Data mining and predictive analytics applications for the delivery of healthcare services: a systematic literature review

Malik, Mohsin; Abdallah, Salam; Ala’raj, Maher

doi:10.1007/s10479-016-2393-z

Cited by 73 publications

(39 citation statements)

References 55 publications

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“…The demand for more sophisticated care delivery services [110] and sites, trying to transform, whenever possible (e.g., whenever acute treatment is unnecessary), inpatients into outpatients and eventually home patients; 6.…”

Section: Discussionmentioning

confidence: 99%

Healthcare Digitalization and Pay-For-Performance Incentives in Smart Hospital Project Financing

Visconti

Morea

2020

IJERPH

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This study aims to explore the impact of healthcare digitalization on smart hospital project financing (PF) fostered by pay-for-performance (P4P) incentives. Digital platforms are a technology-enabled business model that facilitates exchanges between interacting agents. They represent a bridging link among disconnected nodes, improving the scalable value of networks. Application to healthcare public-private partnerships (PPPs) is significant due to the consistency of digital platforms with health issues and the complexity of the stakeholder's interaction. In infrastructural PPPs, public and private players cooperate, usually following PF patterns. This relationship is complemented by digitized supply chains and is increasingly patient-centric. This paper reviews the literature, analyzes some supply chain bottlenecks, addresses solutions concerning the networking effects of platforms to improve PPP interactions, and investigates the cost-benefit analysis of digital health with an empirical case. Whereas diagnostic or infrastructural technology is an expensive investment with long-term payback, leapfrogging digital applications reduce contingent costs. "Digital" savings can be shared by key stakeholders with P4P schemes, incentivizing value co-creation patterns. Efficient sharing may apply network theory to a comprehensive PPP ecosystem where stakeholding nodes are digitally connected. This innovative approach improves stakeholder relationships, which are re-engineered around digital platforms that enhance patient-centered satisfaction and sustainability. Digital technologies are useful even for infectious disease surveillance, like that of the coronavirus pandemic, for supporting massive healthcare intervention, decongesting hospitals, and providing timely big data. not due to population aging per se, but to the increase in demand for new medical technologies that improve and/or extend life as real per-capita incomes grow [3,4].Within this evolving framework, technology is a double-edged sword, since it increases healthcare expenditure [5] but can also bring savings and quality-of-life improvements [6]. The relationship between medical technology and spending is complex and often conflicting. The impact of technology on costs differs across technologies, in that some (e.g., cancer drugs, invasive medical devices) have significant financial implications, while others are cost-neutral or cost-saving [7]. Medicine in the 21 st century is increasingly dependent on technology. Unlike in many other areas, the cost of medical technology is not declining, and its increasing use contributes to spiraling healthcare costs. Many medical professionals equate progress in medicine to the growing use of sophisticated technology, which is often expensive and beyond the reach of the average citizen [8].Whereas technology concerning tangible items, such as diagnostic equipment or physical infrastructure, has an uncertain cost-benefit trade-off, digital investments have a shorter payback, are typically cheaper, and show sounder benef...

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Section: Discussionmentioning

confidence: 99%

Healthcare Digitalization and Pay-For-Performance Incentives in Smart Hospital Project Financing

Visconti

Morea

2020

IJERPH

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“…Our research aims to priority preadmission patients based on historical data using machine‐learning algorithms. Our study reflects the recent trend of using machine‐learning approaches to deal with health‐care–related issues . We applied the latest machine‐learning algorithms, including XGBoost, and created an ensemble model.…”

Section: Discussionmentioning

confidence: 99%

“…Our study reflects the recent trend of using machine-learning approaches to deal with health-care-related issues. 12,32,33 We applied the latest machine-learning algorithms,…”

Section: Discussionmentioning

confidence: 99%

Using machine‐learning methods to support health‐care professionals in making admission decisions

Luo

Shen

2019

Health Planning & Management

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Summary Background Large tertiary hospitals usually face long waiting lines; patients who want to receive hospitalization need to be screened in advance. The patient admission screening process involves a health‐care professional ranking patients by analyzing registration information. Objective The purpose of this study was to develop a machine‐learning approach to screening, using historical data and the experience of health‐care professionals to develop a set of screening rules to help health‐care professionals prioritize patient needs automatically. Methods We used five machine‐learning methods to sequence and predict elective patients: logistic regression (LR), random forest (RF), gradient‐boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and an ensemble model of the four models. Results The results indicate that all of the five models showed a good prioritization performance with high predictive values. In particular, XGBoost had the best predictive performance compared with others in terms of the area under the receiver operating characteristic curve (AUC), with the AUC values of LR, RF, GBDT, XGBoost, and the ensemble model being 0.881, 0.816, 0.820, 0.901, and 0.897, respectively. Conclusion The results reported here indicate that machine‐learning techniques can be valuable for automating the screening process. Our model can assist health‐care professionals in automatically evaluating less complex cases by identifying important factors affecting patient admission.

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“…Of the 27 review papers in Table A2 (Appendix II), six consider process mining or data mining [38,63,78,109,135,168], seven consider simulation [2,105,132,173,175,176,208] and three consider stochastic modelling [59,123,213].…”

Section: Previous Researchmentioning

confidence: 99%

Clinical pathway modelling: a literature review

2019

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Hospital information systems are increasingly used as part of decision support tools for planning at strategic, tactical and operational decision levels. Clinical pathways are an effective and efficient approach in standardising the progression of treatment, to support patient care and facilitate clinical decision making. This literature review proposes a taxonomy of problems related to clinical pathways and explores the intersection between Information Systems (IS), Operational Research (OR) and industrial engineering. A structured search identified 175 papers included in the taxonomy and analysed in this review. The findings suggest that future work should consider industrial engineering integrated with OR techniques, with an aim to improving the handling of multiple scopes within one model, while encouraging interaction between the disjoint care levels and with a more direct focus on patient outcomes. Achieving this would continue to bridge the gap between OR, IS and industrial engineering, for clinical pathways to aid decision support.

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Data mining and predictive analytics applications for the delivery of healthcare services: a systematic literature review

Cited by 73 publications

References 55 publications

Healthcare Digitalization and Pay-For-Performance Incentives in Smart Hospital Project Financing

Healthcare Digitalization and Pay-For-Performance Incentives in Smart Hospital Project Financing

Using machine‐learning methods to support health‐care professionals in making admission decisions

Clinical pathway modelling: a literature review

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