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Mendeley readers
Attention Score in Context
| Title |
Evidence-based rules from family practice to inform family practice; the learning healthcare system case study on urinary tract infections
|
|---|---|
| Published in |
BMC Primary Care, May 2015
|
| DOI | 10.1186/s12875-015-0271-4 |
| Pubmed ID | |
| Authors |
Jean K Soler, Derek Corrigan, Przemyslaw Kazienko, Tomasz Kajdanowicz, Roxana Danger, Marcin Kulisiewicz, Brendan Delaney |
| Abstract |
Analysis of encounter data relevant to the diagnostic process sourced from routine electronic medical record (EMR) databases represents a classic example of the concept of a learning healthcare system (LHS). By collecting International Classification of Primary Care (ICPC) coded EMR data as part of the Transition Project from Dutch and Maltese databases (using the EMR TransHIS), data mining algorithms can empirically quantify the relationships of all presenting reasons for encounter (RfEs) and recorded diagnostic outcomes. We have specifically looked at new episodes of care (EoC) for two urinary system infections: simple urinary tract infection (UTI, ICPC code: U71) and pyelonephritis (ICPC code: U70). Participating family doctors (FDs) recorded details of all their patient contacts in an EoC structure using the ICPC, including RfEs presented by the patient, and the FDs' diagnostic labels. The relationships between RfEs and episode titles were studied using probabilistic and data mining methods as part of the TRANSFoRm project. The Dutch data indicated that the presence of RfE's "Cystitis/Urinary Tract Infection", "Dysuria", "Fear of UTI", "Urinary frequency/urgency", "Haematuria", "Urine symptom/complaint, other" are all strong, reliable, predictors for the diagnosis "Cystitis/Urinary Tract Infection" . The Maltese data indicated that the presence of RfE's "Dysuria", "Urinary frequency/urgency", "Haematuria" are all strong, reliable, predictors for the diagnosis "Cystitis/Urinary Tract Infection". The Dutch data indicated that the presence of RfE's "Flank/axilla symptom/complaint", "Dysuria", "Fever", "Cystitis/Urinary Tract Infection", "Abdominal pain/cramps general" are all strong, reliable, predictors for the diagnosis "Pyelonephritis" . The Maltese data set did not present any clinically and statistically significant predictors for pyelonephritis. We describe clinically and statistically significant diagnostic associations observed between UTIs and pyelonephritis presenting as a new problem in family practice, and all associated RfEs, and demonstrate that the significant diagnostic cues obtained are consistent with the literature. We conclude that it is possible to generate clinically meaningful diagnostic evidence from electronic sources of patient data. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 57 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 10 | 18% |
| Researcher | 8 | 14% |
| Student > Postgraduate | 7 | 12% |
| Other | 4 | 7% |
| Student > Ph. D. Student | 4 | 7% |
| Other | 14 | 25% |
| Unknown | 10 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 20 | 35% |
| Computer Science | 7 | 12% |
| Psychology | 3 | 5% |
| Social Sciences | 3 | 5% |
| Nursing and Health Professions | 2 | 4% |
| Other | 9 | 16% |
| Unknown | 13 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 20 May 2015.
All research outputs
#16,722,190
of 25,374,647 outputs
Outputs from BMC Primary Care
#1,612
of 2,359 outputs
Outputs of similar age
#159,249
of 279,894 outputs
Outputs of similar age from BMC Primary Care
#22
of 29 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,359 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 279,894 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.