DESC:EARLIEST PRIORITY DATE:
This Application claims priority from a provisional patent application filed in India having Patent Application No. 202221036635, filed on October 27, 2022, and titled INFORMATION FOR HEALTHCARE PROVIDERS
FIELD OF INVENTION
[0001] Embodiments of the present disclosure relate to medical services and more particularly to a system and a method for managing data related to healthcare environment.
BACKGROUND
[0002] Currently, the available systems for recording patient information rely on paper-based record forms and charts. This approach is cumbersome, time-consuming, and prone to errors. Also, the current systems lack accessibility as patient data is often scattered across different locations and healthcare facilities, making it challenging for providers to access complete and up-to-date information. Specifically, healthcare providers frequently work in teams, and effective collaboration is crucial for providing comprehensive and coordinated care for patients. However, the current systems do not allow concurrent access to patient records or real-time collaboration, hindering communication and decision-making among the healthcare providers. Further, it is a common tendency to generate patient data in various data formats, such as medical images, test results, and clinical notes. As a result, the current systems struggle to handle such data formats, leading to difficulties in integrating and analyzing the data effectively. Also, using current the systems, protecting patient data is a paramount concern in healthcare. Further, medical records which are based on paper records may be lost, damaged, or accessed by unauthorized individuals. In such cases the patient’s privacy is compromised.
[0003] Hence, there is a need for a system and a method for managing data related to healthcare environment that addresses the aforementioned issues.
OBJECTIVE OF THE INVENTION
[0004] An objective of the present invention is to provide a system for managing data related to healthcare by eliminating the need for paper-based forms thereby improving efficiency.
[0005] Another objective of the present invention is to enable concurrent access to patient records, promoting real-time collaboration and communication among healthcare providers.
[0006] Yet, an objective of the present invention is to enable the healthcare providers to access information on allergies, medication interactions, and practice guidelines thereby enhancing quality of service provided by the healthcare providers.
[0007] Further, an objective of the present invention is to incorporate measures to ensure data security and patient privacy.
BRIEF DESCRIPTION
[0008] In accordance with one embodiment of the disclosure, a system for managing data related to a healthcare environment is provided. The system includes a processing subsystem hosted on a server and configured to execute on a network to control bidirectional communications among a plurality of modules. The plurality of modules includes a receiving module, an analysis module, a data integration module, and a preprocessing module. The receiving module is configured to receive data pertaining to a plurality of patients via a user interface and subsequently store the data in a central repository. The analysis module is operatively connected with the receiving module. The analysis module is configured to analyze the data in the central repository to identify a relationship between the data and a plurality of data patterns to enable a doctor to make decisions and plan treatment for the plurality of patients. The data integration module is operatively connected with the receiving module, and analysis module. The data integration module is configured to integrate legacy data and the analyzed data. The legacy data includes exiting available data of the patient. Relevant data is extracted from handwritten texts of the legacy data via a handwriting recognition module. A medical code used by the legacy data is converted into a standardized medical coding system for keeping medical records of the patient. The medical code is health care information represented in an alphanumeric format. The data is normalized by converting into a standard format with respect to global standard. The preprocessing module is operatively connected with the data integration module. The preprocessing module is configured to preprocess the normalized data by cleaning and correcting text present in the data. The preprocessing module is also configured to validate inconsistent data check for deduplicate data and error in the data for and quality assurance of the data. Further, the preprocessing module is configured to map the legacy data with a corresponding field in the central repository.
[0009] In accordance with another embodiment, a method for managing data related to healthcare is provided. The method includes receiving, by a receiving module of a processing subsystem, data pertaining to a plurality of patients via a user interface and subsequently storing the data in a central repository. The method also includes analyzing, by an analysis module of the processing subsystem, the data in the central repository to identify a relationship between the data and a plurality of data patterns to enable a doctor to make decisions and plan treatment for the plurality of patients. Further, the method includes integrating, by a data integration module of the processing subsystem, a legacy data and the analyzed data. The legacy data comprises exiting available data of the patient. Furthermore, the method includes extracting, by the data integration module of the processing subsystem, relevant data is extracted from a handwritten texts of the legacy data via a handwriting recognition module. Furthermore, the method includes converting, by the data integration module of the processing subsystem, a medical code used by the legacy data is converted into a standardized medical coding system for keeping medical records of the patient. The medical code is a health care information represented in an alphanumeric format. Furthermore, the method includes normalizing, by the data integration module of the processing subsystem, the data by converting into a standard format with respect to global standard. Furthermore, the method includes cleaning and correcting, by a preprocessing module of the processing subsystem, the text present in the data. Furthermore, the method includes validating, by the preprocessing module of the processing subsystem, inconsistent data check for deduplicate data and error in the data for and quality assurance of the data. Furthermore, the method includes mapping, by the preprocessing module of the processing subsystem, the legacy data with a corresponding field in the central repository.
[0010] To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.
BRIEF DESCRIPTION OF THE DRAWINGS
[0011] The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:
[0012] FIG. 1 is a block diagram representing a system for managing data related to a healthcare environment in accordance with an embodiment of the present disclosure;
[0013] FIG. 2 is a block diagram of a computer or a server for the system for managing data related to healthcare environment in accordance with an embodiment of the present disclosure; and
[0014] FIG. 3 is a flow chart representing steps involved in a method for managing data related to healthcare environment in accordance with an embodiment of the present disclosure.
[0015] Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the system, one or more components of the system may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.
DETAILED DESCRIPTION
[0016] For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure.
[0017] The terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process or method that comprises a list of steps does not include only those steps but may include other steps not expressly listed or inherent to such a process or method. Similarly, one or more devices or sub-systems or elements or structures or components preceded by "comprises... a" does not, without more constraints, preclude the existence of other devices, sub-systems, elements, structures, components, additional devices, additional sub-systems, additional elements, additional structures, or additional components. Appearances of the phrase "in an embodiment", "in another embodiment", and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.
[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.
[0019] In the following specification and the claims, reference will be made to a number of terms, which shall be defined to have the following meanings. The singular forms “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise.
[0020] Embodiments of the present disclosure relate to a system for managing data related to a healthcare environment. The system includes a processing subsystem hosted on a server and configured to execute on a network to control bidirectional communications among a plurality of modules. The plurality of modules includes a receiving module, an analysis module, a data integration module, and a preprocessing module. The receiving module is configured to receive data pertaining to a plurality of patients via a user interface and subsequently store the data in a central repository. The analysis module is operatively connected with the receiving module. The analysis module is configured to analyze the data in the central repository to identify a relationship between the data and a plurality of data patterns to enable a doctor to make decisions and plan treatment for the plurality of patients. The data integration module is operatively connected with the receiving module, and analysis module. The data integration module is configured to integrate legacy data and the analyzed data. The legacy data includes exiting available data of the patient. Relevant data is extracted from handwritten texts of the legacy data via a handwriting recognition module. A medical code used by the legacy data is converted into a standardized medical coding system for keeping medical records of the patient. The medical code is health care information represented in an alphanumeric format. The data is normalized by converting into a standard format with respect to global standard. The preprocessing module is operatively connected with the data integration module. The preprocessing module is configured to preprocess the normalized data by cleaning and correcting text present in the data. The preprocessing module is also configured to validate inconsistent data check for deduplicate data and error in the data for and quality assurance of the data. Further, the preprocessing module is configured to map the legacy data with a corresponding field in the central repository.
[0021] FIG. 1 is a block diagram representing a system (100) for managing data related to a healthcare environment in accordance with an embodiment of the present disclosure. The system (100) includes a processing subsystem (102). The processing subsystem (102) is hosted on a server (104), wherein the processing subsystem (102) is configured to execute on a network (106) to enable communications among a plurality of modules. In one embodiment, the network (106) may include a wired network such as local area network (LAN). In another embodiment, the network (106) may include a wireless network such as Wi-Fi, Bluetooth, Zigbee, near field communication (NFC), infra-red communication (RFID) or the like. The plurality of modules includes a receiving module (108), analysis module (116), a data integration module (120), and a preprocessing module (122).
[0022] The receiving module (108) is configured to receive data pertaining to a plurality of patients (110) via a user interface (112) and subsequently store the data in a central repository (114). In one embodiment, the central repository (114) is configured to communicate with a plurality of an external medical systems (124) such as medical laboratory to allow access and exchange of data among the plurality of authorized healthcare providers (118).
[0023] The analysis module (116) is operatively connected with the receiving module (108). The analysis module (116) is configured to analyze the data in the central repository (114) to identify a relationship between the data and a plurality of data patterns to enable an authorized healthcare provider (118) to make decisions and plan treatment for the plurality of patients (110). In one embodiment, the plurality of authorized healthcare providers (118) is allowed to access and update patient data from multiple geographical locations using a plurality of portable digital devices via wireless connectivity. In one embodiment, the plurality of portable devices are such as mobile phones, tablets, and the like. In another embodiment, the plurality of authorized healthcare providers (118) is allowed to access and update patient data from multiple geographical locations using the plurality of portable digital devices via wireless connectivity.
[0024] In one embodiment, the analysis module (116) is configured to identify the plurality of data patterns in the data. In one embodiment, the analysis module (116) is configured for data collection and preprocessing. In one embodiment, the analysis module (116) gathers the data from diverse sources such as electronic health records (EHRs), medical imaging, wearable devices, or clinical trials. The data is preprocessed by cleaning, filtering, and normalizing it to ensure consistency and remove noise. This may include handling missing data and outliers. In another embodiment, the analysis module (116) is configured for feature selection and engineering which identifies relevant features (variables) in the medical data that are likely to contain meaningful information. Perform feature engineering to create new features or transform existing ones to enhance the data's suitability for pattern recognition. Further, in one embodiment, the analysis module (116) is configured for data splitting which divides the dataset into training, validation, and test sets to evaluate model performance accurately. Furthermore, in one embodiment, the analysis module (116) is configured for pattern identification which chooses appropriate machine learning or statistical methods for pattern identification based on the nature of the data and the problem at hand. In one embodiment, the methods include:
- Supervised learning method: For tasks like classification and regression, methods such as Support Vector Machines (SVM), Random Forest, or Neural Networks may be used.
- Unsupervised Learning: For clustering and anomaly detection, methods such as K-Means, Hierarchical Clustering, or Principal Component Analysis (PCA) may be employed.
- Deep Learning: Deep neural networks, including Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data, may be used for more complex patterns.
- Association Rule Mining: For discovering relationships in large datasets, algorithms like Apriori or FP-Growth are useful.
- Time Series Analysis: For time-dependent medical data, techniques such as Autoregressive Integrated Moving Average (ARIMA) or Long Short-Term Memory (LSTM) networks can be applied.
- Furthermore, in one embodiment, the analysis module (116) is configured for Model Training which trains the selected machine learning models using the training dataset. Hyperparameters are optimized and use appropriate evaluation metrics based on the problem (for example accuracy, F1-score, AUC-ROC). Furthermore, in one embodiment, the analysis module (116) is configured for model evaluation to assess the performance of the trained models using the validation dataset. Fine-tune models if necessary. In one embodiment, fine- tuning of models means adjusting the models precisely so as to bring to the highest level of performance or effectiveness.
[0025] Furthermore, in one embodiment, the analysis module (116) is configured for pattern identification which is applied to the trained model to the test dataset or new, unseen data to identify patterns. The model may predict or classify the patterns based on the input data. Furthermore, in one embodiment, the analysis module (116) is configured for interpretation and visualization which interprets the results to understand the discovered patterns' clinical significance. The identified patterns are using charts, graphs, or heatmaps to make the information more accessible to healthcare professionals.
[0026] Furthermore, in one embodiment, the analysis module (116) is configured for validation and clinical testing. If the identified patterns have clinical implications, conduct further validation and testing, possibly through clinical trials or expert consultation. Furthermore, in one embodiment, the analysis module (116) is configured for deployment of the identified patterns. If the identified patterns are deemed useful and accurate, deploy them into clinical practice or healthcare systems for real-time decision support or monitoring. Furthermore, in one embodiment, the analysis module (116) is configured for continuous monitoring and improvement. In one embodiment, the performance of the pattern identification system is monitored continuously, and update models as new data becomes available. This ensures that the system remains accurate and relevant over time.
[0027] The data integration module (120) is operatively connected with the receiving module (108), and analysis module (116). The data integration module (120) is configured to integrate legacy data (126) and the analyzed data. The legacy data (126) includes exiting available data of the patient. The legacy data (126) may include paper records, handwritten notes, scanned documents, or data stored in obsolete electronic formats. Relevant data is extracted from handwritten texts of the legacy data (126) via a handwriting recognition module. A medical code used by the legacy data (126) is converted into a standardized medical coding system for keeping medical records of the patient. The medical code is health care information represented in an alphanumeric format. The data is normalized by converting into a standard format with respect to global standard.
[0028] In one embodiment, the legacy data (126) is integrated by means of an optical character recognition technique. In one embodiment, wherein the data integration module (120) is configured to collaborate in real-time with the plurality of authorized healthcare providers (118) to access the patient data.
[0029] The preprocessing module (122) is operatively connected with the data integration module (120). The preprocessing module (122) is configured to preprocess the normalized data by cleaning and correcting text present in the data. The preprocessing module (122) is also configured to validate inconsistent data check for deduplicate data and error in the data for and quality assurance of the data. Further, the preprocessing module (122) is also configured to map the legacy data (126) with a corresponding field in the central repository (114) to arrange the data in a format that is easily accessible by the healthcare provider.
[0030] In one embodiment, the preprocessing module (122) is configured to organize patient data in a structured manner, facilitating efficient storage, retrieval, and analysis. In one embodiment, the system (100) is configured to secure the data and ensure privacy of the data by means of predetermined security measures, wherein the predetermined security measures comprises at least one of an access control, an encryption, and an audit trail to protect patient information from unauthorized access.
[0031] In one embodiment, the integration of the legacy data (126) into a modern medical information system is a complex process and depends on the format and quality of the existing data.
[0032] FIG. 2 is a block diagram (200) of a computer or a server for the system for managing data related to healthcare environment in accordance with an embodiment of the present disclosure. The server includes processor(s) (202), and memory (206) operatively coupled to the bus (204).
[0033] The processor(s) (202), as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor, a microcontroller, a complex instruction set computing microprocessor, a reduced instruction set computing microprocessor, a very long instruction word microprocessor, an explicitly parallel instruction computing microprocessor, a digital signal processor, or any other type of processing circuit, or a combination thereof.
[0034] The bus (204) used herein refers to internal memory channels or computer network that is used to connect computer components and transfer data between them. The bus (204) includes a serial bus or a parallel bus, wherein the serial bus transmits data in bit-serial format and the parallel bus transmits data across multiple wires. The bus (204) as used herein, may include but not limited to, a system bus, an internal bus, an external bus, an expansion bus, a frontside bus, a backside bus, and the like.
[0035] The memory (206) includes a plurality of subsystems and a plurality of modules stored in the form of executable program which instructs the processor (202) to perform the method steps illustrated in FIG. 1. The memory (206) is substantially similar to the system of FIG.1. The memory (206) has the following submodules includes a receiving module (108), analysis module (116), a data integration module (120), and a preprocessing module (122).
[0036] The receiving module (108) is configured to receive data pertaining to a plurality of patients (110) via a user interface (112) and subsequently store the data in a central repository (114).
[0037] The analysis module (116) operatively connected with the receiving module (108). The analysis module (116) is configured to analyze the data in the central repository (114) to identify a relationship between the data and a plurality of data patterns to enable an authorized healthcare provider (118) to make decisions and plan treatment for the plurality of patients (110).
[0038] The data integration module (120) is operatively connected with the receiving module (108), and analysis module (116). The data integration module (120) is configured to integrate legacy data (126) and the analyzed data. The legacy data (126) includes exiting available data of the patient. Relevant data is extracted from handwritten texts of the legacy data (126) via a handwriting recognition module. A medical code used by the legacy data (126) is converted into a standardized medical coding system for keeping medical records of the patient. The medical code is health care information represented in an alphanumeric format. The data is normalized by converting into a standard format with respect to global standard.
[0039] The preprocessing module (122) is operatively connected with the data integration module (120). The preprocessing module (122) is configured to preprocess the normalized data by cleaning and correcting text present in the data. The preprocessing module (122) is also configured to validate inconsistent data check for deduplicate data and error in the data for and quality assurance of the data. Further, the preprocessing module (122) is also configured to map the legacy data (126) with a corresponding field in the central repository (114).
[0040] Computer memory elements may include any suitable memory device(s) for storing data and executable program, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, hard drive, removable media drive for handling memory cards and the like. Embodiments of the present subject matter may be implemented in conjunction with program modules, including functions, procedures, data structures, and application programs, for performing tasks, or defining abstract data types or low-level hardware contexts. Executable program stored on any of the above-mentioned storage media may be executable by the processor(s) (202).
[0041] FIG. 3 is a flow chart representing steps involved in a method for managing data related to healthcare in accordance with an embodiment of the present disclosure. The method (300) includes receiving, by a receiving module of a processing subsystem, data pertaining to a plurality of patients via a user interface and subsequently storing the data in a central repository in step (302).
[0042] The method (300) also includes analyzing, by an analysis module of the processing subsystem, the data in the central repository to identify a relationship between the data and a plurality of data patterns to enable a doctor to make decisions and plan treatment for the plurality of patients in step (304).
[0043] Further, the method (300) includes integrating, by a data integration module of the processing subsystem, legacy data, and the analyzed data, wherein the legacy data comprises exiting available data of the patient in step (306).
[0044] Furthermore, the method (300) includes extracting, by the data integration module of the processing subsystem, relevant data is extracted from a handwritten texts of the legacy data via a handwriting recognition module in step (308).Furthermore, the method (300) includes converting, by the data integration module of the processing subsystem, a medical code used by the legacy data is converted into a standardized medical coding system for keeping medical records of the patient, wherein the medical code is a health care information represented in an alphanumeric format in step (310). In one embodiment, Legacy data may use older coding systems or terminologies. A plurality of methods are employed to map or convert these legacy codes to standardized medical coding systems such as systematized Nomenclature of Medicine -- Clinical Terms (SNOMED CT), International Classification of Diseases (ICD-10), or cognitive behavioral therapy (CPT).
Furthermore, the method (300) includes normalizing, by the data integration module of the processing subsystem, the data by converting into a standard format with respect to global standard in step (312). In one embodiment, the extracted data is normalized to ensure consistency and conformity with the data structure used in the modern medical information system. This involves converting data into a standardized format, such as HL7 (Health Level Seven) for electronic health records.
[0045] Furthermore, the method (300) includes cleaning and correcting, by a preprocessing module of the processing subsystem, the text present in the data in step (314). Furthermore, the method (300) includes validating, by the preprocessing module of the processing subsystem, inconsistent data check for deduplicate data and error in the data for and quality assurance of the data in step (316). In one embodiment, a plurality of methods are applied to validate the accuracy and completeness of the extracted data. This includes checking for missing or inconsistent information. In one embodiment, extensive testing and validation procedures are performed to ensure that the integrated legacy data is accurate, accessible, and functional within the modern medical information system.
[0046] Furthermore, the method (300) includes mapping, by the preprocessing module of the processing subsystem, the legacy data with a corresponding field in the central repository in step (318).
[0047] In one embodiment, the steps performed for integration of the legacy data integration are collecting and scanning the data. In one embodiment, the legacy data records are collected and scanned into digital format using high-quality scanning equipment. Optical Character Recognition (OCR) is employed to convert printed or handwritten text within the scanned documents into machine-readable text. The method also includes cleaning the data. The OCR output may contain errors or misinterpretations, particularly in the case of handwritten notes. A method for text correction and cleanup is applied to enhance the accuracy of the converted text. The method also includes data Structuring and Extraction. Data extraction methods are used to identify and extract relevant information from the scanned documents. This may include patient demographics, diagnoses, procedures, medications, and other pertinent data. Handwriting recognition is used for more accurate extraction of handwritten text.
[0048] The processed legacy data is integrated into the existing medical information system's database. This often involves mapping the legacy data to the appropriate fields in the central repository. The method also includes integrating via the user interface. The integrated legacy data may be accessible through the modern medical information system's user interface. The methods and interface design are used to present this data to users in a user-friendly and intuitive manner. The method also includes migrating and archiving the data. Once legacy data is successfully integrated, the original paper records or outdated electronic systems may be archived or securely stored as a backup.
[0049] The method also includes considering stringent security and privacy measures, including encryption and access controls, are implemented to protect patient data during the integration process. In one embodiment, a comprehensive documentation and training are provided to healthcare providers and system users to ensure they can effectively utilize the integrated legacy data.
[0050] It's important to note that the specific methods and tools used for legacy data integration may vary depending on the healthcare providers needs and the nature of the legacy data. Additionally, compliance with healthcare data regulations and standards is essential throughout the integration process to safeguard patient privacy and data security.
[0051] A method of organizing and separating data in the system includes identifying data categories. the following. The method also includes identifying different categories or types of data that the user need to store and manage within the system. Common categories may include customer data, financial data, employee records, product information, and more. The method also includes defining data access levels. The method also includes determining the need for access to each category of data and what level of access they require. For example, some data may be accessible to all employees, while other data may be restricted to specific roles or individuals.
[0052] The method also includes creating a structured data model for each data category. This involves defining the fields, attributes, and relationships between data elements. The user may choose to use a relational database management system (RDBMS) or other appropriate data storage solutions. The method also includes determining where and how data may be physically stored. This may involve on-premises servers, cloud storage solutions, or a combination of both. Consider redundancy and backup strategies to safeguard against data loss. The method also includes separating the data into specific environments. In one embodiment, the data is divided into separate environments. The environment may include a development environment, where new applications and features are developed and tested using dummy or sample data. The environment also includes a testing environment, here fully functional applications and systems are tested with real data, often a copy of production data. Further, the environment includes a production environment, where live data is accessed and used for day-to-day operations. Furthermore, the environment includes a backup or archival environment, where historical data is stored for compliance or future reference.
[0053] In one embodiment, a data retention policies are established data retention policies that define how long data should be kept in the system. Different data categories may have different retention periods based on legal requirements or business needs. The method also includes data indexing and searchability.
[0054] In one embodiment, an implement indexing and search mechanisms facilitates efficient data retrieval. This is particularly important for large datasets. The method also includes documenting the data and metadata. In one embodiment, the document data schemas, definitions, and metadata to provide context and aid in data understanding and governance. The method also includes scheduling a routine data maintenance tasks, including data cleaning, updating, and optimizing database performance. In one embodiment, the method ensures that the data organization and separation methods comply with relevant regulations and industry standards, such as general data protection regulation (GDPR), health Insurance Portability and Accountability Act (HIPAA), or international organization for standardization (ISO 27001). The method includes implementing the monitoring and auditing processes to track data access, modifications, and security breaches. Regularly review logs and audit trails. The method also includes preparing a disaster recovery plan to address data loss or system failures. Test the plan periodically to ensure it is effective. The method also includes planning for future data growth and scalability needs. Ensure that user’s data may accommodate increasing data volumes without significant disruptions.
[0055] Various embodiments of the present disclosure provide a system for managing data related to healthcare by eliminating the need for paper-based forms and integrating the legacy data with the data available in the central repository by the data integration module. The central repository in the system disclosed in the present disclosure enables concurrent access to patient records, promoting real-time collaboration and communication among healthcare providers. Also, the preprocessing module of the system enables the healthcare providers access to information on allergies, medication interactions, and practice guidelines, enhancing the quality. The system disclosed in the present disclosure ensure data security and patient privacy.
[0056] Further, the system disclosed in the present disclosure provides error free data by assuring data quality by means of deduplication data and error checking. Also, for data security, validation of inconsistent data is carried out for deduplicate data by the preprocessing module.
[0057] While specific language has been used to describe the disclosure, any limitations arising on account of the same are not intended. As would be apparent to a person skilled in the art, various working modifications may be made to the method in order to implement the inventive concept as taught herein.
[0058] The figures and the foregoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, the order of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts need to be necessarily performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples.
,CLAIMS:1. A system (100) for managing data related to a healthcare environment comprising:
characterized in that:
a processing subsystem (102) hosted on a server (104), and configured to execute on a network (106) to control bidirectional communications among a plurality of modules comprising:
a receiving module (108) configured to receive data pertaining to a plurality of patients (110) via a user interface (112) and subsequently store the data in a central repository (114);
an analysis module (116) operatively connected with the receiving module (108), wherein the analysis module (116) is configured to analyze the data in the central repository (114) to identify a relationship between the data and a plurality of data patterns to enable an authorized healthcare provider (118) to make decisions and plan treatment for the plurality of patients (110); and
a data integration module (120) operatively connected with the receiving module (108), and analysis module (116),
wherein the data integration module (120) is configured to integrate a legacy data (126) and the analyzed data, wherein the legacy data (126) comprises exiting available data of the patient,
wherein a relevant data is extracted from a handwritten texts of the legacy data (126) via a handwriting recognition module,
wherein a medical code used by the legacy data (126) is converted into a standardized medical coding system for keeping medical records of the patient, wherein the medical code is a health care information represented in an alphanumeric format,
wherein the data is normalized by converting into a standard format with respect to global standard;
a preprocessing module (122) operatively connected with the data integration module (120) and configured to:
preprocess the normalized data by cleaning and correcting text present in the data;
validate inconsistent data check for deduplicate data and error in the data for and quality assurance of the data; and
map the legacy data (126) with a corresponding field in the central repository (114).
2. The system (100) as claimed in claim 1, wherein the central repository (114) is configured to communicate with a plurality of an external medical systems (124) to allow access and exchange of data among the plurality of authorized healthcare providers (118).
3. The system (100) as claimed in claim 1, wherein the plurality of authorized healthcare providers (118) is allowed to access and update patient data from multiple geographical locations using a plurality of portable digital devices via a wireless connectivity.
4. The system (100) as claimed in claim 1, comprises a plurality of reference databases for consultation purposes and accessed by the plurality of authorized health care providers (118) to retrieve information related to allergies, medication interactions, and practice guidelines thereby enhancing the quality and safety of the medical laboratory.
5. The system (100) as claimed in claim 1, wherein the data integration module (120) is configured to collaborate in real-time with the plurality of authorized healthcare providers (118) to access the patient data.
6. The system (100) as claimed in claim 1, wherein the legacy data (126) is integrated by means of an optical character recognition technique.
7. The system (100) as claimed in claim 1, wherein the preprocessing module (122) is configured to organize patient data in a structured manner, facilitating efficient storage, retrieval, and analysis.
8. The system (100) as claimed in claim 1, is configured to secure the data and ensure privacy of the data by means of predetermined security measures, wherein the predetermined security measures comprises at least one of an access control, an encryption, and an audit trail to protect patient information from unauthorized access.
9. The system (100) as claimed in claim 1, wherein the legacy data (126) includes at least one of paper files and mainframe data.
10. A method (300) for managing data related to healthcare environment comprising:
receiving, by a receiving module of a processing subsystem, data pertaining to a plurality of patients via a user interface and subsequently store the data in a central repository; (302)
analyzing, by an analysis module of the processing subsystem, the data in the central repository to identify a relationship between the data and a plurality of data patterns to enable a doctor to make decisions and plan treatment for the plurality of patients; (304)
integrating, by a data integration module of the processing subsystem, a legacy data, and the analyzed data, wherein the legacy data comprises exiting available data of the patient; (306)
extracting, by the data integration module of the processing subsystem, a relevant data is extracted from a handwritten texts of the legacy data via a handwriting recognition module; (308)
converting, by the data integration module of the processing subsystem, a medical code used by the legacy data is converted into a standardized medical coding system for keeping medical records of the patient, wherein the medical code is a health care information represented in an alphanumeric format; (310)
normalizing, by the data integration module od the processing subsystem, the data by converting into a standard format with respect to global standard; (312)
cleaning and correcting, by a preprocessing module of the processing subsystem, the text present in the data; (314)
validating, by the preprocessing module of the processing subsystem, inconsistent data check for deduplicate data and error in the data for and quality assurance of the data; (316) and
mapping, by the preprocessing module of the processing subsystem, the legacy data with a corresponding field in the central repository. (318)
Dated this 25th day of October 2023
Signature
Jinsu Abraham
Patent Agent (IN/PA-3267)
Agent for the Applicant