Open Source Health Information Technology Projects

2012 ◽  
pp. 168-185
Author(s):  
Evangelos Katsamakas ◽  
Balaji Janamanchi ◽  
Wullianallur Raghupathi ◽  
Wei Gao
Keyword(s):  
Information Technology ◽  
Open Source ◽  
Health Information Technology ◽  
Health Information ◽  
Open Source Software ◽  
Future Research ◽  
Software Projects ◽  
Development Status ◽  
Future Research Directions ◽  
Using Data

This chapter discusses the growth of open source software projects in healthcare. It proposes a research framework that examines the roles of project sponsorship, license type, development status and technological complements in the success of open source health information technology (HIT) projects, and it develops a systematic method for classifying projects based on their success potential. Using data from Sourceforge, an open source software development portal, we find that although project sponsorship and license restrictiveness influence project metrics, they are not significant predictors of project success categorization. On the other hand, development status, operating system, and programming language are significant predictors of an OSS project’s success categorization. We discuss research and application implications and suggest future research directions.

Open Source Health Information Technology Projects

2011 ◽  
pp. 308-325
Author(s):  
Evangelos Katsamakas ◽  
Balaji Janamanchi ◽  
Wullianallur Raghupathi ◽  
Wei Gao
Keyword(s):  
Information Technology ◽  
Open Source ◽  
Health Information Technology ◽  
Health Information ◽  
Open Source Software ◽  
Future Research ◽  
Software Projects ◽  
Development Status ◽  
Future Research Directions ◽  
Using Data

This chapter discusses the growth of open source software projects in healthcare. It proposes a research framework that examines the roles of project sponsorship, license type, development status and technological complements in the success of open source health information technology (HIT) projects, and it develops a systematic method for classifying projects based on their success potential. Using data from Sourceforge, an open source software development portal, we find that although project sponsorship and license restrictiveness influence project metrics, they are not significant predictors of project success categorization. On the other hand, development status, operating system, and programming language are significant predictors of an OSS project’s success categorization. We discuss research and application implications and suggest future research directions.

A Classification Analysis of the Success of Open Source Health Information Technology Projects

2011 ◽  
pp. 256-273
Author(s):  
Evangelos Katsamakas ◽  
Balaji Janamanchi ◽  
Wullianallur Raghupathi ◽  
Wei Gao
Keyword(s):  
Information Technology ◽  
Open Source ◽  
Health Information Technology ◽  
Health Information ◽  
Open Source Software ◽  
Future Research ◽  
Software Projects ◽  
Information Technology Projects ◽  
Development Status ◽  
Future Research Directions

As the number of open source software (OSS) projects in healthcare grows rapidly, researchers are faced with the challenge of understanding and explaining the success of the open source phenomenon. This article proposes a research framework that examines the roles of project sponsorship, license type, development status and technological complements in the success of open source health information technology (HIT) projects and it develops a systematic method for classifying projects based on their success potential. Drawing from economic theory, a novel proposition in the authors’ framework suggests that higher project-license restrictiveness will increase OSS adoption, because organizations will be more confident that the OSS project will remain open source in the future. Applying the framework to a sample of open source software projects in healthcare, the authors find that although project sponsorship and license restrictiveness influence project metrics, they are not significant predictors of project success categorization. On the other hand, development status, operating system and programming language are significant predictors of an OSS project’s success categorization. Application implications and future research directions are discussed.

A Classification Analysis of the Success of Open Source Health Information Technology Projects

2009 ◽  
Vol 4 (4) ◽  
pp. 19-36 ◽  
Author(s):  
Evangelos Katsamakas ◽  
Balaji Janamanchi ◽  
Wullianallur Raghupathi ◽  
Wei Gao
Keyword(s):  
Information Technology ◽  
Open Source ◽  
Health Information Technology ◽  
Health Information ◽  
Open Source Software ◽  
Future Research ◽  
Software Projects ◽  
Information Technology Projects ◽  
Development Status ◽  
Future Research Directions

As the number of open source software (OSS) projects in healthcare grows rapidly, researchers are faced with the challenge of understanding and explaining the success of the open source phenomenon. This article proposes a research framework that examines the roles of project sponsorship, license type, development status and technological complements in the success of open source health information technology (HIT) projects and it develops a systematic method for classifying projects based on their success potential. Drawing from economic theory, a novel proposition in the authors’ framework suggests that higher project-license restrictiveness will increase OSS adoption, because organizations will be more confident that the OSS project will remain open source in the future. Applying the framework to a sample of open source software projects in healthcare, the authors find that although project sponsorship and license restrictiveness influence project metrics, they are not significant predictors of project success categorization. On the other hand, development status, operating system and programming language are significant predictors of an OSS project’s success categorization. Application implications and future research directions are discussed.

Measuring the Efficiency of Free and Open Source Software Projects Using Data Envelopment Analysis

2007 ◽  
pp. 25-46
Author(s):  
Stefan Koch
Keyword(s):  
Data Envelopment Analysis ◽  
Open Source ◽  
Optimization Method ◽  
Future Research ◽  
Data Envelopment ◽  
Software Projects ◽  
Data Set ◽  
Output Factors ◽  
Future Research Directions ◽  
Using Data

In this chapter, we propose for the first time a method to compare the efficiency of free and open source projects, based on the data envelopment analysis (DEA) methodology. DEA offers several advantages in this context, as it is a non-parametric optimization method without any need for the user to define any relations between different factors or a production function, can account for economies or diseconomies of scale, and is able to deal with multi-input, multi-output systems in which the factors have different scales. Using a data set of 43 large F/OS projects retrieved from SourceForge.net, we demonstrate the application of DEA, and show that DEA indeed is usable for comparing the efficiency of projects. We will also show additional analyses based on the results, exploring whether the inequality in work distribution within the projects, the licensing scheme or the intended audience have an effect on their efficiency. As this is a first attempt at using this method for F/OS projects, several future research directions are possible. These include additional work on determining input and output factors, comparisons within application areas, and comparison to commercial or mixed-mode development projects.

Measuring the Efficiency of Free and Open Source Software Projects Using Data Envelopment Analysis

2009 ◽  
pp. 2963-2977
Author(s):  
Stefan Koch
Keyword(s):  
Data Envelopment Analysis ◽  
Open Source ◽  
Optimization Method ◽  
Future Research ◽  
Data Envelopment ◽  
Software Projects ◽  
Data Set ◽  
Output Factors ◽  
Future Research Directions ◽  
Using Data

In this chapter, we propose for the first time a method to compare the efficiency of free and open source projects, based on the data envelopment analysis (DEA) methodology. DEA offers several advantages in this context, as it is a non-parametric optimization method without any need for the user to define any relations between different factors or a production function, can account for economies or diseconomies of scale, and is able to deal with multi-input, multi-output systems in which the factors have different scales. Using a data set of 43 large F/OS projects retrieved from SourceForge.net, we demonstrate the application of DEA, and show that DEA indeed is usable for comparing the efficiency of projects. We will also show additional analyses based on the results, exploring whether the inequality in work distribution within the projects, the licensing schem,e or the intended audience have an effect on their efficiency. As this is a first attempt at using this method for F/OS projects, several future research directions are possible. These include additional work on determining input and output factors, comparisons within application areas, and comparison to commercial or mixed-mode development projects.

The application profiles and development characteristics of library Open Source Software projects

2014 ◽  
Vol 32 (2) ◽  
pp. 260-275 ◽  
Author(s):  
Namjoo Choi
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Design Methodology ◽  
Project Success ◽  
Future Research ◽  
Software Projects ◽  
Content Type ◽  
Research And Practice ◽  
Development Status ◽  
Steady Decrease

Purpose – Little is known as to the breadth and diversity of Open Source Software (OSS) applications for libraries and the development characteristics that influence the sustainability and success of projects creating them. The purpose of this paper is to address this gap by analyzing a large sample of library OSS projects. Design/methodology/approach – A total of 594 library OSS projects (469 from SourceForge and 125 from Foss4lib) are classified by type and further differentiated and assessed across a number of criteria including, but not limited to, sponsorship status, license type, and development status. Findings – While various types of library OSS applications were found to be under development and in use, the results show that there has been a steady decrease in the number of projects initiated since 2009. Although sponsorship was significantly positively associated with several indicators of OSS project success, the proportion of sponsored projects was relatively small compared to the proportions reported in some other contexts. In total, 71 percent of the projects have a restrictive license scheme, suggesting that the OSS ideology is valued among library OSS projects. The results also indicate that library OSS projects exhibit several characteristics that differ from the traditional developer-oriented OSS projects in terms of their technical environment. Originality/value – This study, as the first of its kind, offers a broader, more quantitative picture of the state of library OSS applications as well as the development characteristics of projects developing them. Several implications for research and practice, and directions for future research are provided.

Prediction Models and Techniques for Open Source Software Projects

2014 ◽  
Vol 5 (2) ◽  
pp. 1-39 ◽  
Author(s):  
M.M. Mahbubul Syeed ◽  
Imed Hammouda ◽  
Tarja Systä
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Prediction Models ◽  
Literature Survey ◽  
Future Research ◽  
Software Projects ◽  
Reliable Prediction ◽  
Research Directions ◽  
Fundamental Knowledge ◽  
Future Research Directions

Open Source Software (OSS) is currently a widely adopted approach to developing and distributing software. For effective adoption of OSS, fundamental knowledge of project development is needed. This often calls for reliable prediction models to simulate project evolution and to envision project future. These models provide help in supporting preventive maintenance and building quality software. This paper reports on a systematic literature survey aimed at the identification and structuring of research that offer prediction models and techniques in analyzing OSS projects. In this review, we systematically selected and reviewed 52 peer reviewed articles that were published between January, 2000 and March, 2013. The study outcome provides insight in what constitutes the main contributions of the field, identifies gaps and opportunities, and distills several important future research directions.

Demography of Open Source Software Prediction Models and Techniques

2021 ◽  
pp. 620-652
Author(s):  
Kaniz Fatema ◽  
M. M. Mahbubul Syeed ◽  
Imed Hammouda
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Preventive Maintenance ◽  
Prediction Models ◽  
Journal Article ◽  
Literature Survey ◽  
Future Research ◽  
Fundamental Knowledge ◽  
Future Research Directions ◽  
Insight Into

Open source software (OSS) is currently a widely adopted approach to developing and distributing software. Many commercial companies are using OSS components as part of their product development. For instance, more than 58% of web servers are using an OSS web server, Apache. For effective adoption of OSS, fundamental knowledge of project development is needed. This often calls for reliable prediction models to simulate project evolution and to envision project future. These models provide help in supporting preventive maintenance and building quality software. This chapter reports on a systematic literature survey aimed at the identification and structuring of research that offers prediction models and techniques in analysing OSS projects. The study outcome provides insight into what constitutes the main contributions of the field, identifies gaps and opportunities, and distils several important future research directions. This chapter extends the authors' earlier journal article and offers the following improvements: broader study period, enhanced discussion, and synthesis of reported results.

scholarly journals A Survey of the Literature on Unintended Consequences Associated with Health Information Technology: 2014–2015

2016 ◽  
Vol 25 (01) ◽  
pp. 13-29 ◽  
Author(s):  
J. Abraham ◽  
L. L. Novak ◽  
T. L. Reynolds ◽  
A. Gettinger ◽  
K. Zheng
Keyword(s):  
Information Technology ◽  
Time Series ◽  
Health Information Technology ◽  
Health Information ◽  
Unintended Consequences ◽  
Adverse Outcomes ◽  
Future Research ◽  
Health It ◽  
Recorded Data ◽  
The Impact

SummaryObjective: To summarize recent research on unintended consequences associated with implementation and use of health information technology (health IT). Included in the review are original empirical investigations published in English between 2014 and 2015 that reported unintended effects introduced by adoption of digital interventions. Our analysis focuses on the trends of this steam of research, areas in which unintended consequences have continued to be reported, and common themes that emerge from the findings of these studies.Method: Most of the papers reviewed were retrieved by searching three literature databases: MEDLINE, Embase, and CINAHL. Two rounds of searches were performed: the first round used more restrictive search terms specific to unintended consequences; the second round lifted the restrictions to include more generic health IT evaluation studies. Each paper was independently screened by at least two authors; differences were resolved through consensus development.Results: The literature search identified 1,538 papers that were potentially relevant; 34 were deemed meeting our inclusion criteria after screening. Studies described in these 34 papers took place in a wide variety of care areas from emergency departments to ophthalmology clinics. Some papers reflected several previously unreported unintended consequences, such as staff attrition and patients’ withholding of information due to privacy and security concerns. A majority of these studies (71%) were quantitative investigations based on analysis of objectively recorded data. Several of them employed longitudinal or time series designs to distinguish between unintended consequences that had only transient impact, versus those that had persisting impact. Most of these unintended consequences resulted in adverse outcomes, even though instances of beneficial impact were also noted. While care areas covered were heterogeneous, over half of the studies were conducted at academic medical centers or teaching hospitals. Conclusion: Recent studies published in the past two years represent significant advancement of unintended consequences research by seeking to include more types of health IT applications and to quantify the impact using objectively recorded data and longitudinal or time series designs. However, more mixed-methods studies are needed to develop deeper insights into the observed unintended adverse outcomes, including their root causes and remedies. We also encourage future research to go beyond the paradigm of simply describing unintended consequences, and to develop and test solutions that can prevent or minimize their impact.

scholarly journals Knowledge Graph Analysis of Human Health Research Related to Climate Change

2020 ◽  
Vol 17 (20) ◽  
pp. 7395
Author(s):  
Yating Zhao ◽  
Jingjing Guo ◽  
Chao Bao ◽  
Changyong Liang ◽  
Hemant K Jain
Keyword(s):  
Climate Change ◽  
Human Health ◽  
Future Research ◽  
Research Directions ◽  
Development Status ◽  
Future Research Directions ◽  
Knowledge Support ◽  
Using Data ◽  
Research Hotspots ◽  
Impacts Of Climate Change

In order to explore the development status, knowledge base, research hotspots, and future research directions related to the impacts of climate change on human health, a systematic bibliometric analysis of 6719 published articles from 2003 to 2018 in the Web of Science was performed. Using data analytics tools such as HistCite and CiteSpace, the time distribution, spatial distribution, citations, and research hotspots were analyzed and visualized. The analysis revealed the development status of the research on the impacts of climate change on human health and analyzed the research hotspots and future development trends in this field, providing important knowledge support for researchers in this field.

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