Evaluation of a pharmacist-led audit and feedback intervention to reduce Gentamicin prescribing errors at admission in neonatal inpatient care in Kenya: A controlled interrupted time series study

Evaluation of a pharmacist-led audit and feedback intervention to reduce Gentamicin prescribing errors at admission in neonatal inpatient care in Kenya: A controlled interrupted time series study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluation of a pharmacist-led audit and feedback intervention to reduce Gentamicin prescribing errors at admission in neonatal inpatient care in Kenya: A controlled interrupted time series study Timothy Tuti, Jalemba Aluvaala, Mercy Mulaku, Dorothy Aywak, Muthoni Ogolla, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9219945/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Approximately 14.9% of medications prescribed during hospital care in low- and middle-income countries contain errors, with neonates—who experience high morbidity and mortality in these settings—being particularly vulnerable. Nevertheless, evidence on effective interventions to improve prescribing practices in such contexts remains limited. Objective Our objective was to evaluate a theory-informed, pharmacist-led audit and feedback intervention aimed at improving routine prescribing practices, with an initial focus on reducing gentamicin prescribing errors in neonatal care. Methods We used interrupted time series analysis to model fluctuations in prescribing errors for neonates ≤ 28 days admitted to newborn units (NBU) in 22 hospitals in Kenya between July 2021 to June 2024 and explored intervention effects in a feedback meeting at the end of the study. The study had three phases, pre-intervention period (July 2021 to June 2022), intervention period (July 2022 to June 2023), and post-intervention period (July 2023 to June 2024). The primary study was a standard single-group interrupted time-series study (ITS) design to evaluate the comparative effectiveness of enhanced A&F in reducing prescribing error trends after its introduction in 16 hospitals. Secondary analysis included comparison to prescribing error outcomes in an additional six hospitals in a contemporaneous control group that received basic A&F reports without pharmacist involvement in the NBU prescribing practices. Results Between July 2021 and June 2024, the 16 hospitals in the primary outcome analysis and the 6 additional hospitals for the secondary outcome analysis had 36,668 and 8,943 neonates with Gentamicin prescriptions at admission retrospectively. From the incidence rate ratios (IRR) of incorrect prescribing at admission, there was no step change (IRR 1.115, 95% CI: 0.920 to 1.352, p-value = 0.265) or trend change (IRR 1.014, 95% CI: 0.986 to 1.042, p-value = 0.344) due to the enhanced pharmacist-led A&F intervention in the 16 hospitals in the primary study. From the secondary study, change in the trend post-intervention in the 16 primary study hospitals in the primary study relative to the 6 hospitals acting as a contemporaneous control group was positive (IRR 0.933, 95% CI: 0.878 to 0.985, p-value = 0.014), despite no step change due to the enhanced A&F intervention. Conclusion We found no statistically significant effect of the team-based pharmacist-led A&F intervention on reducing gentamicin medication errors in neonatal care. Prescribing errors during intervention and post-intervention periods were increasing across all hospitals in both arms of the study during and post-intervention periods. However, relative to control hospitals sites receiving routine feedback but without pharmacist involvement or pharmacist-led CMEs, the primary study sites had a positive trend in reducing Gentamicin prescription error rates at admission during and post-introduction of the pharmacist-led A&F intervention. Trial registration PACTR, PACTR202203869312307. Registered 17th March 2022, https//pactr.samrc.ac.za/Search.aspx?TrialID=PACTR202203869312307 Medical Audit Feedback Inappropriate Prescribing Antimicrobial Stewardship Sub-Saharan Africa Medical Records Routine care Newborns Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Why was this study done? In newborn hospital care where the population with severe illness has a high mortality rate, around 14.9% of drug prescriptions have errors in settings such as sub-Saharan Africa (SSA). However, there is scant research in SSA on actionable audit and feedback interventions over time to reduce the rates of inappropriate and potentially harmful prescribing of antibiotics. Therefore, we evaluated whether such an intervention is associated with sustained changes when it provides continuous feedback championed by pharmacists. What did the researchers do and find? We evaluated the impact of a pharmacist-led audit-and feedback intervention for in-hospital newborn care across Kenya. We found that the intervention was not associated with sustained reduction in the level or trend in incorrect antibiotic prescribing across practices, until the study was completed (after 12 months). Despite the overall increase in prescribing errors during the study period and the 12 months after the study period, a marked difference in inaccurate prescribing trend was also seen between hospital groups where the hospital pharmacist agreed to be involved with the audit and feedback intervention. What do these findings mean? The extent to which actionable audit and feedback interventions reflect the complexity of routine hospital care in SSA determine whether long-term improvements in prescribing practices can be delivered on an ongoing basis. More research is needed to understand why and how to obtain sustained reductions in antibiotic prescribing errors during hospital stay in SSA. Introduction Prescribing practices are a global concern given that failures at any stage of the medication-use process -from prescribing, dispensing, transcription, administration, and monitoring- can result in serious patient harm and additional healthcare expenditure [1–3]. Majority of research on prescribing practices in everyday clinical practice originate from high-income settings [3]. Based on the sparse evidence available, the burden of prescribing errors appears to be considerably greater in low- and middle-income countries (LMICs) [4–6], where the uptake of electronic health records (EHRs), and, consequently, electronic prescribing systems with embedded safety checks, remains low in most healthcare facilities [7, 8]. The challenge of poor medication prescribing practices in hospital care is especially pertinent in the neonatal period (i.e. first 28 days of life) [9, 10] where the population with severe illness has high mortality [11], and dose calculations are often complex. Around 15% of drug prescriptions have errors in neonatal care settings [9], but there is scant research on interventions to improve medication safety in LMICs where neonatal sepsis is common [12] and where concerns over antibiotic resistance and antimicrobial stewardship are growing [13]. In this study, we explored a pharmacist-led, audit and feedback (A&F) intervention guided by the Clinical Performance Feedback Intervention Theory (CP-FIT) to R educe Ge ntamicin dosing errors in N ewborn T reatment (i.e. ReGENT study) in Kenyan hospitals. We build on previously reported studies [14–16] and explain the intervention in detail in this study’s published protocol [17]. Here we report an evaluation of the impact of a pharmacist-led A&F intervention on inaccurate gentamicin prescribing for newborns receiving inpatient care over a three-year period. This study’s objective was to assess whether an enhanced pharmacist-led audit and feedback intervention reduces neonatal gentamicin prescribing errors more effectively than standard hospital feedback over time. Methods Ethics approvals The analyses described in this study were approved by the KEMRI’s Scientific and Ethical Review Committee (SERU #4378 and SERU #3459) with detailed information provided in the protocol [17]. Our description follows the TREND statement [18] for improving the reporting quality of non-randomised evaluations of interventions (Supplementary Materials) and the Template for Intervention Description and Replication (TIDieR) Checklist [19] (Supplementary Materials). Study setting The study was conducted in partnership with 22 hospitals in Kenya. These hospitals were purposefully selected to join a Kenyan Clinical Information Network (CIN, see below) [8, 20, 21], be of at least moderate size and representative of different malaria transmission zones. These hospitals admit small and sick newborns to a New-Born Unit (NBU) with a specific clinical and nursing team. The average age of neonates on admission is 0 or 1 day old and most admitted neonates are inborn [20]. These hospitals joined the CIN, a learning health system in Kenya between 2014 and 2020 [8, 20, 21]. Clinical audit reports which include summaries of gentamicin prescribing error statistics, are shared with CIN hospitals on the routine care they provide. The hospitals are provided with quarterly feedback reports on the quality of care they provide, which include a summary of Gentamicin prescribing errors statistics [22]. The quarterly report is shared to all CIN sites via email and as a printed copy to the paediatrician and the hospital manager, providing a common A&F intervention already in use. Neonatal team leaders (neonatologists, paediatricians, and nurses) also met face-to-face once or twice annually to discuss these standard A&F reports and how to improve multiple facets of clinical care [23, 24]. In these hospitals, in Kenya more widely and in many public sector settings in LMICs, key issues that likely influence prescribing practices and affect dose errors, paying special attention to gentamicin the first-line antibiotic for severe neonatal infection, are listed in Table 1 . Table 1 Contextual and Gentamicin prescribing factors in typical neonatal units in public health facilities in LMICs A) Contextual issues 1) There is absence of clinical decision support for dose validation during prescribing due to low/no EHRs in these sites [35], 2) Majority of patients are admitted and prescribed medication by junior clinicians who rotate quarterly between medical departments [36] – and until recently, are likely to have insufficient training in neonatal care. 3) There are barely more than two consultant doctors in these health facilities [37], so the junior clinicians who prescribe frequently operate with minimal supervision from any consultant. 4) Pharmacists are scarce, and in most settings they have little or no direct involvement in ward-based supervision or in providing prescribing education within newborn units. [31, 38]. 5) There are standardised clinical care guidelines and protocols, similar to and adapted from the World Health Organisation (WHO) guidelines, that are broadly circulated to clinicians, that are meant to inform their prescribing practices [27], 6) Prescriptions of gentamicin and other aminoglycosides are not followed with the expected therapeutic drug monitoring in most public healthcare facilities [25]. 7) Clinicians typically follow empiric antibiotic prescribing during admission due to limited access to diagnostics such as blood cultures . B) Gentamicin prescribing context 8) Gentamicin which is considered as essential medicine by WHO serves as first-line therapy for neonatal sepsis in most LMICs including Kenya, and its use extends to community treatment programmes [13, 26–28]. 9) Gentamicin has well-known toxicity risks with overdoses administered for too long [29] while underdosing is ineffective in killing bacterial; Suboptimal dosing is therefore a major concern, given its contribution to the global rise in antimicrobial resistance [13, 26, 28]. 10) WHO and Kenyan dosing recommendations are determined by both body weight and postnatal age, making gentamicin prescribing more complex than for many other medicines and thereby increasing the potential for prescribing errors [22, 30]. 11) Previous work in the Kenyan hospitals included in this study indicates that roughly 14% of the prescriptions for Gentamicin contain dosing errors, reflecting doses that are outside the recommended levels, with earlier studies from Kenya reporting higher error rates [31, 32]. Table 1 : Contextual and Gentamicin prescribing factors in typical neonatal units in public health facilities in LMICs Study design The study design is described in detail in the published protocol [17]. In summary, the primary study was a standard single-group interrupted time-series study (ITS) design for the primary analysis to evaluate the comparative effectiveness of the enhanced A&F in reducing prescribing error trend after its introduction (which we explain in detail in the intervention section). No randomisation was planned, the aim was to include all hospitals in the intervention but pharmacists from 6 CIN facilities refused to participate [17]. This provided an opportunity for an intervention versus control comparison as a secondary analysis using a parallel group ITS design to evaluate prescribing error trends in the two hospital groups (Fig. 1) while recognising the potential selection bias in the facilities that rejected the pharmacist-led enhanced A&F intervention approach. Figure 1 Flow chart of the intervention roll-out. The ITS starts prior to hospitals being assigned to control or experiment group Intervention: Audit and Feedback (A&F) A&F is hypothesised to be more effective when they are multifaceted, incorporate educational elements, engage key actors such as pharmacists as facilitators or clinical champions, and are aligned with clinicians’ professional goals [14, 25]. There has been limited evaluation practices in empirical studies of the contribution of pharmacists as key facilitators/champions to improving prescribing [26, 27]. While providing performance feedback to individual clinicians is considered highly effective [16] in LMICs, shortages of healthcare workers and constrained time and resources limit opportunities for individual engagement with such feedback [16]. Limitations in routinely available clinical data and digital infrastructure In many LMICs hospital settings constrain the design and implementation of A&F interventions, exacerbated by lack of evidence on the added value of engaging local clinical champions together with the feasibility of delivering feedback at the team versus individual clinician level [16]. The evaluation of the effect of A&F interventions also needs to consider the decay in effect over time by aiming to go beyond a single feedback cycle, short-term intervention period or simple before-after designs [28]. In response to calls for more ‘head to head’ studies of different forms of A&F to help identify ‘what works’ [16], The design of our A&F intervention was informed by the contextual characteristics of these settings as well as by existing empirical evidence and theoretical frameworks [29, 30] in the following ways: The feedback visualisations were limited to three and delivered as a one-pager PDF with infographics to reflect HCWs finite capacity to handle yet another feedback report. These were designed to communicate explicit goals within the control of the recipients and whether clinical team performance had room for improvement, and allow benchmarking with peer hospitals, while improving the specificity and trend reporting of performance [16]. We provided an Android smartphone-based feedback performance dashboard to ensure active A&F delivery and ease automated performance analysis and instantaneous presentation of feedback to recipients [16]. Given the strong views that clinicians hold about appropriate care, which shape how they engage with performance feedback, a WhatsApp channel was introduced to facilitate communication and to assess whether perceptions, acceptability, and intentions arising from interaction with the A&F intervention were consistent with observed practices [16]. We targeted the use of pharmacists as champions in A&F strategy because where HCWs with source knowledge and skill advocated for the feedback intervention, they often influenced others and provided additional resource to enable more meaningful engagement with feedback [16]. We provided pharmacist-led Continuous Medical Education (CME) seminars linked to NBU antimicrobial stewardship and prescribing practices (Supplementary Table 1) to strengthen both the knowledge and skills in quality improvement and in the AMS clinical topics [16]. The theoretical underpinnings of the enhanced A&F report visualisations are provided in detail in the protocol [16, 17]. The enhanced A&F feedback reports in this study provided feedback through: (1) score cards reporting deviation from explicit targets, (2) peer comparison of Gentamicin prescribing errors by newborn patient sub-groups, and (3) hospital-specific performance trends over a six-month period summarised by newborn age-groups [17]. The differences in the A&F components between the study arms are illustrated in Fig. 1. Details of the feedback components, together with the enhanced A&F PDF infographics are provided in Supplementary Table 1–2 and the published protocol’s Supplementary Figs. 1–3 [17]. Outcomes The primary outcome of this study was the ratio of neonates with incorrect Gentamicin dose prescribed at admission. The calculation of the correct prescription according to the Kenyan guidelines is age and weight dependent as illustrated in Fig. 2 where Gentamicin prescription is reported in milligram units. In summary, for neonates < 7 days old, the correct gentamicin dosage is 4 mg/kg to 6 mg/kg, or 2.4 mg/kg to 3.6 mg/kg if birth weight is less than 2000 grams. For neonates who are 7 days old and older, the correct gentamicin dosage is between 6 mg/kg and 9 mg/kg ; The dosage calculations per kilogram for the study outcome allowed for ± 20% deviation, outside which they are considered errors. Figure 2 Adapted from study protocol [17]. Study primary outcome from patients admitted to the NBUs. Dosage calculations per kilogram allow for ± 20% deviation, outside which they are considered errors. From the CP-FIT model, this outcome represents the standard of clinical performance against which clinical behaviour would be measured explicitly ( Goal setting ) [16]. This study’s target process outcome was at least a 35% reduction in prescription errors from individual hospital baseline error rates with this target based on published evidence of 35–50% reductions in prescription errors in neonatal care from before-after studies, which translates to an absolute change from 14% to 9.1% in prescribing error reduction [27]. Data collection procedures and management Methods of collection and cleaning of data in the CIN are reported in detail elsewhere [22, 31]. In summary, clinical data for neonatal admissions to the hospitals within the CIN were captured through structured Neonatal Admission Record (NAR) forms coupled with standard treatment sheets that are approved by the Ministry of Health. The NAR prompts the clinician with a checklist of fields including patient biodata, clinical assessment, admission and discharge diagnoses, and record of outcome (survival or death). The CIN supports one data clerk in each hospital to extract data from paper medical records, nursing charts, treatment charts, and available laboratory reports each day after a newborn’s discharge into the primary data collection tool developed in Research Electronic Data Capture (REDCap). Automated error checking happens at the point of entry by daily review, every week centrally and both are complemented by regular external data quality assurance reviews [31]. A minimal dataset – which is unsuitable for our planned analyses - is collected for (1) admissions during major holiday breaks, (2) admissions when the data clerk was on leave, and (3) on a random selection of records in hospitals where the workload is very high. This process is explained in detail elsewhere [31, 32]. All data in de-identified form derived from medical records was stored in secure KEMRI-Wellcome Trust Research Programme servers with specified researchers provided password protected access. For this study deidentified data from all patients admitted to the selected hospitals’ NBUs who are under the age of 28 days (i.e., neonates) who had a Gentamicin drug prescription on admission were eligible for inclusion in the analysis regardless of gestational age as in Kenya prescriptions are based on weight and postnatal age rather than gestation at birth. Only the patient population with full data on age, weight, and the dose of the prescription were included in the outcome measurement since these data are needed to measure errors. Inadequate Gentamicin prescription documentation occurred in < 1% of all prescriptions documented within CIN in the 24 months before intervention introduction. During the intervention period and the analysis stage, the participating hospitals and clerks capturing prescribing data were blinded to the study arm assignment, but the research staff were not. Protocol deviations In the 16 facilities that received the intervention, contrary to the expectations in the published protocol, none of the pharmacists were willing to join the NBU clinical team WhatsApp group where one existed or form a NBU WhatsApp group where none existed. All healthcare workers save 6 did not use the mobile application to access the performance dashboards, preferring the monthly PDF reports instead. These deviations rendered no substantive difference in the enhanced A&F intervention packages between the intended study arms as envisioned in the published protocol (Table 1 , Fig. 1)[17]; We still provide analysis results based on the intention-to-treat principles. Post-hoc analysis of the 12 months after the intervention had ended was decided upon at the end of the intervention roll-out to evaluate whether any intervention effect would be sustained after the study by comparing the 16 sites that had received the pharmacist led A&F intervention versus the 6 sites that only receiving the standard A&F reports from the CIN. Data analysis and statistical methods This study involved all healthcare workers prescribing gentamicin in the NBUs of the participating hospitals. On average, there is one paediatrician, one medical officer and several medical officer and clinical officer interns who might prescribe. They are supported by three daytime nurses, and two night-nurses in a typical NBU unit. As medical officer and clinical officer interns rotate typically every 3 months the total number of prescribers working in the NBU units over the period was in the hundreds [33] making it difficult to attribute a patient’s Gentamicin prescribing to individual clinicians. Interrupted Time Series analysis We applied a segmented linear mixed effects model with an autoregressive covariance structure on the proposed Interrupted Time Series (ITS) design, that accounts for the pre-intervention trends in the study outcomes [34]. Informed by previous findings [27], our hypothesised impact model assumed month-to-month (slope) changes following the implementation of the intervention, but no immediate (level) change. We adopted a negative binomial modelling approach which we explain in detail in the protocol [17], and report study outcomes as incidence rate ratios (IRR), where values with 95% confidence intervals(CI) > 1 indicate increase in prescribing error rates while the reverse (i.e. 95% CI < 1) indicate decrease in prescribing error rates at admission; IRR values with 95% CI containing 1 indicate no change in the prescribing error rates at admission. The primary analysis for this study was the comparison of pre-intervention to post-intervention trends of the study outcomes in the facilities that consented to participate in the enhanced A&F intervention approach. We also conducted secondary analysis comparing the pre- and post-intervention trends in the study outcome between the facilities that were on the enhanced A&F approach compared to those that were on the basic A&F approach. Interrupted Time Series (ITS) Sample size Detailed explanations on our approach to sample size calculations are provided in our published protocol [17]. The primary analysis followed a single group ITS study design in keeping with a priori study design and entailed the 16/22 facilities that consented to pharmacist-led enhanced A&F intervention approach. All analysis was done in R (v 4.4.3) statistical computing software [35]. Primary analysis The baseline level of erroneous Gentamicin prescribing at admission was 12.07% (95% CI: 11.56% − 12.58%) across the intervention sites (n = 16) from the pre-intervention period. From published reviews, A&F interventions have a median absolute effect size of 4.3% in improving practice behaviour; We estimated that with 950 patients per month across the 16 facilities, our study would have 80% power to detect this 4.3% reduction of the month-to-month prescription error trend over 12 months (i.e. slope change) with a statistical significance of 0.05. The monthly average of admissions to NBUs with a gentamicin prescription at admission during the pre-intervention period in all the 16 facilities that consented to the enhanced pharmacist-led A&F intervention approach was 1325 patients (with a standard deviation of 70 across the 16 hospitals). Secondary analysis The 6/22 CIN sites that chose to not participate in the study were treated as a control group within the ITS design to examine whether improvements in performance (correct prescribing) were more pronounced than improvements that may be linked to underlying secular trends [36]. When considering both the intervention arm and control arm (sites = 22), the baseline monthly level of erroneous Gentamicin prescribing at admission was 12.3% (95% CI: 11.84% − 12.76%) across the 22 facilities from the pre-intervention period. Based on previously published studies [27], we estimated that with 835 patients per month across the 22 facilities, our study would have 85% power to detect an effect size of 15% reduction of the month-to-month prescription error trend over 12 months (i.e. slope change due to arm) with a statistical significance of 0.05. The monthly average admissions to NBUs with a gentamicin prescription at admission in all the 22 facilities during the pre-intervention period was 1617 patients (with a standard deviation of 76 across the 22 hospitals). Sample size analysis at the individual hospital level revealed that all the hospitals did not have sufficient patient numbers per month with gentamicin prescription to facilitate separate within hospital time-series analysis. Process evaluation to assess fidelity We used messages shared over the intervention period in the study’s pharmacists WhatsApp group (which was specifically designed as a group for intended intervention champions) to explore group members’ engagement with the enhanced A&F intervention during the study period. The messages shared were summarised according to their source, target, timing, and content. While the content of the shared messages did not lend itself to the exploration of the reception, comprehension, and acceptance of the feedback by the pharmacists ( Interaction , Perception , and Acceptance respectively) and planned behavioural responses that may be attributed to the feedback ( Intention and Behaviour ), we qualitatively summarised the messages for content on barriers to behaviour change linked to the A&F intervention [16]. We also convened a feedback meeting with the HCWs in the intervention sites to explore challenges and successes from the intervention implementation in CIN hospitals and to learn and gather feedback on how to better design and implement future A&F interventions. Results Participant flow and recruitment The study’s first phase which involved baseline data collection 12 months prior to intervention introduction ran from 01st July 2021 to 30th June 2022 (pre-intervention period). The intervention phase ran for 12 months afterwards from 01st July 2022 to 30th June 2023. The post-intervention period ran for an additional 12 months from 01st July 2023 to 30th June 2024. Of the 46381 patients from the 22 CIN hospitals prescribed Gentamicin at admission and eligible for inclusion in this study, 37109/46381 (80.39%) of patients from 16/22 hospitals were in the arm that received the enhanced A&F intervention (Fig. 3), while 9272/46381 (19.99%) were from 6/22 hospitals in the control arm. While no hospitals were lost to follow-up, 441/37109 (1.19%) and 329/9272 (3.55%) patients from the experiment and control arm were dropped due to missing data on key prescribing variables of age and weight (Fig. 1, Fig. 3). Subsequently, 36668/37109 (98.81%) and 8943/9272 (96.45%) of the patients were in the experiment and control arm respectively were included in the subsequent analysis. Figure 3 Flow-chart of patients included in the ReGENT study analyses Baseline data and numbers analysed Table 2 illustrates the baseline demographic and clinical characteristics for patients in each arm of the study. Patients in both arms had similar gestations, age, birth weight, length of hospital stay and mortality rate despite the large difference in the number of patients in the study arms. From summary descriptive analysis of before and after the intervention, the gentamicin prescribing accuracy after the enhanced A&F intervention was introduced appears no different in the experiment arm compared to the period before the intervention (Fig. 4), the same pattern appears true for the control arm. Figure 4 also provides the absolute error rates by patient sub-group which are important results that are rarely reported from SSA. Table 2 Descriptive Summary Statistics (n = 45611) Indicator Control Arm, n = 8943 Experiment Arm, n = 36668 Summary Statistic 1 Missing Rate Summary Statistic 1 Missing Rate Neonate Age (Days) 1 (IQR: 1–1) 0 (0%) 1 (IQR: 1–1) 0 (0%) Birth Weight 2.65 (IQR: 1.81–3.2) 0 (0%) 2.7 (IQR: 1.9–3.2) 0 (0%) SENSS Score 2 7.6 (IQR: 3.9–16.8) 0 (0%) 7.1 (IQR: 3-14.7) 0 (0%) Gestational Age 3 37 (IQR: 33–39) 826 (9.24%) 38 (IQR: 34–39) 3179 (8.67%) Male 5032 (56.27%) 27 (0.3%) 20774 (56.65%) 87 (0.24%) Length of stay (Days) 6 (IQR: 3–12) 18 (0.2%) 5 (IQR: 3–10) 100 (0.27%) Mortality Rate 1468 (16.42%) 14 (0.16%) 5411 (14.76%) 18 (0.05%) Note: 1 Continuous variable summaries given as median (Interquartile range). Discrete variable summaries given as proportions 2 SENSS (Score of Essential Newborn Symptoms and Signs) score provided as a mortality risk probability (derived from sex, birthweight, presence or absence of difficulty feeding, convulsions, indrawing, central cyanosis and floppy/inability to suck). 3 Provided in weeks Table 2 : Descriptive Summary Statistics (n = 45611) Figure 4 Breakdown of the Gentamicin prescribing errors at admission by study period and ReGENT study participation The trends in Gentamicin prescribing errors in the experiment arm was less variable compared to the control arm for both overdose and underdose errors (Fig. 5). The prescribing error trends were also quite variable in both study arms for the different neonatal inpatient sub-populations (Supplementary Fig. 1–2). There is a strong indication of a large between-hospital variability in the prescribing error trends in both study arms (Supplementary Fig. 3–4). Figure 5 Overall trends in Gentamicin prescribing error at admission Outcomes and estimation Intervention effect in the enhanced A&F group Overall, from the intention-to-treat analysis results (Supplementary Table 3), there was no enhanced A&F intervention effect to reduce the incidence of inaccurate Gentamicin prescription in neonates at admission (Table 3 , Fig. 6), either as a step or trend change. This may be a consequence of very limited fidelity to the intervention as specified in the protocol [17]. Table 3 ReGENT study intervention effect on reducing all Gentamicin prescribing errors at admission (Hospitals = 16) Pre-intervention phase compared to intervention phase Intervention phase compared to post-intervention phase IRR 95% CI p-value IRR 95% CI p-value (intercept) 1 0.158 0.119 to 0.210 < 0.001 0.145 0.099 to 0.211 < 0.001 time 2 0.980 0.962 to 0.999 0.043 0.986 0.945 to 1.030 0.531 intervention 3 1.115 0.920 to 1.352 0.265 0.890 0.695 to 1.138 0.352 intervention × I(time − 12) 4 1.014 0.986 to 1.042 0.344 1.044 0.964 to 1.131 0.289 Note: 1 Baseline performance at the start of the pre-intervention period 2 Pre-intervention trend 3 Step change in performance due to the intervention 4 Change in the trend post-intervention In the 12 months post-intervention for the 16 hospitals receiving the enhanced A&F intervention, the trend in gentamicin prescribing errors at admission worsened, increasing by 4.4% (p-value = 0.289) month-to-month, although it was not statistically significant (Table 3 ). Figure 6 The modelled impact of ReGENT study on all Gentamicin prescribing errors at admission Table 3 : ReGENT study intervention effect on reducing all Gentamicin prescribing errors at admission (Hospitals = 16) Secondary analysis: Intervention effect in the enhanced A&F group in comparison to the control arm Relative to the control arm, the experiment arm had a 7.3% (p-value = 0.009) reduction in prescribing errors during the intervention phase (Table 4 ). After the enhanced A&F intervention ended, the month-to-month trend in prescribing errors at admission increased by 6% (p-value = 0.028). However, relative to the control arm, the experiment arm had 57.6% (p-value = 0.015) less prescribing errors at admission post-study, and the trend in the experiment arm relative to the control arm post-study had a 8.1% (p-value = 0.005) month-to-month reduction in the admission prescribing errors (Table 4 ). Table 4 Secondary analysis of the ReGENT study intervention effect on reducing all Gentamicin prescribing errors at admission (Hospitals = 22) Pre-intervention phase compared to intervention phase Intervention phase compared to post-intervention phase IRR 95% CI p-value IRR 95% CI p-value (intercept) 1 0.199 0.127 to 0.311 < 0.001 0.211 0.110 to 0.403 < 0.001 time 2 0.951 0.922 to 0.981 0.002 0.980 0.950 to 1.011 0.195 intervention 3 1.096 0.922 to 1.302 0.301 1.112 0.934 to 1.322 0.232 intervention × I(time − 12) 4 1.090 1.036 to 1.146 < 0.001 1.060 1.006 to 1.116 0.028 arm 5 (Ref: Control arm) 0.769 0.470 to 1.325 0.371 0.424 0.213 to 0.847 0.015 time × arm 6 1.032 0.998 to 1.067 0.064 1.042 1.009 to 1.077 0.011 arm × intervention × I(time − 12) 7 0.930 0.878 to 0.985 0.014 0.919 0.866 to 0.975 0.005 Note: 1 Baseline performance at the start of the pre-intervention period 2 Pre-intervention trend 3 Step change in performance due to the intervention 4 Change in the trend post-intervention 5 Baseline performance in the experiment arm relative to the control arm 6 Pre-intervention trend in the experiment arm relative to the control arm 7 Change in the trend post-intervention in the experiment arm relative to the control arm Table 4 : Secondary analysis of the ReGENT study intervention effect on reducing all Gentamicin prescribing errors at admission (Hospitals = 22) Fidelity to intervention plan/ participants explanations for observed effects Feedback about the draft findings of the ReGENT study was elicited from the paediatricians and nurses in charge of the newborn units of the hospitals that received the intervention. A key limiting factor was the small number of pharmacists and paediatricians available in the whole hospital vis-à-vis the numbers of neonatal patients and the number of junior clinicians (including interns) that constrained the amount of time they had to provide mentorship on antimicrobial stewardship to junior clinicians. The mentorship challenge was further exacerbated by the high staff turnover coupled with frequent rotation of interns (who provide the bulk of the patient care) every 8–12 weeks that was unsynchronised across the medical and nursing carders making it difficult to sustain any departmental progress in practice. There was clear indication that in some hospitals, the practices being reinforced were in conflict with what was in the clinical guidelines e.g. use of admission weight instead of birth weight to calculate the dosage to prescribe. Additionally, there were only six unique downloads and use of the Android-based smartphone application providing interactive feedback during the study duration. Discussion Summary of findings Applying a theory-informed A&F intervention, the ReGENT study found no statistically significant effect of the team-based pharmacist-led A&F intervention on reducing gentamicin medication errors in neonatal care. Prescribing errors during intervention and post-intervention periods were increasing across all hospitals in both arms of the study. However, relative to control hospitals sites receiving routine feedback but without pharmacist involvement or pharmacist-based CME, the ReGENT study sites had a 7.3% less Gentamicin prescription error rates at admission post-introduction of the pharmacist-led A&F intervention, and 8.1% less Gentamicin prescription error rates at admission in the post-intervention phase. Comparison to other studies. The Gentamicin prescribing error-rates in neonatal care settings of 11%-26.4% at the beginning this study are consistent with previously published studies [9]. Studies using pharmacist-led A&F interventions have demonstrated at 9.6% drop in hazardous prescribing after 12 months, whereas from the findings in our secondary analysis, error rates during intervention and post-intervention periods were increasing across all places but more so in the control hospital sites, where they were significantly higher [15]. Key similarities between our study and other studies remained the use of providing actionable summaries linked to patient-level data that could be used to review patient care retrospectively. There was a fundamental difference between the previous studies where pharmacists reviewed individual at-risk patients, initiating remedial actions or advising doctors to do so. In our study, pharmacists were too few (typically one per hospital) and could not provide individual patient prescription reviews; They were constrained to co-ordinating trainings through CMEs for the clinical teams doing the actual prescribing and reviewing together with the neonatal teams organisational challenges impeding improvement of medication prescribing accuracy (e.g. medication stock-outs, lack of training etc.). In typical Kenyan clinical settings, there is little interaction between clinical teams and pharmacists to guide medication prescribing with medications reconstituted and administered by nurses on the ward; pharmacists typically only get involved for inpatient care when potentially toxic medications (e.g., chemotherapy) are administered but this is a rare event confined to higher level hospitals. Before the onset of this study, pharmacists had not previously been involved in CIN feedback activities except in some hospitals linked to the “ Supportive care and antibiotics for severe pneumonia among hospitalized children (SEARCH) ” trial [37] where their role was to support correct use of study drugs used in the paediatric wards. In this study, while pharmacists did not start routinely visiting the NBUs, they became more involved in the CMEs going through the ReGENT feedback reports. Interpretation of study findings Our study findings indicates that the theory-driven pharmacist-led enhanced A&F intervention did not significantly change the trend in Gentamicin prescribing practices in the participating hospitals, with the overall level of prescribing errors getting worse across all hospitals. Compared to the control sites, in the ReGENT sites the trend in Gentamicin prescribing errors at admission during- and post-intervention was relatively better, which might be due to other events outside the study that might be affecting the provision of inpatient neonatal care (e.g. perhaps new clinicians trained during COVID had less idea what to do on NBUs); The control sites, defined by pharmacists declining to take part in the study, may suggest a lower level of engagement with, and the value placed on, NBU prescribing and care by hospital pharmacists. The need for frequent and varied means of improving the awareness of current guidelines and care-giving norms around prescribing practices was also highlighted. Clinicians argued that the adherence to prescribing guidelines was further complicated where the newborns had other underlying complications – which is common hospitals in most SSA contexts- but the clinical guidelines offered no clear direction on what should happen in that scenario. However, the presence of the WhatsApp group allowed the pharmacists to seek peer support in what advice to offer hospital teams handling such cases. The use of visual aides at patient level (e.g. stickers on patient folders) at the point of care to flag where dosing errors would be critical, involving nurses in advising junior medical doctors and physician assistants (i.e. clinical officers) on the correct practices before prescription administration, and the use of multi-disciplinary induction and ward-round approaches to mentorship were emphasised as means to improve the prescribing practice. While the clinicians argued that they would rapidly respond to and act-on feedback provided through digital platforms, from the almost negligible download and use of the Android-based smartphone application providing interactive feedback, we saw a huge disconnect between the perception and the actual practice that A&F digital platforms would improve access and engagement with feedback, even where the such digital platforms were co-designed with clinicians. Study strengths, limitations and generalisability of findings A key strength of this study was the natural control group that was used in the secondary analysis which was able to mitigate against the potential of history bias (a primary threat to the validity of in single-arm ITS studies) where concurrent interventions or events occurring around the time of the intervention in the facility [36]. Also, we reported patterns and trends in absolute error rates by patient sub-groups which are important results that are rarely reported from SSA studies. A key limitations of this study was the inability of the pharmacists to engage in individual patient reviews (the ideal situation), due to being overstretched (in terms of ongoing typical facility-based tasks) coupled with the high neonatal inpatient admissions. Additionally, the high staff turnover happening every 2–3 months made it difficult to sustain any improvements made through mentorship leading to a stagnation of the prescribing errors at admission. Methodologically, this study did not have sufficient statistical power to allow for ITS analyses at the birthweight and age sub-groups (i.e. the 3mg, 5mg or 7.5mg dosing levels), or the error types sub-groups (i.e. underdose, overdose). However, the findings of this study can be used to inform future pharmacist-led team-based interventions where resources are limited, the teams are large, and there is a high staff turnover. Conclusions We found no statistically significant effect of the team-based pharmacist-led A&F intervention on reducing gentamicin medication errors in neonatal care. Prescribing errors during intervention and post-intervention periods were increasing across all hospitals in both arms of the study during and post-intervention periods. However, relative to control hospitals sites receiving routine feedback but without pharmacist involvement or pharmacist-led CMEs, the primary study sites had a positive trend in reducing Gentamicin prescription error rates at admission during and post-introduction of the pharmacist-led A&F intervention which might be due to other events outside the study that might be affecting the provision of inpatient neonatal care. Abbreviations A&F Audit and Feedback CIN Clinical Information Network CME Continuous Medical Education CP-FIT Clinical Performance Feedback Intervention Theory EHRs Electronic Health Records HICs High Income Countries HCWs Health Care Workers ITS Interrupted Time Series KPA Kenya Paediatric Association LMICs Low and middle-income countries MoH Ministry of Health NBU Newborn Unit QI Quality Improvement SERU KEMRI’s Scientific and Ethics Review Unit SSA Sub-Saharan Africa WHO World Health Organization. Declarations Ethics approval and consent to participate Ethical approval was provided by the KEMRI Scientific and Ethical Review Committee (SERU 4378 and SERU 3459). The Scientific and Ethics Review Unit of the Kenya Medical Research Institute (KEMRI) approved the collection of the de-identified anonymised data for this study waiving the need for individual consent for access to de-identified patient data, with the authors having no access to the information that could identify the patients. Consent for publication This study is published with the permission of the Director of Kenya Medical Research Institute (KEMRI). Availability of Data and Materials The datasets generated and/or analysed during the current study are not publicly available due to the primary data being owned by the hospitals and their counties with the Ministry of Health; The research staff do have permission to share the data without further written approval from both the KEMRI-Wellcome Trust Data Governance Committee and the Facility, County or Ministry of Health as appropriate to the data request. Requests for access to primary data from qualitative research by people other than the investigators will be submitted to the KEMRI-Wellcome Trust Research Programme data governance committee as a first step through [email protected] , who will advise on the need for additional ethical review by the KEMRI Research Ethics Committee. Competing interests The authors have declared that no competing interests exist Funding This work was primarily supported by a Wellcome Trust Senior Fellowship (#207522/Z/17/Z) awarded to ME and a Wellcome Trust Early Career Research Fellowship (#227562/Z/23/Z) awarded to TT. Additional support was provided by a Wellcome Trust core grant awarded to the KEMRI-Wellcome Trust Research Programme (#092654). The funders had no role in the preparation of this report or the decision to submit for publication. Author contributions Authorship eligibility guidelines for the final reports adhere to the Contributor Roles Taxonomy (CRediT) statement guidelines. Conceptualization: TT, ME; Supervision: TT, JA; Methodology: TT, MM, DA, MO, JA; Formal Analysis: TT, MM, JA, ME; Investigation: TT, JA, ME; Resources: TT, ME, DA, MM; Data Curation: TT, GM; Writing – original draft: TT, ME; Writing – review & editing: TT, MO, JA, DA, MM, GM, ME; Funding acquisition: TT, ME; Acknowledgements The Clinical Information Network (CIN) Group : The CIN group hospital teams who are tagged to collaborate in the network’s development, data collection, data management, implementation of audit and feedback interventions and who will participate in this study include the following focal persons: Paediatricians: Juma Vitalis, Nyumbile Bonface, Roselyne Malangachi, Christine Manyasi, Catherine Mutinda, David Kibiwott Kimutai, Rukia Aden, Caren Emadau, Elizabeth Atieno Jowi, Cecilia Muithya, Charles Nzioki, Supa Tunje, Dr. Penina Musyoka, Wagura Mwangi, Agnes Mithamo, Magdalene Kuria, Esther Njiru, Mwangi Ngina, Penina Mwangi, Rachel Inginia, Melab Musabi, Emma Namulala, Grace Ochieng, Lydia Thuranira, Felicitas Makokha, Josephine Ojigo, Beth Maina, Catherine Mutinda, Mary Waiyego, Bernadette Lusweti, Angeline Ithondeka, Julie Barasa, Meshack Liru, Elizabeth Kibaru, Alice Nkirote Nyaribari, Joyce Akuka, Joyce Wangari; Pharmacists: Babra Murila, Beatrice Kamau, Caroline Naliaka, Cynthia Nduta, Evans Gitu, Evans Makumba, Esther Nduta, Lydia Momanyi, Matini Duncan, Sally Mugo, Sarah Kibira, Stephene Gichana, Rogers Omolo, Roy Mwendwa, Wycliffe Dunde Nurses: Amilia Ngoda, Aggrey Nzavaye Emenwa, Patricia Nafula Wesakania, George Lipesa, Jane Mbungu, Marystella Mutenyo, Joyce Mbogho, Joan Baswetty, Ann Jambi, Josephine Aritho, Beatrice Njambi, Felisters Mucheke, Zainab Kioni, Jeniffer, Lucy Kinyua, Margaret Kethi, Alice Oguda, Salome Nashimiyu Situma, Nancy Gachaja, Loise N. Mwangi, Ruth Mwai, irginia Wangari Muruga, Nancy Mburu, Celestine Muteshi, Abigael Bwire, Salome Okisa Muyale, Naomi Situma, Faith Mueni, Hellen Mwaura, Rosemary Mututa, Caroline Lavu, Joyce Oketch, Jane Hore Olum, Orina Nyakina, Faith Njeru, Rebecca Chelimo, Margaret Wanjiku Mwaura, Ann Wambugu, Epharus Njeri Mburu, Linda Awino Tindi, Jane Akumu, Ruth Otieno, Slessor Osok; Health Record Information Officers (HRIOs): Seline Kulubi, Susan Wanjala, Pauline Njeru, Rebbecca Mukami Mbogo, John Ollongo, Samuel Soita, Judith Mirenja, Mary Nguri, Margaret Waweru, Mary Akoth Oruko, Jeska Kuya, Caroline Muthuri, Esther Muthiani, Esther Mwangi, Joseph Nganga, Benjamin Tanui, Alfred Wanjau, Judith Onsongo, Peter Muigai, Arnest Namayi, Elizabeth Kosiom, Dorcas Cherop, Faith Marete, Johanness Simiyu, Collince Danga, Arthur Otieno Oyugi, Fredrick Keya Okoth. The Clinical Information Network (CIN) Group ’s monitored email address is [email protected] and the list can change when new paediatrician(s), nurse(s) or HRIO leave or come into the hospital. Open access This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. References Donaldson, L.J., et al., Medication without harm: who's third global patient safety challenge. The Lancet, 2017. 389 (10080): p. 1680-1681. European Medicines Agency. Medication errors . 2015 [cited 2021 12th March]; Available from: https://www.ema.europa.eu/en/human-regulatory/post-authorisation/pharmacovigilance/medication-errors. 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International journal of epidemiology, 2018. 47 (6): p. 2082-2093. National Institutes of Health. A Study to Compare Different Antibiotics and Different Modes of Fluid Treatment for Children With Severe Pneumonia (SEARCH) . 2019 November 2020 [cited 2022 3rd March]; Available from: https://clinicaltrials.gov/ct2/show/NCT04041791. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx SupplementaryFigure1.tiff SupplementaryFigure2.tiff SupplementaryFigure3.tiff SupplementaryFigure4.tiff Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9219945","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616683702,"identity":"99f8a618-8221-493f-878c-63c042d05d61","order_by":0,"name":"Timothy Tuti","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBAC+/sPGBgSKhKATMYGIGEhQ1ALGzNQacIZuBYJHuK0MLYlwPhEaeExf/BwXpqc7uzDDcw8FRI8/O3diQ8YKu7ZNeDWYtiQuC3H2OxcIlDLGQkeiTNnNxswnClOJqClInHbGcb237ltEjwGErnbJIBOTcbjMKCWORX1QC0NzLn/wFq2/yCspSEnwQyspQFiCwNQix1uLWyFMxKOpRmCbflzDOIXCWCwJ+DWwrzh44+aZHmzM+wPGGfU2Mjxt/du/PChIsEelxYcAGhFYgOJeoBJiWQdo2AUjIJRMFwBAMEMUHKvfAP8AAAAAElFTkSuQmCC","orcid":"","institution":"KEMRI-Wellcome Trust Research Programme","correspondingAuthor":true,"prefix":"","firstName":"Timothy","middleName":"","lastName":"Tuti","suffix":""},{"id":616683703,"identity":"8cf1e509-8b43-44f4-a27f-fcc237e63eea","order_by":1,"name":"Jalemba Aluvaala","email":"","orcid":"","institution":"KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"Jalemba","middleName":"","lastName":"Aluvaala","suffix":""},{"id":616683704,"identity":"929a3bf7-b9e1-47cc-b574-2be3cdb29c06","order_by":2,"name":"Mercy Mulaku","email":"","orcid":"","institution":"University of Nairobi","correspondingAuthor":false,"prefix":"","firstName":"Mercy","middleName":"","lastName":"Mulaku","suffix":""},{"id":616683705,"identity":"ef0e8f16-482b-4d2c-a767-f18521bd9c54","order_by":3,"name":"Dorothy Aywak","email":"","orcid":"","institution":"Kenyatta National Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dorothy","middleName":"","lastName":"Aywak","suffix":""},{"id":616683706,"identity":"7bc268bb-8a65-442b-930e-f87c5d096cfd","order_by":4,"name":"Muthoni Ogolla","email":"","orcid":"","institution":"KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"Muthoni","middleName":"","lastName":"Ogolla","suffix":""},{"id":616683707,"identity":"f7320cd2-a5ca-486d-b01a-5063654322bf","order_by":5,"name":"George Mbevi","email":"","orcid":"","institution":"KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"","lastName":"Mbevi","suffix":""},{"id":616683708,"identity":"4c6889bc-c967-4e9b-af9a-89c71a563ab1","order_by":6,"name":"Mike English","email":"","orcid":"","institution":"KEMRI-Wellcome Trust Research Programme","correspondingAuthor":false,"prefix":"","firstName":"Mike","middleName":"","lastName":"English","suffix":""}],"badges":[],"createdAt":"2026-03-25 07:57:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9219945/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9219945/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107453347,"identity":"eb42d6eb-5865-44b5-8484-987672af93f1","added_by":"auto","created_at":"2026-04-21 15:32:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1905413,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the intervention roll-out. 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4","display":"","copyAsset":false,"role":"figure","size":1712042,"visible":true,"origin":"","legend":"\u003cp\u003eBreakdown of the Gentamicin prescribing errors at admission by study period and ReGENT study participation\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9219945/v1/5ddd3d9ea626fc8ad943b066.png"},{"id":107453353,"identity":"b5c11b62-2af8-4489-9401-852358735129","added_by":"auto","created_at":"2026-04-21 15:32:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":6535199,"visible":true,"origin":"","legend":"\u003cp\u003eOverall trends in Gentamicin prescribing error at admission\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9219945/v1/881a3dc80a894b8879d4044b.png"},{"id":107453356,"identity":"da57a99e-cd82-453f-90c5-1dd0962bfcb3","added_by":"auto","created_at":"2026-04-21 15:32:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1579099,"visible":true,"origin":"","legend":"\u003cp\u003eThe modelled impact of ReGENT study on all Gentamicin prescribing errors at admission\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9219945/v1/276ff4c937d36abe70f29d8a.png"},{"id":109067664,"identity":"431d52e3-d566-4c97-8dd7-cb6b0827b11d","added_by":"auto","created_at":"2026-05-12 09:59:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13700982,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9219945/v1/3a2a3edb-dcf8-43f0-9cce-073f5c8c60ae.pdf"},{"id":107453349,"identity":"380f09d0-c65a-45d3-b3d6-831b60c16f97","added_by":"auto","created_at":"2026-04-21 15:32:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24045,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9219945/v1/f00996a4a47e4d70bf52cd2a.docx"},{"id":107490358,"identity":"ac0805e3-c381-49b6-9a33-58cb02d731e8","added_by":"auto","created_at":"2026-04-22 02:51:55","extension":"tiff","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1572316,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.tiff","url":"https://assets-eu.researchsquare.com/files/rs-9219945/v1/7db486bb7a6c71f8f20a815f.tiff"},{"id":107868497,"identity":"66c99998-452e-4d41-8310-564892a48d7a","added_by":"auto","created_at":"2026-04-27 07:20:16","extension":"tiff","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2433370,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2.tiff","url":"https://assets-eu.researchsquare.com/files/rs-9219945/v1/d6c590615a39cb16c5bfacbf.tiff"},{"id":107490397,"identity":"ae9f52fa-6098-49bc-969d-220f268ca0a7","added_by":"auto","created_at":"2026-04-22 02:52:16","extension":"tiff","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":1918322,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure3.tiff","url":"https://assets-eu.researchsquare.com/files/rs-9219945/v1/6b4801a7fe3585f161c74b8d.tiff"},{"id":107490472,"identity":"8f5641c3-0e18-4314-8f5f-870e7bd992b8","added_by":"auto","created_at":"2026-04-22 02:52:48","extension":"tiff","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":2504136,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure4.tiff","url":"https://assets-eu.researchsquare.com/files/rs-9219945/v1/8c1d038ebda65cc9e07135c1.tiff"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of a pharmacist-led audit and feedback intervention to reduce Gentamicin prescribing errors at admission in neonatal inpatient care in Kenya: A controlled interrupted time series study","fulltext":[{"header":"Why was this study done?","content":"\u003cul\u003e\n \u003cli\u003eIn newborn hospital care where the population with severe illness has a high mortality rate, around 14.9% of drug prescriptions have errors in settings such as sub-Saharan Africa (SSA).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHowever, there is scant research in SSA on actionable audit and feedback interventions over time to reduce the rates of inappropriate and potentially harmful prescribing of antibiotics.\u003c/li\u003e\n \u003cli\u003eTherefore, we evaluated whether such an intervention is associated with sustained changes when it provides continuous feedback championed by pharmacists.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eWhat did the researchers do and find?\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eWe evaluated the impact of a pharmacist-led audit-and feedback intervention for in-hospital newborn care across Kenya.\u003c/li\u003e\n \u003cli\u003eWe found that the intervention was not associated with sustained reduction in the level or trend in incorrect antibiotic prescribing across practices, until the study was completed (after 12 months).\u003c/li\u003e\n \u003cli\u003eDespite the overall increase in prescribing errors during the study period and the 12 months after the study period, a marked difference in inaccurate prescribing trend was also seen between hospital groups where the hospital pharmacist agreed to be involved with the audit and feedback intervention.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eWhat do these findings mean?\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe extent to which actionable audit and feedback interventions reflect the complexity of routine hospital care in SSA determine whether long-term improvements in prescribing practices can be delivered on an ongoing basis.\u003c/li\u003e\n \u003cli\u003eMore research is needed to understand why and how to obtain sustained reductions in antibiotic prescribing errors during hospital stay in SSA.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003ePrescribing practices are a global concern given that failures at any stage of the medication-use process -from prescribing, dispensing, transcription, administration, and monitoring- can result in serious patient harm and additional healthcare expenditure [1\u0026ndash;3]. Majority of research on prescribing practices in everyday clinical practice originate from high-income settings [3]. Based on the sparse evidence available, the burden of prescribing errors appears to be considerably greater in low- and middle-income countries (LMICs) [4\u0026ndash;6], where the uptake of electronic health records (EHRs), and, consequently, electronic prescribing systems with embedded safety checks, remains low in most healthcare facilities [7, 8].\u003c/p\u003e \u003cp\u003eThe challenge of poor medication prescribing practices in hospital care is especially pertinent in the neonatal period (i.e. first 28 days of life) [9, 10] where the population with severe illness has high mortality [11], and dose calculations are often complex. Around 15% of drug prescriptions have errors in neonatal care settings [9], but there is scant research on interventions to improve medication safety in LMICs where neonatal sepsis is common [12] and where concerns over antibiotic resistance and antimicrobial stewardship are growing [13].\u003c/p\u003e \u003cp\u003eIn this study, we explored a pharmacist-led, audit and feedback (A\u0026amp;F) intervention guided by the Clinical Performance Feedback Intervention Theory (CP-FIT) to \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eR\u003c/span\u003eeduce \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGe\u003c/span\u003entamicin dosing errors in \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eN\u003c/span\u003eewborn \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eT\u003c/span\u003ereatment (i.e. ReGENT study) in Kenyan hospitals. We build on previously reported studies [14\u0026ndash;16] and explain the intervention in detail in this study\u0026rsquo;s published protocol [17]. Here we report an evaluation of the impact of a pharmacist-led A\u0026amp;F intervention on inaccurate gentamicin prescribing for newborns receiving inpatient care over a three-year period.\u003c/p\u003e \u003cp\u003eThis study\u0026rsquo;s objective was to assess whether an enhanced pharmacist-led audit and feedback intervention reduces neonatal gentamicin prescribing errors more effectively than standard hospital feedback over time.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEthics approvals\u003c/h2\u003e \u003cp\u003eThe analyses described in this study were approved by the KEMRI\u0026rsquo;s Scientific and Ethical Review Committee (SERU #4378 and SERU #3459) with detailed information provided in the protocol [17]. Our description follows the TREND statement [18] for improving the reporting quality of non-randomised evaluations of interventions (Supplementary Materials) and the Template for Intervention Description and Replication (TIDieR) Checklist [19] (Supplementary Materials).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy setting\u003c/h3\u003e\n\u003cp\u003eThe study was conducted in partnership with 22 hospitals in Kenya. These hospitals were purposefully selected to join a Kenyan Clinical Information Network (CIN, see below) [8, 20, 21], be of at least moderate size and representative of different malaria transmission zones. These hospitals admit small and sick newborns to a New-Born Unit (NBU) with a specific clinical and nursing team. The average age of neonates on admission is 0 or 1 day old and most admitted neonates are inborn [20]. These hospitals joined the CIN, a learning health system in Kenya between 2014 and 2020 [8, 20, 21]. Clinical audit reports which include summaries of gentamicin prescribing error statistics, are shared with CIN hospitals on the routine care they provide. The hospitals are provided with quarterly feedback reports on the quality of care they provide, which include a summary of Gentamicin prescribing errors statistics [22]. The quarterly report is shared to all CIN sites via email and as a printed copy to the paediatrician and the hospital manager, providing a common A\u0026amp;F intervention already in use. Neonatal team leaders (neonatologists, paediatricians, and nurses) also met face-to-face once or twice annually to discuss these standard A\u0026amp;F reports and how to improve multiple facets of clinical care [23, 24]. In these hospitals, in Kenya more widely and in many public sector settings in LMICs, key issues that likely influence prescribing practices and affect dose errors, paying special attention to gentamicin the first-line antibiotic for severe neonatal infection, are listed in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContextual and Gentamicin prescribing factors in typical neonatal units in public health facilities in LMICs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA) Contextual issues\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1) There is absence of clinical decision support for dose validation during prescribing due to low/no EHRs in these sites [35],\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2) Majority of patients are admitted and prescribed medication by junior clinicians who rotate quarterly between medical departments [36] \u0026ndash; and until recently, are likely to have insufficient training in neonatal care.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3) There are barely more than two consultant doctors in these health facilities [37], so the junior clinicians who prescribe frequently operate with minimal supervision from any consultant.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4) Pharmacists are scarce, and in most settings they have little or no direct involvement in ward-based supervision or in providing prescribing education within newborn units. [31, 38].\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5) There are standardised clinical care guidelines and protocols, similar to and adapted from the World Health Organisation (WHO) guidelines, that are broadly circulated to clinicians, that are meant to inform their prescribing practices [27],\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6) Prescriptions of gentamicin and other aminoglycosides are not followed with the expected therapeutic drug monitoring in most public healthcare facilities [25].\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7) Clinicians typically follow empiric antibiotic prescribing during admission due to limited access to diagnostics such as blood cultures .\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eB) Gentamicin prescribing context\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8) Gentamicin which is considered as essential medicine by WHO serves as first-line therapy for neonatal sepsis in most LMICs including Kenya, and its use extends to community treatment programmes [13, 26\u0026ndash;28].\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9) Gentamicin has well-known toxicity risks with overdoses administered for too long [29] while underdosing is ineffective in killing bacterial; Suboptimal dosing is therefore a major concern, given its contribution to the global rise in antimicrobial resistance [13, 26, 28].\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10) WHO and Kenyan dosing recommendations are determined by both body weight and postnatal age, making gentamicin prescribing more complex than for many other medicines and thereby increasing the potential for prescribing errors [22, 30].\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11) Previous work in the Kenyan hospitals included in this study indicates that roughly 14% of the prescriptions for Gentamicin contain dosing errors, reflecting doses that are outside the recommended levels, with earlier studies from Kenya reporting higher error rates [31, 32].\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: \u003cb\u003eContextual and Gentamicin prescribing factors in typical neonatal units in public health facilities in LMICs\u003c/b\u003e\u003c/p\u003e\n\u003ch3\u003eStudy design\u003c/h3\u003e\n\u003cp\u003eThe study design is described in detail in the published protocol [17]. In summary, the primary study was a standard single-group interrupted time-series study (ITS) design for the primary analysis to evaluate the comparative effectiveness of the enhanced A\u0026amp;F in reducing prescribing error trend after its introduction (which we explain in detail in the intervention section). No randomisation was planned, the aim was to include all hospitals in the intervention but pharmacists from 6 CIN facilities refused to participate [17]. This provided an opportunity for an intervention versus control comparison as a secondary analysis using a parallel group ITS design to evaluate prescribing error trends in the two hospital groups (Fig.\u0026nbsp;1) while recognising the potential selection bias in the facilities that rejected the pharmacist-led enhanced A\u0026amp;F intervention approach.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure 1\u003c/strong\u003e \u003cp\u003e \u003cem\u003eFlow chart of the intervention roll-out. The ITS starts prior to hospitals being assigned to control or experiment group\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eIntervention: Audit and Feedback (A\u0026F)\u003c/h3\u003e\n\u003cp\u003eA\u0026amp;F is hypothesised to be more effective when they are multifaceted, incorporate educational elements, engage key actors such as pharmacists as facilitators or clinical champions, and are aligned with clinicians\u0026rsquo; professional goals [14, 25]. There has been limited evaluation practices in empirical studies of the contribution of pharmacists as key facilitators/champions to improving prescribing [26, 27]. While providing performance feedback to individual clinicians is considered highly effective [16] in LMICs, shortages of healthcare workers and constrained time and resources limit opportunities for individual engagement with such feedback [16].\u003c/p\u003e \u003cp\u003eLimitations in routinely available clinical data and digital infrastructure In many LMICs hospital settings constrain the design and implementation of A\u0026amp;F interventions, exacerbated by lack of evidence on the added value of engaging local clinical champions together with the feasibility of delivering feedback at the team versus individual clinician level [16]. The evaluation of the effect of A\u0026amp;F interventions also needs to consider the decay in effect over time by aiming to go beyond a single feedback cycle, short-term intervention period or simple before-after designs [28].\u003c/p\u003e \u003cp\u003eIn response to calls for more \u0026lsquo;head to head\u0026rsquo; studies of different forms of A\u0026amp;F to help identify \u0026lsquo;what works\u0026rsquo; [16], The design of our A\u0026amp;F intervention was informed by the contextual characteristics of these settings as well as by existing empirical evidence and theoretical frameworks [29, 30] in the following ways:\u003c/p\u003e \u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe feedback visualisations were limited to three and delivered as a one-pager PDF with infographics to reflect HCWs finite capacity to handle yet another feedback report. These were designed to communicate explicit goals within the control of the recipients and whether clinical team performance had room for improvement, and allow benchmarking with peer hospitals, while improving the specificity and trend reporting of performance [16].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWe provided an Android smartphone-based feedback performance dashboard to ensure active A\u0026amp;F delivery and ease automated performance analysis and instantaneous presentation of feedback to recipients [16].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eGiven the strong views that clinicians hold about appropriate care, which shape how they engage with performance feedback, a WhatsApp channel was introduced to facilitate communication and to assess whether perceptions, acceptability, and intentions arising from interaction with the A\u0026amp;F intervention were consistent with observed practices [16].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWe targeted the use of pharmacists as champions in A\u0026amp;F strategy because where HCWs with source knowledge and skill advocated for the feedback intervention, they often influenced others and provided additional resource to enable more meaningful engagement with feedback [16].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e We provided pharmacist-led Continuous Medical Education (CME) seminars linked to NBU antimicrobial stewardship and prescribing practices (Supplementary Table\u0026nbsp;1) to strengthen both the knowledge and skills in quality improvement and in the AMS clinical topics [16].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e \u003cp\u003eThe theoretical underpinnings of the enhanced A\u0026amp;F report visualisations are provided in detail in the protocol [16, 17]. The enhanced A\u0026amp;F feedback reports in this study provided feedback through: (1) score cards reporting deviation from explicit targets, (2) peer comparison of Gentamicin prescribing errors by newborn patient sub-groups, and (3) hospital-specific performance trends over a six-month period summarised by newborn age-groups [17]. The differences in the A\u0026amp;F components between the study arms are illustrated in Fig.\u0026nbsp;1. Details of the feedback components, together with the enhanced A\u0026amp;F PDF infographics are provided in Supplementary Table\u0026nbsp;1\u0026ndash;2 and the published protocol\u0026rsquo;s Supplementary Figs.\u0026nbsp;1\u0026ndash;3 [17].\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome of this study was the ratio of neonates with incorrect Gentamicin dose prescribed at admission. The calculation of the correct prescription according to the Kenyan guidelines is age and weight dependent as illustrated in Fig.\u0026nbsp;2 where Gentamicin prescription is reported in milligram units. In summary, for neonates\u0026thinsp;\u0026lt;\u0026thinsp;7 days old, the correct gentamicin dosage is 4 mg/kg to 6 mg/kg, or 2.4 mg/kg to 3.6 mg/kg if birth weight is less than 2000 grams. For neonates who are 7 days old and older, the correct gentamicin dosage is between 6 mg/kg and 9 mg/kg ; The dosage calculations per kilogram for the study outcome allowed for \u0026plusmn;\u0026thinsp;20% deviation, outside which they are considered errors.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure 2\u003c/strong\u003e \u003cp\u003e \u003cem\u003eAdapted from study protocol [17]. Study primary outcome from patients admitted to the NBUs. Dosage calculations per kilogram allow for \u0026plusmn;\u0026thinsp;20% deviation, outside which they are considered errors.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eFrom the CP-FIT model, this outcome represents the standard of clinical performance against which clinical behaviour would be measured explicitly (\u003cem\u003eGoal setting\u003c/em\u003e) [16]. This study\u0026rsquo;s target process outcome was at least a 35% reduction in prescription errors from individual hospital baseline error rates with this target based on published evidence of 35\u0026ndash;50% reductions in prescription errors in neonatal care from before-after studies, which translates to an absolute change from 14% to 9.1% in prescribing error reduction [27].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData collection procedures and management\u003c/h2\u003e \u003cp\u003eMethods of collection and cleaning of data in the CIN are reported in detail elsewhere [22, 31]. In summary, clinical data for neonatal admissions to the hospitals within the CIN were captured through structured Neonatal Admission Record (NAR) forms coupled with standard treatment sheets that are approved by the Ministry of Health. The NAR prompts the clinician with a checklist of fields including patient biodata, clinical assessment, admission and discharge diagnoses, and record of outcome (survival or death). The CIN supports one data clerk in each hospital to extract data from paper medical records, nursing charts, treatment charts, and available laboratory reports each day after a newborn\u0026rsquo;s discharge into the primary data collection tool developed in Research Electronic Data Capture (REDCap). Automated error checking happens at the point of entry by daily review, every week centrally and both are complemented by regular external data quality assurance reviews [31]. A minimal dataset \u0026ndash; which is unsuitable for our planned analyses - is collected for (1) admissions during major holiday breaks, (2) admissions when the data clerk was on leave, and (3) on a random selection of records in hospitals where the workload is very high. This process is explained in detail elsewhere [31, 32]. All data in de-identified form derived from medical records was stored in secure KEMRI-Wellcome Trust Research Programme servers with specified researchers provided password protected access.\u003c/p\u003e \u003cp\u003eFor this study deidentified data from all patients admitted to the selected hospitals\u0026rsquo; NBUs who are under the age of 28 days (i.e., neonates) who had a Gentamicin drug prescription on admission were eligible for inclusion in the analysis regardless of gestational age as in Kenya prescriptions are based on weight and postnatal age rather than gestation at birth. Only the patient population with full data on age, weight, and the dose of the prescription were included in the outcome measurement since these data are needed to measure errors. Inadequate Gentamicin prescription documentation occurred in \u0026lt;\u0026thinsp;1% of all prescriptions documented within CIN in the 24 months before intervention introduction. During the intervention period and the analysis stage, the participating hospitals and clerks capturing prescribing data were blinded to the study arm assignment, but the research staff were not.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProtocol deviations\u003c/h3\u003e\n\u003cp\u003eIn the 16 facilities that received the intervention, contrary to the expectations in the published protocol, none of the pharmacists were willing to join the NBU clinical team WhatsApp group where one existed or form a NBU WhatsApp group where none existed. All healthcare workers save 6 did not use the mobile application to access the performance dashboards, preferring the monthly PDF reports instead. These deviations rendered no substantive difference in the enhanced A\u0026amp;F intervention packages between the intended study arms as envisioned in the published protocol (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;1)[17]; We still provide analysis results based on the intention-to-treat principles.\u003c/p\u003e \u003cp\u003ePost-hoc analysis of the 12 months after the intervention had ended was decided upon at the end of the intervention roll-out to evaluate whether any intervention effect would be sustained after the study by comparing the 16 sites that had received the pharmacist led A\u0026amp;F intervention versus the 6 sites that only receiving the standard A\u0026amp;F reports from the CIN.\u003c/p\u003e\n\u003ch3\u003eData analysis and statistical methods\u003c/h3\u003e\n\u003cp\u003eThis study involved all healthcare workers prescribing gentamicin in the NBUs of the participating hospitals. On average, there is one paediatrician, one medical officer and several medical officer and clinical officer interns who might prescribe. They are supported by three daytime nurses, and two night-nurses in a typical NBU unit. As medical officer and clinical officer interns rotate typically every 3 months the total number of prescribers working in the NBU units over the period was in the hundreds [33] making it difficult to attribute a patient\u0026rsquo;s Gentamicin prescribing to individual clinicians.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eInterrupted Time Series analysis\u003c/h2\u003e \u003cp\u003eWe applied a segmented linear mixed effects model with an autoregressive covariance structure on the proposed Interrupted Time Series (ITS) design, that accounts for the pre-intervention trends in the study outcomes [34]. Informed by previous findings [27], our hypothesised impact model assumed month-to-month (slope) changes following the implementation of the intervention, but no immediate (level) change. We adopted a negative binomial modelling approach which we explain in detail in the protocol [17], and report study outcomes as incidence rate ratios (IRR), where values with 95% confidence intervals(CI)\u0026thinsp;\u0026gt;\u0026thinsp;1 indicate increase in prescribing error rates while the reverse (i.e. 95% CI\u0026thinsp;\u0026lt;\u0026thinsp;1) indicate decrease in prescribing error rates at admission; IRR values with 95% CI containing 1 indicate no change in the prescribing error rates at admission. The primary analysis for this study was the comparison of pre-intervention to post-intervention trends of the study outcomes in the facilities that consented to participate in the enhanced A\u0026amp;F intervention approach. We also conducted secondary analysis comparing the pre- and post-intervention trends in the study outcome between the facilities that were on the enhanced A\u0026amp;F approach compared to those that were on the basic A\u0026amp;F approach.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eInterrupted Time Series (ITS) Sample size\u003c/h2\u003e \u003cp\u003eDetailed explanations on our approach to sample size calculations are provided in our published protocol [17]. The primary analysis followed a single group ITS study design in keeping with a priori study design and entailed the 16/22 facilities that consented to pharmacist-led enhanced A\u0026amp;F intervention approach. All analysis was done in R (v 4.4.3) statistical computing software [35].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePrimary analysis\u003c/h2\u003e \u003cp\u003eThe baseline level of erroneous Gentamicin prescribing at admission was 12.07% (95% CI: 11.56% \u0026minus;\u0026thinsp;12.58%) across the intervention sites (n\u0026thinsp;=\u0026thinsp;16) from the pre-intervention period. From published reviews, A\u0026amp;F interventions have a median absolute effect size of 4.3% in improving practice behaviour; We estimated that with 950 patients per month across the 16 facilities, our study would have 80% power to detect this 4.3% reduction of the month-to-month prescription error trend over 12 months (i.e. slope change) with a statistical significance of 0.05. The monthly average of admissions to NBUs with a gentamicin prescription at admission during the pre-intervention period in all the 16 facilities that consented to the enhanced pharmacist-led A\u0026amp;F intervention approach was 1325 patients (with a standard deviation of 70 across the 16 hospitals).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSecondary analysis\u003c/h2\u003e \u003cp\u003eThe 6/22 CIN sites that chose to not participate in the study were treated as a control group within the ITS design to examine whether improvements in performance (correct prescribing) were more pronounced than improvements that may be linked to underlying secular trends [36]. When considering both the intervention arm and control arm (sites\u0026thinsp;=\u0026thinsp;22), the baseline monthly level of erroneous Gentamicin prescribing at admission was 12.3% (95% CI: 11.84% \u0026minus;\u0026thinsp;12.76%) across the 22 facilities from the pre-intervention period. Based on previously published studies [27], we estimated that with 835 patients per month across the 22 facilities, our study would have 85% power to detect an effect size of 15% reduction of the month-to-month prescription error trend over 12 months (i.e. slope change due to arm) with a statistical significance of 0.05. The monthly average admissions to NBUs with a gentamicin prescription at admission in all the 22 facilities during the pre-intervention period was 1617 patients (with a standard deviation of 76 across the 22 hospitals).\u003c/p\u003e \u003cp\u003eSample size analysis at the individual hospital level revealed that all the hospitals did not have sufficient patient numbers per month with gentamicin prescription to facilitate separate within hospital time-series analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eProcess evaluation to assess fidelity\u003c/h2\u003e \u003cp\u003eWe used messages shared over the intervention period in the study\u0026rsquo;s pharmacists WhatsApp group (which was specifically designed as a group for intended intervention champions) to explore group members\u0026rsquo; engagement with the enhanced A\u0026amp;F intervention during the study period. The messages shared were summarised according to their source, target, timing, and content. While the content of the shared messages did not lend itself to the exploration of the reception, comprehension, and acceptance of the feedback by the pharmacists (\u003cem\u003eInteraction\u003c/em\u003e, \u003cem\u003ePerception\u003c/em\u003e, and \u003cem\u003eAcceptance\u003c/em\u003e respectively) and planned behavioural responses that may be attributed to the feedback (\u003cem\u003eIntention\u003c/em\u003e and \u003cem\u003eBehaviour\u003c/em\u003e), we qualitatively summarised the messages for content on barriers to behaviour change linked to the A\u0026amp;F intervention [16]. We also convened a feedback meeting with the HCWs in the intervention sites to explore challenges and successes from the intervention implementation in CIN hospitals and to learn and gather feedback on how to better design and implement future A\u0026amp;F interventions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eParticipant flow and recruitment\u003c/h2\u003e \u003cp\u003eThe study\u0026rsquo;s first phase which involved baseline data collection 12 months prior to intervention introduction ran from 01st July 2021 to 30th June 2022 (pre-intervention period). The intervention phase ran for 12 months afterwards from 01st July 2022 to 30th June 2023. The post-intervention period ran for an additional 12 months from 01st July 2023 to 30th June 2024. Of the 46381 patients from the 22 CIN hospitals prescribed Gentamicin at admission and eligible for inclusion in this study, 37109/46381 (80.39%) of patients from 16/22 hospitals were in the arm that received the enhanced A\u0026amp;F intervention (Fig.\u0026nbsp;3), while 9272/46381 (19.99%) were from 6/22 hospitals in the control arm. While no hospitals were lost to follow-up, 441/37109 (1.19%) and 329/9272 (3.55%) patients from the experiment and control arm were dropped due to missing data on key prescribing variables of age and weight (Fig.\u0026nbsp;1, Fig.\u0026nbsp;3). Subsequently, 36668/37109 (98.81%) and 8943/9272 (96.45%) of the patients were in the experiment and control arm respectively were included in the subsequent analysis.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure 3\u003c/strong\u003e \u003cp\u003e \u003cem\u003eFlow-chart of patients included in the ReGENT study analyses\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eBaseline data and numbers analysed\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the baseline demographic and clinical characteristics for patients in each arm of the study. Patients in both arms had similar gestations, age, birth weight, length of hospital stay and mortality rate despite the large difference in the number of patients in the study arms. From summary descriptive analysis of before and after the intervention, the gentamicin prescribing accuracy after the enhanced A\u0026amp;F intervention was introduced appears no different in the experiment arm compared to the period before the intervention (Fig.\u0026nbsp;4), the same pattern appears true for the control arm. Figure\u0026nbsp;4 also provides the absolute error rates by patient sub-group which are important results that are rarely reported from SSA.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Summary Statistics (n\u0026thinsp;=\u0026thinsp;45611)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eControl Arm, n\u0026thinsp;=\u0026thinsp;8943\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eExperiment Arm, n\u0026thinsp;=\u0026thinsp;36668\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSummary Statistic\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMissing Rate\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSummary Statistic\u003c/b\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMissing Rate\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeonate Age (Days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (IQR: 1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (IQR: 1\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth Weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.65 (IQR: 1.81\u0026ndash;3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7 (IQR: 1.9\u0026ndash;3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSENSS Score\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.6 (IQR: 3.9\u0026ndash;16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.1 (IQR: 3-14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational Age\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (IQR: 33\u0026ndash;39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e826 (9.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (IQR: 34\u0026ndash;39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3179 (8.67%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5032 (56.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20774 (56.65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87 (0.24%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of stay (Days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (IQR: 3\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (IQR: 3\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100 (0.27%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMortality Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1468 (16.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (0.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5411 (14.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (0.05%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eNote:\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e Continuous variable summaries given as median (Interquartile range). Discrete variable summaries given as proportions\u003c/p\u003e \u003cp\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e SENSS (Score of Essential Newborn Symptoms and Signs) score provided as a mortality risk probability (derived from sex, birthweight, presence or absence of difficulty feeding, convulsions, indrawing, central cyanosis and floppy/inability to suck).\u003c/p\u003e \u003cp\u003e\u003csup\u003e3\u003c/sup\u003e Provided in weeks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: \u003cb\u003eDescriptive Summary Statistics (n\u0026thinsp;=\u0026thinsp;45611)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure 4\u003c/strong\u003e \u003cp\u003e \u003cem\u003eBreakdown of the Gentamicin prescribing errors at admission by study period and ReGENT study participation\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe trends in Gentamicin prescribing errors in the experiment arm was less variable compared to the control arm for both overdose and underdose errors (Fig.\u0026nbsp;5). The prescribing error trends were also quite variable in both study arms for the different neonatal inpatient sub-populations (Supplementary Fig.\u0026nbsp;1\u0026ndash;2). There is a strong indication of a large between-hospital variability in the prescribing error trends in both study arms (Supplementary Fig.\u0026nbsp;3\u0026ndash;4).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure 5\u003c/strong\u003e \u003cp\u003e \u003cem\u003eOverall trends in Gentamicin prescribing error at admission\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes and estimation\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003eIntervention effect in the enhanced A\u0026amp;F group\u003c/h2\u003e \u003cp\u003eOverall, from the intention-to-treat analysis results (Supplementary Table\u0026nbsp;3), there was no enhanced A\u0026amp;F intervention effect to reduce the incidence of inaccurate Gentamicin prescription in neonates at admission (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;6), either as a step or trend change. This may be a consequence of very limited fidelity to the intervention as specified in the protocol [17].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReGENT study intervention effect on reducing all Gentamicin prescribing errors at admission (Hospitals\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePre-intervention phase compared to intervention phase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eIntervention phase compared to post-intervention phase\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIRR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIRR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(intercept)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.119 to 0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.099 to 0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etime\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.962 to 0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.945 to 1.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eintervention\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.920 to 1.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.695 to 1.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eintervention \u0026times; I(time\u0026thinsp;\u0026minus;\u0026thinsp;12)\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.986 to 1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.964 to 1.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eNote:\u003c/p\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eBaseline performance at the start of the pre-intervention period\u003c/p\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003ePre-intervention trend\u003c/p\u003e \u003cp\u003e\u003csup\u003e3\u003c/sup\u003eStep change in performance due to the intervention\u003c/p\u003e \u003cp\u003e\u003csup\u003e4\u003c/sup\u003eChange in the trend post-intervention\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the 12 months post-intervention for the 16 hospitals receiving the enhanced A\u0026amp;F intervention, the trend in gentamicin prescribing errors at admission worsened, increasing by 4.4% (p-value\u0026thinsp;=\u0026thinsp;0.289) month-to-month, although it was not statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFigure 6\u003c/strong\u003e \u003cp\u003e \u003cem\u003eThe modelled impact of ReGENT study on all Gentamicin prescribing errors at admission\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e:\u003cb\u003eReGENT study intervention effect on reducing all Gentamicin prescribing errors at admission (Hospitals\u0026thinsp;=\u0026thinsp;16)\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eSecondary analysis: Intervention effect in the enhanced A\u0026amp;F group in comparison to the control arm\u003c/h2\u003e \u003cp\u003eRelative to the control arm, the experiment arm had a 7.3% (p-value\u0026thinsp;=\u0026thinsp;0.009) reduction in prescribing errors during the intervention phase (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). After the enhanced A\u0026amp;F intervention ended, the month-to-month trend in prescribing errors at admission increased by 6% (p-value\u0026thinsp;=\u0026thinsp;0.028). However, relative to the control arm, the experiment arm had 57.6% (p-value\u0026thinsp;=\u0026thinsp;0.015) less prescribing errors at admission post-study, and the trend in the experiment arm relative to the control arm post-study had a 8.1% (p-value\u0026thinsp;=\u0026thinsp;0.005) month-to-month reduction in the admission prescribing errors (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSecondary analysis of the ReGENT study intervention effect on reducing all Gentamicin prescribing errors at admission (Hospitals\u0026thinsp;=\u0026thinsp;22)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003ePre-intervention phase compared to intervention phase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eIntervention phase compared to post-intervention phase\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIRR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIRR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(intercept)\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.127 to 0.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.110 to 0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etime\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.922 to 0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.950 to 1.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eintervention\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.922 to 1.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.934 to 1.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eintervention \u0026times; I(time\u0026thinsp;\u0026minus;\u0026thinsp;12)\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.036 to 1.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.006 to 1.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003earm\u003csup\u003e5\u003c/sup\u003e (Ref: Control arm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.470 to 1.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.213 to 0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etime \u0026times; arm\u003csup\u003e6\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998 to 1.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.009 to 1.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003earm \u0026times; intervention \u0026times; I(time\u0026thinsp;\u0026minus;\u0026thinsp;12)\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.878 to 0.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.866 to 0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eNote:\u003c/p\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eBaseline performance at the start of the pre-intervention period\u003c/p\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003ePre-intervention trend\u003c/p\u003e \u003cp\u003e\u003csup\u003e3\u003c/sup\u003eStep change in performance due to the intervention\u003c/p\u003e \u003cp\u003e\u003csup\u003e4\u003c/sup\u003eChange in the trend post-intervention\u003c/p\u003e \u003cp\u003e\u003csup\u003e5\u003c/sup\u003eBaseline performance in the experiment arm relative to the control arm\u003c/p\u003e \u003cp\u003e\u003csup\u003e6\u003c/sup\u003ePre-intervention trend in the experiment arm relative to the control arm\u003c/p\u003e \u003cp\u003e\u003csup\u003e7\u003c/sup\u003eChange in the trend post-intervention in the experiment arm relative to the control arm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e: \u003cb\u003eSecondary analysis of the ReGENT study intervention effect on reducing all Gentamicin prescribing errors at admission (Hospitals\u0026thinsp;=\u0026thinsp;22)\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eFidelity to intervention plan/ participants explanations for observed effects\u003c/h2\u003e \u003cp\u003eFeedback about the draft findings of the ReGENT study was elicited from the paediatricians and nurses in charge of the newborn units of the hospitals that received the intervention. A key limiting factor was the small number of pharmacists and paediatricians available in the whole hospital vis-\u0026agrave;-vis the numbers of neonatal patients and the number of junior clinicians (including interns) that constrained the amount of time they had to provide mentorship on antimicrobial stewardship to junior clinicians. The mentorship challenge was further exacerbated by the high staff turnover coupled with frequent rotation of interns (who provide the bulk of the patient care) every 8\u0026ndash;12 weeks that was unsynchronised across the medical and nursing carders making it difficult to sustain any departmental progress in practice. There was clear indication that in some hospitals, the practices being reinforced were in conflict with what was in the clinical guidelines e.g. use of admission weight instead of birth weight to calculate the dosage to prescribe. Additionally, there were only six unique downloads and use of the Android-based smartphone application providing interactive feedback during the study duration.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eSummary of findings\u003c/h2\u003e \u003cp\u003eApplying a theory-informed A\u0026amp;F intervention, the ReGENT study found no statistically significant effect of the team-based pharmacist-led A\u0026amp;F intervention on reducing gentamicin medication errors in neonatal care. Prescribing errors during intervention and post-intervention periods were increasing across all hospitals in both arms of the study. However, relative to control hospitals sites receiving routine feedback but without pharmacist involvement or pharmacist-based CME, the ReGENT study sites had a 7.3% less Gentamicin prescription error rates at admission post-introduction of the pharmacist-led A\u0026amp;F intervention, and 8.1% less Gentamicin prescription error rates at admission in the post-intervention phase.\u003c/p\u003e \u003cp\u003e \u003cb\u003eComparison to other studies.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Gentamicin prescribing error-rates in neonatal care settings of 11%-26.4% at the beginning this study are consistent with previously published studies [9]. Studies using pharmacist-led A\u0026amp;F interventions have demonstrated at 9.6% drop in hazardous prescribing after 12 months, whereas from the findings in our secondary analysis, error rates during intervention and post-intervention periods were increasing across all places but more so in the control hospital sites, where they were significantly higher [15]. Key similarities between our study and other studies remained the use of providing actionable summaries linked to patient-level data that could be used to review patient care retrospectively. There was a fundamental difference between the previous studies where pharmacists reviewed individual at-risk patients, initiating remedial actions or advising doctors to do so. In our study, pharmacists were too few (typically one per hospital) and could not provide individual patient prescription reviews; They were constrained to co-ordinating trainings through CMEs for the clinical teams doing the actual prescribing and reviewing together with the neonatal teams organisational challenges impeding improvement of medication prescribing accuracy (e.g. medication stock-outs, lack of training etc.). In typical Kenyan clinical settings, there is little interaction between clinical teams and pharmacists to guide medication prescribing with medications reconstituted and administered by nurses on the ward; pharmacists typically only get involved for inpatient care when potentially toxic medications (e.g., chemotherapy) are administered but this is a rare event confined to higher level hospitals. Before the onset of this study, pharmacists had not previously been involved in CIN feedback activities except in some hospitals linked to the \u0026ldquo;\u003cem\u003eSupportive care and antibiotics for severe pneumonia among hospitalized children (SEARCH)\u003c/em\u003e\u0026rdquo; trial [37] where their role was to support correct use of study drugs used in the paediatric wards. In this study, while pharmacists did not start routinely visiting the NBUs, they became more involved in the CMEs going through the ReGENT feedback reports.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eInterpretation of study findings\u003c/h2\u003e \u003cp\u003eOur study findings indicates that the theory-driven pharmacist-led enhanced A\u0026amp;F intervention did not significantly change the trend in Gentamicin prescribing practices in the participating hospitals, with the overall level of prescribing errors getting worse across all hospitals. Compared to the control sites, in the ReGENT sites the trend in Gentamicin prescribing errors at admission during- and post-intervention was relatively better, which might be due to other events outside the study that might be affecting the provision of inpatient neonatal care (e.g. perhaps new clinicians trained during COVID had less idea what to do on NBUs); The control sites, defined by pharmacists declining to take part in the study, may suggest a lower level of engagement with, and the value placed on, NBU prescribing and care by hospital pharmacists.\u003c/p\u003e \u003cp\u003e The need for frequent and varied means of improving the awareness of current guidelines and care-giving norms around prescribing practices was also highlighted. Clinicians argued that the adherence to prescribing guidelines was further complicated where the newborns had other underlying complications \u0026ndash; which is common hospitals in most SSA contexts- but the clinical guidelines offered no clear direction on what should happen in that scenario. However, the presence of the WhatsApp group allowed the pharmacists to seek peer support in what advice to offer hospital teams handling such cases. The use of visual aides at patient level (e.g. stickers on patient folders) at the point of care to flag where dosing errors would be critical, involving nurses in advising junior medical doctors and physician assistants (i.e. clinical officers) on the correct practices before prescription administration, and the use of multi-disciplinary induction and ward-round approaches to mentorship were emphasised as means to improve the prescribing practice. While the clinicians argued that they would rapidly respond to and act-on feedback provided through digital platforms, from the almost negligible download and use of the Android-based smartphone application providing interactive feedback, we saw a huge disconnect between the perception and the actual practice that A\u0026amp;F digital platforms would improve access and engagement with feedback, even where the such digital platforms were co-designed with clinicians.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eStudy strengths, limitations and generalisability of findings\u003c/h2\u003e \u003cp\u003eA key strength of this study was the natural control group that was used in the secondary analysis which was able to mitigate against the potential of history bias (a primary threat to the validity of in single-arm ITS studies) where concurrent interventions or events occurring around the time of the intervention in the facility [36]. Also, we reported patterns and trends in absolute error rates by patient sub-groups which are important results that are rarely reported from SSA studies. A key limitations of this study was the inability of the pharmacists to engage in individual patient reviews (the ideal situation), due to being overstretched (in terms of ongoing typical facility-based tasks) coupled with the high neonatal inpatient admissions. Additionally, the high staff turnover happening every 2\u0026ndash;3 months made it difficult to sustain any improvements made through mentorship leading to a stagnation of the prescribing errors at admission. Methodologically, this study did not have sufficient statistical power to allow for ITS analyses at the birthweight and age sub-groups (i.e. the 3mg, 5mg or 7.5mg dosing levels), or the error types sub-groups (i.e. underdose, overdose). However, the findings of this study can be used to inform future pharmacist-led team-based interventions where resources are limited, the teams are large, and there is a high staff turnover.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe found no statistically significant effect of the team-based pharmacist-led A\u0026amp;F intervention on reducing gentamicin medication errors in neonatal care. Prescribing errors during intervention and post-intervention periods were increasing across all hospitals in both arms of the study during and post-intervention periods. However, relative to control hospitals sites receiving routine feedback but without pharmacist involvement or pharmacist-led CMEs, the primary study sites had a positive trend in reducing Gentamicin prescription error rates at admission during and post-introduction of the pharmacist-led A\u0026amp;F intervention which might be due to other events outside the study that might be affecting the provision of inpatient neonatal care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eA\u0026amp;F\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAudit and Feedback\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCIN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical Information Network\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCME\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eContinuous Medical Education\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCP-FIT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinical Performance Feedback Intervention Theory\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEHRs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eElectronic Health Records\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHICs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh Income Countries\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHCWs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealth Care Workers\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eITS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterrupted Time Series\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKenya Paediatric Association\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMICs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow and middle-income countries\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMoH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMinistry of Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNBU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNewborn Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuality Improvement\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSERU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKEMRI\u0026rsquo;s Scientific and Ethics Review Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSub-Saharan Africa\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch3\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eEthical approval was provided by the KEMRI Scientific and Ethical Review Committee (SERU 4378 and SERU 3459). The Scientific and Ethics Review Unit of the Kenya Medical Research Institute (KEMRI) approved the collection of the de-identified anonymised data for this study waiving the need for individual consent for access to de-identified patient data,\u0026nbsp;with the authors having no access to the information that could identify the patients.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThis study is published with the permission of the Director of Kenya Medical Research Institute (KEMRI).\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to the primary data being owned by the hospitals and their counties with the Ministry of Health; The research staff do have permission to share the data without further written approval from both the KEMRI-Wellcome Trust Data Governance Committee and the Facility, County or Ministry of Health as appropriate to the data request.\u003c/p\u003e\n\u003cp\u003eRequests for access to primary data from qualitative research by people other than the investigators will be submitted to the KEMRI-Wellcome Trust Research Programme data governance committee as a first step through [email protected] , who will advise on the need for additional ethical review by the KEMRI Research Ethics Committee.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe authors have declared that no competing interests exist\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThis work was primarily supported by a Wellcome Trust Senior Fellowship (#207522/Z/17/Z) awarded to ME and a Wellcome Trust Early Career Research Fellowship (#227562/Z/23/Z) awarded to TT. Additional support was provided by a Wellcome Trust core grant awarded to the KEMRI-Wellcome Trust Research Programme (#092654). The funders had no role in the preparation of this report or the decision to submit for publication.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAuthorship eligibility guidelines for the final reports adhere to the Contributor Roles Taxonomy (CRediT) statement guidelines.\u003c/p\u003e\n\u003cp\u003eConceptualization: TT, ME; Supervision: TT, JA; Methodology: TT, MM, DA, MO, JA; Formal Analysis: TT, MM, JA, ME; Investigation: TT, JA, ME; Resources: TT, ME, DA, MM; Data Curation: TT, GM; Writing \u0026ndash; original draft: TT, ME; Writing \u0026ndash; review \u0026amp; editing: TT, MO, JA, DA, MM, GM, ME; Funding acquisition: TT, ME;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u003cstrong\u003eClinical Information Network (CIN) Group\u003c/strong\u003e: The CIN group hospital teams who are tagged to collaborate in the network\u0026rsquo;s development, data collection, data management, implementation of audit and feedback interventions and who will participate in this study include the following focal persons:\u0026nbsp;\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003ePaediatricians:\u003c/strong\u003e Juma Vitalis, Nyumbile Bonface, Roselyne Malangachi, Christine Manyasi, Catherine Mutinda, David Kibiwott Kimutai, Rukia Aden, Caren Emadau, Elizabeth Atieno Jowi,\u0026nbsp;Cecilia Muithya, Charles Nzioki, Supa Tunje, Dr. Penina Musyoka, Wagura Mwangi, Agnes Mithamo, Magdalene Kuria, Esther Njiru, Mwangi Ngina, Penina Mwangi, Rachel Inginia,\u0026nbsp;Melab Musabi, Emma Namulala, Grace Ochieng, Lydia\u0026nbsp;Thuranira, Felicitas Makokha, Josephine Ojigo, Beth Maina, Catherine Mutinda, Mary Waiyego, Bernadette Lusweti, Angeline Ithondeka, Julie Barasa, Meshack Liru, Elizabeth Kibaru, Alice Nkirote Nyaribari, Joyce Akuka, Joyce Wangari;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePharmacists:\u0026nbsp;\u003c/strong\u003eBabra Murila,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eBeatrice Kamau, Caroline Naliaka, Cynthia Nduta, Evans Gitu, Evans Makumba, Esther Nduta, Lydia Momanyi, Matini Duncan, Sally Mugo, Sarah Kibira, Stephene Gichana, Rogers Omolo, Roy Mwendwa, Wycliffe Dunde\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eNurses:\u0026nbsp;\u003c/strong\u003eAmilia Ngoda, Aggrey Nzavaye Emenwa, Patricia Nafula Wesakania, \u0026nbsp;George Lipesa, Jane Mbungu, Marystella Mutenyo,\u0026nbsp;Joyce Mbogho, Joan Baswetty,\u0026nbsp;Ann Jambi, Josephine Aritho, Beatrice Njambi, Felisters Mucheke, Zainab Kioni, Jeniffer, Lucy Kinyua, Margaret Kethi, Alice Oguda, Salome Nashimiyu Situma, Nancy Gachaja, Loise N. Mwangi, Ruth Mwai, irginia Wangari Muruga, Nancy Mburu, Celestine Muteshi, Abigael Bwire, Salome\u0026nbsp;Okisa Muyale, Naomi Situma, Faith Mueni, Hellen Mwaura,\u0026nbsp;Rosemary Mututa,\u0026nbsp;Caroline Lavu, Joyce Oketch, Jane Hore Olum, Orina Nyakina, Faith Njeru, Rebecca Chelimo, Margaret Wanjiku Mwaura, Ann Wambugu, Epharus Njeri Mburu, Linda Awino Tindi, Jane Akumu, Ruth Otieno, Slessor Osok;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHealth Record Information Officers (HRIOs):\u0026nbsp;\u003c/strong\u003eSeline Kulubi, Susan Wanjala, Pauline Njeru, Rebbecca Mukami Mbogo, John Ollongo, Samuel Soita, Judith Mirenja, Mary Nguri, Margaret Waweru, Mary Akoth Oruko, Jeska Kuya, Caroline Muthuri, Esther Muthiani, Esther Mwangi, Joseph Nganga, Benjamin Tanui, Alfred Wanjau, Judith Onsongo, Peter Muigai, Arnest Namayi, Elizabeth Kosiom, Dorcas Cherop, Faith Marete, Johanness Simiyu, Collince Danga, Arthur Otieno Oyugi, Fredrick Keya Okoth.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe \u003cstrong\u003eClinical Information Network (CIN) Group\u003c/strong\u003e\u0026rsquo;s\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003emonitored email address is [email protected] and the list can change when new paediatrician(s), nurse(s) or HRIO leave or come into the hospital.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eOpen access\u0026nbsp;\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThis is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDonaldson, L.J., et al., \u003cem\u003eMedication without harm: who\u0026apos;s third global patient safety challenge.\u003c/em\u003e The Lancet, 2017. \u003cstrong\u003e389\u003c/strong\u003e(10080): p. 1680-1681.\u003c/li\u003e\n\u003cli\u003eEuropean Medicines Agency. \u003cem\u003eMedication errors\u003c/em\u003e. 2015 [cited 2021 12th March]; 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Evidence from a longitudinal observational study of an emerging clinical network in Kenya.\u003c/em\u003e BMJ global health, 2017. \u003cstrong\u003e2\u003c/strong\u003e(4): p. e000468.\u003c/li\u003e\n\u003cli\u003eWainaina, J., et al., \u003cem\u003eHypothermia amongst neonatal admissions in Kenya: a retrospective cohort study assessing prevalence, trends, associated factors, and its relationship with all-cause neonatal mortality.\u003c/em\u003e Frontiers in Pediatrics, 2024. \u003cstrong\u003e12\u003c/strong\u003e: p. 1272104.\u003c/li\u003e\n\u003cli\u003eRamdial, K. and A.O. Gupta, \u003cem\u003eIs There a Utility of Gentamicin Trough Level Monitoring in Neonates with Presumed Early Onset Neonatal Sepsis?\u003c/em\u003e Pediatrics, 2022. \u003cstrong\u003e149\u003c/strong\u003e(1 Meeting Abstracts February 2022): p. 690-690.\u003c/li\u003e\n\u003cli\u003eWorld Health Organisation. \u003cem\u003eWHO recommendations on newborn health\u003c/em\u003e. 2017 [cited 2021 15th March]; Available from: https://apps.who.int/iris/bitstream/handle/10665/259269/WHO-MCA-17.07-eng.pdf;jsessionid=FB9C2E7AF310716433EB5CC62A34A041?sequence=1.\u003c/li\u003e\n\u003cli\u003eOrganization, W.H., \u003cem\u003eWHO model list of essential medicines: 17th list, March 2011.\u003c/em\u003e 2011.\u003c/li\u003e\n\u003cli\u003eOkomo, U., et al., \u003cem\u003eAetiology of invasive bacterial infection and antimicrobial resistance in neonates in sub-Saharan Africa: a systematic review and meta-analysis in line with the STROBE-NI reporting guidelines.\u003c/em\u003e The Lancet Infectious diseases, 2019. \u003cstrong\u003e19\u003c/strong\u003e(11): p. 1219-1234.\u003c/li\u003e\n\u003cli\u003eMusiime, G.M., et al., \u003cem\u003eRisk of gentamicin toxicity in neonates treated for possible severe bacterial infection in low‐and middle‐income countries: systematic review.\u003c/em\u003e Tropical Medicine \u0026amp; International Health, 2015. \u003cstrong\u003e20\u003c/strong\u003e(12): p. 1593-1606.\u003c/li\u003e\n\u003cli\u003eMaina, M., et al., \u003cem\u003eAntibiotic use in Kenyan public hospitals: Prevalence, appropriateness and link to guideline availability.\u003c/em\u003e International Journal of Infectious Diseases, 2020. \u003cstrong\u003e99\u003c/strong\u003e: p. 10-18.\u003c/li\u003e\n\u003cli\u003eAluvaala, J., et al., \u003cem\u003eAssessment of neonatal care in clinical training facilities in Kenya.\u003c/em\u003e Archives of disease in childhood, 2015. \u003cstrong\u003e100\u003c/strong\u003e(1): p. 42-47.\u003c/li\u003e\n\u003cli\u003eEnglish, M., et al., \u003cem\u003eA randomised, controlled trial of once daily and multi-dose daily gentamicin in young Kenyan infants.\u003c/em\u003e Archives of disease in childhood, 2004. \u003cstrong\u003e89\u003c/strong\u003e(7): p. 665-669.\u003c/li\u003e\n\u003cli\u003eTuti, T., et al., \u003cem\u003eA systematic review of electronic audit and feedback: intervention effectiveness and use of behaviour change theory.\u003c/em\u003e Implementation Science, 2017. \u003cstrong\u003e12\u003c/strong\u003e(1): p. 1-20.\u003c/li\u003e\n\u003cli\u003eNzinga, J., G. McGivern, and M. English, \u003cem\u003eExamining clinical leadership in Kenyan public hospitals through the distributed leadership lens.\u003c/em\u003e Health policy and planning, 2018. \u003cstrong\u003e33\u003c/strong\u003e(suppl_2): p. ii27-ii34.\u003c/li\u003e\n\u003cli\u003eNguyen, M.-N.R., C. Mosel, and L.E. Grzeskowiak, \u003cem\u003eInterventions to reduce medication errors in neonatal care: a systematic review.\u003c/em\u003e Therapeutic advances in drug safety, 2018. \u003cstrong\u003e9\u003c/strong\u003e(2): p. 123-155.\u003c/li\u003e\n\u003cli\u003eIvers, N., et al., \u003cem\u003eAudit and feedback: effects on professional practice and healthcare outcomes.\u003c/em\u003e Cochrane database of systematic reviews, 2012(6).\u003c/li\u003e\n\u003cli\u003eGrimshaw, J., et al., \u003cem\u003eReinvigorating stagnant science: implementation laboratories and a meta-laboratory to efficiently advance the science of audit and feedback.\u003c/em\u003e BMJ quality \u0026amp; safety, 2019. \u003cstrong\u003e28\u003c/strong\u003e(5): p. 416-423.\u003c/li\u003e\n\u003cli\u003eIvers, N.M., et al., \u003cem\u003eNo more \u0026lsquo;business as usual\u0026rsquo;with audit and feedback interventions: towards an agenda for a reinvigorated intervention.\u003c/em\u003e Implementation Science, 2014. \u003cstrong\u003e9\u003c/strong\u003e(1): p. 1-8.\u003c/li\u003e\n\u003cli\u003eTuti, T., et al., \u003cem\u003eInnovating to enhance clinical data management using non-commercial and open source solutions across a multi-center network supporting inpatient pediatric care and research in Kenya.\u003c/em\u003e Journal of the American Medical Informatics Association, 2016. \u003cstrong\u003e23\u003c/strong\u003e(1): p. 184-192.\u003c/li\u003e\n\u003cli\u003eTuti, T., et al., \u003cem\u003eImproving documentation of clinical care within a clinical information network: an essential initial step in efforts to understand and improve care in Kenyan hospitals.\u003c/em\u003e BMJ global health, 2016. \u003cstrong\u003e1\u003c/strong\u003e(1): p. e000028-e000028.\u003c/li\u003e\n\u003cli\u003eOgero, M., et al., \u003cem\u003eExamining which clinicians provide admission hospital care in a high mortality setting and their adherence to guidelines: an observational study in 13 hospitals.\u003c/em\u003e Archives of disease in childhood, 2020. \u003cstrong\u003e105\u003c/strong\u003e(7): p. 648-654.\u003c/li\u003e\n\u003cli\u003eMascha, E.J. and D.I. Sessler, \u003cem\u003eSegmented regression and difference-in-difference methods: assessing the impact of systemic changes in health care.\u003c/em\u003e Anesthesia \u0026amp; Analgesia, 2019. \u003cstrong\u003e129\u003c/strong\u003e(2): p. 618-633.\u003c/li\u003e\n\u003cli\u003eTeam, R.C., \u003cem\u003eR: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.\u003c/em\u003e http://www. R-project. org/, 2016.\u003c/li\u003e\n\u003cli\u003eLopez Bernal, J., S. Cummins, and A. Gasparrini, \u003cem\u003eThe use of controls in interrupted time series studies of public health interventions.\u003c/em\u003e International journal of epidemiology, 2018. \u003cstrong\u003e47\u003c/strong\u003e(6): p. 2082-2093.\u003c/li\u003e\n\u003cli\u003eNational Institutes of Health. \u003cem\u003eA Study to Compare Different Antibiotics and Different Modes of Fluid Treatment for Children With Severe Pneumonia (SEARCH)\u003c/em\u003e. 2019 November 2020 [cited 2022 3rd March]; Available from: https://clinicaltrials.gov/ct2/show/NCT04041791.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Medical Audit, Feedback, Inappropriate Prescribing, Antimicrobial Stewardship, Sub-Saharan Africa, Medical Records, Routine care, Newborns","lastPublishedDoi":"10.21203/rs.3.rs-9219945/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9219945/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eApproximately 14.9% of medications prescribed during hospital care in low- and middle-income countries contain errors, with neonates\u0026mdash;who experience high morbidity and mortality in these settings\u0026mdash;being particularly vulnerable. Nevertheless, evidence on effective interventions to improve prescribing practices in such contexts remains limited.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003e Our objective was to evaluate a theory-informed, pharmacist-led audit and feedback intervention aimed at improving routine prescribing practices, with an initial focus on reducing gentamicin prescribing errors in neonatal care.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used interrupted time series analysis to model fluctuations in prescribing errors for neonates\u0026thinsp;\u0026le;\u0026thinsp;28 days admitted to newborn units (NBU) in 22 hospitals in Kenya between July 2021 to June 2024 and explored intervention effects in a feedback meeting at the end of the study. The study had three phases, pre-intervention period (July 2021 to June 2022), intervention period (July 2022 to June 2023), and post-intervention period (July 2023 to June 2024). The primary study was a standard single-group interrupted time-series study (ITS) design to evaluate the comparative effectiveness of enhanced A\u0026amp;F in reducing prescribing error trends after its introduction in 16 hospitals. Secondary analysis included comparison to prescribing error outcomes in an additional six hospitals in a contemporaneous control group that received basic A\u0026amp;F reports without pharmacist involvement in the NBU prescribing practices.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBetween July 2021 and June 2024, the 16 hospitals in the primary outcome analysis and the 6 additional hospitals for the secondary outcome analysis had 36,668 and 8,943 neonates with Gentamicin prescriptions at admission retrospectively. From the incidence rate ratios (IRR) of incorrect prescribing at admission, there was no step change (IRR 1.115, 95% CI: 0.920 to 1.352, p-value\u0026thinsp;=\u0026thinsp;0.265) or trend change (IRR 1.014, 95% CI: 0.986 to 1.042, p-value\u0026thinsp;=\u0026thinsp;0.344) due to the enhanced pharmacist-led A\u0026amp;F intervention in the 16 hospitals in the primary study. From the secondary study, change in the trend post-intervention in the 16 primary study hospitals in the primary study relative to the 6 hospitals acting as a contemporaneous control group was positive (IRR 0.933, 95% CI: 0.878 to 0.985, p-value\u0026thinsp;=\u0026thinsp;0.014), despite no step change due to the enhanced A\u0026amp;F intervention.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe found no statistically significant effect of the team-based pharmacist-led A\u0026amp;F intervention on reducing gentamicin medication errors in neonatal care. Prescribing errors during intervention and post-intervention periods were increasing across all hospitals in both arms of the study during and post-intervention periods. However, relative to control hospitals sites receiving routine feedback but without pharmacist involvement or pharmacist-led CMEs, the primary study sites had a positive trend in reducing Gentamicin prescription error rates at admission during and post-introduction of the pharmacist-led A\u0026amp;F intervention.\u003c/p\u003e\u003ch2\u003eTrial registration\u003c/h2\u003e \u003cp\u003ePACTR, PACTR202203869312307. 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