Application of Fourth Industrial Revolution Technologies in Healthcare

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Gama, Nkosinathi; Adebesin, Funmi

Abstract: The aim of this systematic literature review (SLR) is to explore how Fourth Ind ... Expand

Abstract: The aim of this systematic literature review (SLR) is to explore how Fourth Industrial Revolution (4IR) digital technologies are transforming the healthcare sector. Peer-reviewed, full-text research papers published between January 2016 and April 2021, which focused on data-driven healthcare, health data management systems, and the disruptive role of digital technologies in healthcare were retrieved from three electronic databases, namely PubMed Central, Institute of Electrical and Electronics Engineers (IEEE) and MEDLINE. Articles included in the SLR were identified and selected using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline. Three main themes were identified from 84 research papers included in the SLR, namely, (i) digital technologies driving the transformation of healthcare, (ii) the impact of digital transformation on healthcare, and (iii) areas of transformation in healthcare. The results showed that technological innovations can mitigate the impact of unequal distribution of healthcare services, reduce healthcare costs, improve the quality of care, and the quality of life for patients and healthcare professionals. Collapse

Topics: database system electronic health mobile system industry 4.0 production management

Methods: literature sample qualitative content analysis mixed method structured literature research descriptive statistic

Time Series Prediction Using Deep Learning Methods in Healthcare

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Morid, Mohammad Amin; Sheng, Olivia R. Liu; Dunbar, Joseph

Abstract: Traditional machine learning methods face unique challenges when applied to heal ... Expand

Abstract: Traditional machine learning methods face unique challenges when applied to healthcare predictive analytics. The high-dimensional nature of healthcare data necessitates labor-intensive and time-consuming processes when selecting an appropriate set of features for each new task. Furthermore, machine learning methods depend heavily on feature engineering to capture the sequential nature of patient data, oftentimes failing to adequately leverage the temporal patterns of medical events and their dependencies. In contrast, recent deep learning (DL) methods have shown promising performance for various healthcare prediction tasks by specifically addressing the high-dimensional and temporal challenges of medical data. DL techniques excel at learning useful representations of medical concepts and patient clinical data as well as their nonlinear interactions from high-dimensional raw or minimally processed healthcare data. In this article, we systematically reviewed research works that focused on advancing deep neural networks to leverage patient structured time series data for healthcare prediction tasks. To identify relevant studies, we searched MEDLINE, IEEE, Scopus, and ACM Digital Library for relevant publications through November 4, 2021. Overall, we found that researchers have contributed to deep time series prediction literature in 10 identifiable research streams: DL models, missing value handling, addressing temporal irregularity, patient representation, static data inclusion, attention mechanisms, interpretation, incorporation of medical ontologies, learning strategies, and scalability. This study summarizes research insights from these literature streams, identifies several critical research gaps, and suggests future research opportunities for DL applications using patient time series data. Collapse

Topics: missing data health information system database system healthcare data

Methods: longitudinal research time series analysis structured literature research ontological modelling literature study

Intellectual Disability, Digital Technologies, and Independent Transportation -A Scoping Review

2023 | HICSS | Citations: 0

Authors: Safari, M.; Wass, S.; Thygesen, E.; Haugland, S.

Abstract: Transportation is an essential aspect of everyday life. For people with intelle ... Expand

Abstract: Transportation is an essential aspect of everyday life. For people with intellectual disabilities transportation is one the largest barriers to community participation and a cause of inequality. However, digital technologies can reduce barriers for transportation use for people with intellectual disabilities and increase community mobility. The aim of this scoping review was to identify and map existing research on digital technology support for independent transport for people with intellectual disabilities and identify knowledge gaps relevant for further research. The authors conducted a scoping review of articles presenting digital technologies designed to assist in outdoor navigation for people with intellectual disabilities. The results show that while a variation of design elements was utilized, digital technologies can effectively support individuals with intellectual disability in transport. Further research should focus on multiple contexts and types of transportation, different support needs during independent travel, real-world settings, participatory approaches, and the role of user training. Collapse

Topics: system support database system geographic information system Sustainable Development Goal global navigation satellite system

Methods: structured literature research mixed method literature sample literature study

IT-Enabled Self-Monitoring for Chronic Disease Self-Management: An Interdisciplinary Review

2020 | Management Information Systems Quarterly | Citations: 0

Authors: Jinglu Jiang; Cameron, Ann-Frances

Abstract: Self-monitoring is a strategy that patients use to manage their chronic disease ... Expand

Abstract: Self-monitoring is a strategy that patients use to manage their chronic disease and chronic disease risk factors. Technological advances such as mobile apps, web-based tracking programs, sensing devices, wearable technologies, and insideable devices enable IT-based self-monitoring (ITSM) for chronic disease management. Since ITSM is multidisciplinary in nature and our understanding is fragmented, a systematic examination of the literature is performed to build a holistic understanding of the phenomenon. We review 159 studies published in 108 journals and conferences between 2006 and 2017. By adapting affordance actualization theory, we develop an overarching framework to organize the existing literature on ITSM for chronic disease management. Four themes emerge: key ITSM functionalities that enable affordances; effects on ITSM system use; effects on the achievement of chronic care goals; and the role of intermediary outcomes. For each theme, we identify what is known, what is unknown, and opportunities for future research. We also discuss cross-theme opportunities for future research where more diverse theoretical perspectives can contribute to our understanding of the phenomenon. This work provides research directions for IS researchers studying ITSM for chronic disease self-management. Collapse

Topics: affordance usability user experience gamification information system use

Methods: synthesis structured literature research experimental group experiment experimental design

Theories: theory of affordance

Opioid Use Disorder: Decision Support for Healthcare Professionals

2020 | Americas Conference on Information Systems | Citations: 0

Authors: Singh, Neetu; Varshney, Upkar

Abstract: Opioid Use Disorder (OUD) is defined as deviating from the physician's prescrip ... Expand

Abstract: Opioid Use Disorder (OUD) is defined as deviating from the physician's prescription of a specific opioid. The OUD patients will require expensive inpatient treatment followed by a long-term outpatient treatment. We present a decision support system for opioid prescriptions, inpatient treatment (detoxification), and outpatient treatment by healthcare professionals. We analyzed the impact of inaccuracy in PDMP, decision scenarios, and effectiveness of decisions in outpatient scenarios on the opioid resource requirements. The proposed DSS will lead to better decision making using both the risk score and patient's condition. Collapse

Topics: decision making decision support system decision support system support database system

Methods: literature sample literature study Markov chain Monte Carlo self reported survey

A Comprehensive Analysis of Triggers and Risk Factors for Asthma Based on Machine Learning and Large Heterogeneous Data Sources

2020 | Management Information Systems Quarterly | Citations: 0

Authors: Wenli Zhang; Ram, Sudha

Abstract: Asthma is a common chronic health condition affecting millions of people in the ... Expand

Abstract: Asthma is a common chronic health condition affecting millions of people in the United States. While asthma cannot be cured, it can be managed if we identify and understand triggers and risk factors that cause asthma exacerbations. However, this is challenging because these triggers and risk factors are complex and interconnected, and there are limitations to current mainstream approaches for identifying them. The recent availability of massive amounts of heterogeneous data has opened up new possibilities for asthma triggers and risk factors analyses. In this study, we introduce a data-driven framework, adapt and integrate multiple advanced machine learning techniques, and perform an empirical analysis to (1) derive characteristics of selfreported asthma patients from social media, (2) enable integration and repurposing of highly heterogeneous and commonly available datasets, and (3) uncover the sequential patterns of asthma triggers and risk factors, and their relative importance, both of which are difficult to achieve via retrospective cohort-based studies. Our methods and results can provide guidance for developing asthma management plans and interventions for specific subpopulations and, eventually, have the potential to reduce the societal burden of asthma. Collapse

Topics: social media Twitter database system open source wiki

Methods: random forest classification machine learning computational algorithm longitudinal research survey

IT-Based Self-Monitoring Interventions to Promote Physical Activity and Weight Loss: A Meta-Analysis of Change-from-Baseline Effects

2019 | HICSS | Citations: 0

Authors: Jiang, Jinglu; Cameron, Ann-Frances

Abstract: IT-based self-monitoring (ITSM) has attracted increasing interest as a strategy ... Expand

Abstract: IT-based self-monitoring (ITSM) has attracted increasing interest as a strategy for chronic disease self-management and day-to-day fitness promotion. Despite the increasing popularity of various selfmonitoring technologies such as fitness trackers and biosensors, their effectiveness is less certain. The objective of the current review is to determine the effectiveness of ITSM interventions on two types of key chronic care outcomes: weight management and physical activity. A systematic review employing a meta-analysis identified 42 ITSM studies that report change-from-baseline effects on weight and physical activity-related outcomes. Overall, a small effect size is found for body weight, BMI, waist circumferences, and step-based physical activity. The effect estimates on time-based physical activity are moderate. However, the effects on physical activity show variability and potential publication bias. A post-hoc analysis of the effects of ITSM on self-efficacy exhibit a small yet significant effect size, which shows the potential mediating role of patients’ psychological outcomes on the ultimate behavioral outcomes. In summary, ITSM is a potentially useful approach to manage weight and physical activity. Further study is needed to determine the source of heterogeneity as well as the types of ITSM interventions that are effective for weight, physical activity, but also other chronic care outcomes. Collapse

Topics: self efficacy database system missing data smart wearable device mobile system

Methods: meta analysis post-hoc analysis descriptive statistic structured literature research experiment

A Context Free Gramma for Key Noun-Phrase Extraction from Text

2019 | HICSS | Citations: 0

Authors: Liu, Ying

Abstract: Topic extraction is a major field in text mining. Key noun-phrases play a very i ... Expand

Abstract: Topic extraction is a major field in text mining. Key noun-phrases play a very important role in identifying the important document topic because the primary information of a document is described in nounphrases. In this paper, we propose a new topic extraction schema to identify the key noun-phrases by constructing a context free grammar (CFG) from input documents. In our new method, documents are reconstructed as a set of CFG rules using an existing algorithm called Sequitur. The Sequitur algorithm infers the resulting context-free grammatical rules, which can be considered as a hierarchical structure, from a sequence of discrete symbols. The resulting hierarchical structure exposes the underlying structure of input sequence that can help us capture meaningful regularity. Based on this hierarchical structure of the input document, we designed a new algorithm to identify noun-phrases and extract key noun-phrases. Collapse

Topics: IT productivity

Methods: computational algorithm natural language processing experiment machine learning literature sample

Mobile Health Privacy Concerns - A Systematic Review

2018 | Americas Conference on Information Systems | Citations: 3

Authors: Koffi, Bouabre; Yazdanmehr, Adel; Mahapatra, RadhaKanta

Abstract: Mobile health technologies have great potential to improve healthcare delivery. ... Expand

Abstract: Mobile health technologies have great potential to improve healthcare delivery. However, adoption of mobile applications raise many privacy concerns, and these issues must be addressed in order to facilitate technology adoption. We conducted a systematic review of the extant research literature to understand privacy concerns of patients and providers when they use mobile health applications. An extensive search of various databases including Web of Science, CINAHL, Cochrane Library, Embase, MEDLINE, PsycINFO, PubMed, ScienceDirect, and JMIR identified 37 unique articles on this topic. We analyzed these articles using a 3x3 framework with user type and usage type as the two dimensions. Our findings revealed that patient’s privacy concerns are heightened in the presence of certain disease types that carry a social stigma. Providers on the other hand are anxious about staying compliant with privacy regulations. Limitations of the study and directions for future research are discussed. Collapse

Topics: information privacy concern mobile health health information system mobile system privacy

Methods: literature sample structured literature research literature study qualitative interview focus group

A comparative analysis of semi-supervised learning: The case of article selection for medical systematic reviews

2018 | Information Systems Frontiers | Citations: 0

Authors: Liu, Jun; Timsina, Prem; El-Gayar, Omar

Abstract: While systematic reviews are positioned as an essential element of modern eviden ... Expand

Abstract: While systematic reviews are positioned as an essential element of modern evidence-based medical practice, the creation of these reviews is resource intensive. To mitigate this problem, there have been some attempts to leverage supervised machine learning to automate the article triage procedure. This approach has been proved to be helpful for updating existing systematic reviews. However, this technique holds very little promise for creating new reviews because training data is rarely available when it comes to systematic creation. In this research we assess and compare the applicability of semi-supervised learning to overcome this labeling bottleneck and support the creation of systematic reviews. The results indicated that semi-supervised learning could significantly reduce the human effort and is a viable technique for automating medical systematic review creation with a small-sized training dataset. Collapse

Topics: big data

Methods: support vector machine experiment semi-supervised learning structured literature research computational algorithm