| Title |
Toward Data-Driven Radiology Education—Early Experience Building Multi-Institutional Academic Trainee Interpretation Log Database (MATILDA)
|
|---|---|
| Published in |
Journal of Digital Imaging, March 2016
|
| DOI | 10.1007/s10278-016-9872-2 |
| Pubmed ID | |
| Authors |
Po-Hao Chen, Thomas W. Loehfelm, Aaron P. Kamer, Andrew B. Lemmon, Tessa S. Cook, Marc D. Kohli |
| Abstract |
The residency review committee of the Accreditation Council of Graduate Medical Education (ACGME) collects data on resident exam volume and sets minimum requirements. However, this data is not made readily available, and the ACGME does not share their tools or methodology. It is therefore difficult to assess the integrity of the data and determine if it truly reflects relevant aspects of the resident experience. This manuscript describes our experience creating a multi-institutional case log, incorporating data from three American diagnostic radiology residency programs. Each of the three sites independently established automated query pipelines from the various radiology information systems in their respective hospital groups, thereby creating a resident-specific database. Then, the three institutional resident case log databases were aggregated into a single centralized database schema. Three hundred thirty residents and 2,905,923 radiologic examinations over a 4-year span were catalogued using 11 ACGME categories. Our experience highlights big data challenges including internal data heterogeneity and external data discrepancies faced by informatics researchers. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 50% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 23 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 4 | 17% |
| Other | 3 | 13% |
| Student > Bachelor | 3 | 13% |
| Student > Ph. D. Student | 3 | 13% |
| Student > Doctoral Student | 1 | 4% |
| Other | 5 | 22% |
| Unknown | 4 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 5 | 22% |
| Computer Science | 3 | 13% |
| Engineering | 3 | 13% |
| Nursing and Health Professions | 1 | 4% |
| Energy | 1 | 4% |
| Other | 3 | 13% |
| Unknown | 7 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 June 2020.
All research outputs
#12,951,914
of 22,860,626 outputs
Outputs from Journal of Digital Imaging
#587
of 1,050 outputs
Outputs of similar age
#134,984
of 298,940 outputs
Outputs of similar age from Journal of Digital Imaging
#5
of 9 outputs
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So far Altmetric has tracked 1,050 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
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We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 4 of them.