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<p>Heart failure (HF) and chronic kidney disease (CKD) are prevalent comorbidities that significantly impact patient outcomes, with nearly half of HF patients experiencing renal impairment. The challenges associated with implementing guideline-directed medical therapy (GDMT) for patients with HF and CKD emphasize the role of electronic medical records (EMRs) as clinical decision-support tools. Despite the proven benefits of GDMT in improving survival and reducing hospital readmissions, many eligible patients do not receive optimal therapy due to barriers such as alert fatigue, medication costs, and the complexity of managing coexisting conditions. EMR prompts and alerts can help early detection and risk stratification of CKD, utilizing biomarkers such as estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR). EMRs can facilitate the timely initiation and titration of GDMT, ensuring adherence to clinical guidelines. However, the effectiveness of these alerts can be compromised by irrelevant notifications and outdated information, leading to “alert fatigue”. Furthermore, integrating machine learning (ML) and artificial intelligence (AI) into EMR systems can enhance personalized healthcare approaches for HF and CKD patients. Future research directions include developing noninvasive biomarkers and validating ML models to ensure they meet clinical needs. These efforts will ultimately aim to provide individualized treatment strategies for patients with HF and CKD.</p> Graphical Abstract <p></p>

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The challenges of using electronic medical records (EMR) to facilitate guideline-directed medical therapy (GDMT) for patients with heart failure (HF) and chronic kidney disease (CKD)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月10日

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  • Nihar R. Desai,
  • Tariq Ahmad,
  • Francis Perry Wilson

摘要

Heart failure (HF) and chronic kidney disease (CKD) are prevalent comorbidities that significantly impact patient outcomes, with nearly half of HF patients experiencing renal impairment. The challenges associated with implementing guideline-directed medical therapy (GDMT) for patients with HF and CKD emphasize the role of electronic medical records (EMRs) as clinical decision-support tools. Despite the proven benefits of GDMT in improving survival and reducing hospital readmissions, many eligible patients do not receive optimal therapy due to barriers such as alert fatigue, medication costs, and the complexity of managing coexisting conditions. EMR prompts and alerts can help early detection and risk stratification of CKD, utilizing biomarkers such as estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR). EMRs can facilitate the timely initiation and titration of GDMT, ensuring adherence to clinical guidelines. However, the effectiveness of these alerts can be compromised by irrelevant notifications and outdated information, leading to “alert fatigue”. Furthermore, integrating machine learning (ML) and artificial intelligence (AI) into EMR systems can enhance personalized healthcare approaches for HF and CKD patients. Future research directions include developing noninvasive biomarkers and validating ML models to ensure they meet clinical needs. These efforts will ultimately aim to provide individualized treatment strategies for patients with HF and CKD.

Graphical Abstract