Evidence map›Paper›PMID 37740879›Full record

ReviewCurrent pain and headache reports2023

Electronic Health Record Recording of Patient Pain: Challenges and Discrepancies.

Yasmeen Kawji, Hanna Almoaswes, Claire Bise, Lena Kawji, Adrienne Murphy, Tanner D Reed, Rachel J Klapper, Shahab Ahmadzadeh, Sahar Shekoohi, Elyse M Cornett and 1 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current pain and headache reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.0field-weighted citation impact, top 21% of its field
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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Review
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 at 3 institutions in 1 country.

Yasmeen KawjiSchool of Medicine, Louisiana State University Health Sciences Center New Orleans, 433 Bolivar Street, New Orleans, LA, 70112, USA.
Hanna AlmoaswesSchool of Medicine, Louisiana State University Health Sciences Center New Orleans, 433 Bolivar Street, New Orleans, LA, 70112, USA.
Claire BiseSchool of Medicine, Louisiana State University Health Sciences Center New Orleans, 433 Bolivar Street, New Orleans, LA, 70112, USA.
Lena KawjiDepartment of Internal Medicine, School of Medicine, Louisiana State University Health Sciences Center at Shreveport, 1501 Kings Highway, Shreveport, LA, 71103, USA.
Adrienne MurphySchool of Medicine, Louisiana State University Health Sciences Center New Orleans, 433 Bolivar Street, New Orleans, LA, 70112, USA.
Tanner D ReedSchool of Medicine, Louisiana State University Health Sciences Center New Orleans, 433 Bolivar Street, New Orleans, LA, 70112, USA.
Rachel J KlapperDepartment of Radiology, Louisiana State University Health Sciences Center at Shreveport, 1501 Kings Highway, Shreveport, LA, 71103, USA.
Shahab AhmadzadehDepartment of Anesthesiology, Louisiana State University Health Sciences Center at Shreveport, 1501 Kings Highway, Shreveport, LA, 71103, USA.
Sahar ShekoohiDepartment of Anesthesiology, Louisiana State University Health Sciences Center at Shreveport, 1501 Kings Highway, Shreveport, LA, 71103, USA. sahar.shekoohi@lsuhs.edu.
Elyse M CornettDepartment of Anesthesiology, Louisiana State University Health Sciences Center at Shreveport, 1501 Kings Highway, Shreveport, LA, 71103, USA.
Alan D KayeDepartment of Anesthesiology, Louisiana State University Health Sciences Center at Shreveport, 1501 Kings Highway, Shreveport, LA, 71103, USA.
Louisiana State University Health Sciences Center New Orleans · USLouisiana State University Health Sciences Center Shreveport · USLouisiana State University in Shreveport · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewIn the present review, various categories of pain, clinician-observed pain scales, and patient-reported pain scales are evaluated to better understand factors that impact patient pain perceptions. Additionally, the expansion of areas that require further research to determine the optimal way to evaluate pain scale data for treatment and management are discussed. RECENT

findingsElectronic health record (EHR) data provides a starting point for evaluating whether patient predictors influence postoperative pain. There are several ways to assess pain and choosing the most effective form of pain treatment. Identifying individuals at high risk for severe postoperative pain enables more effective pain treatment. However, there are discrepancies in patient pain reporting dependent on instruments used to measure pain and their storage in the EHR. Additionally, whether administered by a physician or another healthcare practitioner, differences in patient pain perception occur. While each scale has distinct advantages and limitations, pain scale data is a valuable therapeutic tool for assisting clinicians in providing patients with optimal pain control. Accurate assessment of patient pain perceptions by data extraction from electronic health records provides a potential for pain alleviation improvement. Predicting high-risk postoperative pain syndromes is a difficult clinical challenge. Numerous studies have been conducted on factors that impact pain prediction. Postoperative pain is significantly predicted by the kind of operation, the existence of prior discomfort, patient anxiety, and age.

Indexed as

Electronic Health RecordsPain PerceptionHumansPostoperative PainAcute painChronic painElectronic health record dataMulti-agent systemsPain managementPatient pain predictions

Identifiers

PMID37740879
OpenAlexW4386988831

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.