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Cockburn N, Osborne C, Withana S, Elsmore A, Nanjappa R, South M, Parry-Smith W, Taylor B, Chandan JS, Nirantharakumar K. Clinical decision support systems for maternity care: a systematic review and meta-analysis. EClinicalMedicine 2024; 76:102822. [PMID: 39296586 PMCID: PMC11408819 DOI: 10.1016/j.eclinm.2024.102822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 08/17/2024] [Accepted: 08/23/2024] [Indexed: 09/21/2024] Open
Abstract
Background The use of Clinical Decision Support Systems (CDSS) is increasing throughout healthcare and may be able to improve safety and outcomes in maternity care, but maternity care has key differences to other disciplines that complicate the use of CDSS. We aimed to identify evaluated CDSS and synthesise evidence of their impact on maternity care. Methods We conducted a systematic review for articles published before 24th May 2024 that described i) CDSS that ii) investigated the impact of their use iii) in maternity settings. Medline, CINAHL, CENTRAL and HMIC were searched for articles relating to evaluations of CDSS in maternity settings, with forward- and backward-citation tracing conducted for included articles. Risk of bias was assessed using the Mixed Methods Assessment Tool, and CDSS were described according to the clinical problem, purpose, design, and technical environment. Quantitative results from articles reporting appropriate data were meta-analysed to estimate odds of a CDSS achieving its desired outcome using a multi-level random effects model, first by individual CDSS and then across all CDSS. PROSPERO ID: CRD42022348157. Findings We screened 12,039 papers and included 87 articles describing 47 unique CDSS. 24 articles (28%) described randomised controlled trials, 30 (34%) described non-randomised interventional studies, 10 (11%) described mixed methods studies, 10 (11%) described qualitative studies, 7 (8%) described quantitative descriptive studies, and 7 (8%) described economic evaluations. 49 (56%) were in High-Income Countries and 38 (44%) in Low- and Middle-Income countries, with no CDSS trialled in both income categories. Meta-analysis of 35 included studies found an odds ratio for improved outcomes of 1.69 (95% confidence interval 1.24-2.30). There was substantial variation in effects, aims, CDSS types, context, study designs, and outcomes. Interpretation Most CDSS evaluations showed improvements in outcomes, but there was heterogeneity in all aspects of design and evaluation of systems. CDSS are increasingly important in delivering healthcare, and Electronic Health Records and mHealth will increase their availability, but traditional epidemiological methods may be limited in guiding design and demonstrating effectiveness due to rapid CDSS development lifecycles and the complex systems in which they are embedded. Development methods that are attentive to context, such as Human Centred Design, will help to meet this need. Funding None.
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Affiliation(s)
- Neil Cockburn
- Department of Applied Health Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Cristina Osborne
- Department of Applied Health Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Supun Withana
- Department of Applied Health Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Amy Elsmore
- Department of Obstetrics and Gynaecology, Shrewsbury and Telford Hospitals NHS Trust, Telford, United Kingdom
| | - Ramya Nanjappa
- Department of Obstetrics and Gynaecology, Shrewsbury and Telford Hospitals NHS Trust, Telford, United Kingdom
| | - Matthew South
- Department of Applied Health Sciences, University of Birmingham, Birmingham, United Kingdom
| | - William Parry-Smith
- Department of Obstetrics and Gynaecology, Shrewsbury and Telford Hospitals NHS Trust, Telford, United Kingdom
- Keele University, Keele, United Kingdom
| | - Beck Taylor
- Warwick Medical School, Warwick University, Coventry, United Kingdom
| | - Joht Singh Chandan
- Department of Applied Health Sciences, University of Birmingham, Birmingham, United Kingdom
- Birmingham Health Partners, University of Birmingham, Birmingham, United Kingdom
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Meher S. Clinical algorithms in labour and childbirth care: Prospects and challenges. BJOG 2024; 131 Suppl 2:3-5. [PMID: 36468347 DOI: 10.1111/1471-0528.17152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Dinh N, Agarwal S, Avery L, Ponnappan P, Chelangat J, Amendola P, Labrique A, Bartlett L. Implementation Outcomes Assessment of a Digital Clinical Support Tool for Intrapartum Care in Rural Kenya: Observational Analysis. JMIR Form Res 2022; 6:e34741. [PMID: 35723911 PMCID: PMC9253974 DOI: 10.2196/34741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 03/29/2022] [Accepted: 03/31/2022] [Indexed: 11/26/2022] Open
Abstract
Background iDeliver, a digital clinical support system for maternal and neonatal care, was developed to support quality of care improvements in Kenya. Objective Taking an implementation research approach, we evaluated the adoption and fidelity of iDeliver over time and assessed the feasibility of its use to provide routine Ministry of Health (MOH) reports. Methods We analyzed routinely collected data from iDeliver, which was implemented at the Transmara West Sub-County Hospital from December 2018 to September 2020. To evaluate its adoption, we assessed the proportion of actual facility deliveries that was recorded in iDeliver over time. We evaluated the fidelity of iDeliver use by studying the completeness of data entry by care providers during each stage of the labor and delivery workflow and whether the use reflected iDeliver’s envisioned function. We also examined the data completeness of the maternal and neonatal indicators prioritized by the Kenya MOH. Results A total of 1164 deliveries were registered in iDeliver, capturing 45.31% (1164/2569) of the facility’s deliveries over 22 months. This uptake of registration improved significantly over time by 6.7% (SE 2.1) on average in each quarter-year (P=.005), from 9.6% (15/157) in the fourth quarter of 2018 to 64% (235/367) in the third quarter of 2020. Across iDeliver’s workflow, the overall completion rate of all variables improved significantly by 2.9% (SE 0.4) on average in each quarter-year (P<.001), from 22.25% (257/1155) in the fourth quarter of 2018 to 49.21% (8905/18,095) in the third quarter of 2020. Data completion was highest for the discharge-labor summary stage (16,796/23,280, 72.15%) and lowest for the labor signs stage (848/5820, 14.57%). The completion rate of the key MOH indicators also improved significantly by 4.6% (SE 0.5) on average in each quarter-year (P<.001), from 27.1% (69/255) in the fourth quarter of 2018 to 83.75% (3346/3995) in the third quarter of 2020. Conclusions iDeliver’s adoption and data completeness improved significantly over time. The assessment of iDeliver’ use fidelity suggested that some features were more easily used because providers had time to enter data; however, there was low use during active childbirth, which is when providers are necessarily engaged with the woman and newborn. These insights on the adoption and fidelity of iDeliver use prompted the team to adapt the application to reflect the users’ culture of use and further improve the implementation of iDeliver.
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Affiliation(s)
- Nhi Dinh
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | - Smisha Agarwal
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | - Lisa Avery
- Centre for Global Public Health, University of Manitoba, Winnipeg, MB, Canada
| | | | | | | | - Alain Labrique
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | - Linda Bartlett
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
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Owoyemi A, Osuchukwu JI, Azubuike C, Ikpe RK, Nwachukwu BC, Akinde CB, Biokoro GW, Ajose AB, Nwokoma EI, Mfon NE, Benson TO, Ehimare A, Irowa-Omoregie D, Olaniran S. Digital Solutions for Community and Primary Health Workers: Lessons From Implementations in Africa. Front Digit Health 2022; 4:876957. [PMID: 35754461 PMCID: PMC9215204 DOI: 10.3389/fdgth.2022.876957] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/11/2022] [Indexed: 11/13/2022] Open
Abstract
The agenda for Universal Health Coverage has driven the exploration of various innovative approaches to expanding health services to the general population. As more African countries have adopted digital health tools as part of the strategic approach to expanding health services, there is a need for defining a standard framework for implementation across board. Therefore, there is a need to review and employ an evidence-based approach to inform managing challenges, adopting best approaches, and implement informed recommendations. We reviewed a variety of digital health tools applied to different health conditions in primary care settings and highlighted the challenges faced, approaches that worked and relevant recommendations. These include limited coverage and network connectivity, lack of technological competence, lack of power supply, limited mobile phone usage and application design challenges. Despite these challenges, this review suggests that mHealth solutions could attain effective usage when healthcare workers receive adequate onsite training, deploying applications designed in an intuitive and easy to understand approach in a manner that fits into the users existing workflows, and involvement of the stakeholders at all levels in the design, planning, and implementation stages of the interventions.
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Affiliation(s)
- Ayomide Owoyemi
- Department of Biomedical and Health Information Sciences, Chicago, IL, United States
- *Correspondence: Ayomide Owoyemi
| | | | - Clark Azubuike
- Social and Behavioral Sciences Department, T.H. Chan School of Public Health, Harvard University, Boston, MA, United States
| | | | - Blessing C. Nwachukwu
- Department of Biomedical and Health Information Sciences, Chicago, IL, United States
| | | | - Grace W. Biokoro
- Department of Human and Health Sciences, Northern Illinois University, DeKalb, IL, United States
| | - Abisoye B. Ajose
- Department of Community Health and Primary Care, College of Medicine, University of Lagos, Lagos, Nigeria
| | | | - Nehemiah E. Mfon
- Department of Obstetrics and Gynecology, National Hospital, Abuja, Nigeria
| | - Temitope O. Benson
- Institute for Computational and Data Sciences, University at Buffalo, State University of New York, Albany, NY, United States
| | - Anthony Ehimare
- Department of Health Informatics, Swansea University, Wales, United Kingdom
| | | | - Seun Olaniran
- Department of Integrated Information Technology, College of Engineering and Computing, University of South Carolina, Columbia, SC, United States
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Adedeji T, Fraser H, Scott P. Implementing Electronic Health Records in Primary Care Using the Theory of Change: A Nigerian Case Study (Preprint). JMIR Med Inform 2021; 10:e33491. [PMID: 35969461 PMCID: PMC9412900 DOI: 10.2196/33491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 03/25/2022] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
Background Objective Methods Results Conclusions
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Affiliation(s)
- Taiwo Adedeji
- School of Computing, University of Portsmouth, Portsmouth, United Kingdom
| | - Hamish Fraser
- Brown Center for Biomedical Informatics, Brown University, Providence, RI, United States
| | - Philip Scott
- Institute of Management and Health, University of Wales Trinity Saint David, Carmarthen, United Kingdom
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