Developing a More Responsive Radiology Resident Dashboard.
Residents have a limited time to be trained. Although having a highly variable caseload should be beneficial for resident training, residents do not necessarily get a uniform distribution of cases. By developing a dashboard where residents and their attendings can track the procedures they have done...
| Publicado en: | Journal of Digital Imaging Vol. 32; no. 1; pp. 81 - 91 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=134830563&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134830563 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2019 vid: 32 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 134830563 134830563 134830563 10.1007/s10278-018-0123-6 134830563 ppf: 81 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Developing a More Responsive Radiology Resident Dashboard. aug: au: Chen, Hongyu Gangaram, Vineeth Shih, George affil: Weill Cornell Medical College, 1300 York Ave, 10065, New York, NY, USA sug: subj: Education, Medical Interns and Residents Website Development Radiology Service Radiology Information Systems Human Natural Language Processing Medical Records Statistics Course Evaluation International Classification of Diseases Current Procedural Terminology Career Planning and Development Machine Learning Coding, Computer-Assisted ab: Residents have a limited time to be trained. Although having a highly variable caseload should be beneficial for resident training, residents do not necessarily get a uniform distribution of cases. By developing a dashboard where residents and their attendings can track the procedures they have done and cases that they have seen, we hope to give residents a greater insight into their training and into where gaps in their training may be occurring. By taking advantage of modern advances in NLP techniques, we process medical records and generate statistics describing each resident's progress so far. We have built the system described and its life within the NYP ecosystem. By creating better tracking, we hope that caseloads can be shifted to better close any individual gaps in training. One of the educational pain points for radiology residency is the assignment of cases to match a well-balanced curriculum. By illuminating the historical cases of a resident, we can better assign future cases for a better educational experience. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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