Evidence map›Paper›PMID 42788993›Full record

Observational studyCritical care explorations2026

Sepsis Diagnostic Excellence: Development and Validation of a Theoretical Framework for Conceptualizing Accurate, Timely Sepsis Diagnosis.

Sarah A Birken, Sachita Shrestha, Laura S Clark, Cheyenne R Wagi, Kandice L Reilly, Marc A Kowalkowski, Jessica A Palakshappa, Jessie E King, Chadwick D Miller, Stefanie S Sebok-Syer and 1 more

Abstract readObservational StudyValidation StudyMulticenter Study
In one paragraph

Observational study in Critical care explorations, 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

11 authors.

Sarah A BirkenDepartment of Implementation Science, Wake Forest University School of Medicine, Winston-Salem, NC.
Sachita ShresthaDivision of Hospital Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI.
Laura S ClarkDepartment of Implementation Science, Wake Forest University School of Medicine, Winston-Salem, NC.
Cheyenne R WagiDepartment of Implementation Science, Wake Forest University School of Medicine, Winston-Salem, NC.
Kandice L ReillyDepartment of Social Sciences and Health Policy, Wake Forest University School of Medicine, Winston-Salem, NC.
Marc A KowalkowskiDepartment of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC.
Jessica A PalakshappaDepartment of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC.
Jessie E KingDivision of Hospital Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI.
Chadwick D MillerDepartment of Emergency Medicine, Wake Forest University School of Medicine, Winston-Salem, NC.
Stefanie S Sebok-SyerDepartment of Emergency Medicine, Stanford University, Stanford, CA.
Stephanie P TaylorDivision of Hospital Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI.

Funding

AHRQ HHS R01 HS029656
6 · The paper itself

Abstract

objectivesDevelop and empirically validate the Sepsis Diagnostic Excellence (SepsisDx) Theoretical Framework of multilevel factors influencing accurate, timely sepsis diagnosis.

designObservational qualitative semi-structured interviews. SETTING AND

participantsTwenty hospitals spanning four states and two health systems within the United States were purposively selected to maximize location, system structure, and patient population representation. ANALYSIS: We conducted 41 interviews with key informants from 13 hospitals, including top managers (e.g., system leaders), mid-level managers (e.g., emergency department (ED) heads; sepsis coordinators), and patient-facing clinicians and staff (e.g., unit nurses). We developed an interview guide by integrating three extant frameworks that collectively conceptualize sepsis diagnosis as a behavior among healthcare providers that is influenced by factors at micro-, meso-, and macroorganization levels. To develop SepsisDx, we used template analysis to identify constructs that influenced accurate, timely sepsis diagnosis and rapid analysis to identify relationships among the framework's constructs. We refined preliminary findings based on feedback from a study advisory board and interview participants.

resultsInterviews validated the central premise of SepsisDx-that is, sepsis diagnosis is influenced by the interaction of factors at micro-, meso-, and macrolevels. The most salient individual-level factor influencing timely sepsis diagnosis was attention (i.e., noticing, encoding, interpreting, and focusing time and effort). Attention was reduced by ambiguous clinical presentations and environmental shocks (e.g., COVID-19). Various strategies increased organization-level attention while decreasing provider-level attention (e.g., audit and feedback). Strategies' effectiveness was moderated by hospital characteristics such as staffing.

conclusionsThe SepsisDx framework clarifies which factors influence sepsis diagnosis and how these factors operate through specific mechanisms. Accurate, timely sepsis diagnosis depends on organizational attention that aligns with individual provider attention to potential sepsis diagnoses. Leaders may improve sepsis diagnosis by pursuing health system- and organization-level strategies that reflect individual-, team-, and emergency-department-level experiences.

Indexed as

SepsisEmergency Service, HospitalHumansInterviews as TopicQualitative ResearchUnited Statesclinical decision-makingdiagnostic errorsemergency serviceshospitalSepsis

Identifiers

PMID42788993
PMCPMC13619180

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.