Modeling Blockchain adoption barriers in the healthcare industry using ISM-Fuzzy MICMAC approach (Preprint)
Authors: Joshi Sudhanshu 
Published: September 2019

BACKGROUND

Blockchain provides a digital platform that keeps the digital history of all transactions across different levels of users. With its emerging presence, no industry is excluded from the benefits that BC can bring into it. BC has disrupted the business processes by enabling ‘Trust-based’ transactions without third party involvement with high level of success(Firdaus et al., 2019; Pan et al., 2019; Queiroz and Wamba, 2019).The intrinsic uniqueness of blockchain design ensures vital features including transparency, robustness, audit-ability, and security(Casino, Dasaklis and Patsakis, 2018; Grover, Kar and Janssen, 2019).Since its inception, BC has expanded its applications and continuously revolutionizing the markets and the society(Nakamoto, 2008; Christidis and Devetsikiotis, 2016; Antonucci et al., 2019; Hsieh and Wu, 2019;Ozdemir, Ar and Erol, 2019; Xie et al., 2019). It is inclusive of variety of technologies that aims to exchange information and digital assets, across distributed e-networks (Ahram et al., 2017; Subramanian, 2018). Thus,the presence of BC is virtually transforming various industries, ranging from service entities (viz., Hospital, Healthcare, Tourism, Event Management, Banking etc.) to operations and supply chain management(Dabbagh, 2019).It is estimated that by 2022, more than 1B people will have data stored on a BC(Forbes,2018), and by 2030, BC will create $3.1 trillion in business value(Gartner, 2019) andit is quite predictable that the number shall further goes up over the coming years(Deloitte, 2018).Both practitioners and academic researchers have confirmed the array of capabilities that BC encapsulates across variety of sectors(Antonucci et al.,2019; Morkunas et al.,2019;Ozdemir et al., 2019; Wamba and Queiroz, 2019; Wang and Qu, 2019; Xu et al., 2019).

OBJECTIVE

The aim of the paper is to identify the barriers towards the adoption of blockchain in Indian healthcare industry and also examines the most pertinent issues related to Blockchain applications in healthcare industry.

METHODS

This study has applied hybrid method (Interpretative Structural Modeling (ISM) and Fuzzy MICMAC) to identify the existing relationship among various Block chain adoption barriers

RESULTS

Our study brings exciting insights for BC and healthcare professionals. First, the study presents the different dimensions those can impact the block chain implementation in healthcare industry and are acting as barriers in the adoption on the basis of literature and experts confirmation. 2nd our results indicate the legal framework and top level management involvement are independent and have maximum impact in the journey of BC adoption. Top management needs to be aware first about the BCT, its architecture and how it is going to benefit the firm overall. For ensuring the top management involvement, they need to understand how they can minimize the transaction delays of their healthcare processes. Also top management need to understand the cost of adoption versus the benefits in terms of increase in productivity, effectiveness and improving the performance of their entire healthcare system. Next major problem is legal issues in BCs. It is challenging to set the rules and regulations for a decentralized environment of BCs. In the case of healthcare systems, the patient is one of the important stakeholders as information provider. Few questions arise such as who will be having the authority to correct or delete the information and who will be responsible for any unintentional incidents that may happen in healthcare systems. Additionally the concerns related to storage capability, trust issues, data ownership, scalability, high maintenance and development cost along with risk of patient data hacking are dependent variables on number of factors and need to be given a fair attention while designing the architecture for BC adoption in healthcare setting. Firms also need pay attention with high driving power and highly dependent variables such as ‘availability of trained people those have skills of handling the complex technology’, ‘provision of adequate infrastructure for the adoption of BCT’, ‘assurance of data privacy and security’ and ‘tools and techniques for data standardization’.

CONCLUSIONS

In this paper, we intended to spot the barriers in adoption of BCT in healthcare setting and the relationship between the identified barriers in terms of their driving and dependence power. Our proposal of identification of these barriers was a combination of ISM and Fuzzy-MICMAC.

CLINICALTRIAL

NA