Search results

1 – 10 of over 3000
Case study
Publication date: 1 January 2024

John McVea, Daniel McLaughlin and Danielle Ailts Campeau

The case is designed to be used with the digital business model framework developed by Peter Weill and Stephanie Woerner of Massachusetts Institute of Technology (MIT) (Weill and…

Abstract

Theoretical basis

The case is designed to be used with the digital business model framework developed by Peter Weill and Stephanie Woerner of Massachusetts Institute of Technology (MIT) (Weill and Woerner, 2015) and is referred to as the W & W framework. This approach provides a useful structure for thinking through the strategic options facing environments ripe for digital transformation.

Research methodology

Research for this case was conducted through face-to-face interviews with the protagonist, as well as through a review of their business planning documents and other data and documentation provided by the founder. Some of the market and industry data were obtained using secondary research and industry reports. Interviews were digitally recorded and transcribed to ensure accuracy.

Case overview/synopsis

The case follows the story of Kurt Waltenbaugh, a Minnesota entrepreneur who shared the dream of using data analytics to reduce costs within the US health-care system. In early 2014, Waltenbaugh and a physician colleague founded Carrot Health to bring together their personal experience and expertise in both consumer data analytics and health care. From the beginning, they focused on how to use data analytics to help identify high-risk/high-cost patients who had not yet sought medical treatment. They believed that they could use these insights to encourage early medical interventions and, as a result, lower the long-term cost of care.

Carrot’s initial success found them in a consultative role, working on behalf of insurance companies. Through this work, they honed their capabilities by helping their clients combine existing claims data with external consumer behavioral data to identify new potential customers. These initial consulting contracts gave Carrot the opportunity to develop its analytic tools, business model and, importantly, to earn some much-needed cash flow during the start-up phase. However, they also learned that, while insurance companies were willing to purchase data insights for one-off market expansion projects, it was much more difficult to motivate them to use data proactively to eliminate costs on an ongoing basis. Waltenbaugh believed that Carrot’s greatest potential lay in their ability to develop predictive models of health outcomes, and this case explores Carrot’s journey through strategic decisions and company transformation.

Complexity academic level

This case is intended for either an undergraduate or graduate course on entrepreneurial strategy. It provides an effective introduction to the unique structure and constraints which apply to an innovative start-up within the health-care industry. The case also serves as a platform to explore the critical criteria to be considered when developing a digital transformation strategy and exposing students to the digital business model developed by Weill and Woerner (2015) at MIT (referred to in this instructor’s manual as the W&W framework). The case was written to be used in an advanced strategy Master of Business Administration (MBA) class, an undergraduate specialty health-care course or as part of a health-care concentration in a regular MBA, Master of Health Care Administration (MHA) or Master of Public Health (MPH). It may be taught toward the end of a course on business strategy when students are building on generic strategy frameworks and adapting their strategic thinking to the characteristics of specific industries or sectors. However, the case can also be taught as part of a course on health-care innovation in which case it also serves well as an introduction to the health-care payments and insurance system in the USA. Finally, the case can be used in a specialized course on digital transformation strategy in which case it serves as an introduction to the MIT W&W framework.

The case is particularly well-suited to students who are familiar with traditional frameworks for business strategy and business models. The analysis builds on this knowledge and introduces students interested in learning about the opportunities and challenges of digital strategy. Equally, the case works well for students with clinical backgrounds, who are interested in how business strategy can influence changes within the health-care sphere. Finally, an important aspect of the case design was to develop students’ analytical confidence by encouraging them to “get their hands dirty” and to carry out some basic exploratory data analytics themselves. As such, the case requires students to combine and correlate data and to experience the potentially powerful combination of clinical and consumer data. Instructors should find that the insights from these activities give students unique insights into the potential for of data analytics to move health care from a reactive/treatment ethos to a proactive/intervention ethos. This experience can be particularly revealing for students with clinical backgrounds who may initially be resistant to the use of clinical data by commercial organizations.

Details

The CASE Journal, vol. ahead-of-print no. ahead-of-print
Type: Case Study
ISSN: 1544-9106

Keywords

Book part
Publication date: 30 September 2020

Suryakanthi Tangirala

With the advent of Big Data, the ability to store and use the unprecedented amount of clinical information is now feasible via Electronic Health Records (EHRs). The massive…

Abstract

With the advent of Big Data, the ability to store and use the unprecedented amount of clinical information is now feasible via Electronic Health Records (EHRs). The massive collection of clinical data by health care systems and treatment canters can be productively used to perform predictive analytics on treatment plans to improve patient health outcomes. These massive data sets have stimulated opportunities to adapt computational algorithms to track and identify target areas for quality improvement in health care.

According to a report from Association of American Medical Colleges, there will be an alarming gap between demand and supply of health care work force in near future. The projections show that, by 2032 there is will be a shortfall of between 46,900 and 121,900 physicians in US (AAMC, 2019). Therefore, early prediction of health care risks is a demanding requirement to improve health care quality and reduce health care costs. Predictive analytics uses historical data and algorithms based on either statistics or machine learning to develop predictive models that capture important trends. These models have the ability to predict the likelihood of the future events. Predictive models developed using supervised machine learning approaches are commonly applied for various health care problems such as disease diagnosis, treatment selection, and treatment personalization.

This chapter provides an overview of various machine learning and statistical techniques for developing predictive models. Case examples from the extant literature are provided to illustrate the role of predictive modeling in health care research. Together with adaptation of these predictive modeling techniques with Big Data analytics underscores the need for standardization and transparency while recognizing the opportunities and challenges ahead.

Details

Big Data Analytics and Intelligence: A Perspective for Health Care
Type: Book
ISBN: 978-1-83909-099-8

Keywords

Article
Publication date: 13 July 2015

Sreenivas R. Sukumar, Ramachandran Natarajan and Regina K. Ferrell

The current trend in Big Data analytics and in particular health information technology is toward building sophisticated models, methods and tools for business, operational and…

6002

Abstract

Purpose

The current trend in Big Data analytics and in particular health information technology is toward building sophisticated models, methods and tools for business, operational and clinical intelligence. However, the critical issue of data quality required for these models is not getting the attention it deserves. The purpose of this paper is to highlight the issues of data quality in the context of Big Data health care analytics.

Design/methodology/approach

The insights presented in this paper are the results of analytics work that was done in different organizations on a variety of health data sets. The data sets include Medicare and Medicaid claims, provider enrollment data sets from both public and private sources, electronic health records from regional health centers accessed through partnerships with health care claims processing entities under health privacy protected guidelines.

Findings

Assessment of data quality in health care has to consider: first, the entire lifecycle of health data; second, problems arising from errors and inaccuracies in the data itself; third, the source(s) and the pedigree of the data; and fourth, how the underlying purpose of data collection impact the analytic processing and knowledge expected to be derived. Automation in the form of data handling, storage, entry and processing technologies is to be viewed as a double-edged sword. At one level, automation can be a good solution, while at another level it can create a different set of data quality issues. Implementation of health care analytics with Big Data is enabled by a road map that addresses the organizational and technological aspects of data quality assurance.

Practical implications

The value derived from the use of analytics should be the primary determinant of data quality. Based on this premise, health care enterprises embracing Big Data should have a road map for a systematic approach to data quality. Health care data quality problems can be so very specific that organizations might have to build their own custom software or data quality rule engines.

Originality/value

Today, data quality issues are diagnosed and addressed in a piece-meal fashion. The authors recommend a data lifecycle approach and provide a road map, that is more appropriate with the dimensions of Big Data and fits different stages in the analytical workflow.

Details

International Journal of Health Care Quality Assurance, vol. 28 no. 6
Type: Research Article
ISSN: 0952-6862

Keywords

Article
Publication date: 6 May 2021

Rajesh Kumar Singh, Saurabh Agrawal, Abhishek Sahu and Yigit Kazancoglu

The proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of…

1680

Abstract

Purpose

The proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.

Design/methodology/approach

Fora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.

Findings

BD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.

Research limitations/implications

The proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.

Originality/value

There are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

Details

The TQM Journal, vol. 35 no. 1
Type: Research Article
ISSN: 1754-2731

Keywords

Book part
Publication date: 30 September 2020

K. Kalaiselvi and A. Thirumurthi Raja

Big Data is one of the most promising area where it can be applied to make a change is health care. Healthcare analytics have the potential to reduce the treatment costs, forecast…

Abstract

Big Data is one of the most promising area where it can be applied to make a change is health care. Healthcare analytics have the potential to reduce the treatment costs, forecast outbreaks of epidemics, avoid preventable diseases, and improve the quality of life. In general, the lifetime of human is increasing along world population, which poses new experiments to today’s treatment delivery methods. Health professionals are skillful of gathering enormous volumes of data and look for best approaches to use these numbers. Big data analytics has helped the healthcare area by providing personalized medicine and prescriptive analytics, medical risk interference and predictive analytics, computerized external and internal reporting of patient data, homogeneous medical terms and patient registries, and fragmented point solutions. The data generated level within healthcare systems is significant. This includes electronic health record data, imaging data, patient-generated data, etc. While widespread information in health care is now mostly electronic and fits under the big data as most is unstructured and difficult to use. The use of big data in health care has raised substantial ethical challenges ranging from risks for specific rights, privacy and autonomy, to transparency and trust.

Details

Big Data Analytics and Intelligence: A Perspective for Health Care
Type: Book
ISBN: 978-1-83909-099-8

Keywords

Book part
Publication date: 30 September 2020

Anam and M. Israrul Haque

The rapid increase in analytics is playing an essential role in enlarging various practices related to the health sector. Big Data Analytics (BDA) provides multiple tools to…

Abstract

The rapid increase in analytics is playing an essential role in enlarging various practices related to the health sector. Big Data Analytics (BDA) provides multiple tools to store, maintain, and analyze large sets of data provided by different systems of health. It is essential to manage and analyze these data to get meaningful information. Pharmaceutical companies are accumulating their data in the medical databases, whereas the payers are digitalizing the records of patients. Biomedical research generates a significant amount of data. There has been a continuous improvement in the health sector for past decades. They have become more advanced by recording the patient’s data on the Internet of Things devices, Electronic Health Records efficiently. BD is undoubtedly going to enhance the productivity and performance of organizations in various fields. Still, there are several challenges associated with BD, such as storing, capturing, and analyzing data, and their subsequent application to a practical health sector.

Details

Big Data Analytics and Intelligence: A Perspective for Health Care
Type: Book
ISBN: 978-1-83909-099-8

Keywords

Open Access
Article
Publication date: 4 December 2023

Ignat Kulkov, Julia Kulkova, Daniele Leone, René Rohrbeck and Loick Menvielle

The purpose of this study is to examine the role of artificial intelligence (AI) in transforming the healthcare sector, with a focus on how AI contributes to entrepreneurship and…

Abstract

Purpose

The purpose of this study is to examine the role of artificial intelligence (AI) in transforming the healthcare sector, with a focus on how AI contributes to entrepreneurship and value creation. This study also aims to explore the potential of combining AI with other technologies, such as cloud computing, blockchain, IoMT, additive manufacturing and 5G, in the healthcare industry.

Design/methodology/approach

Exploratory qualitative methodology was chosen to analyze 22 case studies from the USA, EU, Asia and South America. The data source was public and specialized podcast platforms.

Findings

The findings show that combining technologies can create a competitive advantage for technology entrepreneurs and bring about transitions from simple consumer devices to actionable healthcare applications. The results of this research identified three main entrepreneurship areas: 1. Analytics, including staff reduction, patient prediction and decision support; 2. Security, including protection against cyberattacks and detection of atypical cases; 3. Performance optimization, which, in addition to reducing the time and costs of medical procedures, includes staff training, reducing capital costs and working with new markets.

Originality/value

This study demonstrates how AI can be used with other technologies to cocreate value in the healthcare industry. This study provides a conceptual framework, “AI facilitators – AI achievers,” based on the findings and offer several theoretical contributions to academic literature in technology entrepreneurship and technology management and industry recommendations for practical implication.

Details

International Journal of Entrepreneurial Behavior & Research, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1355-2554

Keywords

Article
Publication date: 12 February 2019

Jenifer Sunrise Winter and Elizabeth Davidson

This paper aims to assess the increasing challenges to governing the personal health information (PHI) essential for advancing artificial intelligence (AI) machine learning…

2947

Abstract

Purpose

This paper aims to assess the increasing challenges to governing the personal health information (PHI) essential for advancing artificial intelligence (AI) machine learning innovations in health care. Risks to privacy and justice/equity are discussed, along with potential solutions.

Design/methodology/approach

This conceptual paper highlights the scale and scope of PHI data consumed by deep learning algorithms and their opacity as novel challenges to health data governance.

Findings

This paper argues that these characteristics of machine learning will overwhelm existing data governance approaches such as privacy regulation and informed consent. Enhanced governance techniques and tools will be required to help preserve the autonomy and rights of individuals to control their PHI. Debate among all stakeholders and informed critique of how, and for whom, PHI-fueled health AI are developed and deployed are needed to channel these innovations in societally beneficial directions.

Social implications

Health data may be used to address pressing societal concerns, such as operational and system-level improvement, and innovations such as personalized medicine. This paper informs work seeking to harness these resources for societal good amidst many competing value claims and substantial risks for privacy and security.

Originality/value

This is the first paper focusing on health data governance in relation to AI/machine learning.

Details

Digital Policy, Regulation and Governance, vol. 21 no. 3
Type: Research Article
ISSN: 2398-5038

Keywords

Article
Publication date: 7 January 2021

Sagar Lotan Chaudhari and Manish Sinha

India ranks third in the global startup ecosystem in the world incubating more than 50,000 startups and witnessing 15% YoY growth per year. Being a center of innovation and…

1324

Abstract

Purpose

India ranks third in the global startup ecosystem in the world incubating more than 50,000 startups and witnessing 15% YoY growth per year. Being a center of innovation and skilled labor, Indian startups have attracted investments from all over the world. This paper aims at exploring the trends that are driving the growth in the Indian startup ecosystem.

Design/methodology/approach

Top 200 startups according to valuation are selected as a sample to find out the major trends in the Indian startup ecosystem. This paper includes surveying the sample startups about the implementation of trends such as big data, crowdfunding and shared economy in their startup and its tangible, as well as intangible impacts on their business. The result of the survey is analyzed to get an overview of the emerging trends in the Indian startup ecosystem.

Findings

Major ten emerging trends that drive growth in the Indian startup ecosystem are discovered and the areas where these trends can be leveraged are identified.

Originality/value

This research has contributed toward structuring and documenting the growth driving trends, and it will help the budding entrepreneurs to get familiar with the contemporary trends, pros and cons associated with it and the ways to leverage these trends to build a successful startup.

Details

International Journal of Innovation Science, vol. 13 no. 1
Type: Research Article
ISSN: 1757-2223

Keywords

Article
Publication date: 8 May 2017

Yichuan Wang and Terry Anthony Byrd

Drawing on the resource-based theory and dynamic capability view, this paper aims to examine the mechanisms by which business analytics (BA) capabilities (i.e. the effective use…

4952

Abstract

Purpose

Drawing on the resource-based theory and dynamic capability view, this paper aims to examine the mechanisms by which business analytics (BA) capabilities (i.e. the effective use of data aggregation, analytics and data interpretation tools) in healthcare units indirectly influence decision-making effectiveness through the mediating role of knowledge absorptive capacity.

Design/methodology/approach

Using a survey method, this study collected data from the hospitals in Taiwan. Of the 155 responses received, three were incomplete, giving a 35.84 per cent response rate with 152 valid data points. Structural equation modeling was used to test the hypotheses.

Findings

This study conceptualizes, operationalizes and measures the BA capability as a multi-dimensional construct that is formed by capturing the functionalities of BA systems in health care, leading to the conclusion that healthcare units are likely to obtain valuable knowledge through using the data analysis and interpretation tools effectively. The effective use of data analysis and interpretation tools in healthcare units indirectly influence decision-making effectiveness, an impact that is mediated by absorptive capacity.

Originality/value

This study adds values to the literature by conceptualizing BA capabilities in healthcare and demonstrating how knowledge absorption matters when implementing BA to the decision-making process. The mediating role of absorptive capacity not only provides a mechanism by which BA can contribute to decision-making practices but also offers a new solution to the puzzle of the IT productivity paradox in healthcare settings.

Details

Journal of Knowledge Management, vol. 21 no. 3
Type: Research Article
ISSN: 1367-3270

Keywords

1 – 10 of over 3000