WO2016093707A1

WO2016093707A1 - System and method for services and collection of data related to health data in big data databases

Info

Publication number: WO2016093707A1
Application number: PCT/NO2015/050243
Authority: WO
Inventors: Harald Jellum
Original assignee: Your.Md As
Priority date: 2014-12-11
Filing date: 2015-12-11
Publication date: 2016-06-16
Also published as: NO20141501A1

Abstract

Method and system for finding new health relationships and services from Dynamically collected health data from large user groups through mobile health applications, "wearables" and other health services using Big Data techniques combined with intelligent algorithms and analysis. This happens through a continuous reading of a user's health information through the users use of the mobile health related applications and use of "wearables" (10..60). This health information is stored in a neutral, secured Big Data Health Storage (110) as it will continuously conduct searches for new relationships and services using algorithms and intelligent analysis (120). This system may lead to new discoveries / services that can proactively warn the users of health conditions that they can prevent and / or avoid. This will provide improved health for many and save society large health expenses. The solution is global and may be for all users of mobile phones.

Description

SYSTEM AND METHOD FOR SERVICES AND COLLECTION OF DATA RELATED TO HEALTH DATA IN BIG DATA DATABASES.

Method and system/arrangement for finding new health contexts and services from dynamically collected health data from major user groups through mobile health applications, "wearables" and other health services using Big Data techniques combined with intelligent algorithms and analysis.

APPLICATION AREA

The invention is a new way to find connections/relationships and new services from dynamically collected health data from large groups through their use of mobile health applications and

"wearables". The invention collects health data from users in a new way and that makes it possible to continuously collect health parameters from large user groups and then look for relationships and patterns that have not previously been possible. These health relationships / patterns will thus form the basis for proactive services that can tell the user if one is about to get into a state of health / disease /illness the user does not want and hence also provide personalized advice based on the user's health. Examples of services could be "Early warnings" related to high blood pressure;

diabetes, heart attacks, strokes, and more.

The market for the invention is global, and for all people with access to a smartphone or equivalent. The invention will be both for the healthy who want proactive notification if they are about to go into an undesirable state of health and for those who are sick and want to follow the development in the best possible way to improve their state of health as efficiently as possible.

STATE OF THE ART

Today's medical practice and health data:

When illness/disease is detected it is today commonplace to see a doctor. Necessary

testing/examination is conducted to determine the patient's current state of health. If different doctors are consulted, the health data will normally be spread across different systems. The doctor will not normally perform any continuous collection of the patient's health data prior to a disease detection or what could have caused it. The patient's health data are neither compared actively and continuously to find other similar cases among similar groups with similar behavior/ course of occurrences of symptoms and the like. It will neither be given any proactive advice that is tailored to the patient and the patient's health condition in the manner enabled by the invention.

Chronically ill patients:

Today, a lot of peoples struggle with chronic diseases. At rare occasions these patients are equipped with measuring apparatuses whereby they are to do various measurements at specific time intervals. These measurement results are then collected at specified times and then post processed and analyzed. These methods are very resource-intensive and are seldom available. Current systems do not continuously analyzes and make no comparisons with others having corresponding state of health as the patient. There is also no "live" intelligent proactive warning as the result of the occurrence of an undesirable result.

Health apps:

Today, there are numerous health apps. Most of these are aimed at training and diet. These often show the user's historical data and development. Some of these are also linked to "wearables" that measures how much the user move / the user's activity level. There are no correlation between these apps and they did not compare with others' data to find relationships or give new advice based on these. HealthKit, Google Fit and S-Health:

These store a user's health data for respectively iOS, Android and Samsung devices. This is merely a system for storing the user's personal health data. If the user allow it, the various apps in the same platform may update / read the users health data. This is a pure storage without any form of processing or attempt to see relationships / patterns with others' health data or the user's past history and development. Nor is there any form of proactive personal advice based on this. And if user wants to switch platform e.g. from iOS to Android the user must be prepared to lose all these stored data.

WHAT IS ACHIEVED Y TEH INVENTION COM PARED WITH PRIOR ART

Building on what is available in various technologies today the present invention defines a novel method to provide new health related relationships and services based on dynamically collected health data from large user groups through mobile health applications, "wearables" (sensors carried by users) and other health services using Big Data techniques combined with intelligent algorithms and analysis. The present invention will additionally store health data in a novel manner in a neutral secure Big Data platform which is optimized for continuous readout of health data from large groups of users. Additionally the present invention will provide a novel method for continuous intelligent searches for relationships / patterns that can imitate/lead to further novel proactive services that can proactively notify/warn the users of health conditions that can be prevented and/or avoided. This will provide improved health for many, and at the same time save society large health expenses. REQUIRED M EANS

The present invention requires users with mobile phones and "wearables" for data collection, and a Big Data platform for continuous storage of health data and a set of intelligent algorithms and analyzes searching for correlations/relationships and patterns that can be translated into rules that lead to personalized proactive health advices.

INDUSTRIAL APPLICABILITY

The present invention will provide services and features for both the healthy persons who want proactive notification/warning if they are about to move into an undesirable state of health and for those who are sick and want to follow development in best possible manner to improve their health as efficiently as possible. The present invention may be of great importance to all who are concerned with their health and may facilitate major societal health savings. The present invention provides a method that will enable a continuous collection of health data from large groups and enable to make visible new connections / patterns that have not previously been possible to see, and which then in turn form the basis for entirely novel proactive advice to avoid undesired health conditions.

The present invention is further exemplified in the attached figures which are:

Fig. 1 - System overview

Fig. 2 - Method flowchart

DETAILED DESCRIPTION OF THE INVENTION

Based on the above described problems, there is a need to find new health relationships / patterns and services from dynamically collected health data from large user groups enabling new proactive automatic advice to avoid unwanted health conditions. The above problems are addressed by the present invention described hereinafter.

The present invention provides a novel method for finding new health relationships / patterns and services based on dynamically collected health data from large user groups using mobile phones, "wearables", and Big Data techniques combined with intelligent algorithms and analysis.

According to the system overview shown in Figure 1, the invention comprising collection of health data from different sources such as apps executed on a mobile phone (10), health data from e.g. Healthkit, GoogleKit, S-Health which is locked storage solutions (20) respectively for iOS, Android and Samsung or others, from the phones storage (30), data from other health services by integration with their APIs (50) or from "wearables" (60) or other sensors measuring health-related parameters. The health data is transmitted continuously by means of an application interface (API) (100) to a Big Health Data Platform (110) as raw data. Other relevant health related data originating from user behavior / interaction (70), along with other medical sources (80) such as health news, social networks, web pages, databases, registers, clinical/medical trials/surveys or other relevant health information and environment parameters such as location, time&date, altitude, weather, movements, statistics, duration or equivalent is also transmitted to the Big Health Data (110) as general additional information.

The health data that is continuously stored in a Big Data structure (110) is made available for a set of intelligent algorithms and analyzes (120) that continuously search through all the data to find relationships / patterns or enough knowledge of the data to be able to provide "rules" that can form the basis for proactive insight / services for improving the user's health and avoid unwanted health conditions. This insight together with services is then transmitted back to the original health sources through an API (100). Examples of this may be a wearable (60) such as a Smart watch that can measures pulse, temperature and stress level and which continuously transmits this to the Big Health Data (110). Based on measurements from this over a period of time will be possible, with the help of algorithms and analysis (120), to highlight changes/variations in the user's pattern, comparing with others having the same parameters such as for example weight, gender, age and compare them with those who for example has got a heart attack. According to this method, one can see that a user is moving toward the same kind of patterns that others have had just before they've got a heart attack. This may then be used to provide a proactive advice / "warning" that may be communicated to for example the Smart watch before the user gets the heart attack himself.

The present invention method flow is described in Figure 2. Initially the method defines to get a user's permission (200) to access the user's health data. This often occurs by the user installing the App on the user's mobile phone or by another verification and acceptance by the user. If the user permits, start reading (220) from the relevant source. This reading of health data will be continuous either at specified time intervals or dynamically when the health data changes. This data is then stored (240) securely in a Big Data storage platform that can handle large amounts of data from many simultaneous users. In addition to health data from users, the present invention will also be able to retrieve general health data (230) from the user's behavior and use of health apps (70), other medical sources (80) such as news, social media, web pages, databases, registers , medical experiments or other relevant health information, and environment parameters (90) such as for example location, time&date, altitude, weather, movement, duration, statistics or other relevant parameters that may have an impact on the user's health and development. The present invention may thus by making using of all collected health information start and look for relationships / patterns using algorithms and intelligent analysis (120) for sending insight and services back to the user (260). Examples of this can be proactive "warnings" that the user is now in the process of developing diabetes, hypertension, heart attack or other. Another example might be to follow patients who already have an illness and being able to provide advise underway so that the patients' health develops in a best possible manner.

This process repeats itself for all users who gives their permission (200). If a user does not give permission, the process proceeds to find next user (210).

Claims

1. Method and system for finding new health relationships and services from dynamically collected health data from large user groups through mobile health applications, "wearables" and other health services using Big Data techniques combined with intelligent algorithms and analyzes that make it possible to find new health relationships / patterns that will lead to new services that can proactively warn users of unwanted health conditions c h a r a c t e r i z e d b y comprising:

a) combining reading health data sources (10-60, 70-90) which is stored using Big Data (100, 240), Algorithms and Intelligent Analysis (120, 250) to find new relationships and insights that can provide the user proactive health advice (260);

b) wherein the readings are dynamically performed through an API (100) that may be integrated with Mobile device Apps (10), common or shared storage structures (20), memory of the mobile device (30), sensors on a mobile phone (40), other health services (50 ) and with "wearables" such as sensors to be carried by a user (60);

c) wherein the information is stored in a Big Data platform (110, 240);

d) and the information is made available for Algorithms and Intelligent Analysis (120); and e) finding new relationships / patterns that form the basis for sending a warning to the user about undesirable health development.

2.

Method and system according to claim 1, c h a r a c t e r i z e d b y that Big Health Data (110) as well as health data from users (10 - 60) also can collect data from user behavior (70) such as a user's search, navigation, page views, and others.

3.

Method and system according to claim 1, c h a r a c t e r i z e d b y that Big Health Data (110) further can also collect data from other sources (80) comprising health information such as news, social networks, forums, blogs, registers, clinical trials, patient records and other relevant health information.

4.

Method and system according to claim 1, c h a r a c t e r i z e d b y that Big Health Data (110) further may also obtain data from environmental sources (90) which can affect the health such as location, time&date, duration, altitude, pressure, weather, movement and activity, statistics and probabilities related to health or other similar envronmental information.

5.

Method and system according to claim 1, c h a r a c t e r i z e d b y reading the health data from the relevant source (220) also comprising measurements taken by a doctor or other health personnel

6.

Method and system according to claim 1, c h a r a c t e r i z e d b y that Big Health Data (110) storing health data in a secure manner in accordance with applicable regulations.

7.

Method and system according to claim 1, c h a r a c t e r i z e d b y that Big Health Data (110) is neutral so that users can access their health data through a variety of devices such as mobile phones, tablets, PC, MAC, "phablet" Portals, or other applications.

8.

Method and system according to claim 1, c h a r a c t e r i z e d b y that API (100) is a generic interface that can be integrated with mobile apps, PC / MAC applications, web based services, instruments or other forms of devices containing health data.

9.

Method and system according to claim 1, c h a r a c t e r i z e d b y that Algorithms and Intelligent Analysis (120) can find relationships / patterns that form the basis for rules which may lead to services that can give users advice if they are in the process of developing an unwanted health condition.

10.

Method and system according to claim 1, c h a r a c t e r i z e d b y that Algorithms and Intelligent Analysis (120) can provide proactive advice to healthy people who want to avoid illness or to diseased users that wants to be well again in the best possible way.

11.

Method and system according to claim 1, c h a r a c t e r i z e d b y that Algorithms and Intelligent analysis (120) can transmit insights and services (260) in many forms back to the user as notifications in an App, directly to 'wearables', via email, via Bluetooth, via WiFi, SMS / M MS or other forms of information transfer.

12.

Method and system according to claim 1, c h a r a c t e r i z e d b y that Algorithms and Intelligent analysis (120) can find other users with the same health conditions and may initiate a contact there between if desired.

13.

Method and system according to claim 1, c h a r a c t e r i z e d b y that Algorithms and Intelligent analysis (120) can find how a user's health is, compared with other groups such as for example having the same age, sex, weight, activity, heart rate, or other relevant factors.

14.

Method and system according to claim 1, c h a r a c t e r i z e d b y that Algorithms and Intelligent analysis (120) can find indications of a user's biological age or other similar simulations.