scholarly journals Adoption of Machine Learning Systems for Medical Diagnostics in Clinics: Qualitative Interview Study

10.2196/29301 ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. e29301
Author(s):  
Luisa Pumplun ◽  
Mariska Fecho ◽  
Nihal Wahl ◽  
Felix Peters ◽  
Peter Buxmann
Keyword(s):  
Machine Learning ◽  
Content Analysis ◽  
Scale Up ◽  
Medical Diagnostics ◽  
Future Research ◽  
Medical Setting ◽  
Adoption Process ◽  
Maturity Model ◽  
Qualitative Interview Study ◽  
Insight Into

Background Recently, machine learning (ML) has been transforming our daily lives by enabling intelligent voice assistants, personalized support for purchase decisions, and efficient credit card fraud detection. In addition to its everyday applications, ML holds the potential to improve medicine as well, especially with regard to diagnostics in clinics. In a world characterized by population growth, demographic change, and the global COVID-19 pandemic, ML systems offer the opportunity to make diagnostics more effective and efficient, leading to a high interest of clinics in such systems. However, despite the high potential of ML, only a few ML systems have been deployed in clinics yet, as their adoption process differs significantly from the integration of prior health information technologies given the specific characteristics of ML. Objective This study aims to explore the factors that influence the adoption process of ML systems for medical diagnostics in clinics to foster the adoption of these systems in clinics. Furthermore, this study provides insight into how these factors can be used to determine the ML maturity score of clinics, which can be applied by practitioners to measure the clinic status quo in the adoption process of ML systems. Methods To gain more insight into the adoption process of ML systems for medical diagnostics in clinics, we conducted a qualitative study by interviewing 22 selected medical experts from clinics and their suppliers with profound knowledge in the field of ML. We used a semistructured interview guideline, asked open-ended questions, and transcribed the interviews verbatim. To analyze the transcripts, we first used a content analysis approach based on the health care–specific framework of nonadoption, abandonment, scale-up, spread, and sustainability. Then, we drew on the results of the content analysis to create a maturity model for ML adoption in clinics according to an established development process. Results With the help of the interviews, we were able to identify 13 ML-specific factors that influence the adoption process of ML systems in clinics. We categorized these factors according to 7 domains that form a holistic ML adoption framework for clinics. In addition, we created an applicable maturity model that could help practitioners assess their current state in the ML adoption process. Conclusions Many clinics still face major problems in adopting ML systems for medical diagnostics; thus, they do not benefit from the potential of these systems. Therefore, both the ML adoption framework and the maturity model for ML systems in clinics can not only guide future research that seeks to explore the promises and challenges associated with ML systems in a medical setting but also be a practical reference point for clinicians.

scholarly journals Adoption of Machine Learning Systems for Medical Diagnostics in Clinics: Qualitative Interview Study (Preprint)

2021 ◽  
Author(s):  
Luisa Pumplun ◽  
Mariska Fecho ◽  
Nihal Wahl ◽  
Felix Peters ◽  
Peter Buxmann
Keyword(s):  
Machine Learning ◽  
Content Analysis ◽  
Scale Up ◽  
Medical Diagnostics ◽  
Future Research ◽  
Medical Setting ◽  
Adoption Process ◽  
Maturity Model ◽  
Qualitative Interview Study ◽  
Insight Into

BACKGROUND Recently, machine learning (ML) has been transforming our daily lives by enabling intelligent voice assistants, personalized support for purchase decisions, and efficient credit card fraud detection. In addition to its everyday applications, ML holds the potential to improve medicine as well, especially with regard to diagnostics in clinics. In a world characterized by population growth, demographic change, and the global COVID-19 pandemic, ML systems offer the opportunity to make diagnostics more effective and efficient, leading to a high interest of clinics in such systems. However, despite the high potential of ML, only a few ML systems have been deployed in clinics yet, as their adoption process differs significantly from the integration of prior health information technologies given the specific characteristics of ML. OBJECTIVE This study aims to explore the factors that influence the adoption process of ML systems for medical diagnostics in clinics to foster the adoption of these systems in clinics. Furthermore, this study provides insight into how these factors can be used to determine the ML maturity score of clinics, which can be applied by practitioners to measure the clinic status quo in the adoption process of ML systems. METHODS To gain more insight into the adoption process of ML systems for medical diagnostics in clinics, we conducted a qualitative study by interviewing 22 selected medical experts from clinics and their suppliers with profound knowledge in the field of ML. We used a semistructured interview guideline, asked open-ended questions, and transcribed the interviews verbatim. To analyze the transcripts, we first used a content analysis approach based on the health care–specific framework of nonadoption, abandonment, scale-up, spread, and sustainability. Then, we drew on the results of the content analysis to create a maturity model for ML adoption in clinics according to an established development process. RESULTS With the help of the interviews, we were able to identify 13 ML-specific factors that influence the adoption process of ML systems in clinics. We categorized these factors according to 7 domains that form a holistic ML adoption framework for clinics. In addition, we created an applicable maturity model that could help practitioners assess their current state in the ML adoption process. CONCLUSIONS Many clinics still face major problems in adopting ML systems for medical diagnostics; thus, they do not benefit from the potential of these systems. Therefore, both the ML adoption framework and the maturity model for ML systems in clinics can not only guide future research that seeks to explore the promises and challenges associated with ML systems in a medical setting but also be a practical reference point for clinicians. CLINICALTRIAL

Issue Emergence, Evolution of Controversy, and Implications for Competitive Framing

2011 ◽  
Vol 17 (2) ◽  
pp. 169-189 ◽  
Author(s):  
Erika Franklin Fowler ◽  
Sarah E. Gollust ◽  
Amanda F. Dempsey ◽  
Paula M. Lantz ◽  
Peter A. Ubel
Keyword(s):  
Content Analysis ◽  
Dynamic Process ◽  
Hpv Vaccine ◽  
Public Discourse ◽  
News Coverage ◽  
Future Research ◽  
Local Newspapers ◽  
The Media ◽  
External Events ◽  
Insight Into

Although scholarship on competitive framing acknowledges that framing is a dynamic process in which the early stages may matter most, very little research has focused on the dynamics of issue emergence. In this article, we draw on several literatures to develop theories for how controversy related to new issues will emerge and expand in news coverage. Through a comprehensive content analysis of 101 local newspapers across the fifty U.S. states, we explore the dynamic and evolving process wherein a new issue—the HPV vaccine—emerged into public discourse and a legislative debate over school requirements for vaccination began. We find that coverage of controversy is a function of proximity, driven primarily by events within a state, although external events also influence local coverage. We also find that the legislative discussion in the media did not necessarily start out as controversial, but as the issue evolved, we observe a large increase in the proliferation of both actors taking positions and the types of arguments made to influence debate. The findings yield important insight into issue emergence with implications for how future research might test competing frames to better understand how the presentation of controversy in the mass media affects public opinion.

Cognitive Interviewing to Improve Questionnaires for Justice-Involved Youth

2019 ◽  
Vol 63 (10) ◽  
pp. 1931-1951
Author(s):  
Jason R. Silva ◽  
Beth Fera ◽  
Susruta Sudula ◽  
Deborah Koetzle ◽  
Craig Schwalbe
Keyword(s):  
Risk Factors ◽  
Content Analysis ◽  
Criminal Justice ◽  
Future Research ◽  
Cognitive Interviewing ◽  
Word Choice ◽  
Justice Research ◽  
Response Sets ◽  
Criminal Justice Research ◽  
Insight Into

This study demonstrates the utility of cognitive interviewing for survey and scale development in criminal justice research and identifies common comprehension problems with survey items for justice-involved youth. A cognitive interviewing strategy was utilized with a sample of youth who completed a survey examining risk factors for recidivism. A content analysis of interviews was used to identify patterns and to classify the nature and type of comprehension issues youth experienced. Five specific comprehension issues were identified including reading comprehension, item ambiguity, precondition binds, double-barreled questions, and double negatives. Findings illustrate the value of cognitive interviewing for pretesting survey items for justice-involved youth and provide further insight into issues surrounding word choice, question structure, and response sets. Results also point to the need to reassess the validity of established scales used in contemporary studies. Limitations of the current study and implications for future research are discussed.

scholarly journals Analysis of Covid-19 impact on virtual hotel operation in Indonesia

2021 ◽  
Vol 20 ◽  
pp. 694-703
Author(s):  
Kenny Roz ◽  
Dicky Wisnu Usdek Riyanto ◽  
Marsudi Marsudi ◽  
Salahudin Salahudin
Keyword(s):  
Content Analysis ◽  
Qualitative Approach ◽  
Future Research ◽  
Online Media ◽  
Media Content ◽  
Data Mapping ◽  
Economic Sector ◽  
Tourism Sector ◽  
The Impact ◽  
Insight Into

The Covid-19 pandemic outbreak had a devastating effect on the economic sector in Indonesia, especially in the tourism sector. This study aims to reveal the impact Covid-19 on the Indonesia tourism sector especially the virtual hotel operation.  This research uses a qualitative approach and content analysis as data mapping of Indonesia online media content. Nvivo-12 plus software is used to manage and analyze the data. The results of this research pandemic covid 19 outbreaks have an impact on the economy of Indonesia and affect startup virtual hotel operations (especially Airy rooms) to the point of closing operations in Indonesia. This research provides some insight into future research opportunities in the new normal conditions in tourism sector operation

scholarly journals An insight into R&D in emerging countries and prominent concepts within the literature

2020 ◽  
Vol 2 (1) ◽  
pp. 01-14
Author(s):  
Fatma Halide Kıvrak
Keyword(s):  
Content Analysis ◽  
Emerging Markets ◽  
Developed Countries ◽  
Multinational Companies ◽  
Emerging Countries ◽  
Systematic Evaluation ◽  
Future Research ◽  
General View ◽  
Research Focus ◽  
Insight Into

Multinational companies (MNCs) show an apparent tendency toward research and development (R&D) for the last three decades. Many kinds of research have recently focused on R&D in emerging countries (ECs) to reveal the differences from R&D in developed countries. This paper presents a literature review consisting of 23 articles from 2004-2019 on R&D in emerging markets. By conducting content analysis, it has been analyzed their prominent concepts and results. According to the research focus, the articles examined have been classified into five main groups – internationalization of R&D, R&D activities, R&D investments, technology-based R&D, R&D offshoring – along with articles that cannot be grouped. The main purpose of this study is to determine the current tendency of R&D in emerging markets. Thus, it provides to understand in which position R&D has in emerging markets, and under which circumstances it is successful. Furthermore, practitioners can benefit from the results of the presented study. The systematic evaluation of the findings contributes to the formation of a general view for future research.

Big Data Security Management

2015 ◽  
pp. 53-66
Author(s):  
Zaiyong Tang ◽  
Youqin Pan
Keyword(s):  
Big Data ◽  
Data Security ◽  
Scale Up ◽  
Big Data Analytics ◽  
Security Management ◽  
Future Research ◽  
Maturity Model ◽  
Research Issues ◽  
Security Standards ◽  
Industry Trends

Big data is a buzzword today, and security of big data is a big concern. Traditional security standards and technologies cannot scale up to deliver reliable and effective security solutions in the big data environment. This chapter covers big data security management from concepts to real-world issues. By identifying and laying out the major challenges, industry trends, legal and regulatory environments, security principles, security management frameworks, security maturity model, big data analytics in solving security problems, current research results, and future research issues, this chapter provides researchers and practitioners with a timely reference and guidance in securing big data processing, management, and applications.

Big Data Security Management

Big Data ◽  
2016 ◽  
pp. 247-260
Author(s):  
Zaiyong Tang ◽  
Youqin Pan
Keyword(s):  
Big Data ◽  
Data Security ◽  
Scale Up ◽  
Big Data Analytics ◽  
Security Management ◽  
Future Research ◽  
Maturity Model ◽  
Research Issues ◽  
Security Standards ◽  
Industry Trends

Big data is a buzzword today, and security of big data is a big concern. Traditional security standards and technologies cannot scale up to deliver reliable and effective security solutions in the big data environment. This chapter covers big data security management from concepts to real-world issues. By identifying and laying out the major challenges, industry trends, legal and regulatory environments, security principles, security management frameworks, security maturity model, big data analytics in solving security problems, current research results, and future research issues, this chapter provides researchers and practitioners with a timely reference and guidance in securing big data processing, management, and applications.

The Future of Organizational Trust Research

2017 ◽  
Author(s):  
Bart A. De Jong ◽  
David P. Kroon ◽  
Oliver Schilke
Keyword(s):  
Content Analysis ◽  
Social Science ◽  
Research Agenda ◽  
Organizational Trust ◽  
Future Research ◽  
The Future ◽  
Insight Into

This chapter contributes to defining a common research agenda on organizational trust, first by content-analyzing scholarly recommendations for future research published between 2007 and 2011 across 347 articles and 58 social science journals and second by reviewing the latest developments in trust research published between 2012 and 2015 across 111 articles and 31 top-tier management journals. This content analysis of scholarly recommendations yields an emergent organizing framework that offers systematic insight into the trust community’s beliefs about how the field should move forward, while the review of the latest developments in the field provide insight into whether these recommendations have recently been followed up on, or whether research has developed in previously unanticipated directions. The chapter concludes with suggestions on how individual researchers and the trust community as a whole can use and build on these findings to help advance the field.

Speech analysis as a tool for detection and monitoring of medical conditions: a review (Preprint)

2020 ◽  
Author(s):  
Mariusz Ziolko
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Voice Quality ◽  
Medical Diagnostics ◽  
Mass Scale ◽  
The State ◽  
Machine Learning Algorithms ◽  
Speech Analysis ◽  
Future Research ◽  
Voice Analysis

BACKGROUND Speech is the main direct means of transmitting information between people. It also carries additional information depending on the state of the speaker: physical features, emotions, psychosocial traits and health conditions. Studying voice features is straightforward, does not require significant investment and can be carried out on a mass scale. However, speech analysis can only be used for initial diagnosis. The outcome of such a pre-diagnosis should indicate the need for standard medical tests. Although speech analysis is currently rarely used in medical diagnostics, it can enable earlier diagnosis and more effective treatment of patients. OBJECTIVE This paper is a systematic review and meta-analysis of recent advancements in using voice analysis for diagnosis and monitoring of some diseases. The goal of this article is to present and compare recent approaches to using speech and voice analysis as biomarkers of diseases. The article takes into account metabolic, respiratory, cardiovascular, endocrine and nervous system disorders. METHODS Articles published between 2010-2019 were selected from PubMed and ISCA Archive, using keywords ‘voice’ and ‘speech’ and respective disorder names. Further selection was performed to identify studies that assessed voice quality quantitatively in selected disorders by acoustic voice analysis (not only perceptual assessment). Information was extracted from each paper in order to compare various aspects of datasets, speech parameters, methods of analysis applied and results obtained. Each chapter starts with a medical description of how each disorder affects voice and contains a summary of different processing approaches, and is supplemented by tables comparing various investigations. Additional diagrams were prepared to illustrate general tendencies and to compare advancements in the state-of-the-art across the analyzed groups of diseases. RESULTS Over 90 research papers were reviewed and over 40 databases were summarized. Basic acoustic parameters which are significantly correlated with each given disorder were developed for cardiovascular, metabolic and endocrine diseases, as well as schizophrenia and amyotrophic lateral sclerosis. The affective and neurodegenerative disorders are well investigated and the majority of papers contain automatic voice recognition and machine learning methods. The main sources of problems were identified and some recommendation for future research were set. CONCLUSIONS Speech analysis is a promising tool for pre-diagnosis of certain disorders. Advanced computerized voice analysis and machine learning algorithms, and the widespread availability of smartphones, means that a diagnosis may be presented during the patient’s appointment with their physician, and even during a telephone conversation.

Sign in / Sign up

Export Citation Format

Share Document