School of Pharmacy, Faculty of Health and Medical Sciences, Taylor's University, Selangor, Malaysia.
HealthMetrics, Selangor, Malaysia.
Int J Pharm Pract. 2022 Jan 7;30(1):59-66. doi: 10.1093/ijpp/riab075.
To determine the prescribing patterns and identify potentially inappropriate prescribing practices among general practitioners in the private primary care sector by analysing a large electronic health insurance claims database.
Medical claims records from February 2019 to February 2020 were extracted from a health insurance claims database. Data cleaning and data analysis were performed using Python 3.7 with the Pandas, NumPy and Matplotlib libraries. The top five most common diagnoses were identified, and for each diagnosis, the most common medication classes and medications prescribed were quantified. Potentially inappropriate prescribing practices were identified by comparing the medications prescribed with relevant clinical guidelines.
The five most common diagnoses were upper respiratory tract infection (41.5%), diarrhoea (7.7%), musculoskeletal pain (7.6%), headache (6.7%) and gastritis (4.0%). Medications prescribed by general practitioners were largely as expected for symptomatic management of the respective conditions. One area of potentially inappropriate prescribing identified was inappropriate antibiotic choice. Same-class polypharmacy that may lead to an increased risk of adverse events were also identified, primarily involving multiple paracetamol-containing products, non-steroidal anti-inflammatory drugs (NSAIDs), and antihistamines. Other areas of non-adherence to guidelines identified included the potential overuse of oral corticosteroids and oral salbutamol, and inappropriate gastroprotection for patients receiving NSAIDs.
While prescribing practices are generally appropriate within the private primary care sector, there remain several areas where some potentially inappropriate prescribing occurs. The areas identified should be the focus in continuing efforts to improve prescribing practices to obtain the optimal clinical outcomes while reducing unnecessary risks and healthcare costs.
通过分析大型电子健康保险索赔数据库，确定私人初级保健领域全科医生的处方模式，并确定潜在的不适当处方行为。
从健康保险索赔数据库中提取 2019 年 2 月至 2020 年 2 月的医疗索赔记录。使用 Python 3.7 及其 Pandas、NumPy 和 Matplotlib 库进行数据清理和数据分析。确定了前五种最常见的诊断，对于每种诊断，量化了最常见的药物类别和开具的药物。通过将开具的药物与相关临床指南进行比较，确定潜在的不适当处方行为。
最常见的五种诊断是上呼吸道感染（41.5%）、腹泻（7.7%）、肌肉骨骼疼痛（7.6%）、头痛（6.7%）和胃炎（4.0%）。全科医生开具的药物主要用于相应病症的对症治疗。确定的一个潜在不适当处方领域是抗生素选择不当。还发现了同一类别的多药并用可能会增加不良事件的风险，主要涉及多种含对乙酰氨基酚的产品、非甾体抗炎药（NSAIDs）和抗组胺药。确定的其他不符合指南的领域包括口服皮质类固醇和口服沙丁胺醇的潜在过度使用，以及 NSAIDs 使用者不当的胃保护。
虽然私人初级保健领域的处方行为总体上是适当的，但仍有几个潜在不适当处方的领域。应关注所确定的领域，继续努力改善处方行为，以获得最佳临床效果，同时降低不必要的风险和医疗保健成本。
