Integration of mHealth Information and Communication Technologies Into the Clinical Settings of Hospitals in Sub-Saharan Africa: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: There is a rapid uptake of mobile-enabled technologies in lower- and upper-middle-income countries because of its portability, ability to reduce mobility, and facilitation of communication. However, there is limited empirical evidence on the usefulness of mobile health (mHealth) information and communication technologies (ICTs) to address constraints associated with the work activities of health care professionals at points of care in hospital settings. OBJECTIVE: This study aims to explore opportunities for integrating mHealth ICTs into the work activities of health care professionals at points of care in clinical settings of hospitals in Sub-Saharan Africa. Thus, the research question is, "How can mHealth ICTs be integrated into the work activities of health care professionals at points of care in hospital settings?" METHODS: A qualitative approach was adopted to understand the work activities and points at which mHealth ICTs could be integrated to support health care professionals. The techniques of inquiry were semistructured interviews and co-design activities. These techniques were used to ensure the participation of frontline end users and determine how mHealth ICTs could be integrated into the point of care in hospital settings. Purposive and snowball sampling techniques were used to select tertiary hospitals and participants for this study from South Africa and Nigeria. A total of 19 participants, including physicians, nurses, and hospital managers, were engaged in the study. Ethical clearance was granted by the University research committee and the respective hospitals. The data collected were sorted and interpreted using thematic analysis and Activity Analysis and Development model. RESULTS: The findings show that mHealth ICTs are suitable at points where health care professionals consult with patients in the hospital clinics, remote communication is needed, and management of referrals and report writing are required. It was inferred that mHealth ICTs could be negatively disruptive, and some participants perceived the use of mobile devices while engaging with patients as unprofessional. These findings were informed by the outcomes of the interplay between human attributes and technology capabilities during the transformation of the motives of work activity into the intended goal, which is enhanced service delivery. CONCLUSIONS: The opportunities to integrate mHealth ICTs into clinical settings depend on the inefficiencies of interaction moments experienced by health care professionals at points of care during patient consultation, remote communication, referrals, and report writing. Thus, the timeliness of mHealth ICTs to address constraints experienced by health care professionals during work activities should take into consideration the type of work activity and the contextual factors that may result in contradictions in relation to technology features. This study contributes toward the design of mHealth ICTs by industry vendors and its usability evaluation for the work activity outcomes of health care professionals.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,009 | 0,005 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».