Evaluating information flow in medication management process in Australian acute care facilities: A multi-professional perspective

Over the years, various interventions have been introduced to improve the medication management process. While these interventions have addressed some aspects predisposing the process to inefficiencies, significant gaps are still prevalent across the process. Studies have suggested that the goal of optimal medication therapy is achievable when information flow integrates across the various medication management process phases, stakeholders and departments involved as the patient moves through the process. To provide a cross-sectional view of the process, this study utilised a systemic philosophy to evaluate the information flow integration across the process. The research approach adopted for this study takes a positivist paradigm, which is guided by the cause and effect (causality) belief. It explored numeric measures to evaluate the relationship between constructs that assessed information flow principles (accessibility, timeliness, granularity and transparency) within the medication process and the information integration. The research design was cross-sectional and analytical, and this ensures that findings are relevant to current situations across the Australian healthcare system. Data for this research was collected using an online self-administered survey and the data assessed information flow principles and technologies used in the medication management process. There were 88 participants in this study, including doctors, nurses and pharmacists. The questions and responses were coded for analysis and data analysis techniques used were frequency analysis, Pearson’s chi-square test and multivariate analysis. Findings from this study indicates that the constructs evaluating accessibility, transparency and granularity had moderate associations with the information integration in the medication management process. Further analysis highlighted accessibility as a significant principle in explaining an increase or decrease in information integration in the medication management process. The accessibility construct referring to information retrieval was significant across the two tests conducted. Accessibility is directly related to information sharing and the assessment and monitoring and evaluation phases in the medication management process were identified as having the highest challenges with information sharing. Furthermore, the hybrid (electronic and paper) channel was preferred to support information integration in the medication management process by the participants. Among the technologies evaluated for the medication process, computer-provider-order-entry was found to be statistically significant in explaining an increase in information integration. Overall, results from this study suggest that interventions for the medication management process in Australian acute care facilities should be directed towards improving accessibility, specifically information retrieval and the sharing of information with emphasis on the assessment and monitoring phases. Implementing strategies to address the gaps identified from this research can improve information integration across the process and thereby reducing medication errors, and improving patient care management. Furthermore, the technology adoption across the process highlights that technology adoption across participants’ facilities remains a challenge in Australia

Factors predictive of successful retention in care among HIV-infected men in a universal test-and-treat setting in Uganda and Kenya: A mixed methods analysis.

BackgroundPrevious research indicates clinical outcomes among HIV-infected men in sub-Saharan Africa are sub-optimal. The SEARCH test and treat trial (NCT01864603) intervention included antiretroviral care delivery designed to address known barriers to HIV-care among men by decreasing clinic visit frequency and providing flexible, patient-centered care with retention support. We sought to understand facilitators and barriers to retention in care in this universal treatment setting through quantitative and qualitative data analysis.MethodsWe used a convergent mixed methods study design to evaluate retention in HIV care among adults (age > = 15) during the first year of the SEARCH (NCT01864603) test and treat trial. Cox proportional hazards regression was used to evaluate predictors of retention in care. Longitudinal qualitative data from n = 190 in-depth interviews with HIV-positive individuals and health care providers were analyzed to identify facilitators and barriers to HIV care engagement.ResultsThere were 1,863 men and 3,820 women who linked to care following baseline testing. Retention in care was 89.7% (95% CI 87.0-91.8%) among men and 89.0% (86.8-90.9%) among women at one year. In both men and women older age was associated with higher rates of retention in care at one year. Additionally, among men higher CD4+ at ART initiation and decreased time between testing and ART initiation was associated with higher rates of retention. Maintaining physical health, a patient-centered treatment environment, supportive partnerships, few negative consequences to disclosure, and the ability to seek care in facilities outside of their community of residence were found to promote retention in care.ConclusionsFeatures of the ART delivery system in the SEARCH intervention and social and structural advantages emerged as facilitators to retention in HIV care among men. Messaging around the health benefits of early ART start, decreasing logistical barriers to HIV care, support of flexible treatment environments, and accelerated linkage to care, are important to men's success in ART treatment programs. Men already benefit from increased social support following disclosure of their HIV-status. Future efforts to shift gender norms towards greater equity are a potential strategy to support high levels of engagement in care for both men and women

Automatic Detection of Adverse Drug Events in Geriatric Care: Study Proposal

BACKGROUND One-third of older inpatients experience adverse drug events (ADEs), which increase their mortality, morbidity, and health care use and costs. In particular, antithrombotic drugs are among the most at-risk medications for this population. Reporting systems have been implemented at the national, regional, and provider levels to monitor ADEs and design prevention strategies. Owing to their well-known limitations, automated detection technologies based on electronic medical records (EMRs) are being developed to routinely detect or predict ADEs. OBJECTIVE This study aims to develop and validate an automated detection tool for monitoring antithrombotic-related ADEs using EMRs from 4 large Swiss hospitals. We aim to assess cumulative incidences of hemorrhages and thromboses in older inpatients associated with the prescription of antithrombotic drugs, identify triggering factors, and propose improvements for clinical practice. METHODS This project is a multicenter, cross-sectional study based on 2015 to 2016 EMR data from 4 large hospitals in Switzerland: Lausanne, Geneva, and Zürich university hospitals, and Baden Cantonal Hospital. We have included inpatients aged ≥65 years who stayed at 1 of the 4 hospitals during 2015 or 2016, received at least one antithrombotic drug during their stay, and signed or were not opposed to a general consent for participation in research. First, clinical experts selected a list of relevant antithrombotic drugs along with their side effects, risks, and confounding factors. Second, administrative, clinical, prescription, and laboratory data available in the form of free text and structured data were extracted from study participants' EMRs. Third, several automated rule-based and machine learning-based algorithms are being developed, allowing for the identification of hemorrhage and thromboembolic events and their triggering factors from the extracted information. Finally, we plan to validate the developed detection tools (one per ADE type) through manual medical record review. Performance metrics for assessing internal validity will comprise the area under the receiver operating characteristic curve, F$_{1}$-score, sensitivity, specificity, and positive and negative predictive values. RESULTS After accounting for the inclusion and exclusion criteria, we will include 34,522 residents aged ≥65 years. The data will be analyzed in 2022, and the research project will run until the end of 2022 to mid-2023. CONCLUSIONS This project will allow for the introduction of measures to improve safety in prescribing antithrombotic drugs, which today remain among the drugs most involved in ADEs. The findings will be implemented in clinical practice using indicators of adverse events for risk management and training for health care professionals; the tools and methodologies developed will be disseminated for new research in this field. The increased performance of natural language processing as an important complement to structured data will bring existing tools to another level of efficiency in the detection of ADEs. Currently, such systems are unavailable in Switzerland. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/40456

Machine learning in drug supply chain management during disease outbreaks: a systematic review

The drug supply chain is inherently complex. The challenge is not only the number of stakeholders and the supply chain from producers to users but also production and demand gaps. Downstream, drug demand is related to the type of disease outbreak. This study identifies the correlation between drug supply chain management and the use of predictive parameters in research on the spread of disease, especially with machine learning methods in the last five years. Using the Publish or Perish 8 application, there are 71 articles that meet the inclusion criteria and keyword search requirements according to Kitchenham's systematic review methodology. The findings can be grouped into three broad groupings of disease outbreaks, each of which uses machine learning algorithms to predict the spread of disease outbreaks. The use of parameters for prediction with machine learning has a correlation with drug supply management in the coronavirus disease case. The area of drug supply risk management has not been heavily involved in the prediction of disease outbreaks

The application of system dynamics modelling to system safety improvement: Present use and future potential

System Dynamics has the potential to study the aspects of complex systems including its likely effect of modifications to structural and dynamic system properties that cannot be achieved with traditional approaches. This paper presents a review of literature addressing safety issues using system dynamics across safety–critical domains. Sixty-three studies were included and classified based on a customised human factors safety taxonomy framework. The thematic analysis of the literature resulted in five themes: external factors, organisational influences, unsafe supervisions, preconditions for unsafe acts and unsafe acts. The findings suggest that using system dynamics can be a potential tool in improving safety. This can be achieved through improved decision-making by basing it on system analysis, analysing past behavioural events in a modelling structure to plan effective safety policies, as well as looking at a holistic approach when analysing accidents

Machine learning risk prediction model for acute coronary syndrome and death from use of non-steroidal anti-inflammatory drugs in administrative data

Our aim was to investigate the usefulness of machine learning approaches on linked administrative health data at the population level in predicting older patients’ one-year risk of acute coronary syndrome and death following the use of non-steroidal anti-inflammatory drugs (NSAIDs). Patients from a Western Australian cardiovascular population who were supplied with NSAIDs between 1 Jan 2003 and 31 Dec 2004 were identified from Pharmaceutical Benefits Scheme data. Comorbidities from linked hospital admissions data and medication history were inputs. Admissions for acute coronary syndrome or death within one year from the first supply date were outputs. Machine learning classification methods were used to build models to predict ACS and death. Model performance was measured by the area under the receiver operating characteristic curve (AUC-ROC), sensitivity and specificity. There were 68,889 patients in the NSAIDs cohort with mean age 76 years and 54% were female. 1882 patients were admitted for acute coronary syndrome and 5405 patients died within one year after their first supply of NSAIDs. The multi-layer neural network, gradient boosting machine and support vector machine were applied to build various classification models. The gradient boosting machine achieved the best performance with an average AUC-ROC of 0.72 predicting ACS and 0.84 predicting death. Machine learning models applied to linked administrative data can potentially improve adverse outcome risk prediction. Further investigation of additional data and approaches are required to improve the performance for adverse outcome risk prediction

Closed loop medication administration using mobile nursing information system

Through this long journey of PhD study including a research on ‘Closed Loop Medication Administration Using Mobile Nursing Information System’ and the thesis writing, I obtained a lot of knowledge and experience about research method and writing. I really very appreciate the help of all my supervisors

Pharmacists’ interventions in minimising medication misadventure in children with cancer

This study evaluated 1741 interventions by clinical pharmacists to reduce medication misadventure in three clinical units of a children’s hospital in Perth, Australia, documented using snapshot self-report and observation. Commonly, pharmacists’ interventions involved taking medication histories, patient counselling and/or drug therapy changes. Active interventions were randomly assessed by an expert panel for their clinical significance. Root cause analysis was used to assess healthcare professionals’ ability to evaluate medication errors causes and formulate preventative strategies