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. 2025 Jan 15;7(1):6.
doi: 10.1186/s42494-024-00192-1.

Using electronic medical records to analyze outpatient visits of persons with epilepsy during the pandemic-experience from a low middle income country

Rajeswari Aghoram  1 , Pradeep P Nair  2 , Anudeep Neelagandan  3
Affiliations

Using electronic medical records to analyze outpatient visits of persons with epilepsy during the pandemic-experience from a low middle income country

Rajeswari Aghoram et al. Acta Epileptol. .

Abstract

Background: Electronic medical records (EMR) can be utilized to understand the impact of the disruption in care provision caused by the pandemic. We aimed to develop and validate an algorithm to identify persons with epilepsy (PWE) from our EMR and to use it to explore the effect of the pandemic on outpatient service utilization.

Methods: EMRs from the neurology specialty, covering the period from January 2018 to December 2023, were used. An algorithm was developed using an iterative approach to identify PWE with a critical lower bound of 0.91 for negative predictive value. Manual internal validation was performed. Outpatient visit data were extracted and modeled as a time series using the autoregressive integrated moving average model. All statistical analyses were performed using STATA version 14.2 (Statacorp, USA).

Results: Four iterations resulted in an algorithm, with a negative predictive value 0.98 (95% CI: 0.95-0.99), positive predictive value of 0.98 (95% CI: 0.85-0.99), and an F-score accuracy of 0.96, which identified 4474 PWE. The outpatient service utilization was abruptly reduced by the pandemic, with a change of -902.1 (95%CI: -936.55 to -867.70), and the recovery has also been slow, with a decrease of -5.51(95%CI: -7.00 to -4.02). Model predictions aligned closely with actual visits with median error of -3.5%.

Conclusions: We developed an algorithm for identifying people with epilepsy with good accuracy. Similar methods can be adapted for use in other resource-limited settings and for other diseases. The COVID pandemic appears to have caused a lasting reduction of service utilization among PWE.

Keywords: Electronic medical records; Interrupted time series analysis; Manual validation.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The study was approved by the institute research board. (JIP/IEC/OS/2021/303). Consent for publication: Not applicable. Competing interests: The authors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
Flow of patient records during algorithm development
Fig. 2
Fig. 2
Time series graph of monthly out patient visits between January 2018 and March 2023
Fig. 3
Fig. 3
Time series graph of out patient visits predicted from the ARIMA model and actual between January 2018 and December 2023
See this image and copyright information in PMC

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