TY - GEN
T1 - A Spatio-Temporal Auto-regressive Model for Generating Savings Calls to a Health Line
AU - Simões, Paula
AU - Carvalho, M. Lucília
AU - Aleixo, Sandra
AU - Gomes, Sérgio
AU - Natário, Isabel
N1 - info:eu-repo/grantAgreement/FCT/5876/147410/PT
This work is financed by national funds through FCT - Foundation for Science and Technology - under the projects UID/MAT/00297/2019 and UID/MAT/00006/2013.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Urgency admission is one of the most important factors regarding hospital costs, which can possibly be mitigated by the use of national health lines such as the Portuguese Saúde24 line (S24). Aiming future development of decision support indicators in a hospital savings context, based on the economic impact of the use of S24 rather than hospital urgency services, this study investigates spatio-temporal dependencies of the number of S24 calls generating savings in each Portuguese municipality, over the period 2010–2016, under an autoregressive approach. An econometric analysis of the savings obtained by the use of S24 is also carried out considering a savings index. Combining insights from classical spatial econometrics and from the analysis of spatio-temporal data, novel Bayesian Poisson spatio-temporal lag models are presented and applied in this paper. This extends to time the ideas of a Bayesian Poisson spatial lag model, considering both a parametric and a non-parametic structure for time and space-time effects. The results obtained for the savings index reveal that, over the last seven years, there has been a more comprehensive spatial effectiveness of the S24 line in solving the non-urgent emergency situations, that could be handled by primary health care services or in a self care basis.
AB - Urgency admission is one of the most important factors regarding hospital costs, which can possibly be mitigated by the use of national health lines such as the Portuguese Saúde24 line (S24). Aiming future development of decision support indicators in a hospital savings context, based on the economic impact of the use of S24 rather than hospital urgency services, this study investigates spatio-temporal dependencies of the number of S24 calls generating savings in each Portuguese municipality, over the period 2010–2016, under an autoregressive approach. An econometric analysis of the savings obtained by the use of S24 is also carried out considering a savings index. Combining insights from classical spatial econometrics and from the analysis of spatio-temporal data, novel Bayesian Poisson spatio-temporal lag models are presented and applied in this paper. This extends to time the ideas of a Bayesian Poisson spatial lag model, considering both a parametric and a non-parametic structure for time and space-time effects. The results obtained for the savings index reveal that, over the last seven years, there has been a more comprehensive spatial effectiveness of the S24 line in solving the non-urgent emergency situations, that could be handled by primary health care services or in a self care basis.
KW - Autoregressive models
KW - Bayesian analysis
KW - Poisson
KW - Space-time correlation
KW - Spatial econometrics
UR - http://www.scopus.com/inward/record.url?scp=85069230867&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-24302-9_7
DO - 10.1007/978-3-030-24302-9_7
M3 - Conference contribution
AN - SCOPUS:85069230867
SN - 9783030243012
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 81
EP - 96
BT - Computational Science and Its Applications – ICCSA 2019 - 19th International Conference, 2019, Proceedings
A2 - Gervasi, Osvaldo
A2 - Apduhan, Bernady O.
A2 - Murgante, Beniamino
A2 - Taniar, David
A2 - Torre, Carmelo
A2 - Tarantino, Eufemia
A2 - Rocha, Ana Maria A.C.
A2 - Stankova, Elena
A2 - Korkhov, Vladimir
A2 - Misra, Sanjay
PB - Springer Verlag
T2 - 19th International Conference on Computational Science and Its Applications, ICCSA 2019
Y2 - 1 July 2019 through 4 July 2019
ER -