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Abstract Aims Sex differences in long‐term post‐discharge clinical outcomes in Asian patients hospitalized for acute decompensated heart failure (HF) persist despite the world‐wide implementation of guideline‐directed medical therapy for decades. The present study aims to elucidate the puzzling dilemma and to depict the directions of solution. Methods and results Between 2011 and 2020, a total of 12 428 patients (6518 men and 5910 women, mean age 73.50 ± 14.85) hospitalized for acute decompensated HF were retrospectively enrolled from a university HF cohort. Compared with men, women hospitalized for acute decompensated HF were older in age (76.40 ± 13.43 vs. 71.20 ± 15.67 years old, P 50%) than men (P 50%) and HF with mildly reduced EF (40%–50%), but not in HF with reduced EF (

BackgroundThe possible association between diabetes mellitus and dementia has raised concerns, given the observed coincidental occurrences. ObjectiveThis study aimed to develop a personalized predictive model, using artificial intelligence, to assess the 5-year and 10-year dementia risk among patients with type 2 diabetes mellitus (T2DM) who are prescribed antidiabetic medications. MethodsThis retrospective multicenter study used data from the Taipei Medical University Clinical Research Database, which comprises electronic medical records from 3 hospitals in Taiwan. This study applied 8 machine learning algorithms to develop prediction models, including logistic regression, linear discriminant analysis, gradient boosting machine, light gradient boosting machine, AdaBoost, random forest, extreme gradient boosting, and artificial neural network (ANN). These models incorporated a range of variables, encompassing patient characteristics, comorbidities, medication usage, laboratory results, and examination data. ResultsThis study involved a cohort of 43,068 patients diagnosed with type 2 diabetes mellitus, which accounted for a total of 1,937,692 visits. For model development and validation, 1,300,829 visits were used, while an additional 636,863 visits were reserved for external testing. The area under the curve of the prediction models range from 0.67 for the logistic regression to 0.98 for the ANNs. Based on the external test results, the model built using the ANN algorithm had the best area under the curve (0.97 for 5-year follow-up period and 0.98 for 10-year follow-up period). Based on the best model (ANN), age, gender, triglyceride, hemoglobin A1c, antidiabetic agents, stroke history, and other long-term medications were the most important predictors. ConclusionsWe have successfully developed a novel, computer-aided, dementia risk prediction model that can facilitate the clinical diagnosis and management of patients prescribed with antidiabetic medications. However, further investigation is required to assess the model’s feasibility and external validity.

BackgroundPreoperative evaluation is important, and this study explored the application of machine learning methods for anesthetic risk classification and the evaluation of the contributions of various factors. To minimize the effects of confounding variables during model training, we used a homogenous group with similar physiological states and ages undergoing similar pelvic organ–related procedures not involving malignancies. ObjectiveData on women of reproductive age (age 20-50 years) who underwent gestational or gynecological surgery between January 1, 2017, and December 31, 2021, were obtained from the National Taiwan University Hospital Integrated Medical Database. MethodsWe first performed an exploratory analysis and selected key features. We then performed data preprocessing to acquire relevant features related to preoperative examination. To further enhance predictive performance, we used the log-likelihood ratio algorithm to generate comorbidity patterns. Finally, we input the processed features into the light gradient boosting machine (LightGBM) model for training and subsequent prediction. ResultsA total of 10,892 patients were included. Within this data set, 9893 patients were classified as having low anesthetic risk (American Society of Anesthesiologists physical status score of 1-2), and 999 patients were classified as having high anesthetic risk (American Society of Anesthesiologists physical status score of >2). The area under the receiver operating characteristic curve of the proposed model was 0.6831. ConclusionsBy combining comorbidity information and clinical laboratory data, our methodology based on the LightGBM model provides more accurate predictions for anesthetic risk classification. Trial RegistrationResearch Ethics Committee of the National Taiwan University Hospital 202204010RINB; https://www.ntuh.gov.tw/RECO/Index.action

Objective The objective of this paper is to provide a comprehensive overview of the development and features of the Taipei Medical University Clinical Research Database (TMUCRD), a repository of real-world data (RWD) derived from electronic health records (EHRs) and other sources.Methods TMUCRD was developed by integrating EHRs from three affiliated hospitals, including Taipei Medical University Hospital, Wan-Fang Hospital and Shuang-Ho Hospital. The data cover over 15 years and include diverse patient care information. The database was converted to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) for standardisation.Results TMUCRD comprises 89 tables (eg, 29 tables for each hospital and 2 linked tables), including demographics, diagnoses, medications, procedures and measurements, among others. It encompasses data from more than 4.15 million patients with various medical records, spanning from the year 2004 to 2021. The dataset offers insights into disease prevalence, medication usage, laboratory tests and patient characteristics.Discussion TMUCRD stands out due to its unique advantages, including diverse data types, comprehensive patient information, linked mortality and cancer registry data, regular updates and a swift application process. Its compatibility with the OMOP CDM enhances its usability and interoperability.Conclusion TMUCRD serves as a valuable resource for researchers and scholars interested in leveraging RWD for clinical research. Its availability and integration of diverse healthcare data contribute to a collaborative and data-driven approach to advancing medical knowledge and practice.

Abstract Introduction Pancreatic cancer is associated with poor prognosis. Considering the increased global incidence of diabetes cases and that individuals with diabetes are considered a high‐risk subpopulation for pancreatic cancer, it is critical to detect the risk of pancreatic cancer within populations of person living = with diabetes. This study aimed to develop a novel prediction model for pancreatic cancer risk among patients with diabetes, using = a real‐world database containing clinical features and employing numerous artificial intelligent approach algorithms. Methods This retrospective observational study analyzed data on patients with Type 2 diabetes from a multisite Taiwanese EMR database between 2009 and 2019. Predictors were selected in accordance with the literature review and clinical perspectives. The prediction models were constructed using machine learning algorithms such as logistic regression, linear discriminant analysis, gradient boosting machine, and random forest. Results The cohort consisted of 66,384 patients. The Linear Discriminant Analysis (LDA) model generated the highest AUROC of 0.9073, followed by the Voting Ensemble and Gradient Boosting machine models. LDA, the best model, exhibited an accuracy of 84.03%, a sensitivity of 0.8611, and a specificity of 0.8403. The most significant predictors identified for pancreatic cancer risk were glucose, glycated hemoglobin, hyperlipidemia comorbidity, antidiabetic drug use, and lipid‐modifying drug use. Conclusion This study successfully developed a highly accurate 4‐year risk model for pancreatic cancer in patients with diabetes using real‐world clinical data and multiple machine‐learning algorithms. Potentially, our predictors offer an opportunity to identify pancreatic cancer early and thus increase prevention and invention windows to impact survival in diabetic patients.

BackgroundPrevious studies have identified COVID-19 risk factors, such as age and chronic health conditions, linked to severe outcomes and mortality. However, accurately predicting severe illness in COVID-19 patients remains challenging, lacking precise methods.ObjectiveThis study aimed to leverage clinical real-world data and multiple machine-learning algorithms to formulate innovative predictive models for assessing the risk of severe outcomes or mortality in hospitalized patients with COVID-19.MethodsData were obtained from the Taipei Medical University Clinical Research Database (TMUCRD) including electronic health records from three Taiwanese hospitals in Taiwan. This study included patients admitted to the hospitals who received an initial diagnosis of COVID-19 between January 1, 2021, and May 31, 2022. The primary outcome was defined as the composite of severe infection, including ventilator use, intubation, ICU admission, and mortality. Secondary outcomes consisted of individual indicators. The dataset encompassed demographic data, health status, COVID-19 specifics, comorbidities, medications, and laboratory results. Two modes (full mode and simplified mode) are used; the former includes all features, and the latter only includes the 30 most important features selected based on the algorithm used by the best model in full mode. Seven machine learning was employed algorithms the performance of the models was evaluated using metrics such as the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity.ResultsThe study encompassed 22,192 eligible in-patients diagnosed with COVID-19. In the full mode, the model using the light gradient boosting machine algorithm achieved the highest AUROC value (0.939), with an accuracy of 85.5%, a sensitivity of 0.897, and a specificity of 0.853. Age, vaccination status, neutrophil count, sodium levels, and platelet count were significant features. In the simplified mode, the extreme gradient boosting algorithm yielded an AUROC of 0.935, an accuracy of 89.9%, a sensitivity of 0.843, and a specificity of 0.902.ConclusionThis study illustrates the feasibility of constructing precise predictive models for severe outcomes or mortality in COVID-19 patients by leveraging significant predictors and advanced machine learning. These findings can aid healthcare practitioners in proactively predicting and monitoring severe outcomes or mortality among hospitalized COVID-19 patients, improving treatment and resource allocation.

Objectives: Lung cancer is a main contributor to all newly diagnosed cancers worldwide. The chemoprotective effect of the influenza vaccine among patients with hypertension remains unclear.Methods: A total of 37,022 patients with hypertension were retrospectively enrolled from the Taiwan National Health Insurance Research Database. These patients were further divided into a vaccinated group (n = 15,697) and an unvaccinated group (n = 21,325).Results: After adjusting for sex, age, comorbidities, medications, level of urbanization and monthly income, vaccinated patients had a significantly lower risk of lung cancer occurrence than unvaccinated patients (adjusted hazard ratio [aHR]: 0.56, 95% confidence interval [CI]: 0.47–0.67). A potential protective effect was observed for both sexes and in the elderly age group. With a greater total number of vaccinations, a potentially greater protective effect was observed (aHR: 0.75, 95% CI 0.60–0.95; aHR: 0.66, 95% CI: 0.53–0.82; aHR: 0.26, 95% CI: 0.19–0.36, after receiving 1, 2–3 and ≥4 vaccinations, respectively).Conclusion: Influenza vaccination was associated with a lower risk of lung cancer among patients with hypertension. The potentially chemoprotective effect appeared to be dose dependent.

Abstract Background This study attempted to illustrate the demographic of inpatient eye careservice from 1997 to 2011 in Taiwan, and also the ophthalmic disease landscape and utilization change over time. These insights might apply to resource allocation planning and trainees’ better understandings of ophthalmic inpatient practice. Methods This study utilized Taiwan’s National Health Insurance Research Database (NHIRD). Admission records of eye service that occurred since 1997 and until 2011 were included. Records were separated into operative and non-operative. The records were further divided according to their time: a group of early time before 2006 and a late one after 2006. Results Patients’ mean age were 56 and 44 years for operative and non-operative records. The sex ratio (male to female) was 1.3, and the average of admission duration was 4 days. The average spending was around 1000 United State Dollars per admission and a gradually upgoing trend was also noted. The number of inpatient eye services decreased over time, from 3,248 to 2,174 in the studied period. Cases admitted for operation primarily underwent cataract surgery, vitrectomy, and scleral buckling during the studied period. Trabeculectomy emerged as another major indication of admission during the later time. Cases admitted for non-operative management were primarily corneal ulcer, glaucoma, and infection, including orbital cellulitis and lid abscess. Corneal ulcers made up a major proportion of admission records in the non-operative group during both periods. Conclusions This study described the demographics of inpatient eye service in Taiwan. Ophthalmologist, especially trainees, and officials could make better policies according to the presented results in this study.

Objective: Although influenza vaccination reduces the risk of atrial fibrillation (AF), its protective effect in patients with gout remains unclear. The present study aimed to evaluate the protective effect of influenza vaccination in patients with gout.Methods: A total of 26,243 patients with gout, aged 55 and older, were enrolled from the National Health Insurance Research Database (NHIRD) between 1 January 2001, and 31 December 2012. The patients were divided into vaccinated (n = 13,201) and unvaccinated groups (n = 13,042). After adjusting comorbidities, medications, sociodemographic characteristics, the risk of AF during follow-up period was analyzed.Results: In influenza, non-influenza seasons and all seasons, the risk of AF was significantly lower in vaccinated than in unvaccinated patients (Adjust hazard ratio [aHR]: 0.59, 95% confidence interval [CI]: 0.50–0.68; aHR: 0.50, 95% CI: 0.42–0.63; aHR: 0.55, 95% CI: 0.49–0.62, respectively). In addition, the risk of AF significantly decreased with increased influenza vaccination (aHR: 0.85, 95% CI: 0.69–1.04; aHR: 0.72, 95% CI: 0.60–0.87; aHR: 0.40, 95% CI: 0.33–0.49, after first, 2–3 times, and ≥4 times of vaccination, respectively). Furthermore, sensitivity analysis indicated that the risk of AF significantly decreased after influenza vaccination for patients with different sexes, medication histories, and comorbidities.Conclusions: Influenza vaccination is associated with a lower risk of AF in patients with gout. This potentially protective effect seems to depend on the dose administered.

Backgrounds: Influenza vaccination could decrease the risk of major cardiac events in patients with hypertension. However, the vaccine’s effects on decreasing the risk of chronic kidney disease (CKD) development in such patients remain unclear. Methods: We retrospectively analysed the data of 37,117 patients with hypertension (≥55 years old) from the National Health Insurance Research Database during 1 January 2001 to 31 December 2012. After a 1:1 propensity score matching by the year of diagnosis, we divided the patients into vaccinated (n = 15,961) and unvaccinated groups (n = 21,156). Results: In vaccinated group, significantly higher prevalence of comorbidities such as diabetes, cerebrovascular disease, dyslipidemia, heart and liver disease were observed compared with unvaccinated group. After adjusting age, sex, comorbidities, medications (anti-hypertensive agents, metformin, aspirin and statin), level of urbanization and monthly incomes, significantly lower risk of CKD occurrence was observed among vaccinated patients in influenza season, non-influenza season and all season (Adjusted hazard ratio [aHR]: 0.39, 95% confidence level [C.I.]: 0.33–0.46; 0.38, 95% C.I.: 0.31–0.45; 0.38, 95% C.I.: 0.34–0.44, respectively). The risk of hemodialysis significantly decreased after vaccination (aHR: 0.40, 95% C.I.: 0.30–0.53; 0.42, 95% C.I.: 0.31–0.57; 0.41, 95% C.I.: 0.33–0.51, during influenza season, non-influenza season and all season). In sensitivity analysis, patients with different sex, elder and non-elder age, with or without comorbidities and with or without medications had significant decreased risk of CKD occurrence and underwent hemodialysis after vaccination. Moreover, the potential protective effect appeared to be dose-dependent. Conclusions: Influenza vaccination decreases the risk of CKD among patients with hypertension and also decrease the risk of receiving renal replacement therapy. Its potential protective effects are dose-dependent and persist during both influenza and noninfluenza seasons.

BackgroundsThe risk of stroke is higher among patients with chronic obstructive pulmonary disease (COPD) than among the healthy population. Moreover, women generally have worse long-term stroke outcomes than men.MethodsThe data of 6681 women with COPD (aged ≥ 65 years) registered in Taiwan’s National Health Insurance Research Database were retrospectively analyzed from January 1, 2001 to December 31, 2011. After 1:1 propensity score matching, the patients were divided into vaccinated and unvaccinated groups.ResultsIn total, 5102 women were enrolled. The vaccinated group had a significantly lower risk of total, hemorrhagic, and ischemic stroke than the unvaccinated group (adjusted hazard ratio [aHR]: 0.60, 95% confidence interval [CI]: 0.54–0.67; aHR: 0.59, 95% CI: 0.43–0.83; and aHR: 0.59, 95% CI: 0.52–0.68, respectively). A lower risk of stroke was observed among the women aged 65–74 and ≥75 years, and the association was dose-dependent in all types of stroke (aHR: 1.08, 95% CI: 0.92–1.26; aHR: 0.70, 95% CI: 0.60–0.82; and aHR: 0.32, 95% CI: 0.26–0.38 for those vaccinated 1, 2 to 3, and ≥4 times, respectively, during the follow-up period). Women with a CHA2DS2-VASc score (conditions and characteristics included congestive heart failure, hypertension, diabetes, stroke, vascular disease, age, and sex) of 2–3 and ≥4 had a significantly lower risk of ischemic stroke while receiving more vaccinations. A smaller significant lower risk of hemorrhagic stroke after more than 4 times of vaccination was noted in the women with a CHA2DS2-VASc score of ≥4. Both interrupted and non-interrupted vaccination was associated with lower risk of stroke occurrence.ConclusionInfluenza vaccination is associated with a lower risk of total, hemorrhagic, and ischemic stroke among women with COPD, and the association is dose-dependent. However, the findings may be limited by unmeasurable confounders. Further investigations on this subject are warranted.

Abstract The aim of this study was to conduct a nationwide survey of the use of emergency ophthalmology services using a sub-dataset of one million beneficiaries sampled from Taiwan’s National Health Insurance Research Database (NHIRD) for the years 2008 through 2012. By analyzing this population dataset, the study illustrates the disease landscape of emergency eye care services. The five-year, one-million-person NHIRD sub-dataset for 2008 through 2012 was used to explore emergency visits and ophthalmology specialty visits and to analyze the associated demographics and diagnosis codes based on the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM). Diagnoses were categorized into three groups: urgent, non-urgent, and intermediate. A total of 2454 emergency eye care visits were identified. The mean age of the patients who made these visits was 34.6 years old, and their sex ratio was 1.36 men to women. The percentages of urgent, non-urgent, and intermediate eye care visits in this study were 48.2%, 30.9%, and 20.9%, respectively. The leading diagnoses in the urgent category were corneal abrasions, foreign bodies in the eyes, eye burns, and blunt eye injuries. The leading diagnoses for the non-urgent visits were conjunctivitis, subconjunctival hemorrhages, trichiasis, and dry eye disease. Those for the intermediate category were superficial punctate keratitis, corneal opacity and degeneration, and lid, orbital, and lacrimal drainage infections. The urgent visit category accounted for nearly half of all the visits identified in this study. Compared to outpatient department visitors, the emergency ophthalmology service patients were younger and more predominantly male. These results were consistent with those of previous reports. Low copays have made emergency ophthalmology services highly accessible in Taiwan. However, future policies can be designed to more effectively allocate resources to urgent cases.

Previous studies have indicated that influenza vaccination reduces the development of lung cancer. However, the protective effects of influenza vaccination on primary liver cancer in patients with chronic kidney disease (CKD) are unclear. This cohort study identified 12,985 patients aged at least 55 years who had received a diagnosis of CKD between 1 January 2001 and 31 December 2012 from the National Health Insurance Research Database of Taiwan. The patients were classified according to vaccination status. Propensity score matching was used to reduce selection bias. Cox proportional hazards regression analysis was used to evaluate the correlation between influenza vaccination and primary liver cancer in patients with CKD. The prevalence of primary liver cancer was lower in patients with CKD who had received an influenza vaccine (adjusted hazard ratio: 0.45, 95% confidence interval [CI]: 0.35–0.58, p < 0.001). The protective effects were observed regardless of sex, age, and comorbidities. Moreover, dose-dependent protective effects were observed. In the subgroup analysis, where the patients were classified by the number of vaccinations received, the adjusted hazard ratios for 1, 2–3, and ≥4 vaccinations were 0.86 (95% CI: 0.63–1.17), 0.45 (95% CI: 0.31–0.63), and 0.21 (95% CI: 0.14–0.33), respectively. In conclusion, influenza vaccination was associated with a lower incidence of liver cancer in patients with CKD.

The risk of stroke in patients with gout is high. The effect of vaccines in lowering the stroke risk in patients with gout remains unclear. We retrospectively analyzed 23,949 patients with gout (age ≥ 55 years) from the National Health Insurance Research Database over a 12-year period. The patients were divided into vaccinated (n = 11,649) and unvaccinated groups (n = 12,300). Overall, the vaccinated group had significantly lower risks of all stroke, hemorrhagic stroke, and ischemic stroke than the unvaccinated group (adjusted hazard ratio [aHR], 0.59 and 95% confidence interval [CI], 0.55–0.63; aHR, 0.60 and 95% CI, 0.49–0.73; and aHR, 0.60 and 95% CI, 0.55–0.65, respectively). The association appeared to be dose-dependent for both hemorrhagic and ischemic stroke (hemorrhagic stroke: aHR, 0.81 and 95% CI, 0.61–1.08; aHR, 0.80 and 95% CI, 0.62–1.02; and aHR, 0.37 and 95% CI, 0.28–0.48; ischemic stroke: aHR, 0.83 and 95% CI, 0.74–0.94; aHR, 0.73 and 95% CI, 0.65–0.81; and aHR, 0.42 and 95% CI, 0.38–0.47 for patients vaccinated 1, 2 or 3, and ≥4 times, respectively, during the follow-up period). Patients with a history of atrial fibrillation did not have a lower risk of hemorrhagic stroke even after receiving four vaccinations (aHR, 0.59; 95% CI, 0.25–1.38). Influenza vaccination was associated with a lower risk of all stroke in people with gout, and the association appeared to be dose-dependent.

Coronavirus disease 2019 (COVID-19) has already raised serious concern globally as the number of confirmed or suspected cases have increased rapidly. Epidemiological studies reported that obesity is associated with a higher rate of mortality in patients with COVID-19. Yet, to our knowledge, there is no comprehensive systematic review and meta-analysis to assess the effects of obesity and mortality among patients with COVID-19. We, therefore, aimed to evaluate the effect of obesity, associated comorbidities, and other factors on the risk of death due to COVID-19. We did a systematic search on PubMed, EMBASE, Google Scholar, Web of Science, and Scopus between January 1, 2020, and August 30, 2020. We followed Cochrane Guidelines to find relevant articles, and two reviewers extracted data from retrieved articles. Disagreement during those stages was resolved by discussion with the main investigator. The random-effects model was used to calculate effect sizes. We included 17 articles with a total of 543,399 patients. Obesity was significantly associated with an increased risk of mortality among patients with COVID-19 (RRadjust: 1.42 (95%CI: 1.24–1.63, p < 0.001). The pooled risk ratio for class I, class II, and class III obesity were 1.27 (95%CI: 1.05–1.54, p = 0.01), 1.56 (95%CI: 1.11–2.19, p < 0.01), and 1.92 (95%CI: 1.50–2.47, p < 0.001), respectively). In subgroup analysis, the pooled risk ratio for the patients with stroke, CPOD, CKD, and diabetes were 1.80 (95%CI: 0.89–3.64, p = 0.10), 1.57 (95%CI: 1.57–1.91, p < 0.001), 1.34 (95%CI: 1.18–1.52, p < 0.001), and 1.19 (1.07–1.32, p = 0.001), respectively. However, patients with obesity who were more than 65 years had a higher risk of mortality (RR: 2.54; 95%CI: 1.62–3.67, p < 0.001). Our study showed that obesity was associated with an increased risk of death from COVID-19, particularly in patients aged more than 65 years. Physicians should aware of these risk factors when dealing with patients with COVID-19 and take early treatment intervention to reduce the mortality of COVID-19 patients.

Purpose: We examined factors associated with health literacy among elders with and without suspected COVID-19 symptoms (S-COVID-19-S).Methods: A cross-sectional study was conducted at outpatient departments of nine hospitals and health centers 14 February−2 March 2020. Self-administered questionnaires were used to assess patient characteristics, health literacy, clinical information, health-related behaviors, and depression. A sample of 928 participants aged 60–85 years were analyzed.Results: The proportion of people with S-COVID-19-S and depression were 48.3 and 13.4%, respectively. The determinants of health literacy in groups with and without S-COVID-19-S were age, gender, education, ability to pay for medication, and social status. In people with S-COVID-19-S, one-score increment of health literacy was associated with 8% higher healthy eating likelihood (odds ratio, OR, 1.08; 95% confidence interval, 95%CI, 1.04, 1.13; p < 0.001), 4% higher physical activity likelihood (OR, 1.04; 95%CI, 1.01, 1.08, p = 0.023), and 9% lower depression likelihood (OR, 0.90; 95%CI, 0.87, 0.94; p < 0.001). These associations were not found in people without S-COVID-19-S.Conclusions: The older people with higher health literacy were less likely to have depression and had healthier behaviors in the group with S-COVD-19-S. Potential health literacy interventions are suggested to promote healthy behaviors and improve mental health outcomes to lessen the pandemic's damage in this age group.

Introduction. A study based on the Taiwanese National Health Insurance Research Database (NHIRD) to reveal the ocular diseases landscape. Materials and Methods. This study comprised all ophthalmological outpatient patient visits (n = 6,341,266) in the Taiwanese longitudinal NHIRD 2000. Descriptive analytics based on 15 disease categories of ICD-9-CM and 10 tiers of age categories was performed with SAS for Windows 9.3 (SAS Institute, Inc., Cary, NC, U.S.A.). Results. The average frequency of visits was 0.7 visits per year. The mean age was 36.2 years old. Bimodal peak of visits in the first, second, and eighth decade of life was revealed. Conjunctiva is the most dominant disease category throughout life while different categories play major roles in each decade of life. The most frequent disease code of each category was listed. Discussion. The bimodal peak of visits revealed the age group of the most prominent ocular disease burden. Peak in school age population can be partially explained by the nationwide vision screening program, while aging accounts for the lens disorder and glaucoma of the senile peak. The disease category frequency variation among age categories reflects the development and aging of the eye. The most frequent disease codes of each category highlight disease of importance for primary practitioners and ophthalmologists. Conclusion. Taiwanese longitudinal NHIRD was used to reveal the ophthalmological disease landscape. The epidemiological insight, while limited in clinical presentation and economic impact, enables physicians and policy makers to improve the overall vision health of the population.

BACKGROUND: Medication errors are common, life threatening, costly but preventable. Information technology and automated systems are highly efficient for preventing medication errors and therefore widely employed in hospital settings. The aim of this study was to construct a probabilistic model that can reduce medication errors by identifying uncommon or rare associations between medications and diseases. METHODS AND FINDINGS: Association rules of mining techniques are utilized for 103.5 million prescriptions from Taiwan's National Health Insurance database. The dataset included 204.5 million diagnoses with ICD9-CM codes and 347.7 million medications by using ATC codes. Disease-Medication (DM) and Medication-Medication (MM) associations were computed by their co-occurrence and associations' strength were measured by the interestingness or lift values which were being referred as Q values. The DMQs and MMQs were used to develop the AOP model to predict the appropriateness of a given prescription. Validation of this model was done by comparing the results of evaluation performed by the AOP model and verified by human experts. The results showed 96% accuracy for appropriate and 45% accuracy for inappropriate prescriptions, with a sensitivity and specificity of 75.9% and 89.5%, respectively. CONCLUSIONS: We successfully developed the AOP model as an efficient tool for automatic identification of uncommon or rare associations between disease-medication and medication-medication in prescriptions. The AOP model helps to reduce medication errors by alerting physicians, improving the patients' safety and the overall quality of care.

BACKGROUND: Turnaround time (TAT) is an important indicator of laboratory performance. It is often difficult to achieve fast TAT for blood tests conducted at clinics in developing countries. This is because clinics where the patient is treated are often far away from the laboratory, and transporting blood samples and test results between the two locations creates significant delay. Recent efforts have sought to mitigate this problem by using Short Message Service (SMS) to reduce TAT. Studies reporting the impact of this technique have not been published in scientific literature however. In this paper we present a study of LabPush, a system developed to test whether SMS delivery of HIV related laboratory results to clinics could shorten TAT time significantly. METHOD: LapPush was implemented in six clinics of the Kingdom of Swaziland. SMS results were sent out from the laboratory as a supplement to normal transport of paper results. Each clinic was equipped with a mobile phone to receive SMS results. The laboratory that processes the blood tests was equipped with a system for digital input of results, and transmission of results via SMS to the clinics. RESULTS: Laboratory results were received for 1041 different clinical cases. The total number of SMS records received (1032) was higher than that of paper records (965), indicating a higher loss rate for paper records. A statistical comparison of TAT for SMS and paper reports indicates a statistically significant improvement for SMS. Results were more positive for more rural clinics, and an urban clinic with high workload. CONCLUSION: SMS can be used to reduce TAT for blood tests taken at clinics in developing countries. Benefits are likely to be greater at clinics that are further away from laboratories, due to the difficulties this imposes on transport of paper records.