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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Assessing the accuracy of an inter-institutional automated patient-specific health problem list
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, February 2010
|
| DOI | 10.1186/1472-6947-10-10 |
| Pubmed ID | |
| Authors |
Lise Poissant, Laurel Taylor, Allen Huang, Robyn Tamblyn |
| Abstract |
Health problem lists are a key component of electronic health records and are instrumental in the development of decision-support systems that encourage best practices and optimal patient safety. Most health problem lists require initial clinical information to be entered manually and few integrate information across care providers and institutions. This study assesses the accuracy of a novel approach to create an inter-institutional automated health problem list in a computerized medical record (MOXXI) that integrates three sources of information for an individual patient: diagnostic codes from medical services claims from all treating physicians, therapeutic indications from electronic prescriptions, and single-indication drugs. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 3% |
| Argentina | 2 | 3% |
| Portugal | 1 | 1% |
| Canada | 1 | 1% |
| Unknown | 74 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 16 | 20% |
| Student > Ph. D. Student | 15 | 19% |
| Student > Postgraduate | 6 | 8% |
| Student > Master | 6 | 8% |
| Student > Doctoral Student | 6 | 8% |
| Other | 22 | 28% |
| Unknown | 9 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 36 | 45% |
| Engineering | 7 | 9% |
| Nursing and Health Professions | 7 | 9% |
| Computer Science | 7 | 9% |
| Social Sciences | 5 | 6% |
| Other | 7 | 9% |
| Unknown | 11 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 03 August 2021.
All research outputs
#6,911,928
of 22,664,644 outputs
Outputs from BMC Medical Informatics and Decision Making
#676
of 1,978 outputs
Outputs of similar age
#32,600
of 93,604 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#3
of 14 outputs
Altmetric has tracked 22,664,644 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 1,978 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 64% of its peers.
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 93,604 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.