ReviewJournal of diabetes science and technology2011
Review of electronic decision-support tools for diabetes care: a viable option for low- and middle-income countries?
Review in Journal of diabetes science and technology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed, 5 syntheses or guidelines pooled it, 69 citations in OpenAlex.
- Association Between Prospective Registration and Quality of Systematic Reviews in Type 2 Diabetes Mellitus: A Meta-epidemiological Study.Frontiers in medicine · 2021Pooled it
- The effect of computerized decision support systems on cardiovascular risk factors: a systematic review and meta-analysis.BMC medical informatics and decision making · 2019Pooled it
- Mobile phone-based mHealth approaches for public health surveillance in sub-Saharan Africa: a systematic review.International journal of environmental research and public health · 2014Pooled it
- Use of mHealth systems and tools for non-communicable diseases in low- and middle-income countries: a systematic review.Journal of cardiovascular translational research · 2014Pooled it
- A meta-analysis of complications following deceased donor liver transplant.American journal of surgery · 2014Pooled it
- Ear biometrics for patient identification in global health: a field study to test the effectiveness of an image stabilization device in improving identification accuracy.BMC medical informatics and decision making · 2019Trial
- A multifaceted strategy using mobile technology to assist rural primary healthcare doctors and frontline health workers in cardiovascular disease risk management: protocol for the SMARTHealth India cluster randomised controlled trial.Implementation science : IS · 2013Trial
- Trial
- Improving diabetes care: multi-component cardiovascular disease risk reduction strategies for people with diabetes in South Asia--the CARRS multi-center translation trial.Diabetes research and clinical practice · 2012Trial
- Evaluation of care and clinical outcomes after the implementation of an electronic medical record system for type 1 diabetes management in Rwanda.Global health action · 2025Article
- The Development of an Electronic Medical Record System to Improve Quality of Care for Individuals With Type 1 Diabetes in Rwanda: Qualitative Study.JMIR diabetes · 2024Article
- Stakeholder Perspectives regarding the Acceptability and Sustainability of a Multi-component Diabetes Care Strategy in South Asia: a longitudinal qualitative analysis.Global implementation research and applications · 2022Article
- Clinical Decision Support for Glycemic Management Reduces Hospital Length of Stay.Diabetes care · 2022Article
- Digital health and diabetes: experience from India.Therapeutic advances in endocrinology and metabolism · 2021Review
- Mitigating The Burden Of Diabetes In Sub-Saharan Africa Through An Integrated Diagonal Health Systems Approach.Diabetes, metabolic syndrome and obesity : targets and therapy · 2019Review
- How does it work? Factors involved in telemedicine home-interventions effectiveness: A review of reviews.PloS one · 2018Review
- e-PC101: an electronic clinical decision support tool developed in South Africa for primary care in low-income and middle-income countries.BMJ global health · 2018Article
- Open-Source Electronic Health Record Systems for Low-Resource Settings: Systematic Review.JMIR medical informatics · 2017Review
- The Effect of Group Discussion-based Education on Self-management of Adults with Type 2 Diabetes Mellitus Compared with Usual Care: A Randomized Control Trial.Oman medical journal · 2017Article
- Ear biometrics for patient identification in global health: a cross-sectional study to test the feasibility of a simplified algorithm.BMC research notes · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
Abstract
contextDiabetes care is complex, requiring motivated patients, providers, and systems that enable guideline-based preventative care processes, intensive risk-factor control, and positive lifestyle choices. However, care delivery in low- and middle-income countries (LMIC) is hindered by a compendium of systemic and personal factors. While electronic medical records (EMR) and computerized clinical decision-support systems (CDSS) have held great promise as interventions that will overcome system-level challenges to improving evidence-based health care delivery, evaluation of these quality improvement interventions for diabetes care in LMICs is lacking. OBJECTIVE AND DATA SOURCES: We reviewed the published medical literature (systematic search of MEDLINE database supplemented by manual searches) to assess the quantifiable and qualitative impacts of combined EMR-CDSS tools on physician performance and patient outcomes and their applicability in LMICs. STUDY SELECTION AND DATA EXTRACTION: Inclusion criteria prespecified the population (type 1 or 2 diabetes patients), intervention (clinical EMR-CDSS tools with enhanced functionalities), and outcomes (any process, self-care, or patient-level data) of interest. Case, review, or methods reports and studies focused on nondiabetes, nonclinical, or in-patient uses of EMR-CDSS were excluded. Quantitative and qualitative data were extracted from studies by separate single reviewers, respectively, and relevant data were synthesized.
resultsThirty-three studies met inclusion criteria, originating exclusively from high-income country settings. Among predominantly experimental study designs, process improvements were consistently observed along with small, variable improvements in risk-factor control, compared with baseline and/or control groups (where applicable). Intervention benefits varied by baseline patient characteristics, features of the EMR-CDSS interventions, motivation and access to technology among patients and providers, and whether EMR-CDSS tools were combined with other quality improvement strategies (e.g., workflow changes, case managers, algorithms, incentives). Patients shared experiences of feeling empowered and benefiting from increased provider attention and feedback but also frustration with technical difficulties of EMR-CDSS tools. Providers reported more efficient and standardized processes plus continuity of care but also role tensions and "mechanization" of care.
conclusionsThis narrative review supports EMR-CDSS tools as innovative conduits for structuring and standardizing care processes but also highlights setting and selection limitations of the evidence reviewed. In the context of limited resources, individual economic hardships, and lack of structured systems or trained human capital, this review reinforces the need for well-designed investigations evaluating the role and feasibility of technological interventions (customized to each LMIC's locality) in clinical decision making for diabetes care.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.