Convenience and price drive online pharmacy usage by veterinary clients.

OBJECTIVE: To evaluate factors that influence client selection of a veterinary pharmacy and assess client perception of the impact of online pharmacies on veterinary clinics. METHODS: Survey data and satisfaction metrics were compared between online pharmacies, a teaching hospital pharmacy (UW Veterinary Care), and primary care veterinary clinics between March and April 2023. Online pharmacy users were asked about perceived impact of online pharmacies on veterinary clinics. Personal values were correlated with the likelihood of using a pharmacy. RESULTS: 158 surveys were analyzed; 32.9% of respondents used an online pharmacy. Of those, 20% used a veterinary-affiliated online pharmacy. Convenience was the value most liked by online pharmacy and primary care clinic users. Online users reported liking price most, whereas veterinary clinic pharmacy users (UW Veterinary Care and primary care clinics) reported liking communication and trust. Online users ranked price as more important, whereas veterinary clinic users ranked personalized experience (caring, explanations, veterinary recommended) as more important. Regardless of pharmacy type, satisfaction scores were high. Most online users perceived a negative impact of online pharmacies on veterinary clinics; this did not change reported usage. CONCLUSIONS: Online and veterinary clinic users prioritized different values. Online users prioritized price and veterinary clinic users prioritized personalized experience. Online usage was not influenced by perceived impact. CLINICAL RELEVANCE: For veterinary clinics attempting to increase revenue, appealing to these 2 distinct driving values is essential. Offering practice-directed online pharmacies with competitive and transparent pricing could procure online consumers, while maintaining a personalized experience remains important for consumers purchasing prescriptions in-house.

Prices for veterinary care of dogs, cats and horses in selected countries in Europe.

Introduction: In recent years, prices for veterinary care have received considerable attention in mainstream media, yet scientific literature has not delved into actual figures. This study aims to elucidate veterinary care costs for dogs, cats, and horses across five countries [Sweden (SE), Norway (NO), Denmark (DK), United Kingdom (UK), and Ireland (IR, with limited data)] through web searches. Methods: Utilising online business directories, we located URLs featuring veterinary care prices in autumn 2022, and repeated tri-monthly five times. Vetpris.se (VP), a price comparison site for SE, NO, and DK, emerged from the search. Additionally, we sought to compare price data from veterinary clinics (ranging from animal hospitals to small private clinics) using a similar approach to VP. We targeted elective procedures (e.g., gonadectomy, GDY) and common procedures (e.g., pyometra surgery in dogs). Results: Comparing data from the same clinics' websites and from VP within extraction from autumn 2022 to winter 2023/2024, median prices for dog and cat GDY were largely consistent. By October 2023, median prices for male cat GDY ranged from €72 (SE) to €230 (DK), and €130 (SE) to €361 (DK) for females; for dog GDY from €390 (SE) to €599 (DK) for males, and €461 (UK) to €1015 (DK) for females. Across sources, median prices for cat and dog GDY increased by 2-24% over a year for procedures with at least 10 clinics per extraction. Equine GDY (per sedation and local analgaesia) in SE saw a 64% increase by year-end, with a median price of €492. Emergency surgeries during regular-hours (e.g., pyometra and caesarean section) in SE were approximately €2,300 at the last extraction, marking a 27% increase for pyometra surgery during regular-hours and 15% after-hours compared to the previous year. Variability existed within and across countries and diagnoses/procedures. Discussion: Cross-validation suggested VP generally provided reliable information, though data points for emergencies were limited. Our web searching tool necessitated extensive manual verification, indicating room for further development. We recommend enhancing price transparency for animal owners to become better informed about the cost of veterinary care and be able to make informed choices.

Tobacco industry pricing strategies during recent tax adjustments in Mexico: evidence from sales data.

INTRODUCTION: Previous studies have identified pricing strategies that the tobacco industry employs to try to minimise the impact of tobacco taxation, but these studies are mostly about high-income countries. This research examines industry price responses to a recent cigarette tax increase in Mexico, including in the capsule cigarette segment that has expanded rapidly in Latin America. METHODS: Data of cigarette prices and sales in Mexico between October 2018 and September 2021 licensed from NielsenIQ were used following a quasi-experimental design to analyse price changes after excise tax increases with fixed effect models by product. To explore heterogeneous responses, estimates were disaggregated by cigarette attributes such as presence of capsules and market segment. Differential shifting was also assessed. RESULTS: Increasing the tobacco tax from 2011MX$0.35(≈US$0.02) to 2020 MX$0.4944(≈US$0.0283) in January 2020 was associated with an overall 8% cigarette price increase in real terms. However, some cigarette types, including premium to discount segments, exhibited price increases larger than the tax increase, which reduced the relative price of ultra-low-priced cigarettes. Instead of a single hike, prices were gradually raised throughout the first months of 2020 for all cigarette types. A combination of both pricing strategies was employed for capsule cigarettes. The 2021 smaller tax adjustment for annual inflation was fully passed onto consumer, maintaining real prices constant. CONCLUSIONS: The industry's ability to raise prices more than the tax increase and manage these price increases smoothly suggests that there was room for larger tobacco tax increases in Mexico. Future developments on tobacco taxes could consider a fully specific tax structure or minimum taxes to mitigate the adverse effects of market segmentation and differential shifting.

Regional wood chip quality parameters decomposition and price linkage with impact on Polish energy sustainability: Time frequency analysis between 2013 and 2019.

The study aims to analyze and decompose the qualitative parameters of wood chips in Poland. The European Green Deal brings the new framework to support sustainability and elimination of emissions. The Wavelet coherence and Wavelet Discrete Decomposition are used for determination of relations among significant qualitative parameters. Thus, the possible uses are discussed. For the obvious relationship between moisture and calorific value there is evidence of strong correlation. The behaviour of these interrelations are different at frequencies in the long and short time. The wood chip price is inter-transmitter from moisture parameter to calorific value in a positive (in-phase) relationship. At both low and high frequencies there is evidence that the variables of moisture and calorific value are highly correlated. The transient effect of linkage is presented at values between 0.1 and 0.3 in coherence map. The empirical findings provide implication for local producers and policymakers.

Toward sustainable environmental management: Insights from Chinese first-tier cities' consumers' response to green electricity payment policies.

China's renewable energy industry is facing the challenge of overcapacity. The environmental management literature suggests that consumers' participation in the green electricity market holds immense potential in addressing renewable energy consumption concerns. However, the question of how payment policies influence China's consumers' willingness to pay for green electricity remains unresolved. Based on 2854 valid questionnaires from a survey conducted in China's four first-tier cities in 2023, our research findings reveal: (1) While 97.9% of consumers express a willingness to use green electricity, only 63.1% are willing to pay a higher cost, indicating the existence of a "value-action" gap between environmental awareness and actual willingness to pay. (2) China's consumers' willingness to pay for green electricity is approximately 38.4 RMB per month. This figure has decreased by 5.7 RMB compared to our survey in 2019. (3) Consumers' willingness to pay will be influenced by the attitudes of those around them. (4) The voluntary payment policy positively impacts consumers' willingness to pay for green electricity. (5) Male, younger, lower education level, higher income, and larger household size consumers exhibit a higher willingness to pay. (6) Electricity price sensitivity weakens the impact of payment policies on willingness to pay.

Cross-company jump spillover and the role of news.

We study how jumps spillover and the cross-company impact of firm-specific unscheduled news on jumps between economic sectors. To this end, we employ high-frequency data of 220 constituents of the Russell 3000 index equally divided into eleven sectors. Using conditional jump probabilities, we find that jump spillover is a pervasive phenomenon enhanced when jumps cluster and that firm-specific news, especially from the financial sector, boosts the jump spillover effect. Volatility following spillover jumps is significantly higher than usual, except when firm-specific news is released around the jump provoking the spillover.

Exploring the interconnection between international crude oil and silver rates volatilities: Novel evidence from the COVID-19 pandemic.

Several research studies globally focus on the volatility of gold and oil prices, neglecting an examination of silver price volatility in relation to other market commodities. The current Covid-19 pandemic has led to various uncertainties and fluctuations in financial and stock exchange markets, yet existing literature primarily concentrates on individual product rates rather than combined rate changes. Our study aims to bridge this research gap by analyzing the relationship between oil rates, silver rates, oil rate transitions, and silver rate transitions on the security exchange in China from 1990 to 2022, using the ARDL approach and Nonlinear ARDL for a comprehensive assessment. The findings reveal a significant impact of silver and oil rates on China's security exchange in the future, with the negative effects of oil rate changes and a positive effect of silver rate transitions. In the short term, oil and silver rates play a crucial role in influencing China's security exchange, highlighting the importance of monitoring these trends for investors. It is recommended that investors respond prudently to market transitions, particularly by considering silver as a secure option during times of uncertainty, while policymakers should implement appropriate measures to manage the rapid fluctuations from oil to the security market.

Do OPEC+ policies help predict the oil price: A novel news-based predictor.

The OPEC+, composed of the Organization of the Petroleum Exporting Countries (OPEC) and non-OPEC oil-producing countries, exerts considerable influence over the global crude oil market. However, existing literature lacks a comprehensive application of this factor in oil price forecasting, primarily due to the complexity of measuring such policy evolutions. To address this research gap, this study develops a news-based OPEC+ policy index based on text mining methods for comprehensive analysis and forecasting of the oil price. First, by crawling and mining news headlines related to OPEC+ production decisions, a dynamic and high-frequency (weekly) OPEC+ policy index is established. Second, the linear and nonlinear relationship between the proposed OPEC+ policy index and the WTI crude oil futures price is thoroughly examined, assessing the potential predictive power of the index in explaining the movements of the crude oil price. Third, the forecasting efficacy of the constructed index on the oil price is rigorously evaluated across eight econometric and machine learning models. Key findings include: (1) The proposed weekly OPEC+ policy index demonstrates strong concordance with OPEC+ production change decisions, exhibiting notable peaks and troughs corresponding to OPEC+ Ministerial Meetings. (2) The relationship analysis demonstrates a strong linear and nonlinear association between the proposed OPEC+ policy index and the crude oil price. (3) For oil price prediction, models incorporating our proposed OPEC+ policy index demonstrate superior performance compared to models without this index. In particular, the index exhibits a more significant predictive effect within three-week forecasting horizons and performs exceptionally well during periods of pandemic and the Russia-Ukraine conflict. In addition, the OPEC+ policy index also exhibits a significant predictive effect on the daily crude oil price and natural gas price, further confirming the robust and powerful forecasting capability of this index within the energy system.

Investigating the impact of fuel price shocks on bicycle sharing usage in Budapest.

The creation of sustainable urban communities is contingent upon the establishment of a sustainable, efficient, and fast transportation system. Bicycle sharing systems (BSS) are one of the most sustainable and inclusive ways of transportation in cities. An important question is how to increase BSS ridership and whether it can effectively replace cars in cities, hence contributing to achieving the 11th Sustainable Development Goal and creating sustainable urban communities. This paper aims to contribute to this stream of research by investigating the effect of fuel prices on BSS ridership. We exploit a natural experiment from Budapest, Hungary, where fuel prices were capped between November 15, 2021, and December 6, 2022. Once the price cap was suddenly eliminated, fuel prices increased by around one-third immediately leading to a very substantial and rarely observable one-time price increase. The difference-in-difference regression results indicate a 2-6% increase in BSS ridership after the elimination of the fuel price cap. The geographical pattern of the change shows that BSS usage mainly increased in the outer part of the city; however, some areas observed a decline. The regression results are also reinforced by survey findings. Understanding these dynamics is crucial for effective urban planning and transportation policymaking.

PMANet: a time series forecasting model for Chinese stock price prediction.

Forecasting stock movements is a crucial research endeavor in finance, aiding traders in making informed decisions for enhanced profitability. Utilizing actual stock prices and correlating factors from the Wind platform presents a potent yet intricate forecasting approach. While previous methodologies have explored this avenue, they encounter challenges including limited comprehension of interrelations among stock data elements, diminished accuracy in extensive series, and struggles with anomaly points. This paper introduces an advanced hybrid model for stock price prediction, termed PMANet. PMANet is founded on Multi-scale Timing Feature Attention, amalgamating Multi-scale Timing Feature Convolution and Ant Particle Swarm Optimization. The model elevates the understanding of dependencies and interrelations within stock data sequences through Probabilistic Positional Attention. Furthermore, the Encoder incorporates Multi-scale Timing Feature Convolution, augmenting the model's capacity to discern multi-scale and significant features while adeptly managing lengthy input sequences. Additionally, the model's proficiency in addressing anomaly points in stock sequences is enhanced by substituting the optimizer with Ant Particle Swarm Optimization. To ascertain the model's efficacy and applicability, we conducted an empirical study using stocks from four pivotal industries in China. The experimental outcomes demonstrate that PMANet is both feasible and versatile in its predictive capability, yielding forecasts closely aligned with actual values, thereby fulfilling application requirements more effectively.

Optimal operation of energy-intensive load considering electricity carbon market.

Energy-intensive load benefits from low electricity tariff and carbon emission, since they occupy certain amounts in the total cost of the product. This paper considers energy-intensive load participation in the electricity as well as carbon trading to reduce the cost. Firstly, an electricity-carbon model is established based on the correlation value method to calculate the carbon emissions of energy-intensive load based on their electricity consumption to realize the carbon amount. Afterwards, the baseline method is used to allocate free carbon emission quotas to energy-intensive load and a reward-penalty carbon trading price mechanism considering offset is proposed. Next, the objective function to achieve maximum benefits, and to reduce output fluctuation, and to improve new energy accommodation is proposed. The case studies show that, by comparing multi-objective function optimization, the optimization target proposed in this paper can effectively reduce wind power output fluctuations and improve wind power accommodation. Through the total participation in carbon trading and electricity market income, multi-objective optimization can increase the system income while ensuring that energy-intensive load meets production requirements under the premise of reducing carbon emissions, verifying the effectiveness of the low-carbon optimal operation model proposed in this paper.

Deep reinforcement learning models in auction item price prediction: an optimisation study of a cross-interval quotation strategy.

In the contemporary digitalization landscape and technological advancement, the auction industry undergoes a metamorphosis, assuming a pivotal role as a transactional paradigm. Functioning as a mechanism for pricing commodities or services, the procedural intricacies and efficiency of auctions directly influence market dynamics and participant engagement. Harnessing the advancing capabilities of artificial intelligence (AI) technology, the auction sector proactively integrates AI methodologies to augment efficacy and enrich user interactions. This study delves into the intricacies of the price prediction challenge within the auction domain, introducing a sophisticated RL-GRU framework for price interval analysis. The framework commences by adeptly conducting quantitative feature extraction of commodities through GRU, subsequently orchestrating dynamic interactions within the model's environment via reinforcement learning techniques. Ultimately, it accomplishes the task of interval division and recognition of auction commodity prices through a discerning classification module. Demonstrating precision exceeding 90% across publicly available and internally curated datasets within five intervals and exhibiting superior performance within eight intervals, this framework contributes valuable technical insights for future endeavours in auction price interval prediction challenges.

Which factor reduces pharmaceutical expenditure, number of entrants or price variance? Updated generic drug markets in South Korea.

BACKGROUND: Introducing more generics has been a successful strategy for lowering pharmaceutical prices and expenditure. However, the effect of the strategy depends on the pricing schemes for generics. We aimed to update the South Korean generic markets in terms of effective competition, and to examine the effects of number of manufacturers and price variance on pharmaceutical expenditure. METHODS: We constructed balanced panel data provided by the Health Insurance Review and Assessment Service covering 726 reimbursed substances from 2019 to 2023. We developed original indicators to analyze the generic markets: the maximum-minimum price variance (MMPV) and the maximum-weighted price variance (MWPV). Panel regression with fixed and time-fixed effects was used. RESULTS: Over the study period, the number of manufacturers increased from 17.81 in 2019 to 20.98 in 2020 and then decreased to 18.70 in 2023. The MMPV increased from 204.70 in 2019 to 230.07 in 2022 and then decreased slightly to 225.34 in 2023. The MWPV increased from 59.70 in 2019 to 72.58 in 2023. Two types of segmented markets were noteworthy: low use of low-cost generics with sufficient manufacturers and high use of low-cost generics with insufficient manufacturers. In the fixed and time-fixed effects panel analyses, the MWPV presented a negative association with the number of manufacturers and a positive association with the MMPV. CONCLUSIONS: A newly introduced tiered pricing scheme, designed to differentiate generic prices, was associated with a decrease in the number of manufacturers and an increase in price dispersion. The pricing schemes for generics should be designed with price variance in mind and limit the number of too many generics in South Korea.

A study on the price variability of branded medicines and Jan aushadi versions of selected commonly prescribed psychiatric medications in India using a cost-comparative approach and a passive evaluation of the Jan aushadhi scheme in India.

Introduction: The cost of medications poses a significant financial burden on patients. It limits access and adherence to treatment. Psychiatric disease burden is rising and it needs treatment for long durations. The high cost of branded medicines and lack of access to medicines at affordable prices can limit adherence. Methodology: A cost comparison study was done to investigate the price difference between branded and Jan aushadhi versions of 20 selected psychiatric drugs was done at the Department of Community Medicine of a Government medical college in Southern India. The average (mean) price of branded medicines of each drug was calculated with minimum, and maximum using online data, and comparison was done by calculating the percentage price difference between branded and Jan aushadhi medicines. The overall percentage price difference between branded and Jan aushadhi medicines was calculated. Results: The overall percentage price difference between the mean price of branded medicine and Jan aushadhi medicine was + 252% for antipsychotics, indicating that the mean branded price was 252% (2.52 times) Jan aushadhi price. Similarly, the overall percentage price difference between the mean branded price and Jan aushadhi price among antidepressants was +277.54%, and the overall percentage price difference between mean branded price and Jan aushadhi was +227.73% for anticonvulsants. Similarly, price differences of maximum and minimum branded prices and Jan aushadhi were high. Conclusion: The study was able to estimate variation in the price of branded drugs and compare the price of branded medicines with Jan aushadhi by estimating price differences. The results of the study are useful in further reference regarding the subject for public, policy makers and healthcare providers. It gives valuable evidence into medication costs in India.

Multi level perspectives in stock price forecasting: ICE2DE-MDL.

This study proposes a novel hybrid model, called ICE2DE-MDL, integrating secondary decomposition, entropy, machine and deep learning methods to predict a stock closing price. In this context, first of all, the noise contained in the financial time series was eliminated. A denoising method, which utilizes entropy and the two-level ICEEMDAN methodology, is suggested to achieve this. Subsequently, we applied many deep learning and machine learning methods, including long-short term memory (LSTM), LSTM-BN, gated recurrent unit (GRU), and SVR, to the IMFs obtained from the decomposition, classifying them as noiseless. Afterward, the best training method was determined for each IMF. Finally, the proposed model's forecast was obtained by hierarchically combining the prediction results of each IMF. The ICE2DE-MDL model was applied to eight stock market indices and three stock data sets, and the next day's closing price of these stock items was predicted. The results indicate that RMSE values ranged from 0.031 to 0.244, MAE values ranged from 0.026 to 0.144, MAPE values ranged from 0.128 to 0.594, and R-squared values ranged from 0.905 to 0.998 for stock indices and stock forecasts. Furthermore, comparisons were made with various hybrid models proposed within the scope of stock forecasting to evaluate the performance of the ICE2DE-MDL model. Upon comparison, The ICE2DE-MDL model demonstrated superior performance relative to existing models in the literature for both forecasting stock market indices and individual stocks. Additionally, to our knowledge, this study is the first to effectively eliminate noise in stock item data using the concepts of entropy and ICEEMDAN. It is also the second study to apply ICEEMDAN to a financial time series prediction problem.

Influencing factors of industrial transformation in resource-based cities: Comprehensive measurement method based on resource price distortion.

The rational allocation of industrial factors in resource-based cities is a necessary condition for promoting high-quality economic development. To systematically measure the mismatch of industrial factors, this study integrates the production function model and accounting framework of Aoki (2012). The capital, labor, and energy factors are integrated into the overall measurement framework, and the factor mismatch calculation model is innovatively constructed at the city level. To effectively reflect the factor mismatch of different cities, the relative distortion coefficient of factor price is introduced to establish the relationship between the distortion of factor-use cost and effectiveness of resource allocation. The degree and direction of factor mismatch are effectively measured. This study takes China's resource-based cities as the research object. To avoid the impact of the COVID-19 pandemic, data on 114 resource-based cities in China from 2003 to 2018 are selected, and 164 non-resource-based cities are used as the control group to evaluate the factors affecting the transformation of such cities. Regarding the diversity of resource-based cities in China, this study compares the characteristics of factor misallocation in these cities for different growth stages, geographical regions, and dominant resources. Finally, this study finds that on average, the cities' degree of energy misallocation is the most serious, and the degree of labor misallocation ranks second. The degree of capital misallocation ranks the lowest, but the misallocation of various factors is very different in different situations. Therefore, different types of cities should make detailed policy adjustments in various transformation directions according to the calculation results.

Access to essential and innovative anti-cancer medicines: a longitudinal study in Nanjing, China.

PURPOSE: To evaluate the availability, cost, affordability of anti-cancer medicines in Nanjing, Jiangsu. METHODS: A longitudinal tracking investigation study was performed to collect information about 24 essential anti-cancer medicines (EAMs) and 17 innovative anti-cancer medicines (IAMs) in 26 healthcare institutions in Nanjing from 2016 to 2020. The availability, cost, drug utilization and affordability of EAMs and IAMs were investigated. RESULTS: The availability of EAMs showed no significant changes in Nanjing, but the availability of IAMs showed a significant increase in 2018 and 2019 and tended to stabilize in 2020. For EAMs, the DDDc(Defined Daily Dose cost) of LPGs (Lowest-Priced Generics) showed no significant changes, and the DDDc of OBs (Originator Brands) and IAMs significantly decreased. The DDDs(Defined Daily Doses) of EAMs (LPGs) showed a decreasing trend since 2016 and rose again in 2019. Overall, the DDDs of EAMs (LPGs) decreased by 25.18% between 2016 and 2020, but the proportion selected for clinical treatment remained at 67.35% in 2020. The DDDs of EAMs (OBs) and IAMs both showed an increasing trend year by year, with a proportional increase of 207.72% and 652.68%, respectively; but the proportion selected for clinical treatment was only 16.09% and 16.56% respectively in 2020. EAMs (LPGs) had good affordability for urban residents but poor affordability for rural residents; the affordability of EAMs (OBs) and IAMs was poor for both urban and rural residents. CONCLUSIONS: There were no significant changes in the availability and cost of EAMs (LPGs), whose lower prices showed better affordability. Although their relative change in drug utilization showed a decreasing trend, they still dominated clinical treatment. Driven by the national drug price negotiation (NDPN) policy, the availability of IAMs was on the rise. It is necessary to further develop and strengthen policies for essential medicines procurement assessment to improve the accessibility of EAMs.

Effect of Shape, Size, and Color of the Food Plate on Consumer Perception of Energy Value, Portion Size, Attractiveness, and Expected Price of Dessert.

The development of new dishes in the catering services market requires an understanding of consumers' needs, expectations, and motivations for their choices. The effect of the serving method of a dessert on customers' perceptions of its visual appeal, portion size, energy value, and expected price was evaluated. The study involved the presentation of desserts on plates of various sizes, shapes, and colors. The study was carried out among 1005 respondents using the CAWI method. Our findings revealed that along with an increasing plate size from a diameter of ϕ24-27 cm to ϕ31 cm, the ratings of the dish's perceived appearance (p ≤ 0.001), portion size (p ≤ 0.001), and energy value (p ≤ 0.01) decreased. Plate shape influenced the perceived appearance of the dessert. When placed on a square platter, round desserts were considerably (p ≤ 0.05) less appealing. The color of the plate had a significant influence (p ≤ 0.001) on the dish's perceived appearance and estimated monetary value, and it evoked more sensory-hedonic impressions. Red-plate and white-plate desserts were liked less than black-plate desserts, but color-plated desserts were perceived as more expensive than those served on white dishes. Consumers perceived bright desserts on white plates as traditional, natural, and boring; those on black plates as modern, appetizing, and aesthetic; and those served on red plates as artificial, unsightly, and unappetizing. Higher consumer food neophobia led to a lower rating related to appearance and price perceptions, but elevated perceptions of portion size and energy value appraisal. Our results may be used in the marketing of gastronomic dishes.

Dynamic Price Application to Prevent Financial Losses to Hospitals Based on Machine Learning Algorithms.

Hospitals that are considered non-profit take into consideration not to make any losses other than seeking profit. A model that ensures that hospital price policies are variable due to hospital revenues depending on patients with appointments is presented in this study. A dynamic pricing approach is presented to prevent patients who have an appointment but do not show up to the hospital from causing financial loss to the hospital. The research leverages three distinct machine learning (ML) algorithms, namely Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB), to analyze the appointment status of 1073 patients across nine different departments in a hospital. A mathematical formula has been developed to apply the penalty fee to evaluate the reappointment situations of the same patients in the first 100 days and the gaps in the appointment system, considering the estimated patient appointment statuses. Average penalty cost rates were calculated based on the ML algorithms used to determine the penalty costs patients will face if they do not show up, such as 22.87% for RF, 19.47% for GB, and 14.28% for AB. As a result, this study provides essential criteria that can help hospital management better understand the potential financial impact of patients missing appointments and can be considered when choosing between these algorithms.

The vacated space of volume/price of the drugs centralized procurement with quantity in secondary and above public hospitals of China.

BACKGROUND: In 2018, the National Centralized Drug Procurement (NCDP) policy has been implemented in 11 provinces, and promoted across the country in 2019. The main feature of the policy is "volume for price", therefore, it is necessary to measure the price relationship, not only to reduce the price of drugs, reduce the burden of patients' medical costs, but also facilitate pharmaceutical companies to access enough innovation incentives. The aim of this study was to assess the vacated space effect of the drug centralized procurement by national organizations in exchange of price for quantity. METHODS: A difference-in-differences (DID) model was employed to analyze the effect of the 4 + 7 pilot drugs centralized purchasing policy on drug sales volume and selected versus clinically substitutable unselected varieties, using observational data from 2018 to 2019. We compared drug procurement data between secondary and above public hospitals in pilot and non-pilot cities throughout China. RESULTS: The study showed that the average treatment effect (ATE) of sales in the in-hospital market for the selected supply varieties in centralized purchasing is -0.42, and with a sales volume of 0.49. This indicates a volume-price vacated space of 1.16 ~ 1.17 DDD (defined daily dose)/Yuan, implying that for every 1 defined daily dose (DDD) increase in reported volume, the standardized price decreased by 1.16-1.17 Yuan. The ATE of in-hospital market sales for drugs not selected in centralized procurement shows a decrease of 0.13. This finding highlights the presence of the price linkage effect. The ATE of sales volume is 0.57, indicating a volume-price space of 4.38 ~ 4.39 DDD/Yuan for unselected drugs, approximately 3.75 higher relative to that of the selected ones. CONCLUSIONS: The ratio of the volume-price space of clinically substitutable unselected and selected drugs may serve as direct evidence for evaluating the shift from centralized purchasing of drug varieties to clinically substitutable other ones. To strengthen the volume-based negotiation approach and maximize the effectiveness of centralized purchasing policies, we recommend the strategic implementation of a three-tiered centralized purchasing system, the expansion of drug coverage, and the introduction of relevant constraints and incentives.