Evidence map›Paper›PMID 28295260›Full record

Observational studyHealth services research2018

Hospital Readmission and Social Risk Factors Identified from Physician Notes.

Amol S Navathe, Feiran Zhong, Victor J Lei, Frank Y Chang, Margarita Sordo, Maxim Topaz, Shamkant B Navathe, Roberto A Rocha, Li Zhou

Abstract readObservational Study
In one paragraph

Observational study in Health services research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
97citing papers in PubMed, 5 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

97 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
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  6. Article
  7. Accelerating real-world prediction and research in Alzheimer's: The M3AD study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
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  19. Maximizing Readmission Reduction in Colon Cancer Patients.The Journal of surgical research · 2024
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37 more citing papers are in PubMed but not listed here.

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.

Amol S NavatheDivision of Health Policy, University of Pennsylvania, Philadelphia, PA.
Feiran ZhongDivision of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Harvard Medical School, Boston, MA.
Victor J LeiDivision of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Harvard Medical School, Boston, MA.
Frank Y ChangClinical Informatics, Partners eCare, Partners Healthcare Inc., Boston, MA.
Margarita SordoDivision of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Harvard Medical School, Boston, MA.
Maxim TopazDivision of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Harvard Medical School, Boston, MA.
Shamkant B NavatheSchool of Computer Science, College of Computing, Georgia Institute of Technology, Atlanta, GA.
Roberto A RochaDivision of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Harvard Medical School, Boston, MA.
Li ZhouDivision of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Harvard Medical School, Boston, MA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate the prevalence of seven social factors using physician notes as compared to claims and structured electronic health records (EHRs) data and the resulting association with 30-day readmissions. STUDY

settingA multihospital academic health system in southeastern Massachusetts. STUDY

designAn observational study of 49,319 patients with cardiovascular disease admitted from January 1, 2011, to December 31, 2013, using multivariable logistic regression to adjust for patient characteristics. DATA COLLECTION/EXTRACTION

methodsAll-payer claims, EHR data, and physician notes extracted from a centralized clinical registry. PRINCIPAL

findingsAll seven social characteristics were identified at the highest rates in physician notes. For example, we identified 14,872 patient admissions with poor social support in physician notes, increasing the prevalence from 0.4 percent using ICD-9 codes and structured EHR data to 16.0 percent. Compared to an 18.6 percent baseline readmission rate, risk-adjusted analysis showed higher readmission risk for patients with housing instability (readmission rate 24.5 percent; p < .001), depression (20.6 percent; p < .001), drug abuse (20.2 percent; p = .01), and poor social support (20.0 percent; p = .01).

conclusionsThe seven social risk factors studied are substantially more prevalent than represented in administrative data. Automated methods for analyzing physician notes may enable better identification of patients with social needs.

Indexed as

PhysiciansAccidental FallsAdolescentAdultAgedAged, 80 and overAge FactorsDepressionDocumentationElectronic Health RecordsFemaleHumansIll-Housed PersonsInsurance Claim ReviewLogistic ModelsMalenatural language processingquality of carereadmissionsSocial determinants of health

Identifiers

PMID28295260
PMCPMC5867172

What OpenQuestion holds

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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.