End-user perspectives of two mHealth decision support tools: Electronic Community Case Management in Northern Malawi

Chirambo, Griphin Baxter; Muula, Adamson S; Thompson, Matthew; Hardy, Victoria; Heavin, Ciara; Connor, Yvonne O’; Mastellos, Nikolaos; Andersson, Bo; Donoghue, John O’

doi:10.1016/j.ijmedinf.2020.104323

Cited by 10 publications

(7 citation statements)

References 20 publications

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“…From an initial 4437 unique articles, 111 (2.5%) were found to be eligible, in which 2 (1.8%) were written in languages other than English [ 33 , 34 ] and the corresponding authors for 3 (2.7%) further studies [ 144 - 146 ], containing potentially relevant data, were not successfully contacted ( Figure 2 [ 147 ]). Specific exclusion criteria were recorded for each excluded article at the full-text review stage ( Multimedia Appendix 2 ), with most exclusions (4115/4326, 95.12%) made at the title and abstract screening stage.…”

Section: Resultsmentioning

confidence: 99%

Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence

Hogg¹,

Al-Zubaidy²,

Talks³

et al. 2023

J Med Internet Res

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Background The rhetoric surrounding clinical artificial intelligence (AI) often exaggerates its effect on real-world care. Limited understanding of the factors that influence its implementation can perpetuate this. Objective In this qualitative systematic review, we aimed to identify key stakeholders, consolidate their perspectives on clinical AI implementation, and characterize the evidence gaps that future qualitative research should target. Methods Ovid-MEDLINE, EBSCO-CINAHL, ACM Digital Library, Science Citation Index-Web of Science, and Scopus were searched for primary qualitative studies on individuals’ perspectives on any application of clinical AI worldwide (January 2014-April 2021). The definition of clinical AI includes both rule-based and machine learning–enabled or non–rule-based decision support tools. The language of the reports was not an exclusion criterion. Two independent reviewers performed title, abstract, and full-text screening with a third arbiter of disagreement. Two reviewers assigned the Joanna Briggs Institute 10-point checklist for qualitative research scores for each study. A single reviewer extracted free-text data relevant to clinical AI implementation, noting the stakeholders contributing to each excerpt. The best-fit framework synthesis used the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework. To validate the data and improve accessibility, coauthors representing each emergent stakeholder group codeveloped summaries of the factors most relevant to their respective groups. Results The initial search yielded 4437 deduplicated articles, with 111 (2.5%) eligible for inclusion (median Joanna Briggs Institute 10-point checklist for qualitative research score, 8/10). Five distinct stakeholder groups emerged from the data: health care professionals (HCPs), patients, carers and other members of the public, developers, health care managers and leaders, and regulators or policy makers, contributing 1204 (70%), 196 (11.4%), 133 (7.7%), 129 (7.5%), and 59 (3.4%) of 1721 eligible excerpts, respectively. All stakeholder groups independently identified a breadth of implementation factors, with each producing data that were mapped between 17 and 24 of the 27 adapted Nonadoption, Abandonment, Scale-up, Spread, and Sustainability subdomains. Most of the factors that stakeholders found influential in the implementation of rule-based clinical AI also applied to non–rule-based clinical AI, with the exception of intellectual property, regulation, and sociocultural attitudes. Conclusions Clinical AI implementation is influenced by many interdependent factors, which are in turn influenced by at least 5 distinct stakeholder groups. This implies that effective research and practice of clinical AI implementation should consider multiple stakeholder perspectives. The current underrepresentation of perspectives from stakeholders other than HCPs in the literature may limit the anticipation and management of the factors that influence successful clinical AI implementation. Future research should not only widen the representation of tools and contexts in qualitative research but also specifically investigate the perspectives of all stakeholder HCPs and emerging aspects of non–rule-based clinical AI implementation. Trial Registration PROSPERO (International Prospective Register of Systematic Reviews) CRD42021256005; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=256005 International Registered Report Identifier (IRRID) RR2-10.2196/33145

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Section: Resultsmentioning

confidence: 99%

Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence

Hogg¹,

Al-Zubaidy²,

Talks³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

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“…The Ligne Verte project has demonstrated that integrating end-user perspectives, especially from diverse ethnic, religious, linguistic, or socioeconomic backgrounds, can potentially add valuable insights to improve the reach and cost-effectiveness of the health program. 9 To ensure that ideas from clients can be implemented, it is necessary to consider the budget for potential program adaptations ahead of deploying evaluations where possible and that recommendations for change come from open discussions between evaluators and implementers. Listening to clients may also provide opportunities for strengthening health system responsiveness beyond the helpline itself.…”

Section: Discussionmentioning

confidence: 99%

Lessons Learned in Improving the Quality of a Free Reproductive Health Hotline in Benin

Zinsou,

Guedegbe,

Dossou

et al. 2023

Glob Health Sci Pract

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“…Speci cally for immunization interventions, a recent study reported high acceptability rating amongst vaccinators and district managers for an iterative mobile-app that was developed to track immunization encounters in Pakistan which shares similar public health demographics with Nigeria [21]. In another study in Malawi, end-users assessed the usability of two digital tools for community case management of under-ve children-highlighting that although both tools are useful in gathering important intervention data, their usability differs by a wide margin and this determined the overall success of the tool in supporting health interventions in the study setting [22]. This implies that the ease-of-use that was recorded with the end-users of the LOMIS Stock App contributed signi cantly to the success of the tool in managing vaccine supply chain management in Nigeria.…”

Section: Discussionmentioning

confidence: 99%

Analyzing the effectiveness of digital technology in Vaccine Supply Management in Kano State, Nigeria

Metiboba,

Katuka,

Adam

et al. 2023

Preprint

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Background Poor vaccine stock management is a major challenge to targeted immunization coverage in Nigeria. Digital health tools are used to promote public health interventions. This study evaluated the impact of LoMIS Stock, a digital health tool, on vaccine stock management in Kano State. We deployed the quasi-experimental design using a mixed method to generate data for impact. We interviewed 109 stakeholders (66 in Kano; the state exposed to LoMIS; 43 in Bauchi; the comparison state). We measured LoMIS Stock usage rate, timeliness of reporting, facility stock-outs, and data-driven decision-making. The Analysis of Variance and T-tests were used to determine effect and compare difference of means. Using time series analysis (expert modeler), we forecasted the trend of stock-outs in Kano State if LoMIS Stock had not been introduced. All qualitative data was analyzed with Atlas Ti software. Results We found that 62% of participants reported using the tool for stock reports three years after its deployment. Twenty-four (24) hour response to stock-out alerts and Cold Chain Equipment breakdown improved by 67% and 49% respectively. Significantly, stock-out alerts reduced by 88% as compared to only 29% in Bauchi State within the same period. It was found that stock-out events would have increased if LoMIS Stock had not been introduced in Kano State.All partners interviewed affirmed the impulse of LoMIS on improved vaccine visibility that led to timely response. Conclusions LoMIS stock has significantly reduced stock-out experiences and improved vaccine visibility and accountability in health facilities. There are more opportunities for LoMIS Stock optimization.

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End-user perspectives of two mHealth decision support tools: Electronic Community Case Management in Northern Malawi

Cited by 10 publications

References 20 publications

Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence

Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence

Lessons Learned in Improving the Quality of a Free Reproductive Health Hotline in Benin

Analyzing the effectiveness of digital technology in Vaccine Supply Management in Kano State, Nigeria

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