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Effects of a computerized feedback intervention on safety performance by junior doctors: results from a randomized mixed method study

Overview of attention for article published in BMC Medical Informatics and Decision Making, June 2013
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  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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4 tweeters

Citations

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9 Dimensions

Readers on

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73 Mendeley
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Title
Effects of a computerized feedback intervention on safety performance by junior doctors: results from a randomized mixed method study
Published in
BMC Medical Informatics and Decision Making, June 2013
DOI 10.1186/1472-6947-13-63
Pubmed ID
Authors

Sabi Redwood, Nothando B Ngwenya, James Hodson, Robin E Ferner, Jamie J Coleman

Abstract

BACKGROUND: The behaviour of doctors and their responses to warnings can inform the effective design of Clinical Decision Support Systems. We used data from a University hospital electronic prescribing and laboratory reporting system with hierarchical warnings and alerts to explore junior doctors' behaviour. The objective of this trial was to establish whether a Junior Doctor Dashboard providing feedback on prescription warning information and laboratory alerting acceptance rates was effective in changing junior doctors' prescribing behaviour. METHODS: A mixed methods approach was employed which included a parallel group randomised controlled trial, and individual and focus group interviews. Junior doctors below the specialty trainee level 3 grade were recruited and randomised to two groups. Every doctor (N = 42) in the intervention group was e-mailed a link to a personal dashboard every week for 4 months. Nineteen participated in interviews. The 44 control doctors did not receive any automated feedback. The outcome measures were the difference in responses to prescribing warnings (of two severities) and laboratory alerting (of two severities) between the months before and the months during the intervention, analysed as the difference in performance between the intervention and the control groups. RESULTS: No significant differences were observed in the rates of generating prescription warnings, or in the acceptance of laboratory alarms. However, responses to laboratory alerts differed between the pre-intervention and intervention periods. For the doctors of Foundation Year 1 grade, this improvement was significantly (p = 0.002) greater in the group with access to the dashboard (53.6% ignored pre-intervention compared to 29.2% post intervention) than in the control group (47.9% ignored pre-intervention compared to 47.0% post intervention). Qualitative interview data indicated that while junior doctors were positive about the electronic prescribing functions, they were discriminating in the way they responded to other alerts and warnings given that from their perspective these were not always immediately clinically relevant or within the scope of their responsibility. CONCLUSIONS: We have only been able to provide weak evidence that a clinical dashboard providing individualized feedback data has the potential to improve safety behaviour and only in one of several domains. The construction of metrics used in clinical dashboards must take account of actual work processes.Trial registration: ISRCTN: ISRCTN72253051.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 3%
United Kingdom 1 1%
South Africa 1 1%
Canada 1 1%
Spain 1 1%
United States 1 1%
Unknown 66 90%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 19%
Student > Ph. D. Student 12 16%
Other 10 14%
Student > Postgraduate 6 8%
Student > Doctoral Student 5 7%
Other 18 25%
Unknown 8 11%
Readers by discipline Count As %
Medicine and Dentistry 29 40%
Computer Science 7 10%
Business, Management and Accounting 6 8%
Nursing and Health Professions 4 5%
Engineering 3 4%
Other 9 12%
Unknown 15 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 26 June 2013.
All research outputs
#1,994,426
of 8,014,601 outputs
Outputs from BMC Medical Informatics and Decision Making
#375
of 957 outputs
Outputs of similar age
#31,825
of 123,080 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#15
of 23 outputs
Altmetric has tracked 8,014,601 research outputs across all sources so far. This one has received more attention than most of these and is in the 61st percentile.
So far Altmetric has tracked 957 research outputs from this source. They receive a mean Attention Score of 4.6. This one has gotten more attention than average, scoring higher than 57% 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 123,080 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 69% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.