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Taxonomy of delays in the implementation of hospital computerized physician order entry and clinical decision support systems for prescribing: a longitudinal qualitative study

Overview of attention for article published in BMC Medical Informatics and Decision Making, February 2016
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  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

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Title
Taxonomy of delays in the implementation of hospital computerized physician order entry and clinical decision support systems for prescribing: a longitudinal qualitative study
Published in
BMC Medical Informatics and Decision Making, February 2016
DOI 10.1186/s12911-016-0263-x
Pubmed ID
Authors

Hajar Mozaffar, Kathrin M. Cresswell, Lisa Lee, Robin Williams, Aziz Sheikh, On behalf of the NIHR ePrescribing Programme Team

Abstract

Implementation delays are common in health information technology (HIT) projects. In this paper, we sought to explore the reasons for delays in implementing major hospital-based HIT, through studying computerized physician order entry (CPOE) and clinical decision support (CDS) systems for prescribing and to develop a provisional taxonomy of causes of implementation delays. We undertook a series of longitudinal, qualitative case studies to investigate the implementation and adoption of CPOE and CDS systems for prescribing in hospitals in the U.K. We used a combination of semi-structured interviews from six case study sites and two whole day expert roundtable discussions to collect data. Interviews were carried out with users, implementers and suppliers of CPOE/CDS systems. We used thematic analysis to examine the results, drawing on perspectives surrounding the biography of artefacts. We identified 15 major factors contributing to delays in implementation of CPOE and CDS systems. These were then categorized in a two-by-two delay classification matrix: one axis distinguishing tactical versus unintended causes of delay, and the second axis illustrating internal i.e., (the adopting hospital) versus external (i.e., suppliers, other hospitals, policymakers) related causes. Our taxonomy of delays in HIT implementation should enable system developers, implementers and policymakers to better plan and manage future implementations. More detailed planning at the outset, considering long-term strategies, sustained user engagement, and phased implementation approaches appeared to reduce the risks of delays. It should however be noted that whilst some delays are likely to be preventable, other delays cannot be easily avoided and taking steps to minimize these may negatively affect the longer-term use of the system.

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X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Unknown 74 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 21 28%
Student > Ph. D. Student 12 16%
Researcher 8 11%
Lecturer 4 5%
Student > Doctoral Student 3 4%
Other 10 13%
Unknown 17 23%
Readers by discipline Count As %
Medicine and Dentistry 18 24%
Nursing and Health Professions 12 16%
Computer Science 8 11%
Social Sciences 6 8%
Business, Management and Accounting 5 7%
Other 7 9%
Unknown 19 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 April 2016.
All research outputs
#6,712,992
of 25,386,384 outputs
Outputs from BMC Medical Informatics and Decision Making
#583
of 2,139 outputs
Outputs of similar age
#86,891
of 313,103 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#13
of 31 outputs
Altmetric has tracked 25,386,384 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 2,139 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one has gotten more attention than average, scoring higher than 72% 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 313,103 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 72% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.