Evidence map›Paper›PMID 33537952›Full record

ReviewJournal of general internal medicine2021

Approach to Human-Centered, Evidence-Driven Adaptive Design (AHEAD) for Health Care Interventions: a Proposed Framework.

Meredith Fischer, Nadia Safaeinili, Marie C Haverfield, Cati G Brown-Johnson, Dani Zionts, Donna M Zulman

Abstract readReview
In one paragraph

Review in Journal of general internal medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing 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

43 citing papers in PubMed.

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  15. Development and implementation of "BMC digital health · 2026
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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

6 authors.

Meredith FischerDivision of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
Nadia SafaeiniliDivision of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
Marie C HaverfieldDepartment of Communication Studies, San José State University, San Jose, CA, USA.
Cati G Brown-JohnsonDivision of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
Dani ZiontsDivision of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
Donna M ZulmanDivision of Primary Care and Population Health, Stanford University School of Medicine, Stanford, CA, USA. dzulman@stanford.edu.ORCID 0000-0001-8904-2810

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human-centered design (HCD), an empathy-driven approach to innovation that focuses on user needs, offers promise for the rapid design of health care interventions that are acceptable to patients, clinicians, and other stakeholders. Reviews of HCD in healthcare, however, note a need for greater rigor, suggesting an opportunity for integration of elements from traditional research and HCD. A strategy that combines HCD principles with evidence-grounded health services research (HSR) methods has the potential to strengthen the innovation process and outcomes. In this paper, we review the strengths and limitations of HCD and HSR methods for intervention design, and propose a novel Approach to Human-centered, Evidence-driven Adaptive Design (AHEAD) framework. AHEAD offers a practical guide for the design of creative, evidence-based, pragmatic solutions to modern healthcare challenges.

Indexed as

Delivery of Health CareResearch DesignEmpathyHealth Services ResearchHumanshealth services researchhuman-centered designimplementation scienceintervention design

Identifiers

PMID33537952
PMCPMC8042058

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.