IADIS International Conference. e-health July, Prague, Czech Republic

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1 IADIS International Conference e-health July, Prague, Czech Republic MAURO OLIVEIRA & ODORICO MONTEIRO FEDERAL INSTITUTE OF CEARÁ FEDERAL UNIVERSITY OF CEARÁ LAR-A : Laboratório de Redes de Computadores - Aracati RIPASS : Rede Interdisciplinar de Pesquisa e Avaliação em Sistemas de Saúde

2 SISA APPLYING ONTOLOGY AND CONTEXT AWARENESS CONCEPTS ON HEALTH MANAGEMENT SYSTEM: A DENGUE CRISIS STUDY CASE Mauro Oliveira Federal Institute of Ceará (IFCE) Fortaleza, Brazil amauroboliveira@gmail.com César Olavo Federal Institute of Ceará (IFCE) Fortaleza, Brazil cesarolavo@ifce.edu.br Jose Bringel Filho Federal University of Piaui (UFPI) Terezina, Brazil bringelfilho@gmail.com Odorico Andrade Federal University of Ceará (UFC) Fortaleza, Brazil odorico0811@gmail.com Anilton Garcia Federal University of Espírito Santo Vitória, Brazil anilton@inf.ufes.br Franciano Antunes State University of Mato Grosso Cárceres, Brazil franciano@unemat.br Leonardo Gardini State University of Ceará (UECE) Fortaleza, Brazil lgardini@gmail.com Reinaldo Braga Federal University of Ceará (UFC) Fortaleza, Brazil reinalbraga@gmail.com Prague, Czech Republic July/2013

3 Outline 1. Motivation 2. LARIISA Project 3. Ontology and Health Application 4. LARIISA Framework 5. SISA Prototype 6. CLARIISA Project 7. Conclusion

4 1. Motivation

5 Health System s Evolution Fonte: K. Jennings, K. Miller, S. Materna (1997) Industrial Era Information Era $ Cost Cost $ Disease Treatment Disease Prevention HOSPITAL HOMECARE

6 LARIISA, A Context-Aware Framework for Health Care Governance Decision-Making Systems: A model based on the Brazilian Digital TV Mauro Oliveira & Odorico Monteiro

7 GENESIS of the LARIISA Project The objective of the LARIISA is to provide a software platform that allows the decision-making model for public health system. Health Knowledge Decision-making Application ONTOLOGY Real Time Information Inference Mechanism

8 The LARIISA Environment Decision Making in Knowledge Management Decision Making in Systemic Normative Decision Making in Clinical and Epidemiology Decision Making in Share Management Decision Making in Administration Decision: Creating an Emergency for the clinical management of severe cases (ER-SC) Rule: IF the patient had Dengue more than once AND lives in an area with high infestation rate AND has symptoms A,B and C, THEN you must consult the ER-SC about this case Results: lower mortality due to a series of actions, in special the use of the rule above.

Example of the LARIISA Environment - 2010 Decision Making in Administration 1)

management of severe cases (ER-SC) Administration Case IF the patient had Dengue

A,B and C, 2) LARIISA Data Acquisition THEN you must consult the ER-SC about this

9 Example of the LARIISA Environment Decision Making in Administration 1) LARIISA Application Decision: Creating an Emergency (ER) for the clinical management of severe cases (ER-SC) Administration Case IF the patient had Dengue more than once AND lives in an area with high infestation indice AND has symptoms A,B and C, 2) LARIISA Data Acquisition THEN you must consult the ER-SC about this case Results: lower mortality due to a series of actions, in special the use of the rule above

10 2. LARIISA Project

11 LARIISA, A Context-Aware Framework for Health Care Governance Decision- Making Systems: A model based on the Brazilian Digital TV SISA APPLYING ONTOLOGY AND CONTEXT AWARENESS CONCEPTS ON HEALTH MANAGEMENT SYSTEM: A DENGUE CRISIS STUDY CASE SISA Application DIGA SAUDE A HOME CARE PROTOTYPE BASED ON THE BRAZILIAN DIGITAL TV MODEL FOR A HEALTH MANAGEMENT SYSTEM Project Lariisa Data Acquisition NextSAUDE 2013 Fortaleza Ceara - Brazil

12 LARIISA Data Acquisition Scenario DIGA Saúde

13 Mobile health System (Lariisa module) LARIISA Application Scenario (SISA) 1st step Send diagnosis data to the Lariisa Database Additional questions (based on patient history and current diagnosis) patienthistory(sus_id) 2nd step Lariisa Database (diagnosis_data, patienthistory) Ontology Base Patient 3rd step (DIGA Saúde) Patient s house Health Agent Reply to the questions raised by the system High-level Local decision rule(1) IF(patient s diagnosis is critical) AND(thereisa Medical Center nearthe patient s house (GPS)) THEN(sendmessage togo tothe nearestmedical Center (GPS)) High-level Local decision rule(2) IF(patient s diagnosis is critical) AND(thereisno Medical Center near the patient s house(gps)) THEN(rellocatingthenearestHealth Agent (basedonspecificspecialities) for the patient shouse(gps)) Health Agents Agenda Health Agents Database I II III 4th step Inference Rules Local Context Repository Global Context Repository Decision-Making Support Administrative Context-Aware Service(CAS) High-level Global decision rule IF(there are several diagnoses with insurgent signs of Dengue) AND(more thanx cases came fromthe same location(personal tracking)) THEN(rellocatingY Health Agents for thisregion(gps)) NextSAUDE 2013 Fortaleza Ceara - Brazil

14 3. Ontologies and Health Application

15 Why do we need Ontologies in healthcare Aplication? REPRESENTING INFORMATION REAL INFORMATION SEMANTIC INTEROPERABILITY Problem An ontologyis a "formal, explicit specificationof a shared conceptualization". Itformally represents knowledge as a set of concepts within adomain, and the relationships between theses concepts. It can be used to model a domain and support reasoningabout concepts

16 Why do we need Ontologies in healthcare Aplication? ELECTROCARDIOGRAM PROBLEM: Data integration with diferent standarts AHA/MIT-BIH (Physionet) SCP-ECG (ISO/DIS ) HL7 aecg

17 Ontology Why do we need Ontologies in healthcare Aplication?

18 Why do we need Ontologies in healthcare Aplication? Ontology AHA/MIT -BIH SCP-ECG HL7 aecg

19 Why do we need Ontologies in healthcare Aplication? Reference Model Standart 1 Standart 2 Standart 3 Standart 4

20 SemanticDistance Conceitualization Interpretation Tan Tan Tan TAN...

21 SemanticDistance Conceitualization Interpretation

22 4. LARIISA Framework

23 Global health context model An ontology is a "formal, explicit specification of a shared conceptualization. Prague, Czech Republic July/2013

24 Local health context model Prague, Czech Republic July/2013

26 5. SISA Prototype

27 Logical Architecture SISA Prague, Czech Republic July/2013

28 Case Study: Health Agent Scheduling Local health context model Global health context model Prague, Czech Republic July/2013

29 SISA Prototype TV module capturing context of citizen Mobile module Web module displaying epidemiological map of Fortaleza/CE for managers Prague, Czech Republic July/2013

30 6. CLARIISA Project

31 CLARIISA: Applying Personal Tracking Technology in a Context-Aware Health Management System CAPTAIN (2012) A Context-Aware Web Content Generator Based on Personal Tracking Captain Project LARIISA (2010) A Context-Aware Framework for Health Care Governance Decision-Making Systems: A model based on the Brazilian Digital TV Project Clariisa Lariisa Project NextSAUDE 2013 Fortaleza Ceara - Brazil

32 CaptainproposesaContext-Aware Web Content Generator Based on Personal Tracking Temperature Humidity Atmospheric Internet Global Sensor Pressure Proximity Accelerometer Digital Positioning System Light Geographical Gyroscope Compass Connection Camera Sensor (GPS) Sensor Information System (GIS)...many device sensors to explore and collect information from users... NextSAUDE 2013 Fortaleza Ceara - Brazil

33 How could our team improve the LARIISA Project using the Captain architecture? Global Positioning System (GPS) Compass Digital Camera Proximity Sensor Internet Connection Gyroscope Accelerometer Light Sensor Temperature Sensor Humidity Sensor Geographical Information System (GIS) Atmospheric Pressure Sensor Medical Sensors NextSAUDE 2013 Fortaleza Ceara - Brazil

34 CAPTAIN + LARIISA The diagnosis < > was taken at <S 3 45' >, <W 38 36' >, <Av. F, Conj. Ceará, Fortaleza CE> on <March 2nd, 2013> at <17:00h>. Heart rate was <110 bpm>, body temperature was <40 C> and blood pressure was <140/90>. Symptoms: <A, B, C>. SUS ID: < >. Proposed approach <diagnosis_id> </diagnosis_id> <lat>3 45' "</lat> <lon>38 36' "</lon> <time>17:00</time> <date>03/02/2013</date> <body_temp>40 C</body_temp> Personal Tracking Diagnosis data metadata file <heart_rate>110bpm</heart_rate> <blood_pressure>140/90</blood_pressure> <loc_name>av. F, Conj. Ceará, Fortaleza - CE</loc_name> NextSAUDE <symptom>a, B, C</symptom> <sus_id> </sus_id> 2013 Patient Identification!! Fortaleza Ceara - Brazil

Mobile health System (Lariisa module) 1st step <diagnosis_id=0023821992>, <lat=s 3 45' 48.6429 >, <lon=w 38 36' 28.7434 >, <loc_name=av. F, 126-298- Conj.

Lariisa Database Additional questions (based on patient history and current diagnosis) patienthistory(sus_id) 2nd step Lariisa Database (diagnosis_data, patienthistory) Ontology Base Patient Patient

(GPS)) THEN(sendmessage togo tothe nearestmedical Center (GPS)) High-level Local decision rule(2) IF(patient s diagnosis is critical) AND(thereisno Medical Center near the patient s house(gps))

35 Mobile health System (Lariisa module) 1st step <diagnosis_id= >, <lat=s 3 45' >, <lon=w 38 36' >, <loc_name=av. F, Conj. Ceará, Fortaleza CE>, <date=march 2nd, 2013>, <time=17:00h>, <heart_rate=110 bpm>, <body_temp=40 C>, <blood_pressure=140/90>, <symptom=a, B, C>, <sus_id= > Send diagnosis data to the Lariisa Database Additional questions (based on patient history and current diagnosis) patienthistory(sus_id) 2nd step Lariisa Database (diagnosis_data, patienthistory) Ontology Base Patient Patient s house 3rd step Health Agent Reply to the questions raised by the system High-level Local decision rule(1) IF(patient s diagnosis is critical) AND(thereisa Medical Center nearthe patient s house (GPS)) THEN(sendmessage togo tothe nearestmedical Center (GPS)) High-level Local decision rule(2) IF(patient s diagnosis is critical) AND(thereisno Medical Center near the patient s house(gps)) THEN(rellocatingthenearestHealth Agent (basedonspecificspecialities) for the patient shouse(gps)) Health Agents Agenda Health Agents Database I II III 4th step Inference Rules Local Context Repository Global Context Repository Decision-Making Support Administrative Context-Aware Service(CAS) High-level Global decision rule IF(there are several diagnoses with insurgent signs of Dengue) AND(more thanx cases came fromthe same location(personal tracking)) THEN(rellocatingY Health Agents for thisregion(gps)) NextSAUDE 2013 Fortaleza Ceara - Brazil

36 7. Conclusion

37 LARIISA, A Context-Aware Framework for Health Care Governance Decision- Making Systems: A model based on the Brazilian Digital TV SISA APPLYING ONTOLOGY AND CONTEXT AWARENESS CONCEPTS ON HEALTH MANAGEMENT SYSTEM: A DENGUE CRISIS STUDY CASE SISA Application DIGA SAUDE A HOME CARE PROTOTYPE BASED ON THE BRAZILIAN DIGITAL TV MODEL FOR A HEALTH MANAGEMENT SYSTEM Project Lariisa Data Acquisition NextSAUDE 2013 Fortaleza Ceara - Brazil

CLARIISA: Applying Personal Tracking Technology in a Context-Aware Health Management System CAPTAIN (2012) A Context-Aware Web Content Generator Based on Personal Tracking Captain Project LARIISA

38 CLARIISA: Applying Personal Tracking Technology in a Context-Aware Health Management System CAPTAIN (2012) A Context-Aware Web Content Generator Based on Personal Tracking Captain Project LARIISA (2010) A Context-Aware Framework for Health Care Governance Decision-Making Systems: A model based on the Brazilian Digital TV Project Clariisa Lariisa Project NextSAUDE 2013 Fortaleza Ceara - Brazil

39 LARIISA Environment

40 The CLARIISAprojectwillbe applied tothe SUS, The brazilian public health system ACADEMIC RESULTS 10 Master of Science 12 papers 03 research projects INOVATION ASPECTS A Proof of Concept of the LARIISA Applications is being implemented by the IT companies in Brazil The DIGA Saúde prototype is held by the COELCE (Electricity Company of Ceará State Brazil)

41 MUITO OBRIGADO

Model of Good Practice tools for risk reduction and clinical governance

Model of Good Practice tools for risk reduction and clinical governance Delphine Smagghe Luxembourg, 7 April 2005 Introduction Main challenges in healthcare Risk reduction Improvement of the quality of