Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/27342
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dc.contributor.authorRathi, Sawan-
dc.contributor.TAC-ChairChakrabarti, Anindya-
dc.contributor.TAC-MemberChatterjee, Chirantan-
dc.contributor.TAC-MemberKapoor, Anuj-
dc.contributor.TAC-MemberMohaghegh, Mohsen-
dc.date.accessioned2024-05-15T04:13:21Z-
dc.date.available2024-05-15T04:13:21Z-
dc.date.issued2024-
dc.identifier.urihttp://hdl.handle.net/11718/27342-
dc.description.abstractWhat explains differential rates of healthcare technology adoption in low- and middle-income countries (LMICs)? Three determinants that can help determine the technology adoption trajectory in LMICs are – demand-pull from patients, technology push through actors with an appropriate network, and ecosystem provided by the market and non-market institutions. This thesis aims to study these three determinants to understand the changing dynamics of technology adoption and innovation in healthcare. In the first chapter, we exploit the COVID-19 shock to examine how intra-organization technology replacements occurred due to concurrent shifts in the demand and supply side. We use unique electronic medical records data from LV Prasad Eye Institute (LVPEI), which is one of the largest not-for-profit eye hospitals in India. We focus on the adoption of high-end medical technology by ophthalmologists to diagnose prevalent eye diseases – replacing less costly and older technology. We find that visual acuity among the non-paying patients worsened during the lockdown. Demand-pull generated through increased impairment propelled new technology adoption, predominantly facilitated through technology- push by young physicians. Higher adoption of new technology, in turn, contributed to improving the eyesight of non-paying patients. In the second chapter, we focus on the demand-side factors to examine how sudden change in the opportunity cost of time changes technology engagement. We analyzed two million call records of enrolled users of ARMMAN. ARMMAN is an internationally recognized NGO that leverages mHealth to send timely informational calls to mobile phones of underprivileged pregnant women in Mumbai. We find that during the COVID-19 induced lockdown period, the hearing duration of these calls significantly increased; however, technology engagement behavior exhibited demographic heterogeneity. In the third chapter, we focus on the institutional side and propose a framework for the governments in the LMIC setting. We conceptualize the role of margins of digital endowment and methods of information dissemination through digitalization in healthcare. We substantiate this framework with empirical evaluation based on Indian data to find that when public health information is disseminated individuals with digital endowments report better health. However, the distribution of this benefit is not uniform, and heterogeneities exist based on caste and location.en_US
dc.language.isoenen_US
dc.publisherIndian Institute of Management Ahmedabaden_US
dc.subjectLMICsen_US
dc.subjectHealthcareen_US
dc.subjectTechnologyen_US
dc.titleTechnology adoption and innovation in healthcareen_US
dc.typeThesisen_US
Appears in Collections:Thesis and Dissertations

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