System for personalized, condition-based health networks using AI and population health data

Abstract

System (100) for personalized condition-based matching of health networks using artificial intelligence and population health data, comprising: (a) a patient profiling module configured to collect and analyse individual health data from electronic health records, wearable devices and patient inputs to identify current medical conditions and generate dynamic health profiles; (b) a population health intelligence engine configured to aggregate and analyze population-level health data to identify regional disease trends, treatment outcomes, and provider performance; c) an AI-based network matching algorithm configured to compare individual health conditions with provider capabilities, historical outcomes, availability, and other contextual parameters to generate rated recommendations for healthcare providers; (d) a provider profiling and network indexing module configured to store, index and update provider and network information, including specialization, certifications, outcomes, availability and treatment capacities; (e) a decision support interface configured to present patients or clinicians with personalized, explainable recommendations for healthcare providers based on clinical relevance, patient preferences and system intelligence; (f) a feedback and learning optimization module configured to continuously collect patient outcomes, satisfaction, and treatment effectiveness to refine and retrain AI models; and g) a data security and compliance layer configured to ensure the secure handling of patient data, enforce access control, and ensure compliance with data protection standards such as HIPAA and GDPR.

Description

[0001] The present invention relates to the field of artificial intelligence-driven healthcare systems. More specifically, it relates to personalized healthcare network matching using real-time analytics of individual health status and the health status of a broader population. The invention enables dynamic, condition-based provider recommendations and optimized patient-provider matching to improve healthcare outcomes.

[0002] In the current healthcare ecosystem, patients often face challenges finding the most appropriate healthcare providers or networks tailored to their specific health conditions. Traditional referral systems and provider directories rely heavily on static parameters such as location or specialty, without considering dynamic clinical needs or evolving personal health data. This lack of personalization can lead to delayed treatments, suboptimal care coordination, and lower patient satisfaction, particularly for individuals with chronic or complex conditions that require specialized and timely interventions.

[0003] Furthermore, health systems often fail to harness the power of rich health data to direct patients to the most appropriate health networks. While population health analytics provides insights into regional disease trends, provider performance, and patient outcomes, there is a gap in translating these insights into actionable, individualized matching. As a result, many patients are routed into either overcrowded or misaligned care pathways, leading to inefficiencies, higher costs, and poorer outcomes for health systems.

[0004] To address these problems, there is an urgent need for a condition-aware, AI-based system that continuously learns from individual and population-based health data. Such a system can proactively recommend the most appropriate healthcare providers or networks by evaluating clinical needs, treatment history, provider expertise, network performance, and availability. The present invention aims to close this gap, providing patients with intelligent, personalized access to care while enabling healthcare systems to optimize resource utilization and improve care delivery.

[0005] One goal of this disclosure is to enable highly personalized recommendations for healthcare networks based on the patient's real-time condition.

[0006] Another goal of this disclosure is to improve care outcomes through AI-driven coordination with the most appropriate providers.

[0007] Another objective of this disclosure is to increase efficiency by reducing referral delays and inappropriate care.

[0008] Another objective of this disclosure is to integrate population health data to support contextual provider selection.

[0009] Another goal of this disclosure is to continuously learn and adapt from patient outcomes and feedback.

[0010] Another objective of this disclosure is to ensure the secure and compliant handling of sensitive health data.

[0011] Another goal of this disclosure is to provide a transparent, user-friendly interface for patients and clinicians.

[0012] Another objective of this disclosure is to optimize resource allocation across healthcare networks and systems.

[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 relates to AI-driven analytics for creating an evolving, real-time health profile for each patient by integrating clinical records, wearable data, and patient input. This enables dynamic identification of health conditions, severity, and care needs. The module ensures personalized insights tailored to each individual's unique clinical status.

[0015] Another embodiment of the present invention is that the system leverages comprehensive population health data to derive meaningful trends regarding disease prevalence, treatment effectiveness, and provider performance. It correlates individual conditions with broader epidemiological patterns.

[0016] Another embodiment of the present invention is an advanced AI matching algorithm that compares the patient's condition with provider attributes, such as specialization, previous outcomes, and availability. The system uses machine learning models to evaluate healthcare networks and providers, ensuring accurate selection of the most appropriate care pathways for improved outcomes.

[0017] Another embodiment of the present invention is that the provider module continuously updates profiles of healthcare professionals and facilities based on expertise, infrastructure, treatment data, and network connectivity. It maintains a real-time searchable index for matching algorithms.

[0018] Another embodiment of the present invention is that the user-friendly interface displays personalized recommendations for care networks along with rationales, ratings, and supporting data. It takes into account user preferences such as gender, language, or availability of teleconsultations.

[0019] In another embodiment of the present invention, the system integrates feedback from patient outcomes and treatment experiences into the AI ​​model. It uses adaptive learning techniques to improve future matching logic.

[0020] Another embodiment of the present invention provides for data protection and regulatory compliance through encryption, role-based access, and federated learning protocols. This ensures that patient data remains secure and compliant with HIPAA, GDPR, and other regulatory standards.

[0021] Another embodiment of the present invention is that the invention combines individual health needs with system-level intelligence to guide patients to effective care networks. It optimizes provider utilization, reduces misuse, and improves overall healthcare efficiency.

[0022] The present invention relates to an AI-based system designed to connect patients with the most appropriate healthcare networks based on their real-time health status and population health insights. It integrates personal medical data, wearable data, and clinical records to create dynamic patient profiles. Advanced machine learning evaluates provider skills, availability, and outcomes to provide precise, disease-based recommendations. A feedback loop refines the system over time, ensuring continuous learning and improved accuracy. The system also ensures robust data security and healthcare regulatory compliance for safe and reliable use.

[0023] Patient profiling and condition detection module: This module continuously ingests structured and unstructured health data from electronic health records (EHRs), wearable devices, lab results, and patient self-reports to create a dynamic health profile. It applies natural language processing (NLP) and AI-based diagnostic models to detect existing conditions, monitor changes, and classify the severity of ongoing health problems. The patient profile is updated in real time to reflect current clinical status, lifestyle risks, and disease progression, thus providing highly personalized insights. Population Health Intelligence Engine:

[0024] This component collects anonymized population-level health data from various sources such as public health databases, claims data, and regional health information exchanges. It analyzes epidemiological patterns, disease clusters, provider outcomes, and treatment effectiveness to create a predictive map of healthcare delivery across regions and specialties. The resulting insights support contextual recommendations based on success rates and provider specialization in treating similar cases. KL-based network matching algorithm:

[0025] At the heart of the invention is a powerful AI matching engine that compares the patient's health status in real time with the skills, experience, and availability of providers and healthcare networks. This takes into account parameters such as specialization, treatment success rates, patient feedback, geographic proximity, insurance compatibility, and historical care outcomes. Using advanced graph-based machine learning and reinforcement learning models, the most relevant care networks for the patient's specific health status are dynamically ranked and recommended. Provider profiling and network indexing module:

[0026] This module manages an intelligent, continuously updated directory of healthcare providers and networks. It indexes data on specialties, certifications, affiliations, treatment history, availability, capacity, and quality of infrastructure. Integration with hospital systems and professional registries ensures data accuracy and real-time availability updates. The module supports dynamic filtering and ranking to match patient needs with the most suitable and accessible providers. Interface for decision support and recommendations:

[0027] This is the user-facing layer of the invention, which presents personalized recommendations to patients, care managers, or referring physicians. An intuitive dashboard allows users to view appropriate providers, rationale ratings, supporting documentation, and route options. The module also integrates patient preferences such as language, physician gender, appointment availability, and digital consultation options, ensuring holistic personalization of access to healthcare. Feedback loop and learning optimizer:

[0028] To ensure continuous improvement and adaptation, this module captures post-matching patient outcomes, feedback, treatment experiences, and satisfaction scores. It feeds this information into the AI ​​model to refine future matching logic and prediction accuracy. The optimizer regularly trains the models using supervised and semi-supervised learning approaches, taking into account success indicators, error patterns, and changing healthcare trends to further evolve the recommendation system. Security, compliance and data governance:

[0029] To ensure data protection and regulatory compliance, this module monitors encryption, access control, audit trails, and anonymization processes. It ensures that patient health data is securely transmitted, stored, and processed in compliance with HIPAA, GDPR, and other regional health data regulations. Role-based access and federated learning techniques are used to maintain data sovereignty while enabling cross-network insights and AI training.

[0030] The invention is explained again below with reference to the figure. It shows: Fig. : a system (100) for a personalized, condition-based health network using AI and population health data.

[0031] Fig.illustrates a system (100) for a personalized, condition-based healthcare network that leverages AI and population health data. The system begins with the aggregation of individual health data through the patient profiling and condition detection module, which continuously monitors clinical records, wearable device inputs, and patient self-reports to create a dynamic health profile and detect specific medical conditions in real time. At the same time, the Population Health Intelligence Engine collects and analyzes population-level health data to uncover regional trends, provider performance metrics, and treatment effectiveness benchmarks.These insights feed into the AI-based network matching algorithm, which compares the patient's current health status with the provider's capabilities, availability, outcomes, and compatibility, creating a ranked list of optimal healthcare networks or specialists. The provider profiling and network indexing module ensures this matching engine has access to up-to-date provider information, specialties, treatment histories, and operational data. Recommendations are then presented through the decision support and recommendation interface, which provides patients and physicians with transparent, customizable guidance, including evidence-based rationales and preference-based filters.After the patient has been cared for, the feedback loop and learning optimizer collect data on treatment outcomes and satisfaction to retrain and fine-tune the AI ​​models for a better fit in the future. Throughout the process, the security, compliance, and data governance layer protects patient data privacy, manages access rights, and ensures compliance with healthcare data protection regulations to enable secure, compliant, and intelligent personalized healthcare network matching.

Claims

[1] System (100) for personalized state-based matching of health networks using artificial intelligence and population health data, comprising: (a) a patient profiling module configured to collect and analyse individual health data from electronic health records, wearable devices and patient inputs to identify current medical conditions and generate dynamic health profiles; (b) a population health intelligence engine configured to aggregate and analyze population-level health data to identify regional disease trends, treatment outcomes, and provider performance; c) an AI-based network matching algorithm configured to compare individual health conditions with provider capabilities, historical outcomes, availability, and other contextual parameters to generate rated recommendations for healthcare providers; (d) a provider profiling and network indexing module configured to store, index and update provider and network information, including specialization, certifications, outcomes, availability and treatment capacities; (e) a decision support interface configured to present patients or clinicians with personalized, explainable recommendations for healthcare providers based on clinical relevance, patient preferences and system intelligence; (f) a feedback and learning optimization module configured to continuously collect patient outcomes, satisfaction, and treatment effectiveness to refine and retrain AI models; and g) a data security and compliance layer configured to ensure the secure handling of patient data, enforce access control, and ensure compliance with data protection standards such as HIPAA and GDPR. [2] The system (100) of claim 1, wherein the patient profiling module uses natural language processing (NLP) to extract insights from unstructured clinical records and patient communications. [3] The system (100) of claim 1, wherein the population health intelligence engine includes predictive analytics for disease outbreaks and regional healthcare resource needs. [4] The system (100) of claim 1, wherein the AI-based network adaptation algorithm uses reinforcement learning to improve recommendation accuracy over time based on previous patient outcomes. [5] The system (100) of claim 1, wherein the provider profiling module is integrated with national and regional health registries for real-time validation of provider credentials and affiliations. [6] The system (100) of claim 1, wherein the decision support interface includes filtering options for patient preferences such as language, gender, telemedicine availability, and insurance compatibility. [7] The system (100) of claim 1, wherein the feedback and learning optimizer applies supervised and semi-supervised machine learning techniques to periodically retrain the recommendation models. [8] The system (100) of claim 1, wherein the data security and compliance layer uses federated learning to train AI models across distributed healthcare data sources without sharing raw patient data. [9] The system (100) of claim 1, wherein the decision support interface generates a visual explanation of the reasons for the match with the provider, including treatment history and probability of success values. [10] The system (100) of claim 1, wherein the AI-based matching algorithm considers comorbidities, treatment urgency, and continuity of care while generating network recommendations.

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