Browsing by Subject "clinical decision-making"

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    ASGE Guideline on the Role of Ergonomics for Prevention of Endoscopy-related Injury (ERI): Methodology and Review of Evidence
    (ASGE, 2023-10) Pawa, Swati; Kwon, Richard S.; Fishman, Douglas S.; Thosani, Nirav C.; Shergill, Amandeep; Grover , Samir C.; Al-Haddad , Mohammad; Amateau, Stuart K.; Buxbaum , James L.; Calderwood , Audrey H.; Chalhoub, Jean M.; Coelho-Prabhu, Nayantara; Desai, Madhav; Elhanafi, Sherif E.; Forbes, Nauzer; Fujii-Lau, Larissa L.; Kohli, Divyanshoo R.; Machicado , Jorge D.; Marya, Neil B.; Ruan, Wenly; Sheth, Sunil G.; Storm, Andrew C.; Thiruvengadam, Nikhil R.; Wani, Sachin; Qumseya, Bashar J.; Medicine, School of Medicine
    This guideline document was prepared by the Standards of Practice Committee of the American Society for Gastrointestinal Endoscopy using the best available scientific evidence and considering a multitude of variables including but not limited to adverse events, patient values, and cost implications. The purpose of these guidelines is to provide the best practice recommendations, which may help standardize patient care, improve patient outcomes, and reduce variability in practice. We recognize that clinical decision-making is complex. Guidelines, therefore, are not a substitute for a clinician’s judgment. Such judgements may at times seem contradictory to our guidance because of many factors that are impossible to fully consider by guideline developers. Any clinical decisions should be based on the clinician’s experience, local expertise, resource availability, and patient values and preferences. This document is not a rule and should not be construed as establishing a legal standard of care or as encouraging, advocating for, mandating, or discouraging any particular treatment. Our guidelines should not be used in support of medical complaints, legal proceedings, and/or litigation, as they were not designed for this purpose.
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    Can we predict orthodontic extraction patterns by using machine learning?
    (Wiley, 2023) Leavitt, Landon; Volovic, James; Steinhauer, Lily; Mason, Taylor; Eckert, George; Dean, Jeffrey A.; Dundar, M. Murat; Turkkahraman, Hakan; Orthodontics and Oral Facial Genetics, School of Dentistry
    Objective To investigate the utility of machine learning (ML) in accurately predicting orthodontic extraction patterns in a heterogeneous population. Materials and Methods The material of this retrospective study consisted of records of 366 patients treated with orthodontic extractions. The dataset was randomly split into training (70%) and test sets (30%) and was stratified according to race/ethnicity and gender. Fifty-five cephalometric and demographic input data were used to train and test multiple ML algorithms. The extraction patterns were labelled according to the previous treatment plan. Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) algorithms were used to predict the patient's extraction patterns. Results The highest class accuracy percentages were obtained for the upper and lower 1st premolars (U/L4s) (RF: 81.63%, LR: 63.27%, SVM: 63.27%) and upper 1st premolars only (U4s) extraction patterns (RF: 61.11%, LR: 72.22%, SVM: 72.22%). However, all methods revealed low class accuracy rates (<50%) for the upper 1st and lower 2nd premolars (U4/L5s), upper 2nd and lower 1st premolars (U5/L4s), and upper and lower 2nd premolars (U/L5s) extraction patterns. For the overall accuracy, RF yielded the highest percentage with 54.55%, followed by SVM with 52.73% and LR with 49.09%. Conclusion All tested supervised ML techniques yielded good accuracy in predicting U/L4s and U4s extraction patterns. However, they predicted poorly for the U4/L5s, U5/L4s, and U/L5s extraction patterns. Molar relationship, mandibular crowding, and overjet were found to be the most predictive indicators for determining extraction patterns.
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    Getting Under the Skin of Clinical Inertia in Insulin Initiation: The Translating Research Into Action for Diabetes (TRIAD) Insulin Starts Project
    (2012-01) Ratanawongsa, Neda; Crosson, Jesse C.; Schillinger, Dean; Karter, Andrew J.; Saha, Chandan K.; Marrero, David G.
    Purpose The purpose of this cross-sectional study is to explore primary care providers’ (PCPs) perceptions about barriers to initiating insulin among patients. Studies suggest that many patients with poorly controlled type 2 diabetes do not receive insulin initiation by PCPs. Methods As part of the Translating Research Into Action for Diabetes study, the authors conducted structured interviews in health systems in Indiana, New Jersey, and California, asking PCPs about the importance of insulin initiation and factors affecting this decision. The authors calculated proportions choosing each multiple-choice response option and listed the most frequently offered open-ended response categories. Results Among 83 PCPs, 45% were women; 60% were white; and they averaged 13.4 years in practice. Four-fifths of PCPs endorsed guideline-concordant glycemic targets, but 54% individualized targets based on patient age, life expectancy, medical comorbidities, self-management capacity, and willingness. Most (64%) reported that many patients were resistant to new oral or insulin therapies due to fears about the therapy and what it meant about their disease progression. Two-thirds (64%) cited patient resistance as a barrier to insulin initiation, and 43% cited problems with patient self-management, including cognitive or mental health issues, dexterity, or ability to adhere. Eighty percent felt that patient nonadherence would dissuade them from initiating insulin at least some of the time. Conclusions PCPs perceived that patient resistance and poor self- management skills were significant barriers to initiating insulin. Future studies should investigate whether systems-level interventions to improve patient-provider communication about insulin and enhance providers’ perceptions of patient self-management capacity can increase guideline-concordant, patient-centered insulin initiation.