3. System would effectively notify the concerned official.

Description:Field of the Invention

[0001] This invention is of the domain computer science using machine learning techniques.

[0002] The current invention has taken the proximity of healthcare facilities as the main parameter.

Background

[0003] Information from the background description may help you comprehend the current invention. It is not an admission that any of the data presented here is previous knowledge or pertinent to the invention currently being claimed, nor is it an admission that any publication directly or implicitly referred to herein is prior knowledge.
[0004] Location-based analysis of healthcare facilities based on specific performance involves using geographic information system (GIS) technology to map and analyze the performance of healthcare facilities in specific geographic areas
[0005] This analysis can help healthcare organizations and policymakers identify areas where healthcare services are lacking or where improvements in healthcare services are needed.
[0006] The first step in this analysis is to define the specific performance metrics that will be analyzed
[0007] This could include metrics such as patient outcomes, patient satisfaction, quality of care, wait times, or cost of care
[0008] Once the performance metrics have been defined, the next step is to collect data from healthcare facilities in the target geographic area.
[0009] The data can be collected through surveys, interviews, or by analyzing healthcare records. Once the data has been collected, it can be geocoded to create a spatial database.
[0010] This database can then be used to create maps and visualizations of the performance metrics for each healthcare facility in the area.

[0011] Using GIS software, analysts can then overlay the performance metrics maps with other geographic data such as population demographics, transportation infrastructure, and socio-economic indicators
[0012] This can help identify areas where there is a high demand for healthcare services but low availability, or where there are disparities in healthcare access based on income or ethnicity.
[0013] The U.S. Patent No. 7,996,187 describes a personal health monitor that wirelessly gathers and sends physiologic data to a distant entity. [0014] U.S. Patent Publication No. 2009/0076405 details a wireless respiration monitoring system. When an alert condition is triggered by information from a medical practitioner, a remote monitoring system, or a medical treatment system, data from the patient-worn monitor can be utilised to confirm the alert condition.
[0015] The healthcare information management system described in U.S. Pat. No. 7,801,591 can display patient data at various levels of analysis depending on the user's needs and sophistication.
[0016] By analyzing the location-based data, healthcare organizations and policymakers can develop targeted interventions and strategies to improve healthcare services in specific geographic areas
[0017] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the
definition of that term provided herein applies and the definition of that term in the reference does not apply.
[0018] In some embodiments, the numbers expressing quantities of ingredients, properties such as concentration, reaction conditions, and so forth, used to describe and claim certain embodiments of the invention are to be understood as being modified in some instances by the term “about.” Accordingly, in some embodiments, the numerical parameters set forth in the written description and attached claims are approximations that can vary depending upon the desired properties sought to be obtained by a particular embodiment. In some embodiments, the numerical parameters should be construed in light of the number of reported significant digits and by applying ordinary rounding techniques. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of some embodiments of the invention are approximations, the numerical values set forth in the specific examples are reported as precisely as practicable. The numerical values presented in some embodiments of the invention may contain certain errors necessarily resulting from the standard deviation found in their respective testing measurements.
[0019] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0020] The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non- claimed element essential to the practice of the invention.
[0021] Groupings of alternative elements or embodiments of the invention disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all Markush groups used in the appended claims.

 
Objects of the Invention

[0022] Identifying areas where healthcare services are lacking: By mapping the performance of healthcare facilities in a specific geographic area, analysts can identify areas where healthcare services are lacking or where there is a high demand for healthcare services.
[0023] Identifying areas where healthcare services are needed the most: By overlaying the performance metrics maps with other geographic data such as population demographics, transportation infrastructure, and socio-economic indicators, analysts can identify areas where healthcare services are needed the most.
[0024] Improving healthcare services: By identifying areas where healthcare services are lacking or needed the most, healthcare organizations and policymakers can develop targeted interventions and strategies to improve healthcare services in specific geographic areas. [0025] Identifying disparities in healthcare access: By overlaying the performance metrics maps with demographic and socio-economic data, analysts can identify disparities in healthcare access based on income or ethnicity. This information can be used to develop targeted outreach and education programs to improve health literacy and promote preventive care in underserved communities.
[0026] Optimizing resource allocation: By analyzing the performance of healthcare facilities in a specific geographic area, healthcare organizations and policymakers can optimize resource allocation to ensure that healthcare services are being delivered efficiently and effectively.
Summary

[0027] it is important to ensure that the privacy and confidentiality of patients' health information are protected during the collection, storage, and analysis of data.
[0028] This can be achieved by using de-identified data, implementing data security measures, and complying with relevant data protection regulations
[0029] By analyzing the location-based data, healthcare organizations and policymakers can develop targeted interventions and strategies to improve healthcare services in specific geographic areas
[0030] For example, they can identify areas where new healthcare facilities are needed, or where existing facilities need to be upgraded or expanded. They can also develop targeted outreach and education programs to improve health literacy and promote preventive care in underserved communities.

 
Drawings

 
Brief Description of the Drawings

[0031] The example architecture of a health care system is seen in FIG. 1 in accordance with an embodiment of the present disclosure.

 
Detailed Description

[0033] The current invention 100 pertains to a proximity based health monitoring system
[0034] Below is a list of several illustrative implementations of the innovative subject matter. Although each embodiment represents a unique combination of creative components, the inventive subject matter encompasses all potential combinations of the disclosed elements. Hence, if one embodiment has components A, B, and C, and a second embodiment includes elements B and D, then the innovative subject matter is also deemed to contain other remaining combinations of A, B, C, or D, even if they are not specifically stated.
[0035] The current invention pertains to a system and method for location based health monitoring. Specifically, the invention relates to a system and method for storing all healthcare data for future accessibility and usage.
[0036] FIG. 1 depicts an illustrative architecture of a system 100 for health monitoring including the user 102 and the wearables 104 on the body, i.e., different sensors and equipment as labelled 1, 2, 3, 4, 5, 6.
[0037] Fig. 1 demonstrates, in accordance with one embodiment, the wireless communication 106 between the hospitals and the cloud storage 108. The correct wireless connection has been established for data transfer.
[0038] Once the data has been acquired by sensors, it is stored on cloud 108. The stored data are processed via interface 110, where the machine learning and fuzzy inference system is available to process the algorithms and calculate the data, 112.
[0039] Once the data is analyzed and normal activities are determined, the notice is sent back to the user and the repository. Nevertheless, if abnormalities are detected, the notification is sent to the user's family or physicians through communication network linkages 114.
[0040] In accordance with aspects of the present disclosure, FIG. 2 depicts example operational instructions for the health monitoring system 200.
[0041] As seen, the functional processes involve the placement of wearables 202 in the form of sensors and communication modules, including a GPS module for location tracking.
[0042] With IOT Support 204, cloud-based data processing will commence after the data has been acquired by sensors.
[0043] IOT Support 204 must process the supplied data values and utilize fuzzy algorithms to map them to the user's stored conditions and prior saved results.
[0044] Outputs are monitored closely. If no irregularity is detected, the notice is delivered solely to the user; otherwise, it is sent to the user's family or physicians, based on the information stored in the database, so that the person may be reached as soon as possible 206.
[0045] In an aspect, any or a combination of machine learning mechanisms such as decision tree learning, Bayesian network, deep learning, random forest, supervised vector machines, reinforcement learning, prediction models, Statistical Algorithms, Classification, Logistic Regression, Support Vector Machines, Linear Discriminant Analysis, K- Nearest Neighbors, Decision Trees, Random Forests, Regression, Linear Regression, Support Vector Regression, Logistic Regression, Ridge Regression, Partial Least-Squares Regression, Non-Linear Regression, Clustering, Hierarchical Clustering – Agglomerative, Hierarchical Clustering
– Divisive, K-Means Clustering, K-Nearest Neighbors Clustering, EM (Expectation Maximization) Clustering, Principal Components Analysis Clustering (PCA), Dimensionality Reduction, Non-Negative Matrix Factorization (NMF), Kernel PCA, Linear Discriminant Analysis (LDA), Generalized Discriminant Analysis (kernel trick again), Ensemble Algorithms, Deep Learning, Reinforcement Learning, AutoML (Bonus) and the like can be employed to learn sensor/hardware components.
[0046] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[0047] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-
exclusive manner, indicating that the referenced elements, components, or
steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refer to at least one of something selected from the group consisting of A, B, C …. and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.

 
Advantages of the Invention

[0048] Improved Resource Allocation: Location-based analysis of healthcare facilities allows healthcare providers to identify gaps in the provision of healthcare services and allocate resources more effectively.
[0049] Location-based analysis of healthcare facilities enables healthcare providers to identify areas where quality of care may be lacking and take appropriate measures to improve it.
[0050] Location-based analysis of healthcare facilities can help improve patient outcomes by identifying areas where healthcare services are lacking or where quality of care needs improvement.
[0051] Location-based analysis of healthcare facilities can help healthcare providers to optimize their resources and reduce costs.
[0052] Location-based analysis of healthcare facilities can help improve access to care for underserved communities..
, Claims:We Claim:

1.A system and method for analysis of healthcare facility monitoring using proximity as primary criteria.

2.The system of Claim 1 is a preventative method that warns the governments and doctors of the concerned regions.
3. System would effectively notify the concerned official.