scholarly journals Identifying priority policy issues and health system research questions associated with recovery outcomes for burns survivors in India: a qualitative inquiry

BMJ Open ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. e020045 ◽  
Author(s):  
Jagnoor Jagnoor ◽  
Sheree Bekker ◽  
Shobha Chamania ◽  
Tom Potokar ◽  
Rebecca Ivers
Keyword(s):  
Health System ◽  
Healthcare Providers ◽  
Qualitative Inquiry ◽  
Health System Research ◽  
System Research ◽  
Group Discussions ◽  
Policy Issues ◽  
Referral Networks ◽  
Research Questions ◽  
Priority Policy

ObjectivesThis study aimed to identify priority policy issues and health system research questions associated with recovery outcomes for burns survivors in India.DesignQualitative inquiry; data were collected through semistructured in-depth interviews and focus group discussions.SettingNine sites in urban and rural settings across India, through primary, secondary and tertiary health facilities.ParticipantsHealthcare providers, key informants, burns survivors and/or their carers.ResultsParticipants acknowledged the challenges of burns care and recovery, and identified the need for prolonged rehabilitation. Challenges identified included poor communication between healthcare providers and survivors, limited rehabilitation services, difficulties with transportation to health facility and high cost associated with burns care. Burns survivors and healthcare providers identified the stigma attached with burns as the biggest challenge within the healthcare system, as well as in the community. Systems barriers (eg, limited infrastructure and human resources), lack of economic and social support, and poor understanding of recovery and rehabilitation were identified as major barriers to recovery.ConclusionsThough further research is needed for addressing gaps in data, strengthening of health systems can enable providers to address issues such as developing/providing, protocols, capacity building, effective coordination between key organisations and referral networks.

scholarly journals Planning Future Health: Developing Big Data and System Modelling Pipelines for Health System Research

2017 ◽  
Vol 27 (4) ◽  
pp. 203-208 ◽  
Author(s):  
Niki Popper ◽  
Florian Endel ◽  
Rudolf Mayer ◽  
Martin Bicher ◽  
Barbara Glock
Keyword(s):  
Big Data ◽  
Health System ◽  
Health System Research ◽  
System Modelling ◽  
System Research ◽  
Future Health

scholarly journals Eliciting policymakers' and stakeholders' opinions to help shape health system research priorities in the Middle East and North Africa region

2009 ◽  
Vol 25 (1) ◽  
pp. 15-27 ◽  
Author(s):  
F. El-Jardali ◽  
J. Makhoul ◽  
D. Jamal ◽  
M. K. Ranson ◽  
N. M Kronfol ◽  
...  
Keyword(s):  
Middle East ◽  
Health System ◽  
North Africa ◽  
Research Priorities ◽  
Health System Research ◽  
System Research

P6371Network characteristics of a hypertension referral system in western kenya

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
A Thakkar ◽  
T Valente ◽  
J Andesia ◽  
B Njuguna ◽  
J Miheso ◽  
...  
Keyword(s):  
Health System ◽  
Network Model ◽  
Healthcare Providers ◽  
Pressure Control ◽  
Centrality Measures ◽  
Western Kenya ◽  
Improve Blood Pressure Control ◽  
Referral Networks ◽  
Network Cluster ◽  
Referral Network

Abstract Introduction The Strengthening Referral Networks for Management of Hypertension Across the Health System (STRENGTHS) trial is creating and testing interventions to improve the effectiveness of referral networks for patients with hypertension in Western Kenya. Purpose Network analysis of facility-based healthcare providers was used to understand the existing network of referrals. The ultimate goal was to identify both structural gaps and opportunities for implementation of the planned intervention. Methods A network survey was administered to providers who deliver care to patients with hypertension asking individuals to nominate a) individuals to whom, and b) facilities to which they refer patients, both up and down the health system. We analyzed survey data using centrality measures of in-degree and out-degree (number of links each provider received and sent, respectively), as well as fitting a core-periphery (CP) model. A higher CP indicates a strong referral network, while a lower CP indicates a relatively weaker network. Results Data were collected from 130 providers across 39 sites within 7 geographically separate network clusters. Each cluster consists of a mix of primary, secondary, and/or tertiary facilities. Compared to a perfect CP referral network model (Correlation Score [CP] = 1.00) and a random referral network model (CP = 0.200), the provider referral networks within each cluster showed a weak tendency for CP structure. There was a large range in CP from 0.334 to 0.639. In contrast, cluster-level facility networks showed a strong tendency for CP structure, with a CP range of 0.857 to 0.949. Core Periphery Correlation Scores [CP] Network Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Provider Referrals 0.433 0.424 0.334 0.639 0.535 0.448 0.407 Facility Referrals 0.949 0.894 0.871 0.949 0.949 0.904 0.857 Each cluster represents a geographically separate referral network. A random referral network would reveal a CP score of 0.200; while a perfect referral network would give a CP of 1.00. Referral Network Models Conclusions The current health system across Western Kenya does not demonstrate a strong network of referrals between providers for patients with hypertension. While facility-to-facility referrals are more in-line with a perfect referral model, there are gaps in communication between the specific providers. These results highlight the need for STRENGTHS to design and test interventions that strengthen provider referral patterns in order to improve blood pressure control and reduce cardiovascular risk. Acknowledgement/Funding National Institutes of Health: National Heart Lung and Blood Institute, Doris Duke Charitable Foundation:International Clinical Research Fellowship

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