Development of an obstetrics triage tool for pharmacists in an urban medical centre

Obstetrics services are a high-throughput and high-risk environment poised for pharmacist involvement, but determining how to ideally allocate services is difficult. There is recent interest in the development of tools for service prioritisation, but none are specifically targeted to obstetrics. Therefore, the aim of this study was (1) to conduct a practice audit surveying the demographics of patients attending obstetrics wards at a high-capacity maternity hospital, and (2) to evaluate a triage tool developed to prioritise pharmacy services. A retrospective case review of women discharged after birth admissions was undertaken at a hospital in National Health Service (NHS) Scotland during June 2014. Demographic and admission data were collected, as well as pharmacist interventions and missed opportunities in patient care on postnatal wards. A pharmacy triage tool was developed and retrospectively applied to each case to ascertain a risk category that would trigger and target pharmacist review. Interventions/opportunities were classified as either clinical (medication-related) or administrative (potential for error development). 175 cases were reviewed with a median age of 29 years old. Eighty-six patients (49.1%) were retrospectively classified with elevated risk using the triage tool. A total of 117 charts (66.9%) were identified with missed opportunities for pharmacist intervention, which was significantly higher among patients classified as higher risk (75.6 vs. 58.4%, p=0.017). Compared to low risk patients, patients with a higher risk classification had lower rates of administrative missed opportunities (55.4 vs. 80.8%, p=0.015), but numerically higher rates of clinical (26.2 vs. 9.6%, p=NS) and mixed clinical/administrative (18.5 vs. 9.6%, p=NS) missed opportunities, although this failed to reach statistical significance. Evaluation of a triage tool for obstetric services demonstrated potential for prioritising higher risk patients for pharmacist review and addressing opportunities for clinical improvements

Investigating the impact of poverty on colonization and infection with drug-resistant organisms in humans: a systematic review

Background Poverty increases the risk of contracting infectious diseases and therefore exposure to antibiotics. Yet there is lacking evidence on the relationship between income and non-income dimensions of poverty and antimicrobial resistance. Investigating such relationship would strengthen antimicrobial stewardship interventions. Methods A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed, Ovid, MEDLINE, EMBASE, Scopus, CINAHL, PsychINFO, EBSCO, HMIC, and Web of Science databases were searched in October 2016. Prospective and retrospective studies reporting on income or non-income dimensions of poverty and their influence on colonisation or infection with antimicrobial-resistant organisms were retrieved. Study quality was assessed with the Integrated quality criteria for review of multiple study designs (ICROMS) tool. Results Nineteen articles were reviewed. Crowding and homelessness were associated with antimicrobial resistance in community and hospital patients. In high-income countries, low income was associated with Streptococcus pneumoniae and Acinetobacter baumannii resistance and a seven-fold higher infection rate. In low-income countries the findings on this relation were contradictory. Lack of education was linked to resistant S. pneumoniae and Escherichia coli. Two papers explored the relation between water and sanitation and antimicrobial resistance in low-income settings. Conclusions Despite methodological limitations, the results suggest that addressing social determinants of poverty worldwide remains a crucial yet neglected step towards preventing antimicrobial resistance

A stimulus to define informatics and health information technology

<p>Abstract</p> <p>Background</p> <p>Despite the growing interest by leaders, policy makers, and others, the terminology of health information technology as well as biomedical and health informatics is poorly understood and not even agreed upon by academics and professionals in the field.</p> <p>Discussion</p> <p>The paper, presented as a Debate to encourage further discussion and disagreement, provides definitions of the major terminology used in biomedical and health informatics and health information technology. For informatics, it focuses on the words that modify the term as well as individuals who practice the discipline. Other categories of related terms are covered as well, from the associated disciplines of computer science, information technolog and health information management to the major application categories of applications used. The discussion closes with a classification of individuals who work in the largest segment of the field, namely clinical informatics.</p> <p>Summary</p> <p>The goal of presenting in Debate format is to provide a starting point for discussion to reach a documented consensus on the definition and use of these terms.</p

Prescriptive variability of drugs by general practitioners

<div><p>Prescription drug spending is growing faster than any other sector of healthcare. However, very little is known about patterns of prescribing and cost of prescribing between general practices. In this study, we examined variation in prescription rates and prescription costs through time for 55 GP surgeries in Northern Ireland Western Health and Social Care Trust. Temporal changes in variability of prescribing rates and costs were assessed using the Mann–Kendall test. Outlier practices contributing to between practice variation in prescribing rates were identified with the interquartile range outlier detection method. The relationship between rates and cost of prescribing was explored with Spearman's statistics. The differences in variability and mean number of prescribing rates associated with the practice setting and socioeconomic deprivation were tested using t-test and <i>F</i>-test respectively. The largest between-practice difference in prescribing rates was observed for Apr-Jun 2015, with the number of prescriptions ranging from 3.34 to 8.36 per patient. We showed that practices with outlier prescribing rates greatly contributed to between-practice variability. The largest difference in prescribing costs was reported for Apr-Jun 2014, with the prescription cost per patient ranging from £26.4 to £64.5. In addition, the temporal changes in variability of prescribing rates and costs were shown to undergo an upward trend. We demonstrated that practice setting and socio-economic deprivation accounted for some of the between-practice variation in prescribing. Rural practices had higher between practice variability than urban practices at all time points. Practices situated in more deprived areas had higher prescribing rates but lower variability than those located in less deprived areas. Further analysis is recommended to assess if variation in prescribing can be explained by demographic characteristics of patient population and practice features. Identification of other factors contributing to prescribing variability can help us better address potential inappropriateness of prescribing.</p></div

Evaluation of a joint Bioinformatics and Medical Informatics international course in Peru

Background: New technologies that emerge at the interface of computational and biomedical science could drive new advances in global health, therefore more training in technology is needed among health care workers. To assess the potential for informatics training using an approach designed to foster interaction at this interface, the University of Washington and the Universidad Peruana Cayetano Heredia developed and assessed a one-week course that included a new Bioinformatics (BIO) track along with an established Medical/Public Health Informatics track (MI) for participants in Peru. Methods: We assessed the background of the participants, and measured the knowledge gained by track-specific (MI or BIO) 30-minute pre- and post-tests. Participants' attitudes were evaluated both by daily evaluations and by an end-course evaluation. Results: Forty-three participants enrolled in the course - 20 in the MI track and 23 in the BIO track. Of 20 questions, the mean % score for the MI track increased from 49.7 pre-test (standard deviation or SD = 17.0) to 59.7 (SD = 15.2) for the post-test (P = 0.002, n = 18). The BIO track mean score increased from 33.6 pre-test to 51.2 post-test (P less than 0.001, n = 21). Most comments (76%) about any aspect of the course were positive. The main perceived strength of the course was the quality of the speakers, and the main perceived weakness was the short duration of the course. Overall, the course acceptability was very good to excellent with a rating of 4.1 (scale 1-5), and the usefulness of the course was rated as very good. Most participants (62.9%) expressed a positive opinion about having had the BIO and MI tracks come together for some of the lectures. Conclusion: Pre- and post-test results and the positive evaluations by the participants indicate that this first joint Bioinformatics and Medical/Public Health Informatics (MI and BIO) course was a success.The University of Washington AMAUTA Global Training in Health Informatics, a Fogarty International Center/NIH funded grant (5D43TW007551), and the AMAUTA Research Practica Program, a Puget Sound Partners for Global Health-funded grant