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Projects / Programmes source: ARIS

The detection of irregularities and fraud in the financing of the public health services

Research activity

Code Science Field Subfield
1.07.01  Natural sciences and mathematics  Computer intensive methods and applications  Algorithms 

Code Science Field
P170  Natural sciences and mathematics  Computer science, numerical analysis, systems, control 

Code Science Field
1.01  Natural Sciences  Mathematics 
Keywords
Diagnosis related groups, Financing of health care system, anomaly detection, care pathways, Digitalized clinical pathways, eHealth, mobile devices, data mining, large datasets analysis
Evaluation (rules)
source: COBISS
Researchers (28)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  13919  PhD Tit Albreht  Public health (occupational safety)  Researcher  2011 - 2014  526 
2.  35646  PhD Benjamin Bizjan  Mechanics  Researcher  2013 - 2014  128 
3.  35021  PhD Patricia Blatnik  Economics  Researcher  2013 - 2014  74 
4.  19284  PhD Marko Boben  Computer intensive methods and applications  Researcher  2011 - 2012  84 
5.  33987  PhD Monika Cerinšek  Computer intensive methods and applications  Researcher  2014  21 
6.  13430  PhD Gregor Cigler  Mathematics  Researcher  2011 - 2014  61 
7.  32939  Gašper Derganc  Computer intensive methods and applications  Researcher  2011 
8.  33506  Katja Grašič  Mathematics  Researcher  2011 - 2012  10 
9.  24421  PhD Boris Horvat  Computer intensive methods and applications  Researcher  2011 - 2014  143 
10.  34024  Sonja Jerše  Computer intensive methods and applications  Researcher  2012 
11.  20269  PhD Iztok Kavkler  Mathematics  Researcher  2011 - 2014  59 
12.  22353  PhD Igor Klep  Mathematics  Researcher  2013 - 2014  312 
13.  34751  PhD Boštjan Kovač  Mathematics  Researcher  2012 - 2014  10 
14.  05484  PhD Edvard Kramar  Mathematics  Researcher  2011 - 2012  93 
15.  20037  PhD Marjeta Kramar Fijavž  Mathematics  Researcher  2011 - 2014  185 
16.  26450  PhD Primož Lukšič  Computer intensive methods and applications  Researcher  2011 - 2014  96 
17.  21656  PhD Štefko Miklavič  Mathematics  Researcher  2011 - 2014  201 
18.  33024  Rok Okorn  Mathematics  Researcher  2011 - 2012 
19.  21436  MSc Mojca Omerzu  Public health (occupational safety)  Researcher  2011 - 2014  44 
20.  21658  PhD Alen Orbanić  Computer intensive methods and applications  Researcher  2011 - 2014  141 
21.  01941  PhD Tomaž Pisanski  Mathematics  Researcher  2011 - 2014  866 
22.  18838  PhD Primož Potočnik  Mathematics  Head  2011 - 2014  239 
23.  18170  PhD Gregor Šega  Natural sciences and mathematics  Researcher  2014  41 
24.  30826  PhD Janez Šter  Mathematics  Researcher  2013  31 
25.  22354  PhD Jernej Tonejc  Mathematics  Researcher  2013 - 2014  13 
26.  27610  PhD Eva Turk  Public health (occupational safety)  Researcher  2011  152 
27.  23962  PhD Dejan Velušček  Energy engineering  Researcher  2014  52 
28.  33790  MSc Anne-Marie Yazbeck  Public health (occupational safety)  Researcher  2011 - 2013  63 
Organisations (4)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  0106  Jožef Stefan Institute  Ljubljana  5051606000  91,855 
2.  1669  University of Primorska, Andrej Marušič Insitute  Koper  1810014007  10,879 
3.  2975  ABELIUM d.o.o., research and development  Ljubljana  3557952  458 
4.  3333  National Institut of Public Health  Ljubljana  6462642  18,544 
Abstract
In the project we will carry out research targeting inefficiencies and possible anomalies in the Slovenian public health care system. On a macro level we will focus on detecting misuse in DRG system reporting, while on a micro level we will address the efficiency of certain aspects of health care process management in hospitals. In Slovenia, health care institutions are financed through diagnosis related groups (DRG) model.  This is a way of classifying patients into groups of related diagnosis, in respect to the amount of resources spent. The basis for classification of patients is the statistical analysis of clinical data and data on consumption of resources on large samples of patients. Each hospital treatment is assigned to a specific group in the SPP system based on the main diagnosis and taking into account other possible information, e.g. additional diagnoses and operations. Groups are formed on the basis of medical information; however, economic parameters are also taken into account. For each hospital treatment of the defined SPP group, a weight is assigned, which then becomes the basis for the calculation of funding. Hospitals behave economically and often make costs appear higher than they are. This often results in miscoding the diagnosis or adding certain additional medical complications to the patient. In result, the data we get from DRGs and on which all the important strategic decisions in health management are made is often erroneous. In the research we plan to investigate the extent of the anomalies. This will be done by analyzing the current data in the health system with different analytical methods and data mining. Furthermore, in order to provide ideas for possible optimizations, we intend to approach the problem from the micro level. We will select certain test hospitals and evaluate operational management and patient flow for selected critical processes, regarding money, time or volume. Through research on the macro level, relevant critical diagnoses will be detected. For those and related care processes we will, together with medical staff, create and implement care pathways. Carefully selected effectiveness indicators will be used for measuring influence of care pathways on the operational management in hospitals. Later, advanced information support will be provided to the care pathways (to establish e-pathways). Using the methods from information support in logistics, we will improve the availability of important data about the patient, thereby increasing the possibility of successful treatment, making the patient flow more efficient and quicker as well as improving the reliability of the data that is reported in the DRG system. Care pathways will enable hospitals to handle 60-80% of patient cases for certain diagnoses in an efficient and controllable way. The main challenge will be to provide and use relevant advanced information support and powerful analytic tools. Digitized data will also reduce possibilities for anomalies and frauds. Finally, we will test e-pathways in the selected hospitals and evaluate their impact on DRG reporting. We will provide suggestions, directions and ideas for improvements of the current system of DRG reporting as well as provide prototype solutions for efficient information support of DRG reporting through e-pathways. This will improve the patient flow by making it faster, more organized and more efficient. The project will be highly interdisciplinary and will involve groups of public health care experts, economists, mathematicians and computer scientists. We believe that the project will provide better insight into financing and management of Slovenian public health care system, and directions for improvements using advanced information technologies.
Significance for science
The project will provide a strong base for cooperation between scientists from different areas of knowledge: economists in the section of public health, information scientists and mathematicians. Information support for public health care systems is a continuously increasing trend. We believe that analyzing the data and optimizing the management of health care systems will represent a completely new research field, motivated by concrete management problems in health care. There were three doctoral students from the fields of health-care economics, statistics and computer science working on this project. The results and experience gained in the project contributed to their doctoral thesis.
Significance for the country
It is clear that health care system and institutions have many benefits from efficient information support, which, combined with intelligent analytics provides support for more efficient management of certain, treatment related processes. This has a potential to make the whole process of treating a patient faster, more efficient and economically more stable. By controlling the reporting we provided more transparency in the system. This will make the financing of health care fairer as well as improve the real value of the data about the patients which is then used for making national health care directives. With aging population in western world, reforms and optimizations in management of health care systems are a must. Even small improvements in health care process management can introduce vast potentials for savings, higher quality of services, improve their reliability and make them more time efficient.
Most important scientific results Annual report 2011, 2012, 2013, final report, complete report on dLib.si
Most important socioeconomically and culturally relevant results Annual report 2011, 2012, 2013, final report, complete report on dLib.si
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