Dataset Information

Reliability and Validity of the Checklist for Early Recognition and Treatment of Acute Illness and Injury as a Charting Tool in the Medical Intensive Care Unit.

ABSTRACT: Background:Resuscitation of critically ill patients is complex and potentially prone to diagnostic errors and therapeutic harm. The Checklist for early recognition and treatment of acute illness and injury (CERTAIN) is an electronic tool that aims to provide decision-support, charting, and prompting for standardization. This study sought to evaluate the validity and reliability of CERTAIN in a real-time Intensive Care Unit (ICU). Materials and Methods:This was a prospective pilot study in the medical ICU of a tertiary care medical center. A total of thirty patient encounters over 2 months period were charted independently by two CERTAIN investigators. The inter-observer recordings and comparison to the electronic medical records (EMR) were used to evaluate reliability and validity, respectively. The primary outcome was reliability and validity measured using Cohen's Kappa statistic. Secondary outcomes included time to completion, user satisfaction, and learning curve. Results:A total of 30 patients with a median age of 59 (42-78) years and median acute physiology and chronic health evaluation III score of 38 (23-50) were included in this study. Inter-observer agreement was very good (? = 0.79) in this study and agreement between CERTAIN and the EMR was good (? = 0.5). CERTAIN charting was completed in real-time that was 121 (92-150) min before completion of EMR charting. The subjective learning curve was 3.5 patients without differences in providers with different levels of training. Conclusions:CERTAIN provides a reliable and valid method to evaluate resuscitation events in real time. CERTAIN provided the ability to complete data in real-time.

SUBMITTER: Kogan A

PROVIDER: S-EPMC5699002 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Reliability and Validity of the Checklist for Early Recognition and Treatment of Acute Illness and Injury as a Charting Tool in the Medical Intensive Care Unit.

Kogan Alexander A Pennington Kelly M KM Vallabhajosyula Saraschandra S Dziadzko Mikhail M Bennett Courtney E CE Jensen Jeffrey B JB Gajic Ognjen O O'Horo John C JC

Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine 20171101 11

<h4>Background</h4>Resuscitation of critically ill patients is complex and potentially prone to diagnostic errors and therapeutic harm. The Checklist for early recognition and treatment of acute illness and injury (CERTAIN) is an electronic tool that aims to provide decision-support, charting, and prompting for standardization. This study sought to evaluate the validity and reliability of CERTAIN in a real-time Intensive Care Unit (ICU).<h4>Materials and methods</h4>This was a prospective pilot ...[more]

PMID: 29279635

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Project description:BackgroundIt is unlikely that applications of artificial intelligence (AI) will completely replace physicians. However, it is very likely that AI applications will acquire many of their roles and generate new tasks in medical care. To be ready for new roles and tasks, medical students and physicians will need to understand the fundamentals of AI and data science, mathematical concepts, and related ethical and medico-legal issues in addition with the standard medical principles. Nevertheless, there is no valid and reliable instrument available in the literature to measure medical AI readiness. In this study, we have described the development of a valid and reliable psychometric measurement tool for the assessment of the perceived readiness of medical students on AI technologies and its applications in medicine.MethodsTo define medical students' required competencies on AI, a diverse set of experts' opinions were obtained by a qualitative method and were used as a theoretical framework, while creating the item pool of the scale. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were applied.ResultsA total of 568 medical students during the EFA phase and 329 medical students during the CFA phase, enrolled in two different public universities in Turkey participated in this study. The initial 27-items finalized with a 22-items scale in a four-factor structure (cognition, ability, vision, and ethics), which explains 50.9% cumulative variance that resulted from the EFA. Cronbach's alpha reliability coefficient was 0.87. CFA indicated appropriate fit of the four-factor model (?2/df?=?3.81, RMSEA?=?0.094, SRMR?=?0.057, CFI?=?0.938, and NNFI (TLI)?=?0.928). These values showed that the four-factor model has construct validity.ConclusionsThe newly developed Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) was found to be valid and reliable tool for evaluation and monitoring of perceived readiness levels of medical students on AI technologies and applications. Medical schools may follow 'a physician training perspective that is compatible with AI in medicine' to their curricula by using MAIRS-MS. This scale could be benefitted by medical and health science education institutions as a valuable curriculum development tool with its learner needs assessment and participants' end-course perceived readiness opportunities.

Dataset Information

Reliability and Validity of the Checklist for Early Recognition and Treatment of Acute Illness and Injury as a Charting Tool in the Medical Intensive Care Unit.

Publications

Reliability and Validity of the Checklist for Early Recognition and Treatment of Acute Illness and Injury as a Charting Tool in the Medical Intensive Care Unit.

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