Background Medication Errors are among the most common types of incidents reported in Australian and international hospitals. There is no uniform method of reporting these errors and no uniform method of reducing them. This study aims to identify the incidence, time trends, types and factors associated factors with medication errors in a large regional hospital. Methods A five-year cross sectional study Results The incidence of medication errors was 1.05 per 100 admitted patients. The highest frequency of errors was observed during the colder months of May to August. When distributed by day, Mondays and Tuesdays had the highest frequency of errors. When distributed by hour of the day, time intervals from 7am to 8am and 7 pm to 8pm showed a sharp increase in the frequency of errors. 1088 (57.8%) MEs belonged to ISR 4 and 787 (41.8%) belonged to ISR 3. There were 6 incidents of ISR level 2 and only 1 incident of ISR level 1 reported in the last 5 years. Administration only errors were the most common accounting for 1070 (56.8%) followed by prescribing only errors (433, 23%). High risk medications were associated with half the number of errors, the most common of which were narcotics (17.9%) and anti-microbials (13.2%). Conclusions Medication errors continue to be a problem faced by hospitals internationally. Inexperience of health professionals and nurse-patient ratios might be the fundamental challenges to overcome. Specific training of junior staff in prescribing and administering medication and nurse workload management could be possible solutions to reducing MEs in hospitals.

Purpose This study aims to develop a decision-making tool that assesses the economic feasibility of converting commercial and industrial buildings into rented residential accommodation. This tool also enables developers to provide high-quality rented residential accommodation that contribute to the gentrification of formerly industrialised inner city or developed areas. Design/methodology/approach The overarching epistemological approach adopted used inductive reasoning and a postpositivist philosophical design to structure the research problem and devise new theories about the phenomena under investigation. From an operational perspective, a two-phase “waterfall” research approach was adopted. Phase one used extant literature to identify development factors and variables for consideration, risks posed and conversion appraisal criteria. Two case studies formed the basis of a cross comparative analysis, namely, a new build and conversion of a former industrial building into rented residential accommodation. Phase two identified development appraisal criteria, conducted a cost analysis and premised upon the findings, developed a decision support appraisal tool as a “proof of concept”. Findings The research combined key decision factors and variables that assist property developers when evaluating whether to convert commercial and industrial property into rented residential accommodation. The appraisal tool’s functionality was validated via a focus group discussion with senior property developers to ensure that assessment criteria and development weightings were appropriate. Feedback revealed that the tool was suitable for purpose and should now be adopted in practice and refined as appropriate and with usage. Research limitations/implications The appraisal tool presented could yield a far more accurate means of decision-making which, in turn, could ensure that predicted investment returns are received (thus reducing errors and lowering risk for investors). Future work is required to robustly test and validate the tool’s accuracy in practice. It is envisaged that future projects will provide a rich stream of data for such testing. Originality/value To the best of the authors’ knowledge, this work constitutes the first attempt to conceptualise a decision support tool for rented residential property development.

Introduction: The literature on the use of AI in prehospital emergency care (PEC) settings is scattered and diverse, making it difficult to understand the current state of the field. In this scoping review, we aim to provide a descriptive analysis of the current literature and to visualise and identify knowledge and methodological gaps using an evidence map. Methods: We conducted a scoping review from inception until 14 December 2021 on MEDLINE, Embase, Scopus, IEEE Xplore, ACM Digital Library, and Cochrane Central Register of Controlled Trials (CENTRAL). We included peer-reviewed, original studies that applied AI to prehospital data, including applications for cardiopulmonary resuscitation (CPR), automated external defibrillation (AED), out-of-hospital cardiac arrest, and emergency medical service (EMS) infrastructure like stations and ambulances. Results: The search yielded 4350 articles, of which 106 met the inclusion criteria. Most studies were retrospective (n=88, 83.0%), with only one (0.9%) randomised controlled trial. Studies were mostly internally validated (n=96, 90.6%), and only ten studies (9.4%) reported on calibration metrics. While the most studied AI applications were Triage/Prognostication (n=52, 49.1%) and CPR/AED optimisation (n=26, 24.5%), a few studies reported unique use cases of AI such as patient-trial matching for research and Internet-of-Things (IoT) wearables for continuous monitoring. Out of 49 studies that identified a comparator, 39 reported AI performance superior to either clinicians or non-AI status quo algorithms. The minority of studies utilised multimodal inputs (n=37, 34.9%), with few models using text (n=8), audio (n=5), images (n=1), or videos (n=0) as inputs. Conclusion: AI in PEC is a growing field and several promising use cases have been reported, including prognostication, demand prediction, resource optimisation, and IoT continuous monitoring systems. Prospective, externally validated studies are needed before applications can progress beyond the proof-of-concept stage to real-world clinical settings.

Culture, Lean Thinking, MetricsBlink during a Formula 1 pit-stop and you'll probably miss it. But this wasn't always the case. Fifty years ago, a pit-crew would take over a minute to change the wheels and refuel. Today, anything more than three seconds is considered a fail.It's the same in software development, where teams once tasked with updating enterprise applications at a sedate pace must now deliver new software services as a continuous flow of value to customers.The problem for today's enterprise, however, is that software teams don't work like Formula 1 pit-crews. Rather than working in tandem, IT teams often work serially-development codes, then QA tests, and finally IT operations monitors. However, with application software released, enhanced, and retired over more compressed timeframes (months and even days), this stop-start method of development falls short. It's as ineffective as each member of a Formula 1 pit-crew replacing a tire and checking wheel nut tension before the next one could start-the race would be over before the car left the pits.While we can celebrate the heroics and skill of great racing car drivers, what sets successful constructors apart is their ability to build a winning culture irrespective of role and responsibility, be that driver, team manager, telemetry engineer, or aerodynamics chief, everyone is focused on a singular goal-winning races. It's why drivers thank the teams before they spray champagne on the podium. C H A P T E R 3 Chapter 3 | DevOps Foundations 28Like Formula 1 drivers, technological advancements have improved the efficiency and effectiveness of IT professionals. However, in organizations that traditionally measure and incentivize based on technical specialization within functional areas, relying on tools alone will never build the collaborative culture needed for business growth and profitability.What Characterizes DevOps Culture?DevOps is very different from traditional thinking because it places great emphasis on culture. It instills a shared sense of vision across multiple teams, directly aligned to the business and its customers. To this end, maverick behavior, such a cutting corners and allowing defect ridden code to go into production, or blaming operations when a software release fails, is counter to a DevOps thinking. With DevOps unified IT is the hero and no one is singularly to blame for problems.But this is challenging in IT because of the friction existing between development and other IT teams-especially IT operations. On the one hand, developers are focused on accelerating change by faster delivery of applications, while the operational mantra has been resilience and stability at all costs, even if that means holding back change.Evidence suggests, however, that while both these goals are equally important, they are not mutually exclusive. For example, the 2016 Puppet Labs "State of DevOps" report illustrated that high-performing IT organizations are well able to achieve faster software delivery along with increased resilience and stability.1 Cl...