Challenges related to such algorithms include the necessity of a previously defined architecture for the model, sensitivity to the initial conditions used in training [ ]. A hybrid model of an ANN and decision tree classifier has been used to predict university admissions using data related to student academic merits, background and university admission criteria. Another advantage reported was improved generalizability, e. The integration of ANN with secondary AI and meta-heuristic methods such as fuzzy logic, genetic, bee colony algorithms, or artificial immune systems have been proposed to reduce or eliminate challenges related to ANN e.

Our findings suggest a possible correlation between advancements made in the field of ANN and publication rates related to the application of ANN in health care organizational decision-making. Despite the variety of study contexts and applications, ANN continues to be mainly used for classification, prediction and diagnosis. As suggested by the literature, the most commonly used taxonomy of ANN found was the feed-forward neural network.

However, our study showed a significant use of hybrid models. A primary strength of this review is its comprehensive scope and search strategy involving multiple databases. Variables selected for data collection were based on bodies of work with similar inquiry and well aligned with the methods of a scoping review. The complex nature of artificial neural networks required a fundamental understanding for the authors who were otherwise novice to the field.

Studies included in this review did not always use standardized reporting measures and may include publications of lower quality. Current and anticipated advancements in the field of AI will play an influential role in decision-making related to adopting novel and innovative machine learning based techniques in health care. Our findings warrant the understanding of perspectives and beliefs of those adopting ANN-based solutions in clinical and non-clinical decision-making.

Patients and families are accessing health information in real-time with the array of AI or ANN based health care solutions available to them in an open and unstructured market. Clinical applications of ANN-based solutions can have implications on the changing role of health care providers as well team dynamics and patterns in workflow.

Artificial Neural Network | NVIDIA Developer

The changing role of the physicians has been at the forefront of recent debates on AI, with some anticipating the positive impacts of augmenting clinical service with AI based technologies, e. Literature suggests a need for bridging disciplines in order to enable of clinicians to benefit from rapid advancements in technology [ ] In addition to the implications for clinical decision-making, interprofessional team dynamics and processes can be expected to change.

For example, a US based hospital has collaborated with a game development company to create a virtual world in which surgeons are guided through scenarios in the operating room using rules, conditions and scripts to practice making decisions, team communication, and leadership [ ]. As policy-makers adopt strategies towards a value-based, patient-centred model of care delivery, decision-makers are required to consider the readiness of health care organizations for successful implementation and wide-scale adoption of AI or ANN based decision-support tools.

Factors such as easier integration with hospital workflows, patient-centric treatment plans leading to improved patient outcomes, elimination of unnecessary hospital procedures and reduced treatment costs can influence wider adoption of AI-based solutions in the health care industry [ ]. Challenges in uptake include the current inability of AI-based solutions to read unstructured data, the perspectives of health care providers using AI-based solutions, and the lack of supportive infrastructure required for wide-scale implementation [ ].

For improved organizational readiness, the governance and operating model of health care organizations need to enable a workforce and culture that will support the use of AI to enhance efficiency, quality and patient outcomes [ ]. Machine learning from unstructured data e. Currently, most of the data in health care is unstructured and difficult to share [ ] Wide-scale implementation and adoption of AI service solutions requires strong partnerships between AI technology vendors and health care organizations [ ].

Policies encouraging transparency and sharing of core datasets across public and private sectors can stimulate higher levels of innovation-oriented competition and research productivity [ ]. Several theoretical implications emerge from our study findings. Healthcare organizations are complex adaptive systems embedded in larger complex adaptive systems[ ]; health care organizational decision-making can appropriately rely on ANN as an internalized rule set.

Application of Artificial Neural Networks in the Heart Electrical Axis Position Conclusion Modeling

The change of health care delivery from single to multiple settings and providers has led to new complexities around how health care delivery needs are being structured and managed e. Traditional decision-making processes based on stable and predictable systems are no longer relevant, due to the complex and emergent nature of contemporary health care delivery systems [ 1 ]. Yet the health care organizational decision-making literature suggests the focus of decision-making persistently remains on problems that are visible, while the larger system within which health care delivery organizations exist remains unacknowledged [ 1 ].

Using complex adaptive systems CAS theory to understand the functionality of AI can provide critical insights: first, AI enhances adaptability to change by strengthening communication among agents, which in turn fosters rapid collective response to change, and further, AI possesses the potential to generate a collective memory for social systems within an organization [ ].

References

The theory of CAS has been used as an alternative approach to improve our understanding and scaling up of health services; CAS theory shifts decision-making towards embracing uncertainty, non-linear processes, varying context and emergent characteristics [ ]. Interdependent organizational factors such as clinical practice, organization, information management research education and professional development, are built around multiple self-adjusting interacting systems [ ].

Agents e. Although lacking the ability to explain decision-making, ANN-based decision-support tools enable health care organizational decision-makers to respond to complex and emergent environments using incoming and evolving data. Our study found artificial neural networks can be applied across all levels of health care organizational decision-making.

Influenced by advancements in the field, decision-makers are taking advantage of hybrid models of neural networks in efforts to tailor solutions to a given problem. We found ANN-based solutions applied on the meso- and macro-level of decision-making suggesting the promise of its use in contexts involving complex, unstructured or limited information. Successful implementation and adoption may require an improved understanding of the ethical, societal, and economic implications of applying ANN in health care organizational decision-making. Browse Subject Areas?

Click through the PLOS taxonomy to find articles in your field. Abstract Health care organizations are leveraging machine-learning techniques, such as artificial neural networks ANN , to improve delivery of care at a reduced cost. Funding: The authors received no specific funding for this work. Introduction As health care systems in developed countries transform towards a value based, patient-centered model of care delivery, we face new complexities relating to improving the structure and management of health care delivery; for example, improving integration of processes in care delivery for patient-centered chronic disease management [ 1 ].

Overview According to an overview by Kononenko , as a sub-field of AI, machine learning provides indispensable tools for intelligent data analysis. Artificial neural networks Originally developed as mathematical theories of the information-processing activity of biological nerve cells, the structural elements used to describe an ANN are conceptually analogous to those used in neuroscience, despite it belonging to a class of statistical procedures [ 23 ].

Basics ANN can have single or multiple layers [ 23 ], and consist of processing units nodes or neurons that are interconnected by a set of adjustable weights that allows signals to travel through the network in parallel and consecutively[ 13 , 26 ]. Download: PPT. Fig 1. Conceptual model of a feed-forward and recurrent neural network. Artificial neural networks and regression models Neural networks are similar to linear regression models in their nature and use.

Data collection Screening of articles occurred in two stages. Results Overall, 3, articles were imported for screening, out of which after removal of duplicates 3, were screened for titles and abstracts to give a total of articles used for full-text review Fig 2. Study characteristics Publication dates ranged from to with the number of studies fluctuating each year Fig 3A. Fig 4. Types of applications of artificial neural networks identified in the review. Context and key findings ANN was primarily applied to organizational decision-making at a micro-level 61 articles between patients and health care providers in addition to meso-, macro-levels out of which 48 articles referenced to micro-level decision-making only; with 29 articles referencing meso-level applications between patients, health care providers, hospital managers and decision-makers, out of which 10 referenced meso- only.

Discussion This review provides a comprehensive review of the various applications of artificial neural networks in health care organizational decision-making.

• Applications of artificial neural networks in medical science. - PubMed - NCBI.
• Medical decision making.
• Applications of Artificial Neural Networks in Medical Science: Ingenta Connect.

Strengths and limitations A primary strength of this review is its comprehensive scope and search strategy involving multiple databases. Implications Practical implications Current and anticipated advancements in the field of AI will play an influential role in decision-making related to adopting novel and innovative machine learning based techniques in health care. Theoretical implications Several theoretical implications emerge from our study findings. Conclusion Our study found artificial neural networks can be applied across all levels of health care organizational decision-making.

A note on combining ML and distributed ledger technology

Supporting information. S1 Checklist.

S1 Appendix. Search strategy and syntax. S2 Appendix. Summary of findings. S3 Appendix. Glossary of terms. S1 Workflow. References 1. Kuziemsky C. Decision-making in healthcare as a complex adaptive system. Healthc Manage Forum. Global health care outlook: The evolution of smart health care.