Emergency department utilization can indicate early diagnosis of digestive tract cancers: A population-based study in Taiwan

Effectiveness of hospital emergency department regionalization and categorization policy on appropriate patient emergency care use: a nationwide observational study in Taiwan

BACKGROUND

Emergency department (ED) overcrowding is a health services issue worldwide. Modern health policy emphasizes appropriate health services utilization. However, the relationship between accessibility, capability, and appropriateness of ED use is unknown. Thus, this study aimed to examine the effect of hospital ED regionalization policy and categorization of hospital emergency capability policy (categorization policy) on patient-appropriate ED use.

METHODS

Taiwan implemented a nationwide three-tiered hospital ED regionalization and categorization of hospital emergency capability policies in 2007 and 2009, respectively. We conducted a retrospective observational study on the effect of emergency care policy intervention on patient visit. Between 2005 and 2011, the Taiwan National Health Insurance Research Database recorded 1,835,860 ED visits from 1 million random samples. ED visits were categorized using the Yang-Ming modified New York University-ED algorithm. A time series analysis was performed to examine the change in appropriate ED use rate after policy implementation.

RESULTS

From 2005 to 2011, total ED visits increased by 10.7%. After policy implementation, the average appropriate ED visit rate was 66.9%. The intervention had no significant effect on the trend of appropriate ED visit rate.

CONCLUSIONS

Although regionalization and categorization policies did increase emergency care accessibility, it had no significant effect on patient-appropriate ED use. Further research is required to improve data-driven policymaking.

Gastrointestinal endoscopy in early diagnosis and treatment of gastrointestinal tumors

Objective

To explore the value of gastrointestinal endoscopy in the early diagnosis and treatment of gastrointestinal tumors and lay a foundation for the diagnosis and treatment of gastrointestinal tumors.

Methods

One hundred and eight patients with gastrointestinal tumors who were admitted to our hospital from August 2016 to April 2018 were retrospectively analyzed and divided into observation group and control group according to different diagnostic methods, 54 cases in each group. The control group was treated with traditional endoscopy (white light imaging) and traditional surgery, while the observation group underwent narrow band imaging (NBI) based on endoscopic examination and endoscopic mucosal resection. The image quality scores (morphological image, gastric pit image and capillary image), diagnostic accuracy, surgery related clinical indicators (operation time, intraoperative bleeding volume, hospitalization days) and complications were observed and compared between the two groups.

Results

The morphological image, gastric pit image and capillary image scores of the observation group were higher than those of the control group (P<0.05). The diagnostic accuracy rate of the observation group was 96.30%, which was significantly higher than 75.93% (P<0.05). The operation time and hospitalization days of the observation group were shorter than those of the control group, and the intraoperative bleeding volume of the observation group was less than that of the control group; the differences were statistically significant (P<0.05). The incidence of complications of the observation group was lower than that of the control group, and the difference was statistically significant (P<0.05).

Conclusion

Gastrointestinal endoscopy can accurately identify the pathological changes of tumors in the early diagnosis and treatment of gastrointestinal tumors, improve the diagnostic accuracy rate, and guide the implementation of treatment measures to improve clinical indicators. Moreover the incidence of postoperative complications is low. It is worth clinical promotion.

Effect of motorcycle helmet types on head injuries: evidence from eight level-I trauma centres in Taiwan

Background

Motorcycle full-coverage helmet use may reduce fatalities and head injuries.

Methods

This retrospective cohort study extracted injury data from eight level-I trauma centres in Taiwan and performed a questionnaire survey to investigate injuries sustained by motorcyclists for the period between January 2015 and June 2017.

Results

As many as 725 patients participated in the questionnaire survey and reported their helmet types or phone use during crashes. The results of multivariate logistic models demonstrated that nonstandard helmet (half or open-face helmet) use was associated with an increased risk of head injuries and more severe injuries (injury severity score ≥ 8). Drunk riding and phone use appeared to be two important risk factors for head injuries and increased injury severity. Anaemia was also found to be a determinant of head injuries.”

Conclusions

Compared to full-coverage helmets, nonstandard provide less protection against head injuries and increased injury severity among motorcyclists.

Rider factors associated with severe injury after a light motorcycle crash: A multicentre study in an emerging economy setting

Objective

In Taiwan, light motorcycles (LMCs) with cylinder capacities between 50 and 250 cc are widely used for daily commute. These vehicles are operated in a mixed traffic environment and prohibited on highways. In light of increasing motorcycle casualties, we conducted a multicentre study to analyse rider factors affecting injury severity.

Methods

Riders hospitalised upon LMC crashes were contacted. Information on demographics, comorbidities, and riding behaviours was collected through questionnaires and linked to hospital data. The injury severity score (ISS) and length of hospitalisation (LOH) were used as injury severity measures.

Results

In total, 725 patients (mean age: 37.7 years; 64% men) completed their questionnaires. Multivariate analysis results showed that age ≥ 65 years, half-face helmets, protective clothing, collisions with a bus/truck or car, and fatigue riding were risk factors for having an ISS of ≥9. Age ≥ 65 years; motorcycle crashes ≥2 times in the previous year; anaemia; rural crashes; half-face helmets; protective boots; collisions with a bus/truck, car, or a stationary object; alcohol/stimulating refreshment consumption; and fatigue riding were risk factors for increased LOH. A protective factor was individuals working in commerce. Collisions with opening car doors caused low risks of having an ISS of ≥9 and a short LOH.

Conclusion

Certain factors were significantly associated with riders’ injury severity and related medical resource consumption. Because of differences in the power output, use, and riding environment, risk factors for severe injuries in LMC crashes are dissimilar from those for heavy motorcycles (cylinder capacities > 250 cc) in developed countries and deserve more attention for injury prevention. Further in-depth evaluation of significant factors based on this study’s results can yield valuable information to reduce severe injuries after LMC crashes in countries and areas with a high dependency on motorcycles, even considering the popularity of electric motorcycles.

eDRAM: Effective early disease risk assessment with matrix factorization on a large-scale medical database: A case study on rheumatoid arthritis

Recently, a number of analytical approaches for probing medical databases have been developed to assist in disease risk assessment and to determine the association of a clinical condition with others, so that better and intelligent healthcare can be provided. The early assessment of disease risk is an emerging topic in medical informatics. If diseases are detected at an early stage, prognosis can be improved and medical resources can be used more efficiently. For example, if rheumatoid arthritis (RA) is detected at an early stage, appropriate medications can be used to prevent bone deterioration. In early disease risk assessment, finding important risk factors from large-scale medical databases and performing individual disease risk assessment have been challenging tasks. A number of recent studies have considered risk factor analysis approaches, such as association rule mining, sequential rule mining, regression, and expert advice. In this study, to improve disease risk assessment, machine learning and matrix factorization techniques were integrated to discover important and implicit risk factors. A novel framework is proposed that can effectively assess early disease risks, and RA is used as a case study. This framework comprises three main stages: data preprocessing, risk factor optimization, and early disease risk assessment. This is the first study integrating matrix factorization and machine learning for disease risk assessment that is applied to a nation-wide and longitudinal medical diagnostic database. In the experimental evaluations, a cohort established from a large-scale medical database was used that included 1007 RA-diagnosed patients and 921,192 control patients examined over a nine-year follow-up period (2000-2008). The evaluation results demonstrate that the proposed approach is more efficient and stable for disease risk assessment than state-of-the-art methods.

A hybrid classifier combining Borderline-SMOTE with AIRS algorithm for estimating brain metastasis from lung cancer: a case study in Taiwan

Classifying imbalanced data in medical informatics is challenging. Motivated by this issue, this study develops a classifier approach denoted as BSMAIRS. This approach combines borderline synthetic minority oversampling technique (BSM) and artificial immune recognition system (AIRS) as global optimization searcher with the nearest neighbor algorithm used as a local classifier. Eight electronic medical datasets collected from University of California, Irvine (UCI) machine learning repository were used to evaluate the effectiveness and to justify the performance of the proposed BSMAIRS. Comparisons with several well-known classifiers were conducted based on accuracy, sensitivity, specificity, and G-mean. Statistical results concluded that BSMAIRS can be used as an efficient method to handle imbalanced class problems. To further confirm its performance, BSMAIRS was applied to real imbalanced medical data of lung cancer metastasis to the brain that were collected from National Health Insurance Research Database, Taiwan. This application can function as a supplementary tool for doctors in the early diagnosis of brain metastasis from lung cancer.