METHOD: Type 2 diabetes mellitus patients (n = 73) attending endocrine clinic at Universiti Kebangsaan Malaysia Medical Centre (UKMMC) were randomised to either control (n = 36) or intervention group (n = 37) after screening. Patients in the intervention group received an intervention from a pharmacist during the enrolment, after three and six months of the enrolment. Outcome measures such as HbA1c, BMI, lipid profile, Morisky scores and quality of life (QoL) scores were assessed at the enrolment and after 6 months of the study in both groups. Patients in the control group did not undergo intervention or educational module other than the standard care at UKMMC.
RESULTS: HbA1c values reduced significantly from 9.66% to 8.47% (P = 0.001) in the intervention group. However, no significant changes were noted in the control group (9.64-9.26%, P = 0.14). BMI values showed significant reduction in the intervention group (29.34-28.92 kg/m(2); P = 0.03) and lipid profiles were unchanged in both groups. Morisky adherence scores significantly increased from 5.83 to 6.77 (P = 0.02) in the intervention group; however, no significant change was observed in the control group (5.95-5.98, P = 0.85). QoL profiles produced mixed results.
CONCLUSION: This randomised controlled study provides evidence about favourable impact of a pharmacist led diabetes intervention programme on HbA1c, medication adherence and QoL scores amongst type 2 diabetes patients at UKMMC, Malaysia.
Participants and methods: A total of 11 stakeholders comprising health care providers, administrators, caretakers and residents were recruited from a list of registered government, nongovernmental organization and private RACFs in Malaysia from September 2016 to April 2017. An exploratory qualitative study adhering to Consolidated Criteria for Reporting Qualitative Studies was conducted. In-depth interview was conducted with consent of all participants, and the interviews were audio recorded for later verbatim transcription. Observational analysis was also conducted in a noninterfering manner.
Results and discussion: Three themes, namely medication use process, personnel handling medications and culture, emerged in this study. Medication use process highlighted an unclaimed liability for residents' medication by the RACFs, whereas personnel handling medications were found to lack sufficient training in medication management. Culture of the organization did affect the medication safety and quality improvement. The empowerment of the residents in their medication management was limited. There were unclear roles and responsibility of who manages the medication in the nongovernment-funded RACFs, although they were well structured in the private nursing homes.
Conclusion: There are important issues related to medication management in RACFs which require a need to establish policy and guidelines.
Objectives: To identify and prioritize learning needs based on self-perceived competence of ward pharmacists in AMS, to identify predictors of self-perceived competence, learning methods in AMS and perceived barriers to learning.
Methods: A cross-sectional survey involving ward pharmacists from Hospital Canselor Tuanku Muhriz (HCTM) and hospitals under the Ministry of Health was conducted from May to July 2018.
Results: A total of 553 ward pharmacists from 67 hospitals responded to this survey (71.3% response rate). Knowledge of infections, antimicrobials and AMS systems, confidence to advise on various issues relating to antimicrobial therapy and participation in clinical audit and evaluation were among the learning needs identified (median score 3.00). Meanwhile, knowledge on the epidemiology of infections, off-label use of antimicrobials and pharmacoeconomics relating to antimicrobials had lower median scores (2.00) and were thus prioritized as high learning needs. Significant predictors of self-perceived competence in AMS were: gender (P
Methods: Data from 160 hypertensive patients from a tertiary hospital in Kuala Lumpur, Malaysia, were used in this study. Variables were ranked based on their significance to adherence levels using the RF variable importance method. The backward elimination method was then performed using RF to obtain the variables significantly associated with the patients' adherence levels. RF, SVR and ANN models were developed to predict adherence using the identified significant variables. Visualizations of the relationships between hypertensive patients' adherence levels and variables were generated using SOM.
Result: Machine learning models constructed using the selected variables reported RMSE values of 1.42 for ANN, 1.53 for RF, and 1.55 for SVR. The accuracy of the dichotomised scores, calculated based on a percentage of correctly identified adherence values, was used as an additional model performance measure, resulting in accuracies of 65% (ANN), 78% (RF) and 79% (SVR), respectively. The Wilcoxon signed ranked test reported that there was no significant difference between the predictions of the machine learning models and the actual scores. The significant variables identified from the RF variable importance method were educational level, marital status, General Overuse, monthly income, and Specific Concern.
Conclusion: This study suggests an effective alternative to conventional methods in identifying the key variables to understand hypertensive patients' adherence levels. This can be used as a tool to educate patients on the importance of medication in managing hypertension.
Purpose: This study aimed to explore the roles of culture, religiosity, and spirituality on adherence to anti-hypertensive medications.
Methodology: A semi-structured qualitative interview was used to explore promoters and barriers to medication adherence among hypertensive individuals residing in urban and rural areas of Perak State, West Malaysia. Study participants were individuals who are able to comprehend either in Malay or English, above 18 years old and on antihypertensive medications. Interview transcriptions from 23 participants were coded inductively and analyzed thematically. Codes generated were verified by three co-investigators who were not involved in transcribing process. The codes were matched with quotations and categorized using three levels of themes named as organizing, classifying and general themes.
Results: Cultural aspects categorized as societal and communication norms were related to non-adherence. The societal norms related to ignorance, belief in testimony and anything "natural is safe" affected medication adherence negatively. Communication norms manifested as superficiality, indirectness and non-confrontational were also linked to medication non-adherence. Internal and organizational religiosity was linked to increased motivation to take medication. In contrast, religious misconception about healing and treatment contributed towards medication non-adherence. The role of spirituality remains unclear and seemed to be understood as related to religiosity.
Conclusion: Culture and religiosity (C/R) are highly regarded in many societies and shaped people's health belief and behaviour. Identifying the elements and mechanism through which C/R impacted adherence would be useful to provide essential information for linking adherence assessment to the interventions that specifically address causes of medication non-adherence.
OBJECTIVE: This study aimed to estimate and critically appraise the evidence on the prevalence, causes and severity of medication administration errors (MAEs) amongst neonates in Neonatal Intensive Care Units (NICUs).
METHODS: A systematic review and meta-analysis was conducted by searching nine electronic databases and the grey literature for studies, without language and publication date restrictions. The pooled prevalence of MAEs was estimated using a random-effects model. Data on error causation were synthesised using Reason's model of accident causation.
RESULTS: Twenty unique studies were included. Amongst direct observation studies reporting total opportunity for errors as the denominator for MAEs, the pooled prevalence was 59.3% (95% confidence interval [CI] 35.4-81.3, I2 = 99.5%). Whereas, the non-direct observation studies reporting medication error reports as the denominator yielded a pooled prevalence of 64.8% (95% CI 46.6-81.1, I2 = 98.2%). The common reported causes were error-provoking environments (five studies), while active failures were reported by three studies. Only three studies examined the severity of MAEs, and each utilised a different method of assessment.
CONCLUSIONS: This is the first comprehensive systematic review and meta-analysis estimating the prevalence, causes and severity of MAEs amongst neonates. There is a need to improve the quality and reporting of studies to produce a better estimate of the prevalence of MAEs amongst neonates. Important targets such as wrong administration-technique, wrong drug-preparation and wrong time errors have been identified to guide the implementation of remedial measures.
METHODS AND ANALYSIS: This is a prospective direct observational study that will be conducted in five neonatal intensive care units. A minimum sample size of 820 drug preparations and administrations will be observed. Data including patient characteristics, drug preparation-related and administration-related information and other procedures will be recorded. After each round of observation, the observers will compare his/her observations with the prescriber's medication order, hospital policies and manufacturer's recommendations to determine whether MAE has occurred. To ensure reliability, the error identification will be independently performed by two clinical pharmacists after the completion of data collection for all study sites. Any disagreements will be discussed with the research team for consensus. To reduce overfitting and improve the quality of risk predictions, we have prespecified a priori the analytical plan, that is, prespecifying the candidate predictor variables, handling missing data and validation of the developed model. The model's performance will also be assessed. Finally, various modes of presentation formats such as a simplified scoring tool or web-based electronic risk calculators will be considered.
METHODS: This national-level, multicentre, prospective direct observational study was conducted in neonatal intensive care units (NICUs) of five public hospitals in Malaysia. Randomly selected nurses were directly observed during medication preparation and administration. Each observation was independently assessed for errors. Ten machine learning (ML) algorithms were applied with features derived from systematic reviews, incident reports, and expert consensus. Model performance, prioritising F1-score for MAEs, was evaluated using various measures. Feature importance was determined using the permutation-feature importance for robust comparison across ML algorithms.
RESULTS: A total of 1093 doses were administered to 170 neonates, with mean age and birth weight of 33.43 (SD ± 5.13) weeks and 1.94 (SD ± 0.95) kg, respectively. F1-scores for the ten models ranged from 76.15% to 83.28%. Adaptive boosting (AdaBoost) emerged as the best-performing model (F1-score: 83.28%, accuracy: 77.63%, area under the receiver operating characteristic: 82.95%, precision: 84.72%, sensitivity: 81.88% and negative predictive value: 64.00%). The most influential features in AdaBoost were the intravenous route of administration, working hours, and nursing experience.
CONCLUSIONS: This study developed and validated an ML-based model to predict the presence of MAEs among neonates in NICUs. AdaBoost was identified as the best-performing algorithm. Utilising the model's predictions, healthcare providers can potentially reduce MAE occurrence through timely interventions.