scholarly journals Evaluating the impact of healthcare provider training to improve tuberculosis management: a systematic review of methods and outcome indicators used

2017 ◽  
Vol 56 ◽  
pp. 105-110 ◽  
Author(s):  
Shishi Wu ◽  
Imara Roychowdhury ◽  
Mishal Khan
BMJ Open ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. e045704
Author(s):  
Emily D Carter ◽  
Hannah H Leslie ◽  
Tanya Marchant ◽  
Agbessi Amouzou ◽  
Melinda K Munos

ObjectiveTo assess existing knowledge related to methodological considerations for linking population-based surveys and health facility data to generate effective coverage estimates. Effective coverage estimates the proportion of individuals in need of an intervention who receive it with sufficient quality to achieve health benefit.DesignSystematic review of available literature.Data sourcesMedline, Carolina Population Health Center and Demographic and Health Survey publications and handsearch of related or referenced works of all articles included in full text review. The search included publications from 1 January 2000 to 29 March 2021.Eligibility criteriaPublications explicitly evaluating (1) the suitability of data, (2) the implications of the design of existing data sources and (3) the impact of choice of method for combining datasets to obtain linked coverage estimates.ResultsOf 3805 papers reviewed, 70 publications addressed relevant issues. Limited data suggest household surveys can be used to identify sources of care, but their validity in estimating intervention need was variable. Methods for collecting provider data and constructing quality indices were diverse and presented limitations. There was little empirical data supporting an association between structural, process and outcome quality. Few studies addressed the influence of the design of common data sources on linking analyses, including imprecise household geographical information system data, provider sampling design and estimate stability. The most consistent evidence suggested under certain conditions, combining data based on geographical proximity or administrative catchment (ecological linking) produced similar estimates to linking based on the specific provider utilised (exact match linking).ConclusionsLinking household and healthcare provider data can leverage existing data sources to generate more informative estimates of intervention coverage and care. However, existing evidence on methods for linking data for effective coverage estimation are variable and numerous methodological questions remain. There is need for additional research to develop evidence-based, standardised best practices for these analyses.


2020 ◽  
Author(s):  
Emily D Carter ◽  
Hannah H Leslie ◽  
Tanya Marchant ◽  
Agbessi Amouzou ◽  
Melinda Munos

Effective coverage measures assess the proportion of a population that receive a health intervention with sufficient quality to achieve health benefit. Linking population-based surveys and health facility data is a promising means of generating effective coverage estimates, however, little guidance exists on methodological considerations for these analyses. We conducted a systematic review to assess existing knowledge related to 1) the suitability of data used in linking analyses, 2) the implications of the design of existing data sources commonly used in linking analyses, and 3) the impact of choice of method for combining datasets to obtain linked coverage estimates. The primary search was completed in Medline, with additional reviews of select sources. Of 3192 papers reviewed, 62 publications addressed issues related to linking household and provider datasets. Limited data suggest household surveys can be used to identify sources of care, but their validity in estimating a denominator of intervention need was variable. Methods for collecting provider data and constructing quality indices were variable and presented limitations. There was little empirical data supporting an association between structural, process, and outcome quality. Few studies addressed the influence of the design of common data sources on linking analyses, including imprecise household GIS data, provider sampling frame and sampling design, and estimate stability. There was a lack of concrete evidence around the impact of these factors on linked effective coverage estimates. The most consistent evidence suggested under certain conditions, combining data sets based on geographical proximity (ecological linking) produced similar estimates to linking based on the specific provider utilized (exact-match linking). Linking household and healthcare provider can leverage existing data sources to generate more informative estimates of intervention coverage and care. However, there is need for additional research to develop evidence-based, standardized best practices for these analyses.


2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1867.2-1868
Author(s):  
J. De Fonss Gandrup ◽  
S. Mustafa Ali ◽  
S. Van der Veer ◽  
J. Mcbeth ◽  
W. Dixon

Background:Patients with long-term conditions (LTCs), including many RMDs, often require continuous management of care. Patient-generated health data (PGHD) collected between visits could inform ongoing care management and provide important insights into patient health and well-being. There is increasing interest in integrating PGHD in electronic health records (EHRs). However, integration is still largely aspirational with limited evidence of successful systems.Objectives:To map the landscape of EHR-integrated remote symptom monitoring systems in the field of LTCs. The objectives were to 1) characterise state of the art systems, 2) describe their clinical use, and 3) outline anticipated and realized benefits for clinical practice.Methods:A systematic search was conducted in three electronic databases up until November 2019. Titles and abstracts were independently screened by two reviewers. One reviewer screened full-text articles, identified those relevant for review and extracted data. Inclusion criteria included 1) symptom reporting systems in adult patients suffering a LTC, 2) integration of data into the EHR, 3) symptom data collected remotely, 4) evidence of use in clinical care. We did not exclude studies based on study design, quality, or sample size. Synthesis focused on describing system specifications and their use. For objective three we adopted a list of outcome indicators [1], which each of the studies were assessed against.Results:The initial search yielded 2040 articles. Only 12 studies reporting on ten unique systems were identified. Two systems were used in rheumatology, but the majority were used in oncology. Systems were highly heterogeneous in terms of technical and functional specifications. Nine systems were fully integrated (data viewable in the EHR) while the remaining system represented a partial integration (data viewable via link in the EHR). Five systems allowed repeated data collection at pre-defined intervals between visits with frequencies varying from daily to monthly. The remaining five made a single request before a scheduled clinic visit. The number of items requested from patients ranged from 9-48 per session. We identified three different clinical workflows: Simple (data only used during consultation, n=5), moderate (real-time alerts for providers when severe symptoms were reported, n=4) and on-demand (patient-initiated visits, n=1). Benefits of symptom reporting from each of the studies were categorised as anticipated, realized quantitative, and realized qualitative. We present summarised counts of these benefits in Figure 1. The most common anticipated benefits were better communication, changes to patient management and improved health outcomes. Most common realized benefits were detecting unrecognised problems and changes to patient management.Figure 1.Summarized counts of benefits from each included study assessed against Chen et al.’s 10 outcome indicators. Categorized in anticipated (orange), realized quantitative (light purple), and realized qualitative benefits (dark purple).Conclusion:There is growing interest and urge for integrating symptom data in the EHR and clinical care. Yet, this review has illustrated that there are limited published efforts to learn from. The heterogeneity in approaches underpins the need for a common framework. There is growing evidence from qualitative work in support of remote symptom-reporting in enabling better and patient-centred care in LTCs. The next step will be for robust, quantitative studies to provide evidence of benefits.References:[1]Chen J, Ou L, Hollis SJ. A systematic review of the impact of routine collection of patient reported outcome measures on patients, providers and health organisations in an oncologic setting. BMC Health Serv Res. 2013 Jun 11;13:211.Disclosure of Interests:Julie de Fonss Gandrup: None declared, Syed Mustafa Ali: None declared, Sabine van der Veer: None declared, John McBeth: None declared, William Dixon Consultant of: Bayer and Google


2018 ◽  
Vol 43 (1) ◽  
pp. 65-77 ◽  
Author(s):  
Carina Van Rooyen ◽  
Ruth Stewart ◽  
Thea De Wet

Big international development donors such as the UK’s Department for International Development and USAID have recently started using systematic review as a methodology to assess the effectiveness of various development interventions to help them decide what is the ‘best’ intervention to spend money on. Such an approach to evidence-based decision-making has long been practiced in the health sector in the US, UK, and elsewhere but it is relatively new in the development field. In this article we use the case of a systematic review of the impact of microfinance on the poor in sub-Saharan African to indicate how systematic review as a methodology can be used to assess the impact of specific development interventions.


Sign in / Sign up

Export Citation Format

Share Document