Öz
Kaynakça
- Willcocks, L.P., Sauer, C. and Lacity, M.C (2016). “Enacting Research Method in Information Systems:Volume 3”, Springer ISBN: 9783319292724.
- Ashrafi, N., Kelleher, L., & Kuilboer, J-P. (2014). ”The impact of business intelligence on healthcare delivery in the USA" Interdisciplinary Journal of Information, Knowledge, and Management, Vol. 9, pp. 117-130, (2014).
- Blind, A. (2012). “Four-layer Data Model Implementation for Business Intelligence”, Thesis Study, (2012).
- Özdoğan, O., (2016). “Büyük Veri Denizi”, (1st Edition), Elma Yayınevi, ISBN: 9786059367059.
- Ki, P.Y., Sawy, E., Omar, A. and Peer, F., (2017). "The Role of Business Intelligence and Communication Technologies in Organizational Agility: A Configurational Approach," Journal of the Association for Information Systems: http://doi.org/10.17705/1jais.00467.
- Schultz, N.O., Collins, A.B. & McCulloch, M., (1994). “The ethics of business intelligence”, Journal of Business Ethics https://doi.org/10.1007/BF00871677. Ivan, M. and Velicanu, M.,(2015). “Healthcare Industry Improvement with Business Intelligence”, Informatica Economica, https://doi.org/10.12948/issn14531305/19.2.2015.08.
- Vest, J.R., Grannis, S.J., Haut D.P., Halverson P.K. and Menachemi N., (2017). “Using structured and unstructured data to identify patients’ need for services that address the social determinants of health”, International Journal of Medical Informatics, Vol. 107, pp.101-106, (2017).
- Jothia, N., Aini, N., Rashidb, A. and Husai, W., (2015). “Data Mining in Healthcare – A Review”, (2015), Procedia Computer Science, https://doi.org/10.1016/j.procs.2015.12.145.
- Iatan, I.F., (2016). “Issues in the Use of Neural Networks in Information Retrieval”, Springer, ISBN: 9783319438702
- Kudyba, S. and Hoptroff R, (2001). “Data Mining and Business Intelligence A Guide to Productivity”, IGI Global, ISBN:9781930708808
- Kao, Y. H., Yu, C.M., Masud, M., Wu, W.H., Chen, L.J., Chun, Y. and Wu J., (2016). “Design and evaluation of hospital-based business intelligence system (HBIS): A foundation for design science research methodology”, Computers in Human Behaviour, https://doi.org/10.1016/j.chb.2016.04.021.
- Richards, G., Yeoh, W., Chong, A.Y.L and Popovic, A, (2017). “ Business Intelligence Effectiveness and Corporate Performance Management: An Empirical Analysis”, Journal of Computer Information Systems https://doi.org/10.1080/08874417.2017.1334244
- Dwivedi, A., Niranjan, M., and Sahu, K., (2013). “A Business Intelligence Technique for Forecasting the Automobile Sales using Adaptive Intelligent Systems (ANFIS and ANN)”, International Journal of Computer Applications, Vol. 74, No: 9, pp. 7-13, (2013).
- Dinçerden, E., (2017). “İş Zekası ve Stratejik Yönetim”, 1st Edition, Beta Yayınları
- Olszak, C. M., (2016). “Toward Better Understanding and Use of Business Intelligence in Organizations”, Information Systems Management, https://doi.org/10.1080/10580530.2016.1155946
- Gök, M, Akçetin, E. and Çelik, U., (2017). “Rapıdmıner ile Uygulmalı Veri Madenciliği”, 1st Edition, Pusula Yayıncılık.
- Parida, A., Kumar, U., Galar, D. and Stenström, C., (2015). "Performance measurement and management for maintenance: a literature review", Journal of Quality in Maintenance Engineering, Vol. 21 Iss 1 pp.
- Kowalczyk, M., (2017). “The Support of Decision Processes with Business Intelligence and Analytics”, Springer, ISBN : 9783658192297
- Muehlen, M., and Shapiro, R., (2014). “Business Process Analytics”, Springer, Handbook on Business Process Management 2, ISBN: 9783642019821.
- Amayri, M., Arora, A., Ploix, S., et all, (2016). “Estimating occupancy in heterogeneous sensor enviorment”, Energy and Buildings, https://doi.org/10.1016/j.enbuild.2016.07.026
- Babiceanu, R.F. and Seker, R., (2016). “ Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook”, Computers in Industry, https://doi.org/10.1016/j.compind.2016.02.004
- Shim, J.P., Warkentin, M., Courtney, J. F. et all. (2002). “Past, present, and future of decision support technology”, Decision Support System, Vol. 33, Issue2, pp.111-126, (2002).
- Yeaoh, W., Popovic, A., (2015). “Extending the understanding of critical success factors for implementing business intelligence systems”, JASIST, https://doi.org/10.1002/asi.23366
- Kaklauskas, A., (2015). “Intelligent Decision Support Systems”, Springer, ISBN: 9783319136585
- Intezari, A., and Gressel, S., (2017). “Information and reformation in KM systems: big data and strategic decision making”, Journal of Knowledge Management, Vol. 21, No.1, pp. 71-91, (2017).
- Duan, L., Xiong, Y., (2015). “Big data analytics and business analytics”, Journal of Management Analaytics, Vol. 2, Issue 1.
- Brooks, P., Gayar, O. E, and Sarnikar, S, (2015). “A framework for developing a domain specific business intelligence maturity model: Application to health care”, International Journal of Information Management, Vol. 35, pp. 337-345.
- Şeker, Ş.A. (2013). İş Zekası ve Veri Madenciliği Weka İle, 1. Baskı, Cinius Yayınları, 2013.
- Wang, Y, Hajli, N. (2016). “Exploring the Path to Big Data Analytics Success in Healthcare’’, Journal of Business Research, 2016. Işık, F. (2016). ‘’Busıness Intellıgence Requırement Analysıs ın Small and Medıum Enterprıses a Masters Thesis (Tez). Atılım Üniversitesi, Endüstri Mühendisliği Ana Bilim Dalı Uzmanlık Tezi; 2016.
- Najah, R., Rahman, N.R. (2017). ‘’Bilgi Sistemlerinin Performans Değerlemeye Etkisi: Sanayi İşletmeleri Araştırması (Tez). Selçuk Üniversitesi, Sosyal Bilimler Enstitüsü İşletme Anabilim Dalı Yönetim Organizasyon Bilim Dalı Uzmanlık Tezi; 2017.
- Özçam, Y, Çoşkun, E. (2016). “Türkiye’de Faaliyet Gösteren İşletmelerin İş Zekası Kullanım Düzeylerinin İncelenmesi Üzerine Bir Araştırma’’, Aksaray Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 2016, 8(1): 73-81.
MEASUREMENT OF THE EFFECTS OF BUSINESS INTELLIGENCE APPLICATIONS ON PERFORMANCE IN HOSPITALS ACCORDING TO THE MANAGERIAL LEVELS: A CHAIN HOSPITAL APPLICATION
Öz
MEASUREMENT OF THE EFFECTS OF BUSINESS INTELLIGENCE APPLICATIONS ON PERFORMANCE IN HOSPITALS ACCORDING TO THE MANAGERIAL LEVELS: A CHAIN HOSPITAL APPLICATION
Mustafa IŞIK [1]
Onur YARAR [2]
Didem Söylemez SUR [3]
ABSTRACT
Problem of the Research: Hospitals are matrix and complex organizations with many areas of expertise. In hospitals, multi-modular and integrated systems that can have multiple software and databases provide support in the execution of business processes within this complex organizational structure. Getting the right data at the right time for decision support purposes is often an important problem. In overcoming these problems, the problem of the research is what the contribution of integrated business intelligence applications solutions can be in decision-making stages from all levels of management.
Purpose of the research: This research was conducted to investigate the effect of business intelligence reporting practices on performance according to managerial levels in hospital management systems. The research is a study conducted to measure the effect of business intelligence practices on performance in a chain hospital group that uses business intelligence in connection with the ERP (Enterprise Resource Planning) system with the highest number of hospitals in Turkey. The questionnaire study was conducted on 383 people. In this context, BI (Business Intelligence) architecture has been examined with all its components and information has been given about the benefits it provides to the enterprises. In the application developed as an example, a data warehouse modeling in accordance with the information required by the top management in hospitals, preparation of analyses, creation of presentation layer and presentation of the prepared analyses and reports on the control table were carried out.
Material &Method: The universe of the research is composed of people who have the authority and access to use BI at MLP care group for this study. The number of authorized people on the ERP system was 591 and 65% of them have filled out the requested survey. 231 was our minimum number and for increasing the reliability we have modified our number of samples to 383. So 383 samples were reached to strengthen the validity and the reliability of the survey. In this sample range, it was also aimed to compare the performance impact between senior, intermediate and operational level managers.
Conclusion: As a result of the research, it was determined that corporate business intelligence application screens at all types of managerial levels have a positive and significant effect on measurable performance indicators. In this context, when businesses monitor and control their operational activities through corporate business intelligence, it has been concluded that performance indicators provide less time loss, high reliability, integrated data, quality and accurate valuation advantages in the evaluation process. In the research, it was observed that the effect of performance results of operational managers' business intelligence applications from management levels was higher than that of senior and middle level managers.
Keywords: Business Intelligence, Big Data, Corporate Performance Management, Hospital Information Management Systems, Data Analysis
[1] Mustafa IŞIK, Asst. Prof. İstinye University Faculty of Economics, Administrative and Social Sciences, Department of Health Management/ mustafa.isik@mlpcare.com
[2] Onur YARAR, Asst. Prof. Okan University, Healthcare Management, Turkey / onur.yarar@okan.edu.tr
[3] Didem Söylemez SUR, Asst. Prof. Kent University, Healthcare Management, Turkey /didem.sur@kent.edu.tr
Anahtar Kelimeler
Business Inteligence Big Data Corporate Performance Management
Kaynakça
- Willcocks, L.P., Sauer, C. and Lacity, M.C (2016). “Enacting Research Method in Information Systems:Volume 3”, Springer ISBN: 9783319292724.
- Ashrafi, N., Kelleher, L., & Kuilboer, J-P. (2014). ”The impact of business intelligence on healthcare delivery in the USA" Interdisciplinary Journal of Information, Knowledge, and Management, Vol. 9, pp. 117-130, (2014).
- Blind, A. (2012). “Four-layer Data Model Implementation for Business Intelligence”, Thesis Study, (2012).
- Özdoğan, O., (2016). “Büyük Veri Denizi”, (1st Edition), Elma Yayınevi, ISBN: 9786059367059.
- Ki, P.Y., Sawy, E., Omar, A. and Peer, F., (2017). "The Role of Business Intelligence and Communication Technologies in Organizational Agility: A Configurational Approach," Journal of the Association for Information Systems: http://doi.org/10.17705/1jais.00467.
- Schultz, N.O., Collins, A.B. & McCulloch, M., (1994). “The ethics of business intelligence”, Journal of Business Ethics https://doi.org/10.1007/BF00871677. Ivan, M. and Velicanu, M.,(2015). “Healthcare Industry Improvement with Business Intelligence”, Informatica Economica, https://doi.org/10.12948/issn14531305/19.2.2015.08.
- Vest, J.R., Grannis, S.J., Haut D.P., Halverson P.K. and Menachemi N., (2017). “Using structured and unstructured data to identify patients’ need for services that address the social determinants of health”, International Journal of Medical Informatics, Vol. 107, pp.101-106, (2017).
- Jothia, N., Aini, N., Rashidb, A. and Husai, W., (2015). “Data Mining in Healthcare – A Review”, (2015), Procedia Computer Science, https://doi.org/10.1016/j.procs.2015.12.145.
- Iatan, I.F., (2016). “Issues in the Use of Neural Networks in Information Retrieval”, Springer, ISBN: 9783319438702
- Kudyba, S. and Hoptroff R, (2001). “Data Mining and Business Intelligence A Guide to Productivity”, IGI Global, ISBN:9781930708808
- Kao, Y. H., Yu, C.M., Masud, M., Wu, W.H., Chen, L.J., Chun, Y. and Wu J., (2016). “Design and evaluation of hospital-based business intelligence system (HBIS): A foundation for design science research methodology”, Computers in Human Behaviour, https://doi.org/10.1016/j.chb.2016.04.021.
- Richards, G., Yeoh, W., Chong, A.Y.L and Popovic, A, (2017). “ Business Intelligence Effectiveness and Corporate Performance Management: An Empirical Analysis”, Journal of Computer Information Systems https://doi.org/10.1080/08874417.2017.1334244
- Dwivedi, A., Niranjan, M., and Sahu, K., (2013). “A Business Intelligence Technique for Forecasting the Automobile Sales using Adaptive Intelligent Systems (ANFIS and ANN)”, International Journal of Computer Applications, Vol. 74, No: 9, pp. 7-13, (2013).
- Dinçerden, E., (2017). “İş Zekası ve Stratejik Yönetim”, 1st Edition, Beta Yayınları
- Olszak, C. M., (2016). “Toward Better Understanding and Use of Business Intelligence in Organizations”, Information Systems Management, https://doi.org/10.1080/10580530.2016.1155946
- Gök, M, Akçetin, E. and Çelik, U., (2017). “Rapıdmıner ile Uygulmalı Veri Madenciliği”, 1st Edition, Pusula Yayıncılık.
- Parida, A., Kumar, U., Galar, D. and Stenström, C., (2015). "Performance measurement and management for maintenance: a literature review", Journal of Quality in Maintenance Engineering, Vol. 21 Iss 1 pp.
- Kowalczyk, M., (2017). “The Support of Decision Processes with Business Intelligence and Analytics”, Springer, ISBN : 9783658192297
- Muehlen, M., and Shapiro, R., (2014). “Business Process Analytics”, Springer, Handbook on Business Process Management 2, ISBN: 9783642019821.
- Amayri, M., Arora, A., Ploix, S., et all, (2016). “Estimating occupancy in heterogeneous sensor enviorment”, Energy and Buildings, https://doi.org/10.1016/j.enbuild.2016.07.026
- Babiceanu, R.F. and Seker, R., (2016). “ Big Data and virtualization for manufacturing cyber-physical systems: A survey of the current status and future outlook”, Computers in Industry, https://doi.org/10.1016/j.compind.2016.02.004
- Shim, J.P., Warkentin, M., Courtney, J. F. et all. (2002). “Past, present, and future of decision support technology”, Decision Support System, Vol. 33, Issue2, pp.111-126, (2002).
- Yeaoh, W., Popovic, A., (2015). “Extending the understanding of critical success factors for implementing business intelligence systems”, JASIST, https://doi.org/10.1002/asi.23366
- Kaklauskas, A., (2015). “Intelligent Decision Support Systems”, Springer, ISBN: 9783319136585
- Intezari, A., and Gressel, S., (2017). “Information and reformation in KM systems: big data and strategic decision making”, Journal of Knowledge Management, Vol. 21, No.1, pp. 71-91, (2017).
- Duan, L., Xiong, Y., (2015). “Big data analytics and business analytics”, Journal of Management Analaytics, Vol. 2, Issue 1.
- Brooks, P., Gayar, O. E, and Sarnikar, S, (2015). “A framework for developing a domain specific business intelligence maturity model: Application to health care”, International Journal of Information Management, Vol. 35, pp. 337-345.
- Şeker, Ş.A. (2013). İş Zekası ve Veri Madenciliği Weka İle, 1. Baskı, Cinius Yayınları, 2013.
- Wang, Y, Hajli, N. (2016). “Exploring the Path to Big Data Analytics Success in Healthcare’’, Journal of Business Research, 2016. Işık, F. (2016). ‘’Busıness Intellıgence Requırement Analysıs ın Small and Medıum Enterprıses a Masters Thesis (Tez). Atılım Üniversitesi, Endüstri Mühendisliği Ana Bilim Dalı Uzmanlık Tezi; 2016.
- Najah, R., Rahman, N.R. (2017). ‘’Bilgi Sistemlerinin Performans Değerlemeye Etkisi: Sanayi İşletmeleri Araştırması (Tez). Selçuk Üniversitesi, Sosyal Bilimler Enstitüsü İşletme Anabilim Dalı Yönetim Organizasyon Bilim Dalı Uzmanlık Tezi; 2017.
- Özçam, Y, Çoşkun, E. (2016). “Türkiye’de Faaliyet Gösteren İşletmelerin İş Zekası Kullanım Düzeylerinin İncelenmesi Üzerine Bir Araştırma’’, Aksaray Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 2016, 8(1): 73-81.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Sağlık Kurumları Yönetimi |
| Bölüm | Orginal Research |
| Yazarlar | |
| Yayımlanma Tarihi | 30 Nisan 2021 |
| Yayımlandığı Sayı | Yıl 2021 Cilt: 7 Sayı: 13 |
