Better antimicrobial resistance data analysis and reporting in less time

Objectives: Insights about local antimicrobial resistance (AMR) levels and epidemiology are essential to guide decision-making processes in antimicrobial use. However, dedicated tools for reliable and reproducible AMR data analysis and reporting are often lacking. We aimed to compare traditional data analysis and reporting versus a new approach for reliable and reproducible AMR data analysis in a clinical setting.Methods: Ten professionals who routinely work with AMR data were provided with blood culture test results including antimicrobial susceptibility results. Participants were asked to perform a detailed AMR data analysis in a two-round process: first using their software of choice and next using our newly developed software tool. Accuracy of the results and time spent were compared between both rounds. Finally, participants rated the usability using the System Usability Scale (SUS).Results: The mean time spent on creating the AMR report reduced from 93.7 to 22.4 min (P Conclusions: This study demonstrated the significant improvement in efficiency and accuracy in standard AMR data analysis and reporting workflows through open-source software. Integrating these tools in clinical settings can democratize the access to fast and reliable insights about local microbial epidemiology and associated AMR levels. Thereby, our approach can support evidence-based decision-making processes in the use of antimicrobials

A personalised and adaptive insulin dosing decision support system for type 1 diabetes

People with type 1 diabetes (T1D) rely on exogenous insulin to maintain stable glucose levels. Despite the advent of diabetes technologies such as continuous glucose monitors and insulin infusion pumps, the majority of people with T1D do not manage to bring back glucose levels into a healthy target after meals. In addition to patient compliance, this is due to the complexity of the decision-making on how much insulin is required. Commercial insulin bolus calculators exist that help with the calculation of insulin for meals but these lack fine-tuning and adaptability. This thesis presents a novel insulin dosing decision support system for people with T1D that is able to provide individualised insulin dosing advice. The proposed research utilises Case-Based Reasoning (CBR), an artificial intelligence methodology, that is able to learn over time based on the behaviour of the patient and optimises the insulin therapy for various diabetes scenarios. The decision support system has been implemented into a user-friendly smartphone-based patient platform and communicates with a clinical platform for remote supervision. In-silico studies are presented demonstrating the overall performance of CBR as well as metrics used to adapt the insulin therapy. Safety and feasibility of the developed system have been assessed incrementally in clinical trials; initially during an eight-hour study in hospital settings followed by a six-week study in the home environment of the user. Human factors play an important role in the clinical adoption of technologies such as the one proposed. System usability and acceptability were evaluated during the second study phase based on feedback obtained from study participants. Results from in-silico tests show the potential of the proposed research to safely automate the process of optimising the insulin therapy for people with T1D. In the six-week study, the system demonstrated safety in maintaining glycemic control with a trend suggesting improvement in postprandial glucose outcomes. Feedback from participants showed favourable outcomes when assessing device satisfaction and usability. A six-month large-scale randomised controlled study to evaluate the efficacy of the system is currently ongoing.Open Acces

A six-year repeated evaluation of computerized clinical decision support system user acceptability

OBJECTIVE: Long-term acceptability among computerized clinical decision support system (CDSS) users in pediatrics is unknown. We examine user acceptance patterns over six years of our continuous computerized CDSS integration and updates. MATERIALS AND METHODS: Users of Child Health Improvement through Computer Automation (CHICA), a CDSS integrated into clinical workflows and used in several urban pediatric community clinics, completed annual surveys including 11 questions covering user acceptability. We compared responses across years within a single healthcare system and between two healthcare systems. We used logistic regression to assess the odds of a favorable response to each question by survey year, clinic role, part-time status, and frequency of CHICA use. RESULTS: Data came from 380 completed surveys between 2011 and 2016. Responses were significantly more favorable for all but one measure by 2016 (OR range 2.90-12.17, all p < 0.01). Increasing system maturity was associated with improved perceived function of CHICA (OR range 4.24-7.58, p < 0.03). User familiarity was positively associated with perceived CDSS function (OR range 3.44-8.17, p < 0.05) and usability (OR range 9.71-15.89, p < 0.01) opinions. CONCLUSION: We present a long-term, repeated follow-up of user acceptability of a CDSS. Favorable opinions of the CDSS were more likely in frequent users, physicians and advanced practitioners, and full-time workers. CHICA acceptability increased as it matured and users become more familiar with it. System quality improvement, user support, and patience are important in achieving wide-ranging, sustainable acceptance of CDSS

Comparison of a prototype for indications-based prescribing with 2 commercial prescribing systems

Importance: The indication (reason for use) for a medication is rarely included on prescriptions despite repeated recommendations to do so. One barrier has been the way existing electronic prescribing systems have been designed. Objective: To evaluate, in comparison with the prescribing modules of 2 leading electronic health record prescribing systems, the efficiency, error rate, and satisfaction with a new computerized provider order entry prototype for the outpatient setting that allows clinicians to initiate prescribing using the indication. Design, Setting, and Participants: This quality improvement study used usability tests requiring internal medicine physicians, residents, and physician assistants to enter prescriptions electronically, including indication, for 8 clinical scenarios. The tool order assignments were randomized and prescribers were asked to use the prototype for 4 of the scenarios and their usual system for the other 4. Time on task, number of clicks, and order details were captured. User satisfaction was measured using posttask ratings and a validated system usability scale. The study participants practiced in 2 health systems\u27 outpatient practices. Usability tests were conducted between April and October of 2017. Main Outcomes and Measures: Usability (efficiency, error rate, and satisfaction) of indications-based computerized provider order entry prototype vs the electronic prescribing interface of 2 electronic health record vendors. Results: Thirty-two participants (17 attending physicians, 13 residents, and 2 physician assistants) used the prototype to complete 256 usability test scenarios. The mean (SD) time on task was 1.78 (1.17) minutes. For the 20 participants who used vendor 1\u27s system, it took a mean (SD) of 3.37 (1.90) minutes to complete a prescription, and for the 12 participants using vendor 2\u27s system, it took a mean (SD) of 2.93 (1.52) minutes. Across all scenarios, when comparing number of clicks, for those participants using the prototype and vendor 1, there was a statistically significant difference from the mean (SD) number of clicks needed (18.39 [12.62] vs 46.50 [27.29]; difference, 28.11; 95% CI, 21.47-34.75; P \u3c .001). For those using the prototype and vendor 2, there was also a statistically significant difference in number of clicks (20.10 [11.52] vs 38.25 [19.77]; difference, 18.14; 95% CI, 11.59-24.70; P \u3c .001). A blinded review of the order details revealed medication errors (eg, drug-allergy interactions) in 38 of 128 prescribing sessions using a vendor system vs 7 of 128 with the prototype. Conclusions and Relevance: Reengineering prescribing to start with the drug indication allowed indications to be captured in an easy and useful way, which may be associated with saved time and effort, reduced medication errors, and increased clinician satisfaction

Study protocol for the Anesthesiology Control Tower—Feedback Alerts to Supplement Treatments (ACTFAST-3) trial: A pilot randomized controlled trial in intraoperative telemedicine [version 1; referees: 2 approved]

Background: Each year, over 300 million people undergo surgical procedures worldwide. Despite efforts to improve outcomes, postoperative morbidity and mortality are common. Many patients experience complications as a result of either medical error or failure to adhere to established clinical practice guidelines. This protocol describes a clinical trial comparing a telemedicine-based decision support system, the Anesthesiology Control Tower (ACT), with enhanced standard intraoperative care. Methods: This study is a pragmatic, comparative effectiveness trial that will randomize approximately 12,000 adult surgical patients on an operating room (OR) level to a control or to an intervention group. All OR clinicians will have access to decision support software within the OR as a part of enhanced standard intraoperative care. The ACT will monitor patients in both groups and will provide additional support to the clinicians assigned to intervention ORs. Primary outcomes include blood glucose management and temperature management. Secondary outcomes will include surrogate, clinical, and economic outcomes, such as incidence of intraoperative hypotension, postoperative respiratory compromise, acute kidney injury, delirium, and volatile anesthetic utilization. Ethics and dissemination: The ACTFAST-3 study has been approved by the Human Resource Protection Office (HRPO) at Washington University in St. Louis and is registered at clinicaltrials.gov (NCT02830126). Recruitment for this protocol began in April 2017 and will end in December 2018. Dissemination of the findings of this study will occur via presentations at academic conferences, journal publications, and educational materials

A study of general practitioners' perspectives on electronic medical records systems in NHS Scotland

&lt;b&gt;Background&lt;/b&gt; Primary care doctors in NHSScotland have been using electronic medical records within their practices routinely for many years. The Scottish Health Executive eHealth strategy (2008-2011) has recently brought radical changes to the primary care computing landscape in Scotland: an information system (GPASS) which was provided free-of-charge by NHSScotland to a majority of GP practices has now been replaced by systems provided by two approved commercial providers. The transition to new electronic medical records had to be completed nationally across all health-boards by March 2012. &lt;p&gt;&lt;/p&gt;&lt;b&gt; Methods&lt;/b&gt; We carried out 25 in-depth semi-structured interviews with primary care doctors to elucidate GPs' perspectives on their practice information systems and collect more general information on management processes in the patient surgical pathway in NHSScotland. We undertook a thematic analysis of interviewees' responses, using Normalisation Process Theory as the underpinning conceptual framework. &lt;p&gt;&lt;/p&gt; &lt;b&gt;Results&lt;/b&gt; The majority of GPs' interviewed considered that electronic medical records are an integral and essential element of their work during the consultation, playing a key role in facilitating integrated and continuity of care for patients and making clinical information more accessible. However, GPs expressed a number of reservations about various system functionalities - for example: in relation to usability, system navigation and information visualisation. &lt;b&gt;Conclusion &lt;/b&gt;Our study highlights that while electronic information systems are perceived as having important benefits, there remains substantial scope to improve GPs' interaction and overall satisfaction with these systems. Iterative user-centred improvements combined with additional training in the use of technology would promote an increased understanding, familiarity and command of the range of functionalities of electronic medical records among primary care doctors

inPractice: a practical nursing package for clinical decisions

This paper examines the recent development of a computer-assisted learning program-in Practice-at the School of Health Science, in the University of Wales Swansea. The project, which began in 2001, was developed in close collaboration with The Meningitis Trust, the aim being to produce a software package to increase nursing students knowledge of meningitis-related illnesses, and to enhance their decision-making and problem-solving skills by using lifelike scenarios. It incorporates two multimedia meningitis modules incorporating the use of text, film, and sound, in which students are presented with information about the illness (symptoms, treatment etc.), and are required to use their knowledge to make decisions at various key points. A general discussion of decision-making theories and CAL design principles is presented, which has provided a foundation for the main design aspects of the package. This is followed by an outline of how the program was created to promote students application of knowledge and their decision-making and problem-solving skills. Results from an evaluation questionnaire are presented. Consideration is also given as to how the program can be extended

Health Figures: An Open Source JavaScript Library for Health Data Visualization

The way we look at data has a great impact on how we can understand it, particularly when the data is related to health and wellness. Due to the increased use of self-tracking devices and the ongoing shift towards preventive medicine, better understanding of our health data is an important part of improving the general welfare of the citizens. Electronic Health Records, self-tracking devices and mobile applications provide a rich variety of data but it often becomes difficult to understand. We implemented the hFigures library inspired on the hGraph visualization with additional improvements. The purpose of the library is to provide a visual representation of the evolution of health measurements in a complete and useful manner. We researched the usefulness and usability of the library by building an application for health data visualization in a health coaching program. We performed a user evaluation with Heuristic Evaluation, Controlled User Testing and Usability Questionnaires. In the Heuristics Evaluation the average response was 6.3 out of 7 points and the Cognitive Walkthrough done by usability experts indicated no design or mismatch errors. In the CSUQ usability test the system obtained an average score of 6.13 out of 7, and in the ASQ usability test the overall satisfaction score was 6.64 out of 7. We developed hFigures, an open source library for visualizing a complete, accurate and normalized graphical representation of health data. The idea is based on the concept of the hGraph but it provides additional key features, including a comparison of multiple health measurements over time. We conducted a usability evaluation of the library as a key component of an application for health and wellness monitoring. The results indicate that the data visualization library was helpful in assisting users in understanding health data and its evolution over time.Comment: BMC Medical Informatics and Decision Making 16.1 (2016

CAMMD: Context Aware Mobile Medical Devices

Telemedicine applications on a medical practitioners mobile device should be context-aware. This can vastly improve the effectiveness of mobile applications and is a step towards realising the vision of a ubiquitous telemedicine environment. The nomadic nature of a medical practitioner emphasises location, activity and time as key context-aware elements. An intelligent middleware is needed to effectively interpret and exploit these contextual elements. This paper proposes an agent-based architectural solution called Context-Aware Mobile Medical Devices (CAMMD). This framework can proactively communicate patient records to a portable device based upon the active context of its medical practitioner. An expert system is utilised to cross-reference the context-aware data of location and time against a practitioners work schedule. This proactive distribution of medical data enhances the usability and portability of mobile medical devices. The proposed methodology alleviates constraints on memory storage and enhances user interaction with the handheld device. The framework also improves utilisation of network bandwidth resources. An experimental prototype is presented highlighting the potential of this approach