PhD Scientific Days

Poster Presentation: Mental Health

P12: Mining procurement data of the Hungarian healthcare sector

Előadó neve

Merész, Gergő

Előadó munkahelye

Health Services Management Training Centre, Semmelweis University

Előadó telefonszáma

0036203244372

Előadó e-mail címe

meresz@emk.sote.hu

Az előadás címe

Mining procurement data of the Hungarian healthcare sector

Szerző(k) neve és munkahelye

Gergő Merész
Health Services Management Training Centre, Semmelweis University, Budapest, Hungary

Szekció

Poster Presentation: Mental Health

Data of the presenter

Assistant lecturer (HE & Quantitative Analyst), Health Services Management Training Centre

Doctoral Scool: Mental Health Doctoral School
Program: Behavioural Sciences
Supervisor: Péter Gaál
email: meresz@emk.sote.hu

Text of the abstract

AIMS
Data on procurement processes in the Hungarian healthcare sector is not available in a well-structured format. Although there is a publicly available, searchable database (www.kozbeszerzes.hu) and unique data queries can be filed under the Act CXII of 2011 on information self-determination and freedom of information, these channels of data access complicate the analysis, or often wearisome with low flexibility. The aim of this poster is to present the challenges which occured during the development of the algorithm.

METHODS
An open-source environment had to be chosen for the development of the data mining algorithm targeting the publicly available database of the procurement authority. The algorithm had to be able to identify the proper type of notification documents containing data about the results of a procurement process and extract as much information as possible on the procurement proccess.

RESULTS
The algorithm was developed using the R Statistical Software, and able to cover three different document models at the time of the abstract submisson. Document models were found to be stratified by timing of the procurement proccess. Data on timing, value, subject, applicants and customer in the procurement process can be collected. The collected data can be further linked to company databases, or can be analysed by applying network layouts.

CONCLUSIONS
The constructed algorithm is able to collect sufficient amount of information to proceed with data analysis; however, the integration of further document models is desirable to elaborate the time horizon of the analysis. Further testing is also needed to ensure the reliability of the code.

Azonosító

P12

Kind

Szabad

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

1151

Authors (legacy)