Naveen R. Gowda, H. Vikas, Sidhartha Satpathy, Anjali Ramaswamy, Meghana Prabhu, Atul Kumar, Ananth Kini, Angel Rajan Singh, D. K. Sharma, Devashish Desai, J. B. Sharma, Praveen R. Gowda, Rajkumar, Bharath Gopinath, Chandrashekhar Huded, K. P. Sowmya, T. K. Divya, Khyati Vakharia, Somanath Viswanath, Dhayal C. John, Neeraj Gudipati
{"title":"Digital elixir for healthcare: market intelligence and policy implications","authors":"Naveen R. Gowda, H. Vikas, Sidhartha Satpathy, Anjali Ramaswamy, Meghana Prabhu, Atul Kumar, Ananth Kini, Angel Rajan Singh, D. K. Sharma, Devashish Desai, J. B. Sharma, Praveen R. Gowda, Rajkumar, Bharath Gopinath, Chandrashekhar Huded, K. P. Sowmya, T. K. Divya, Khyati Vakharia, Somanath Viswanath, Dhayal C. John, Neeraj Gudipati","doi":"10.1007/s40622-023-00370-z","DOIUrl":null,"url":null,"abstract":"<p>There is an increasing emphasis on digital health. However, success of digital health depends on voluntary adoption, which requires good product–market fit for a wide range of users. A national-level survey through snowball sampling was conducted from November 2020 to March 2021 among all MBBS doctors willing to participate. A total of 1010 doctors from different sectors, locations, qualifications with wide range of experience and patient load participated. Doctors from across the board felt going digital would entail long learning curves, additional workload, more screen time and that they do not improve overall quality of care. Majority feel digital solutions do not help in increasing net revenue and consequently prefer free-of-cost digital solutions. Among those willing to pay, onetime investment for hardware/equipment (38%) followed by annual subscription for software licenses (34%) are the preferred modalities. Seventy-four percent of doctors expressed not being comfortable with government providing digital solutions or controlling the data. In order to make the findings more practical and relevant, digital health adoption curve and market intelligence grid have been proposed. Digital health companies can use the adoption curve to understand how adoption can fluctuate with cost, ease of use and data policy. The grid can help companies identify the requirements of their target segment of doctors and therefore achieve better product–market fit.</p>","PeriodicalId":43923,"journal":{"name":"Decision","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Decision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s40622-023-00370-z","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0
Abstract
There is an increasing emphasis on digital health. However, success of digital health depends on voluntary adoption, which requires good product–market fit for a wide range of users. A national-level survey through snowball sampling was conducted from November 2020 to March 2021 among all MBBS doctors willing to participate. A total of 1010 doctors from different sectors, locations, qualifications with wide range of experience and patient load participated. Doctors from across the board felt going digital would entail long learning curves, additional workload, more screen time and that they do not improve overall quality of care. Majority feel digital solutions do not help in increasing net revenue and consequently prefer free-of-cost digital solutions. Among those willing to pay, onetime investment for hardware/equipment (38%) followed by annual subscription for software licenses (34%) are the preferred modalities. Seventy-four percent of doctors expressed not being comfortable with government providing digital solutions or controlling the data. In order to make the findings more practical and relevant, digital health adoption curve and market intelligence grid have been proposed. Digital health companies can use the adoption curve to understand how adoption can fluctuate with cost, ease of use and data policy. The grid can help companies identify the requirements of their target segment of doctors and therefore achieve better product–market fit.
期刊介绍：
The aim of the Journal, Decision, is to publish qualitative, quantitative, survey-based, simulation-based research articles at the national and sub-national levels. While there is no stated regional focus of the journal, we are more interested in examining if and how individuals, firms and governments in emerging economies may make decisions differently. Published for the management scholars, business executives and managers, the Journal aims to advance the management research by publishing empirically and theoretically grounded articles in management decision making process. The Editors aim to provide an efficient and high-quality review process to the authors.
The Journal accepts submissions in several formats such as original research papers, case studies, review articles and book reviews (book reviews are only by invitation).
The Journal welcomes research-based, original and insightful articles on organizational, individual, socio-economic-political, environmental decision making with relevance to theory and practice of business. It also focusses on the managerial decision-making challenges in private, public, private-public partnership and non-profit organizations. The Journal also encourages case studies that provide a rich description of the business or societal contexts in managerial decision-making process including areas – but not limited to – conflict over natural resources, product innovation and copyright laws, legislative or policy change, socio-technical embedding of financial markets, particularly in developing economy, an ethnographic understanding of relations at a workplace, or social network in marketing management, etc.
Research topics covered in the Journal include (but not limited to):
Finance and Accounting
Organizational Theory and Behavior
Decision Science
Public Policy-Economic Insights
Operation Management
Innovation and Entrepreneurship
Information Technology and Systems Management
Optimization and Modelling
Supply Chain Management
Data Analytics
Marketing Management
Human Resource Management
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