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Accepted for/Published in: JMIR Medical Informatics

Date Submitted: Sep 27, 2022
Date Accepted: Jan 15, 2023

The final, peer-reviewed published version of this preprint can be found here:

Identification of Postpartum Depression in Electronic Health Records: Validation in a Large Integrated Health Care System

Slezak J, Sacks D, Chiu V, Avila C, Khadka N, Chen JC, Wu J, Getahun D

Identification of Postpartum Depression in Electronic Health Records: Validation in a Large Integrated Health Care System

JMIR Med Inform 2023;11:e43005

DOI: 10.2196/43005

PMID: 36857123

PMCID: 10018380

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Validation of postpartum depression in electronic health records in a large integrated health care system

  • Jeff Slezak; 
  • David Sacks; 
  • Vicki Chiu; 
  • Chantal Avila; 
  • Nehaa Khadka; 
  • Jiu-Chiuan Chen; 
  • Jun Wu; 
  • Darios Getahun

ABSTRACT

Background:

Background:

The accuracy of electronic health records (EHR) for the identification of postpartum depression (PPD) is not well studied.

Objective:

Objective:

To evaluate the accuracy of PPD reporting in EHR and compare the quality of postpartum depression data collected before and after the implementation of International Classification of Diseases [ICD]-10 coding in the healthcare system.

Methods:

Methods:

Information on PPD was extracted from a random sample of 400 eligible Kaiser Permanente Southern California patients’ EHRs. Clinical diagnosis codes and pharmacy records were abstracted for two time periods: 1/1/2012 through 12/31/2014 (ICD-9 period) and 1/1/2017 through 12/31/2019 (ICD-10 period). Manual chart review of clinical records for PPD were considered the gold standard and were compared with corresponding electronically coded diagnosis and pharmacy records using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Kappa statistic was calculated to measure agreement.

Results:

Results:

Overall agreement between the identification of depression using combined diagnosis codes and pharmacy records with that of medical record review was strong (k: 0.85, sensitivity: 98.3%, specificity: 83.3%, PPV: 93.7%, NPV: 95.0%). Using only diagnosis codes resulted in much lower sensitivity (65.4%) and NPV (50.5%), but good specificity (88.6%) and PPV (93.5%). Separately, examining agreement between chart review and electronic coding among diagnosis codes and pharmacy records showed sensitivity, specificity, and NPV higher with prescription utilization records than with clinical diagnosis coding for postpartum depression, 96.5% vs. 72.0%, 96.5% vs. 65.0%, and 96.5% vs. 65.0%, respectively. There was no notable difference in agreement between ICD-9 (overall k: 0.86) and ICD-10 (overall k: 0.83) coding periods.

Conclusions:

Conclusion: PPD is not reliably captured in clinical diagnosis coding of EHRs. The accuracy of PPD identification can be improved by supplementing clinical diagnosis with pharmacy utilization records. The completeness of PPD data remained unchanged after the implementation of the ICD-10 diagnosis coding.


 Citation

Please cite as:

Slezak J, Sacks D, Chiu V, Avila C, Khadka N, Chen JC, Wu J, Getahun D

Identification of Postpartum Depression in Electronic Health Records: Validation in a Large Integrated Health Care System

JMIR Med Inform 2023;11:e43005

DOI: 10.2196/43005

PMID: 36857123

PMCID: 10018380

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