| 1. |
- Andersson, Ann-Christine, et al.
(författare)
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Evaluating a questionnaire to measure improvement initiatives in Swedish healthcare
- 2013
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Ingår i: BMC Health Services Research. - : BioMed Central. - 1472-6963. ; 13:48
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Tidskriftsartikel (refereegranskat)abstract
- Background: Quality improvement initiatives have expanded recently within the healthcare sector. Studies have shown that less than 40% of these initiatives are successful, indicating the need for an instrument that can measure the progress and results of quality improvement initiatives and answer questions about how quality initiatives are conducted. The aim of the present study was to develop and test an instrument to measure improvement process and outcome in Swedish healthcare. Methods: A questionnaire, founded on the Minnesota Innovation Survey (MIS), was developed in several steps. Items were merged and answer alternatives were revised. Employees participating in a county council improvement program received the web-based questionnaire. Data was analysed by descriptive statistics and correlation analysis. The questionnaire psychometric properties were investigated and an exploratory factor analysis was conducted. Results: The Swedish Improvement Measurement Questionnaire consists of 27 items. The Improvement Effectiveness Outcome dimension consists of three items and has a Cronbach’s alpha coefficient of 0.67. The Internal Improvement Processes dimension consists of eight sub-dimensions with a total of 24 items. Cronbach’s alpha coefficient for the complete dimension was 0.72. Three significant item correlations were found. A large involvement in the improvement initiative was shown and the majority of the respondents were satisfied with their work. Conclusions: The psychometric property tests suggest initial support for the questionnaire to study and evaluate quality improvement initiatives in Swedish healthcare settings. The overall satisfaction with the quality improvement initiative correlates positively to the awareness of individual responsibilities.
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| 2. |
- Bosch, Jan, 1967, et al.
(författare)
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Digital for real: A multicase study on the digital transformation of companies in the embedded systems domain
- 2021
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Ingår i: Journal of Software: Evolution and Process. - : Wiley. - 2047-7481 .- 2047-7473. ; 33:5
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Tidskriftsartikel (refereegranskat)abstract
- With digitalization and with technologies such as software, data, and artificial intelligence, companies in the embedded systems domain are experiencing a rapid transformation of their conventional businesses. While the physical products and associated product sales provide the core revenue, these are increasingly being complemented with service offerings, new data-driven services, and digital products that allow for continuous value creation and delivery to customers. However, although there is significant research on digitalization and digital transformation, few studies highlight the specific needs of embedded systems companies and what it takes to transform from a traditional towards a digital company within business domains characterized by high complexity, hardware dependencies, and safety-critical system functionality. In this paper, we capture the difference between what constitutes a traditional and a digital company and we detail the typical evolution path embedded systems companies take when transitioning towards becoming digital companies.
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| 3. |
- Caramaschi, Sara, 1998-, et al.
(författare)
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Device Orientation Independent Human Activity Recognition Model for Patient Monitoring Based on Triaxial Acceleration
- 2023
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 13:7, s. 4175-4175
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Tidskriftsartikel (refereegranskat)abstract
- Tracking a person’s activities is relevant in a variety of contexts, from health and group-specific assessments, such as elderly care, to fitness tracking and human–computer interaction. In a clinical context, sensor-based activity tracking could help monitor patients’ progress or deterioration during their hospitalization time. However, during routine hospital care, devices could face displacements in their position and orientation caused by incorrect device application, patients’ physical peculiarities, or patients’ day-to-day free movement. These aspects can significantly reduce algorithms’ performances. In this work, we investigated how shifts in orientation could impact Human Activity Recognition (HAR) classification. To reach this purpose, we propose an HAR model based on a single three-axis accelerometer that can be located anywhere on the participant’s trunk, capable of recognizing activities from multiple movement patterns, and, thanks to data augmentation, can deal with device displacement. Developed models were trained and validated using acceleration measurements acquired in fifteen participants, and tested on twenty-four participants, of which twenty were from a different study protocol for external validation. The obtained results highlight the impact of changes in device orientation on a HAR algorithm and the potential of simple wearable sensor data augmentation for tackling this challenge. When applying small rotations (<20 degrees), the error of the baseline non-augmented model steeply increased. On the contrary, even when considering rotations ranging from 0 to 180 along the frontal axis, our model reached a f1-score of 0.85±0.110.85±0.11 against a baseline model f1-score equal to 0.49±0.120.49±0.12.
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| 4. |
- Dakkak, Anas, et al.
(författare)
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Customer Support In The Era of Continuous Deployment: A Software-Intensive Embedded Systems Case Study
- 2022
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Ingår i: Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 914-923
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Konferensbidrag (refereegranskat)abstract
- Supporting customers after they acquire the prod-uct is essential for companies producing and selling software-intensive embedded systems products. Generally, customer sup-port is the first interaction point between the product users and the product vendor. Customer support is often engaged with answering customers' questions, troubleshooting, fault identification, and fixing product faults. While continuous deployment advocates for closer cooperation between the ones operating the software and the ones developing it, the means of such collaboration in general and the role of customer support, in particular, has not been addressed in the context of software-intensive embedded systems. Therefore, to better understand the impact that continuous deployment has on customer support and the role customer support should play in this context, we conducted a case study at a multinational company developing and selling telecommunications networks infrastructure. We focused on the 4th and 5th Generation (4G and 5G) Radio Access Networks (RAN) products, which can be considered a high volume product as they cover more than 80% of the world's population. Our study reveals that customer support needs to transition from a transaction-based and passive function triggered by customer support requests, to take an active role characterized by being proactive and preemptive to cope with the shorter operational time of a software version introduced by continuous deployment. In addition, customer support plays an essential role in making the feedback actionable by aggregating and consolidating feedback data to the R&D organization.
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| 5. |
- Dzhusupova, Rimman, et al.
(författare)
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Challenges in developing and deploying AI in the engineering, procurement and construction industry
- 2022
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Ingår i: Proceedings - 2022 IEEE 46th Annual Computers, Software, and Applications Conference, COMPSAC 2022. - : IEEE. ; , s. 1070-1075
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Konferensbidrag (refereegranskat)abstract
- AI in the Engineering, Procurement and Construction (EPC) industry has not yet a proven track record in large-scale projects. Since AI solutions for industrial applications became available only recently, deployment experience and lessons learned are still to be built up. Several research papers exist describing the potential of AI, and many surveys and white papers have been published indicating the challenges of AI deployment in the EPC industry. However, there is a recognizable shortage of in-depth studies of deployment experience in academic literature, particularly those focusing on the experiences of EPC companies involved in large-scale project execution with high safety standards, such as the petrochemical or energy sector. The novelty of this research is that we explore in detail the challenges and obstacles faced in developing and deploying AI in a large-scale project in the EPC industry based on real-life use cases performed in an EPC company. Those identified challenges are not linked to specific technology or a company's know-how and, therefore, are universal. The findings in this paper aim to provide feedback to academia to reduce the gap between research and practice experience. They also help reveal the hidden stones when implementing AI solutions in the industry.
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| 6. |
- Fabijan, Aleksander, et al.
(författare)
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Effective Online Controlled Experiment Analysis at Large Scale
- 2018
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Ingår i: Proceedings of the EUROMICRO Conference. - : IEEE. ; , s. 64-67
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Konferensbidrag (refereegranskat)abstract
- Online Controlled Experiments (OCEs) are the norm in data-driven software companies because of the benefits they provide for building and deploying software. Product teams experiment to accurately learn whether the changes that they do to their products (e.g. adding new features) cause any impact (e.g. customers use them more frequently). Experiments also help reduce the risk from deploying software by minimizing the magnitude and duration of harm caused by software bugs, allowing software to be shipped more frequently. To make informed decisions in product development, experiment analysis needs to be granular with a large number of metrics over heterogeneous devices and audiences. Discovering experiment insights by hand, however, can be cumbersome. In this paper, and based on case study research at a large-scale software development company with a long tradition of experimentation, we (1) describe the standard process of experiment analysis, and (2) introduce an artifact to improve the effectiveness and comprehensiveness of this process.
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| 7. |
- Fabijan, Aleksander, et al.
(författare)
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Experimentation growth: Evolving trustworthy A/B testing capabilities in online software companies
- 2018
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Ingår i: Journal of Software: Evolution and Process. - : Wiley. - 2047-7481 .- 2047-7473. ; 30:12
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Tidskriftsartikel (refereegranskat)abstract
- Companies need to know how much value their ideas deliver to customers. One of the most powerful ways to accurately measure this is by conducting online controlled experiments (OCEs). To run experiments, however, companies need to develop strong experimentation practices as well as align their organization and culture to experimentation. The main objective of this paper is to demonstrate how to run OCEs at large scale using the experience of companies that succeeded in scaling. Based on case study research at Microsoft, Booking.com, Skyscanner, and Intuit, we present our main contribution-The Experiment Growth Model. This four-stage model addresses the seven critical aspects of experimentation and can help companies to transform their organizations into learning laboratories where new ideas can be tested with scientific accuracy. Ultimately, this should lead to better products and services.
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| 8. |
- Fabijan, Aleksander, et al.
(författare)
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Online Controlled Experimentation at Scale : An Empirical Survey on the Current State of A/B Testing
- 2018
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Ingår i: Proceedings of the EUROMICRO Conference. - : IEEE. ; , s. 68-72
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Konferensbidrag (refereegranskat)abstract
- Online Controlled Experiments (OCEs, aka A/B tests) are one of the most powerful methods for measuring how much value new features and changes deployed to software products bring to users. Companies like Microsoft, Amazon, and Booking.com report the ability to conduct thousands of OCEs every year. However, the competences of the remainder of the online software industry remain unknown. The main objective of this paper is to reveal the current state of A/B testing maturity in the software industry based on a maturity model from our previous research. We base our findings on 44 responses from an online empirical survey. Our main contribution of this paper is the current state of experimentation maturity as operationalized by the ExG model for a convenience sample of companies doing online controlled experiments. Our findings show that, among others, companies typically develop in-house experimentation platforms, that these platforms are of various levels of maturity, and that designing key metrics - Overall Evaluation Criteria - remains the key challenge for successful experimentation.
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| 9. |
- Fabijan, Aleksander, et al.
(författare)
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Three Key Checklists and Remedies for Trustworthy Analysis of Online Controlled Experiments at Scale
- 2019
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Ingår i: Proceedings - 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice, ICSE-SEIP 2019. - : IEEE. ; May 2019, s. 1-10, s. 1-10
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Konferensbidrag (refereegranskat)abstract
- Online Controlled Experiments (OCEs) are transforming the decision-making process of data-driven companies into an experimental laboratory. Despite their great power in identifying what customers actually value, experimentation is very sensitive to data loss, skipped checks, wrong designs, and many other 'hiccups' in the analysis process. For this purpose, experiment analysis has traditionally been done by experienced data analysts and scientists that closely monitored experiments throughout their lifecycle. Depending solely on scarce experts, however, is neither scalable nor bulletproof. To democratize experimentation, analysis should be streamlined and meticulously performed by engineers, managers, or others responsible for the development of a product. In this paper, based on synthesized experience of companies that run thousands of OCEs per year, we examined how experts inspect online experiments. We reveal that most of the experiment analysis happens before OCEs are even started, and we summarize the key analysis steps in three checklists. The value of the checklists is threefold. First, they can increase the accuracy of experiment set-up and decision-making process. Second, checklists can enable novice data scientists and software engineers to become more autonomous in setting-up and analyzing experiments. Finally, they can serve as a base to develop trustworthy platforms and tools for OCE set-up and analysis.
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| 10. |
- Figalist, Iris, et al.
(författare)
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Breaking the Vicious Circle : Why AI for software analytics and business intelligence does not take off in practice
- 2020
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Ingår i: 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). - : IEEE. - 9781728195322 - 9781728195339 ; , s. 5-12
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Konferensbidrag (refereegranskat)abstract
- In recent years, the application of artificial intelligence (AI) has become an integral part of a wide range of areas, including software engineering. By analyzing various data sources generated in software engineering, it can provide valuable insights into customer behavior, product performance, bugs and errors, and many more. In practice, however, AI for software analytics and business intelligence often gets stuck in a prototypical stage and the results are rarely used to make decisions based on data. To understand the underlying root causes of this phenomenon, we conduct both an explanatory case study and a survey on the challenges of realizing and utilizing artificial intelligence in the context of software-intensive businesses. As a result, we identify a vicious circle that prevents practitioners from moving from prototypical analytics to continuous and productively usable software analytics and business intelligence based on AI.
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