Evidence map›Paper›PMID 40229955›Full record

ArticleJournal of hospital medicine2025

A multi-institutional multi-methods analysis of jeopardy systems in academic hospital medicine.

Kirsten N Kangelaris, Angela Keniston, Andrew D Auerbach, Gregory Bowling, Marisha Burden, Shradha A Kulkarni, Luci K Leykum, Anne S Linker, Matthew Sakumoto, Jeffrey Schnipper and 1 more

Abstract readMulticenter Study
In one paragraph

Article in Journal of hospital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Kirsten N KangelarisDivision of Hospital Medicine, University of California, San Francisco School of Medicine, San Francisco, California, USA.ORCID http://orcid.org/0000-0001-8152-994X
Angela KenistonDivision of Hospital Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA.ORCID http://orcid.org/0000-0003-1399-2881
Andrew D AuerbachDivision of Hospital Medicine, University of California, San Francisco School of Medicine, San Francisco, California, USA.ORCID http://orcid.org/0000-0001-8440-0527
Gregory BowlingDivision of Hospital Medicine, University of Texas Health Science Center San Antonio, San Antonio, Texas, USA.
Marisha BurdenDivision of Hospital Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA.ORCID http://orcid.org/0000-0002-8262-3994
Shradha A KulkarniSection of General Internal Medicine, Baylor College of Medicine, Houston, Texas, USA.
Luci K LeykumDivision of Hospital Medicine, The University of Texas at Austin, Dell Medical School, South Texas Veterans Health Care System, San Antonio, Texas, USA.
Anne S LinkerDivision of Hospital Medicine, Mount Sinai Hospital/Icahn School of Medicine at Mount Sinai, New York, New York, USA.ORCID http://orcid.org/0000-0003-2151-5065
Matthew SakumotoDivision of General Internal Medicine, University of California, San Francisco School of Medicine, San Francisco, California, USA.ORCID http://orcid.org/0000-0002-8192-471X
Jeffrey SchnipperDivision of General Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.ORCID http://orcid.org/0000-0001-7072-0781
Gopi AstikDivision of Hospital Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.ORCID http://orcid.org/0000-0001-8979-3243

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHospital medicine programs use backup ("jeopardy") systems to cover unexpected staffing gaps, but little is known about their structures or optimal practices.

objectivesTo describe jeopardy structures, assess clinician perceptions, and identify potential approaches across a broad sample of hospital medicine groups.

methodsThis multi-methods study, conducted within a national hospitalist consortium, used virtual focus groups and an email survey to (1) describe the presence and structure of jeopardy systems and (2) explore features perceived as fair, equitable, and tolerable. Rapid qualitative analysis identified major themes, while descriptive methods analyzed survey data.

resultsTwenty-five individuals participated in focus groups, and 26 completed the survey, representing 31 unique institutions. Participants were primarily physicians in academic hospital medicine groups. Three themes emerged: (1) jeopardy systems are widely used but vary in structure, activation criteria, and compensation, leading to inconsistencies in clinician experiences; (2) many clinicians report stress and dissatisfaction due to unpredictability, perceived inequities in assignment, and concerns about inappropriate use; and (3) strategies to improve fairness, equity and tolerability include structured scheduling, support for sick days, and compensation for the burden of jeopardy coverage. Survey data confirmed high variability in jeopardy systems across institutions. Common practices included jeopardy activation over redistributing patients and compensating clinicians for covered shifts.

conclusionsJeopardy systems are essential for hospital medicine staffing but contribute to clinician dissatisfaction due to unpredictability and perceived inequities in coverage. Implementing deliberate scheduling, formalized support for absences, and equitable compensation models may reduce dissatisfaction and improve jeopardy system sustainability.

Indexed as

Academic Medical CentersHospitalistsHospital MedicinePersonnel Staffing and SchedulingAttitude of Health PersonnelFemaleFocus GroupsHumansMaleSurveys and Questionnaires

Identifiers

PMID40229955
PMCPMC12406760

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.