exploring the amihealth paradigm. monitoring in healthcare: building mhealth ecosystems

Exploring the AmIHEALTH paradigm

Jesús Fontecha

University of Castilla-La ManchaEscuela Superior de Informática de Ciudad Real

Ciudad Real, Spain

MAmI Research Lab

Santiago, Chile, Nov. 25-27, 2015

Summer School

Monitoring in Healthcare: Building mHealth ecosystems

2

Exploring the AmIHEALTH paradigm. Monitoring in Healthcare: Building mHealth ecosystems

CONTENT• Definition of concepts

• AmI, IoT, Healthcare, AmIHEALTH• mHealth

• Goals, Ecosystems, Limitations, Scenarios, Interoperability

• Monitoring fundamentals and study cases• Frailty monitoring• Disease monitoring• Analysis tool

• Conclusions

3

FROM AMI TO AMIHEALTHIntroduction. Definition of concepts

4

• Introduction

Internet of Things

eHealth mHealth

Smart environments & devices

Healthcare

Distributed systems

“diagnosis, treatment, and prevention of diseases and impairments in human beings”

Use of technology for supporting Healthcare

Use of mobile technology for supporting Healthcare

SensorsNetworks

Services

Devices

Embedded

AmI HEALTH

AmI

Study casesMonitoringmHealthIntroduction


Conclusions

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• AmIHEALTH• Ambient Intelligence for Health

More & better infrastructure- Technologies- Resources- Devices- Communication possibilities

mHEALTH AmIHEALTH

AmI

Integration of mobile technologies in Healthcare + • Context Aware

• Personalized• Anticipatory• Adaptive• Ubiquity• Transparency

Source: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3890262/



Conclusions

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DEFINITION, GOALS, ECOSYSTEMS, LIMITATIONS, SCENARIOS, INTEROPERABILITY

mHealth

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• mHEALTH• Mobile Health• An evolving concept• Keys

• Use of smart and mobile devices• Inclusion of wireless technology• Easy social adoption

“the delivery of healthcare services via mobile communication devices”

…are converging

Source: 2010 mHealth Summit of the Foundation for the National Institutes of Health (FNIH)



Conclusions

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• Goals of mHEALTH solutions• Better management of health• Make better healthcare decisions• Find appropriate care

• Engage people and access providers• Management of ongoing health

(monitoring)



Conclusions

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• Objectives for mHealth usersSource: National eHealth Collaborative

Importance (%)Objectives

• Uses of mHealth (in developed countries)• Collect data• Self monitoring (patient)• Remember events• Appointment• Remote monitoring (doctor)

Underdeveloped countries

Increasing use of mobile phones

Lack of infrastructures



Conclusions

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• The mHealth Ecosystem

Source: Wikipedia

• “Ecosystem is a community of living organisms in conjunction with the nonliving components of their environments (things like air, water and mineral soil), interacting as a system”.

• “mHealth ecosystem is a community of people who interact with mobile devices of an environment to get clinical benefits”.

There is no standard definition for mHealth ecosystem

• Aspects to consider in development of mHealth ecosystems• Right information• Secure communication• Good system adherence & accessibility• Right time• Reduce technology impact on disease• Sustainable use of resources



Conclusions

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• mHealth Ecosystems

Source: http://www.mdtmag.com/article/2013/05/wireless-enabled-remote-patient-monitoring-solutions

Source: H. T. Cheng and W. Zhuang, "Bluetooth-enabled in-home patient monitoring system: early detection of Alzheimer's disease," IEEE Wireless Communications, vol. 17, no. 1, pp. 74-79, Feb. 2010

Source: http://www.ece.uah.edu/~jovanov/whrms/

• But, it is not easy…



Conclusions

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• Limitations of mHEALTH solutions and ecosystems• Technological education• Infrastructure• Communications• System Friendliness • Budget

“Everything is possible if there is an unlimited amount of time and resources”

Theoretically anything is feasible, but in practise…• There is no device that monitor this parameter!• We have commercial devices but, this API is not open!• I do not know how it works!• Here we have not network connection!• Implementation of the project is expensive! • I prefer traditional methods!• …

Scientific and technological advances improve our life quality



Conclusions

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• Elements of a mHealth system• Users



Conclusions

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• Elements of a mHealth system• Environment



Conclusions

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• Elements of a mHealth system• Sensors and devices



Conclusions

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• Elements of a mHealth system• Communication technologies



Conclusions

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• Simulating a real scenario

Obese patient

Smartphone + smartwatch with HR monitor

Computer, tablet, smartphone

Server

Doctor

Monitoring data

Patient information

RelativesTablet, smartphone

Patie

nt d

ata

Updated data

Personal flows



Conclusions

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• Technology convergence and consistency

Connectivity

Data

Sensors

Mobile

Users

mHealth care



Conclusions

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• Standards and interoperability

Continua Health Alliance

• Interoperability between health devices• Fundamentals of data exchange• Define the interfaces that enable the secure flow of medical data

among sensors, gateways, and end services, removing ambiguity

Not Open APIs!

Source: http://www.continuaalliance.org/



Conclusions

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STAGES, BIG DATA, MHEALTH SYSTEMSMonitoring fundamentals and study cases

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• Monitoring fundamentals

Data acquisition Data segmentation & filtering Data analysis

Not only signal analysis!... Data analysis

Not only monitoring!... Many processes



Conclusions

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• Big data• Set of mechanisms to process large amounts of data

• Data is too big, moves too fast, doesn’t fit the structures of traditional database architectures

• Characteristics• Volume. Quantity of data• Variety. Type of content• Velocity. Speed of data generation and retrieving• Variability. Deal with data inconsistency effectively• Veracity. Quality of data• Complexity. Deal with complex data management.

• Big data is very useful in data monitoring!



Conclusions

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• Study cases• Three mHealth systems

• [Completed] Mobile system for detection and assessment of frailty syndrome in seniors. • <<Frailty monitoring>>

• [Ongoing Work] A smart and sensorized framework for continuous monitoring of diseases based on health aspects.• <<Disease monitoring>>

• [Completed] PIA: Personal IADL Assistant. Development of a web analysis tool. • <<Analysis tool>>



Conclusions

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MOBILE SYSTEM FOR DETECTION AND ASSESSMENT OF FRAILTY SYNDROME IN SENIORS

First study case <<frailty monitoring>>

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• Goal

Design and development of a system which uses mobile devices to provide a support to physicians in the frailty assessment, taking into account a set of relevant clinical variables and movement data.

Patient record

Accelerometer

+Clinical factors Physical activity

Analysis of patients and factors

Assessment

Adquisition

Similarity study

Tinetti test

Mobile system for detection and assessment of frailty syndrome in seniors

Study casesMonitoringmHealthIntroductio

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Conclusions

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• Conceptual model



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Conclusions

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• Service-oriented mHealth approach

Mobile Web

Services

- Accelerometer data acquisition- Accelerometer data processing- Visualization of frailty assessment

- Patient record extraction- Comparison and analysis procedure- Setting up a built result- Storage into patient stack

12



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Conclusions

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• Accelerometer data acquisition and processing


1


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Conclusions

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• Clinical factors acquisition


2

Analysis of patients and factors

Assessment

Similarity study

Tinetti test

Anthropometric Assessment Functional AssessmentNutritional Assessment Cognitive Assessment

Geriatric Syndromes Independence in ADL Pathologies & Diseases

Gender, Age, Size, Weight, Body Mass Index, Body Mass,

Fat Mass, Lean Mass, Total Water, Drug Number

Tinetti gait score, Tinetti balnce score, Barthel index, Lawton score, Get-Up and Go, Need

help

Total protein, Serum albumin, Cholesterol level, Triglycerides,

Blood iron, Ferritin, Vitamin B12, Serum folic acid, Serum

transferrin, Leukocytes, Lymphocytes, Hemoglobin,

Calcium

Mini Mental Status, CRP

Dementia, Depression, Incontinence, Immobility,

Recurrent falls, Polypharmacy, Comorbidity, Sensory

deprivation, Pressure ulcer, Malnutrition, Terminally illness

Independent, Mild dependence, Moderate

dependence, Great dependence, Serious

dependence

Cardiovascular, Neurological, Respiratory, Digestive, Endocrine, Orthopedic,

Osteomuscular, Eyes, ENT, Dermatological

Dispersion Measures

Arithmetic mean, Standard deviation, Absolute mean diff.,

Amplitude, Pearson’s coefficient of variation,

Variance, Acceleration mean


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Conclusions

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• Clustering and similarities calculation


3

Selection of relevant variables • Frailty risk factors

Normalization of variables

Calculation of similarity measures• Strength of relationship between 2

objects• Gower coefficient

1

2

3

Gower similarity coefficient


n


Conclusions

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• System overview


Mobile Services Infrastructure for Frailty Diagnosis Support based on Gower’s Similarity Coefficient and Treemaps. Jesús Fontecha, Ramón Hervás, José Bravo. Journal of Mobile Information System. July 2013. DOI: 10.3233/MIS-130174


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Conclusions

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• Evaluation and results summary


• Descriptive analysis• 20 elderly people (10 men, 10 women)• 60 patient instances (data from 3 times on 20

users)• Global evaluation• More values in variables -> more accuracy• Improve decissions making frailty diagnosis by

physicians• Specific evaluation• Results adapted to different domains• Modifying some parameters in the mobile

system• Useful in evolutionary studies (nutritional &

functional)


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Conclusions

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A SMART AND SENSORIZED FRAMEWORK FOR CONTINUOUS MONITORING OF DISEASES BASED ON HEALTH ASPECTS

Second study case <<disease monitoring>>

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• GoalDevelopment of a modular framework based on health aspects for monitoring multiple diseases by using smartphones and smart devices.

A smart and sensorized framework for continuous monitoring of diseases based on health aspects

• Monitoring and treatment of a disease• Primary aspects

• Common to most diseases• Directly related to the disease

• Vital signs, physical activity, clinical profile, education, relatives, diet.

• Complementary aspects• Depending on the disease• Improve the monitoring

• Environment, social relationships, emotions, stress, incomes,…• Patient side (Self-control) & Doctor side (remote monitoring)• New mobile technologies, communication networks, new devices…

• New possibilities and opportunities


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Conclusions

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• Monitoring cycle


Vital signs monitoring

Interaction

Primary aspectsComplementary aspects

Information flow

Clinical tr

eatment

Self-monitoring

Patient profile

Physician

PatientSmart devices & sensors

Smartphone

Diet

Education Relatives

Exercise


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Conclusions

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• Levels of monitoring


Intensive

Moderate

Mild

Aspects coverage +

-

Self-care

+

-

Level 1 – Basic aspects monitoring

Level 3 – Complete monitoring

Level 2 – Usual aspects monitoring

• Adapted to the patient• Different action levels• Considered aspects

• Variables• Level of supervision• Temporary or permanent

• Some examples

Aspects

Devices & sensors

Monitoring level


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Conclusions

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• Monitoring behavior (trends and objectives)


• Main goal of the framework!• It depends on the disease• Objectives proposed by doctors• Trends calculated by the system (prevention!)

Diabetes

Trends Objectives• Glucose trend • Glucose level

• Physical exercise• Carbohydrate intake

Hypertension

Trends Objectives• Blood pressure

trend• Blood pressure level• Physical activity• Diet

Diabetes behavior Hypertension behavior

Examples


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Conclusions

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• Study cases. Framework overview


• A generic framework to deal with specific disesases


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Conclusions

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Conclusions

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Conclusions

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Conclusions

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• Conclusions


• Proposal of health aspect-based framework for smart monitoring• Monitoring of chronic and non-chronic diseases• Interaction with sensors & smart devices

• Reducing human interaction• Promoting patient self-control and remote supervision

• Future work• Development of software pieces covering

each health aspect• Deal with gathering of data from smart

devices through open API• Application to specific domain (Diabetes)

Endocrine Diet

Education

Physical activityGlucose level


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Conclusions

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PIA: PERSONAL IADL ASSISTANT. DEVELOPMENT OF A WEB ANALYSIS TOOL

Third study case <<analysis tool>>

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• Goal & ScenarioDesign and development of a Analysis tool for a AAL system which uses mobile devices and web platforms to assess IADL of elderly people at home.

PIA: Personal IADL Assistant. Development of a web analysis tool


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Conclusions

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• How user’s information (from interaction with environmental objects and system applications) is collected?


System QuestionnairesEnvironmental Interaction

Caregivers and physicians complete questionnaires during the use of AAL system.

Involves recording the subjects behavior in their AAL environment.

Questionnaires collect factual information about individuals

Collecting data from user actions in the environment

Analysis tool


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Conclusions

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• Questionnaires system• Creation of questionnaires adapted

to the person



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Conclusions

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• Recommendations based on results• Completion of the questionnaire provides

• Recommendations• Results


Need of help in telephone tasks

scoreResults

Recommendations


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Conclusions

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• Some conclusions• The system saves information about

• Minimum interaction of elderly people with environmental objects at home (NFC technology)• Questionnaires completed by caregivers and doctors

• Information is used with analysis purposes• Evaluated on 10 caregivers• Useful to assess IADL activities in elderly people and the burden of

caregivers by means of interactions and questionnaire results

http://mamilab.esi.uclm.es/PIAToolv16/web/


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Conclusions

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• Conclusions• mHealth as part of AmIHEALTH• Everything is possible with unlimited time and resources!• Most important element in mHealth ecosystems -> End User• Main drawbacks

• Technology• Communication networks• Environmental resources and interoperability• UX and UI

• Functional and friendly systems -> successful proposal!• Scenarios where monitoring is quite relevant• Integration of mHealth ecosystems in bigger environments (Smart

cities?)



Conclusions

Exploring the AmIHEALTH paradigm

Jesús Fontechajesus.fontecha[at]uclm[dot]es

http://jesusfontecha.name

University of Castilla-La ManchaEscuela Superior de Informática de Ciudad Real

Ciudad Real, Spain

MAmI Research Lab

Santiago, Chile, Nov. 25-27, 2015

Summer School

Monitoring in Healthcare: Building mHealth ecosystems

https://www.linkedin.com/pub/jes%C3%BAs-fontecha/28/896/b98

https://www.researchgate.net/profile/Jesus_Fontecha

http://www.slideshare.net/JessFontecha/

Thank you!

http://jesusfontecha.name/






