Strengthening data systems within learning health systems in Kenya
Notice bibliographique
Résumé
Introduction Many countries are working towards the Sustainable Development Goal 3.2 target of reducing neonatal mortality rate to under 12 per 1,000 live births. However, in Sub-Saharan Africa, the neonatal mortality rate remains high at 27 deaths per 1,000 live births in 2019. With more births occurring in hospitals, it is important to strengthen inpatient newborn care by improving newborn monitoring charts as a step towards improving the quality and quantity of documentation and subsequently quality of care. Documentation can be done using paper-based or electronic data systems which generate data that can be used for daily care provision as well as quality improvement. Research question: How can routine data systems be strengthened in an LMIC to support a learning health system agenda? This study was conducted within the Clinical Information Network for Newborns (CIN-N) in Kenya. The CIN-N is a network of 22 county referral hospitals in Kenya. Its aim is to improve the quality and use of information for decision making and therefore improve the patient outcomes. The network data system comprises a clerk who extracts discharge data from hospital records onto a customised database. Overall, a multi-methods approach was adopted through qualitative studies, evidence synthesis, and quantitative studies. Findings showed that the electronic medical record systems that were in place at the public hospitals, could not support the data technical block of a learning health system as envisioned for inpatient newborn care because there were no functioning inpatient modules. Therefore, a hybrid data solution that combines paper-based and electronic data system was found to be contextually appropriate. Next, the study sought to understand the global evidence on designing monitoring charts through a scoping review which showed that studies followed a general non-systematic process of designing paper-based monitoring charts. The next step of the project involved designing, piloting a newly designed monitoring chart using a Human centered design approach. The implementation process varied across hospitals, for example, some hospitals opted to train all staff together during continuous medical education sessions while others trained staff during hand-over sessions at the end of a shift. The chart was well received at the newborn wards within the network of hospitals with users citing benefits such as reduced writing, consolidated information, and improved communication. However, challenges emerged relating to the work environment and staffing, inadequate supply of charts and inadequate equipment to support monitoring tasks. These challenges also provide opportunities to improve processes within the hospital to overcome them and improve documentation. Lastly, we evaluated the documentation of key vital signs – temperature, pulse, respiratory rate, and oxygen saturation, by assessing the number of times each was documented and the number of times the set was documented in the first 48hours. This quantitative evaluation showed that all vital signs recorded an improvement with oxygen saturation recording the highest improvement. Further, sicker babies were likely to receive more frequent documentation of vital signs as is the recommended practice. However, there was still room for improvement as nearly half of the newborns did not have a single full set of documented post-admission TPRS by the end of the 48 hours study period, and there was variability in hospital performance. Conclusions While this PhD showed that the design process and technical design of the chart was important, it also illustrated that an enabling environment is crucial to ensure successful implementation and chart uptake. Hospitals, are considered as complex systems with people, processes equipment and institutions working together. Therefore, a systems perspective is required to facilitate implementation and explore emerging issues to strengthen documentation of newborn care and subsequently improve care.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,025 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,006 | 0,007 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».