Event Title

Analysing Complex Healthcare Systems: from Big Data to Deep Data via System Simulation

Session

Computer Science and Communication Engineering

Description

Complexity of manmade systems increases rapidly and so do costs spent for them. National health systems invest annually more billions of Euros as the demand for health services increases (because of demographic change), but resources are limited. In addition complexity of processes increases: From diagnosis to therapy – Interventions are complex hybrid processes including e-health, decentralized services and personalized medicine. To measure efficiency and effectiveness becomes more and more complex but is an urgent need. Development of new methods, models and technologies is needed in order to support analyzing, planning and controlling. Quantity and quality of available data strongly increases and therefore facilitates the description and analysis of all areas in complex systems like health care. Based on data for healthy expenditures the evaluation for health care systems has a market of 75 to 120 Billion Euro only in the European Union.

To provide state of the art analysis for Health Technology Assessment (HTA), Comparative Effectiveness Research (CER) and Evidence Based Medicine (EBM) processes combining health system domain knowledge, knowledge of professional data processes and – last but not least – mathematical modelling & simulation will be vital to transform Big Data into Deep Data: Evidence based and reproducible knowledge.

Bringing together these technologies is an enormous challenge. Data Based Demographic models have to be combined with models for the spread of diseases. Time dependent treatment paths have to be parametrized with data sets from clinical routine joined with large-scale health system data. For system simulation an important aspect is the possibility to implement changes inside the system, like interventions within the computer model, and to analyse their effects. On basis of experiences of the Austrian DEXHELPP Competence Centre for Decision Support in Health Policy and Planning, the contribution points out how big, interdisciplinary teams can handle these complex processing future and what are similarities and differences to other complex manmade systems. Analysis is supported by an innovative research infrastructure – the DEXHELPP Research Server - developed to enable researchers and other stakeholders to share data and methods for research and decision making.

Keywords:

Complex Systems, System Simulation, Big Data, Deep Data, Health Care

Session Chair

Kozeta Sevran

Session Co-Chair

Bertan Karahoda

Proceedings Editor

Edmond Hajrizi

ISBN

978-9951-437-54-7

Location

Durres, Albania

Start Date

27-10-2017 3:00 PM

End Date

27-10-2017 4:30 PM

DOI

10.33107/ubt-ic.2017.92

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Oct 27th, 3:00 PM Oct 27th, 4:30 PM

Analysing Complex Healthcare Systems: from Big Data to Deep Data via System Simulation

Durres, Albania

Complexity of manmade systems increases rapidly and so do costs spent for them. National health systems invest annually more billions of Euros as the demand for health services increases (because of demographic change), but resources are limited. In addition complexity of processes increases: From diagnosis to therapy – Interventions are complex hybrid processes including e-health, decentralized services and personalized medicine. To measure efficiency and effectiveness becomes more and more complex but is an urgent need. Development of new methods, models and technologies is needed in order to support analyzing, planning and controlling. Quantity and quality of available data strongly increases and therefore facilitates the description and analysis of all areas in complex systems like health care. Based on data for healthy expenditures the evaluation for health care systems has a market of 75 to 120 Billion Euro only in the European Union.

To provide state of the art analysis for Health Technology Assessment (HTA), Comparative Effectiveness Research (CER) and Evidence Based Medicine (EBM) processes combining health system domain knowledge, knowledge of professional data processes and – last but not least – mathematical modelling & simulation will be vital to transform Big Data into Deep Data: Evidence based and reproducible knowledge.

Bringing together these technologies is an enormous challenge. Data Based Demographic models have to be combined with models for the spread of diseases. Time dependent treatment paths have to be parametrized with data sets from clinical routine joined with large-scale health system data. For system simulation an important aspect is the possibility to implement changes inside the system, like interventions within the computer model, and to analyse their effects. On basis of experiences of the Austrian DEXHELPP Competence Centre for Decision Support in Health Policy and Planning, the contribution points out how big, interdisciplinary teams can handle these complex processing future and what are similarities and differences to other complex manmade systems. Analysis is supported by an innovative research infrastructure – the DEXHELPP Research Server - developed to enable researchers and other stakeholders to share data and methods for research and decision making.