Evidence map›Paper›PMID 42367370›Full record

ArticleSSM. Qualitative research in health2026

"That's not my silo": Navigating fragmented long COVID care in the mid-Atlantic United States.

Heang-Lee Tan, Erica Rosser, Angélica Lopez Hernandez, Y Christine Fei, Alba Azola, Ann Parker, Panagis Galiatsatos, Chimére L Smith, Patrick Shea, Andrea L Cox and 2 more

Abstract read
In one paragraph

Article in SSM. Qualitative research in health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Heang-Lee TanDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD, 21205, USA.ORCID 0000-0001-6375-9362
Erica RosserDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD, 21205, USA.ORCID 0009-0001-3893-869X
Angélica Lopez HernandezDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD, 21205, USA.
Y Christine FeiDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD, 21205, USA.
Alba AzolaDepartment of Physical Medicine and Rehabilitation and Pediatrics, Johns Hopkins University School of Medicine, 733 N. Broadway, Baltimore, MD, 21205, USA.
Ann ParkerDivision of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, 733 N. Broadway, Baltimore, MD, 21205, USA.
Panagis GaliatsatosDivision of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, 733 N. Broadway, Baltimore, MD, 21205, USA.
Chimére L SmithThe Black Long Covid Experience, Poughkeepsie, NY, 12601, USA.
Patrick SheaW. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD, 21205, USA.
Andrea L CoxW. Harry Feinstone Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD, 21205, USA.ORCID 0000-0002-9331-2462
Sabra L KleinDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD, 21205, USA.
Rosemary MorganDepartment of International Health, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD, 21205, USA.ORCID 0000-0001-5009-8470

Funding

Virology Resource CoreU54CA260492 · NCI · JOHNS HOPKINS UNIVERSITY · PI COX, ANDREA L, KLEIN, SABRA L. · 2020 to 2024
$10.4M
NCI NIH HHS U54 CA260492
6 · The paper itself

Abstract

Background: Long COVID is a chronic illness affecting multiple organ systems, which can be at odds with a highly specialized and siloed U.S. healthcare system. Patients frequently see multiple specialists for diverse symptoms, creating substantial coordination challenges. Methods: We conducted a qualitative study using semi-structured interviews with 69 U.S. Mid-Atlantic adults diagnosed with or suspected of having Long COVID, recruited through a Long COVID clinic, MyChart patient-portal outreach, and snowball sampling. Interviews were conducted via Zoom, phone, or in person, audio-recorded, transcribed, and analyzed using the Framework Approach. Results: Participants described significant difficulty navigating a fragmented healthcare system characterized by siloed care, communication breakdowns, long wait times, and unclear provider responsibilities, as well as difficulties obtaining a diagnosis. When coordination failed, patients were often forced to organize and direct their own healthcare, a task that was cognitively and physically taxing, particularly for those experiencing brain fog and fatigue, and, for some, ultimately led to delaying or forgoing care. Conclusion: Strengthening care coordination for Long COVID could reduce patient burden and improve access to care. Needed strategies include standardized diagnostic definitions, enhanced primary care roles, interoperable referral pathways, and institutional case conferencing to clarify provider responsibilities. Patient navigators, expanded telehealth options, and community-based support, especially culturally tailored services for disproportionately affected groups, may further reduce fragmentation, scheduling strain, and inequities. Addressing these systemic barriers is essential to creating a more efficient, patient-centered care experience in which patients are no longer responsible for coordinating their own complex care.

Identifiers

PMID42367370
PMCPMC13297947

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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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.