Evidence map›Paper›PMID 25980019›Full record

ArticleJournal of oncology practice2015

Detecting unplanned care from clinician notes in electronic health records.

Suzanne Tamang, Manali I Patel, Douglas W Blayney, Julie Kuznetsov, Samuel G Finlayson, Yohan Vetteth, Nigam Shah

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of oncology practice, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 3 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

19 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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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

7 authors.

Suzanne TamangStanford University School of Medicine; Stanford Health Care, Stanford, CA; and Harvard Medical School, Boston, MA stamang@stanford.edu.
Manali I PatelStanford University School of Medicine; Stanford Health Care, Stanford, CA; and Harvard Medical School, Boston, MA.
Douglas W BlayneyStanford University School of Medicine; Stanford Health Care, Stanford, CA; and Harvard Medical School, Boston, MA.
Julie KuznetsovStanford University School of Medicine; Stanford Health Care, Stanford, CA; and Harvard Medical School, Boston, MA.
Samuel G FinlaysonStanford University School of Medicine; Stanford Health Care, Stanford, CA; and Harvard Medical School, Boston, MA.
Yohan VettethStanford University School of Medicine; Stanford Health Care, Stanford, CA; and Harvard Medical School, Boston, MA.
Nigam ShahStanford University School of Medicine; Stanford Health Care, Stanford, CA; and Harvard Medical School, Boston, MA.

Funding

POSTDOCTORAL TRAINING IN MEDICAL INFORMATION SCIENCEST15LM007033 · NLM · STANFORD UNIVERSITY · PI SYLVIA KATINA PLEVRITIS · 1985 to 2026
$25.5M
Methods for generalized ontology terms enrichment analysisR01LM011369 · NLM · STANFORD UNIVERSITY · PI SHAH, NIGAM H · 2013 to 2020
$5.0M
NLM NIH HHS R01 LM011369NLM NIH HHS T15 LM007033
6 · The paper itself

Abstract

purposeReduction in unplanned episodes of care, such as emergency department visits and unplanned hospitalizations, are important quality outcome measures. However, many events are only documented in free-text clinician notes and are labor intensive to detect by manual medical record review.

methodsWe studied 308,096 free-text machine-readable documents linked to individual entries in our electronic health records, representing care for patients with breast, GI, or thoracic cancer, whose treatment was initiated at one academic medical center, Stanford Health Care (SHC). Using a clinical text-mining tool, we detected unplanned episodes documented in clinician notes (for non-SHC visits) or in coded encounter data for SHC-delivered care and the most frequent symptoms documented in emergency department (ED) notes.

resultsCombined reporting increased the identification of patients with one or more unplanned care visits by 32% (15% using coded data; 20% using all the data) among patients with 3 months of follow-up and by 21% (23% using coded data; 28% using all the data) among those with 1 year of follow-up. Based on the textual analysis of SHC ED notes, pain (75%), followed by nausea (54%), vomiting (47%), infection (36%), fever (28%), and anemia (27%), were the most frequent symptoms mentioned. Pain, nausea, and vomiting co-occur in 35% of all ED encounter notes.

conclusionThe text-mining methods we describe can be applied to automatically review free-text clinician notes to detect unplanned episodes of care mentioned in these notes. These methods have broad application for quality improvement efforts in which events of interest occur outside of a network that allows for patient data sharing.

Indexed as

Delivery of Health CareElectronic Health RecordsMedical OncologyPatient Care PlanningAcademic Medical CentersCaliforniaData MiningEmergency Service, HospitalHospitalizationHumansNeoplasmsRisk AssessmentRisk FactorsTime Factors

Identifiers

PMID25980019
PMCPMC4438112

What OpenQuestion holds

Textmetadata
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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.