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Intelligent Risk Detection in Health Care: Integrating Social and Technical Factors to Manage Health Outcomes

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Delivering Superior Health and Wellness Management with IoT and Analytics

Part of the book series: Healthcare Delivery in the Information Age ((Healthcare Delivery Inform. Age))

Abstract

The rapid increase of service demands in healthcare contexts today has reignited the importance of a robust risk assessment framework supported by real-time service handling in order to ensure superior decision-making and successful healthcare outcomes. Big data and analytics have the potential to provide numerous opportunities in healthcare for the application of information technology (IT) and decision sciences to real-time intelligent risk detection and management. In this article, we suggest that this intersection of decision sciences and IT should be the focus when looking to the future of health risk management. To demonstrate the power and benefits of integrating these domains, this exploratory study develops a solution framework that combines a real-time intelligent risk detection solution with decision support for a specific healthcare context. An intelligent risk detection model called HOUSE (Health Outcomes around Uncertainty, Stakeholders, and Efficacy) is proffered for risk detection and management in the context of congenital heart disease (CHD) surgeries in children. The model builds on the principles of user-centered design, network-centric healthcare operations, and intelligence continuum. The article elaborates on elements of this model, describes the fundamental research that supports its design, and concludes with a research agenda and design recommendations for extension into other healthcare domains.

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Author information

Authors and Affiliations

  1. Health Informatics Management, Epworth HealthCare, Richmond, VIC, Australia

    Hoda Moghimi

  2. Epworth HealthCare, Richmond, VIC, Australia

    Nilmini Wickramasinghe

  3. Swinburne University of Technology, Hawthorn, VIC, Australia

    Nilmini Wickramasinghe

  4. Management, Marquette University, Milwaukee, WI, USA

    Monica Adya

Authors
  1. Hoda Moghimi
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  2. Nilmini Wickramasinghe
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  3. Monica Adya
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Editor information

Editors and Affiliations

  1. Epworth HealthCare, Richmond, VIC, Australia, Swinburne University of Technology, Hawthorn, VIC, Australia

    Nilmini Wickramasinghe

  2. Friedrich-Alexander-University, Erlangen-Nuremberg, Erlangen, Germany

    Freimut Bodendorf

Appendix A

Appendix A

Name of study

Disease state

Application of KD

Kaur and Wasan (2006)

Diabetes

Rule-based, decision tree, and artificial neural network were used to classify diabetic patients

Meyer et al. (2014)

Type 2 diabetes mellitus

Data mining classification techniques were applied to improve treatment of patients with type 2 diabetes through better prediction and elimination of treatment failures

Chandola et al. (2013)

Healthcare fraud

Three case studies using big data and data mining are developed for identifying fraudulent healthcare providers using insurance claims data. Hadoop/Hive data platforms were used for text mining, network analysis, and analysis of claims sequences

Marschollek et al. (2012)

Identification of patients with risk of falling

Data mining techniques were used to detect patients with high risk of falling. Techniques classified patients as fallers or nonfallers. Prediction accuracies ranged from 55% to 68%

Holzinger et al. (2012)

Strokes

Use of data visualization techniques for mapping brain activity patterns and improved sense-making of brain functions

Patrick et al. (2011)

Extraction of clinical data

Machine-learning and rule-based system is used to extract clinical data from text and natural language notes. A pipeline system was developed to extract clinical data

Batal and Hauskrecht (2010)

Antibody test orders

Using data mining, authors elicit minimally predictive rules (MPRs) that are applied to predict heparin–platelet factor 4 (HPF4) antibody test orders from electronic health records. MPR-based classification models were comparable with those obtained from using a complete set of association rules

Srinivas et al. (2010)

Cardiovascular disease

Data mining techniques were used to predict the likelihood of patients experiencing heart disease. Medical profiles such as age, gender, blood pressure, and blood sugar were used

Peng et al. (2006)

Breast cancer detection

Use of genetic algorithms to search for bright spots in mammograms improved detection

Wright and Sittig (2006)

Physician order entry

Data mining techniques were used to learn from past ordering behaviors. Compared to manual ordering, data mining increased order efficiency, better accounted for local preferences, and enabled improved integration into existing clinical systems

Gago et al. (2005)

Organ failure and mortality assessment

KD and agent-based technologies were used to predict organ failure in hospital ICU

Ceglowski et al. (2005)

Emergency department data

Mining of emergency data resulted in discovery of definitive treatment pathways that fully modelled patient treatment. Analysis provided insights into emergency department workload and types of procedures carried out

Kraft et al. (2003)

Spinal cord injuries

Nursing diagnosis and neural networks were used to predict length of stay for patients with spinal cord injuries. Algorithms correctly predicted 77% of the stays

 

Cardiology

Predictive analytics model was effective in predicting readmission of patients diagnosed with congestive heart failure

Abidi and Goh (1998)

Infectious diseases

Neural networks were used to forecast bacterial infections using past data

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Moghimi, H., Wickramasinghe, N., Adya, M. (2020). Intelligent Risk Detection in Health Care: Integrating Social and Technical Factors to Manage Health Outcomes. In: Wickramasinghe, N., Bodendorf, F. (eds) Delivering Superior Health and Wellness Management with IoT and Analytics. Healthcare Delivery in the Information Age. Springer, Cham. https://doi.org/10.1007/978-3-030-17347-0_11

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  • DOI: https://doi.org/10.1007/978-3-030-17347-0_11

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