Can Real-World Data From Hospital Information Systems Be Directly Utilized for Efficacy Studies: An Analysis of Data Time Points

Jiaying Luo , Aomeng Zhang , Wulin Gao , Xiangwei Bu , Runming Li , Zehui Ye , Xinyi Zhang , Lili Ren , Yin Jiang , Zhiyue Guan , Hui Guan , Liyuan Tao , Guohua Dai , Chen Zhao , Hongcai Shang

Journal of Evidence-Based Medicine ›› 2025, Vol. 18 ›› Issue (4) : e70079

PDF
Journal of Evidence-Based Medicine ›› 2025, Vol. 18 ›› Issue (4) :e70079 DOI: 10.1111/jebm.70079
ARTICLE
Can Real-World Data From Hospital Information Systems Be Directly Utilized for Efficacy Studies: An Analysis of Data Time Points
Author information +
History +
PDF

Abstract

Background: Data from Hospital Information Systems are crucial components of real-world data, but concerns arise regarding the transparency of measurement time-points when directly utilizing for efficacy studies. The objective of the study was to analyze the distribution characteristics of time-points for laboratory test records from HIS.

Method: Medical records before December 31, 2019 from Affiliated Hospital of Shandong University of Traditional Chinese Medicine, for patients primarily diagnosed with coronary heart disease (CHD), or heart failure combined with secondary diagnosis of CHD were retrieved from HIS. Fifteen test groups were extracted. The number of records, average of test times, and distribution characteristics of time points were analyzed.

Results: The renal function tests have the most records, and the blood glucose have the most times. Tests time-points distribution showed concentration in the early stage of hospitalization, with the majority occurring within the first 0–10%. For those measured ≥2 times, their first tests are also centrally distributed in the early stage, while the lasts in all period of hospitalization. Besides, substantial difference is showed in the time span of the first and last test. Abnormal value may be a trigger that promotes more intensive examination, and normal or abnormal status of the first examination is significantly weak to moderate correlated with the number of examinations and time span.

Discussion: The disclosure of time-points of clinical studies based on HIS should be encouraged and the pre-survey of data measuring time point is essential to the design of trial.

Keywords

Hospital Information System / real-world data / time points / transparency

Cite this article

Download citation ▾
Jiaying Luo, Aomeng Zhang, Wulin Gao, Xiangwei Bu, Runming Li, Zehui Ye, Xinyi Zhang, Lili Ren, Yin Jiang, Zhiyue Guan, Hui Guan, Liyuan Tao, Guohua Dai, Chen Zhao, Hongcai Shang. Can Real-World Data From Hospital Information Systems Be Directly Utilized for Efficacy Studies: An Analysis of Data Time Points. Journal of Evidence-Based Medicine, 2025, 18(4): e70079 DOI:10.1111/jebm.70079

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

V. L. Bartlett, S. S. Dhruva, N. D. Shah, P. Ryan, and J. S. Ross, “Feasibility of Using Real-World Data to Replicate Clinical Trial Evidence,” JAMA Network Open 2, no. 10 (2019): e1912869.

[2]

O. Efthimiou, H. Taipale, J. Radua, et al., “Efficacy and Effectiveness of Antipsychotics in Schizophrenia: Network Meta-Analyses Combining Evidence From Randomised Controlled Trials and Real-World Data,” Lancet Psychiatry 11, no. 2 (2024): 102–111.

[3]

P. H. Gabrielle, H. Mehta, D. Barthelmes, et al., “From Randomised Controlled Trials to Real-world Data: Clinical Evidence to Guide Management of Diabetic Macular Oedema,” Progress in Retinal and Eye Research 97 (2023): 101219.

[4]

R. Martina, D. Jenkins, S. Bujkiewicz, P. Dequen, and K. Abrams, “The Inclusion of Real World Evidence in Clinical Development Planning,” Trials 19, no. 1 (2018): 468.

[5]

N. Gökbuget, M. Kelsh, V. Chia, et al., “Blinatumomab vs Historical Standard Therapy of Adult Relapsed/Refractory Acute Lymphoblastic Leukemia,” Blood Cancer Journal 6, no. 9 (2016): e473.

[6]

J. H. Kim, A. M. Butler, C. N. Ta, Y. Sun, M. S. Maurer, and C. Weng, “The Potential Role of EHR Data in Optimizing Eligibility Criteria Definition for Cardiovascular Outcome Trials,” International Journal of Medical Informatics 156 (2021): 104587.

[7]

V. van Baalen, E. M. Didden, D. Rosenberg, K. Bardenheuer, M. van Speybroeck, and M. Brand, “Increase Transparency and Reproducibility of Real-World Evidence in Rare Diseases Through Disease-Specific Federated Data Networks,” Pharmacoepidemiology and Drug Safety 33, no. 4 (2024): e5778.

[8]

O. Ben-Assuli, I. Shabtai, and M. Leshno, “Using Electronic Health Record Systems to Optimize Admission Decisions: The Creatinine Case Study,” Health Informatics Journal 21, no. 1 (2015): 73–88.

[9]

R. S. Evans, “Electronic Health Records: Then, Now, and in the Future,” Yearbook of Medical Informatics Suppl 1, no. Suppl 1 (2016): S48–S61.

[10]

A. M. Kucharska-Newton, M. S. Loop, M. Bullo, et al., “Use of Troponins in the Classification of Myocardial Infarction From Electronic Health Records. The Atherosclerosis Risk in Communities (ARIC) Study,” International Journal of Cardiology 348 (2022): 152–156.

[11]

K. W. Lemke, K. A. Gudzune, H. Kharrazi, and J. P. Weiner, “Assessing Markers From Ambulatory Laboratory Tests for Predicting High-Risk Patients,” The American Journal of Managed Care 24, no. 6 (2018): e190–e195.

[12]

J. Lou, Y. Wang, L. Li, and D. Zeng, “Learning Latent Heterogeneity for Type 2 Diabetes Patients Using Longitudinal Health Markers in Electronic Health Records,” Statistics in Medicine 40, no. 8 (2021): 1930–1946.

[13]

R. Pivovarov, D. J. Albers, J. L. Sepulveda, and N. Elhadad, “Identifying and Mitigating Biases in EHR Laboratory Tests,” Journal of Biomedical Informatics 51 (2014): 24–34.

[14]

Group F-NBW. BEST (Biomarkers, EndpointS, and Other Tools) Resource. Monitoring Biomarker. [cited 2024 10/10], https://www.ncbi.nlm.nih.gov/books/NBK402282/2021.

[15]

Group F-NBW. BEST (Biomarkers, EndpointS, and Other Tools) Resource-Validated Surrogate Endpoint. [cited 2024 10/10], https://www.ncbi.nlm.nih.gov/books/NBK453484/2020.

[16]

G. Hripcsak, D. J. Albers, and A. Perotte, “Exploiting Time in Electronic Health Record Correlations,” Journal of the American Medical Informatics Association: JAMIA 18, no. Suppl 1 (2011): i109–i115.

[17]

W. R. Hersh, M. G. Weiner, P. J. Embi, et al., “Caveats for the Use of Operational Electronic Health Record Data in Comparative Effectiveness Research,” Medical Care 51, no. 8 Suppl 3 (2013): S30–S37.

[18]

K. Khambholja and M. Gehani, “Use of Structured Template and Reporting Tool for Real-World Evidence for Critical Appraisal of the Quality of Reporting of Real-World Evidence Studies: A Systematic Review,” Value in Health: The Journal of the International Society for Pharmacoeconomics and Outcomes Research 26, no. 3 (2023): 427–434.

[19]

C. M. Hamersky, M. Fridman, C. L. Gamble, and N. N. Iyer, “Injectable Antihyperglycemics: A Systematic Review and Critical Analysis of the Literature on Adherence, Persistence, and Health Outcomes,” Diabetes Therapy: Research, Treatment and Education of Diabetes and Related Disorders 10, no. 3 (2019): 865–890.

[20]

O. T. Inan, P. Tenaerts, S. A. Prindiville, et al., “Digitizing Clinical Trials,” NPJ Digital Medicine 3 (2020): 101.

[21]

L. A. Adang, F. Gavazzi, R. D'Aiello, D. Isaacs, N. Bronner, and Z. S. Arici, “Hematologic Abnormalities in Aicardi Goutières Syndrome,” Molecular Genetics and Metabolism 136, no. 4 (2022): 324–329.

[22]

L. A. Adang, A. Sevagamoorthy, O. Sherbini, et al., “Longitudinal Natural History Studies Based on Real-World Data in Rare Diseases: Opportunity and a Novel Approach,” Molecular Genetics and Metabolism 142, no. 1 (2024): 108453.

[23]

O. Ciani, A. M. Manyara, P. Davies, et al., “A Framework for the Definition and Interpretation of the Use of Surrogate Endpoints in Interventional Trials,” EClinicalMedicine 65 (2023): 102283.

[24]

S. Dagenais, L. Russo, A. Madsen, J. Webster, and L. Becnel, “Use of Real-World Evidence to Drive Drug Development Strategy and Inform Clinical Trial Design,” Clinical Pharmacology & Therapeutics 111, no. 1 (2022): 77–89.

[25]

W. N. Robiner, “Enhancing Adherence in Clinical Research,” Contemporary Clinical Trials 26, no. 1 (2005): 59–77.

[26]

N. J. Butcher, A. Monsour, E. J. Mew, et al., “Guidelines for Reporting Outcomes in Trial Reports: The CONSORT-Outcomes 2022 Extension,” Jama 328, no. 22 (2022): 2252–2264.

[27]

A. W. Chan, J. M. Tetzlaff, P. C. Gøtzsche, et al., “SPIRIT 2013 Explanation and Elaboration: Guidance for Protocols of Clinical Trials,” BMJ (Clinical Research Ed) 346 (2013): e7586.

[28]

R. White, “Building Trust in Real World Evidence (RWE): Moving Transparency in RWE Towards the Randomized Controlled Trial Standard,” Current Medical Research and Opinion 39, no. 12 (2023): 1737–1741.

[29]

L. S. Orsini, M. Berger, W. Crown, et al., “Improving Transparency to Build Trust in Real-World Secondary Data Studies for Hypothesis Testing-Why, What, and How: Recommendations and a Road Map From the Real-World Evidence Transparency Initiative,” Value in Health: The Journal of the International Society for Pharmacoeconomics and Outcomes Research 23, no. 9 (2020): 1128–1136.

RIGHTS & PERMISSIONS

2025 The Author(s). Journal of Evidence-Based Medicine published by Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd.

PDF

6

Accesses

0

Citation

Detail

Sections
Recommended

/