Evidence map›Paper›PMID 35977714›Full record

ArticleApplied clinical informatics2022

Provider Perspectives on Patient- and Provider-Facing High Blood Pressure Clinical Decision Support.

David A Dorr, Joshua E Richardson, Michelle Bobo, Christopher D'Autremont, Robert Rope, M J Dunne, Steven Z Kassakian, Lipika Samal

Open access · greenAbstract read
In one paragraph

Article in Applied clinical informatics, 2022. 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 26% 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, 6 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

8 authors at 3 institutions in 1 country.

David A DorrDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States.
Joshua E RichardsonCenter for Health Informatics and Evidence Synthesis, RTI International, Chicago, Illinois, United States.
Michelle BoboDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States.
Christopher D'AutremontDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States.
Robert RopeDepartment of Medicine, Oregon Health and Science University, Portland, Oregon, United States.
M J DunneDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States.
Steven Z KassakianDepartment of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States.
Lipika SamalDivision of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States.
Oregon Health & Science University · USHarvard University · USRTI International · US

Funding

Oregon Clinical and Translational Research Institute - The National COVID Cohort Collaborative (N3C)UL1TR002369 · NCATS · OREGON HEALTH & SCIENCE UNIVERSITY · PI Cynthia D Morris, Christopher G. Slatore · 2017 to 2026
$78.4M
Translating Hypertension Guidelines into Practice: Development of Interoperable Clinical Decision SupportU18HS026849 · AHRQ · OREGON HEALTH & SCIENCE UNIVERSITY · PI DORR, DAVID A. · 2019 to 2020
$939k
AHRQ HHS U18 HS026849NCATS NIH HHS UL1 TR002369
6 · The paper itself

Abstract

backgroundHypertension, persistent high blood pressures (HBP) leading to chronic physiologic changes, is a common condition that is a major predictor of heart attacks, strokes, and other conditions. Despite strong evidence, care teams and patients are inconsistently adherent to HBP guideline recommendations. Patient-facing clinical decision support (CDS) could help improve recommendation adherence but must also be acceptable to clinicians and patients.

objectiveThis study aimed to partly address the challenge of developing a patient-facing CDS application, we sought to understand provider variations and rationales related to HBP guideline recommendations and perceptions regarding patient role and use of digital tools.

methodsWe engaged hypertension experts and primary care respondents to iteratively develop and implement a pilot survey and a final survey which presented five clinical cases that queried clinicians' attitudes related to actions; variations; prioritization; patient input; importance; and barriers for HBP diagnosis, monitoring, and treatment. Analysis of Likert's scale scores was descriptive with content analysis for free-text answers.

resultsFifteen hypertension experts and 14 providers took the pilot and final version of the surveys, respectively. The majority (>80%) of providers felt the recommendations were important, yet found them difficult to follow-up to 90% of the time. Perceptions of relative amounts of patient input and patient work for effective HBP management ranged from 22 to 100%. Stated reasons for variation included adverse effects of treatment, patient comorbidities, shared decision-making, and health care cost and access issues. Providers were generally positive toward patient use of electronic CDS applications but worried about access to health care, nuance of recommendations, and patient understanding of the tools.

conclusionAt baseline, provider management of HBP is heterogeneous. Providers were accepting of patient-facing CDS but reported preferences for that CDS to capture the complexity and nuance of guideline recommendations.

Indexed as

Decision Support Systems, ClinicalHypertensionHumansSurveys and Questionnaires

Identifiers

PMID35977714
PMCPMC9713301
OpenAlexW4292424910

What OpenQuestion holds

Textmetadata
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.