The burden of recording and reporting health data in primary health care facilities in five low- and lower-middle income countries
Notice bibliographique
Résumé
BACKGROUND: Recording and reporting health data in facilities is the backbone of routine health information systems which provide data collected by health facility workers during service provision. Data is firstly collected in a register, to record patient health data and care process, and tallied into nationally designed reporting forms. While there is anecdotal evidence of large numbers of registers and reporting forms for primary health care (PHC) facilities, there are few systematic studies to document this potential burden on health workers. This multi-country study aimed to document the numbers of registers and reporting forms use at the PHC level and to estimate the time it requires for health workers to meet data demands. METHODS: In Cambodia, Ghana, Mozambique, Nigeria and Tanzania, a desk review was conducted to document registers and reporting forms mandated at the PHC level. In each country, visits to 16 randomly selected public PHC facilities followed to assess the time spent on paper-based recording and reporting. Information was collected through self-reports of estimated time use by health workers, and observation of 1360 provider-patient interactions. Data was primarily collected in outpatient care (OPD), antenatal care (ANC), immunization (EPI), family planning (FP), HIV and Tuberculosis (TB) services. RESULT: Cross-countries, the average number of registers was 34 (ranging between 16 and 48). Of those, 77% were verified in use and each register line had at least 20 cells to be completed per patient. The mean time spent on recording was about one-third the total consultation time for OPD, FP, ANC and EPI services combined. Cross-countries, the average number of monthly reporting forms was 35 (ranging between 19 and 52) of which 78% were verified in use. The estimated time to complete monthly reporting forms was 9 h (ranging between 4 to 15 h) per month per health worker. CONCLUSIONS: PHC facilities are mandated to use many registers and reporting forms pausing a considerable burden to health workers. Service delivery systems are expected to vary, however an imperative need remains to invest in international standards of facility-based registers and reporting forms, to ensure regular, comparable, quality-driven facility data collection and use.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».