As early as in 1956, John McCarthy and his colleagues, Marvin Minskly, Claude Shannon and Nathaniel Rochester coined the term ‘Artificial Intelligence’ (AI). More commonly, AI is defined as, “the science and engineering of making intelligent machines. Artificial Intelligence refers to the computer programmes that execute a task like that of human intelligence, especially intelligently and independently. The main objective of AI is to develop a machine that can exhibit human intelligence. It is also a promising tool for supporting the healthcare administration. Several studies have shown that AI algorithms are capable of managing patient flow and thus augmenting clinical care by reducing the administrative demands on clinicians. Artificial intelligence is not about robots completing the jobs and rendering people obsolete. AI in healthcare is set to help healthcare works and stakeholders manage the vast data and transform them into potentially life-saving information.
Concepts in AI
Intelligence is defined by learning and reasoning. Learning is an essential element in AI and is realised through machine learning. Reasoning is another component of AI, which encompasses data manipulation to produce actions. The AI is designed to work through two ways – symbolic-based and data-based (machine learning). Human’s process information through the eyes and that could be equated to the computer vision. In AI it includes methods for acquiring, processing, analysing, and understanding images.
Predictive modelling AI in healthcare
Artificial Intelligence has several applications in medicine including hospitals, clinical laboratories, and research facilities. Healthcare administration and operations; clinical decision support; predictions in healthcare; patient monitoring; and healthcare interventions are key domains where AI is applied.
Predictive modelling in healthcare is a proactive step towards identifying patients at risk of disease or adverse outcomes. One of the most common AI predictive model is the patient inflow into emergency department; re-admissions into emergency departments; disease or other outcomes; and in-patient mortality.
AI for improving operational efficiency
Resource optimisation and patient crowding in the emergency department is a challenging issue. Resource requirement forecasting is essential to reduce the rising healthcare cost by optimising the use and availability of healthcare resources. Yousefi et al., utilised machine learning and the genetic algorithm (GA) to determine optimal resource allocation in emergency departments. Yousefi et al., constructed a meta-model, with three power machine learning approaches (adaptive neuro-fuzzy inference system, feed forward neural network and recurrent neural network) using the bootstrap aggregating (bagging) and adaptive boosting (AdaBoost) ensemble algorithm. When applied to an emergency department, the GA algorithm was able to reduce the average length of stay by 15 per cent. Predicting the waiting time and appointment delays can help in optimising hospital resources and increasing patient satisfaction.
Curtis et al. utilised several machine learning algorithms to predict waiting times at a walk-in radiology centres or delay times at scheduled radiology facilities across all four modalities (computed tomography, MRI, ultrasound, and radiography). Several variables were extracted from the radiology information system. Nine machine learning algorithms (neural network, random forest, support vector machine (SVM), elastic net, multivariate adaptive regression splines, kth nearest neighbour, gradient boosting machine, bagging, and classification and regression tree) were used to fine-tune their parameters into the best possible training data fit. The root mean square error metric was used to determine the predictive accuracy of the algorithms. Among the nine machine learning algorithms, the elastic net was found to be better than other algorithms in accurately and efficiently predict the waiting time and delay time.
Automated diagnostic decision support applications can fast track diagnostic decisions in the emergency department as well as within the hospital departments and wards. Feature-rich AI models with several predictor variables were found to recognise patients at risk of experiencing an unplanned intensive care unit transfer. AI algorithms are capable of predicting hospital readmissions within a specified duration of time and that indeed can reduce the cost in the healthcare system.
Discussion
Early identification of patients in the emergency department requiring admission may perhaps help in optimising the hospital resources. Hong et al used triage information and patient history to predict hospital admission at the time of emergency triage. In general, the prediction of patient admission to ward from the emergency department was based solely on the triage (demographics, vital signs, chief complaint, nursing notes, and early diagnostics). Triage-based prediction models include the Sydney Triage to Admission Risk Tool and the Glasgow Admission Prediction Score. Hong et al. used 972 variables extracted per visit from the electronic health records. Logistic regression (LR), gradient boosting (XGBoost), and DNN were trained on three dataset types (only triage, only patient history and full set (both triage and patient history). The addition of historical information to triage information significantly improved predictive performance significantly vs. triage information alone. Moreover, XGBoost and DNN were better than LR in predicting hospital admission when the full dataset was used. The predictive value of XGBoost and DNN across all three dataset types was similar. Hong et al showed that the addition of patient history to the triage information could enable machine learning to strongly predict hospital mission.
Conclusion
The application of AI ranges from hospital administration to therapeutic decisions. It is changing the medical landscape. AI is designed to work through symbolic-based and data-based (machine learning). Computer vision and robotics uses symbolic- based data to process the information. Artificial neural network is a data-based AI that is enabled with cognitive capabilities of a human. Healthcare generates big data, which may be structured, unstructured and semi-structured. These data will be redundant unless it is interpreted and integrated into various algorithms, especially to predict outcomes.
In AI, the algorithms are created in such a way that they can not only modify themselves in response to patterns in data set, they can also derive inferences when applied to new data. In lieu of availability of humongous data, several predictive models have been developed in the context of healthcare administration and operations; clinical decision support; predictions in healthcare; patient monitoring; and healthcare interventions.
AI has been applied to predicting the flow of patients into the emergency department; streamlining patient flow to hospital; monitoring patients in ward and emergency department and predicting the availability of bed in in-patients. Various forecasting methods that have been employed in predicting patient flow include linear regression, exponential smoothing, time series regression, and artificial neural network.
AI seems to be an ideal tool for optimising patient management in hospitals. A wide range of AI algorithms are available for managing and predicting patient flow into the various departments of a hospital.
References available on request.
