Design and implementation of an automatic nursing assessment system based on CDSS technology

BACKGROUND: Various quantitative and quality assessment tools are currently used in nursing to evaluate a patient's physiological, psychological, and socioeconomic status. The results play important roles in evaluating the efficiency of healthcare, improving the treatment plans, and lowing relevant clinical risks. However, the manual process of the assessment imposes a substantial burden and can lead to errors in digitalization. To fill these gaps, we proposed an automatic nursing assessment system based on clinical decision support system (CDSS). The framework underlying the CDSS included experts, evaluation criteria, and voting roles for selecting electronic assessment sheets over paper ones.METHODS: We developed the framework based on an expert voting flow to choose electronic assessment sheets. The CDSS was constructed based on a nursing process workflow model. A multilayer architecture with independent modules was used. The performance of the proposed system was evaluated by comparing the adverse events' incidence and the average time for regular daily assessment before and after the implementation.RESULTS: After implementation of the system, the adverse nursing events' incidence decreased significantly from 0.43 % to 0.37 % in the first year and further to 0.27 % in the second year (p-value: 0.04). Meanwhile, the median time for regular daily assessments further decreased from 63 s to 51 s.CONCLUSIONS: The automatic assessment system helps to reduce nurses' workload and the incidence of adverse nursing events.</p

Artificial Intelligence and Medicine

The introduction of artificial intelligence (AI) has resulted in numerous technological advancements in the medical profession and a radical transformation of the old medical model. Artificial intelligence in medicine consists mostly of machine learning, deep learning, expert systems, intelligent robotics, the internet of medical things, and other prevalent and new AI technology. The primary applications of AI in the medical industry are intelligent screening, intelligent diagnosis, risk prediction, and supplemental treatment. Presently, medical AI has achieved significant advances, and big data quality management, new technology empowerment innovation, multi-domain knowledge integration, and personalized medical decision-making will exhibit greater growth potential in the clinical arena

Evaluating information flow in medication management process in Australian acute care facilities: A multi-professional perspective

Over the years, various interventions have been introduced to improve the medication management process. While these interventions have addressed some aspects predisposing the process to inefficiencies, significant gaps are still prevalent across the process. Studies have suggested that the goal of optimal medication therapy is achievable when information flow integrates across the various medication management process phases, stakeholders and departments involved as the patient moves through the process. To provide a cross-sectional view of the process, this study utilised a systemic philosophy to evaluate the information flow integration across the process. The research approach adopted for this study takes a positivist paradigm, which is guided by the cause and effect (causality) belief. It explored numeric measures to evaluate the relationship between constructs that assessed information flow principles (accessibility, timeliness, granularity and transparency) within the medication process and the information integration. The research design was cross-sectional and analytical, and this ensures that findings are relevant to current situations across the Australian healthcare system. Data for this research was collected using an online self-administered survey and the data assessed information flow principles and technologies used in the medication management process. There were 88 participants in this study, including doctors, nurses and pharmacists. The questions and responses were coded for analysis and data analysis techniques used were frequency analysis, Pearson’s chi-square test and multivariate analysis. Findings from this study indicates that the constructs evaluating accessibility, transparency and granularity had moderate associations with the information integration in the medication management process. Further analysis highlighted accessibility as a significant principle in explaining an increase or decrease in information integration in the medication management process. The accessibility construct referring to information retrieval was significant across the two tests conducted. Accessibility is directly related to information sharing and the assessment and monitoring and evaluation phases in the medication management process were identified as having the highest challenges with information sharing. Furthermore, the hybrid (electronic and paper) channel was preferred to support information integration in the medication management process by the participants. Among the technologies evaluated for the medication process, computer-provider-order-entry was found to be statistically significant in explaining an increase in information integration. Overall, results from this study suggest that interventions for the medication management process in Australian acute care facilities should be directed towards improving accessibility, specifically information retrieval and the sharing of information with emphasis on the assessment and monitoring phases. Implementing strategies to address the gaps identified from this research can improve information integration across the process and thereby reducing medication errors, and improving patient care management. Furthermore, the technology adoption across the process highlights that technology adoption across participants’ facilities remains a challenge in Australia

Data-driven approaches for predicting asthma attacks in adults in primary care

Background Asthma attacks cause approximately 270 hospitalisations and four deaths per day in the United Kingdom (UK). Previous attempts to construct data-driven risk prediction models of asthma attacks have lacked clinical utility: either producing inaccurate predictions or requiring patient data which are not cost-effective to collect on a large scale (such as electronic monitoring device data). Electronic Health Record (EHR) use throughout the UK enables researchers to harness comprehensive and panoramic patient data, but their cleaning and pre-processing requires sophisticated empirical experimentation and data analytics approaches. My objectives were to appraise the previously utilised methods in asthma attack risk prediction modelling for feature extraction, model development, and model selection, and to train and test a model in Scottish EHRs. Methods In this thesis, I used a Scottish longitudinal primary care EHR dataset with linked secondary care records, to investigate the optimisation of an asthma attack risk prediction model. To inform the model, I refined methods for estimation of asthma medication adherence from EHRs, compared model training data enrichment procedures, and evaluated measures for validating model performance. After conducting a critical appraisal of the methods employed in the literature, I trained and tested four statistical learning algorithms for prediction in the next four weeks, i.e. logistic regression, naïve Bayes classification, random forests, and extreme gradient boosting, and validated model performance in an unseen hold-out dataset. Training data enrichment methods were compared across all algorithms to establish whether the sensitivity of estimating relatively uncommon event incidence, such as asthma attacks in the general asthma population, could be improved. Secondary event horizons were also examined, such as prediction in the next six months. Empirical experimentation established the balanced accuracy to be the most appropriate prediction model performance measure, and the calibration between estimated and observed risk was additionally assessed using the Area Under the Receiver-Operator Curve (AUC). Results Data were available for over 670,000 individuals, followed for up to 17 years (177,306 person-years in total). Binary prediction of asthma attacks in the following four-week period resulted in 1,203,476 data samples, of which 1% contained one or more attacks (12,193 total attacks). In the preliminary model selection phase, the random forest algorithm provided the best balance between accuracy in those with asthma attacks (sensitivity) and in those predicted to have attacks (positive predictive value) in the following four weeks. In an unseen data partition, the final random forest model, with optimised hyper-parameters, achieved an AUC of 0.91, and a balanced accuracy of 73.6% after the application of an optimised decision threshold. Accurate predictions were made for a median of 99.6% of those who did not go on to have attacks (specificity). As expected with rare event predictions, the sensitivity was lower at 47.7%, but this was well balanced with the positive predictive value of 48.9%. Furthermore, several of the secondary models, including predicting asthma attacks in the following 12 weeks, achieved state-of-the-art performance and still had high potential clinical utility. Conclusions I successfully developed an EHR-based model for predicting asthma attacks in the next four weeks. Accurately predicting asthma attacks occurrence may facilitate closer monitoring to ensure that preventative therapy is adequately managing symptoms, reinforce the need to keep abreast of triggers, and allow rescue treatments to be administered quickly when necessary