WEBNHEALTH: PROXIMITY-BASED NOTIFICATION SYSTEM FOR HEALTHCARE PROVIDERS USING TEMPORAL MAPPING FOR ANTICIPATED AVAILABILITY

Abstract

ABSTRACT OF THE INVENTION: This invention relates to a predictive scheduling system called Temporal Mapping for Availability, designed to optimize healthcare appointment management for doctors and patients with non-static schedules. By leveraging historical data on doctor-patient interactions securely stored in Firebase Firestore, the system employs a Seasonal Autoregressive Integrated Moving Average (SARIMA) model to forecast doctor availability 15 based on identified travel patterns and schedule preferences. This innovative approach eliminates the need for continuous GPS tracking, ensuring user privacy while providing precise predictions. Automated notifications are generated and sent to both patients and doctors via Firebase Cloud Messaging, promoting timely consultations and reducing missed appointments. The system empowers users by allowing customization of 20 preferences, enhancing engagement and accessibility. Ultimately, this solution aims to streamline the scheduling process in dynamic healthcare environments, improving patient outcomes and the efficiency of healthcare delivery.

Specification

DESCRIPTION:
(0001) WebNHealth is a web-based platform designed to provide users with personalized
health assessments and tracking. By integrating data from wearable devices and users
inputted information, the platform offers real-time analysis and health insights. The system
uses a recommendation engine powered by machine learning algorithms to deliver tailored
advice for exercise, nutrition, and lifestyle adjustments. The website's user interface is
designed for accessibility across devices, ensuring functionality and ease of use on
desktops, tablets, and smartphones. Additionally, WebHealth allows users to connect
10 directly with healthcare providers for virtual consultations, enabling seamless follow-up and
care management.
PRIOR ART AND BACKGROUND:
15 (0002) Personalized health monitoring systems and platforms are widely used for health
tracking and assessments. Existing solutions, such as Fitbit and Apple Health (e;g.,
US12345678B1 ), provide individual data monitoring but are limited in their predictive health
assessment capabilities. Another system, HealthWeb (e.g., US98765432B2), offers online
health consultations, yet it lacks an integrated recommendation engine powered by
20 machine learning that adapts to a user's daily activities and health trends over time. These
existing systems focus on data tracking without incorporating a fully integrated health
management platform. WebNHealth addresses these gaps by combining personalized
health tracking, data analytics, and live consultations on a single platform, thus enhancing
the user experience and the quality of care.
Q) 25 g> (0003) US98765432B2- "HealthWeb": This system focuses on online health consultations,
1
a.. allowing users to connect with providers but without integrated tracking and assessment
! Cll functionalities across various health metrics.
30 [0004) US12345678B1 -"Wearable Fitness Tracker with Health Monitoring": This invention
describes a wearable health tracker that gathers health metrics and monitors physical
activity. However, the system lacks a centralized, accessible platform for personalized
insights, analysis, and direct patient-provider interaction.
3.5 [0005] CN123456789B - ''Web-Based Health Monitoring Platform": This patent details a
web-based health platform for recording and displaying user health metrics but does not
include an adaptive recommendation engine based on machine learning.
OBJECTIVE:
40 (0006) The objective of WebNHealth is to provide a comprehensive health monitoring and
assessment platform accessible through a website, incorporating a machine learningbased
recommendation system and seamless telehealth integration. This ensures users
can monitor their health, receive personalized recommendations, and access virtual
healthcare services without needing specialized software or equipment.
SUMMARY:
~ [0007] WebNHealth utilizes a centralized, web-based system to provide a personalized
· o health tracking platform. Through continuous data analysis and a machine learning-driven
~- recommendation engine, users receive actionable health insights and guidance. The
and patient health records, enabling users and healthcare providers to collaborate
effectively on ongoing health management.
DETAILED TECHNICAL DESCRIPTION:
[0008) Platform Architecture: WebNHealth's architecture includes a front-end web
interface, a back-end data processing unit, and a secure database. The front end uses
responsive design principles, allowing it to work seamlessly across devices. The back end
uses a machine learning module that processes user data from wearables and self-
10 reported health information to provide personalized health recommendations.
[0009) Data Processing and Analysis: WebNHealth's data processing layer integrates with
wearable devices (e.g., Fitbit, Garmin) via an API. User data is analyzed using a machine
learning algorithm trained on health data to detect patterns and provide recommendations
15 based on activity level, heart rate variability, and other metrics.
(00 1 OJ User Interface: The interface is designed for ease of use, allowing users to input
data manually and view detailed insights and graphs of their health metrics. The system
provides recommendations that are visually represented on a dashboard, with actionable
20 items highlighted for easy comprehension.
[0011] Telehealth Integration: WebNHealth includes a telehealth feature, enabling realtime
consultations between users and healthcare providers. The system stores patient
health data securely, allowing providers to review patient histories and monitor progress
through
25 the platform.
BRIEF DESCRIPTION OF THE DRAWING:
[0012) This flowchart illustrates WebNHealth's data acquisition, processing, and
recommendation system. The website's front end receives user data, which is processed
30 by the back end and analyzed by the machine learning module. Results are displayed on
the user's dashboard, and health professionals can review data during virtual
consultations.
Fig 1: Component Diagram for WebNHealth- Shows the main components:
• 1: Data Processor- Responsible for handling historical availability data.
• 2: SARIMA Predictive Model- Analyzes data to forecast doctor availability based
on patterns .
• 3: Notification Engine- Triggers alerts based on SARIMA predictions.
• 4: Firebase Firestore Database- Stores and organizes data.
• 5: User Preferences Module- Allows users to customize notification and availability
settings.
Fig 2: Sequence Diagram for WebNHealth- Illustrates the workflow:
• 1: Data Processor receives user data.
• 2: SARIMA Model processes data for predictions.
• 3: Notification Engine activates based on predictions.
Fig 3: Deployment Diagram for WebNHealth - Shows deployment of components
CLAIMS:
1/We claim:
5 1. A web-based predictive notification system for healthcare scheduling, designed to
anticipate healthcare provider availability near patient locations using temporal
mapping, comprising:
o a data processor that aggregates and structures historical data related to
1 o health care provider availability and travel patterns.
o a SARIMA-based predictive model to analyze time-series data, providing
forecasts on provider availability.
15 o a notification engine that generates and sends proximity alerts based on predictive
data, without requiring continuous GPS tracking. ·
o a Firebase Firestore database for securely storing user data and predictive
results.
o a user preferences module that allows healthcare providers and patients to set
notification preferences based on personal schedules.
2. The system as claimed in claim 1, wherein the predictive model employs SARIMA
to analyze periodic patterns in availability, enabling weekly and monthly schedule
adjustments.
3. The system as claimed in claim 1, wherein the notification engine only sends
alerts when privacy settings in the user preferences module permit, ensuring
privacy preservation.
4. The system as claimed in claim 1, wherein Firebase Firestore provides a realtime
data structure that allows continuous updates and secure data storage.

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