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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.