The Experts below are selected from a list of 336252 Experts worldwide ranked by ideXlab platform
Campoy Guerrero Carme - One of the best experts on this subject based on the ideXlab platform.
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Design and Validation of a Computer Application for Diagnosis of Shoulder Locomotor System Pathology
Korean Society of Medical Informatics, 2019Co-Authors: Bigordà Sagué Albert, Trujillano Cabello Javier, Ariza Carrió Gemma, Campoy Guerrero CarmeAbstract:Objectives: To design and validate a Computer Application for the diagnosis of shoulder locomotor system pathology. Meth-ods: The first phase involved the construction of the Application using the Delphi method. In the second phase, the applica-tion was validated with a sample of 250 patients with shoulder pathology. Validity was measured for each diagnostic group using sensitivity, specificity, and positive and negative likelihood ratio (LR(+) and LR(–)). The correct classification ratio (CCR) for each patient and the factors related to worse classification were calculated using multivariate binary logistic regres-sion (odds ratio, 95% confidence interval). Results: The mean time to complete the Application was 15 ± 7 minutes. The va-lidity values were the following: LR(+) 7.8 and LR(–) 0.1 for cervical radiculopathy, LR(+) 4.1 and LR(–) 0.4 for glenohumeral arthrosis, LR(+) 15.5 and LR(–) 0.2 for glenohumeral instability, LR(+) 17.2 and LR(–) 0.2 for massive rotator cuff tear, LR(+) 6.2 and LR(–) 0.2 for capsular syndrome, LR(+) 4.0 and LR(–) 0.3 for subacromial impingement/rotator cuff tendinopathy, and LR(+) 2.5 and LR(–) 0.6 for acromioclavicular arthropathy. A total of 70% of the patients had a CCR greater than 85%. Factors that negatively affected accuracy were massive rotator cuff tear, acromioclavicular arthropathy, age over 55 years, and high pain intensity (p < 0.05). Conclusions: The developed Application achieved an acceptable validity for most pathologies. Because the tool had a limited capacity to identify the full clinical picture in the same patient, improvements and new studies applied to other groups of patients are required
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Design and Validation of a Computer Application for Diagnosis of Shoulder Locomotor System Pathology
'The Korean Society of Medical Informatics', 2019Co-Authors: Bigordà Sagué Albert, Trujillano Cabello Javier, Ariza Carrió Gemma, Campoy Guerrero CarmeAbstract:Objectives: To design and validate a Computer Application for the diagnosis of shoulder locomotor system pathology. Meth-ods: The first phase involved the construction of the Application using the Delphi method. In the second phase, the applica-tion was validated with a sample of 250 patients with shoulder pathology. Validity was measured for each diagnostic group using sensitivity, specificity, and positive and negative likelihood ratio (LR(+) and LR(–)). The correct classification ratio (CCR) for each patient and the factors related to worse classification were calculated using multivariate binary logistic regres-sion (odds ratio, 95% confidence interval). Results: The mean time to complete the Application was 15 ± 7 minutes. The va-lidity values were the following: LR(+) 7.8 and LR(–) 0.1 for cervical radiculopathy, LR(+) 4.1 and LR(–) 0.4 for glenohumeral arthrosis, LR(+) 15.5 and LR(–) 0.2 for glenohumeral instability, LR(+) 17.2 and LR(–) 0.2 for massive rotator cuff tear, LR(+) 6.2 and LR(–) 0.2 for capsular syndrome, LR(+) 4.0 and LR(–) 0.3 for subacromial impingement/rotator cuff tendinopathy, and LR(+) 2.5 and LR(–) 0.6 for acromioclavicular arthropathy. A total of 70% of the patients had a CCR greater than 85%. Factors that negatively affected accuracy were massive rotator cuff tear, acromioclavicular arthropathy, age over 55 years, and high pain intensity (p < 0.05). Conclusions: The developed Application achieved an acceptable validity for most pathologies. Because the tool had a limited capacity to identify the full clinical picture in the same patient, improvements and new studies applied to other groups of patients are required.This research has received a grant from the Chartered Soci-ety of Physiotherapy in Catalonia (n. R04/13)
Susan Restaino - One of the best experts on this subject based on the ideXlab platform.
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a tablet Computer Application for patients to participate in their hospital care
American Medical Informatics Association Annual Symposium, 2011Co-Authors: David K. Vawdrey, Lauren Wilcox, Steve Feiner, Aurelia Boyer, Sarah A. Collins, Suzanne Bakken, Susan RestainoAbstract:Building on our institution’s commercial electronic health record and custom personal health record Web portal, we developed a tablet Computer Application to provide interactive information to hospital patients. Using Apple iPad devices, the prototype Application was provided to five patients in a cardiology step-down unit. We conducted detailed interviews to assess patients’ knowledge of their inpatient care, as well as their perceptions of the usefulness of the Application. While patients exhibited varying levels of comfort with using the tablet Computer, they were highly enthusiastic about the Application’s ability to supply health information such as their inpatient medication histories and photographs of their care providers. Additional research is warranted to assess the benefit such Applications may have for addressing inpatient information needs, enhancing patient-provider communication and improving patient satisfaction.
Bigordà Sagué Albert - One of the best experts on this subject based on the ideXlab platform.
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Design and Validation of a Computer Application for Diagnosis of Shoulder Locomotor System Pathology
Korean Society of Medical Informatics, 2019Co-Authors: Bigordà Sagué Albert, Trujillano Cabello Javier, Ariza Carrió Gemma, Campoy Guerrero CarmeAbstract:Objectives: To design and validate a Computer Application for the diagnosis of shoulder locomotor system pathology. Meth-ods: The first phase involved the construction of the Application using the Delphi method. In the second phase, the applica-tion was validated with a sample of 250 patients with shoulder pathology. Validity was measured for each diagnostic group using sensitivity, specificity, and positive and negative likelihood ratio (LR(+) and LR(–)). The correct classification ratio (CCR) for each patient and the factors related to worse classification were calculated using multivariate binary logistic regres-sion (odds ratio, 95% confidence interval). Results: The mean time to complete the Application was 15 ± 7 minutes. The va-lidity values were the following: LR(+) 7.8 and LR(–) 0.1 for cervical radiculopathy, LR(+) 4.1 and LR(–) 0.4 for glenohumeral arthrosis, LR(+) 15.5 and LR(–) 0.2 for glenohumeral instability, LR(+) 17.2 and LR(–) 0.2 for massive rotator cuff tear, LR(+) 6.2 and LR(–) 0.2 for capsular syndrome, LR(+) 4.0 and LR(–) 0.3 for subacromial impingement/rotator cuff tendinopathy, and LR(+) 2.5 and LR(–) 0.6 for acromioclavicular arthropathy. A total of 70% of the patients had a CCR greater than 85%. Factors that negatively affected accuracy were massive rotator cuff tear, acromioclavicular arthropathy, age over 55 years, and high pain intensity (p < 0.05). Conclusions: The developed Application achieved an acceptable validity for most pathologies. Because the tool had a limited capacity to identify the full clinical picture in the same patient, improvements and new studies applied to other groups of patients are required
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Design and Validation of a Computer Application for Diagnosis of Shoulder Locomotor System Pathology
'The Korean Society of Medical Informatics', 2019Co-Authors: Bigordà Sagué Albert, Trujillano Cabello Javier, Ariza Carrió Gemma, Campoy Guerrero CarmeAbstract:Objectives: To design and validate a Computer Application for the diagnosis of shoulder locomotor system pathology. Meth-ods: The first phase involved the construction of the Application using the Delphi method. In the second phase, the applica-tion was validated with a sample of 250 patients with shoulder pathology. Validity was measured for each diagnostic group using sensitivity, specificity, and positive and negative likelihood ratio (LR(+) and LR(–)). The correct classification ratio (CCR) for each patient and the factors related to worse classification were calculated using multivariate binary logistic regres-sion (odds ratio, 95% confidence interval). Results: The mean time to complete the Application was 15 ± 7 minutes. The va-lidity values were the following: LR(+) 7.8 and LR(–) 0.1 for cervical radiculopathy, LR(+) 4.1 and LR(–) 0.4 for glenohumeral arthrosis, LR(+) 15.5 and LR(–) 0.2 for glenohumeral instability, LR(+) 17.2 and LR(–) 0.2 for massive rotator cuff tear, LR(+) 6.2 and LR(–) 0.2 for capsular syndrome, LR(+) 4.0 and LR(–) 0.3 for subacromial impingement/rotator cuff tendinopathy, and LR(+) 2.5 and LR(–) 0.6 for acromioclavicular arthropathy. A total of 70% of the patients had a CCR greater than 85%. Factors that negatively affected accuracy were massive rotator cuff tear, acromioclavicular arthropathy, age over 55 years, and high pain intensity (p < 0.05). Conclusions: The developed Application achieved an acceptable validity for most pathologies. Because the tool had a limited capacity to identify the full clinical picture in the same patient, improvements and new studies applied to other groups of patients are required.This research has received a grant from the Chartered Soci-ety of Physiotherapy in Catalonia (n. R04/13)
Trujillano Cabello Javier - One of the best experts on this subject based on the ideXlab platform.
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Design and Validation of a Computer Application for Diagnosis of Shoulder Locomotor System Pathology
Korean Society of Medical Informatics, 2019Co-Authors: Bigordà Sagué Albert, Trujillano Cabello Javier, Ariza Carrió Gemma, Campoy Guerrero CarmeAbstract:Objectives: To design and validate a Computer Application for the diagnosis of shoulder locomotor system pathology. Meth-ods: The first phase involved the construction of the Application using the Delphi method. In the second phase, the applica-tion was validated with a sample of 250 patients with shoulder pathology. Validity was measured for each diagnostic group using sensitivity, specificity, and positive and negative likelihood ratio (LR(+) and LR(–)). The correct classification ratio (CCR) for each patient and the factors related to worse classification were calculated using multivariate binary logistic regres-sion (odds ratio, 95% confidence interval). Results: The mean time to complete the Application was 15 ± 7 minutes. The va-lidity values were the following: LR(+) 7.8 and LR(–) 0.1 for cervical radiculopathy, LR(+) 4.1 and LR(–) 0.4 for glenohumeral arthrosis, LR(+) 15.5 and LR(–) 0.2 for glenohumeral instability, LR(+) 17.2 and LR(–) 0.2 for massive rotator cuff tear, LR(+) 6.2 and LR(–) 0.2 for capsular syndrome, LR(+) 4.0 and LR(–) 0.3 for subacromial impingement/rotator cuff tendinopathy, and LR(+) 2.5 and LR(–) 0.6 for acromioclavicular arthropathy. A total of 70% of the patients had a CCR greater than 85%. Factors that negatively affected accuracy were massive rotator cuff tear, acromioclavicular arthropathy, age over 55 years, and high pain intensity (p < 0.05). Conclusions: The developed Application achieved an acceptable validity for most pathologies. Because the tool had a limited capacity to identify the full clinical picture in the same patient, improvements and new studies applied to other groups of patients are required
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Design and Validation of a Computer Application for Diagnosis of Shoulder Locomotor System Pathology
'The Korean Society of Medical Informatics', 2019Co-Authors: Bigordà Sagué Albert, Trujillano Cabello Javier, Ariza Carrió Gemma, Campoy Guerrero CarmeAbstract:Objectives: To design and validate a Computer Application for the diagnosis of shoulder locomotor system pathology. Meth-ods: The first phase involved the construction of the Application using the Delphi method. In the second phase, the applica-tion was validated with a sample of 250 patients with shoulder pathology. Validity was measured for each diagnostic group using sensitivity, specificity, and positive and negative likelihood ratio (LR(+) and LR(–)). The correct classification ratio (CCR) for each patient and the factors related to worse classification were calculated using multivariate binary logistic regres-sion (odds ratio, 95% confidence interval). Results: The mean time to complete the Application was 15 ± 7 minutes. The va-lidity values were the following: LR(+) 7.8 and LR(–) 0.1 for cervical radiculopathy, LR(+) 4.1 and LR(–) 0.4 for glenohumeral arthrosis, LR(+) 15.5 and LR(–) 0.2 for glenohumeral instability, LR(+) 17.2 and LR(–) 0.2 for massive rotator cuff tear, LR(+) 6.2 and LR(–) 0.2 for capsular syndrome, LR(+) 4.0 and LR(–) 0.3 for subacromial impingement/rotator cuff tendinopathy, and LR(+) 2.5 and LR(–) 0.6 for acromioclavicular arthropathy. A total of 70% of the patients had a CCR greater than 85%. Factors that negatively affected accuracy were massive rotator cuff tear, acromioclavicular arthropathy, age over 55 years, and high pain intensity (p < 0.05). Conclusions: The developed Application achieved an acceptable validity for most pathologies. Because the tool had a limited capacity to identify the full clinical picture in the same patient, improvements and new studies applied to other groups of patients are required.This research has received a grant from the Chartered Soci-ety of Physiotherapy in Catalonia (n. R04/13)
Ariza Carrió Gemma - One of the best experts on this subject based on the ideXlab platform.
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Design and Validation of a Computer Application for Diagnosis of Shoulder Locomotor System Pathology
Korean Society of Medical Informatics, 2019Co-Authors: Bigordà Sagué Albert, Trujillano Cabello Javier, Ariza Carrió Gemma, Campoy Guerrero CarmeAbstract:Objectives: To design and validate a Computer Application for the diagnosis of shoulder locomotor system pathology. Meth-ods: The first phase involved the construction of the Application using the Delphi method. In the second phase, the applica-tion was validated with a sample of 250 patients with shoulder pathology. Validity was measured for each diagnostic group using sensitivity, specificity, and positive and negative likelihood ratio (LR(+) and LR(–)). The correct classification ratio (CCR) for each patient and the factors related to worse classification were calculated using multivariate binary logistic regres-sion (odds ratio, 95% confidence interval). Results: The mean time to complete the Application was 15 ± 7 minutes. The va-lidity values were the following: LR(+) 7.8 and LR(–) 0.1 for cervical radiculopathy, LR(+) 4.1 and LR(–) 0.4 for glenohumeral arthrosis, LR(+) 15.5 and LR(–) 0.2 for glenohumeral instability, LR(+) 17.2 and LR(–) 0.2 for massive rotator cuff tear, LR(+) 6.2 and LR(–) 0.2 for capsular syndrome, LR(+) 4.0 and LR(–) 0.3 for subacromial impingement/rotator cuff tendinopathy, and LR(+) 2.5 and LR(–) 0.6 for acromioclavicular arthropathy. A total of 70% of the patients had a CCR greater than 85%. Factors that negatively affected accuracy were massive rotator cuff tear, acromioclavicular arthropathy, age over 55 years, and high pain intensity (p < 0.05). Conclusions: The developed Application achieved an acceptable validity for most pathologies. Because the tool had a limited capacity to identify the full clinical picture in the same patient, improvements and new studies applied to other groups of patients are required
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Design and Validation of a Computer Application for Diagnosis of Shoulder Locomotor System Pathology
'The Korean Society of Medical Informatics', 2019Co-Authors: Bigordà Sagué Albert, Trujillano Cabello Javier, Ariza Carrió Gemma, Campoy Guerrero CarmeAbstract:Objectives: To design and validate a Computer Application for the diagnosis of shoulder locomotor system pathology. Meth-ods: The first phase involved the construction of the Application using the Delphi method. In the second phase, the applica-tion was validated with a sample of 250 patients with shoulder pathology. Validity was measured for each diagnostic group using sensitivity, specificity, and positive and negative likelihood ratio (LR(+) and LR(–)). The correct classification ratio (CCR) for each patient and the factors related to worse classification were calculated using multivariate binary logistic regres-sion (odds ratio, 95% confidence interval). Results: The mean time to complete the Application was 15 ± 7 minutes. The va-lidity values were the following: LR(+) 7.8 and LR(–) 0.1 for cervical radiculopathy, LR(+) 4.1 and LR(–) 0.4 for glenohumeral arthrosis, LR(+) 15.5 and LR(–) 0.2 for glenohumeral instability, LR(+) 17.2 and LR(–) 0.2 for massive rotator cuff tear, LR(+) 6.2 and LR(–) 0.2 for capsular syndrome, LR(+) 4.0 and LR(–) 0.3 for subacromial impingement/rotator cuff tendinopathy, and LR(+) 2.5 and LR(–) 0.6 for acromioclavicular arthropathy. A total of 70% of the patients had a CCR greater than 85%. Factors that negatively affected accuracy were massive rotator cuff tear, acromioclavicular arthropathy, age over 55 years, and high pain intensity (p < 0.05). Conclusions: The developed Application achieved an acceptable validity for most pathologies. Because the tool had a limited capacity to identify the full clinical picture in the same patient, improvements and new studies applied to other groups of patients are required.This research has received a grant from the Chartered Soci-ety of Physiotherapy in Catalonia (n. R04/13)
