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A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining.

ABSTRACT: The growing healthcare industry is generating a large volume of useful data on patient demographics, treatment plans, payment, and insurance coverage—attracting the attention of clinicians and scientists alike. In recent years, a number of peer-reviewed articles have addressed different dimensions of data mining application in healthcare. However, the lack of a comprehensive and systematic narrative motivated us to construct a literature review on this topic. In this paper, we present a review of the literature on healthcare analytics using data mining and big data. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a database search between 2005 and 2016. Critical elements of the selected studies—healthcare sub-areas, data mining techniques, types of analytics, data, and data sources—were extracted to provide a systematic view of development in this field and possible future directions. We found that the existing literature mostly examines analytics in clinical and administrative decision-making. Use of human-generated data is predominant considering the wide adoption of Electronic Medical Record in clinical care. However, analytics based on website and social media data has been increasing in recent years. Lack of prescriptive analytics in practice and integration of domain expert knowledge in the decision-making process emphasizes the necessity of future research.

SUBMITTER: Islam MS

PROVIDER: S-EPMC6023432 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining.

Islam Md Saiful MS Hasan Md Mahmudul MM Wang Xiaoyi X Germack Hayley D HD Noor-E-Alam Md M

Healthcare (Basel, Switzerland) 20180523 2

The growing healthcare industry is generating a large volume of useful data on patient demographics, treatment plans, payment, and insurance coverage—attracting the attention of clinicians and scientists alike. In recent years, a number of peer-reviewed articles have addressed different dimensions of data mining application in healthcare. However, the lack of a comprehensive and systematic narrative motivated us to construct a literature review on this topic. In this paper, we present a re ...[more]

PMID: 29882866

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Project description:Introduction: Healthcare systems increasingly move toward "value-based healthcare" (VBHC), aiming to further improve quality and performance of care as well as the sustainable use of resources. Evidence about healthcare professionals' contributions to VBHC, experienced job demands and resources as well as employee well-being in VBHC is scattered. This systematic review synthesizes this evidence by exploring how VBHC relates to the healthcare professional, and vice versa. Method: Seven databases were systematically searched for relevant studies. The search yielded 3,782 records, of which 45 were eligible for inclusion based on a two-step screening process using exclusion criteria performed by two authors independently. The quality of the included studies was appraised using the Mixed Methods Appraisal Tool (MMAT). Based on inductive thematic analysis, the Job Demands-Resources (JD-R) model was modified. Subsequently, this modified model was applied deductively for a second round of thematic analysis. Results: Ten behaviors of healthcare professionals to enhance value in care were identified. These behaviors and associated changes in professionals' work content and work environment impacted the experienced job demands and resources and, in turn, employee well-being and job strain. This review revealed 16 constructs as job demand and/or job resource. Examples of these include role strain, workload and meaning in work. Four constructs related to employee well-being, including engagement and job satisfaction, and five constructs related to job strain, including exhaustion and concerns, were identified. A distinction was made between job demands and resources that were a pure characteristic of VBHC, and job demands and resources that resulted from environmental factors such as how care organizations shaped VBHC. Conclusion and Discussion: This review shows that professionals experience substantial job demands and resources resulting from the move toward VBHC and their active role therein. Several job demands are triggered by an unsupportive organizational environment. Hence, increased organizational support may contribute to mitigating or avoiding adverse psychosocial factors and enhance positive psychosocial factors in a VBHC context. Further research to estimate the effects of VBHC on healthcare professionals is warranted.

Project description:BACKGROUND:While the application of learning analytics in tertiary education has received increasing attention in recent years, a much smaller number have explored its use in health care-related educational studies. OBJECTIVE:This systematic review aims to examine the use of e-learning analytics data in health care studies with regards to how the analytics is reported and if there is a relationship between e-learning analytics and learning outcomes. METHODS:We performed comprehensive searches of papers from 4 electronic databases (MEDLINE, EBSCOhost, Web of Science, and ERIC) to identify relevant papers. Qualitative studies were excluded from this review. Papers were screened by 2 independent reviewers. We selected qualified studies for further investigation. RESULTS:A total of 537 papers were screened, and 19 papers were identified. With regards to analytics undertaken, 11 studies reported the number of connections and time spent on e-learning. Learning outcome measures were defined by summative final assessment marks or grades. In addition, significant statistical results of the relationships between e-learning usage and learning outcomes were reported in 12 of the identified papers. In general, students who engaged more in e-learning resources would get better academic attainments. However, 2 papers reported otherwise with better performing students consuming less e-learning videos. A total of 14 papers utilized satisfaction questionnaires for students, and all were positive in their attitude toward e-learning. Furthermore, 6 of 19 papers reported descriptive statistics only, with no statistical analysis. CONCLUSIONS:The nature of e-learning activities reported in this review was varied and not detailed well. In addition, there appeared to be inadequate reporting of learning analytics data observed in over half of the selected papers with regards to definitions and lack of detailed information of what the analytic was recording. Although learning analytics data capture is popular, a lack of detail is apparent with regards to the capturing of meaningful and comparable data. In particular, most analytics record access to a management system or particular e-learning materials, which may not necessarily detail meaningful learning time or interaction. Hence, learning analytics data should be designed to record the time spent on learning and focus on key learning activities. Finally, recommendations are made for future studies.

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A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining.

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A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining.

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