Exploring the Effects of Data-Driven Hospital Operations on Operational Performance From the Resource Orchestration Theory Perspective

Yu, Wantao; Liu, Qi; Zhao, Gen; Song, Yamin

doi:10.1109/tem.2021.3098541

Cited by 21 publications

(31 citation statements)

References 71 publications

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“…Health institutions must "... embrace the value of data to drive improved outcomes of care" (p.125) [2] and promote a non punitive environment to facilitate discussions around success and failure as depicted by data [2]. This is consistent with other work surrounding healthcare digitisation [85] that recommends healthcare organisations promote a strong data culture in order for digital technologies to impact behaviour.…”

Section: Factors That Influence Sensemakingmentioning

confidence: 53%

An exploration into physician and surgeon data sensemaking: a qualitative systematic review using thematic synthesis

Whitelock-Wainwright

Koh

Whitelock‐Wainwright

et al. 2022

BMC Med Inform Decis Mak

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Providing electronic health data to medical practitioners to reflect on their performance can lead to improved clinical performance and quality of care. Understanding the sensemaking process that is enacted when practitioners are presented with such data is vital to ensure an improvement in performance. Thus, the primary objective of this research was to explore physician and surgeon sensemaking when presented with electronic health data associated with their clinical performance. A systematic literature review was conducted to analyse qualitative research that explored physicians and surgeons experiences with electronic health data associated with their clinical performance published between January 2010 and March 2022. Included articles were assessed for quality, thematically synthesised, and discussed from the perspective of sensemaking. The initial search strategy for this review returned 8,829 articles that were screened at title and abstract level. Subsequent screening found 11 articles that met the eligibility criteria and were retained for analyses. Two articles met all of the standards within the chosen quality assessment (Standards for Reporting Qualitative Research, SRQR). Thematic synthesis generated five overarching themes: data communication, performance reflection, infrastructure, data quality, and risks. The confidence of such findings is reported using CERQual (Confidence in the Evidence from Reviews of Qualitative research). The way the data is communicated can impact sensemaking which has implications on what is learned and has impact on future performance. Many factors including data accuracy, validity, infrastructure, culture can also impact sensemaking and have ramifications on future practice. Providing data in order to support performance reflection is not without risks, both behavioural and affective. The latter of which can impact the practitioner’s ability to effectively make sense of the data. An important consideration when data is presented with the intent to improve performance.Registration This systematic review was registered with Prospero, registration number: CRD42020197392.

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Section: Factors That Influence Sensemakingmentioning

confidence: 53%

An exploration into physician and surgeon data sensemaking: a qualitative systematic review using thematic synthesis

Whitelock-Wainwright

Koh

Whitelock‐Wainwright

et al. 2022

BMC Med Inform Decis Mak

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“…As earlier studies highlighted the importance of strong datadriven decision-making culture in hospitals [24], this study can also help healthcare executives better facilitate the use of survey data in resource allocation and prioritization. Considering staff involvement and engagement positively affects technology management [15], service innovation [26], and digital transformation [71], this study can also encourage healthcare managers to look for further opportunities to leverage the organizational culture in their organizations.…”

Section: A Contributions and Managerial Implicationsmentioning

confidence: 90%

Exploring Drivers of Staff Engagement in Healthcare Organizations Using Tree-Based Machine Learning Algorithms

Al-Nammari

Simsekler

Gabor

et al. 2023

IEEE Trans. Eng. Manage.

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Staff engagement in the work environment is vital to organizational success. Engaged staff are motivated and indulged in their work as they have a sense of belonging, commitment, and loyalty toward their employer, which eventually leads to better performance and outcomes. While various organizational factors are related to staff engagement, limited research is available regarding what drives staff engagement and the degree of their importance in healthcare. Leveraging data-driven approaches, in this article, we employ three machine learning algorithms, random forests, gradient boosting, and extra trees, to identify the relative importance of organizational factors affecting staff engagement. We use hospitallevel aggregate survey data from hospitals in the U.K. While staff engagement is the outcome variable, the following factors are used as organizational factors in the prediction model and feature importance analysis: equality, diversity, and inclusion, safety culture, health and wellbeing, immediate managers, quality of appraisals, quality of care, bullying and harassment, violence, and team working.All the algorithms provide comparable prediction results with similar feature importance ranking with respect to prediction accuracy. The results suggest that safety culture is the most influential factor related to staff engagement, followed by the team working. Healthcare managers and decision makers can benefit from this data-driven application to make informed decisions in resource allocation and prioritization efforts to improve staff engagement.

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“…Through data-driven insights, corporate BDA can serve as an explanatory element to learn from previous behaviors and identify potential impact on forthcoming consequences (Awan et al, 2021). For instance, Yu et al (2021) studied hospitals' capacity to analyze large amounts of data and found that such institutions need to foster a data-driven culture that promotes evidence-based decisionmaking among healthcare professionals.…”

Section: Basic Resources For Bdacmentioning

confidence: 99%

The impact of big data analytics on innovation capability and sustainability performance of hotels: evidence from an emerging economy

Aziz,

Al Mamun,

Reza

et al. 2024

JEIM

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PurposeThis study aimed to examine the role of big data analytics capabilities (BDAC) in fostering organizational innovation capabilities and, consequently, in achieving economic, social and environmental sustainability.Design/methodology/approachThrough the lens of dynamic capability theory, this study surveyed 115 hotels using purposive sampling to gain in-depth insights regarding the factors affecting organizational sustainability in the hospitality industry. The data analysis was conducted using partial least squares-structural equation modeling (PLS-SEM).FindingsThe findings reported a substantial impact of seven core dimensions (i.e. technology, data, basic resources, technological skills, managerial skills, organizational learning and data-driven culture) in building BDAC among hotels. Moreover, BDAC was also revealed to significantly influence innovation capabilities, positively impacting all three sorts of sustainability performance. Innovation capability also mediated the relationship between BDAC and all sustainability factors.Practical implicationsThe findings will assist policymakers and practitioners in developing effective initiatives to enhance the adoption and implementation of data science and technologies, substantially contributing to the “National IR 4.0 Policy” and “Malaysia Digital Economy Blueprint” and achieving sustainable development goals (SDGs).Originality/valueThe originality of this study is established by investigating the interplay between BDAC, innovation capability and sustainability performance, particularly in the context of the hotel industry, whereas the existing studies focus on exploring the advantages of BDA.

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Exploring the Effects of Data-Driven Hospital Operations on Operational Performance From the Resource Orchestration Theory Perspective

Cited by 21 publications

References 71 publications

An exploration into physician and surgeon data sensemaking: a qualitative systematic review using thematic synthesis

An exploration into physician and surgeon data sensemaking: a qualitative systematic review using thematic synthesis

Exploring Drivers of Staff Engagement in Healthcare Organizations Using Tree-Based Machine Learning Algorithms

The impact of big data analytics on innovation capability and sustainability performance of hotels: evidence from an emerging economy

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