Promoting data-driven decision-making in Jordan: strengthening national health information system and achieving consensus on core set of health system indicators

Findings from this study can contribute to the broader discourse on HIS reforms in Jordan, emphasizing the need for ongoing efforts to enhance data quality, stakeholder collaboration, and infrastructure.

Authors

Institutions

Publication Details

Journal
Reproductive Health
Published
2025-05-30
DOI
https://doi.org/10.1186/s12978-025-01988-1
Primary Topic
Healthcare Systems and Reforms
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Promoting data-driven decision-making in Jordan: strengthening national health information system and achieving consensus on core set of health system indicators

Racha Fadlallah, Diana Jamal, Fadi El‐Jardali, Najla Daher et al.
Reproductive Health
Healthcare Systems and Reforms
article

Promoting data-driven decision-making in Jordan: strengthening national health information system and achieving consensus on core set of health system indicators

Racha Fadlallah, Diana Jamal, Fadi El‐Jardali, Najla Daher, Raeda Abu AlRub
article en

Abstract

Findings from this study can contribute to the broader discourse on HIS reforms in Jordan, emphasizing the need for ongoing efforts to enhance data quality, stakeholder collaboration, and infrastructure.

Reproductive HealthVol. 22(S1)
American University of Beirut (LB)
International Development Research Centre
Partnerships for the goals
Openalex Percentile: Top 84%
Healthcare Systems and Reforms
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.