Can an mhealth clinical decision-making support system improve adherence to neonatal healthcare protocols in a low-resource setting?

Amoakoh, Hannah Brown; Klipstein‐Grobusch, Kerstin; Agyepong, Irène Akua; Amoakoh-Coleman, Mary; Kayode, Gbenga A; Reitsma, Johannes B.; Grobbee, Diederick E.; Ansah, Evelyn

doi:10.1186/s12887-020-02378-1

Cited by 10 publications

(10 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Health care workers need to be adequately and continuously trained and empowered on the use of these technologies ( 24 , 39 , 46 , 49 , 53 , 56 , 63 , 71 , 72 ). There is a need to consider the integration of interventions into existing workflows alongside regular training, case management guidelines, emergency supplies, equipment, and drugs, this should also include taking into account the possibility of other ongoing interventions in the system ( 47 , 73 ). Measuring and establishing the acceptability of the mobile health interventions by health care workers is very critical in the sustainability and scalability of any mHealth pilot project ( 74 ).…”

Section: Resultsmentioning

confidence: 99%

Digital Solutions for Community and Primary Health Workers: Lessons From Implementations in Africa

Owoyemi¹,

Osuchukwu

Azubuike

et al. 2022

Front. Digit. Health

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Digital Solutions for Community and Primary Health Workers: Lessons From Implementations in Africa

Owoyemi¹,

Osuchukwu

Azubuike

et al. 2022

Front. Digit. Health

View full text Add to dashboard Cite

show abstract

“…Our study also supports mHealth as an educational tool and as an easy to access form of medical guidelines. Evidence however has been inconclusive in this area with improvements noted with or without mHealth interventions in compliance to neonatal protocols in Ghana [ 18 ]. Similarly in rural India improvements were noted in reducing barriers to healthcare rapport but did not lead to a significant improvement in knowledge [ 19 ].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of an ARF diagnosis calculator: a survey and content analysis

Fisher

James²,

Mosca³

et al. 2022

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

Background Acute Rheumatic Fever (ARF) is a critically important condition for which there is no diagnostic test. Diagnosis requires the use of a set of criteria comprising clinical, laboratory, electrocardiographic and echocardiographic findings. The complexity of the algorithm and the fact that clinicians lack familiarity with ARF, make ARF diagnosis ideally suited to an electronic decision support tool. The ARF Diagnosis Calculator was developed to assist clinicians in diagnosing ARF and correctly assign categories of ‘possible, ‘probable’ or ‘definite’ ARF. This research aimed to evaluate the acceptability, accuracy, and test performance of the ARF Diagnosis Calculator. Methods Three strategies were used to provide triangulation of data. Users of the calculator employed at Top End Health Service, Northern Territory, Australia were invited to participate in an online survey, and clinicians with ARF expertise were invited to participate in semi-structured interviews. Qualitative data were analysed using inductive analysis. Performance of the calculator in correctly diagnosing ARF was assessed using clinical data from 35 patients presenting with suspected ARF. Diagnoses obtained from the calculator were compared using the Kappa statistic with those obtained from a panel of expert clinicians. Results Survey responses were available from 23 Top End Health Service medical practitioners, and interview data were available from five expert clinicians. Using a 6-point Likert scale, participants highly recommended the ARF Diagnosis Calculator (median 6, IQR 1), found it easy to use (median 5, IQR 1) and believed the calculator helped them diagnose ARF (median 5, IQR 1). Clinicians with ARF expertise noted that electronic decision making is not a substitute for clinical experience. There was high agreement between the ARF Diagnosis Calculator and the ‘gold standard’ ARF diagnostic process (κ = 0.767, 95% CI: 0.568–0.967). Incorrect assignment of diagnosis occurred in 4/35 (11%) patients highlighting the greater accuracy of expert clinical input for ambiguous presentations. Sixteen changes were incorporated into a revised version of the calculator. Conclusions The ARF Diagnosis Calculator is an easy-to-use, accessible tool, but it does not replace clinical expertise. The calculator performed well amongst clinicians and is an acceptable tool for use within the clinical setting with a high level of accuracy in comparison to the gold standard diagnostic process. Effective resources to support clinicians are critically important for improving the quality of care of ARF.

show abstract

“…[21][22][23] The efficient management of admissions is vital for ensuring prompt access to necessary medical interventions and resources. [24][25][26] In Ghana, however, there are several factors affecting this process. [27][28][29][30] Limited resources and infrastructure pose significant challenges in delivering optimal care to neonates.…”

Section: Standards Of Care In Neonatal Care Systemsmentioning

confidence: 99%

“…By tailoring protocols according to local realities, healthcare providers can bridge the gap between international best practices and local implementation, ensuring optimal care for neonates in Ghana and West Africa. From the comprehensive analysis of relevant selected studies in Ghana and West Africa [160][161][162][163] the specific challenges faced by healthcare providers in delivering high-quality care to neonates were revealed. Limited resources and infrastructure for neonatal care have been identified as a major obstacle, with inadequate facilities, equipment, and trained personnel hindering the provision of optimal care.…”

Section: Need For Localized Evidence-based Guidelines For Neonatal Carementioning

confidence: 99%

Challenges Faced by Healthcare Providers in Delivering High-Quality Care to Neonates in Ghana and West Africa

et al. 2023

IJFMR

View full text Add to dashboard Cite

The challenges faced by healthcare providers in delivering high-quality care to neonates in Ghana and West Africa are numerous. Limited resources, including equipment, medications, and skilled personnel, hinder the ability to provide optimal care. Additionally, cultural beliefs and practices often pose obstacles to effective healthcare delivery. Furthermore, inadequate infrastructure and transportation systems make it difficult to reach remote areas where many neonates reside. Despite these challenges, healthcare providers strive to overcome these barriers through education, training programs, and partnerships with international organizations. By addressing these challenges head-on, the goal of providing high-quality care to neonates in Ghana and West Africa can be achieved.

show abstract

Can an mhealth clinical decision-making support system improve adherence to neonatal healthcare protocols in a low-resource setting?

Cited by 10 publications

References 35 publications

Digital Solutions for Community and Primary Health Workers: Lessons From Implementations in Africa

Digital Solutions for Community and Primary Health Workers: Lessons From Implementations in Africa

Evaluation of an ARF diagnosis calculator: a survey and content analysis

Challenges Faced by Healthcare Providers in Delivering High-Quality Care to Neonates in Ghana and West Africa

Contact Info

Product

Resources

About