Evidence map›Paper›PMID 38904478›Full record

ArticleJMIR medical informatics2024

Effect of Implementing an Informatization Case Management Model on the Management of Chronic Respiratory Diseases in a General Hospital: Retrospective Controlled Study.

Yi-Zhen Xiao, Xiao-Jia Chen, Xiao-Ling Sun, Huan Chen, Yu-Xia Luo, Yuan Chen, Ye-Mei Liang

Abstract read
In one paragraph

Article in JMIR medical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

7 authors.

Yi-Zhen XiaoDepartment of Pulmonary and Critical Care Medicine, Yulin First People's Hospital, Yulin, China.ORCID http://orcid.org/0009-0004-0087-6288
Xiao-Jia ChenDepartment of Pulmonary and Critical Care Medicine, Yulin First People's Hospital, Yulin, China.ORCID http://orcid.org/0009-0001-1325-593X
Xiao-Ling SunDepartment of Pulmonary and Critical Care Medicine, Yulin First People's Hospital, Yulin, China.ORCID http://orcid.org/0009-0005-2633-4230
Huan ChenDepartment of Pulmonary and Critical Care Medicine, Yulin First People's Hospital, Yulin, China.ORCID http://orcid.org/0009-0005-9304-2107
Yu-Xia LuoDepartment of Pulmonary and Critical Care Medicine, Yulin First People's Hospital, Yulin, China.ORCID http://orcid.org/0009-0000-5185-5285
Yuan ChenDepartment of Pulmonary and Critical Care Medicine, Yulin First People's Hospital, Yulin, China.ORCID http://orcid.org/0009-0004-8732-0652
Ye-Mei LiangDepartment of Nursing, Yulin First People's Hospital, Yulin, China.ORCID http://orcid.org/0009-0001-0016-9872

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The use of chronic disease information systems in hospitals and communities plays a significant role in disease prevention, control, and monitoring. However, there are several limitations to these systems, including that the platforms are generally isolated, the patient health information and medical resources are not effectively integrated, and the "Internet Plus Healthcare" technology model is not implemented throughout the patient consultation process. Objective: The aim of this study was to evaluate the efficiency of the application of a hospital case management information system in a general hospital in the context of chronic respiratory diseases as a model case. Methods: A chronic disease management information system was developed for use in general hospitals based on internet technology, a chronic disease case management model, and an overall quality management model. Using this system, the case managers provided sophisticated inpatient, outpatient, and home medical services for patients with chronic respiratory diseases. Chronic respiratory disease case management quality indicators (number of managed cases, number of patients accepting routine follow-up services, follow-up visit rate, pulmonary function test rate, admission rate for acute exacerbations, chronic respiratory diseases knowledge awareness rate, and patient satisfaction) were evaluated before (2019-2020) and after (2021-2022) implementation of the chronic disease management information system. Results: Before implementation of the chronic disease management information system, 1808 cases were managed in the general hospital, and an average of 603 (SD 137) people were provided with routine follow-up services. After use of the information system, 5868 cases were managed and 2056 (SD 211) patients were routinely followed-up, representing a significant increase of 3.2 and 3.4 times the respective values before use (U=342.779; P<.001). With respect to the quality of case management, compared to the indicators measured before use, the achievement rate of follow-up examination increased by 50.2%, the achievement rate of the pulmonary function test increased by 26.2%, the awareness rate of chronic respiratory disease knowledge increased by 20.1%, the retention rate increased by 16.3%, and the patient satisfaction rate increased by 9.6% (all P<.001), while the admission rate of acute exacerbation decreased by 42.4% (P<.001) after use of the chronic disease management information system. Conclusions: Use of a chronic disease management information system improves the quality of chronic respiratory disease case management and reduces the admission rate of patients owing to acute exacerbations of their diseases.

Indexed as

breathingcare managementcase managementchronic disease managementchronic obstructive pulmonary diseasechronic respiratory diseasedisease managementhospital information systemimplementationinformation systeminformatizationpulmonaryrespiratory

Identifiers

PMID38904478
PMCPMC11199924

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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.