Optimizing adolescent HIV care: a review of EMR system quality for clinical monitoring in Zambia

TL;DR

This study evaluates the quality of electronic medical record data for adolescent HIV patients in Zambia, finding strong demographic data but significant gaps in clinical information like CD4 counts and pregnancy status.

Contribution

The study identifies specific data quality gaps in EMR systems for adolescent HIV care in Zambia, offering targeted recommendations for improvement.

Findings

Socio-demographic data in SmartCare EMR systems showed high completeness, correctness, and consistency.

Clinical variables like CD4 counts and pregnancy status had significant completeness gaps, with pregnancy data completeness at only 4%.

Tuberculosis history and viral load results were reliably captured, indicating system strengths in specific clinical areas.

Abstract

Adolescents living with HIV (ALHIV) in Zambia experience poorer treatment outcomes than adults, with lower viral suppression and higher loss to follow-up rates. Electronic medical record (EMR) systems such as SmartCare aim to strengthen patient monitoring, but their utility is contingent on high data quality. Accurate monitoring of ALHIV treatment outcomes is critical for improving patient care and supporting progress toward UNAIDS 95–95-95 targets. We conducted a retrospective cross-sectional review of EMR data for ALHIV on antiretroviral therapy in selected Lusaka facilities (January–December 2023). Data were extracted from SmartCare and assessed using the WHO Routine Data Quality Assessment framework across three dimensions: completeness, correctness, and consistency. Records from 3,978 ALHIV were analysed. Socio-demographic variables (gender, date of birth, age at ART initiation) and treatment data (ARV regimen) performed strongly, with ≥98% completeness, correctness, and consistency. In contrast, clinical variables showed substantial gaps. Completeness for baseline (n = 1,707) and current (n = 2,149) CD4 counts was 43% and 54%, respectively, though correctness and consistency exceeded 99%. Pregnancy and breastfeeding data among female adolescents (n = 2,177) were particularly poor, with completeness of 4% and 12%. By comparison, history of tuberculosis (100%) and current viral load results (91%) were reliably captured. Whereas SmartCare demonstrated strong reliability for demographic and treatment indicators, notable weaknesses in the completeness of key clinical variables, such as CD4 count and pregnancy/breastfeeding status were observed. These gaps may reflect variability in data entry workflows and system-level factors, including EMR upgrades, highlighting areas for targeted improvement. We recommend targeted training, system redesign to enforce mandatory entry of critical fields, and routine data quality monitoring to ensure EMR systems provide accurate and actionable data. Addressing these gaps would facilitate optimising HIV care and support progress toward achieving the UNAIDS 95–95-95 goals for ALHIV in Zambia.

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