Artificial intelligence in orthopaedics:false hope or not? A narrative review along the line of Gartner's hype cycle

Artificial Intelligence (AI) in general, and Machine Learn-ing (ML)-based applications in particular, have the potential to change the scope of healthcare, including orthopaedic surgery.The greatest benefit of ML is in its ability to learn from real world clinical use and experience, and thereby its capability to improve its own performance.Many successful applications are known in orthopaedics, but have yet to be adopted and evaluated for accuracy and efficacy in patients' care and doctors' workflows.The recent hype around AI triggered hope for development of better risk stratification tools to personalize orthopaedics in all subsequent steps of care, from diagnosis to treatment.Computer vision applications for fracture recognition show promising results to support decision-making, overcome bias, process high-volume workloads without fatigue, and hold the promise of even outperforming doctors in certain tasks.In the near future, AI-derived applications are very likely to assist orthopaedic surgeons rather than replace us. 'If the computer takes over the simple stuff, doctors will have more time again to practice the art of medicine'.(76)</p

Decision support by machine learning systems for acute management of severely injured patients: A systematic review

Introduction Treating severely injured patients requires numerous critical decisions within short intervals in a highly complex situation. The coordination of a trauma team in this setting has been shown to be associated with multiple procedural errors, even of experienced care teams. Machine learning (ML) is an approach that estimates outcomes based on past experiences and data patterns using a computer-generated algorithm. This systematic review aimed to summarize the existing literature on the value of ML for the initial management of severely injured patients. Methods We conducted a systematic review of the literature with the goal of finding all articles describing the use of ML systems in the context of acute management of severely injured patients. MESH search of Pubmed/Medline and Web of Science was conducted. Studies including fewer than 10 patients were excluded. Studies were divided into the following main prediction groups: (1) injury pattern, (2) hemorrhage/need for transfusion, (3) emergency intervention, (4) ICU/length of hospital stay, and (5) mortality. Results Thirty-six articles met the inclusion criteria; among these were two prospective and thirty-four retrospective case series. Publication dates ranged from 2000 to 2020 and included 32 different first authors. A total of 18,586,929 patients were included in the prediction models. Mortality was the most represented main prediction group (n = 19). ML models used were artificial neural network ( n = 15), singular vector machine (n = 3), Bayesian network (n = 7), random forest (n = 6), natural language processing (n = 2), stacked ensemble classifier [SuperLearner (SL), n = 3], k-nearest neighbor (n = 1), belief system (n = 1), and sequential minimal optimization (n = 2) models. Thirty articles assessed results as positive, five showed moderate results, and one article described negative results to their implementation of the respective prediction model. Conclusions While the majority of articles show a generally positive result with high accuracy and precision, there are several requirements that need to be met to make the implementation of such models in daily clinical work possible. Furthermore, experience in dealing with on-site implementation and more clinical trials are necessary before the implementation of ML techniques in clinical care can become a reality

Influence of artificial intelligence on the work design of emergency department clinicians:a systematic literature review

Objective: This systematic literature review aims to demonstrate how Artificial Intelligence (AI) is currently used in emergency departments (ED) and how it alters the work design of ED clinicians. AI is still new and unknown to many healthcare professionals in emergency care, leading to unfamiliarity with its capabilities. Method: Various criteria were used to establish the suitability of the articles to answer the research question. This study was based on 34 selected peer-reviewed papers on the use of Artificial Intelligence (AI) in the Emergency Department (ED), published in the last five years. Drawing on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, all articles were scanned, read full-text, and analyzed afterward. Results: The majority of the AI applications consisted of AI-based tools to aid with clinical decisions and to relieve overcrowded EDs of their burden. AI support was mostly offered during triage, the moment that sets the patient trajectory. There is ample evidence that AI-based applications could improve the clinical decision-making process. Conclusion: The use of AI in EDs is still in its nascent stages. Many studies focus on the question of whether AI has clinical utility, such as decision support, improving resource allocation, reducing diagnostic errors, and promoting proactivity. Some studies suggest that AI-based tools essentially have the ability to outperform human skills. However, it is evident from the literature that current technology does not have the aims or power to do so. Nevertheless, AI-based tools can impact clinician work design in the ED by providing support with clinical decisions, which could ultimately help alleviate a portion of the increasing clinical burden

Prediction Model for 30-day Outcomes Among Emergency Department Patients with Lower Gastrointestinal Bleeding

Introduction: There are currently no robust tools available for risk stratification of emergency department (ED) patients with lower gastrointestinal bleed (LGIB). Our aim was to identify risk factors and develop a preliminary model to predict 30-day serious adverse events among ED LGIB patients.Methods: We conducted a health records review including adult ED patients with acute LGIB. We used a composite outcome of 30-day all-cause death, recurrent LGIB, need for intervention to control the bleeding, and severe adverse events resulting in intensive care unit admission. One researcher collected data for variables and a second researcher independently collected 10% of the variables for inter-observer reliability. We used backward multivariable logistic regression analysis and SELECTION=SCORE option to create a preliminary risk-stratification tool. We assessed the diagnostic accuracy of the final model.Results: Of 372 patients, 48 experienced an adverse outcome. We found that age ≥75 years, hemoglobin ≤100 g/L, international normalized ratio ≥2.0, ongoing bleed in the ED, and a medical history of colorectal polyps were statistically significant predictors in the multivariable regression analysis. The area under the curve (AUC) for the model was 0.83 (95% confidence interval, 0.77-0.89). We developed a scoring system based on the logistic regression model and found a sensitivity 0.96 (0.90-1.00) and specificity 0.53 (0.48-0.59) for a cut-off score of 1.Conclusion: This model showed good ability to differentiate patients with and without serious outcomes as evidenced by the high AUC and sensitivity. The results of this study could be used in the prospective derivation of a clinical decision tool

A study to derive a clinical decision rule for triage of emergency department patients with chest pain: design and methodology

<p>Abstract</p> <p>Background</p> <p>Chest pain is the second most common chief complaint in North American emergency departments. Data from the U.S. suggest that 2.1% of patients with acute myocardial infarction and 2.3% of patients with unstable angina are misdiagnosed, with slightly higher rates reported in a recent Canadian study (4.6% and 6.4%, respectively). Information obtained from the history, 12-lead ECG, and a single set of cardiac enzymes is unable to identify patients who are safe for early discharge with sufficient sensitivity. The 2007 ACC/AHA guidelines for UA/NSTEMI do not identify patients at low risk for adverse cardiac events who can be safely discharged without provocative testing. As a result large numbers of low risk patients are triaged to chest pain observation units and undergo provocative testing, at significant cost to the healthcare system. Clinical decision rules use clinical findings (history, physical exam, test results) to suggest a diagnostic or therapeutic course of action. Currently no methodologically robust clinical decision rule identifies patients safe for early discharge.</p> <p>Methods/design</p> <p>The goal of this study is to derive a clinical decision rule which will allow emergency physicians to accurately identify patients with chest pain who are safe for early discharge. The study will utilize a prospective cohort design. Standardized clinical variables will be collected on all patients at least 25 years of age complaining of chest pain prior to provocative testing. Variables strongly associated with the composite outcome acute myocardial infarction, revascularization, or death will be further analyzed with multivariable analysis to derive the clinical rule. Specific aims are to: i) apply standardized clinical assessments to patients with chest pain, incorporating results of early cardiac testing; ii) determine the inter-observer reliability of the clinical information; iii) determine the statistical association between the clinical findings and the composite outcome; and iv) use multivariable analysis to derive a highly sensitive clinical decision rule to guide triage decisions.</p> <p>Discussion</p> <p>The study will derive a highly sensitive clinical decision rule to identify low risk patients safe for early discharge. This will improve patient care, lower healthcare costs, and enhance flow in our busy and overcrowded emergency departments.</p

Artificial intelligence and statistical techniques to predict probability of injury survival

The aim of this study is to design, develop and evaluate artificial intelligence and statistical techniques to predict the probability of survival in traumas using knowledge acquired from a database of confirmed traumas outcomes (survivors and not survivors). Trauma in this study refers to body injuries from accidents or other means. Quantifying the effects of traumas on individuals is challenging as they have many forms, affect different organs, differ in severity and their consequence could be related to the individual's physiological attributes (e.g. age, fragility, premedical condition etc). It is known that appropriate intervention improves survival and may reduce disabilities in traumas. Determining the probability of survival in traumas is important as it can inform triage, clinical research and audit. A number of methods have been reported for this purpose. These are based on a combination of physiological and anatomical examination scores. However, these methods have shortcomings as for example, combining the scores from injuries for different organs is complicated. A method for predicting probability of survival in traumas needs to be accurate, practical and accommodate broad cases. In this study Sheffield Hallam University, Sheffield Children's Hospital, Sheffield University and the Trauma Audit and Research Network (TARN) collaborated to develop improved means of predicting probability of survival in traumas. The data used in this study were trauma cases and their outcomes provided by the TARN. The data included 47568 adults (age: mean = 59.9 years, standard deviation = 24.7 years) with various injuries. In total, 93.3% of cases had survived and 6.7% of cases had not survived. The data were partitioned into calibration (2/3 of the data) and evaluation (1/3 of the data). The trauma parameters used in the study were: age, respiration rate (RR), systolic blood pressure (SBP), pulse (heart) rate (PR) and the values obtained from two trauma scoring systems called Abbreviated Injury Score (AIS) and Glasgow Coma Score (GCS). Intubation and Pre-exiting Medical Condition (PMC) data were also considered. Initially a detailed statistical exploration of the manner trauma these trauma parameters related to the probability of survival outcomes was carried out and the results were interpreted. The resulting information assisted the development of three methods to predict probability of survival. These were based on Bayesian statistical approach called predictive statistical diagnosis (PSD), a new method called Iterative Random Comparison classification (IRCC) and the third method combined the IRCC with the fuzzy inference system (FIS). The performance of these methods was compared with each other as well as the method of predicating survival used by the TARN called Ps14 (the name refers to probability of survival method reported in 2014). The study primarily focused on Trauma Brain Injury (TBI) as they represented the majority of the cases. For TBI, the developed IRCC performed best amongst all methods including Ps14. It predicted survivors and not survivors with 97.2% and 75.9% accuracies respectively. In comparison, the predication accuracy for Ps14 for survivors and not survivors were 97.4% and 40.2%. The study provided resulted in new findings that indicated the manner trauma parameters affect probability of survival and resulted in new artificial intelligence and statistical methods of determining probability survival in trauma

Expert systems in optometry

Expert systems in optometr

Predictive analytics framework for electronic health records with machine learning advancements : optimising hospital resources utilisation with predictive and epidemiological models

The primary aim of this thesis was to investigate the feasibility and robustness of predictive machine-learning models in the context of improving hospital resources’ utilisation with data- driven approaches and predicting hospitalisation with hospital quality assessment metrics such as length of stay. The length of stay predictions includes the validity of the proposed methodological predictive framework on each hospital’s electronic health records data source. In this thesis, we relied on electronic health records (EHRs) to drive a data-driven predictive inpatient length of stay (LOS) research framework that suits the most demanding hospital facilities for hospital resources’ utilisation context. The thesis focused on the viability of the methodological predictive length of stay approaches on dynamic and demanding healthcare facilities and hospital settings such as the intensive care units and the emergency departments. While the hospital length of stay predictions are (internal) healthcare inpatients outcomes assessment at the time of admission to discharge, the thesis also considered (external) factors outside hospital control, such as forecasting future hospitalisations from the spread of infectious communicable disease during pandemics. The internal and external splits are the thesis’ main contributions. Therefore, the thesis evaluated the public health measures during events of uncertainty (e.g. pandemics) and measured the effect of non-pharmaceutical intervention during outbreaks on future hospitalised cases. This approach is the first contribution in the literature to examine the epidemiological curves’ effect using simulation models to project the future hospitalisations on their strong potential to impact hospital beds’ availability and stress hospital workflow and workers, to the best of our knowledge. The main research commonalities between chapters are the usefulness of ensembles learning models in the context of LOS for hospital resources utilisation. The ensembles learning models anticipate better predictive performance by combining several base models to produce an optimal predictive model. These predictive models explored the internal LOS for various chronic and acute conditions using data-driven approaches to determine the most accurate and powerful predicted outcomes. This eventually helps to achieve desired outcomes for hospital professionals who are working in hospital settings

A Case for Delirium Risk Prediction Models to Aid in Triaging Resources to those Most at Risk an Integrative Literature Review

Abstract Delirium is a complex syndrome resulting from compounding effects of acute illness, comorbidities, and the environment. It results in adverse outcomes: elevated mortality rates, length of stay, readmissions, institutionalization, long-term cognitive changes, and diminished quality of life. The rate of iatrogenic delirium is astounding, ranging from 10%-89%. There are no curative treatments; thus, primary prevention is the key. The purpose of this literature review is to identify and critique the research for the accuracy of risk stratification and feasibility in practice. Support for interventions that prevent delirium is mounting; however, interventions are resource-intensive and often not implemented. Researchers have responded to this problem by developing risk stratification tools to triage interventions toward those of the highest risk. There is evidence that some of the models\u27 implementation is successful; however, they are not yet widely operationalized. A compilation of seven published models of risk prediction was critiqued and compared using the Stetler Model of Evidence-Based Practice as a guiding model. The Newcastle-Ottawa Scale and the Critical Appraisal and the Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS checklist) are employed to aid in the critical appraisal, evaluation of the study\u27s quality, and aid in data abstraction. The models show the ability to stratify risk. Still, their effectiveness in practice cannot be studied without directed interventions because they risk prediction models are created to aid healthcare staff in making clinical decisions. Therefore, a complete clinical pathway with evidence-based interventions should be employed with a delirium risk prediction model to triage the interventions to patients at the highest risk. Recommendations are to implement an automated electronic model (automatic calculation using the EMR or a machine learning model) into clinical practice along with a delirium prevention care pathway. Electronic versions of risk scores allow for an opportunity to achieve clinical efficiency and show statistical superiority to the other models. Published evidence on the impact of the models is diminutive. Their ability to triage patients and aid in clinical decision-making should be published in an impact study. Keywords: Delirium, risk assessment, risk prediction, risk model, risk score, patient safety, patient-centered outcomes researc