| Title |
Electronic Medical Record Integration for Streamlined DXA Reporting
|
|---|---|
| Published in |
Journal of Digital Imaging, September 2017
|
| DOI | 10.1007/s10278-017-0023-1 |
| Pubmed ID | |
| Authors |
Jason Wachsmann, Kyle Blain, Mathew Thompson, Solomon Cherian, Orhan K. Oz, Travis Browning |
| Abstract |
Dual-energy X-ray absorptiometry (DXA) is the most frequently performed examination to assess bone mineral density in clinical practice. Aside from images and graphical displays, many numerical values are part of DXA reports. These values are typically manually entered into the formal report through the electronic medical record or PACS workstation. The process takes time and is prone to errors. Exporting the DXA numerical data via HL7 engine to the electronic medical record was proposed to improve reporting efficiency and accuracy. The output from the DXA unit computer was reconfigured to export the report content via the HL7 interface engine into the electronic medical record. Radiology interpretive reporting was subsequently done directly in the electronic medical record. In the evaluation of errors, 100 preliminary DXA reports before the change and 100 after the change were examined. These reports were analyzed for errors that included decimal change, number transposition, negative number issue, other incorrect number error, and failure to include prior exam for comparison. In addition, report turnaround times were evaluated before and after the changes were made. Reporting time evaluations included 1-year volume prior to change (3915 reports) and 1 month post-change (206 reports). Of 100 DEXA exams before the change, 15 final reports contained 25 numerical errors. After the change, no numerical errors in the reports were identified. Exam end to final report time decreased from 2159 to 625 min on average. Automating data transmittal from the DXA modality for report generation improves accuracy and turnaround time. This approach did not require any third party software, and healthcare information security concerns were negated since we are using our standard workstations. Secondary to the affordability and applicability to the large percentage of the population using electronic medical record systems, this type of automated workflow is recommended. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
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 | 6 | 26% |
| Student > Bachelor | 4 | 17% |
| Student > Doctoral Student | 1 | 4% |
| Student > Ph. D. Student | 1 | 4% |
| Professor | 1 | 4% |
| Other | 2 | 9% |
| Unknown | 8 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 3 | 13% |
| Medicine and Dentistry | 3 | 13% |
| Nursing and Health Professions | 2 | 9% |
| Computer Science | 1 | 4% |
| Biochemistry, Genetics and Molecular Biology | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 12 | 52% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 12 February 2018.
All research outputs
#17,916,739
of 23,003,906 outputs
Outputs from Journal of Digital Imaging
#813
of 1,061 outputs
Outputs of similar age
#229,531
of 320,342 outputs
Outputs of similar age from Journal of Digital Imaging
#14
of 25 outputs
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