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Background <p>Exploring records from entire cities to make decisions, particularly within public health systems, remains challenging.</p> Methods <p>This study investigates the public health data of São Caetano do Sul (SCS), in Brazil, to uncover patterns of antimicrobial prescriptions for infectious diseases using electronic health system records from primary care. Data science techniques such as preprocessing, transformation, loading, and analytics were also applied to achieve this goal.</p> Results <p>From January to September 2023, a total of 575,616 records of medical appointments were analyzed, and 67,023 patients underwent one or more medical appointments of which 16,572 had infectious diagnoses. There were 7,938 prescriptions of antimicrobials for infections of which the most frequent were upper respiratory infections (37%), gingivitis/periodontal disease (20%), and urinary tract infections (9%). The most frequently prescribed antimicrobials were amoxicillin (23%), azithromycin (15%), amoxicillin/clavulanate (13%), ciprofloxacin (11%), and cephalexin (11%). A preliminary evaluation of the data highlighted several points for targeted interventions, as well as challenges in obtaining certain information. For instance, some infections lacked documented antimicrobial treatment, while others were managed with medications not considered first-line options.</p> Conclusion <p>Implementing a system that can extract data directly from electronic records and automatically present it in a logical and relevant way to health professionals—including policymakers and administrators—would enable the identification of potential problems, the planning of interventions to improve antimicrobial use, and the monitoring of their impact. Our findings highlight opportunities to improve antimicrobial prescribing through data-driven tracking, analysis, and feedback mechanisms.</p>

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Evaluating antimicrobial prescriptions in primary health care across an entire Brazilian city through the analysis of electronic medical records: where public health and data science converge

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月12日

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  • Ana R. C. Maita,
  • Marcio K. Oikawa,
  • Vítor Falcão de Oliveira,
  • Viviane Aparecida Marto do Prado,
  • Robson Pereira,
  • Gabriela T. O. Xavier,
  • Maria Laura Mariano de Matos,
  • Erika Regina Manuli,
  • Lucia H. A. R. Salvi,
  • Monica Tilli Reis Pessoa Conde,
  • Maria Clara Padoveze,
  • Maria Tereza Razzolini,
  • Nazareno Scaccia,
  • Maura Salaroli de Oliveira,
  • Ícaro Boszczowski,
  • Cibele Cristine Remondes Sequeira,
  • Regina Maura Zetone Graspan,
  • Fabio Eudes Leal,
  • Ester Cerdeira Sabino,
  • Alison Holmes,
  • Silvia Figueiredo Costa,
  • Anna S. Levin,
  • Fátima L. S. Nunes

摘要

Background

Exploring records from entire cities to make decisions, particularly within public health systems, remains challenging.

Methods

This study investigates the public health data of São Caetano do Sul (SCS), in Brazil, to uncover patterns of antimicrobial prescriptions for infectious diseases using electronic health system records from primary care. Data science techniques such as preprocessing, transformation, loading, and analytics were also applied to achieve this goal.

Results

From January to September 2023, a total of 575,616 records of medical appointments were analyzed, and 67,023 patients underwent one or more medical appointments of which 16,572 had infectious diagnoses. There were 7,938 prescriptions of antimicrobials for infections of which the most frequent were upper respiratory infections (37%), gingivitis/periodontal disease (20%), and urinary tract infections (9%). The most frequently prescribed antimicrobials were amoxicillin (23%), azithromycin (15%), amoxicillin/clavulanate (13%), ciprofloxacin (11%), and cephalexin (11%). A preliminary evaluation of the data highlighted several points for targeted interventions, as well as challenges in obtaining certain information. For instance, some infections lacked documented antimicrobial treatment, while others were managed with medications not considered first-line options.

Conclusion

Implementing a system that can extract data directly from electronic records and automatically present it in a logical and relevant way to health professionals—including policymakers and administrators—would enable the identification of potential problems, the planning of interventions to improve antimicrobial use, and the monitoring of their impact. Our findings highlight opportunities to improve antimicrobial prescribing through data-driven tracking, analysis, and feedback mechanisms.