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Enregistrement W7128424862 · doi:10.70082/z7r33q29

The Role Of Health Informatics In Collaborative Interventions Between Pharmacists, Nurses, And Midwives To Reduce Medication Errors In Obstetrics: A Systematic Review

2024· article· W7128424862 sur OpenAlexaboutno aff
Mshal Abdullah A. Almaqbal, Nawal Mohammad M. Motambak, Thuraya Saleem Suliman Alhwity, Amal Saleh Abdullah Almughalig, Batlaa Samir Abdullah Alali, Abdallah Faisal Suliman Alsharari, Smerah Wahlan Ghazi Alruwaili, Shamsah Hulayyil Khulaif Alanazi, Mamdouh Hussein Salim Alatawi, Abdullah Dhafer Ayed Alshahrani, Waheedah Ali Abdu Daghriri, Tahani Mohammed Mandeel Al-Anzi

Notice bibliographique

RevueThe Review of Diabetic Studies · 2024
Typearticle
Langue
DomaineHealth Professions
ThématiqueElectronic Health Records Systems
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPsychological interventionSociotechnical systemHealth informaticsHealth careInformaticsClinical decision support systemHealth Administration InformaticsPatient safety

Résumé

récupéré en direct d'OpenAlex

Background: Medication errors in obstetrics constitute a pervasive and critical threat to patient safety, contributing substantially to preventable maternal and neonatal morbidity and mortality globally. The unique physiological adaptations of pregnancy—including altered pharmacokinetics, hemodynamics, and renal function—combined with the high-acuity, unpredictable nature of the labor and delivery environment, create a clinical landscape exceptionally vulnerable to adverse drug events (ADEs). The prevailing standard of care (Intervention 2), characterized by manual prescribing, paper-based medication administration records (MARs), and reliance on verbal coordination among the interdisciplinary team, has historically been the backbone of obstetric practice. However, this conventional approach is fraught with systemic limitations, including illegibility of handwriting, transcription errors, lack of integrated decision support, and communication failures between the triad of care providers: pharmacists, nurses, and midwives. Health informatics (Intervention 1)—specifically the integration of Computerized Provider Order Entry (CPOE), Barcode Medication Administration (BCMA), and Clinical Decision Support Systems (CDSS)—has emerged as a transformative alternative. These technologies promise to close the loop on medication management, potentially mitigating the human factors associated with errors. Objective: The primary objective of this systematic review is to exhaustively compare the effectiveness of health informatics interventions (Intervention 1) versus standard manual care (Intervention 2) in reducing the incidence of medication errors (prescribing, dispensing, and administration) and adverse drug events for pregnant women and neonates (Population). A secondary but equally critical objective is to evaluate the impact of these technological interventions on the quality and efficacy of interprofessional collaboration between pharmacists, nurses, and midwives, hypothesizing that technology alters the sociotechnical dynamics of the ward. Methods: This review was conducted in strict adherence to the PRISMA 2020 guidelines. A comprehensive and systematic search strategy was employed across major electronic databases, including MEDLINE, EMBASE, CINAHL, and The Cochrane Library, targeting literature published between 2010 and 2024. The review incorporated a diverse range of study designs, including Randomized Controlled Trials (RCTs), quasi-experimental pre-post studies, prospective cohort studies, and qualitative ethnographic assessments to capture both quantitative safety metrics and qualitative workflow impacts. The PICO framework was utilized to define the Population (obstetric patients), Intervention (CPOE, BCMA, CDSS), Comparison (paper-based/manual care), and Outcomes (primary: error rates; secondary: collaboration quality). Quality assessment of included studies was rigorously performed using the Cochrane Risk of Bias tool (RoB 2.0) for RCTs and the Newcastle-Ottawa Scale for observational studies. Results: Thirty-two (32) studies meeting the inclusion criteria were identified and analyzed, representing data from over 600,000 medication orders and qualitative insights from hundreds of clinicians. The synthesis of evidence reveals that CPOE systems are associated with a reduction in prescribing errors ranging from 48% to 70% compared to manual methods, largely driven by the standardization of orders for high-alert medications such as oxytocin and magnesium sulfate. BCMA implementation demonstrated a significant capacity to intercept administration errors, specifically "wrong patient" and "wrong dose" errors, although efficacy was modulated by compliance rates, which frequently dropped during obstetric emergencies due to "workarounds". CDSS showed marked success in improving adherence to complex clinical protocols for preeclampsia and gestational diabetes. However, qualitative results indicated a "paradox of automation," where increased digital reliance inadvertently created communication silos, reducing face-to-face interaction between midwives and pharmacists. Conclusion: Health informatics interventions demonstrate superior efficacy in reducing technical medication errors compared to standard manual care in obstetric settings. The transition to digital systems creates a robust safety net that addresses the cognitive limitations of human providers in high-stress environments. However, the technology profoundly impacts the collaborative ecosystem, necessitating a sociotechnical approach to implementation that preserves the vital communicative roles of the pharmacist, nurse, and midwife. Implications for clinical practice include the need for "human-in-the-loop" protocols during emergencies and ergonomic hardware design to minimize workarounds. Future research must address the long-term impact of automation on clinical skill retention and the specific needs of resource-limited obstetric settings.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,033
score de la tête « metaresearch » (Gemma)0,028
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict)
Catégories consensuellesMétarecherche
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,093
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0330,028
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0040,000
Bibliométrie0,0000,005
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,094
Tête enseignante GPT0,502
Écart entre enseignants0,409 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeRevue systématique
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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