Hui-Chu Lang

Lung cancer patients suffer from numerous symptoms that impact their quality of life. This study aims to identify the symptom burden on quality of life in lung cancer patients. This survey used a structured questionnaire to collect data from 8 March 2021 to 12 May 2021. Patient demographic information was collected. The data on symptom burden and quality of life (QOL) of patients were obtained from the QLQ-C30 and the QLQ-LC13. The stepwise multiple regression analysis was used to estimate lung cancer-related symptom burden in relation to quality of life. The study included 159 patients with lung cancer who completed the questionnaire. The mean age of the patients was 63.12 ± 11.4 years, and 64.8% of them were female. The Global Quality of Life score of the QLQ-C30 was 67.87 ± 22.24, and the top five lung cancer-related symptoms were insomnia, dyspnea, and fatigue from the QLQ-C30, and coughing and dyspnea from the QLQ-LC13. The multiple regression analysis showed that appetite loss was the most frequently associated factor for global QOL (β = −0.32; adjusted R²: 27%) and cognitive function (β = −0.15; adjusted R²: 11%), while fatigue was associated with role function (β = −0.35; adjusted R²: 43%), emotional function (β = −0.26; adjusted R²: 9%), and social function (β = −0.26; adjusted R²: 27%). Dyspnea was associated with physical function (β = −0.45; adjusted R²: 42%). Appetite loss, fatigue, and dyspnea were the main reasons causing symptom burdens on quality of life for lung cancer patients. Decreasing these symptoms can improve the quality of life and survival for patients with lung cancer.

Purpose:This study aimed to examine the symptom burden conditions and quality of life in patients with lung cancer. Methods: A structured questionnaire was utilized to collect data from patients with lung cancer at a medical center. Demographic information of the patients was obtained, and their symptom burden and quality of life (QOL) were assessed using the QLQ-C30 and QLQ-LC13 instruments. Stepwise multiple regression analysis was employed to estimate the relationship between lung cancer-related symptom burden and quality of life. Results: A total of 159 patients completed the questionnaire. The mean age of the patients was 63.12±11.4 years, with 64.8% of them being female. The Global Quality of Life score on the QLQ-C30 was 67.87±22.24. The top five lung cancer-related symptoms reported were insomnia, dyspnea, fatigue (from the QLQ-C30), and coughing and dyspnea (from the QLQ-LC13). The multiple regression analysis revealed that appetite loss was significantly associated with global quality of life (β = -0.32; adjusted R2: 27%) and cognitive function (β = -0.15; adjusted R2: 11%). Fatigue was associated with role function (β = -0.35; adjusted R2: 43%), emotional function (β = -0.26; adjusted R2: 9%), and social function (β= -0.26; adjusted R2: 27%). Dyspnea was associated with physical function (β= -0.45; adjusted R2: 42%). Conclusion: The main contributors to symptom burdens and reduced quality of life in lung cancer patients were appetite loss, fatigue, and dyspnea. Alleviating these symptoms can potentially improve the quality of life and overall survival outcomes for patients with lung cancer.

Background: Although the COVID-19 pandemic has accelerated the adoption of telemedicine and virtual consultations worldwide, complex factors that may affect the use of virtual clinics are still unclear. Objective: This study aims to identify factors associated with the utilization of virtual clinics in the experience of virtual clinic service implementation in Taiwan. Methods: We retrospectively analyzed a total of 187,742 outpatient visits (176,815, 94.2%, in-person visits and 10,927, 5.8%, virtual visits) completed at a large general hospital in Taipei City from May 19 to July 31, 2021, after rapid implementation of virtual outpatient clinic visits due to the COVID-19 pandemic. Data of patients’ demographic characteristics, disease type, physicians’ features, and specialties/departments were collected, and physicians’ opinions regarding virtual clinics were surveyed and evaluated using a 5-point Likert scale. Multilevel analysis was conducted to determine the factors associated with the utilization of virtual clinics. Results: Patient-/visit-, physician-, and department-level factors accounted for 67.5%, 11.1%, and 21.4% of the total variance in the utilization of virtual clinics, respectively. Female sex (odds ratio [OR] 1.27, 95% CI 1.22-1.33, P<.001); residing at a greater distance away from the hospital (OR 2.36, 95% CI 2.15-2.58 if distance>50 km, P<.001; OR 3.95, 95% CI 3.11-5.02 if extensive travel required, P<.001); reimbursement by the National Health Insurance (NHI; OR 7.29, 95% CI 5.71-9.30, P<.001); seeking care for a major chronic disease (OR 1.33, 95% CI 1.24-1.42, P<.001); the physician’s positive attitude toward virtual clinics (OR 1.50, 95% CI 1.16-1.93, P=.002); and visits within certain departments, including the heart center, psychiatry, and internal medicine (OR 2.55, 95% CI 1.46-4.46, P=.004), were positively associated with the utilization of virtual clinics. The patient’s age, the physician’s age, and the physician’s sex were not associated with the utilization of virtual clinics in our study. Conclusions: Our results show that in addition to previously demonstrated patient-level factors that may influence telemedicine use, including the patient’s sex and distance from the hospital, factors at the visit level (insurance type, disease type), physician level (physician’s attitude toward virtual clinics), and department level also contribute to the utilization of virtual clinics. Although there was a more than 300-fold increase in the number of virtual visits during the pandemic compared with the prepandemic period, the majority (176,815/187,742, 94.2%) of the outpatient visits were still in-person visits during the study period. Therefore, it is of great importance to understand the factors impacting the utilization of virtual clinics to accelerate the implementation of telemedicine. The findings of our study may help direct policymaking for expanding the use of virtual clinics, especially in countries struggling with the development and promotion of telemedicine virtual clinic services.

Background: People in Taiwan enjoy comprehensive National Health Insurance coverage. However, under the global budget constraint, hospitals encounter enormous challenges. This study was designed to examine Taiwan medical centers' efficiency and factors that influence it.Methods: We obtained data from open sources of government routine publications and hospitals disclosed by law to the National Health Insurance Administration, Ministry of Health and Welfare, Taiwan. The dynamic data envelopment analysis(DDEA) model was adopted to estimate all medical centers' efficiencies during 2015–2018. Beta regression models were used to model the efficiency level obtained from the DDEA model. We applied an input-oriented approach under both the constant returns-to-scale (CRS) and variable returns-to-scale (VRS) assumptions to estimate efficiency.Results: The findings indicated that 68.4% (13 of 19) of medical centers were inefficient according to scale efficiency. The mean efficiency scores of all medical centers during 2015–2018 under the CRS, VRS, and Scale were 0.85, 0.930, and 0.95,respectively. Regression results showed that an increase in the population less than 14 years of age, assets, nurse-patient ratio and bed occupancy rate could increase medical centers' efficiency. The rate of emergency return within 3-day and patient self-pay revenues were associated significantly with reduced hospital efficiency (p < 0.05). The result also showed that the foundation owns medical center has the highest efficiency than other ownership hospitals.Conclusions: The study results provide information for hospital managers to consider ways they could adjust available resources to achieve high efficiency.

Readmission is an important indicator of the quality of care. The purpose of this study was to explore the probabilities and predictors of 30-day and 1-year potentially preventable hospital readmission (PPR) after a patient’s first stroke. We used claims data from the National Health Insurance (NHI) from 2010 to 2018. Multinomial logistic regression was used to assess the predictors of 30-day and 1-year PPR. A total of 41,921 discharged stroke patients was identified. We found that hospital readmission rates were 15.48% within 30-days and 47.25% within 1-year. The PPR and non-PPR were 9.84% (4123) and 5.65% (2367) within 30-days, and 30.65% (12,849) and 16.60% (6959) within 1-year, respectively. The factors of older patients, type of stroke, shorter length of stay, higher Charlson Comorbidity Index (CCI), higher stroke severity index (SSI), regional hospital, public and private hospital, and hospital in the lower urbanized area were associated significantly with the 30-day PPR. In addition, the factors of male, hospitalization year, and monthly income were associated significantly with 1-year PPR. The ORs of long-term PPR showed a decreasing trend since implementing the national health insurance post-acute care (PAC) program in 2014 and a dramatic drop in 2018 after the government expanded the long-term care plan-LTC 2.0 in 2017. The results showed that better discharge planning, implementing post-acute care programs and long-term care plan-LTC 2.0 may benefit the care of stroke patients and help reduce long-term readmission in Taiwan.

Objectives: To evaluate the cost–effectiveness of immune checkpoint inhibitors versus docetaxel in patients with advanced non-small-cell lung cancer. Methods: A Markov model was constructed to simu... Plain language summary Lung cancer is the first leading cause of cancer death in Taiwan. About 75% of patients have advanced disease at the time of diagnosis (stage III/IV) with a median survival o...

This study estimates the efficiency of 19 tertiary hospitals in Taiwan using a two-stage analysis of Data Envelopment Analysis (DEA) and TOBIT regression. It is a retrospective panel-data study and includes all the tertiary hospitals in Taiwan. The data were sourced from open information hospitals legally required to disclose to the National Health Insurance (NHI) Administration, Ministry of Health and Welfare. The variables, including five inputs (total hospital beds, total physicians, gross equipment, fixed assets net value, the rate of emergency transfer in-patient stay over 48 h) and six outputs (surplus or deficit of appropriation, length of stay, the total relative value units [RVUs] for outpatient services, total RVUs for inpatient services, self-pay income, modified EBITDA) were adopted into the Charnes, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BCC) model. In the CCR model, the technical efficiency (TE) from 2015–2018 increases annually, and the average efficiency of all tertiary hospitals is 96.0%. In the BCC model, the highest pure technical efficiency (PTE) was in 2018 and the average efficiency of all medical centers is 99.1%. The average scale efficiency of all medical centers was 96.8% in the BBC model, meaning investment can be reduced by 3.2% and the current production level can be maintained with a fixed return to scale. Correlation coefficient analysis shows that all variables are correlated positively; the highest was the number of beds and the number of days in hospital (r = 0.988). The results show that TE in the CCR model was similar to PTE in the BCC model in four years. The difference analysis shows that more hospitals must improve regarding surplus or deficit of appropriation, modified EBITDA, and self-pay income. TOBIT regression reveals that the higher the bed-occupancy rate and turnover rate of fixed assets, the higher the TE; and the higher number of hospital beds per 100,000 people and turnover rate of fixed assets, the higher the PTE. DEA and TOBIT regression are used to analyze the other factors that affect medical center efficiency, and different categories of hospitals are chosen to assess whether different years or different types of medical centers affect operational performance. This study provides reference values for the improvable directions of relevant large hospitals’ inefficiency decision-making units through reference group analysis and slack variable analysis.

Readmission is an important indicator of the quality of care. The purpose of this study was to explore the probabilities and predictors of 30-day and 1-year potentially preventable hospital readmission (PPR) after a patient’s first stroke. We used claims data from the National Health Insurance (NHI) from 2010 to 2018. Multinomial logistic regression was used to assess the predictors of 30-day and 1-year PPR. A total of 41,921 discharged stroke patients was identified. We found that hospital readmission rates were 15.48% within 30-days and 47.25% within 1-year. The PPR and non-PPR were 9.84% (4,123) and 5.65% (2,367) within 30-days, and 30.65% (12,849) and 16.60% (6,959) within 1-year, respectively. The factors of older patients, type of stroke, shorter length of stay, higher Charlson Comorbidity Index (CCI), higher stroke severity index (SSI), hospital level, hospital ownership, and urbanization level were associated significantly with the 30-day PPR. In addition, the factors of gender, hospitalization year, and monthly income were associated significantly with 1-year PPR. The results showed that better discharge planning and post-discharge follow-up programs could reduce PPR substantially. Also, implementing a post-acute care program for stroke patients has helped reduce the long-term PPR in Taiwan.

Objective: To investigate the association between the risk of stroke and exposure to particulate matter with an aerodynamic diameter less than 2.5 μm (PM_2.5) over various exposure periods. Methods: This was a nationwide population-based case–control study in which 10,035 incident patients with a primary diagnosis of ischemic stroke each were matched with two randomly selected controls for sex, age, Charlson Comorbidity Index, year of stroke diagnosis, and level of urbanization. Multiple logistic models adjusted for potential confounders were used to assess the association of PM_2.5 with ischemic stroke incidence. Results: There were significant short-term, medium-term, and long-term relationships between PM_2.5 exposure and ischemic stroke incidence. Conclusions: This study supports existing evidence that PM_2.5 should be considered a risk factor for ischemic stroke.

Continuity of care is considered to be an important principle of stroke care; however, few analyses of empirically related outcomes have been reported. This study examined the correlation between the continuity of care for outpatients after a stroke event and the survival of stroke patients over the year following hospital discharge. Data from the Taiwan National Health Insurance Database were used in this study. We defined stroke as the ICD-9-CM codes 430 to 437, and all patients were followed up regarding their survival for at least one year. The modified modified continuity index (MMCI) was used as the indicator of continuity of care. Cox proportional hazard models with robust sandwich variance estimates were employed to analyze the correlation between continuity of care and stroke-related death. A total of 9,252 stroke patients were included in the analysis. Those patients who had a high and a completed COC had a higher percentage of survival (97.25% and 95.39%) compared to the other two groups. After controlling for other variables, compared with the low-level continuity of care group, the moderate-level, high-level and completed continuity of care groups still showed a significantly lower risk of death HR (95% CI) were: 0.63 (0.49–0.80), 0.56 (0.40–0.79) and 0.50 (0.39–0.63), respectively. Continuity of care may increase the survival among stroke patients and therefore plays an important role in management of stroke after survival.