Scalable analytics engineering platform for intelligent insights in healthcare

Abstract

A computer-implemented, scalable analytics engineering platform for intelligent insights in healthcare, consisting of: • A data acquisition module configured to collect and import health data from various sources, including electronic health records (EHRs), wearable devices, diagnostic imaging, genomic data, and patient-generated health data; • A data standardization module designed to ensure the consistency and compatibility of the captured data for seamless integration and analysis; • A data curation module configured to organize, filter, and prepare standardized data for advanced analysis; • A data analytics module that uses machine learning algorithms to detect patterns, predict outcomes and provide actionable insights, including predictive analytics, anomaly detection and natural language processing; • A feedback loop mechanism that updates and retrains machine learning models based on real-world data and outcomes to improve analysis accuracy and treatment recommendations; and • A cloud-based infrastructure configured to dynamically scale computing resources, thus ensuring efficient and cost-effective data processing for varying workloads.

Description

Technical area:

[0001] The present invention relates to the field of healthcare technology, with a particular focus on the development and implementation of scalable analytics engineering platforms designed to deliver intelligent insights into healthcare. This field encompasses a broad range of interdisciplinary technologies and methods aimed at leveraging advanced data analytics, machine learning, artificial intelligence (AI), and cloud computing to improve the efficiency, accuracy, and effectiveness of healthcare delivery systems.

[0002] The invention lies at the intersection of health informatics and data science and focuses on integrating and analyzing large amounts of healthcare data to generate actionable insights. It involves creating robust, scalable architectures capable of processing diverse data sources, including electronic health records (EHRs), wearable devices, diagnostic imaging, genomic data, and other health-related information. The goal is to transform raw data into meaningful insights that can support clinical decisions, improve treatment outcomes, and optimize healthcare operations.

[0003] The technical field of health technology encompasses the use of innovative tools and systems to address the challenges of the healthcare industry. This includes the development of software platforms that facilitate the collection, integration, and analysis of patient data from diverse sources. The invention utilizes AI and machine learning algorithms to detect patterns, predict outcomes, and recommend personalized treatment plans. This improves the ability of healthcare providers to provide timely and precise interventions, thus improving the overall quality of care.

[0004] The invention also addresses the need for interoperability between different healthcare systems and standards, enabling seamless data exchange and communication between various stakeholders, including hospitals, clinics, laboratories, and research institutions. By providing a unified platform for data aggregation and analysis, the invention supports the creation of comprehensive health profiles for individual patients and populations, thus facilitating preventive care, disease management, and health promotion.

[0005] The technical field of data analytics encompasses the application of statistical and computational techniques to gain insights from large and complex data sets. In the context of this invention, data analytics plays a crucial role in processing and analyzing healthcare data to uncover hidden trends, correlations, and anomalies. The platform uses advanced data processing pipelines to clean, normalize, and transform raw data into a structured format suitable for analysis.

[0006] Machine learning models are trained to perform tasks such as predictive analytics, anomaly detection, and natural language processing. These models enable the platform to predict disease outbreaks, identify high-risk patients, and recommend personalized treatment regimens based on individual health profiles. The invention also includes visualization tools that present the findings in an intuitive and actionable way, enabling healthcare providers to make informed decisions.

[0007] Furthermore, the invention addresses the scalability issues associated with processing large amounts of healthcare data. By leveraging a cloud-based infrastructure, the platform can dynamically scale its computing resources to accommodate varying workloads, thus ensuring efficient and cost-effective data processing. By leveraging distributed computing and parallel processing techniques, the platform can process data from multiple sources simultaneously and provide real-time analytics and insights.

[0008] In summary, the technical field of the invention encompasses healthcare technology and data analytics, with a focus on developing scalable, AI-driven platforms for intelligent healthcare insights. The invention aims to revolutionize healthcare by enabling data-driven decision-making, personalized treatment, and improved patient outcomes. Background:

[0009] The healthcare industry is undergoing profound change, driven by the exponential growth of data from diverse sources such as electronic health records (EHRs), wearable devices, diagnostic imaging systems, genomic data, and patient-generated health data. This surge in data, often referred to as "big data," presents both opportunities and challenges for healthcare providers, researchers, and policymakers. The potential to leverage these vast amounts of data to generate actionable insights is immense, yet the complexity and volume of the data also pose significant challenges in terms of data integration, analysis, and interpretation.

[0010] Traditional healthcare systems are often siloed, with data scattered across different platforms and formats, making it difficult to obtain a holistic view of patient health. Furthermore, the enormous amount of data generated daily can overwhelm existing data processing and analytics capabilities. These challenges underscore the need for a scalable, integrated platform that can efficiently process, analyze, and derive insights from healthcare data in real time.

[0011] In response to these challenges, the development of scalable analytics engineering platforms has emerged as a critical area of ​​innovation. These platforms leverage advanced technologies such as cloud computing, machine learning, and artificial intelligence (AI) to effectively manage and analyze large data sets. By integrating data from diverse sources, these platforms can deliver comprehensive, real-time insights that support clinical decisions, personalized treatments, and improved patient outcomes.

[0012] The proposed utility model, titled "Design of a Scalable Analytics Engineering Platform for Intelligent Healthcare Insights," aims to address the aforementioned challenges by providing a robust and scalable architecture for healthcare data analytics. The platform is designed to ingest, standardize, curate, and analyze healthcare data from diverse sources. It leverages AI and machine learning algorithms to detect patterns, predict outcomes, and recommend personalized treatments.

[0013] One of the key advantages of the proposed platform is its scalability. By leveraging a cloud-based infrastructure, the platform can dynamically scale its computing resources to accommodate varying workloads, ensuring efficient and cost-effective data processing. This is particularly important in the healthcare sector, where data volumes can fluctuate significantly and timely analysis is critical for effective decision-making.

[0014] Furthermore, the platform's ability to integrate and standardize data from multiple sources ensures interoperability and facilitates seamless data exchange between different healthcare systems. This enables the creation of comprehensive health profiles for individual patients and populations and supports preventive care, disease management, and health promotion.

[0015] In summary, the background of the proposed utility model underscores the growing importance of data analytics in healthcare and the need for scalable, integrated platforms to harness the potential of big data. By addressing the challenges of data integration, analytics, and scalability, the proposed platform aims to revolutionize healthcare and improve patient outcomes through intelligent, data-driven insights. Summary of the invention:

[0016] The present invention, entitled "Design of a Scalable Analytics Engineering Platform for Intelligent Healthcare Insights," addresses the urgent need for an integrated and scalable platform capable of processing large amounts of healthcare data to generate actionable insights. This invention leverages advanced technologies such as artificial intelligence (AI), machine learning, and cloud computing to provide a robust healthcare data analytics solution. The platform is designed to ingest, standardize, curate, and analyze health data from diverse sources, including electronic health records (EHRs), wearable devices, diagnostic imaging, and genomic data. By integrating these heterogeneous data sources, the platform ensures interoperability and provides a comprehensive view of patient health.

[0017] The main features of the invention include: 1. Data acquisition and standardization: Efficient collection and import of health data from various sources, followed by standardization to ensure consistency and compatibility for further processing. 2. Data curation: Organizing and preparing standardized data for analysis to improve data quality and usability. 3. Advanced data analytics: Leveraging machine learning algorithms to identify patterns, predict outcomes, and recommend personalized treatment plans. Analytics includes predictive analytics, anomaly detection, and natural language processing. 4. Scalability: The platform uses a cloud-based infrastructure to dynamically scale computing resources, accommodate different workloads, and ensure cost-effective data processing. 5. Real-time insights: Providing real-time analytics and insights through distributed computing and parallel processing techniques that enable timely and informed decisions. 6. Feedback loop: Continuously update and feed back data into the system to improve the accuracy and effectiveness of disease detection and treatment recommendations.

[0018] The invention aims to revolutionize healthcare by enabling data-driven decision-making, personalized treatment, and improved patient outcomes. By addressing the challenges of data integration, analysis, and scalability, the platform offers a comprehensive solution for harnessing the potential of big data in healthcare.

[0019] Overall, the invention represents a significant advance in the field of healthcare technology and data analytics, providing a scalable and intelligent platform for generating valuable healthcare insights. Description of the drawings:Figure 1 1. Patient data in healthcare: This is the starting point of the flowchart, represented by a document icon. It indicates the collection of patient data. 2. Health data enrollment: This step is represented by a cloud icon with a downward-pointing arrow and involves enrolling health data into the system. 3. Standardization of health data: This step, represented by a funnel symbol, standardizes the collected data to ensure consistency and compatibility. 4. Health data curation: This step is indicated by the icon of a document with a check mark and involves curating the standardized data for further analysis. 5. Health data analysis: This step is represented by a bar chart icon and involves analyzing the curated health data. 6. Machine learning model: This step is represented by the symbol of a neural network and involves applying a machine learning model to the analyzed data. 7. Disease Detection : This step is represented by a diamond with a heartbeat symbol and determines whether a disease is detected or not. 8. Detected: If a disease is detected, the flowchart branches into the following steps: ◯ Early intervention: This step is represented by the symbol of a heart with a cross and includes early intervention measures. ◯ Health Recommendations : This step is represented by the clipboard icon and provides health recommendations based on the detected disease. ◯ Patient notification: This step, represented by a bell icon, involves notifying the patient about the health recommendations. 9. Not detected: If no disease is detected, the flowchart branches to the following step: ◯ Continuous monitoring: This step is indicated by the symbol of a monitor and involves the continuous monitoring of the patient's health data. 10. Feedback loop: This step is represented by the symbol of a winding arrow and involves updating the data and feeding it back into the system for continuous improvement.

[0020] The image provides a comprehensive and systematic approach to using AI and machine learning for disease detection, which can significantly improve healthcare outcomes through early intervention and continuous monitoring. Figure 2

[0021] illustrates a data processing pipeline in the healthcare context: 1. Batch sources: ◯ Patient-Provider Databases: Represents databases that store patient-provider information and contain comprehensive patient data. ◯ Electronic health record: Represents electronic health records that store detailed patient health information. 2. Sensors for healthcare: ◯ Stream processing: Represents the processing of real-time data streams from health sensors and continuously collects health metrics. 3. ETL processing: ◯ Extract, transform, and load (ETL): This process integrates data from different sources and ensures that it is extracted, transformed for consistency, and loaded into a unified data repository. 4. Cloud data lake: ◯ Standardized and curated : Represents a cloud-based data lake where data is standardized and curated to be ready for further analysis and use. 5. Data Science Platform: ◯ Analytics Platform: Represents a platform used by healthcare data scientists to analyze and process standardized and curated data. 6. SQL Lakehouse Platform: ◯ Data Lake and Warehouse Integration: Represents a platform that combines the functions of data lakes and data warehouses and enables SQL-based data processing and analysis. 7. BI tools: ◯ Business Intelligence Tools: Represents tools used by data analysts in healthcare to gain insights from processed data and thus support decision-making. 8. Batch reports: ◯ Reporting: Represents the creation of batch reports that provide valuable insights and summaries for healthcare providers. 9. Data Scientists in Healthcare: ◯ Data Science Platform Users: Represents data scientists who use the Data Science Platform for advanced data analysis and modeling. 10. Data analysts in healthcare: ◯ BI tool users: Represents data analysts who use business intelligence tools to derive actionable insights from data. 11. Healthcare providers: ◯ Report end users: Represents healthcare providers who receive the batch reports and use the insights for clinical decisions and patient care. Figure 3

[0022] Data sources • Wearables: Devices that collect health data from individuals, such as fitness trackers and smartwatches. • Diagnosis: Information about medical diagnoses made by healthcare professionals. • Disease management: Data related to the ongoing management and treatment of chronic diseases. • Prevention: Information on preventive measures to prevent the occurrence of diseases. • Screening: Data from health examinations and tests carried out to detect diseases early. • Treatment: Information about treatments given to patients, including medications and therapies. 2. Data integration • Medical health records: A central repository that integrates data from all of the above sources. This ensures that all relevant health information is available in a consistent format for further analysis. 3. Analysis • AI Tools: Advanced artificial intelligence tools that analyze integrated medical health records. These tools use machine learning algorithms to identify patterns, predict outcomes, and provide insights based on the data. 4. Personalized treatment • Personalized treatment recommendations: The system's end result is a set of personalized treatment recommendations tailored to the individual patient's health needs. These recommendations are generated based on the analysis performed by the AI ​​tools, taking into account all integrated data sources.

Claims

[1] A computer-implemented scalable analytics engineering platform for intelligent healthcare insights, consisting of: • A data ingestion module configured to collect and import health data from various sources, including electronic health records (EHRs), wearable devices, diagnostic imaging, genomic data, and patient-generated health data; • A data standardization module designed to ensure the consistency and compatibility of the ingested data for seamless integration and analysis; • A data curation module configured to organize, filter, and prepare standardized data for advanced analytics; • A data analytics module that uses machine learning algorithms to identify patterns, predict outcomes, and provide actionable insights, including predictive analytics, anomaly detection, and natural language processing; • A feedback loop mechanism that updates and retrains machine learning models based on real-world data and outcomes to improve analysis accuracy and treatment recommendations; and • A cloud-based infrastructure configured to dynamically scale computing resources, ensuring efficient and cost-effective data processing for diverse workloads. [2] A device for providing intelligent healthcare insights, the device comprising: • A visualization module that presents analysis results and insights in an intuitive and actionable format, enabling healthcare providers to make informed clinical decisions. [3] A computer system for intelligent health data ingestion and analysis, where the data ingestion module supports real-time data acquisition and integration from distributed sources using interoperability standards such as HL7 and FHIR.

Images