| Title |
Optimising Use of Electronic Health Records to Describe the Presentation of Rheumatoid Arthritis in Primary Care: A Strategy for Developing Code Lists
|
|---|---|
| Published in |
PLOS ONE, February 2013
|
| DOI | 10.1371/journal.pone.0054878 |
| Pubmed ID | |
| Authors |
Amanda Nicholson, Elizabeth Ford, Kevin A. Davies, Helen E. Smith, Greta Rait, A. Rosemary Tate, Irene Petersen, Jackie Cassell |
| Abstract |
Research using electronic health records (EHRs) relies heavily on coded clinical data. Due to variation in coding practices, it can be difficult to aggregate the codes for a condition in order to define cases. This paper describes a methodology to develop 'indicator markers' found in patients with early rheumatoid arthritis (RA); these are a broader range of codes which may allow a probabilistic case definition to use in cases where no diagnostic code is yet recorded. |
X Demographics
The data shown below were collected from the profiles of 5 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 Kingdom | 3 | 60% |
| United States | 1 | 20% |
| Unknown | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Practitioners (doctors, other healthcare professionals) | 1 | 20% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 86 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 3 | 3% |
| United Kingdom | 2 | 2% |
| Netherlands | 1 | 1% |
| Switzerland | 1 | 1% |
| Indonesia | 1 | 1% |
| Unknown | 78 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 23 | 27% |
| Researcher | 21 | 24% |
| Student > Master | 6 | 7% |
| Student > Postgraduate | 5 | 6% |
| Other | 5 | 6% |
| Other | 12 | 14% |
| Unknown | 14 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 36 | 42% |
| Computer Science | 5 | 6% |
| Engineering | 4 | 5% |
| Economics, Econometrics and Finance | 3 | 3% |
| Social Sciences | 3 | 3% |
| Other | 15 | 17% |
| Unknown | 20 | 23% |