Literature DB >> 22921485

The effect of electronic medical record-based clinical decision support on HIV care in resource-constrained settings: a systematic review.

Tom Oluoch1, Xenophon Santas, Daniel Kwaro, Martin Were, Paul Biondich, Christopher Bailey, Ameen Abu-Hanna, Nicolette de Keizer.   

Abstract

BACKGROUND: It is estimated that one million people infected with HIV initiate anti-retroviral therapy (ART) in resource-constrained countries annually. This occurs against a background of overburdened health workers with limited skills to handle rapidly changing treatment standards and guidelines hence compromising quality of care. Electronic medical record (EMR)-based clinical decision support systems (CDSS) are considered a solution to improve quality of care. Little evidence, however, exists on the effectiveness of EMR-based CDSS on quality of HIV care and treatment in resource-constrained settings.
OBJECTIVE: The aim of this systematic review was to identify original studies on EMR-based CDSS describing process and outcome measures as well as reported barriers to their implementation in resource-constrained settings. We characterized the studies by guideline adherence, data and process, and barriers to CDSS implementation.
METHODS: Two reviewers independently assessed original articles from a search of the MEDLINE, EMBASE, CINAHL and Global Health Library databases until January 2012. The included articles were those that evaluated or described the implementation of EMR-based CDSS that were used in HIV care in low-income countries.
RESULTS: A total of 12 studies met the inclusion criteria, 10 of which were conducted in sub-Saharan Africa and 2 in the Caribbean. None of the papers described a strong (randomized controlled) evaluation design. Guideline adherence: One study showed that ordering rates for CD4 tests were significantly higher when reminders were used. Data and process: Studies reported reduction in data errors, reduction in missed appointments, reduction in missed CD4 results and reduction in patient waiting time. Two studies showed a significant increase in time spent by clinicians on direct patient care. Barriers to CDSS implementation: Technical infrastructure problems such as unreliable electric power and erratic Internet connectivity, clinicians' limited computer skills and failure by providers to comply with the reminders are key impediments to the implementation and effective use of CDSS.
CONCLUSION: The limited number of evaluation studies, the basic and heterogeneous study designs, and varied outcome measures make it difficult to meaningfully conclude on the effectiveness of CDSS on quality of HIV care and treatment in resource-limited settings. High quality evaluation studies are needed. Factors specific to implementation of EMR-based CDSS in resource-limited setting should be addressed before such countries can demonstrate its full benefits. More work needs to be done to overcome the barriers to EMR and CDSS implementation in developing countries such as technical infrastructure and care providers' computer illiteracy. However, simultaneously evaluating and describing CDSS implementation strategies that work can further guide wise investments in their wider rollout. Published by Elsevier Ireland Ltd.

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Year:  2012        PMID: 22921485     DOI: 10.1016/j.ijmedinf.2012.07.010

Source DB:  PubMed          Journal:  Int J Med Inform        ISSN: 1386-5056            Impact factor:   4.046


  34 in total

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Authors:  Nancy Puttkammer; Jane M Simoni; Tracy Sandifer; Jean Marcxime Chéry; Witson Dervis; Jean Gabriel Balan; Jean Geto Dubé; Guirlaine Calixte; Ermane Robin; Kesner François; Cameron Casey; Ira Wilson; Jean Guy Honoré
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2.  User Requirements for an Electronic Medical Records System for Oncology in Developing Countries: A Case Study of Uganda.

Authors:  Johnblack K Kabukye; Sabine Koch; Ronald Cornet; Jackson Orem; Maria Hagglund
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3.  Validation of the Behavior of a Knowledge Base Implementing Clinical Guidelines for Point-of-Care Antiretroviral Toxicity Monitoring.

Authors:  William Ogallo; Carol Friedman; Andrew S Kanter
Journal:  AMIA Annu Symp Proc       Date:  2018-12-05

4.  Ear biometrics for patient identification in global health: a cross-sectional study to test the feasibility of a simplified algorithm.

Authors:  Elizabeth J Ragan; Courtney Johnson; Jacqueline N Milton; Christopher J Gill
Journal:  BMC Res Notes       Date:  2016-11-02

5.  Pediatric Provider Utilization of a Clinical Decision Support Alert and Association with HIV Pre-exposure Prophylaxis Prescription Rates.

Authors:  Carrie T Chan; Megen Vo; Jennifer Carlson; Tzielan Lee; Marcello Chang; Geoffrey Hart-Cooper
Journal:  Appl Clin Inform       Date:  2022-01-12       Impact factor: 2.342

6.  Designing Colorectal Cancer Screening Decision Support: A Cognitive Engineering Enterprise.

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7.  Effect of a clinical decision support system on early action on immunological treatment failure in patients with HIV in Kenya: a cluster randomised controlled trial.

Authors:  Tom Oluoch; Abraham Katana; Daniel Kwaro; Xenophon Santas; Patrick Langat; Samuel Mwalili; Kimeu Muthusi; Nicky Okeyo; James K Ojwang; Ronald Cornet; Ameen Abu-Hanna; Nicolette de Keizer
Journal:  Lancet HIV       Date:  2015-12-17       Impact factor: 12.767

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9.  Interventions to improve district-level routine health data in low-income and middle-income countries: a systematic review.

Authors:  Jieun Lee; Caroline A Lynch; Lauren Oliveira Hashiguchi; Robert W Snow; Naomi D Herz; Jayne Webster; Justin Parkhurst; Ngozi A Erondu
Journal:  BMJ Glob Health       Date:  2021-06

10.  Evaluation Framework for Successful Artificial Intelligence-Enabled Clinical Decision Support Systems: Mixed Methods Study.

Authors:  Mengting Ji; Georgi Z Genchev; Hengye Huang; Ting Xu; Hui Lu; Guangjun Yu
Journal:  J Med Internet Res       Date:  2021-06-02       Impact factor: 5.428

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