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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Implementation of the Austrian Nursing Minimum Data Set (NMDS-AT): A Feasibility Study
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, September 2015
|
| DOI | 10.1186/s12911-015-0198-7 |
| Pubmed ID | |
| Authors |
Renate Ranegger, Werner O. Hackl, Elske Ammenwerth |
| Abstract |
An Austrian Nursing Minimum Data Set (NMDS-AT) has been developed to describe the diversity of patient populations and variability of nursing care based on nursing diagnoses, nursing interventions, and nursing outcomes. The aim of this study is to test the feasibility of using this NMDS-AT by assessing the availability of data needed for the NMDS-AT in routine nursing documentation, and to assess its reliability and usefulness. Data were collected in a general hospital from patient records of 20 patients representing 457 patient days. Availability of needed data was assessed by two raters in a chart review based on an NMDS-AT form. The interrater reliability (n = 20) and intrarater reliability (n = 5) was assessed using Cohen's kappa coefficient and intraclass correlation coefficient (ICC). Usefulness was assessed by verifying whether typical analysis questions can be answered by the documented NMDS-AT data. In the 20 patient records, thirteen nursing diagnoses, 50 nursing interventions, and five nursing outcomes occurred, representing 68 (58.6 %) of the overall 116 data elements of the NMDS-AT. The data were found at different data sources (e.g., electronic nursing record or paper-based fever chart) and in various forms (e.g., standardized or free text). The interrater reliability of the thirteen nursing diagnoses showed kappa values (percentage of agreement) ranging from 0.35 (85 %) to 1.00 (100 %). The 50 nursing interventions showed ICCs ranging from 0.03 to 1.00. All nursing outcomes showed an ICC of 1.00. The intrarater reliability showed 100 % agreement. Performing typical analysis questions showed that the extracted NMDS-AT data are able to answer questions of clinical management, of policy makers, and of nursing science. The NMDS-AT was found to be feasible: needed data was available in the analysed patient records, data extraction showed good reliability, and typical analysis could be performed and showed interesting results. Before the NMDS-AT can be introduced in healthcare institutions, the following challenges need to be addressed: 1. improve the quality of nursing documentation; 2. reduce fragmentation of documentation; 3. use a standardized nursing classification system; and 4. establish mappings between nursing classification systems and the NMDS-AT. |
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 % |
|---|---|---|
| India | 1 | 50% |
| Unknown | 1 | 50% |
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 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 21 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 3 | 14% |
| Researcher | 3 | 14% |
| Student > Doctoral Student | 2 | 10% |
| Student > Ph. D. Student | 2 | 10% |
| Professor | 1 | 5% |
| Other | 3 | 14% |
| Unknown | 7 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Nursing and Health Professions | 6 | 29% |
| Social Sciences | 2 | 10% |
| Medicine and Dentistry | 2 | 10% |
| Computer Science | 1 | 5% |
| Chemistry | 1 | 5% |
| Other | 0 | 0% |
| Unknown | 9 | 43% |
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 21 September 2015.
All research outputs
#15,347,611
of 22,829,083 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,312
of 1,988 outputs
Outputs of similar age
#159,364
of 272,396 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#25
of 32 outputs
Altmetric has tracked 22,829,083 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,988 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
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We're also able to compare this research output to 32 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.