PREDIX: A New Tool for Measuring Disaster Resilience Index Performance of Community Health Center

Rina Suryani Oktari (1), Safrizal Rahman (2), Tita Menawati Liansyah (3), - Nasliati (4)
(1) Universitas Syiah Kuala
(2) Faculty of Medicine, Universitas Syiah Kuala
(3) Faculty of Medicine, Universitas Syiah Kuala
(4) Faculty of Computer Science, Universitas Bung Karno
Fulltext View | Download
How to cite (IJASEIT) :
Oktari, Rina Suryani, et al. “PREDIX: A New Tool for Measuring Disaster Resilience Index Performance of Community Health Center”. International Journal on Advanced Science, Engineering and Information Technology, vol. 9, no. 5, Oct. 2019, pp. 1570-6, doi:10.18517/ijaseit.9.5.9389.
A Puskesmas is the singular Indonesian term for a Community Health Center, hereinafter termed as a Puskesmas Community Health Center or simply a PCHC. A PCHC operates under government directives at the sub-district level in Indonesia. A PCHC provides extensive services for the development of health and bolsters such health endeavors at the public-level and at the individual-level. A PCHC is a critical protagonist in reducing the problems created by a disasters hence the concept of the resilience of the PCHC in the face of disaster needs to be promoted. The aim of the present study was to design a Dashboard Screen Toolkit (DST) for performance monitoring and evaluation based on a canvas of PCHC resilience. The authors have developed a “PCHC’ Resilience to Disasters Index (PREDIX)” which is a computer interface or dashboard for visualizing PCHC resilience indicators. This means that disaster prevention personnel can monitor of PCHC resilience index performance data using a transparent and easy-to-operate medium. The stages of creating a DST for PREDIX has these steps: identifying parameters and indicators/indices of the PCHC resilience; evidence of design feasibility; building the technology connectivity roadmap for hosting the DST; catering for multiple platforms, and creating pertinent systems as a basis for the incorporation of fine-scale data. A conceptual model was built for documenting the components of the measurement process of the disaster resilience of a PCHC, incorporating potential measures for the production of a real-time evaluation tool-kit. The model has five parameters for measuring the disaster resilience of a PCHC, which are: real-time conditions of structures, institutional issues, the work force and personnel, external networks, and exposure to disaster. The PREDIX DST, hereinafter termed as PDST gives a visual interface to channel bulk information onto an interface monitor. The PDST complements strategic decision making with the goal of increasing the disaster resilience of a PCHC. The PDST provides charts that display structural and non-structural resilience measurements of PCHC. The PDST can also become enabled for the Internet. This innovative tool kit will help to tracking the progress of the disaster resilience of a PCHC. The PDST will aid policy makers regarding any necessary increase in PCHC resilience to disasters.

D. Guha-Sapir, Ph. Hoyois, and R. Below, “Annual Disaster Statistical Review 2016: The Numbers and Trends,” Brussels: CRED, 2017.

Pan American Health Organization Health Sector Self-Assessment Tool for Disaster Risk Reduction. Washington, DC, 2010.

R. Berariu, C. Fikar, M. Gronalt, and P. Hirsch, “Understanding the impact of cascade effects of natural disasters on disaster relief operations,” International Journal of Disaster Risk Reduction, vol. 12, pp. 350-356, 2015.

A.M. Chand, and M. Loosemore, “Hospital disaster management’s understanding of built environment impacts on healthcare services during extreme weather events,” Engineering, Construction and Architectural Management, vol. 23(3), pp.385-402, 2016.

M. L. Birnbaum, E. K. Daily, A. P. O’Rourke, and A. Loretti, “Research and evaluations of the health aspects of disasters, Part II: the disaster health conceptual framework revisited,” Prehospital and disaster medicine, vol. 30(5), pp. 523-538, 2015.

UNISDR. Sendai framework for disaster risk reduction 2015-2030. Geneva: UNISDR, 2015.

A. Aitsi-Selmi, S. Egawa, H. Sasaki, C. Wannous, and V. Murray, “The Sendai framework for disaster risk reduction: Renewing the global commitment to people’s resilience, health, and well-being,” International Journal of Disaster Risk Science, vol. 6(2), pp. 164-176, 2015.

A. Aitsi-Selmi and V. Murray, “Protecting the health and well-being of populations from disasters: Health and health care in the Sendai framework for disaster risk reduction 2015-2030,” Prehospital and disaster medicine, vol. 31(1), pp. 74-78, 2016.

L. Reifels, P. Arbon, A. Capon, J. Handmer, A. Humphrey, V. Murray, and C. Spencer, “Health and disaster risk reduction regarding the Sendai Framework,” Australian Journal of Emergency Management, vol. 33(1), pp. 23-24, 2018.

Regulation of the Minister of Health of the Republic of Indonesia Number 75 Year 2014 on Community Health Center (Puskesmas).

World Health Organization. Risk reduction and emergency preparedness: WHO six-year strategy for the health sector and community capacity development, 2007.

F. Mulyasari, S. Inoue, S. Prashar, K. Isayama, M. Basu, N. Srivastava, and R. Shaw, “Disaster preparedness: Looking through the lens of hospitals in Japan,” International Journal of Disaster Risk Science, vol. 4(2), pp.89-100, 2013.

Oktari, R.S. and Kurniawan, H., Framework Ketahanan Puskesmas Dalam Menghadapi Bencana. Jurnal Kedokteran Syiah Kuala, vol. 16 (1), pp.44-52, 2016.

UNISDR. Hyogo framework for action 2005-2015: building the resilience of nations and communities to disasters. Extract from the final report of the World Conference on Disaster Reduction (A/CONF. 206/6), 2005.

J. Carthey, M. R. De Leval, and J. T. Reason, “Institutional resilience in healthcare systems,” Quality in health care, vol. 10.1, pp. 29-32, 2001.

S. Fukuma, S. Ahmed, R. Goto, T. S. Inui, R. Atun, and S. Fukuhara, “Fukushima after the Great East Japan Earthquake: lessons for developing responsive and resilient health systems,” Journal of global health, vol. 7(1), pp. 010501, 2017.

S. Zhong, M. Clark, X. Y. Hou, Y. Zang, and G. FitzGerald, “Development of key indicators of hospital resilience: a modified Delphi study,” Journal of health services research & policy, vol. 20(2), pp. 74-82, 2015.

World Health Organization. Safe hospitals in emergencies and disasters: structural, non-structural and functional indicators, 2010.

O. M. Yigitbasioglu and O. Velcu, “A Review of Dashboards in Performance Management: Implications for Design and Research,” International Journal of Accounting Information Systems, vol. 13(1), pp.41-59, 2012.

D. Arnott and G. Pervan, “A critical analysis of decision support systems research”, Journal of Information Technology, vol. 20, pp. 67-87, 2005.

M. O. Ward, G. Grinstein, and D. Keim, “Interactive data visualization: foundations, techniques, and applications,” AK Peters/CRC Press, 2015.

K. Pauwels, T. Ambler, H. C. Bruce, P. Lapointe, D. Reibstein, B. Skiera, B. Wierenga, and T. Wiesel, “ Dashboards as a Service: Why, What, How, and What Research Is Needed?,” Journal of Service Research, vol. 12 (2), pp. 175-189, 2009.

E. O’Donnell, and J. S. David, “How information systems influence user decisions: a research framework and literature review,” International Journal of Accounting Information Systems, no.1, pp.178-203, 2000.

E. B. Goldstein, Sensation and Perception. Thomson Wadsworth, 7th Edition, 2007.

S. Few, Information Dashboard Design, The Effective Visual Communication of Data, O’Reilly Media, Inc, First Edition, 2006.

A. Navarro-Galera, F. J. Alcaraz-Quiles, and D. Ortiz-Rodrí­guez, "Online dissemination of information on sustainability in regional governments: Effects of technological factors,” Government Information Quarterly, vol. 33(1), pp. 53-66, 2016.

R. P. Lourení§o, “An analysis of open government portals: A perspective of transparency for accountability,” Government Information Quarterly, vol. 32(3), pp. 323-332, 2015.

E. W. Welch, M. K. Feeney, and C. H. Park, “Determinants of data sharing in US city governments,” Government Information Quarterly, vol. 33(3), pp. 393-403, 2016.

A. Vetrí², L. Canova, M. Torchiano, C. O. Minotas, R. Iemma, and F. Morando, “Open data quality measurement framework: Definition and application to Open Government Data,” Government Information Quarterly, vol. 33(2), pp. 325-337, 2016.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
    2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
    3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).