Work scheduling research typically prescribes task sequences implemented by managers. Yet employees often have discretion to deviate from their prescribed sequence. Using data from 2.4 million ...radiological diagnoses, we find that doctors prioritize similar tasks (batching) and those tasks they expect to complete faster (shortest expected processing time). Moreover, they exercise more discretion as they accumulate experience. Exploiting random assignment of tasks to doctors’ queues, instrumental variable models reveal that these deviations erode productivity. This productivity decline lessens as doctors learn from experience. Prioritizing the shortest tasks is particularly detrimental to productivity. Actively grouping similar tasks also reduces productivity, in stark contrast to productivity gains from exogenous grouping, indicating deviation costs outweigh benefits from repetition. By analyzing task completion times, our work highlights the trade-offs between the time required to exercise discretion and the potential gains from doing so, which has implications for how discretion over scheduling should be delegated.
The online appendix is available at
https://doi.org/10.1287/mnsc.2017.2810
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This paper was accepted by Serguei Netessine, operations management.
Radiology and Value-Based Health Care Brady, Adrian; Brink, James; Slavotinek, John
JAMA : the journal of the American Medical Association,
10/2020, Letnik:
324, Številka:
13
Journal Article
Recenzirano
The importance of transitioning radiology from a volume-based to a value-based practice and specialty is examined. The transition begins from within with an effort by radiologists to help define and ...create that value for referring clinicians and the patients and health care systems they serve.
Abstract Background To evaluate the efficiency of artificial intelligence (AI)-assisted diagnosis system in the pulmonary nodule detection and diagnosis training of junior radiology residents and ...medical imaging students. Methods The participants were divided into three groups. Medical imaging students of Grade 2020 in the Jinzhou Medical University were randomly divided into Groups 1 and 2; Group 3 comprised junior radiology residents. Group 1 used the traditional case-based teaching mode; Groups 2 and 3 used the ‘AI intelligent assisted diagnosis system’ teaching mode. All participants performed localisation, grading and qualitative diagnosed of 1,057 lung nodules in 420 cases for seven rounds of testing after training. The sensitivity and number of false positive nodules in different densities (solid, pure ground glass, mixed ground glass and calcification), sizes (less than 5 mm, 5–10 mm and over 10 mm) and positions (subpleural, peripheral and central) of the pulmonary nodules in the three groups were detected. The pathological results and diagnostic opinions of radiologists formed the criteria. The detection rate, diagnostic compliance rate, false positive number/case, and kappa scores of the three groups were compared. Results There was no statistical difference in baseline test scores between Groups 1 and 2, and there were statistical differences with Group 3 ( P = 0.036 and 0.011). The detection rate of solid, pure ground glass and calcified nodules; small-, medium-, and large-diameter nodules; and peripheral nodules were significantly different among the three groups ( P <0.05). After seven rounds of training, the diagnostic compliance rate increased in all three groups, with the largest increase in Group 2. The average kappa score increased from 0.508 to 0.704. The average kappa score for Rounds 1–4 and 5–7 were 0.595 and 0.714, respectively. The average kappa scores of Groups 1,2 and 3 increased from 0.478 to 0.658, 0.417 to 0.757, and 0.638 to 0.791, respectively. Conclusion The AI assisted diagnosis system is a valuable tool for training junior radiology residents and medical imaging students to perform pulmonary nodules detection and diagnosis.
Study Design Clinical measurement, cross-sectional. Background Individuals who have undergone anterior cruciate ligament (ACL) reconstruction commonly experience long-term impairments in quality of ...life (QoL), which may be related to persistent knee symptoms or radiographic osteoarthritis (ROA). Understanding the impact of knee symptoms and ROA on QoL after ACL reconstruction may assist in the development of appropriate management strategies. Objectives To (1) compare QoL between groups of individuals after ACL reconstruction (including those who are symptomatic with ROA, symptomatic without ROA, and asymptomatic unknown ROA status), and (2) identify specific aspects of QoL impairment in symptomatic individuals with and without ROA post ACL reconstruction. Methods One hundred thirteen participants completed QoL measures (Knee injury and Osteoarthritis Outcome Score QoL subscale KOOS-QoL, Anterior Cruciate Ligament Quality of Life ACL-QoL, Assessment of Quality of Life-8 Dimensions AQoL-8D) 5 to 20 years after ACL reconstruction. Eighty-one symptomatic individuals underwent radiographs, and 32 asymptomatic individuals formed a comparison group. Radiographic osteoarthritis was defined as a Kellgren-Lawrence grade of 2 or greater for the tibiofemoral and/or patellofemoral joints. Mann-Whitney U tests compared outcomes between groups. Individual ACL-QoL items were used to explore specific aspects of QoL. Results In symptomatic individuals after ACL reconstruction, ROA was related to worse knee-related outcomes on the KOOS-QoL (median, 50; interquartile range IQR, 38-69 versus median, 69; IQR, 56-81; P<.001) and the ACL-QoL (median, 51; IQR, 38-71 versus median, 66; IQR, 50-82; P = .04). The AQoL-8D scores showed that health-related QoL was impaired in both symptomatic groups compared to the asymptomatic group. The ACL-QoL item scores revealed greater limitations and concern surrounding sport and exercise and social/emotional difficulties in the symptomatic group with ROA. Conclusion Osteoarthritis is associated with worse knee-related QoL in symptomatic individuals after ACL reconstruction. Diagnosing ROA in symptomatic individuals after ACL reconstruction may be valuable, because these individuals may require unique management. Targeted strategies to facilitate participation in satisfying activities have potential to improve QoL in symptomatic people with ROA after ACL reconstruction. J Orthop Sports Phys Ther 2018;48(5):398-408. doi:10.2519/jospt.2018.7830.
Radiomics, which extract large amount of quantification image features from diagnostic medical images had been widely used for prognostication, treatment response prediction and cancer detection. The ...treatment options for lung nodules depend on their diagnosis, benign or malignant. Conventionally, lung nodule diagnosis is based on invasive biopsy. Recently, radiomics features, a non-invasive method based on clinical images, have shown high potential in lesion classification, treatment outcome prediction.
Lung nodule classification using radiomics based on Computed Tomography (CT) image data was investigated and a 4-feature signature was introduced for lung nodule classification. Retrospectively, 72 patients with 75 pulmonary nodules were collected. Radiomics feature extraction was performed on non-enhanced CT images with contours which were delineated by an experienced radiation oncologist.
Among the 750 image features in each case, 76 features were found to have significant differences between benign and malignant lesions. A radiomics signature was composed of the best 4 features which included Laws_LSL_min, Laws_SLL_energy, Laws_SSL_skewness and Laws_EEL_uniformity. The accuracy using the signature in benign or malignant classification was 84% with the sensitivity of 92.85% and the specificity of 72.73%.
The classification signature based on radiomics features demonstrated very good accuracy and high potential in clinical application.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Objectives
Why is there a major gap between the promises of AI and its applications in the domain of diagnostic radiology? To answer this question, we systematically review and critically analyze the ...AI applications in the radiology domain.
Methods
We systematically analyzed these applications based on their focal modality and anatomic region as well as their stage of development, technical infrastructure, and approval.
Results
We identified 269 AI applications in the diagnostic radiology domain, offered by 99 companies. We show that AI applications are primarily narrow in terms of tasks, modality, and anatomic region. A majority of the available AI functionalities focus on supporting the “perception” and “reasoning” in the radiology workflow.
Conclusions
Thereby, we contribute by (1) offering a systematic framework for analyzing and mapping the technological developments in the diagnostic radiology domain, (2) providing empirical evidence regarding the landscape of AI applications, and (3) offering insights into the current state of AI applications. Accordingly, we discuss the potential impacts of AI applications on the radiology work and we highlight future possibilities for developing these applications.
Key Points
•
Many AI applications are introduced to the radiology domain and their number and diversity grow very fast.
•
Most of the AI applications are narrow in terms of modality, body part, and pathology.
•
A lot of applications focus on supporting “perception” and “reasoning” tasks.
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