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AI and Data Analytics for Omnichannel Health Care Business

AI and Data Analytics for Omnichannel Health Care Business

SUBMISSION WINDOW: Dec 1 2021 to March 31 2022

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Introduction and Background

In the past few decades, while the healthcare industry has grown exponentially due to the increased awareness and technological advances (Dixit, Routroy & Dubey, 2019). Inaccessibility, unavailability, un-affordability, over-crowdedness causing high waiting times, and length of stays are other disruptions that health systems have faced (Almeida & Vales, 2020; Davis et al., 2019; Supeekit et al., 2016). Unexpected occurrences of endemics, epidemics, and pandemics cause off-balance for system actors and push these systems into bottlenecks, resulting in system-related adverse outcomes and economic and social disorders. Overcoming such challenges, particularly under epidemic and pandemic situations, can only be possible by the involvement of various system actors (Meijboom, Schmidt-Bakx, & Westert, 2011). A new disease that has not been seen before, such as COVID-19, can only be diagnosed and treated by multidisciplinary efforts of various health service providers acting all around the world and requires a range of diagnostic tests, medicines, and many other medical supplies from different industries as well as developing new and effective vaccines or drugs. In such an environment, developing an operative health supply chains becomes inevitable.

In recent past years, the healthcare business is drastically changed, and recent pandemic is catalyst to it. Many healthcare operators have started using the omnichannel retailing to contact with the customer (Dahl et al., 2021; Kraus et al., 2021). Omni means everywhere, and omnichannel means through every possible channel. Many healthcare services providers like Nuance Communication Inc. are coming up with the “patient engagement virtual assistance”. Traditional consultation from the doctor from the hospital is now-a-days replaced with the telehealth and video visits. It decreases unnecessary time in travelling and waiting in hospitals. Mobile apps and diagnostic tools empower patients to avoid unnecessary doctor visits, and tests. Also, with the help of the connected medical devices and IoT, the doctors can monitor the patients in the real time. It helps to reduce the unnecessary visit to the hospitals and readmissions. Patients are using the websites/apps of online medicine seller like 1mg, PharmEasy etc. to order medicines apart from traditional buying of prescribed medicines from pharmacy. The buy online pickup in store options is available to the patients to pick medicines from the pharmacy store.

Health supply chains are a network of system entities that aim to deliver supplies in the right quantity, at the right quality, at the right place, at the right price, and at the right time (Kochan et al., 2018). Health supply chains are unique and different from typical supply chains due to their high level of complexity, diversity, and customization of services provided, dynamic environment, uncertainty underlying fundamental processes, the existence of high valued medical supplies, and most importantly, the fact that they deal with human lives (Kochan et al., 2018). The health supply chain consists of manufacturers (primary and secondary), distributors, wholesalers, and retailers (Mustaffa & Potter, 2009). Medical raw materials get into the health supply chain by the task of the primary manufacturer. These materials are then transformed into medical products, such as medicine, drug, vaccine, medical or surgical equipment, by secondary manufacturers. Hospitals or other health facilities and pharmacies buy these medical supplies from distributors if a large amount is needed or from wholesalers otherwise. The key entities of health supply chains are hospitals and other health facilities, chemistry and pharmaceuticals, medical and surgical equipment suppliers, blood centers, etc. Typically, the health supply chain begins with these medical equipment suppliers and finishes at patients consuming these supplies and health services. Since patients form the last link of the health supply chain, any mistakes can threaten human lives and cause irreversible outcomes.

On the other hand, the era of advanced digital technologies allows storing huge volumes of medical data. This exponential growth in the electronic medical records (EMR) stored by health services is noticeable and requires in-depth investigations to generate valuable information from the stored raw data. Artificial Intelligence (AI) which was introduced by Alan Turing (1950) is the branch of computer science concerned with the developing smart machines which have ability to perform cognitive functions such as perceiving, reasoning, learning, interacting with the environment and problem solving which usually requires human intelligence. In the past decades, expeditious development, wide applications and outstanding achievements of AI in healthcare sector has been witnessed. AI based healthcare machines are used to monitor and store every sensitive data of patients more accurately and acting as vital force for making impossible acts possible. With the use of AI, the capability of healthcare systems is enhanced in terms of increased working speed and decreased error rate. The intelligent data driven applications of AI in healthcare systems are acting as the major shift in man-machine relationship in terms of transparency, efficacy, privacy, safety, improved productivity, automated decision making approach, human-machine partnering and so-forth. The growing number of solutions, provided by AI, is impacting daily lives of people around the globe. The most recent example is the use of AI algorithms to aid in the development of vaccine for the 2019 novel coronavirus CIVID 2019. Additionally, in the future, AI will be one of the important technologies to run the healthcare sector efficiently. The major notable players in the domain of applications of AI in healthcare sector include Amazon Web Services, General Electric, Google, IBM, Microsoft, Siemens Healthineers, and among others.

Furthermore, artificial intelligence (AI) is emerging to influence many aspects of healthcare management (from hospital resource optimization, image analysis for radiology, population level disease prevention, and voice recognition systems to medical applications such as healthcare delivery services, etc.), businesses and government institutions are deploying and integrating AI into their business process at large scales. In this context, exploring the opportunity of AI and big data analytics to effectively manage the omnichannel health supply chain has become inevitable since these emerging technologies are useful in identifying and operationalizing practical solutions to each actor’s problems. AI and Big data analytics are founded on and combines multiple disciplines such as statistics, database, pattern recognition, machine learning, data visualization, and expert systems. It can be used for different goals such as prediction, classification, clustering, exploration, and big data association.

This contemporary topic receives increasing attention in the literature. A few recent studies focus on the use of data analytics to emphasize their potential application areas in supply chain operations. In improving various supply chain operations, such as demand forecasting and shaping (Chase, 2016; Roßmann et al., 2018), supplier selection (Choi, Lee & Irani, 2018), risk management (Huang & Handfield, 2015), quality management (Zhang et al., 2017), logistics planning (Mehmood et al., 2017), assessing coordination complexity (Ivanov, Dolgui & Sokolov, 2019), and evaluating hidden costs after implementation of these technologies (Guha & Kumar, 2018), data analytics has been implemented.

However, minimal work has been done in managing the omnichannel health supply chains, particularly by using big data and data analytics. Clauson et al. (2018) also noted that ineffective management of health supply chains and utilization of these cutting-edge technologies and innovations are still insufficient, and thus healthcare technologists should take them under consideration to move beyond the existing modus operandi. In addition to this identified literature gap, a recent outbreak of COVID-19 showed that although supply chains are the backbones of healthcare delivery, they were not competent enough to manage health systems effectively, especially under pandemic situations. It was seen that the unexpected increase in patient volume due to COVID-19 led to a crash in the health systems of most of the countries, which caused undesirable increases in new case and death numbers. This observation showed that most health systems could not satisfy demand sufficiently in overcrowded environments, and thus demand management gets more complicated for health systems. The issue of overcrowding has the potential to increase exponentially during outbreaks. For example, during the H1N1/2009 pandemic, the rate of visits to emergency services which is related with influenza is estimated to increase to 1000 per 100000, doubling the average annual rate of 500 per 100,000 population for seasonal influenza (Schanzer & Schwartz, 2013). This phenomenon is also observed in current COVID-19 outbreak which is caused by the rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2), affecting all countries in the world in a very short time. Using experiences from past and current outbreaks, it is clearly seen that adopting better strategies to achieve operational excellence in emergency services is a necessity.

Besides, most health systems had difficulties finding qualified medical staff with required quantities, showing that health systems had problems managing capacities, particularly under pandemic situations. Similarly, most of the health systems had insufficient intensive bed capacities. It was observed that health systems had significant challenges in satisfying medical equipment, especially ventilators, medical masks, and disinfectants under these circumstances. This shows that inventory planning is one of the other major concerns in most healthcare systems. From a holistic perspective, it is concluded that health supply chains are not managed effectively, which creates an obstacle particularly for fighting against pandemic situations. On the other hand, it should be noticed that COVID-19 shows flu-like symptoms where other flu cases increase and create crowdedness in health systems in its season. In the coming periods, since COVID-19 and other flu cases will be seen together with very similar symptoms, differentiating these cases and providing proper treatments for each of them will get much more complicated.

Aim and Scope of this Call

As learnt from the COVID-19 pandemic, it can be concluded that health supply chains require further research and innovations (Donthu & Gustafsson, 2020). In the era of Industry 4.0, managing omnichannel health supply chains effectively is only possible when big data and data analytics are taken into consideration. Big data analytics is proven to help forecast and decision-making, and hence can be powerful in enhancing health supply chains. Data analytics-enabled technologies can be implemented in demand planning, procurement, production, inventory, logistics, and many other supply chain topics. This special issue aims to discuss the opportunities and challenges of big data enabled omnichannel health supply chains and show how the performances of supply chain operations should be improved by using AI and data analytics techniques. This special issue also highlights data-driven supply chain strategies in coping with unexpected increases in demand or overcrowded health environments, such as exploring pandemic situations. Manuscripts with conceptual, empirical, theory elaboration and theory building research are suited for the special issue. Papers focused with entire mathematical/OR/modelling background would not be so suited for this special issue. There should a clear contribution to the theory and impact on busienss and society. In this call, manuscripts that use case research methodology to investigate underlying phenomena will be given preference over others.

The special issue aims to address the following, but not limited to, potential topics in health supply chain business research by the use of AI and data analytics techniques:

Omni-channel Hospital supply chain managementOvercrowding modeling and managementAI based diagnostic medical equipment modeling and managementTherapeutic and protective medical equipment modeling and managementData analytics for capacity and resource management of hospitalHealthcare supply chain coordination managementManagement of critical equipment and materialsRisk modeling and managementHealth waste managementFlu vaccine supply chain managementData analytics for healthcare managementAdoption of AI for Pharmaceutical supply chain management

Guest Editors (Listed in an alphabetical order)

Dr. Guo Li,

Professor, School of Management and Economics Beijing Institute of Technology, China

Email -


Dr. Satish Kumar,

Department of Management Studies, Malaviya National Institute of Technology, Jaipur, India


Dr. Sachin Kumar Mangla (Managing Guest Editor),

1Jindal Global Business School, O P Jindal University, India

2Plymouth Business School, University of Plymouth, UK

Email –;;

Dr. Suresh P. Sethi,

Professor, Naveen Jindal School of Management, The University of Texas at Dallas, USA

Email -

Dr. Yigit Kazancoglu

Yasar University, Izmir, Turkey

Email -


Almeida, A., & Vales, J. (2020). The impact of primary health care reform on hospital emergency department overcrowding: Evidence from the Portuguese reform. The International Journal of Health Planning and Management, 35(1): 368-377.

Chase, C. W. (2016). Next generation demand management: People, process, analytics, and technology. John Wiley & Sons.

Cho, S-H., & Zhao, H. (2018). Healthcare supply chain. Dai, T. & Tayur, S. (eds.). Handbook of Healthcare Analytics: Theoretical Minimum for Conducting 21st century on Healthcare Operations. John Wiley & Sons.

Choi, Y., Lee, H., & Irani, Z. (2018). Big data-driven fuzzy cognitive map for prioritising IT service procurement in the public sector. Annals of Operations Research, 270(1-2): 75-104.

Dahl, A. J., Milne, G. R., & Peltier, J. W. (2021). Digital health information seeking in an omni-channel environment: A shared decision-making and service-dominant logic perspective. Journal of Business Research, 125: 840-850.

Davis, Z., Zobel, C. W., Khansa, L., & Glick, R. E. (2020). Emergency department resilience to disaster?level overcrowding: A component resilience framework for analysis and predictive modeling. Journal of Operations Management, 66(1-2): 54-66.

Dixit, A., Routroy, S., & Dubey, S. K. (2019). A systematic literature review of healthcare supply chain and implications of future research. International Journal of Pharmaceutical and Healthcare Marketing, 13(4): 405-435

Donthu, N., & Gustafsson, A. (2020). Effects of COVID-19 on business and research. Journal of Business Research, 117: 284.

Guha, S., & Kumar, S. (2018). Emergence of big data research in operations management, information systems, and healthcare: Past contributions and future roadmap. Production and Operations Management, 27(9): 1724-1735.

Huang, Y. Y., & Handfield, R. B. (2015). Measuring the benefits of ERP on supply management maturity model: “a big data” method. International Journal of Operations and Production Management, 35(1): 2-25.

Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3): 829-846.

Kochan, C. G., Nowicki, D. R., Sauser, B., & Randall, W. S. (2018). Impact of cloud-based information sharing on hospital supply chain performance: A system dynamics framework. International Journal of Production Economics, 195: 168-185.

Kraus, S., Schiavone, F., Pluzhnikova, A., & Invernizzi, A. C. (2021). Digital transformation in healthcare: Analyzing the current state-of-research. Journal of Business Research, 123: 557-567.

Mehmood, R., Meriton, R., Graham, G., Hennelly, P., & Kumar, M. (2017). Exploring the influence of big data on city transport operations: a Markovian approach. International Journal of Operations & Production Management, 37(1): 75-104.

Meijboom, B., Schmidt-Bakx, S. & Westert, G. (2011). Supply Chain Management Practices for Improving Patient-Oriented Care.” Supply Chain Management: An International Journal, 16(3): 166–175.

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Schanzer, D. L., & Schwartz, B. (2013). Impact of seasonal and pandemic influenza on emergency department visits, 2003–2010, Ontario, Canada. Academic Emergency Medicine, 20(4): 388-397.

Supeekit, T., Somboonwiwat, T., & Kritchanchai, D. (2016). DEMATEL-modified ANP to evaluate internal hospital supply chain performance. Computers & Industrial Engineering, 102: 318-330.

Zhang, Y., Ren, S., Liu, Y., & Si, S. (2017). A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products. Journal of Cleaner Production, 142: 626-641.

Clauson, K. A., Breeden, E. A., Davidson, C., & Mackey, T. K. (2018). Leveraging blockchain technology to enhance supply chain management in healthcare: an exploration of challenges and opportunities in the health supply chain. Blockchain in healthcare today, 1(3): 1-12.