Structuring and Visualizing Healthcare Claims Data Using Systems Architecture Methodology
Healthcare delivery systems around the world are in
crisis. The need to improve health outcomes while decreasing
healthcare costs have led to an imminent call to action to transform
the healthcare delivery system. While Bioinformatics and Biomedical
Engineering have primarily focused on biological level data and
biomedical technology, there is clear evidence of the importance
of the delivery of care on patient outcomes. Classic singular
decomposition approaches from reductionist science are not capable
of explaining complex systems. Approaches and methods from
systems science and systems engineering are utilized to structure
healthcare delivery system data. Specifically, systems architecture is
used to develop a multi-scale and multi-dimensional characterization
of the healthcare delivery system, defined here as the Healthcare
Delivery System Knowledge Base. This paper is the first to contribute
a new method of structuring and visualizing a multi-dimensional and
multi-scale healthcare delivery system using systems architecture in
order to better understand healthcare delivery.
[1] A. Case and A. Deaton, “Rising morbidity and mortality in midlife
among white non-Hispanic Americans in the 21st century,” Proceedings
of the National Academy of Sciences, vol. 112, no. 49, pp.
15 078–15 083, 2015.
[2] A. Alwan, Global status report on noncommunicable diseases. Geneva,
Switzerland: World Health Organization, 2010.
[3] C. f. M. &. M. Service, “National Health Expenditures 2013 Highlights,”
Centers for Medicare & Medicaid Services, Office of the Actuary,
National Health Statistics Group, Tech. Rep., 2013.
[4] Institute of Medicine (U.S.), “Best Care at Lower Cost: The Path to
Continuously Learning Health Care in America,” Tech. Rep., 2012.
[5] P. Proctor, W. Compton, J. H. Grossman, and G. Fanjiang, Building a
Better Delivery System: A New Engineering/Health Care Partnership.
National Academies Press, 2005.
[6] D. M. Buede, The engineering design of systems: models and methods.
John Wiley & Sons, 2011, vol. 55.
[7] Wiley, INCOSE Systems Engineering Handbook: A Guide for System
Life Cycle Processes and Activities. John Wiley & Sons, 2015.
[8] I. Khayal and A. M. Farid, “Axiomatic Design Based Human Resources
Management for the Enterprise Transformation of the Abu Dhabi
Healthcare Labor Pool,” Journal of Enterprise Transformation, vol. 5,
no. 3, pp. 162–191, 2015.
[9] A. M. Farid, “Reconfigurability measurement in automated
manufacturing systems,” Ph.D. dissertation, University of Cambridge,
2007.
[10] A. M. Farid and D. C. McFarlane, “Production degrees of freedom as
manufacturing system reconfiguration potential measures,” Proceedings
of the Institution of Mechanical Engineers, Part B: Journal of
Engineering Manufacture, vol. 222, no. 10, pp. 1301–1314, 2008.
[11] A. M. Farid, “Product Degrees of Freedom as Manufacturing System
Reconfiguration Potential Measures,” International Transactions on
Systems Science and Applications – invited paper, vol. 4, no. 3, pp.
227–242, 2008.
[12] “Static Resilience of Large Flexible Engineering Systems :
Axiomatic Design Model and Measures,” IEEE Systems Journal (in
press), vol. PP, no. 99, pp. 1–12, 2015.
[13] “An Axiomatic Design Approach to Non-Assembled Production
Path Enumeration in Reconfigurable Manufacturing Systems,” in 2013
IEEE International Conference on Systems Man and Cybernetics,
Manchester, UK, 2013, pp. 1–8.
[14] A. M. Farid and L. Ribeiro, “An Axiomatic Design of a Multi-Agent
Reconfigurable Mechatronic System Architecture,” IEEE Transactions
on Industrial Informatics, vol. 11, no. 5, pp. 1142–1155, 2015.
[15] W. C. Schoonenberg and A. M. Farid, “A Dynamic Energy Management
Model for Microgrid-Enabled Production Systems,” IEEE Transactions
on Industrial Informatics (under revision), vol. 1, no. 1, pp. 1–7, 2016.
[16] J. E. Wennberg, The Dartmouth Atlas of Health Care in the United
States (incl. Diskette). American Hospital Association, 1996.
[17] J. E. Wennberg, E. S. Fisher, and J. S. Skinner, “Geography and the
debate over medicare reform,” Health Affairs, p. W96, 2003.
[18] J. Weaver, “Feds break up $1 billion Medicare scam in Miami biggest
in U.S. history,,” Miami, jul 2016.
[1] A. Case and A. Deaton, “Rising morbidity and mortality in midlife
among white non-Hispanic Americans in the 21st century,” Proceedings
of the National Academy of Sciences, vol. 112, no. 49, pp.
15 078–15 083, 2015.
[2] A. Alwan, Global status report on noncommunicable diseases. Geneva,
Switzerland: World Health Organization, 2010.
[3] C. f. M. &. M. Service, “National Health Expenditures 2013 Highlights,”
Centers for Medicare & Medicaid Services, Office of the Actuary,
National Health Statistics Group, Tech. Rep., 2013.
[4] Institute of Medicine (U.S.), “Best Care at Lower Cost: The Path to
Continuously Learning Health Care in America,” Tech. Rep., 2012.
[5] P. Proctor, W. Compton, J. H. Grossman, and G. Fanjiang, Building a
Better Delivery System: A New Engineering/Health Care Partnership.
National Academies Press, 2005.
[6] D. M. Buede, The engineering design of systems: models and methods.
John Wiley & Sons, 2011, vol. 55.
[7] Wiley, INCOSE Systems Engineering Handbook: A Guide for System
Life Cycle Processes and Activities. John Wiley & Sons, 2015.
[8] I. Khayal and A. M. Farid, “Axiomatic Design Based Human Resources
Management for the Enterprise Transformation of the Abu Dhabi
Healthcare Labor Pool,” Journal of Enterprise Transformation, vol. 5,
no. 3, pp. 162–191, 2015.
[9] A. M. Farid, “Reconfigurability measurement in automated
manufacturing systems,” Ph.D. dissertation, University of Cambridge,
2007.
[10] A. M. Farid and D. C. McFarlane, “Production degrees of freedom as
manufacturing system reconfiguration potential measures,” Proceedings
of the Institution of Mechanical Engineers, Part B: Journal of
Engineering Manufacture, vol. 222, no. 10, pp. 1301–1314, 2008.
[11] A. M. Farid, “Product Degrees of Freedom as Manufacturing System
Reconfiguration Potential Measures,” International Transactions on
Systems Science and Applications – invited paper, vol. 4, no. 3, pp.
227–242, 2008.
[12] “Static Resilience of Large Flexible Engineering Systems :
Axiomatic Design Model and Measures,” IEEE Systems Journal (in
press), vol. PP, no. 99, pp. 1–12, 2015.
[13] “An Axiomatic Design Approach to Non-Assembled Production
Path Enumeration in Reconfigurable Manufacturing Systems,” in 2013
IEEE International Conference on Systems Man and Cybernetics,
Manchester, UK, 2013, pp. 1–8.
[14] A. M. Farid and L. Ribeiro, “An Axiomatic Design of a Multi-Agent
Reconfigurable Mechatronic System Architecture,” IEEE Transactions
on Industrial Informatics, vol. 11, no. 5, pp. 1142–1155, 2015.
[15] W. C. Schoonenberg and A. M. Farid, “A Dynamic Energy Management
Model for Microgrid-Enabled Production Systems,” IEEE Transactions
on Industrial Informatics (under revision), vol. 1, no. 1, pp. 1–7, 2016.
[16] J. E. Wennberg, The Dartmouth Atlas of Health Care in the United
States (incl. Diskette). American Hospital Association, 1996.
[17] J. E. Wennberg, E. S. Fisher, and J. S. Skinner, “Geography and the
debate over medicare reform,” Health Affairs, p. W96, 2003.
[18] J. Weaver, “Feds break up $1 billion Medicare scam in Miami biggest
in U.S. history,,” Miami, jul 2016.
@article{"International Journal of Biological, Life and Agricultural Sciences:76132", author = "Inas S. Khayal and Weiping Zhou and Jonathan Skinner", title = "Structuring and Visualizing Healthcare Claims Data Using Systems Architecture Methodology", abstract = "Healthcare delivery systems around the world are in
crisis. The need to improve health outcomes while decreasing
healthcare costs have led to an imminent call to action to transform
the healthcare delivery system. While Bioinformatics and Biomedical
Engineering have primarily focused on biological level data and
biomedical technology, there is clear evidence of the importance
of the delivery of care on patient outcomes. Classic singular
decomposition approaches from reductionist science are not capable
of explaining complex systems. Approaches and methods from
systems science and systems engineering are utilized to structure
healthcare delivery system data. Specifically, systems architecture is
used to develop a multi-scale and multi-dimensional characterization
of the healthcare delivery system, defined here as the Healthcare
Delivery System Knowledge Base. This paper is the first to contribute
a new method of structuring and visualizing a multi-dimensional and
multi-scale healthcare delivery system using systems architecture in
order to better understand healthcare delivery.", keywords = "Health informatics, systems thinking, systems
architecture, healthcare delivery system, data analytics.", volume = "11", number = "4", pages = "342-5", }
