Reducing Antibiotic Administration Time in the NICU: A Quality Improvement Study to Improve Neonatal Outcomes

International Journal of Medical Science and Innovative Research (IJMSIR) IJMSIR : A Medical Publication Hub Available Online at: www.ijmsir.com Volume – 10, Issue – 4, July – 2025, Page No. : 52 – 66 Corresponding Author: Dr. Neha Khadke, IJMSIR, Volume – 10 Issue - 4, Page No. 52 – 66 Page 52 ISSNO: 2458 - 868X, ISSN–P: 2458 – 8687 National Library of Medicine - ID: 101731606 Reducing Antibiotic Administration Time in the NICU: A Quality Improvement Study to Improve Neonatal Outcomes 1Dr. Neha Khadke, Junior Resident, Department of Paediatrics, Dr. Vikhe Patil Medical College and Hospital, Ahilyanagar, India 2Dr. Abhijit Shinde, Associate Professor, Department of Paediatrics, Dr. Vikhe Patil Medical College and Hospital, Ahilyanagar, India 3Dr. Sunil Natha Mhaske, Professor, Department of Paediatrics, Dr. Vikhe Patil Medical College and Hospital, Ahilyanagar, India Corresponding Author: Dr. Neha Khadke, Junior Resident, Department of Paediatrics, Dr. Vikhe Patil Medical College and Hospital, Ahilyanagar, India Citation this Article: Dr. Neha Khadke, Dr. Abhijit Shinde, Dr. Sunil Natha Mhaske, “Reducing Antibiotic Administration Time in the NICU: A Quality Improvement Study to Improve Neonatal Outcomes”, IJMSIR - July – 2025, Vol – 10, Issue - 4, P. No. 52 – 66. Type of Publication: Original Research Article Conflicts of Interest: Nil Abstract Background: Delays in antibiotic administration in neonatal intensive care units (NICUs) are associated with increased morbidity and mortality. This study aimed to evaluate the impact of targeted quality improvement (QI) interventions on reducing antibiotic administration delays and improving neonatal outcomes. Methods: A mixed-methods QI study was conducted in a tertiary care NICU over six months. Root cause analysis (RCA) identified key barriers to timely antibiotic administration, including staff shortages, pharmacy bottlenecks, and IV access delays. Four Plan-Do-StudyAct (PDSA) cycles were implemented, involving staff training, pharmacy workflow optimisation, and standardised sepsis protocols. A structured questionnaire assessed staff-reported barriers preand postintervention. Quantitative outcomes were evaluated using paired t-tests, chi-square tests, and binary logistic regression to assess the independent impact of key factors on delays. Results: The mean time to antibiotic administration decreased significantly from 1.5 hours pre-intervention to 0.25 hours post-intervention (p < 0.0001). Neonatal sepsis cases declined from 60 to 30 (p < 0.001), while meningitis rates decreased from 27 to 19 (p = 0.025). The mean NICU stay duration reduced significantly (p < 0.0001). Logistic regression identified staff shortages (AOR = 2.1; 95% CI: 1.5–2.8) and pharmacy delays (AOR = 1.8; 95% CI: 1.3–2.4) as the strongest predictors of antibiotic delays. Despite improvements in workflow, no statistically significant reductions in mortality (p = 0.23) or pneumonia rates (p = 0.32) were observed. Conclusions: Structured QI interventions effectively reduced antibiotic administration delays and improved select clinical outcomes in NICU patients. Further strategies targeting pneumonia prevention, enhanced Dr. Neha Khadke, et al. International Journal of Medical Sciences and Innovative Research (IJMSIR) © 2025 IJMSIR, All Rights Reserved Page53 Page53 Page53 Page53 Page53 Page53 Page53 Page53 Page53 Page53 Page53 Page53 Page53 Page53 Page53 Page53 Page53 Page53 maternal infection screening, and improved respiratory support protocols may improve mortality and pneumonia outcomes. Sustained improvements will require ongoing staff education, pharmacy workflow monitoring, and antibiotic stewardship expansion. Keywords: Neonatal sepsis, Quality improvement, Antibiotic delivery time, NICU outcomes, Antibiotic stewardship, PDSA cycle, Root Cause Analysis, Logistic regression, Thematic Analysis Introduction Neonatal sepsis remains a significant cause of morbidity and mortality, accounting for nearly 3 million cases annually worldwide1. The timely administration of antibiotics, ideally within the first 60 minutes of suspected sepsis, is critical in preventing infection-related complications and reducing mortality2. However, systemic inefficiencies within NICUs, including workflow delays, inadequate communication, and pharmacy-related bottlenecks, frequently lead to antibiotic administration delays exceeding 60–120 minutes 3. These delays have been associated with increased risk of multi-organ dysfunction syndrome (MODS), prolonged hospitalisation, and higher sepsisrelated mortality 4. Despite established sepsis management protocols, real-world adherence remains inconsistent, particularly in resource-limited settings 5. Studies have shown that delays in antibiotic administration persist even in well-equipped NICUs, often due to a lack of streamlined workflows, inefficient antibiotic ordering systems, and staff-related barriers 6. Addressing these systemic challenges requires a structured, real-time Quality Improvement (QI) approach that targets NICU operational inefficiencies and ensures timely antibiotic administration. Methodology Study Design and Setting This mixed-methods quality improvement (QI) study employed a convergent parallel design over 24 weeks in a tertiary care NICU in Ahilyanagar, India. The 14-bed NICU operates 24/7 and is staffed by multidisciplinary teams. The study integrated both qualitative and quantitative data to identify barriers, implement interventions, and assess their impact on reducing antibiotic administration time and improving neonatal outcomes. Study Population A total of 300 neonates were included — 150 in the preintervention phase and 150 in the post-intervention phase. Study subjects were selected using defined inclusion and exclusion criteria to minimise confounding and ensure comparability across both cohorts. Inclusion and Exclusion Criteria Inclusion Criteria  Term neonates (37–42 weeks gestation) with suspected or confirmed sepsis  Out born neonates admitted within 24 hours of birth with signs of sepsis  Neonates developing new-onset sepsis, MODS, pneumonia, or meningitis during NICU stay Exclusion Criteria  Preterm neonates (<37 weeks gestation)  Neonates previously treated with antibiotics or hospitalised >24 hours before NICU admission  Neonates with congenital anomalies or noninfectious conditions  Neonates with severe birth asphyxia (Apgar ≤3 at 5 minutes) Ethical Considerations Ethical approval was obtained from the Institutional Ethics Committee (IEC Approval No: 2023/80). Written Dr. Neha Khadke, et al. International Journal of Medical Sciences and Innovative Research (IJMSIR) © 2025 IJMSIR, All Rights Reserved Page54 Page54 Page54 Page54 Page54 Page54 Page54 Page54 Page54 Page54 Page54 Page54 Page54 Page54 Page54 Page54 Page54 Page54 informed consent was obtained from NICU staff participating in FGDs, KIIs, and questionnaires. Parental consent was waived as neonatal data were collected retrospectively with confidentiality maintained. Phase 1: Pre-Intervention (Weeks 1–6) This phase involved baseline data collection and problem identification. Qualitative Assessment Root Cause Analysis (RCA) was conducted to identify barriers to timely antibiotic delivery. FGDs (n=8) and KIIs (n=5) were conducted with NICU nurses, paediatricians, residents, and pharmacists. A structured questionnaire was distributed to 20 staff to identify perceived workflow challenges. Quantitative Assessment Baseline clinical data from 150 neonates were collected, including time to antibiotic administration, sepsis-related morbidities, NICU length of stay, and mortality. Data were recorded using clinical logs and hospital records. Phase 2: Intervention (Weeks 7–18) Targeted interventions were implemented through four PDSA cycles. PDSA Cycles and Interventions Table 1: Summary of Plan-Do-Study-Act (PDSA) Cycles Implemented to Reduce Antibiotic Administration Time in the NICU PDSA Cycle Intervention Implementation Period Assessment Method PDSA 1 Standardised sepsis protocols for timely antibiotic initiation Weeks 7–9 Root Cause Analysis, real-time observations PDSA 2 Dedicated NICU pharmacy counter to streamline antibiotic delivery Weeks 10–12 Time-tracking logs, NICU workflow audits PDSA 3 Nurse-led antibiotic preparation and staffing optimisation Weeks 13–15 Staff surveys, thematic analysis of Focus Group Discussions PDSA 4 Structured communication between NICU, laboratory, and pharmacy Weeks 16–18 Key Informant Interviews, workflow efficiency analysis Legend/ Footnote: PDSA: Plan-Do-Study-Act; NICU: Neonatal Intensive Care Unit. Each cycle was designed based on staff feedback and Root Cause Analysis (RCA) findings to address delays in antibiotic administration. Antibiotic Stewardship Measures Standardised sepsis protocols included predefined antibiotic order sets, culture-based reassessments at 48– 72 hours, and training on stewardship principles. Antibiotic stocks were secured in advance and stored for rapid access. Phase 3: Post-Intervention (Weeks 19–24) This phase focused on evaluating the effect of interventions. Qualitative Evaluation Post-intervention FGDs and KIIs assessed staff satisfaction, intervention impact, and sustainability. Structured surveys were repeated to capture feedback. Quantitative Evaluation Post-intervention data from 150 neonates were analysed to assess changes in:  Time to antibiotic administration  Sepsis, MODS, pneumonia, meningitis rates Dr. Neha Khadke, et al. International Journal of Medical Sciences and Innovative Research (IJMSIR) © 2025 IJMSIR, All Rights Reserved Page55 Page55 Page55 Page55 Page55 Page55 Page55 Page55 Page55 Page55 Page55 Page55 Page55 Page55 Page55 Page55 Page55 Page55  NICU length of stay  Mortality Data Management Complete-case analysis was used. Neonates with incomplete clinical data were excluded. Non-clinical data with minor gaps (e.g., surveys) were used for thematic insight but excluded from outcome analysis. Statistical Analysis Descriptive statistics were used to summarise demographic variables. Paired t-tests were applied to compare continuous variables before and after the intervention, while chi-square tests were used for categorical outcomes. Stratified analysis was performed based on birth weight and gestational age to account for subgroup variability. Binary logistic regression was employed to identify independent predictors of delayed antibiotic administration, with adjusted odds ratios (AORs) and 95% confidence intervals reported. The Mantel-Haenszel test was used to evaluate effect modification across stratified subgroups. Model fit for the logistic regression was assessed using the HosmerLemeshow goodness-of-fit test, with a p-value greater than 0.05 considered acceptable. Results This study evaluated the impact of targeted quality improvement (QI) interventions on reducing antibiotic administration delays and improving neonatal outcomes in a NICU setting. A mixed-methods approach was employed, integrating qualitative data from staff experiences and quantitative statistical analysis of clinical outcomes. Qualitative Results The qualitative analysis explored staff-reported barriers to timely antibiotic administration and assessed the effectiveness of implemented interventions. Data were derived from focus group discussions (FGDs), key informant interviews (KIIs), and structured questionnaire responses. Thematic analysis of responses before and after the intervention provided insights into workflow inefficiencies, staffing constraints, and communication barriers. Root Cause Analysis (RCA): A systematic Root Cause Analysis (RCA) framework was applied during focus group discussions (FGDs) and key informant interviews (KIIs) to identify and categorise key barriers contributing to delays in antibiotic administration. The identified barriers were grouped into three main categories: Human Factors, Process-Related Barriers, and Communication & Workflow Barriers. Human factors included staffing shortages, unclear protocols, and delays in clinical decision-making, which collectively contributed to inefficiencies in antibiotic delivery. Process-related barriers were linked to pharmacy bottlenecks, inadequate antibiotic stock, and delays in establishing intravenous (IV) access, further hindering timely treatment. Lastly, communication and workflow barriers stemmed from inefficiencies in coordination between NICU staff, laboratory services, and the pharmacy, resulting in avoidable delays. Addressing these root causes was pivotal in designing targeted interventions to improve antibiotic administration processes. Thematic Analysis: Thematic analysis of preand postintervention FGDs and KIIs revealed significant improvements following the QI interventions. Table 2 shows Comparison of preand post-intervention thematic findings from FGDs and KIIs, highlighting improvements in workflow efficiency, interdepartmental coordination, and staffing adjustments. Dr. Neha Khadke, et al. International Journal of Medical Sciences and Innovative Research (IJMSIR) © 2025 IJMSIR, All Rights Reserved Page56 Page56 Page56 Page56 Page56 Page56 Page56 Page56 Page56 Page56 Page56 Page56 Page56 Page56 Page56 Page56 Page56 Page56 Table 2: Comparison of Preand Post-Intervention Findings Across Key Themes Identified Through Thematic Analysis Theme Pre-Intervention Findings Post-Intervention Findings Workflow Efficiency Unclear protocols causing frequent delays Standardised order sets improved workflow Communication & Coordination Poor NICU–laboratory–pharmacy communication Structured escalation pathways reduced response time Pharmacy Bottlenecks Delayed order processing; lack of emergency antibiotic stock Dedicated NICU pharmacy counter improved medication availability Nurse Staffing & Duty Allocation Inconsistent staffing led to IV access delays Optimised shift schedules minimised procedural delays Legend/Footnote Themes were derived from qualitative data through Focus Group Discussions (FGDs), Key Informant Interviews (KIIs), and structured surveys. Postintervention improvements reflect outcomes from iterative PDSA cycles targeting each barrier. Questionnaire-Based Staff Survey: A structured Google Forms-based questionnaire was distributed to 20 NICU staff members, including physicians, nurses, and pharmacists, to assess perceived barriers to timely antibiotic administration before implementing the quality improvement (QI) interventions. The analysis revealed three primary areas of concern: clinical decision-making delays (35%), pharmacy-related bottlenecks (30%), and staff shortages (40%), which significantly contributed to antibiotic administration delays. Parental decisionmaking was reported as a minor factor (5%), indicating that most delays originated from healthcare system inefficiencies rather than caregiver reluctance. After the intervention, follow-up responses indicated improved workflow efficiency, enhanced interdepartmental communication, and reduced pharmacy processing times. Figure 1 illustrates the reduction in key factors contributing to delayed antibiotic administration. Staff shortages and pharmacy issues showed the greatest improvement, aligning with intervention strategies such as enhanced staffing models and streamlined pharmacy workflows. Figure 1: Comparison of Reasons for Antibiotic Administration Delays Preand Post-Intervention Quantitative Results The quantitative analysis assessed preand postintervention clinical outcomes using statistical methods to determine the effectiveness of the implemented interventions. Reduction in Delays in Antibiotic Administration A significant reduction in delays across multiple workflow components was observed. Pre-intervention, the mean time to antibiotic administration was 1.5 hours, which was reduced to 0.25 hours post-intervention (p < 0.05). Statistical comparisons were performed using paired t-tests, which assess whether there was a significant mean difference between two related groups (preand post-intervention) as shown in table 3. Dr. Neha Khadke, et al. International Journal of Medical Sciences and Innovative Research (IJMSIR) © 2025 IJMSIR, All Rights Reserved Page57 Page57 Page57 Page57 Page57 Page57 Page57 Page57 Page57 Page57 Page57 Page57 Page57 Page57 Page57 Page57 Page57 Page57 Table 3: Statistical Analysis of Delay Reduction in Antibiotic Administration Cause of Delay Mean Time Before Intervention (hours) Mean Time After Intervention (hours) Difference (hours) Statistical Test p-value Interpretation Staff Shortages 1.5 0.25 1.25 Paired t-test < 0.05 Significant Parental DecisionMaking 0.75 0.75 0.00 Paired t-test > 0.05 Not Significant Pharmacy Processing Issues 0.75 0.25 0.50 Paired t-test < 0.05 Significant Intravenous Access Delays 0.75 0.25 0.50 Paired t-test < 0.05 Significant Antibiotic DecisionMaking 1.5 0.75 0.75 Paired t-test < 0.05 Significant Inadequate Stocking 1.5 0.75 0.75 Paired t-test < 0.05 Significant Footnote/Legend: Time delays were measured from the point of clinical decision to actual antibiotic administration. Paired t-tests were used to compare preand post-intervention means. A p-value < 0.05 was considered statistically significant. Figure 2: Time to Antibiotic Administration – Prevs. Post-Intervention Figure 2 illustrates the reduction in time to antibiotic administration over five weeks following the intervention. While the pre-intervention data (red) shows fluctuating values with times ranging between 1.3 to 1.6 hours, the post-intervention data (blue) demonstrates a consistent decline from 0.8 hours in Week 1 to 0.3 hours in Week 5. This improvement reflects the effectiveness of streamlined processes and enhanced staff coordination. Impact of QI Interventions on Neonatal Outcomes Post-intervention clinical outcomes demonstrated significant improvements in neonatal sepsis rates, hospital stay duration, and incidence of meningitis, suggesting that timely antibiotic administration had a direct impact on neonatal morbidity. A chi-square test was used to compare categorical variables (e.g., number of cases of neonatal sepsis, MODS, and pneumonia preand post-intervention). The t-test was used for continuous variables, such as length of NICU stay. The study demonstrated a significant reduction in neonatal sepsis cases (p < 0.001) following the implementation of structured quality improvement (QI) interventions, emphasising the impact of timely antibiotic administration on infection control. Similarly, meningitis incidence declined significantly (p = 0.025), indicating that early antibiotic initiation played a crucial role in preventing severe infections. The mean length of NICU stay was significantly reduced (p < 0.0001), suggesting that optimised antibiotic delivery contributed to faster recovery and reduced hospitalisation duration. Mortality Dr. Neha Khadke, et al. International Journal of Medical Sciences and Innovative Research (IJMSIR) © 2025 IJMSIR, All Rights Reserved Page58 Page58 Page58 Page58 Page58 Page58 Page58 Page58 Page58 Page58 Page58 Page58 Page58 Page58 Page58 Page58 Page58 Page58 rates decreased from 20 to 15 cases, but this was not statistically significant (p = 0.23), indicating that factors beyond antibiotic timing—such as gestational age and respiratory distress—may influence survival outcomes. The incidence of pneumonia reduced from 60 to 50 cases (p = 0.32), suggesting that while early antibiotic administration aids sepsis prevention, additional infection control measures (e.g., improved respiratory support) may be required to impact pneumonia rates. MODS incidence significantly decreased from 15 to 9 cases (p = 0.045), suggesting that timely antibiotic administration may contribute to reducing multi-organ dysfunction, all of which can be seen in table 4. Table 4: Comparison of Clinical Outcomes before and After Quality Improvement Interventions Outcome Before Intervention (n = 150) After Intervention (n = 150) Statistical Test p-value Interpretation Time to Antibiotic Administration (Mean ± SD) 1.5 ± 0.5 hours 0.25 ± 0.1 hours Paired t-test < 0.0001 Highly Significant Neonatal Sepsis Cases (New onset) 60 30 Chi-Square Test < 0.001 Statistically Significant Meningitis Cases (New onset) 27 19 Chi-Square Test 0.032 Statistically Significant Pneumonia Cases (New onset) 60 50 Chi-Square Test 0.32 Not Significant Multiple Organ Dysfunction Syndrome (MODS) 15 9 Chi-Square Test 0.045 Significant Length of NICU Stay (Mean ± SD) 19.5 ± 4.1 days 10.4 ± 3.5 days T-Test < 0.0001 Highly Significant Mortality Rate (Direct NICU Admissions Only) 20 15 Chi-Square Test 0.23 Not Significant Legend/Footnote: Post-intervention outcomes showed significant improvements in time to antibiotic administration, neonatal sepsis, meningitis, MODS, and NICU stay. Mortality and pneumonia rates showed nonsignificant reductions. All p-values < 0.05 were considered statistically significant. Figure 3: Control Chart Showing Time to Antibiotic Administration Across PDSA Cycles Dr. Neha Khadke, et al. International Journal of Medical Sciences and Innovative Research (IJMSIR) © 2025 IJMSIR, All Rights Reserved Page59 Page59 Page59 Page59 Page59 Page59 Page59 Page59 Page59 Page59 Page59 Page59 Page59 Page59 Page59 Page59 Page59 Page59 To assess the sustained impact of QI interventions across multiple PDSA cycles, a control chart was plotted as shown in figure 3. The control chart demonstrates a significant reduction in antibiotic administration time following QI interventions. Initially, delays exceeded 1.5 hours, nearing the Upper Control Limit (UCL), indicating inefficiencies. After implementing PDSA cycles, administration times steadily declined, reaching a stable mean of 0.25 hours. By Week 8, values remained within control limits, confirming a sustained improvement. No major deviations suggest that the interventions effectively streamlined NICU workflows, ensuring antibiotics were administered within 30 minutes consistently. This validates the success and long-term stability of the QI model in optimising neonatal sepsis management. Figure 4: Distribution of Neonatal Clinical Outcomes before and after intervention phase. As shown in Figure 4, In the pre-intervention phase sepsis and pneumonia were the most prevalent outcomes, each accounting for 40% of cases. Meningitis occurred in 20%, while MODS and mortality were reported in 10% and 13.3% of cases. Whereas in post-intervention phase, sepsis rates decreased to 20%, while meningitis rates reduced to 12.7%. Pneumonia remained prominent at 33.3%, though lower than pre-intervention rates. Additionally, MODS and mortality rates improved, reducing to 6% and 10%, respectively. Figure 5: Distribution of Length of Stay – Pre vs. Post Intervention As illustrated in Figure 5, the proportion of neonates with shorter hospital stays increased following the intervention. Specifically, the percentage of patients with stays of 5–10 days increased from 10% to 25%, and those with stays of 11–15 days increased from 25% to 40%. Conversely, the proportion of patients with longer stays decreased, with those staying 16–20 days reducing from 40% to 20%, and those staying 21+ days decreasing from 25% to 15%. This shift reflects improved clinical efficiency and faster recovery following the quality improvement interventions. A binary logistic regression model was used to assess the independent effect of the QI interventions on neonatal sepsis occurrence, while adjusting for potential confounders. Table 5 presents the results of the logistic regression analysis identifying key factors contributing to delayed antibiotic administration. Staff shortages emerged as the most prominent contributor, with an adjusted odd ratio (AOR) of 2.1 (95% CI: 1.5–2.8, p < 0.001), indicating that neonates treated during periods of staff shortages were over twice as likely to experience antibiotic delays. Pharmacy delays (AOR = 1.8, 95% CI: 1.3–2.4, p = 0.002) and IV access delays (AOR = 1.5, 95% CI: 1.1–2.0, p = 0.015) also had statistically Dr. Neha Khadke, et al. International Journal of Medical Sciences and Innovative Research (IJMSIR) © 2025 IJMSIR, All Rights Reserved Page60 Page60 Page60 Page60 Page60 Page60 Page60 Page60 Page60 Page60 Page60 Page60 Page60 Page60 Page60 Page60 Page60 Page60 significant impacts, reinforcing their role in workflow inefficiencies. Decision-making delays showed an AOR of 1.3 (95% CI: 0.9–1.8, p = 0.12), suggesting a weaker and non-significant association with antibiotic delays. To assess the consistency of intervention outcomes across subgroups, a Mantel-Haenszel chi-square test was conducted. The test showed no significant interaction effect for gestational age groups (p = 0.38) or birth weight categories (p = 0.41), confirming that the intervention’s impact was consistent across these subgroups. The logistic regression model’s overall fit was confirmed using the Hosmer-Lemeshow test, which yielded a p-value = 0.72, indicating that the model demonstrated a good fit for predicting sepsis reduction trends and ensuring the results were not biased by baseline patient characteristics. These findings confirm that staff shortages, pharmacy delays, and IV access issues were the primary contributors to delayed antibiotic administration, while decision-making delays had a limited impact. The observed improvements in antibiotic delivery and neonatal outcomes were consistent across gestational age and birth weight subgroups, further supporting the effectiveness of the implemented QI interventions. Table 5: Binary Logistic Regression Analysis of Factors Associated with Delayed Antibiotic Administration in the NICU Factor Adjusted Odds Ratio (AOR) Lower 95% CI Upper 95% CI Interpretation Staff Shortages 2.1 1.5 2.8 Staff shortages significantly increased the odds of delayed antibiotic administration, indicating a strong association. Pharmacy Delays 1.8 1.3 2.4 Pharmacy workflow inefficiencies contributed notably to delays, with a moderate but significant impact. IV Access Delays 1.5 1.1 2.0 Delays in obtaining IV access moderately increased the risk of delayed antibiotics. DecisionMaking Delay 1.3 0.9 1.8 Decision-making delays showed a weaker association with antibiotic delays, with a confidence interval crossing 1, indicating marginal significance. Legend/Footnote: Adjusted odds ratios (AORs) were derived from a multivariate binary logistic regression model, with delayed antibiotic administration as the dependent variable. AOR > 1 indicates increased odds of delay. Confidence intervals (CI) that do not cross 1 indicate statistical significance. Model fit was verified using the Hosmer-Lemeshow test (p > 0.05). Figure 6: Adjusted Odds Ratio of Factors Contributing to Delayed Antibiotic Administration As shown in Figure 6, staff shortages and pharmacy delays had the highest adjusted odds ratios (AOR) for