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Health Information Technologies—Academic and Commercial Evaluation (HIT-ACE) methodology: description and application to clinical feedback systems

Overview of attention for article published in Implementation Science, September 2016
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Title
Health Information Technologies—Academic and Commercial Evaluation (HIT-ACE) methodology: description and application to clinical feedback systems
Published in
Implementation Science, September 2016
DOI 10.1186/s13012-016-0495-2
Pubmed ID
Authors

Aaron R. Lyon, Cara C. Lewis, Abigail Melvin, Meredith Boyd, Semret Nicodimos, Freda F. Liu, Nathaniel Jungbluth

Abstract

Health information technologies (HIT) have become nearly ubiquitous in the contemporary healthcare landscape, but information about HIT development, functionality, and implementation readiness is frequently siloed. Theory-driven methods of compiling, evaluating, and integrating information from the academic and commercial sectors are necessary to guide stakeholder decision-making surrounding HIT adoption and to develop pragmatic HIT research agendas. This article presents the Health Information Technologies-Academic and Commercial Evaluation (HIT-ACE) methodology, a structured, theory-driven method for compiling and evaluating information from multiple sectors. As an example demonstration of the methodology, we apply HIT-ACE to mental and behavioral health measurement feedback systems (MFS). MFS are a specific class of HIT that support the implementation of routine outcome monitoring, an evidence-based practice. HIT-ACE is guided by theories and frameworks related to user-centered design and implementation science. The methodology involves four phases: (1) coding academic and commercial materials, (2) developer/purveyor interviews, (3) linking putative implementation mechanisms to hit capabilities, and (4) experimental testing of capabilities and mechanisms. In the current demonstration, phase 1 included a systematic process to identify MFS in mental and behavioral health using academic literature and commercial websites. Using user-centered design, implementation science, and feedback frameworks, the HIT-ACE coding system was developed, piloted, and used to review each identified system for the presence of 38 capabilities and 18 additional characteristics via a consensus coding process. Bibliometic data were also collected to examine the representation of the systems in the scientific literature. As an example, results are presented for the application of HIT-ACE phase 1 to MFS wherein 49 separate MFS were identified, reflecting a diverse array of characteristics and capabilities. Preliminary findings demonstrate the utility of HIT-ACE to represent the scope and diversity of a given class of HIT beyond what can be identified in the academic literature. Phase 2 data collection is expected to confirm and expand the information presented and phases 3 and 4 will provide more nuanced information about the impact of specific HIT capabilities. In all, HIT-ACE is expected to support adoption decisions and additional HIT development and implementation research.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 89 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 1%
Netherlands 1 1%
United States 1 1%
Unknown 86 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 16%
Student > Master 13 15%
Student > Ph. D. Student 10 11%
Student > Doctoral Student 6 7%
Student > Bachelor 5 6%
Other 25 28%
Unknown 16 18%
Readers by discipline Count As %
Psychology 18 20%
Medicine and Dentistry 12 13%
Computer Science 10 11%
Social Sciences 9 10%
Unspecified 5 6%
Other 16 18%
Unknown 19 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 2017.
All research outputs
#9,099,426
of 16,750,089 outputs
Outputs from Implementation Science
#1,239
of 1,551 outputs
Outputs of similar age
#117,235
of 272,015 outputs
Outputs of similar age from Implementation Science
#7
of 10 outputs
Altmetric has tracked 16,750,089 research outputs across all sources so far. This one is in the 45th percentile – i.e., 45% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,551 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.0. This one is in the 18th percentile – i.e., 18% 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 272,015 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 55% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.