1,678 research outputs found

    Providers’ Perception of Alert Fatigue After Implementation of User-Filtered Warnings

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    Alert fatigue is a complex problem that many health institutions face when using an electronic health record (EHR). The addition of user-filtered warnings (UFW) is a physicians’ proposed intervention at Inova Health System (IHS), a large 5-hospital health system in Northern Virginia, that allows prescribers to filter out specific drug-drug interactions and pregnancy and lactation medication alerts for a 30-day period. This study aims to determine the impact of UFW on physicians’ perception of alert fatigue and to calculate the reduction of medication alerts. It was hypothesized that the reduction in alerts will significantly impact physicians’ perception of alert fatigue in a positive manner. Physician perception of alert fatigue was assessed using online surveys before and after the implementation of UFW. Data from Medications Warnings Statistics reports were used to assess the reduction of alerts fired post-implementation of UFW. For the primary outcome, there was no significant difference in the overall perception of alert fatigue before and after the implementation of UFW. For the secondary outcome, the number of medication alerts was decreased by 16.7% post UFW implementation. Overall, the data does not support UFW to reduce alert fatigue

    Combating Alarm Fatigue: The Quest for More Accurate and Safer Clinical Monitoring Equipment

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    As the demand for health-care services continues to increase, clinically efficient and cost-effective patient monitoring takes on a critically important role. Key considerations inherent to this area of concern include patient safety, reliability, ease of use, and cost containment. Unfortunately, even the most modern patient monitoring systems carry significant drawbacks that limit their effectiveness and/or applicability. Major opportunities for improvement in both equipment design and monitor utilization have been identified, including the presence of excessive false and nuisance alarms. When poorly optimized, clinical alarm activity can affect patient safety and may have a negative impact on care providers, leading to inappropriate alarm response time due to the so-called alarm fatigue (AF). Ultimately, consequences of AF include missed alerts of clinical significance, with substantial risk for patient harm and potentially fatal outcomes. Targeted quality improvement initiatives and staff training, as well as the proactive incorporation of technological improvements, are the best approaches to address key barriers to the optimal utilization of clinical alarms, AF reduction, better patient care, and improved provider job satisfaction

    Can the NHS learn about human factors from the Ministry of Defence?

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    The National Health Service (NHS) in England has ambitious plans to drive innovation in health information technology (HIT) to improve patient safety, quality and cost effectiveness. Acute trusts are complex socio-technical systems that are required to implement a number of large information technology projects in order to meet national targets for digital maturity. This research explored whether the Ministry of Defence (MOD) Human Factors Integration Model for the acquisition process could be applied to a HIT project. A qualitative research study was undertaken in a large English NHS acute trust using the experience of implementing an electronic observation system to explore transferability of the MOD approach to acute healthcare. Data were collected using semi-structured interviews and focus groups and analysed thematically with reference to SEIPS 2.0 (Holden et al, 2013) healthcare systems model and the MOD framework. Key findings included limited awareness of Human Factors in healthcare; information system design/specification to deliver positive outcomes around patient safety and financial savings. Human Factors negative systems issues included alert fatigue, changing mental models, inability to maximise data for patient benefit, system resilience, local and national interoperability issues

    Aerospace Medicine and Biology: A continuing bibliography, supplement 191

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    A bibliographical list of 182 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1979 is presented

    Boredom and Distraction in Multiple Unmanned Vehicle Supervisory Control

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    Operators currently controlling Unmanned Aerial Vehicles report significant boredom, and such systems will likely become more automated in the future. Similar problems are found in process control, commercial aviation, and medical settings. To examine the effect of boredom in such settings, a long duration low task load experiment was conducted. Three low task load levels requiring operator input every 10, 20, or 30 minutes were tested in a our-hour study using a multiple unmanned vehicle simulation environment that leverages decentralized algorithms for sometimes imperfect vehicle scheduling. Reaction times to system-generated events generally decreased across the four hours, as did participants’ ability to maintain directed attention. Overall, participants spent almost half of the time in a distracted state. The top performer spent the majority of time in directed and divided attention states. Unexpectedly, the second-best participant, only 1% worse than the top performer, was distracted almost one third of the experiment, but exhibited a periodic switching strategy, allowing him to pay just enough attention to assist the automation when needed. Indeed, four of the five top performers were distracted more than one-third of the time. These findings suggest that distraction due to boring, low task load environments can be effectively managed through efficient attention switching. Future work is needed to determine optimal frequency and duration of attention state switches given various exogenous attributes, as well as individual variability. These findings have implications for the design of and personnel selection for supervisory control systems where operators monitor highly automated systems for long durations with only occasional or rare input.This work was supported by Aurora Flight Sciences under the ONR Science of Autonomy program as well as the Office of Naval Research (ONR) under Code 34 and MURI [grant number N00014-08-C-070]

    OntoPharma: ontology based clinical decision support system to reduce medication prescribing errors

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    Background: Clinical decision support systems (CDSS) have been shown to reduce medication errors. However, they are underused because of different challenges. One approach to improve CDSS is to use ontologies instead of relational databases. The primary aim was to design and develop OntoPharma, an ontology based CDSS to reduce medication prescribing errors. Secondary aim was to implement OntoPharma in a hospital setting. Methods: A four-step process was proposed. (1) Defining the ontology domain. The ontology scope was the medication domain. An advisory board selected four use cases: maximum dosage alert, drug-drug interaction checker, renal failure adjustment, and drug allergy checker. (2) Implementing the ontology in a formal representation. The implementation was conducted by Medical Informatics specialists and Clinical Pharmacists using Protégé-OWL. (3) Developing an ontology-driven alert module. Computerised Physician Order Entry (CPOE) integration was performed through a REST API. SPARQL was used to query ontologies. (4) Implementing OntoPharma in a hospital setting. Alerts generated between July 2020/ November 2021 were analysed. Results: The three ontologies developed included 34,938 classes, 16,672 individuals and 82 properties. The domains addressed by ontologies were identification data of medicinal products, appropriateness drug data, and local concepts from CPOE. When a medication prescribing error is identified an alert is shown. OntoPharma generated 823 alerts in 1046 patients. 401 (48.7%) of them were accepted. Conclusions: OntoPharma is an ontology based CDSS implemented in clinical practice which generates alerts when a prescribing medication error is identified. To gain user acceptance OntoPharma has been designed and developed by a multidisciplinary team. Compared to CDSS based on relational databases, OntoPharma represents medication knowledge in a more intuitive, extensible and maintainable manner

    Implementation of an Innovative Early Warning System: Evidenced-based Strategies for Ensuring System-wide Nursing Adoption

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    Early deterioration in adult medical-surgical patients is associated with increased intensive care unit and hospital mortality (Goldhill, 2001). Failure to recognize deterioration is a preventable patient safety and quality issue. To address this problem, since 2013, Kaiser Permanente Northern California (KP NCAL) has piloted Advance Alert Monitor (AAM) at two hospitals. This early warning system employs a set of predictive models developed by the KP NCAL Division of Research, which automatically predicts patient deterioration within the next 12 hours based on a complex algorithm of laboratory and clinical data points. Improvements in mortality and length of stay have been realized at the two pilot hospitals. In anticipation of expansion to additional NCAL facilities, major changes to the AAM workflows and processes were developed that increased the sensitivity of the patients identified at risk for clinical deterioration, as well as the timeliness and clarity of clinical response. Expansion to two additional pilot hospitals using these revised processes rely on the evidence-based implementation strategies found in this Doctor of Nursing Practice project. This paper examines the planning, assessment, and implementation of early warning systems at two NCAL facilities using Rogers’ diffusion of innovation theory and Greenhalgh’s extension of Rogers’ theory. Key attributes need to be considered from a cultural and organizational perspective to both start and sustain an implementation. The success of AAM implementation is validated using specific outcome and process measures, including compliance with documentation and timeliness of workflows
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