Complexity of clinical cases in simulated learning environments: proposal for a scoring system.
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ABSTRACT:
SUBMITTER: Braun LT
PROVIDER: S-EPMC6905356 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
Publications
Complexity of clinical cases in simulated learning environments: proposal for a scoring system.
Braun Leah Theresa LT Lenzer Benedikt B Fischer Martin R MR Schmidmaier Ralf R
GMS journal for medical education 20191115 6
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Project description:ObjectivesTo establish principles informing a new scoring system for the UK's Clinical Impact Awards and pilot a system based on those principles.DesignA three-round online Delphi process was used to generate consensus from experts on principles a scoring system should follow. We conducted a shadow scoring exercise of 20 anonymised, historic applications using a new scoring system incorporating those principles.SettingAssessment of clinical excellence awards for senior doctors and dentists in England and Wales.ParticipantsThe Delphi panel comprised 45 members including clinical excellence award assessors and representatives of professional bodies. The shadow scoring exercise was completed by 24 current clinical excellence award assessors.Main outcome measuresThe Delphi panel rated the appropriateness of a series of items. In the shadow scoring exercise, a novel scoring system was used with each of five domains rated on a 0-10 scale.ResultsConsensus was achieved around principles that could underpin a future scoring system; in particular, a 0-10 scale with the lowest point on the scale reflecting someone operating below the expectations of their job plan was agreed as appropriate. The shadow scoring exercise showed similar levels of reliability between the novel scoring system and that used historically, but with potentially better distinguishing performance at higher levels of performance.ConclusionsClinical excellence awards represent substantial public spending and thus far the deployment of these funds has lacked a strong evidence base. We have developed a new scoring system in a robust manner which shows improvements over current arrangements.
| S-EPMC10790597 | biostudies-literature
Project description:ObjectiveTo assess usability and usefulness of a machine learning-based order recommender system applied to simulated clinical cases.Materials and methods43 physicians entered orders for 5 simulated clinical cases using a clinical order entry interface with or without access to a previously developed automated order recommender system. Cases were randomly allocated to the recommender system in a 3:2 ratio. A panel of clinicians scored whether the orders placed were clinically appropriate. Our primary outcome included the difference in clinical appropriateness scores. Secondary outcomes included total number of orders, case time, and survey responses.ResultsClinical appropriateness scores per order were comparable for cases randomized to the order recommender system (mean difference -0.11 order per score, 95% CI: [-0.41, 0.20]). Physicians using the recommender placed more orders (median 16 vs 15 orders, incidence rate ratio 1.09, 95%CI: [1.01-1.17]). Case times were comparable with the recommender system. Order suggestions generated from the recommender system were more likely to match physician needs than standard manual search options. Physicians used recommender suggestions in 98% of available cases. Approximately 95% of participants agreed the system would be useful for their workflows.DiscussionUser testing with a simulated electronic medical record interface can assess the value of machine learning and clinical decision support tools for clinician usability and acceptance before live deployments.ConclusionsClinicians can use and accept machine learned clinical order recommendations integrated into an electronic order entry interface in a simulated setting. The clinical appropriateness of orders entered was comparable even when supported by automated recommendations.
| S-EPMC7727352 | biostudies-literature
Project description:PurposeSeveral nephrometry scoring systems have been developed based on two-dimensional computerized tomography images to quantify anatomical features of renal tumors. We have developed an accurate three-dimensional nephrometry scoring system to respond to the urgent need for advanced systems based on three-dimensional images.Materials and methodsWe retrospectively reviewed 135 patients who underwent partial nephrectomy in our institution. Stereoscopic models were reconstructed from preoperative computerized tomography images and three-dimensional scores were assigned directly on stereoscopic models. All tumors were analyzed for following features: tumor volume; endophytic tumor proportion; renal vascular variations; tumor's relationships with urinary collecting system or renal sinus; longitudinal distance from tumor to equatorial plane. Correlation between three-dimensional score and warm ischemic time was calculated compared with existing classical nephrometry scoring systems. The value of nephrometry scoring systems predicting longer warm ischemic time was explored by receiver operating characteristic curves.ResultsMean tumor volume was 31.25 ml; endophytic volume was less than 50% in 42 cases, more than 50% in 79 cases, and 100% in 14 cases; mean longitudinal distance from tumor to equatorial plane was 1.41 cm; 30 patients (22.2%) presented renal vascular variations; 18 cases (13.3%) involved both urinary collecting system and sinus. Mean three-dimensional score was 8.3. Variance analysis and covariance analysis revealed warm ischemic time a significant association with all evaluated tumor features. Furthermore, three-dimensional scores most highly correlated with warm ischemic time (rs = 0.64, p < 0.001), followed by R.E.N.A.L. scores (rs = 0.21, p = 0.012), centrality index (rs = - 0.20, p = 0.019) and Preoperative Aspects and Dimensions Used for Anatomy score (rs = 0.20, p = 0.019). Area under curve of above nephrometry scoring systems was 0.91, 0.67, 0.68 and 0.67 respectively (p < 0.05).ConclusionsThe three-dimensional scoring system developed in this study was a highly-accurate system to quantify the anatomical features of renal tumors. It was identified to have a value in predicting duration of warm ischemic time.
| S-EPMC7049461 | biostudies-literature
Project description:Optimal scoring system for clinical prognostic factors in patients with unresectable hepatocellular carcinoma (HCC) is currently uncertain. We aimed to develop and externally validate an easy to use tool, particularly for this population, and named it the "unresectable hepatocellular carcinoma prognostic index" (UHPI). We evaluated the data of patients with treatment-naive unresectable HCC who were diagnosed in the training center from 2010 to 2019 (n = 209). A simple prognostic model was developed by assigning points for each covariate in proportion to the beta coefficients in the Cox multivariable model. Predictive performance and distinction ability of the UHPI were further evaluated in an independent European validation cohort (n = 147) and compared with 11 other available models. A simple scoring system was derived, assigning 0.5/1/2 scores for six independent covariates including, the Child-Pugh score, Eastern Cooperative Oncology Group performance status, maximum tumor size, vascular invasion or extrahepatic metastasis, lymph node involvement, and alpha-fetoprotein. The UHPI score, ranging from 0 to 6, showed superior performance in prognosis prediction and outperformed 11 other staging or prognostic models, giving the highest homogeneity (c-index, 6-month and 1-year area under the receiver operator characteristic curves), lowest Akaike information criterion, and -2 log-likelihood ratio values. The UHPI score allocated well the risk of patients with unresectable HCC for mortality within the first year, using two cut-off values (low-risk, <0.5; intermediate-risk, 0.5-2; high-risk, >2). Conclusion: The UHPI score can predict prognosis better than other systems in subjects with unresectable HCC and can be used in clinical practice or trials to estimate the 6-month and 1-year survival probabilities for this group.
| S-EPMC8870011 | biostudies-literature
Project description:Hepatoid perianal gland tumors are relatively common in dogs, accounting for 25% of all skin tumors. However, the specific factors involved in their development are still not completely clear. It has been established that hormonal influences can impact the formation of these tumors. The prognosis for dogs with perianal tumors depends largely on histology (benign vs. malignant) and, in case of malignancy, it has been suggested that the stage of the disease is important, with a more favorable outcome in dogs having small (under 5 cm in diameter), non-metastatic adenocarcinomas which are surgically removed with non-infiltrated margins. Nevertheless, there is a paucity of studies which thoroughly relate hepatoid gland histotypes to their prognosis; therefore, it is possible that a well-differentiated adenocarcinoma could be misclassified. Based on a retrospective review of 76 dogs with hepatoid gland tumors having clinical follow-up, the aims of this study were (1) to establish a histological grading system capable of potentially predicting prognosis and (2) to explore the role of Ki67 as a potential prognostic marker. Based on histopathological features only, the proposed grading system effectively differentiated tumors with a favorable prognosis from those with a worse prognosis to support histological diagnosis. The evaluation of the Ki67 index was not useful to predict prognosis in this study.
| S-EPMC11841504 | biostudies-literature
Project description:Background/aimsEnteroenteric intussusception in Peutz-Jeghers syndrome (EI-PJS) is traditionally treated by surgery. However, enteroscopic treatment is a minimally invasive approach worth attempting. We aimed to develop a risk scoring system to facilitate decision-making in the treatment of EI-PJS.MethodsThis was a single-center case-control study, including 80 patients diagnosed with PJS and coexisting intussusception between January 2015 and January 2021 in Air Force Medical Center. We performed logistic regression analysis to identify independent risk factors and allocated different points to each subcategory of risk factors; the total score of individuals ranged from 0 to 9 points. Then, we constructed a risk stratification system based on the possibility of requiring surgery: 0-3 points for "low-risk," 4-6 points for "moderate-risk," and 7-9 points for "high-risk."ResultsSixty-one patients (76.25%) were successfully treated with enteroscopy. Sixteen patients (20.0%) failed enteroscopic treatment and subsequently underwent surgery, and three patients (3.75%) received surgery directly. Abdominal pain, the diameter of the responsible polyp, and the length of intussusception were independent risk factors for predicting the possibility of requiring surgery. According to the risk scoring system, the incidence rates of surgery were 4.44% in the low-risk tier, 30.43% in the moderate-risk tier, and 83.33% in the high-risk tier. From low- to high-risk tiers, the trend of increasing risk was significant (p<0.001).ConclusionsWe developed a risk scoring system based on abdominal pain, diameter of the responsible polyps, and length of intussusception. It can preoperatively stratify patients according to the risk of requiring surgery for EI-PJS to facilitate treatment decision-making.
| S-EPMC10018310 | biostudies-literature
Project description:IntroductionThe objective of this study was to describe and analyze residents' perceptions of characteristics on the expansive/restrictive continuum of their clinical learning environment.MethodsWe conducted a quantitative, descriptive and cross-sectional study. A self-administered questionnaire was designed, programmed and applied to residents at the Faculty of Medicine of the National Autonomous University of Mexico. The instrument was structured in eight sections, and for this article, Section 3, which referred to clinical environments and violence was considered. The questionnaire had an 85% response rate, with 12,612 residents from 113 medical units and 78 specialties participating. The reliability and internal consistency measured with alpha omega obtained a value of ω 0.835 (CI; 0.828-0.843).ResultsUnpleasant, competitive, tense and conflictive contexts were related to restrictive environments. Sexual orientation influenced the perception of intolerance in the clinical setting with respect to discriminatory comments, such that for gender minorities, the environment was experienced as exclusionary. First-year residents perceived environments as more aggressive, a perception that tended to decrease in later years of residency.DiscussionAbuses in power relations, rigid hierarchical positions and offensive clinical interactions may foster restrictive environments. In such settings, the reproduction of socio-culturally learned violence is feasible; however, asymmetrical relationships may be deconstructed and transformed.
| S-EPMC10531318 | biostudies-literature
Project description:IntroductionThe Medication Regimen Complexity -Intensive Care Unit (MRC-ICU) is the first tool for measuring medication regimen complexity in critically ill patients. This study tested machine learning (ML) models to investigate the relationship between medication regimen complexity and patient outcomes.MethodsThis study was a single-center, retrospective observational evaluation of 130 adults admitted to the medical ICU. The MRC-ICU score was utilized to improve the inpatient model's prediction accuracy. Three models were proposed: model I, demographic data without medication data; model II, demographic data and medication regimen complexity variables; and model III: demographic data and the MRC-ICU score. A total of 6 ML classifiers was developed: k-nearest neighbor (KNN), naïve Bayes (NB), random forest, support vector machine, neural network, and logistic classifier (LC). They were developed and tested using electronic health record data to predict inpatient mortality.ResultsThe results demonstrated that adding medication regimen complexity variables (model II) and the MRC-ICU score (model III) improved inpatient mortality prediction.. The LC outperformed the other classifiers (KNN and NB), with an overall accuracy of 83%, sensitivity (Se) of 87%, specificity of 67%, positive predictive value of 93%, and negative predictive value of 46%. The APACHE III score and the MRC-ICU score at the 24-hour interval were the 2 most important variables.Conclusion and relevanceInclusion of the MRC-ICU score improved the prediction of patient outcomes on the previously established APACHE III score. This novel, proof-of-concept methodology shows promise for future application of the MRC-ICU scoring tool for patient outcome predictions.
| S-EPMC8106768 | biostudies-literature
Project description:The human monkeypox disease has mainly been described in Western and Central Africa. Since May 2022, the monkeypox virus has been spreading worldwide in a new epidemiological pattern, where cases result from person-to-person transmission, and develop clinically milder or less typical illness than during previous outbreaks in endemic areas. The newly-emerging monkeypox disease needs to be described over the long term, to improve cases definitions, to implement prompt control measures against epidemics, and to provide supportive care. Hence, we first conducted a review of historical and recent outbreaks to define the full clinical spectrum of the monkeypox disease and its course known so far. Then, we built a self-administrated questionnaire collecting daily symptoms of the monkeypox infection to follow cases and their contacts, even remotely. This tool will assist in the management of cases, the surveillance of contacts, and the conduct of clinical studies.
| S-EPMC9946014 | biostudies-literature
Project description:ObjectivesTo simulate clinical deployment, evaluate performance, and establish quality assurance of a deep learning algorithm (U-Net) for detection, localization, and segmentation of clinically significant prostate cancer (sPC), ISUP grade group ≥ 2, using bi-parametric MRI.MethodsIn 2017, 284 consecutive men in active surveillance, biopsy-naïve or pre-biopsied, received targeted and extended systematic MRI/transrectal US-fusion biopsy, after examination on a single MRI scanner (3 T). A prospective adjustment scheme was evaluated comparing the performance of the Prostate Imaging Reporting and Data System (PI-RADS) and U-Net using sensitivity, specificity, predictive values, and the Dice coefficient.ResultsIn the 259 eligible men (median 64 [IQR 61-72] years), PI-RADS had a sensitivity of 98% [106/108]/84% [91/108] with a specificity of 17% [25/151]/58% [88/151], for thresholds at ≥ 3/≥ 4 respectively. U-Net using dynamic threshold adjustment had a sensitivity of 99% [107/108]/83% [90/108] (p > 0.99/> 0.99) with a specificity of 24% [36/151]/55% [83/151] (p > 0.99/> 0.99) for probability thresholds d3 and d4 emulating PI-RADS ≥ 3 and ≥ 4 decisions respectively, not statistically different from PI-RADS. Co-occurrence of a radiological PI-RADS ≥ 4 examination and U-Net ≥ d3 assessment significantly improved the positive predictive value from 59 to 63% (p = 0.03), on a per-patient basis.ConclusionsU-Net has similar performance to PI-RADS in simulated continued clinical use. Regular quality assurance should be implemented to ensure desired performance.Key points• U-Net maintained similar diagnostic performance compared to radiological assessment of PI-RADS ≥ 4 when applied in a simulated clinical deployment. • Application of our proposed prospective dynamic calibration method successfully adjusted U-Net performance within acceptable limits of the PI-RADS reference over time, while not being limited to PI-RADS as a reference. • Simultaneous detection by U-Net and radiological assessment significantly improved the positive predictive value on a per-patient and per-lesion basis, while the negative predictive value remained unchanged.
| S-EPMC7755653 | biostudies-literature