Evidence map›Paper›PMID 27805795›Full record

ArticleJournal of hospital medicine2016

Piloting electronic medical record-based early detection of inpatient deterioration in community hospitals.

Gabriel J Escobar, Benjamin J Turk, Arona Ragins, Jason Ha, Brian Hoberman, Steven M LeVine, Manuel A Ballesca, Vincent Liu, Patricia Kipnis

Abstract read
In one paragraph

Article in Journal of hospital medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed, 2 pooled it
–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

46 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Derivation and validation of a prediction model for primary and recurrentAntimicrobial stewardship & healthcare epidemiology : ASHE · 2026
    Article
  5. Observational
  6. Article
  7. Review
  8. Article
  9. Article
  10. Use of an electronic medical record to optimize a neonatal sepsis score for mortality prediction.Journal of perinatology : official journal of the California Perinatal Association · 2023
    Article
  11. Article
  12. Article
  13. The role of machine learning in HIV risk prediction.Frontiers in reproductive health · 2022
    Article
  14. Article
  15. Review
  16. Article
  17. Observational
  18. Article
  19. A framework for making predictive models useful in practice.Journal of the American Medical Informatics Association : JAMIA · 2021
    Article
  20. 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

9 authors.

Gabriel J EscobarDivision of Research, Kaiser Permanente Northern California, Oakland, California. schorr-christa@cooperhealth.edu, alex.dummett@kp.org, russ.granich@kp.org, gabriel.escobar@kp.org, gabriel.escobar@kp.org.
Benjamin J TurkDivision of Research, Kaiser Permanente Northern California, Oakland, California.
Arona RaginsDivision of Research, Kaiser Permanente Northern California, Oakland, California.
Jason HaIMG-Systems Integration, Kaiser Permanente, Pleasanton, California.
Brian HobermanKaiser Permanente HealthConnect, Pleasanton, California.
Steven M LeVineKaiser East Bay Medical Center, Oakland, California.
Manuel A BallescaKaiser Permanente Vacaville Medical Center, Vacaville, California.
Vincent LiuDivision of Research, Kaiser Permanente Northern California, Oakland, California.
Patricia KipnisDecision Support, Kaiser Foundation Health Plan, Oakland, California.

Funding

Data-driven Precision medicine in sepsisK23GM112018 · NIGMS · KAISER FOUNDATION RESEARCH INSTITUTE · PI LIU, VINCENT · 2014 to 2017
$768k
NIGMS NIH HHS K23 GM112018
6 · The paper itself

Abstract

Patients who deteriorate in the hospital outside the intensive care unit (ICU) have higher mortality and morbidity than those admitted directly to the ICU. As more hospitals deploy comprehensive inpatient electronic medical records (EMRs), attempts to support rapid response teams with automated early detection systems are becoming more frequent. We aimed to describe some of the technical and operational challenges involved in the deployment of an early detection system. This 2-hospital pilot, set within an integrated healthcare delivery system with 21 hospitals, had 2 objectives. First, it aimed to demonstrate that severity scores and probability estimates could be provided to hospitalists in real time. Second, it aimed to surface issues that would need to be addressed so that deployment of the early warning system could occur in all remaining hospitals. To achieve these objectives, we first established a rationale for the development of an early detection system through the analysis of risk-adjusted outcomes. We then demonstrated that EMR data could be employed to predict deteriorations. After addressing specific organizational mandates (eg, defining the clinical response to a probability estimate), we instantiated a set of equations into a Java application that transmits scores and probability estimates so that they are visible in a commercially available EMR every 6 hours. The pilot has been successful and deployment to the remaining hospitals has begun. Journal of Hospital Medicine 2016;11:S18-S24. © 2016 Society of Hospital Medicine.

Indexed as

Early DiagnosisInpatientsCritical CareElectronic Health RecordsHospital Rapid Response TeamHospitals, CommunityHumans

Identifiers

PMID27805795
PMCPMC5510649

What OpenQuestion holds

Textmetadata
LicenceTDM
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.