Suchergebnisse

At present, the vast majority of Chinese farmers' income comes from factor income. The distortion of factor market affects the sustainable growth of farmers' income and the optimization of income structure to a certain extent. The development of factor market is particularly important to solve the problem of increasing farmers' income. Based on the panel data of 28 provinces (cities, autonomous regions) in China from 1997 to 2021, this study constructs a spatial panel model to empirically analyze the spatial impact of the three major factor market distortions on Farmers' income growth. The results show that Chinese farmers' income has a strong spatial dependence, and there is a significant spatial correlation effect in different regions. There are serious market distortions, which have significantly hindered the growth of farmers' income. The distortion of China's factor market not only inhibits the growth of farmers' income in this region, but also has a significant negative spatial spillover effect. The distortion of factor market restricts the improvement of farmers' income in adjacent areas. In the future, we need to further straighten out the influencing factors and give full play to the income increasing efficiency of various factors.

ObjectiveTo evaluate validity of hospital-discharged abstract administrative health data (DAD) over years through chart reviews.
MethodsWe analyzed three chart review cohorts (4,008 patients in 2003, 3,045 in 2015, and 9,024 in 2022) in Calgary, Canada. Nurse reviewers determined the presence or absence of 17 clinical conditions employing similar protocols. The reviews were linked with DAD using a unique lifetime identifier, chart number, and admission date. We evaluated the validity of DAD, coded in ICD-10-Canada version, in recording conditions by comparing against chart reviews. The C-statistics was calculated in predicting in-hospital mortality.
ResultsThe mean difference in prevalence between chart reviews and DAD for these 17 conditions was 2.1% in 2003, 7.6% in 2015, and 6.3% in 2022. However, some conditions were relatively stable, such as diabetes (1.9%, 2.1%, and 1.1%) and metastatic cancer (0.3%, 1.1%, and 0.4%). For 17 conditions, the sensitivity ranged 39.6-85.1% in 2003, 1.3-85.2% in 2015, and 3.0-89.7% in 2022. The C-statistics for in-hospital mortality based on DAD was 0.84 in 2003, 0.81 in 2015, and 0.78 in 2022.
ConclusionDAD increasingly under-coded conditions over 19 years. The validity of DAD decreased but remained relatively stable for certain conditions mandated for coding. The under-coding exerted minimal impact on in-hospital mortality prediction.
ImplicationsThe under-coding could be primarily due to the increase of hospital patient volumes and the limited time allocated to coders. Consequently, there is a need to develop artificial intelligence methods based on electronic medical records to support coding practices and improve coding quality.

ObjectiveAdministrative data often underrepresents hospital adverse events (AEs) due to limitations in International Classification of Diseases (ICD) coding. By leveraging electronic medical records (EMRs), we aim to mitigate these discrepancies and enhance the precision of healthcare surveillance and performance evaluations. To this end, we have developed a machine learning (ML)-based approach that utilizes EMR text data to detect common AEs.
MethodsWe sampled adult admissions from four Calgary hospitals (2017 - 2022). Registered nurses assessed charts for 17 AEs, and the results were used as reference standard. We compared two AE detection methods: the standard ICD-based method following Canadian guidelines, and our ML algorithm applied to EMR narratives. Sensitivity, positive predictive value (PPV), negative predictive value (NPV), and specificity for both methods were calculated and compared against the reference standard.
ResultsWe analyzed 9,566 patients, of whom 1,506 were identified with AEs. Of the 17 AEs, the sensitivity in ICD-coded data ranged from 0-37%, and in EMRs, it was between 75-100%. Both showed low PPV (0-50% ICD vs.1-34% EMR). ICD data had high specificity ranging from 99-100% and NPV (99%-100%), while EMRs had specificity between 68-94% and an NPV of 100%.
Conclusion and ImplicationsML significantly enhances sensitivity for AE detection compared to ICD-10-CA coding, despite both methods experiencing low PPV due to imbalances in EMR data. This marked improvement in sensitivity highlights ML's potential to transform AE surveillance and reporting, promising significant advancements in patient safety and healthcare quality by enabling more accurate and comprehensive identification of AEs.