to monitor patients at the individual, ward, and hospital-wide levels. Feedback learning and model update module:

[0029] To ensure the system evolves with changing clinical practices and population health profiles, this module collects feedback from healthcare providers regarding the accuracy and usefulness of predictions. It continuously evaluates model performance and regularly retrains the algorithms using new clinical data. This self-learning loop helps maintain the model's relevance and accuracy over time.

[0030] The invention is explained again below with reference to the figure. It shows: Fig. : an AI-powered real-time patient risk scoring system (100) for predictive health management.

[0031] Fig.illustrates an AI-powered real-time patient risk scoring system for predictive healthcare management." The AI-powered real-time patient risk scoring system functions through the seamless integration of several intelligent modules, each playing a crucial role in predictive healthcare. Its operation begins with the Data Ingestion and Integration Module, which continuously collects patient data from various sources such as electronic health records (EHRs), bedside monitoring systems, laboratory information systems, and wearable healthcare devices. This real-time data is then cleaned, standardized, and fed into the Feature Extraction and Data Processing Module, where vital signs, laboratory test values, clinical observations, and temporal trends are transformed into structured features suitable for predictive modeling.These features are passed to the machine learning-based risk prediction module, where sophisticated AI algorithms such as deep learning networks or ensemble models analyze patterns and generate dynamic risk scores for each patient, indicating the likelihood of critical events such as sepsis, cardiac arrest, or intensive care unit admission. The resulting risk scores are interpreted by the clinical decision support and alerting module, which prioritizes high-risk cases and sends real-time alerts with contextual recommendations to clinicians to support timely interventions. At the same time, the visualization and dashboard module provides clinicians with a live, interactive view of patient risk trajectories, indicating deteriorating patient conditions and enabling hospital-wide situational awareness.To ensure continuous improvement and adaptation to evolving clinical environments, the feedback learning and model update module collects results and user input to refine the AI models and ensure that the system remains current, accurate, and effective over time.

Claims

[1] An AI-powered real-time patient risk assessment system (100) for predictive health management, comprising: (a) a data acquisition and integration module configured to collect and standardise patient data from multiple clinical sources, including electronic health records, monitoring devices and laboratory systems; b) a feature extraction and data processing module for pre-processing the collected data and extracting clinically relevant features; (c) a machine learning-based risk prediction module configured to generate real-time risk scores based on the extracted features using trained AI models; (d) a clinical decision support and alerting module configured to trigger alerts and suggest interventions based on risk thresholds; (e) a visualisation and dashboard module for displaying patient risk scores and trends via an interactive interface; and f) a feedback learning and model update module configured to incorporate clinical feedback and retrain the AI models to improve prediction accuracy over time. [2] The system (100) of claim 1, wherein the data ingestion and integration module supports Health Level Seven (HL7) and FHIR protocols for seamless integration with hospital information systems. [3] The system (100) of claim 1, wherein the feature extraction and data processing module applies temporal pattern recognition and dimensionality reduction techniques to improve model input quality. [4] The system (100) of claim 1, wherein the machine learning-based risk prediction module uses recurrent neural networks (RNNs) or long short-term memory models (LSTM) to capture sequential clinical patterns. [5] The system (100) of claim 1, wherein the clinical decision support and alerting module generates risk-graded alerts based on user-defined clinical thresholds and priority levels. [6] The system (100) of claim 1, wherein the visualization and dashboard module includes heatmaps, risk timelines, and drill-down views for monitoring patients at the ward level and across the hospital. [7] The system (100) of claim 1, wherein the feedback learning and model updating module supports continuous evaluation of model performance using real patient outcomes and clinician inputs. [8] The system (100) of claim 1, wherein the AI models are trained using a historical dataset characterized by clinical events such as the onset of sepsis, cardiac arrest, or unplanned admissions to the intensive care unit.

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