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Table 2 Distribution by value targets

From: The path from big data analytics capabilities to value in hospitals: a scoping review

Value targets N References Description
VT1 – Decision making 62   
Care – Diagnostic 12 Hu et al. (2018) [71] Develop models based on machine learning to aid the diagnosis of hyperlemia at point of care.
Care – Risk detection 22 Genevès et al. (2018) [72] Use machine learning on prescription data to detect, on the day of hospital admissions, patients at risks of developing complications during their hospital stay.
Admin. – Assessing hospital activities 20 Mahajan et al. (2019) [73] Develop a data-driven methodology for decision-making supported by the use of quarterly strategic analytics for improvement and learning (SAIL) reports to visualize data, study trends and provide actionable recommendations.
Admin. – Resource allocation 14 McNair (2015) [61] Use statistical model to forecast the optimal safety level of nurse staffing in intensive care units.
Research – Hypothesis setting 2 Hendricks (2019) [62] Use process mining to explore available hospital logs and identify areas in clinical operations to further investigate.
VT2 – Innovation 54   
Care – Precision medicine 27 An et al. (2018) [74] Develop algorithms using machine learning methods to predict drug-resistant epilepsy in order to ensure these patients receive specific care and interventions following their diagnosis.
Care – Preventative medicine 22 Zolbanin and Delen (2018) [75] Propose new data processing approaches to predict preventable readmissions for patient with chronic diseases and prescribe the best course of actions for each patient at discharge to prevent readmission.
Admin. – Adapt strategies 10 Navarro et al. (2018) [67] Develop a machine learning algorithm using perioperative data to predict length of stay and inpatients costs after primary total knee arthroplasty and propose a patient-specific payment model better reflecting patient complexity.
Research – New research tools 2 Johnson et al. (2016) [63] Develop a dynamic simulation tool suitable for data visualization of both human-designed and data-driven process which can be used for “what if” analysis and used to deep-dive on big data.
VT3 – Performance 36   
Care – Patient flow 25 Krämer et al. (2019) [76] Use supervised machine learning techniques to train a model to classify inpatient admissions as either emergency or elective care to reduce the number of hospitals admissions from the emergency department.
Admin. – Operations management 7 Guan et al. (2017) [54] Use statistical model to investigate platelet usage patterns and better forecast future demand to reduce wastage.
Research – Research performance 6 Karanastasis et al. [68] Develop a platform with tools and services necessary to explore big data in clinical research to improve the efficiency of clinical trials design and the effectiveness and speed of subject recruitment.