scholarly journals Predicting and Responding to Clinical Deterioration in Hospitalized Patients by Using Artificial Intelligence: Protocol for a Mixed Methods, Stepped Wedge Study

10.2196/27532 ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. e27532
Author(s):  
Laura M Holdsworth ◽  
Samantha M R Kling ◽  
Margaret Smith ◽  
Nadia Safaeinili ◽  
Lisa Shieh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Mixed Methods ◽  
Clinical Outcomes ◽  
Systems Engineering ◽  
Multidisciplinary Team ◽  
Hospital Medicine ◽  
Clinical Deterioration ◽  
Emergent Properties ◽  
Stepped Wedge ◽  
Work System

Background The early identification of clinical deterioration in patients in hospital units can decrease mortality rates and improve other patient outcomes; yet, this remains a challenge in busy hospital settings. Artificial intelligence (AI), in the form of predictive models, is increasingly being explored for its potential to assist clinicians in predicting clinical deterioration. Objective Using the Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 model, this study aims to assess whether an AI-enabled work system improves clinical outcomes, describe how the clinical deterioration index (CDI) predictive model and associated work processes are implemented, and define the emergent properties of the AI-enabled work system that mediate the observed clinical outcomes. Methods This study will use a mixed methods approach that is informed by the SEIPS 2.0 model to assess both processes and outcomes and focus on how physician-nurse clinical teams are affected by the presence of AI. The intervention will be implemented in hospital medicine units based on a modified stepped wedge design featuring three stages over 11 months—stage 0 represents a baseline period 10 months before the implementation of the intervention; stage 1 introduces the CDI predictions to physicians only and triggers a physician-driven workflow; and stage 2 introduces the CDI predictions to the multidisciplinary team, which includes physicians and nurses, and triggers a nurse-driven workflow. Quantitative data will be collected from the electronic health record for the clinical processes and outcomes. Interviews will be conducted with members of the multidisciplinary team to understand how the intervention changes the existing work system and processes. The SEIPS 2.0 model will provide an analytic framework for a mixed methods analysis. Results A pilot period for the study began in December 2020, and the results are expected in mid-2022. Conclusions This protocol paper proposes an approach to evaluation that recognizes the importance of assessing both processes and outcomes to understand how a multifaceted AI-enabled intervention affects the complex team-based work of identifying and managing clinical deterioration. International Registered Report Identifier (IRRID) PRR1-10.2196/27532

scholarly journals Predicting and Responding to Clinical Deterioration in Hospitalized Patients by Using Artificial Intelligence: Protocol for a Mixed Methods, Stepped Wedge Study (Preprint)

2021 ◽  
Author(s):  
Laura M Holdsworth ◽  
Samantha M R Kling ◽  
Margaret Smith ◽  
Nadia Safaeinili ◽  
Lisa Shieh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Mixed Methods ◽  
Clinical Outcomes ◽  
Systems Engineering ◽  
Multidisciplinary Team ◽  
Hospital Medicine ◽  
Clinical Deterioration ◽  
Emergent Properties ◽  
Stepped Wedge ◽  
Work System

BACKGROUND The early identification of clinical deterioration in patients in hospital units can decrease mortality rates and improve other patient outcomes; yet, this remains a challenge in busy hospital settings. Artificial intelligence (AI), in the form of predictive models, is increasingly being explored for its potential to assist clinicians in predicting clinical deterioration. OBJECTIVE Using the Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 model, this study aims to assess whether an AI-enabled work system improves clinical outcomes, describe how the clinical deterioration index (CDI) predictive model and associated work processes are implemented, and define the emergent properties of the AI-enabled work system that mediate the observed clinical outcomes. METHODS This study will use a mixed methods approach that is informed by the SEIPS 2.0 model to assess both processes and outcomes and focus on how physician-nurse clinical teams are affected by the presence of AI. The intervention will be implemented in hospital medicine units based on a modified stepped wedge design featuring three stages over 11 months—stage 0 represents a baseline period 10 months before the implementation of the intervention; stage 1 introduces the CDI predictions to physicians only and triggers a physician-driven workflow; and stage 2 introduces the CDI predictions to the multidisciplinary team, which includes physicians and nurses, and triggers a nurse-driven workflow. Quantitative data will be collected from the electronic health record for the clinical processes and outcomes. Interviews will be conducted with members of the multidisciplinary team to understand how the intervention changes the existing work system and processes. The SEIPS 2.0 model will provide an analytic framework for a mixed methods analysis. RESULTS A pilot period for the study began in December 2020, and the results are expected in mid-2022. CONCLUSIONS This protocol paper proposes an approach to evaluation that recognizes the importance of assessing both processes and outcomes to understand how a multifaceted AI-enabled intervention affects the complex team-based work of identifying and managing clinical deterioration. CLINICALTRIAL INTERNATIONAL REGISTERED REPORT PRR1-10.2196/27532

Real-World Experience with Artificial Intelligence-Based Triage in Transferred Large Vessel Occlusion Stroke Patients

2021 ◽  
pp. 1-6
Author(s):  
Jacob R. Morey ◽  
Xiangnan Zhang ◽  
Kurt A. Yaeger ◽  
Emily Fiano ◽  
Naoum Fares Marayati ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Outcomes ◽  
Warning System ◽  
Large Vessel ◽  
Time Interval ◽  
Large Vessel Occlusion ◽  
Time Intervals ◽  
Vessel Occlusion ◽  
Left Behind ◽  
Stroke Center

<b><i>Background and Purpose:</i></b> Randomized controlled trials have demonstrated the importance of time to endovascular therapy (EVT) in clinical outcomes in large vessel occlusion (LVO) acute ischemic stroke. Delays to treatment are particularly prevalent when patients require a transfer from hospitals without EVT capability onsite. A computer-aided triage system, Viz LVO, has the potential to streamline workflows. This platform includes an image viewer, a communication system, and an artificial intelligence (AI) algorithm that automatically identifies suspected LVO strokes on CTA imaging and rapidly triggers alerts. We hypothesize that the Viz application will decrease time-to-treatment, leading to improved clinical outcomes. <b><i>Methods:</i></b> A retrospective analysis of a prospectively maintained database was assessed for patients who presented to a stroke center currently utilizing Viz LVO and underwent EVT following transfer for LVO stroke between July 2018 and March 2020. Time intervals and clinical outcomes were compared for 55 patients divided into pre- and post-Viz cohorts. <b><i>Results:</i></b> The median initial door-to-neuroendovascular team (NT) notification time interval was significantly faster (25.0 min [IQR = 12.0] vs. 40.0 min [IQR = 61.0]; <i>p</i> = 0.01) with less variation (<i>p</i> &#x3c; 0.05) following Viz LVO implementation. The median initial door-to-skin puncture time interval was 25 min shorter in the post-Viz cohort, although this was not statistically significant (<i>p</i> = 0.15). <b><i>Conclusions:</i></b> Preliminary results have shown that Viz LVO implementation is associated with earlier, more consistent NT notification times. This application can serve as an early warning system and a failsafe to ensure that no LVO is left behind.

scholarly journals An evaluation of service user experience, clinical outcomes and service use associated with urgent care services that utilise telephone-based digital triage: a systematic review protocol

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Vanashree Sexton ◽  
Jeremy Dale ◽  
Helen Atherton
Keyword(s):  
Systematic Review ◽  
Mixed Methods ◽  
Clinical Outcomes ◽  
User Experience ◽  
Service User ◽  
Service Use ◽  
Urgent Care ◽  
Narrative Synthesis ◽  
Service User Experience ◽  
Synthesis Approach

Abstract Background Telephone-based digital triage is widely used by services that provide urgent care. This involves a call handler or clinician using a digital triage tool to generate algorithm-based care advice, based on a patient’s symptoms. Advice typically takes the form of signposting within defined levels of urgency to specific services or self-care advice. Despite wide adoption, there is limited evaluation of its impact on service user experience, service use and clinical outcomes; no previous systematic reviews have focussed on services that utilise digital triage, and its impact on these outcome areas within urgent care. This review aims to address this need, particularly now that telephone-based digital triage is well established in healthcare delivery. Methods Studies assessing the impact of telephone-based digital triage on service user experience, health care service use and clinical outcomes will be identified through searches conducted in Medline, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science and Scopus. Search terms using words relating to digital triage and urgent care settings (excluding in-hours general practice) will be used. The review will include all original study types including qualitative, quantitative and mixed methods studies; studies published in the last 20 years and studies published in English. Quality assessment of studies will be conducted using the Mixed Methods Appraisal Tool (MMAT); a narrative synthesis approach will be used to analyse and summarise findings. Discussion This is the first systematic review to evaluate service user experience, service use and clinical outcomes related to the use of telephone-based digital triage in urgent care settings. It will evaluate evidence from studies of wide-ranging designs. The narrative synthesis approach will enable the integration of findings to provide new insights on service delivery. Models of urgent care continue to evolve rapidly, with the emergence of self-triage tools and national help lines. Findings from this review will be presented in a practical format that can feed into the design of digital triage tools, future service design and healthcare policy. Systematic review registration This systematic review is registered on the international database of prospectively registered systematic reviews in health and social care (PROSPERO 2020 CRD42020178500).

CONCEPTUAL DESIGN OF BOMB DETECTOR AS ANTICIPATION OF TERRORISM THREAT TO PUBLIC SERVICES

2021 ◽  
Vol 7 (1) ◽  
pp. 170
Author(s):  
Iris Sumariyanto ◽  
Asep Adang Supriyadi ◽  
I Nengah Putra A
Keyword(s):  
Artificial Intelligence ◽  
Conceptual Design ◽  
Systems Engineering ◽  
Public Services ◽  
Mass Casualties ◽  
Intelligence System ◽  
Three Stages ◽  
Metal Detectors ◽  
Security Surveillance ◽  
Processing Device

<p>Acts of terrorism are crimes and serious violations of human rights, also the threat of violence that can cause mass casualties and destruction of vital strategic objects. This is an urgent threat that needs to be prepared by designing a bomb detector conceptual design as anticipation of the threat of terrorism in public services. This study aims to obtain operational requirements and conceptual design of bomb detectors as detection of terrorism threats in public services. This study uses a mixed-method with a systems engineering approach and a life cycle model to produce a technological design. The results of operational requirements are sensors, standards, artificial intelligence, integration capability, reliability, calibration mode, portable, and easy to maintain. The configuration design is divided into three stages, namely, 1) sensors including a camera security surveillance system vector image, metal detectors, explosive detectors, and A-jamming; 2) as a processing device, processes an order with the help of an artificial intelligence system; and 3)  a security computer (surveillance), early warning, and mobile information to provide information to related agencies, especially the anti-terror unit.</p>

scholarly journals Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA): Employees’ perceptions of our future workplace

2017 ◽  
Vol 24 (2) ◽  
pp. 239-257 ◽  
Author(s):  
David Brougham ◽  
Jarrod Haar
Keyword(s):  
Artificial Intelligence ◽  
Mixed Methods ◽  
Career Satisfaction ◽  
Turnover Intentions ◽  
Well Being ◽  
Career Progression ◽  
Organisational Commitment ◽  
Smart Technology ◽  
Mixed Methods Approach ◽  
Technology Knowledge

AbstractFuturists predict that a third of jobs that exist today could be taken by Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA) by 2025. However, very little is known about how employees perceive these technological advancements in regards to their own jobs and careers, and how they are preparing for these potential changes. A new measure (STARA awareness) was created for this study that captures the extent to which employees feel their job could be replaced by these types of technology. Due to career progression and technology knowledge associated with age, we also tested age as a moderator of STARA. Using a mixed-methods approach on 120 employees, we tested STARA awareness on a range of job and well-being outcomes. Greater STARA awareness was negatively related to organisational commitment and career satisfaction, and positively related to turnover intentions, cynicism, and depression.

Will the Medium Become the Message? A Framework for Understanding the Coming Automation of the Audit Process

2019 ◽  
Vol 34 (2) ◽  
pp. 109-130 ◽  
Author(s):  
Michael G Alles ◽  
Glen L. Gray
Keyword(s):  
Artificial Intelligence ◽  
Financial Statements ◽  
Audit Process ◽  
Accounting Firms ◽  
Work System ◽  
Key Questions

ABSTRACT Frey and Osborne (2017), estimated that there is a 94 percent probability that automation will replace accountants and auditors. The leading accounting firms are concerned that a tech company like Google could enter and disrupt the auditing industry. In response, they are themselves investing in artificial intelligence and other technologies. Two key questions need to be answered when automating audits: What is the role played by technology, whether client or auditor owned, in the audit process? And how will the use of that technology impact trust in audited financial statements, recognizing that trust is the rationale for auditing in the first place? We develop a framework for the audit process as an affective work system that takes into account that technology is both a production input and an affective mechanism shaping the level of trust consumers of the audited financial statements have in the audit process that assured those statements.

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