scholarly journals Health Care Facility Mapping with GIS in Majuli, India

2019 ◽  
Vol 9 (2) ◽  
pp. 5250-5253
Keyword(s):  
Health Care ◽  
Community Health ◽  
Health Center ◽  
Community Health Centers ◽  
Care Facility ◽  
Health Care Facility ◽  
Secondary Data ◽  
Health Centers ◽  
Primary Health ◽  
Development Block

This paper concerns the availability of healthcare facilities including primary health centers (PHC), sub-centers and community health centers in the Majuli region, Jorhat district of Assam, India. Majuli Consist of two development blocks i.e. Ujoni Majuli and Majuli blocks. The paper is based on secondary data and analyses are done in GIS environment. It is identified that primary health care centers are not equally distributed in Majuli development block but instead of PHC there are lots of sub-centers and community health centers are available in the study area. Again availability of sub-centers is found satisfactory in both of these blocks. The number of community health centers is very low in the whole region of Majuli. The result also shows served areas of primary health center in Ujoni Majuli block (77.13%) is much higher than the Majuli Development block (43.70%), again for sub-center and community health center, it is found satisfactory than the PHC service area in both of the blocks.

scholarly journals Beban biaya penyakit demam berdarah dengue di rumah sakit dan puskesmas

2017 ◽  
Vol 33 (7) ◽  
pp. 357
Author(s):  
Evitrisna Warni Sihite ◽  
Yodi Mahendradata ◽  
Tri Baskoro
Keyword(s):  
Health Insurance ◽  
Dengue Fever ◽  
Care Facility ◽  
Health Care Facility ◽  
Secondary Data ◽  
Indirect Costs ◽  
Health Centers ◽  
Primary Data ◽  
Budget Support ◽  
Primary Health

PurposeThis study aimed to determine the cost caused by dengue fever disease based on the perspectives of the patients/family in hospitals and primary health centers in Banjarnegara district in 2016.MethodsThis study used a descriptive design with prospective survey. Secondary data were obtained from hospitals and primary health centers, and primary data was collected by interviews. The data were processed by MS Excel software and analyzed by using STATA software version 12.ResultsSubjects in this study was 57 respondents. The direct cost was 207.290.500 IDR (mean: 3.636.676 IDR). Indirect cost was 68.0169.900 IDR (mean: 1.193.300 IDR). Cost of dengue fever disease was 275.307.500 IDR (mean: 4.829.955 IDR). Cost of dengue fever disease was higher in males, age group more than 15 years, worker group, with disease duration more than 7 days, no insurance ownership, no insurance use, health care facility use, private practice and hospital, more than 2 visits, and travel time more than 15 minutes. The analysis showed correlations with age (p= 0.0209), employment status (p= 0.0389), the ownership of health insurance (p= 0.0022), and the use of health insurance (p= 0.0003).ConclusionDengue fever disease cost was 275.307.500 IDR, where 75.29% was direct costs and 24.71% indirect costs. There was no increase in the active participation of each sector nor a clear division of roles for each sector so that control of dengue can be properly directed with more budget support for the prevention and control of dengue.

scholarly journals Can public sector community health workers deliver a nurturing care intervention in South Africa? The Amagugu Asakhula feasibility study

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Sonja Klingberg ◽  
Esther M. F. van Sluijs ◽  
Stephanie T. Jong ◽  
Catherine E. Draper
Keyword(s):  
South Africa ◽  
Health Care ◽  
Public Sector ◽  
Health Workers ◽  
Care Facility ◽  
Health Care Facility ◽  
Preschool Age ◽  
Primary Health Care Facility ◽  
Community Based ◽  
Primary Health

Abstract Background Nurturing care interventions have the potential to promote health and development in early childhood. Amagugu Asakhula was designed to promote developmentally important dietary and movement behaviours among children of preschool age (3–5 years) in South Africa. An initial formative study in Cape Town found the intervention to be feasible and acceptable when delivered by community health workers (CHWs) linked to a community-based organisation. This study evaluated the delivery of the Amagugu Asakhula intervention by CHWs linked to a public sector primary health care facility in Soweto, as this mode of delivery could have more potential for sustainability and scalability. Methods A qualitative design was utilised to assess feasibility, acceptability, adoption, appropriateness, implementation, fidelity and context. CHWs (n = 14) delivered the intervention to caregivers (n = 23) of preschool-age children in Soweto over 6 weeks. Following the completion of the intervention, focus group discussions were held with CHWs and caregivers. Further data were obtained through observations, study records and key informant interviews (n = 5). Data were analysed using deductive thematic analysis guided by a process evaluation framework. Results The delivery of the Amagugu Asakhula intervention through CHWs linked to a primary health care facility in Soweto was not found to be feasible due to contextual challenges such as late payment of salaries influencing CHW performance and willingness to deliver the intervention. CHWs expressed dissatisfaction with their general working conditions and were thus reluctant to take on new tasks. Despite barriers to successful delivery, the intervention was well received by both CHWs and caregivers and was considered a good fit with the CHWs’ scope of work. Conclusions Based on these findings, delivery of the Amagugu Asakhula intervention is not recommended through public sector CHWs in South Africa. This feasibility study informs the optimisation of implementation and supports further testing of the intervention’s effectiveness when delivered by CHWs linked to community-based organisations. The present study further demonstrates how implementation challenges can be identified through qualitative feasibility studies and subsequently addressed prior to large-scale trials, avoiding the wasting of research and resources.

After the Affordable Care Act: Health Reform and the Safety Net

2016 ◽  
Vol 44 (4) ◽  
pp. 585-588
Author(s):  
Peter Shin ◽  
Marsha Regenstein
Keyword(s):  
Health Care ◽  
Community Health ◽  
Affordable Care Act ◽  
Health Reform ◽  
Community Health Centers ◽  
Safety Net ◽  
Public Hospitals ◽  
Health Centers ◽  
Safety Net Providers ◽  
The Affordable Care Act

Two major safety net providers – community health centers and public hospitals – continue to play a key role in the health care system even in the wake of coverage reform. This article examines the gains and threats they face under the Affordable Care Act.

scholarly journals Deployment of Analytics into the Healthcare Safety Net: Lessons Learned

2016 ◽  
Vol 8 (3) ◽  
Author(s):  
David Hartzband ◽  
Feygele Jacobs
Keyword(s):  
Data Quality ◽  
Community Health ◽  
Health Center ◽  
Community Health Centers ◽  
Health Resources ◽  
Population Level ◽  
Safety Net ◽  
Lessons Learned ◽  
Health Centers ◽  
Effective Manner

To better understand existing capacity and help organizations plan for the strategic and expanded uses of data, a project was initiated that deployed contemporary, Hadoop-based, analytic technology into several multi-site community health centers (CHCs) and a primary care association (PCA). An initial data quality exercise was carried out after deployment, in which a number of analytic queries were executed using both the existing electronic health record (EHR) applications and in parallel, the analytic stack. Each organization carried out the EHR analysis using the definitions typically applied for routine reporting. The analysis using the analytic stack was carried out using those common definitions established for the Uniform Data System (UDS) by the Health Resources and Service Administration.  In addition, interviews with health center leadership and staff were completed to understand the context for the findings.The analysis uncovered many challenges and inconsistencies with respect to the definition of core terms (patient, encounter, etc.), data formatting, and missing, incorrect and unavailable data. At a population level, apparent underreporting of a number of diagnoses, specifically obesity and heart disease, was also evident in the results of the data quality exercise, for both the EHR-derived and stack analytic results.Data awareness, that is, an appreciation of the importance of data integrity, data hygiene and the potential uses of data, needs to be prioritized and developed by health centers and other healthcare organizations if analytics are to be used in an effective manner to support strategic objectives. While this analysis was conducted exclusively with community health center organizations, its conclusions and recommendations may be more broadly applicable. 

Organizational Factors Influencing the Use of Clinical Decision Support for Improving Cancer Screening Within Community Health Centers

2016 ◽  
pp. 118-148 ◽  
Author(s):  
Timothy Jay Carney ◽  
Michael Weaver ◽  
Anna M. McDaniel ◽  
Josette Jones ◽  
David A. Haggstrom
Keyword(s):  
Colorectal Cancer ◽  
Health Care ◽  
Decision Support ◽  
Cancer Screening ◽  
Community Health ◽  
Clinical Decision Support ◽  
Community Health Centers ◽  
Clinical Decision ◽  
Health Centers ◽  
Practice Setting

Adoption of clinical decision support (CDS) systems leads to improved clinical performance through improved clinician decision making, adherence to evidence-based guidelines, medical error reduction, and more efficient information transfer and to reduction in health care disparities in under-resourced settings. However, little information on CDS use in the community health care (CHC) setting exists. This study examines if organizational, provider, or patient level factors can successfully predict the level of CDS use in the CHC setting with regard to breast, cervical, and colorectal cancer screening. This study relied upon 37 summary measures obtained from the 2005 Cancer Health Disparities Collaborative (HDCC) national survey of 44 randomly selected community health centers. A multi-level framework was designed that employed an all-subsets linear regression to discover relationships between organizational/practice setting, provider, and patient characteristics and the outcome variable, a composite measure of community health center CDS intensity-of-use. Several organizational and provider level factors from our conceptual model were identified to be positively associated with CDS level of use in community health centers. The level of CDS use (e.g., computerized reminders, provider prompts at point-of-care) in support of breast, cervical, and colorectal cancer screening rate improvement in vulnerable populations is determined by both organizational/practice setting and provider factors. Such insights can better facilitate the increased uptake of CDS in CHCs that allows for improved patient tracking, disease management, and early detection in cancer prevention and control within vulnerable populations.

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