Evidence map›Paper›PMID 39897572›Full record

ArticleIntegrative medicine research2025

Methods for identifying health status from routinely collected health data: An overview.

Mei Liu, Ke Deng, Mingqi Wang, Qiao He, Jiayue Xu, Guowei Li, Kang Zou, Xin Sun, Wen Wang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Integrative medicine research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Mei LiuInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, China.
Ke DengInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, China.
Mingqi WangInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, China.
Qiao HeInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, China.
Jiayue XuInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, China.
Guowei LiDepartment of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON, Canada.
Kang ZouInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, China.
Xin SunInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, China.
Wen WangInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine and Cochrane China Center, West China Hospital, Sichuan University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Routinely collected health data (RCD) are currently accelerating publications that evaluate the effectiveness and safety of medicines and medical devices. One of the fundamental steps in using these data is developing algorithms to identify health status that can be used for observational studies. However, the process and methodologies for identifying health status from RCD remain insufficiently understood. While most current methods rely on International Classification of Diseases (ICD) codes, they may not be universally applicable. Although machine learning methods hold promise for more accurately identifying the health status, they remain underutilized in RCD studies. To address these significant methodological gaps, we outline key steps and methodological considerations for identifying health statuses in observational studies using RCD. This review has the potential to boost the credibility of findings from observational studies that use RCD.

Indexed as

Health statusMachine learning algorithmsRoutinely collected health dataRule-based algorithms

Identifiers

PMID39897572
PMCPMC11786076

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.