scholarly journals LO81: Interrater agreement and time it takes to assign a Canadian Triage and Acuity Scale score pre and post implementation of eCTAS

CJEM ◽  
2019 ◽  
Vol 21 (S1) ◽  
pp. S37
Author(s):  
S. McLeod ◽  
J. McCarron ◽  
T. Ahmed ◽  
S. Scott ◽  
H. Ovens ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Improve Patient Safety ◽  
Weighted Kappa ◽  
Clinical Decision ◽  
Interrater Agreement ◽  
Kappa Statistics ◽  
Support Tool ◽  
Number Of Patients ◽  
Research Assistants ◽  
Triage Time

Introduction: In addition to its clinical utility, the Canadian Triage and Acuity Scale (CTAS) has become an administrative metric used by governments to estimate patient care requirements, emergency department (ED) funding and workload models. The electronic Canadian Triage and Acuity Scale (eCTAS) initiative aims to improve patient safety and quality of care by establishing an electronic triage decision support tool that standardizes that application of national triage guidelines across Ontario. The objective of this study was to evaluate triage times and score agreement in ED settings where eCTAS has been implemented. Methods: This was a prospective, observational study conducted in 7 hospital EDs, selected to represent a mix of triage processes (electronic vs. manual), documentation practices (electronic vs. paper), hospital types (rural, community and teaching) and patient volumes (annual ED census ranged from 38,000 to 136,000). An expert CTAS auditor observed on-duty triage nurses in the ED and assigned independent CTAS in real time. Research assistants not involved in the triage process independently recorded triage time. Interrater agreement was estimated using unweighted and quadratic-weighted kappa statistics with 95% confidence intervals (CIs). Results: 1491 (752 pre-eCTAS, 739 post-implementation) individual patient CTAS assessments were audited over 42 (21 pre-eCTAS, 21 post-implementation) seven-hour triage shifts. Exact modal agreement was achieved for 567 (75.4%) patients pre-eCTAS, compared to 685 (92.7%) patients triaged with eCTAS. Using the auditor's CTAS score as the reference standard, eCTAS significantly reduced the number of patients over-triaged (12.0% vs. 5.1%; Δ 6.9, 95% CI: 4.0, 9.7) and under-triaged (12.6% vs. 2.2%; Δ 10.4, 95% CI: 7.9, 13.2). Interrater agreement was higher with eCTAS (unweighted kappa 0.89 vs 0.63; quadratic-weighted kappa 0.91 vs. 0.71). Research assistants captured triage time for 3808 patients pre-eCTAS and 3489 post implementation of eCTAS. Median triage time was 312 seconds pre-eCTAS and 347 seconds with eCTAS (Δ 35 seconds, 95% CI: 29, 40 seconds). Conclusion: A standardized, electronic approach to performing CTAS assessments improves both clinical decision making and administrative data accuracy without substantially increasing triage time.

scholarly journals LO70: Interrater agreement and time it takes to assign a Canadian Triage and Acuity Scale score pre and post implementation of eCTAS

CJEM ◽  
2018 ◽  
Vol 20 (S1) ◽  
pp. S31-S32
Author(s):  
S. McLeod ◽  
J. McCarron ◽  
T. Ahmed ◽  
S. Scott ◽  
H. Ovens ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Improve Patient Safety ◽  
Weighted Kappa ◽  
Clinical Decision ◽  
Interrater Agreement ◽  
Kappa Statistics ◽  
Support Tool ◽  
Number Of Patients ◽  
Research Assistants ◽  
Triage Time

Introduction: In addition to its clinical utility, the Canadian Triage and Acuity Scale (CTAS) has become an administrative metric used by governments to estimate patient care requirements, ED funding and workload models. The Electronic Canadian Triage and Acuity Scale (eCTAS) initiative aims to improve patient safety and quality of care by establishing an electronic triage decision support tool that standardizes the application of national triage guidelines (CTAS) across Ontario. The objective of this study was to evaluate the implementation of eCTAS in a variety of ED settings. Methods: This was a prospective, observational study conducted in 7 hospital EDs, selected to represent a mix of triage processes (electronic vs. manual), documentation practices (electronic vs. paper), hospital types (rural, community and teaching) and patient volumes (annual ED census ranged from 38,000 to 136,000). An expert CTAS auditor observed on-duty triage nurses in the ED and assigned independent CTAS in real time. Research assistants not involved in the triage process independently recorded the triage time. Interrater agreement was estimated using unweighted and quadratic-weighted kappa statistics with 95% confidence intervals (CIs). Results: 1200 (738 pre-eCTAS, 462 post-implementation) individual patient CTAS assessments were audited over 33 (21 pre-eCTAS, 11 post-implementation) seven-hour triage shifts. Exact modal agreement was achieved for 554 (75.0%) patients pre-eCTAS, compared to 429 (93.0%) patients triaged with eCTAS. Using the auditors CTAS score as the reference standard, eCTAS significantly reduced the number of patients over-triaged (12.1% vs. 3.2%; 8.9, 95% CI: 5.7, 11.7) and under-triaged (12.9% vs. 3.9%; 9.0, 95% CI: 5.9, 12.0). Interrater agreement was higher with eCTAS (unweighted kappa 0.90 vs 0.63; quadratic-weighted kappa 0.79 vs. 0.94). Research assistants captured triage time for 4403 patients pre-eCTAS and 1849 post implementation of eCTAS. Median triage time was 304 seconds pre-eCTAS and 329 seconds with eCTAS ( 25 seconds, 95% CI: 18, 32 seconds). Conclusion: A standardized, electronic approach to performing CTAS assessments improves both clinical decision making and administrative data accuracy without substantially increasing triage time.

scholarly journals LO75: Interrater agreement and time it takes to assign a Canadian Triage and Acuity Scale score in 7 emergency departments

CJEM ◽  
2017 ◽  
Vol 19 (S1) ◽  
pp. S54
Author(s):  
S.L. McLeod ◽  
J. McCarron ◽  
K. Stein ◽  
S. Scott ◽  
H.J. Ovens ◽  
...  
Keyword(s):  
Emergency Departments ◽  
Clinical Decision Making ◽  
Weighted Kappa ◽  
Clinical Decision ◽  
Primary Objective ◽  
Interrater Agreement ◽  
Kappa Statistics ◽  
Care Requirements ◽  
Research Assistants ◽  
Triage Time

Introduction: The Canadian Triage and Acuity Scale (CTAS) is the standard used in all Canadian (and many international) emergency departments (EDs) for establishing the priority by which patients should be assessed. In addition to its clinical utility, CTAS has become an important administrative metric used by governments to estimate patient care requirements, ED funding and workload models. Despite its importance, the process by which CTAS scores are derived is highly variable. Emphasis on ED wait times has also drawn attention to the length of time the triage process takes. The primary objective of this study was to determine the interrater agreement of CTAS in current clinical practice. The secondary objective was to determine the time it takes to triage in a variety of ED settings. Methods: This was a prospective, observational study conducted in 7 hospital EDs, selected to represent a mix of triage processes (electronic vs. manual), documentation practices (electronic vs. paper), hospital types (rural, community and teaching) and patient volumes (annual ED census ranged from 38,000 to 136,000). An expert CTAS auditor observed on-duty triage nurses in the ED and assigned independent CTAS in real time. Research assistants not involved in the triage process independently recorded the triage time. Interrater agreement was estimated using unweighted and quadratic-weighted kappa statistics with 95% confidence intervals (CIs). Results: 738 consecutive patient CTAS assessments were audited over 21 seven-hour triage shifts. Exact modal agreement was achieved for 554 (75.0%) patients. Using the auditor’s CTAS score as the reference standard, on-duty triage nurses over-triaged 89 (12.1%) and under-triaged 95 (12.9%) patients. Interrater agreement was “good” with an unweighted kappa of 0.63 (95% CI: 0.58, 0.67) and quadratic-weighted kappa of 0.79 (95% CI: 0.67, 0.90). Research assistants captured triage time for 3808 patients over 69 shifts at 7 different EDs. Median (IQR) triage time was 5.2 (3.8, 7.3) minutes and ranged from 3.9 (3.1, 4.8) minutes to 7.5 (5.8, 10.8) minutes. Conclusion: Variability in the accuracy, and length of time taken to perform CTAS assessments suggest that a standardized approach to performing CTAS assessments would improve both clinical decision making, and administrative data accuracy.

scholarly journals Clinicians’ experiences of using and implementing a medical mobile phone app (QUiPP V2) designed to predict the risk of preterm birth and aid clinical decision making

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
N. Carlisle ◽  
H. A. Watson ◽  
J. Carter ◽  
K. Kuhrt ◽  
P. T. Seed ◽  
...  
Keyword(s):  
Decision Making ◽  
Preterm Birth ◽  
Cultural Context ◽  
Clinical Decision Making ◽  
Qualitative Interviews ◽  
Scale Up ◽  
Clinical Decision ◽  
Clinical Trial Registry ◽  
Support Tool ◽  
The Impact

Abstract Background As the vast majority of women who present in threatened preterm labour (TPTL) will not deliver early, clinicians need to balance the risks of over-medicalising the majority of women, against the potential risk of preterm delivery for those discharged home. The QUiPP app is a free, validated app which can support clinical decision-making as it produces individualised risks of delivery within relevant timeframes. Recent evidence has highlighted that clinicians would welcome a decision-support tool that accurately predicts preterm birth. Methods Qualitative interviews were undertaken as part of the EQUIPTT study (The Evaluation of the QUiPP app for Triage and Transfer) (REC: 17/LO/1802) which aimed to evaluate the impact of the QUiPP app on management of TPTL. Individual semi-structured telephone interviews were used to explore clinicians’ (obstetricians’ and midwives’) experiences of using the QUiPP app and how it was implemented at their hospital sites. Thematic analysis was chosen to explore the meaning of the data, through a framework approach. Results Nineteen participants from 10 hospital sites in England took part. Data analysis revealed three overarching themes which were: ‘experience of using the app’, ‘how QUiPP risk changes practice’ and ‘successfully adopting QUiPP: context is everything’. With these final themes we appeared to have achieved our aim of exploring the clinicians’ experiences of using and implementing the QUiPP app. Conclusion This study explored different clinician’s experiences of implementing the app. The organizational and cultural context at different sites appeared to have a large impact on how well the QUiPP app was implemented. Future work needs to be undertaken to understand how best to embed the intervention within different settings. This will inform scale up of QUiPP app use across the UK and ensure that clinicians have access to this free, easy-to-use tool which can positively aid clinical decision making when caring for women in TPTL. Clinical trial registry and registration number ISRCTN 17846337, registered 08th January 2018, https://doi.org/10.1186/ISRCTN17846337.

scholarly journals ACADEMIA’S ROLE TO DRIVE CHANGE IN THE ORTHOTICS AND PROSTHETICS PROFESSION

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Géza Kogler ◽  
Christopher Hovorka
Keyword(s):  
Technology Use ◽  
Clinical Training ◽  
Clinical Decision Making ◽  
New Technologies ◽  
Clinical Care ◽  
Clinical Decision ◽  
The United States ◽  
Academic Program ◽  
Number Of Patients ◽  
Curricular Emphasis

This position paper outlines the important role of academia in shaping the orthotics and prosthetics (O&P) profession and preparing for its future. In the United States, most healthcare professions including O&P are under intense pressure to provide cost effective treatments and quantifiable health outcomes. Pivotal changes are needed in the way O&P services are provided to remain competitive. This will require the integration of new technologies and data driven processes that have the potential to streamline workflows, reduce errors and inform new methods of clinical care and device manufacturing. Academia can lead this change, starting with a restructuring in academic program curricula that will enable the next generation of professionals to cope with multiple demands such as the provision of services for an increasing number of patients by a relatively small workforce of certified practitioners delivering these services at a reduced cost, with the expectation of significant, meaningful, and measurable value. Key curricular changes will require replacing traditional labor-intensive and inefficient fabrication methods with the integration of newer technologies (i.e., digital shape capture, digital modeling/rectification and additive manufacturing). Improving manufacturing efficiencies will allow greater curricular emphasis on clinical training and education – an area that has traditionally been underemphasized. Providing more curricular emphasis on holistic patient care approaches that utilize systematic and evidence-based methods in patient assessment, treatment planning, dosage of O&P technology use, and measurement of patient outcomes is imminent. Strengthening O&P professionals’ clinical decision-making skills and decreasing labor-intensive technical fabrication aspects of the curriculum will be critical in moving toward a digital and technology-centric practice model that will enable future practitioners to adapt and survive. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/36673/28349 How To Cite: Kogler GF, Hovorka CF. Academia’s role to drive change in the orthotics and prosthetics profession. Canadian Prosthetics & Orthotics Journal. 2021; Volume 4, Issue 2, No.21. https://doi.org/10.33137/cpoj.v4i2.36673 Corresponding Author: Géza F. KoglerOrthotics and Prosthetics Unit, Kennesaw State University.E-Mail: [email protected] ID: https://orcid.org/0000-0003-0212-5520

scholarly journals Deep Learning: A New Horizon for Personalized Treatment of Depression?

2018 ◽  
Vol 16 (1) ◽  
Author(s):  
David Benrimoh ◽  
Robert Fratila ◽  
Sonia Israel ◽  
Kelly Perlman
Keyword(s):  
Deep Learning ◽  
Decision Aid ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Personalized Care ◽  
Treatment Of Depression ◽  
Number Of Patients ◽  
Treatment Plans ◽  
Patient Responses ◽  
Deep Learning Model

Globally, depression affects 300 million people and is projected be the leading cause of disability by 2030. While different patients are known to benefit from different therapies, there is no principled way for clinicians to predict individual patient responses or side effect profiles. A form of machine learning based on artificial neural networks, deep learning, might be useful for generating a predictive model that could aid in clinical decision making. Such a model’s primary outcomes would be to help clinicians select the most effective treatment plans and mitigate adverse side effects, allowing doctors to provide greater personalized care to a larger number of patients. In this commentary, we discuss the need for personalization of depression treatment and how a deep learning model might be used to construct a clinical decision aid.

scholarly journals Development of a stepwise tool to aide primary health care professionals in the process of deprescribing in older persons

2020 ◽  
Vol 18 (4) ◽  
pp. 2033
Author(s):  
Roxana De las Salas ◽  
Javier Eslava-Schmalbach ◽  
Claudia Vaca-González ◽  
Dolores Rodríguez ◽  
Albert Figueras
Keyword(s):  
Health Care ◽  
Primary Health Care ◽  
Health Care Professionals ◽  
Older Persons ◽  
Clinical Decision Making ◽  
Inappropriate Medication ◽  
Clinical Decision ◽  
Potentially Inappropriate Medication ◽  
Kappa Statistics ◽  
Primary Health

Objective: The aim of this study was to develop and validate a stepwise tool to aid primary health care professionals in the process of deprescribing potentially inappropriate medication in older persons. Methods: We carried out a systematic review to identify previously published tools. A composite proposal of algorithm was made by following the steps from clinical experience to deprescribe medications. A 2-round electronic Delphi method was conducted to establish consensus. Eighteen experts from different countries (Colombia, Spain and Argentina) accepted to be part of the panel representing geriatricians, internists, endocrinologist, general practitioners, pharmacologists, clinical pharmacists, family physicians and nurses. Panel members were asked to mark a Likert Scale from 1 to 9 points (1= strongly disagree, 9= strongly agree). The content validity‏ ratio, item-level content validity, and Fleiss’ Kappa statistics was measured to establish reliability. The same voting method was used for round 2. Results: A 7-question algorithm was proposed. Each question was part of a domain and conduct into a decision. In round 1, a consensus was not reached but statements were grouped and organized. In round 2, the tool met consensus. The inter-rater reliability was between substantial and almost perfect for questions with Kappa=0.77 (95% CI 0.60-0.93), for domains with Kappa= 0.73 (95%CI 0.60-0.86) and for decisions with Kappa= 0.97 (95%CI 0.90-1.00). Conclusions: This is a novel tool that captures and supports healthcare professionals in clinical decision-making for deprescribing potentially inappropriate medication. This includes patient’s and caregiver’s preferences about medication. This tool will help to standardize care and provide guidance on the prescribing/deprescribing process of older persons’ medications. Also, it provides a holistic way to reduce polypharmacy and inappropriate medications in clinical practice.

scholarly journals A “Lymphocyte MicroRNA Signature” as Predictive Biomarker of Immunotherapy Response and Plasma PD-1/PD-L1 Expression Levels in Patients with Metastatic Renal Cell Carcinoma: Pointing towards Epigenetic Reprogramming

Cancers ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3396
Author(s):  
Lorena Incorvaia ◽  
Daniele Fanale ◽  
Giuseppe Badalamenti ◽  
Chiara Brando ◽  
Marco Bono ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Checkpoint Inhibitors ◽  
Predictive Biomarkers ◽  
Predictive Biomarker ◽  
Clinical Decision ◽  
Mirna Expression Profile ◽  
Immune Checkpoints ◽  
Cancer Evolution ◽  
Specific Subset ◽  
Number Of Patients

Introduction of checkpoint inhibitors resulted in durable responses and improvements in overall survival in advanced RCC patients, but the treatment efficacy is widely variable, and a considerable number of patients are resistant to PD-1/PD-L1 inhibition. This variability of clinical response makes necessary the discovery of predictive biomarkers for patient selection. Previous findings showed that the epigenetic modifications, including an extensive microRNA-mediated regulation of tumor suppressor genes, are key features of RCC. Based on this biological background, we hypothesized that a miRNA expression profile directly identified in the peripheral lymphocytes of the patients before and after the nivolumab administration could represent a step toward a real-time monitoring of the dynamic changes during cancer evolution and treatment. Interestingly, we found a specific subset of miRNAs, called “lymphocyte miRNA signature”, specifically induced in long-responder patients (CR, PR, or SD to nivolumab >18 months). Focusing on the clinical translational potential of miRNAs in controlling the expression of immune checkpoints, we identified the association between the plasma levels of soluble PD-1/PD-L1 and expression of some lymphocyte miRNAs. These findings could help the development of novel dynamic predictive biomarkers urgently needed to predict the potential response to immunotherapy and to guide clinical decision-making in RCC patients.

Using imaging to identify psychogenic parkinsonism before deep brain stimulation surgery

2012 ◽  
Vol 116 (1) ◽  
pp. 114-118 ◽  
Author(s):  
Michael H. Pourfar ◽  
Chris C. Tang ◽  
Alon Y. Mogilner ◽  
Vijay Dhawan ◽  
David Eidelberg
Keyword(s):  
Deep Brain Stimulation ◽  
Brain Stimulation ◽  
Metabolic Networks ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Number Of Patients ◽  
Normal Expression ◽  
Deep Brain Stimulation Surgery ◽  
Deep Brain

The frequency with which patients with atypical parkinsonism and advanced motor symptoms undergo deep brain stimulation (DBS) procedures is unknown. However, the potential exposure of these patients to unnecessary surgical risks makes their identification critical. As many as 15% of patients enrolled in recent early Parkinson disease (PD) trials have been found to lack evidence of a dopaminergic deficit following PET or SPECT imaging. This suggests that a number of patients with parkinsonism who are referred for DBS may not have idiopathic PD. The authors report on 2 patients with probable psychogenic parkinsonism who presented for DBS surgery. They found that both patients had normal caudate and putamen [18F]-fluorodopa uptake on PET imaging, along with normal expression of specific disease-related metabolic networks for PD and multiple system atrophy, a common form of atypical neurodegenerative parkinsonism. The clinical and PET findings in these patients highlight the role of functional imaging in assisting clinical decision making when the diagnosis is uncertain.

Stratification of patients by tumor type using molecular profiling in real-world data.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e19262-e19262
Author(s):  
Alyssa Antonopoulos ◽  
Elizabeth Eldridge ◽  
George Managadze ◽  
Elia Stupka ◽  
Hakim Lakhani ◽  
...  
Keyword(s):  
Real World ◽  
Clinical Decision Making ◽  
Single Gene ◽  
Data Access ◽  
Clinical Decision ◽  
Molecular Data ◽  
Tumor Type ◽  
Real World Data ◽  
Number Of Patients ◽  
Biomarker Data

e19262 Background: While Next-Generation Sequencing (NGS) tests become increasingly more common for diagnosis, molecular characterization, and treatment, a significant amount of molecular data derives from single-gene or analyte tests. Single gene test information is stored in disparate sources including electronic medical record (EMR) and data access for clinical use remains a challenge. A solution that harmonizes biomarker data beyond standard NGS-centric data and linked to rich clinical data is required for the complete patient picture. Methods: Health Catalyst’s extended real-world database, Touchstone includes a molecular data mart that integrates data from provider and life sciences proprietary NGS panels, Laboratory Information Systems, and other repositories. A portion of the data is derived from single-gene tests documented in the EMR. Biomarker data from EMRs was extracted from six health systems via a proprietary pipeline for extracting biomarker data. The algorithm relies on a curated ontology for molecular terms and publicly available terminologies for human genetics. Minor transformations extract pertinent variant information where available to harmonize with NGS-level data. Results: Over 44 thousand molecular labs from over 24 thousand patients were identified with this method. The oncology classes for which molecular data was identified in the greatest number of patients include skin, hematological, breast, digestive, and lung cancers (Table). PRTN3, EGFR, BRAF, JAK2, ERBB2, and KRAS are among the most commonly tested genes. Conclusions: Integrated real-world clinical and biomarker data from single gene tests can inform clinical decision-making and support clinical trial recruitment across a broader set of patient population. [Table: see text]

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