Evidence map›Paper›PMID 36644330›Full record

ArticleACI open2020

Deploying Clinical Decision Support for Familial Hypercholesterolemia.

Hana Bangash, Joseph Sutton, Justin H Gundelach, Laurie Pencille, Ahmed Makkawy, Omar Elsekaily, Ozan Dikilitas, Ali Mir, Robert Freimuth, Pedro J Caraballo and 1 more

Open access · diamondAbstract read
In one paragraph

Article in ACI open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.8field-weighted citation impact, top 25% of its field
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

5 citing papers in PubMed, 5 citations in OpenAlex.

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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

11 authors at 2 institutions in 1 country.

Hana BangashDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States.
Joseph SuttonDepartment of Information Technology, Mayo Clinic, Rochester, Minnesota, United States.
Justin H GundelachDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States.
Laurie PencilleCenter for Science of HealthCare Delivery, Mayo Clinic, Rochester, Minnesota, United States.
Ahmed MakkawyUser Experience Research, Saharafox Creative Agency, Rochester, Minnesota, United States.
Omar ElsekailyDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States.
Ozan DikilitasDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States.
Ali MirDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States.
Robert FreimuthDepartment of Digital Health Sciences, Mayo Clinic, Rochester, Minnesota, United States.
Pedro J CaraballoDepartment of General Internal Medicine, Mayo Clinic, Rochester, Minnesota, United States.
Iftikhar J KulloDepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, United States.
Mayo Clinic · USMayo Clinic in Florida · US

Funding

EHR-Based Strategies to Improve Outcomes in Familial HypercholesterolemiaR01HL135879 · NHLBI · MAYO CLINIC ROCHESTER · PI KULLO, IFTIKHAR J · 2017 to 2020
$2.5M
Patient-Oriented Research in Genomic Discovery and ImplementationK24HL137010 · NHLBI · MAYO CLINIC ROCHESTER · PI KULLO, IFTIKHAR J · 2017 to 2021
$590k
NHLBI NIH HHS K24 HL137010NHLBI NIH HHS R01 HL135879
6 · The paper itself

Abstract

Objective: Familial hypercholesterolemia (FH), a prevalent genomic disorder that increases risk of coronary heart disease, remains significantly underdiagnosed. Clinical decision support (CDS) tools have the potential to increase FH detection. We describe our experience in the development and implementation of a genomic CDS for FH at a large academic medical center. Methods: CDS development and implementation were conducted in four phases: (1) development and validation of an algorithm to identify "possible FH"; (2) obtaining approvals from institutional committees to develop the CDS; (3) development of the initial prototype; and (4) use of an implementation science framework to evaluate the CDS. Results: The timeline for this work was approximately 4 years; algorithm development and validation occurred from August 2018 to February 2020. During this 4-year period, we engaged with 15 stakeholder groups to build and integrate the CDS, including health care providers who gave feedback at each stage of development. During CDS implementation six main challenges were identified: (1) need for multiple institutional committee approvals; (2) need to align the CDS with institutional knowledge resources; (3) need to adapt the CDS to differing workflows; (4) lack of institutional guidelines for CDS implementation; (5) transition to a new institutional electronic health record (EHR) system; and (6) limitations of the EHR related to genomic medicine. Conclusion: We identified multiple challenges in different domains while developing CDS for FH and integrating it with the EHR. The lessons learned herein may be helpful in streamlining the development and deployment of CDS to facilitate genomic medicine implementation.

Indexed as

clinical decision supportfacilitators and barriershuman–computer interactionimplementation and deploymentinterfaces and usabilityprecision medicinetesting and evaluationworkflows and human interactions

Identifiers

PMID36644330
PMCPMC9838214
OpenAlexW3119604147

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.