An estimation model for hypertension drug demand in retail pharmacies with the aid of big data analytics

The unpredictability of consumer preference observed in the last few years has coincided with the global digital data explosion as consumers are increasingly relying on internet information to guide their buying behaviour. The emergence of this trend has resulted in demand volatility and uncertainty in the retail industry, leading to negative consequences on inventory control and on shareholder profits in the long-run. This paper examines whether retail pharmacies in Abuja, Nigeria may exploit the increasing availability of relevant big data (structured, semi-structured and unstructured) from different sources to anticipate the changes on demand profiles for antihypertensive medication. In order to examine this, we consider a VARX model with non-structured data as exogenous to obtain the best estimatio

Coping with demand volatility in retail pharmacies with the aid of big data exploration

Data management tools and analytics have provided managers with the opportunity to contemplate inventory performance as an ongoing activity by no longer examining only data agglomerated from ERP systems, but also, considering internet information derived from customers' online buying behaviour. The realisation of this complex relationship has increased interest in business intelligence through data and text mining of structured, semi-structured and unstructured data, commonly referred to as "big data" to uncover underlying patterns which might explain customer behaviour and improve the response to demand volatility. This paper explores how sales structured data can be used in conjunction with non-structured customer data to improve inventory management either in terms of forecasting or treating some inventory as "top-selling" based on specific customer tendency to acquire more information through the internet. A medical condition is considered - namely pain - by examining 129 weeks of sales data regarding analgesics and information seeking data by customers through Google, online newspapers and YouTube. In order to facilitate our study we consider a VARX model with non-structured data as exogenous to obtain the best estimation and we perform tests against several univariate models in terms of best fit performance and forecasting

Sales Prediction of a Pharmaceutical Distribution Company

The study aims to find an appropriate model to extract insights from the sales of a Pharmaceutical Distribution Company (PDC) and make it available in an interactive and readable manner for the company. In PDCs, it is highly important to obtain a good approximation of the medicine needs, due to the short shelf life of many medicines and the need to control stock levels. The presented method is a combination of analysis and interactive visualization tools along with prediction. In this paper, we explore the use of Support Vector Regression algorithm for the sales prediction of individual products. The proposed model helps to present the sales data in a better way such that understanding the trends and seasonality becomes easier for the PDCs. The dataset has information of hourly, daily, weekly and monthly sales of the drugs and hence the end results also give us a likely classified understanding of the sales. The study of the results obtained, suggest that the proposed model may be considered appropriate for product sales prediction

Sales Prediction of a Pharmaceutical Distribution Company

The study aims to find an appropriate model to extract insights from the sales of a Pharmaceutical Distribution Company (PDC) and make it available in an interactive and readable manner for the company. In PDCs, it is highly important to obtain a good approximation of the medicine needs, due to the short shelf life of many medicines and the need to control stock levels. The presented method is a combination of analysis and interactive visualization tools along with prediction. In this paper, we explore the use of Support Vector Regression algorithm for the sales prediction of individual products. The proposed model helps to present the sales data in a better way such that understanding the trends and seasonality becomes easier for the PDCs. The dataset has information of hourly, daily, weekly and monthly sales of the drugs and hence the end results also give us a likely classified understanding of the sales. The study of the results obtained, suggest that the proposed model may be considered appropriate for product sales prediction

Pharmacist Services

The overall goal of this book is to give the reader a state-of-the-art synopsis of the pharmacist services domain. To accomplish this goal, the authors have addressed the social, psychosocial, political, legal, historic, clinical, and economic factors that are associated with pharmacist services. In this book, you will gain cutting-edge insights from learning about the research of experts throughout the world. The findings have relevance for enhancing pharmacist professionalism, pharmacist practice, and the progression of pharmacist services in the future

Smart Healthcare solutions in China and Europe, an international business perspective

The thesis is part of the Marie Curie Fellowship project addressing health related challenges with IoT solutions. The author tries to address the challenge for the implementation of telehealth solutions by finding out the demand of the telehealth solution in selected European economies and in China (chapter 1), analyzing the emerging business models for telehealth solution ecosystems in China (chapter 2), how to integrate telehealth solutions with institutional stakeholders (chapter 3) and why are elderly users willing to use telehealth solutions in China. Chapter 1 and chapter 2 form the theoretical background for empirical work in chapter 3 and chapter 4. The thesis addressed four research questions, namely “Which societal and social-economics unmet needs that Internet of Healthcare Things can help to resolve?”, “What are the business model innovation for tech companies in China for the smart health industry?”, “What are the facilitators and hurdles for implementing telehealth solutions”, “Are elderly users willing to use telehealth solutions in China?”. Both qualitative study and quantitative analysis has been made based on data collected by in depth interviews with stakeholders, focus group study work with urban and rural residents in China. The digital platform framework was used in chapter 2 as the theoretical framework where as the stakeholder power mapping framework was used in chapter 3. The discretion choice experiment was used in chapter 4 to design questionnaire study while ordered logit regression was used to analyze the data. Telehealth solutions have great potential to fill in the gap for lack of community healthcare and ensuring health continuity between home care setting, community healthcare and hospitals. There is strong demand for such solutions if they can prove the medical value in managing chronic disease by raising health awareness and lowering health risks by changing the patients’ lifestyle. Analyzing how to realize the value for preventive healthcare by proving the health-economic value of digital health solutions (telehealth solutions) is the focus of research. There remain hurdles to build trust for telehealth solutions and the use of AI in healthcare. Next step of research can also be extended to addressing such challenges by analyzing how to improve the transparency of algorithms by disclosing the data source, and how the algorithms were built. Further research can be done on data interoperability between the EHR systems and telehealth solutions. The medical value of telehealth solutions can improve if doctors could interpret data collected from telehealth solutions; furthermore, if doctors could make diagnosis and provide treatment, adjust healthcare management plans based on such data, telehealth solutions then can be included in insurance packages, making them more accessible

Impact of a detailing restriction policy on prescription behavior

In the pharmaceutical industry, physicians control more than four fifths of health care expenditures, situation leading to a high investment of the pharmaceutical companies in marketing, aiming to influence phsyicians in their prescription behavior. Marketing-related factors influencing prescription behavior include detailing and detailing ceilings are a form of government-imposed regulation on companies’ promotion. Counterfactual simulations made by previous researchers suggest that a detailing ceiling may have a negative effect on drugs sales. Our thesis focuses on the impact of detailing ceilings on physicians’ prescription behavior, contributing to this stream of research. We used a mixed method approach, starting with a quantitative phase using a time series of drug sales and promotion investments (IQVIA). We used four models applied by Leeflang & Wieringa (2010) and applied seven other models to 18 products in four markets. We performed a series break test on detailing elasticities (before and after the ceiling). We then made 20 in-depth interviews with officers from the pharmaceutical market, to understand the quantitative results

Administrative Data Linkage in Brazil: Potentials for Health Technology Assessment.

Health technology assessment (HTA) is the systematic evaluation of the properties and impacts of health technologies and interventions. In this article, we presented a discussion of HTA and its evolution in Brazil, as well as a description of secondary data sources available in Brazil with potential applications to generate evidence for HTA and policy decisions. Furthermore, we highlighted record linkage, ongoing record linkage initiatives in Brazil, and the main linkage tools developed and/or used in Brazilian data. Finally, we discussed the challenges and opportunities of using secondary data for research in the Brazilian context. In conclusion, we emphasized the availability of high quality data and an open, modern attitude toward the use of data for research and policy. This is supported by a rigorous but enabling legal framework that will allow the conduct of large-scale observational studies to evaluate clinical, economical, and social impacts of health technologies and social policies