PDF(Pubmed)
Abstract：
BACKGROUND: Diagnosis can often be recorded in electronic medical records (EMRs) as free-text or using a term with a diagnosis code. Researchers, governments, and agencies, including organisations that deliver incentivised primary care quality improvement programs, frequently utilise coded data only and often ignore free-text entries. Diagnosis data are reported for population healthcare planning including resource allocation for patient care. This study sought to determine if diagnosis counts based on coded diagnosis data only, led to under-reporting of disease prevalence and if so, to what extent for six common or important chronic diseases.
METHODS: This cross-sectional data quality study used de-identified EMR data from 84 general practices in Victoria, Australia. Data represented 456,125 patients who attended one of the general practices three or more times in two years between January 2021 and December 2022. We reviewed the percentage and proportional difference between patient counts of coded diagnosis entries alone and patient counts of clinically validated free-text entries for asthma, chronic kidney disease, chronic obstructive pulmonary disease, dementia, type 1 diabetes and type 2 diabetes.
RESULTS: Undercounts were evident in all six diagnoses when using coded diagnoses alone (2.57-36.72% undercount), of these, five were statistically significant. Overall, 26.4% of all patient diagnoses had not been coded. There was high variation between practices in recording of coded diagnoses, but coding for type 2 diabetes was well captured by most practices.
CONCLUSIONS: In Australia clinical decision support and the reporting of aggregated patient diagnosis data to government that relies on coded diagnoses can lead to significant underreporting of diagnoses compared to counts that also incorporate clinically validated free-text diagnoses. Diagnosis underreporting can impact on population health, healthcare planning, resource allocation, and patient care. We propose the use of phenotypes derived from clinically validated text entries to enhance the accuracy of diagnosis and disease reporting. There are existing technologies and collaborations from which to build trusted mechanisms to provide greater reliability of general practice EMR data used for secondary purposes.
METHODS: This cross-sectional data quality study used de-identified EMR data from 84 general practices in Victoria, Australia. Data represented 456,125 patients who attended one of the general practices three or more times in two years between January 2021 and December 2022. We reviewed the percentage and proportional difference between patient counts of coded diagnosis entries alone and patient counts of clinically validated free-text entries for asthma, chronic kidney disease, chronic obstructive pulmonary disease, dementia, type 1 diabetes and type 2 diabetes.
RESULTS: Undercounts were evident in all six diagnoses when using coded diagnoses alone (2.57-36.72% undercount), of these, five were statistically significant. Overall, 26.4% of all patient diagnoses had not been coded. There was high variation between practices in recording of coded diagnoses, but coding for type 2 diabetes was well captured by most practices.
CONCLUSIONS: In Australia clinical decision support and the reporting of aggregated patient diagnosis data to government that relies on coded diagnoses can lead to significant underreporting of diagnoses compared to counts that also incorporate clinically validated free-text diagnoses. Diagnosis underreporting can impact on population health, healthcare planning, resource allocation, and patient care. We propose the use of phenotypes derived from clinically validated text entries to enhance the accuracy of diagnosis and disease reporting. There are existing technologies and collaborations from which to build trusted mechanisms to provide greater reliability of general practice EMR data used for secondary purposes.
摘要：
背景：诊断通常可以以自由文本或使用带有诊断代码的术语记录在电子病历（EMR）中。研究人员,政府,和机构，包括提供激励初级保健质量改进计划的组织，经常只使用编码数据，经常忽略自由文本条目。报告了用于人口医疗保健计划的诊断数据，包括用于患者护理的资源分配。这项研究试图确定诊断是否仅基于编码的诊断数据，导致疾病患病率报告不足，如果是这样，六种常见或重要的慢性疾病在多大程度上。
方法：这项横断面数据质量研究使用了来自维多利亚州84个一般实践的去识别EMR数据，澳大利亚。数据代表了456,125名患者，他们在2021年1月至2022年12月之间的两年内三次或更多次参加了一般实践之一。我们回顾了仅编码诊断条目的患者计数与哮喘临床验证的自由文本条目的患者计数之间的百分比和比例差异，慢性肾病,慢性阻塞性肺疾病,痴呆症,1型糖尿病和2型糖尿病。
结果：当单独使用编码诊断（2.57-36.72％的低估）时，在所有六个诊断中都有明显的低估。其中，五个有统计学意义。总的来说,所有患者诊断中有26.4％未编码。记录编码诊断的实践之间存在很大差异，但是大多数实践都很好地记录了2型糖尿病的编码。
结论：在澳大利亚，临床决策支持和向政府报告依赖于编码诊断的汇总患者诊断数据，与同样纳入临床验证的自由文本诊断的计数相比，可能导致诊断的严重漏报。诊断漏报会影响人群健康，医疗保健规划,资源分配,和病人护理。我们建议使用来自临床验证文本条目的表型来提高诊断和疾病报告的准确性。存在现有技术和协作,从中构建可信机制以提供用于次要目的的一般实践EMR数据的更大可靠性。
方法：这项横断面数据质量研究使用了来自维多利亚州84个一般实践的去识别EMR数据，澳大利亚。数据代表了456,125名患者，他们在2021年1月至2022年12月之间的两年内三次或更多次参加了一般实践之一。我们回顾了仅编码诊断条目的患者计数与哮喘临床验证的自由文本条目的患者计数之间的百分比和比例差异，慢性肾病,慢性阻塞性肺疾病,痴呆症,1型糖尿病和2型糖尿病。
结果：当单独使用编码诊断（2.57-36.72％的低估）时，在所有六个诊断中都有明显的低估。其中，五个有统计学意义。总的来说,所有患者诊断中有26.4％未编码。记录编码诊断的实践之间存在很大差异，但是大多数实践都很好地记录了2型糖尿病的编码。
结论：在澳大利亚，临床决策支持和向政府报告依赖于编码诊断的汇总患者诊断数据，与同样纳入临床验证的自由文本诊断的计数相比，可能导致诊断的严重漏报。诊断漏报会影响人群健康，医疗保健规划,资源分配,和病人护理。我们建议使用来自临床验证文本条目的表型来提高诊断和疾病报告的准确性。存在现有技术和协作,从中构建可信机制以提供用于次要目的的一般实践EMR数据的更大可靠性。
