Evidence map›Paper›PMID 38883873›Full record

ArticleLearning health systems2024

Healthcare systems collaborating to implement a shared decision-making tool in the electronic health record and build evidence on its adoption and use.

Megan E Branda, Jennifer L Ridgeway, Devin Mann, Jeff Wieser, Yvonne Gomez, Ashlee Dagoberg, Vivek Nautiyal, Hugh Jackson, Patrick Jahn, Kathy Yaple and 6 more

Abstract read
In one paragraph

Article in Learning health systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Megan E BrandaDepartment of Quantitative Health Sciences Mayo Clinic Rochester Minnesota USA.ORCID https://orcid.org/0000-0003-0779-6876
Jennifer L RidgewayKnowledge and Evaluation Research Unit Mayo Clinic Rochester Minnesota USA.
Devin MannNYU Langone Health New York City New York USA.
Jeff WieserAltru Health System Grand Forks North Dakota USA.
Yvonne GomezAltru Health System Grand Forks North Dakota USA.
Ashlee DagobergAltru Health System Grand Forks North Dakota USA.
Vivek NautiyalWellstar Health System Marietta Georgia USA.
Hugh JacksonWellstar Health System Marietta Georgia USA.
Patrick JahnWellstar Health System Marietta Georgia USA.
Kathy YapleWellstar Health System Marietta Georgia USA.
Charanjit KhuranaVHC Health Physicians Arlington Virginia USA.
Hooman GharaiVHC Health Physicians Arlington Virginia USA.
Briana GieseVHC Health Arlington Virginia USA.
Tate CorcoranVHC Health Arlington Virginia USA.
Victor MontoriKnowledge and Evaluation Research Unit Mayo Clinic Rochester Minnesota USA.
Victor M MontoriKnowledge and Evaluation Research Unit Mayo Clinic Rochester Minnesota USA.

Funding

Implementing Shared Decision Making (SDM) for Individualized CV Prevention (SDM4IP)R01HL151662 · NHLBI · MAYO CLINIC ROCHESTER · PI MONTORI, VICTOR MANUEL, RIDGEWAY, JENNIFER L · 2020 to 2023
$2.7M
NHLBI NIH HHS R01 HL151662
6 · The paper itself

Abstract

Introduction: Shared decision-making (SDM) is a method of care by which patients and clinicians work together to co-create a plan of care. Electronic health record (EHR) integration of SDM tools may increase adoption of SDM. We conducted a "lightweight" integration of a freely available electronic SDM tool, CV Prevention Choice, within the EHRs of three healthcare systems. Here, we report how the healthcare systems collaborated to achieve integration. Methods: This work was conducted as part of a stepped wedge randomized pragmatic trial. CV Prevention Choice was developed using guidelines for HTML5-based web applications. Healthcare systems integrated the tool in their EHR using documentation the study team developed and refined with lessons learned after each system integrated the electronic SDM tool into their EHR. CV Prevention Choice integration populates the tool with individual patient data locally without sending protected health information between the EHR and the web. Data abstraction and secure transfer systems were developed to manage data collection to assess tool implementation and effectiveness outcomes. Results: Time to integrate CV Prevention Choice in the EHR was 12.1 weeks for the first system, 10.4 weeks for the second, and 9.7 weeks for the third. One system required two 1-hour meetings with study team members and two healthcare systems required a single 1-hour meeting. Healthcare system information technology teams collaborated by sharing information and offering improvements to documentation. Challenges included tracking CV Prevention Choice use for reporting and capture of combination medications. Data abstraction required refinements to address differences in how each healthcare system captured data elements. Conclusion: Targeted documentation on tool features and resource mapping supported collaboration of IT teams across healthcare systems, enabling them to integrate a web-based SDM tool with little additional research team effort or oversight. Their collaboration helped overcome difficulties integrating the web application and address challenges to data harmonization for trial outcome analyses.

Indexed as

data harmonizationelectronic health recordembedded researchimplementation sciencepragmatic trialsshared decision‐making

Identifiers

PMID38883873
PMCPMC11176581

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.