The Role Of Health Informatics In Collaborative Interventions Between Pharmacists, Nurses, And Midwives To Reduce Medication Errors In Obstetrics: A Systematic Review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".