An Assessment of Medication Therapy and Readmission of Comorbid Diabetic Patients

Understanding the therapeutic effects of diabetes medications is an essential aspect of ongoing care for maintaining wellness and reducing early readmission (\u3c 30 days). We analysed comorbid diabetic patients to establish the impacts of medication dosage on their readmission predisposition. After statistically analysing 101,766 patients’ records with Analysis of Variance (ANOVA), Students’ t-test with Bonferroni-Holm correction, and multivariate logistics regression, we affirmed that metformin, insulin, and repaglinide administered dosages significantly influence readmission at 95% confidence level. This result shows that therapeutic dosages influence “\u3c 30 days” and “No” readmissions for those treated with insulin and metformin while those treated with repaglinide are impacted on “\u3c 30 days” and “\u3e 30 days” readmissions. This finding opens a new frontier to study the factors influencing unplanned readmissions for comorbid diabetic patients treated with these medications

Dynamic Prediction of ICU Mortality Risk Using Domain Adaptation

Early recognition of risky trajectories during an Intensive Care Unit (ICU) stay is one of the key steps towards improving patient survival. Learning trajectories from physiological signals continuously measured during an ICU stay requires learning time-series features that are robust and discriminative across diverse patient populations. Patients within different ICU populations (referred here as domains) vary by age, conditions and interventions. Thus, mortality prediction models using patient data from a particular ICU population may perform suboptimally in other populations because the features used to train such models have different distributions across the groups. In this paper, we explore domain adaptation strategies in order to learn mortality prediction models that extract and transfer complex temporal features from multivariate time-series ICU data. Features are extracted in a way that the state of the patient in a certain time depends on the previous state. This enables dynamic predictions and creates a mortality risk space that describes the risk of a patient at a particular time. Experiments based on cross-ICU populations reveals that our model outperforms all considered baselines. Gains in terms of AUC range from 4% to 8% for early predictions when compared with a recent state-of-the-art representative for ICU mortality prediction. In particular, models for the Cardiac ICU population achieve AUC numbers as high as 0.88, showing excellent clinical utility for early mortality prediction. Finally, we present an explanation of factors contributing to the possible ICU outcomes, so that our models can be used to complement clinical reasoning

Accuracy of Machine Learning to Predict the Outcomes of Shoulder Arthroplasty: A Systematic Review

BACKGROUND: Artificial intelligence (AI) uses computer systems to simulate cognitive capacities to accomplish goals like problem-solving and decision-making. Machine learning (ML), a branch of AI, makes algorithms find connections between preset variables, thereby producing prediction models. ML can aid shoulder surgeons in determining which patients may be susceptible to worse outcomes and complications following shoulder arthroplasty (SA) and align patient expectations following SA. However, limited literature is available on ML utilization in total shoulder arthroplasty (TSA) and reverse TSA. METHODS: A systematic literature review in accordance with PRISMA guidelines was performed to identify primary research articles evaluating ML\u27s ability to predict SA outcomes. With duplicates removed, the initial query yielded 327 articles, and after applying inclusion and exclusion criteria, 12 articles that had at least 1 month follow-up time were included. RESULTS: ML can predict 30-day postoperative complications with a 90% accuracy, postoperative range of motion with a higher-than-85% accuracy, and clinical improvement in patient-reported outcome measures above minimal clinically important differences with a 93%-99% accuracy. ML can predict length of stay, operative time, discharge disposition, and hospitalization costs. CONCLUSION: ML can accurately predict outcomes and complications following SA and healthcare utilization. Outcomes are highly dependent on the type of algorithms used, data input, and features selected for the model. LEVEL OF EVIDENCE: III

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research

Real-time prediction of mortality, readmission, and length of stay using electronic health record data

Objective: To develop a predictive model for real-time predictions of length of stay, mortality, and readmission for hospitalized patients using electronic health records (EHRs). Materials and Methods: A Bayesian Network model was built to estimate the probability of a hospitalized patient being "at home," in the hospital, or dead for each of the next 7 days. The network utilizes patient-specific administrative and laboratory data and is updated each time a new pathology test result becomes available. Electronic health records from 32 634 patients admitted to a Sydney metropolitan hospital via the emergency department from July 2008 through December 2011 were used. The model was tested on 2011 data and trained on the data of earlier years. Results: The model achieved an average daily accuracy of 80% and area under the receiving operating characteristic curve (AUROC) of 0.82. The model's predictive ability was highest within 24 hours from prediction (AUROC = 0.83) and decreased slightly with time. Death was the most predictable outcome with a daily average accuracy of 93% and AUROC of 0.84. Discussion: We developed the first non-disease-specific model that simultaneously predicts remaining days of hospitalization, death, and readmission as part of the same outcome. By providing a future daily probability for each outcome class, we enable the visualization of future patient trajectories. Among these, it is possible to identify trajectories indicating expected discharge, expected continuing hospitalization, expected death, and possible readmission. Conclusions: Bayesian Networks can model EHRs to provide real-time forecasts for patient outcomes, which provide richer information than traditional independent point predictions of length of stay, death, or readmission, and can thus better support decision making.9 page(s

Developing Clinical Decision Support Systems for Sepsis Prediction Using Temporal and Non-Temporal Machine Learning Methods

In healthcare, diagnostic errors represent the biggest challenge to synthesize accurate treatments. In the United States, patient deaths due to misdiagnoses are estimated at 40,000 to 80,000 per year. It was also found that 30% of the annual healthcare spending was consumed on unnecessary services and other inefficiencies. The diagnostic errors could be reduced, and public health can be improved by applying machine learning and artificial intelligence in healthcare problems. This dissertation is an attempt to formulate clinical decision support systems and to develop new algorithms to reduce clinical errors.This dissertation aims at developing clinical decision support systems to diagnose sepsis in the early stages. The key feature of our work is that we captured the dynamics among body organs using Bayesian networks. The richness of the proposed model is measured not only by achieving high accuracy but also by utilizing fewer lab results.To further improve the accuracy of the clinical decision support system, we utilize longitudinal data to develop a mortality progression model. This part of the dissertation proposes a hidden Markov model (HMM) framework to model the mortality progression. In comparison to existing approaches, the proposed framework leverages the longitudinal data available in the electronic health records (EHR).In addition, this dissertation proposes an initialization procedure to train the parameters of HMM efficiently. The current HMM learning algorithms are sensitive to initialization. The proposed method computes an initial set of parameters by relaxing the time dependency in sequential time series data and incorporating the multinomial logistic regression.Finally, this dissertation compares the prognostic accuracy of two popularly used early sepsis diagnostic criteria: Systemic Inflammatory Response Syndrome (SIRS) and quick Sepsis-related Organ Failure Assessment (qSOFA). Using statistical and machine learning methods, we found that qSOFA is a better diagnostic criteria than SIRS. These findings will guide healthcare providers in selecting the best bedside diagnostic criteria

O impacto da inteligência artificial na área da saúde

Nos últimos anos, os projetos de Inteligência Artificial na área da saúde têm atraído mais investimento do que qualquer outro setor da economia. Esse investimento tem impulsionado o desenvolvimento de ferramentas com capacidade de auxiliar profissionais de saúde e gestores na tomada de decisão em várias vertentes de atuação, nomeadamente, na vertente que envolve a prática clínica e na vertente que envolve o processo de gestão. A investigação desenvolvida pretende perceber quais são os fatores que impulsionam e condicionam o processo de implementação desses sistemas por parte de profissionais e gestores de saúde. Para obter conclusões relevantes foi realizada uma extensa revisão de literatura, assim como uma análise estatística dos questionários online, com recurso à ferramenta SmartPLS 3. Os resultados obtidos demonstraram que o grau de conhecimento e os benefícios gerados pelos sistemas inteligentes favorecem a intenção de implementar essas ferramentas, suportando a maioria das hipóteses formuladas. Adicionalmente e contrariamente ao expectável, foi possível verificar que os desafios não representam uma ameaça para esses mesmos profissionais durante o processo de adoção de ferramentas de Inteligência Artificial. Com a investigação foi possível concluir que seria importante melhorar a integração da Inteligência Artificial na carreira dos profissionais ligados à área da saúde. Segundos os autores encontrados quanto maior for o nível de conhecimento sobre sistemas inteligentes, maior é a intenção de aplicar esses sistemas. Nesse sentido, devem ser tomadas algumas medidas para que esses profissionais entendam melhor o modo de funcionamento dos algoritmos que sustentam as ferramentas de Inteligência Artificial.In recent years, Artificial Intelligence health projects have attracted more investment than any other sector of the economy. This investment has driven the development of tools with the capacity to assist health professionals and managers in decision-making in various aspects of action, namely, in the aspect that involves clinical practice and in the aspect that involves the management process. The research developed aims to understand what are the main factors that condition the process of implementation of these systems by health professionals and managers. To obtain relevant conclusions, an extensive literature review was carried out, as well as a statistical analysis of online questionnaires, using the SmartPLS 3 tool. The results showed that the degree of knowledge and the benefits generated by intelligent systems favor the intention to implement these tools, supporting most of the hypotheses formulated. Additionally, and contrary to what is expected, it was possible to verify that the challenges do not pose a threat to these same professionals during the process of adoption of Artificial Intelligence tools. With the investigation it was possible to conclude that it would be important to improve the integration of Artificial Intelligence in the career of professionals related to health. According to the authors found the higher the level of knowledge about intelligent systems, the greater the intention to apply these systems. In this sense, some measures should be taken to better understand the way the algorithms that support Artificial Intelligence tools work

Two Essays on Analytical Capabilities: Antecedents and Consequences

Although organizations are rapidly embracing business analytics (BA) to enhance organizational performance, only a small proportion have managed to build analytical capabilities. While BA continues to draw attention from academics and practitioners, theoretical understanding of antecedents and consequences of analytical capabilities remain limited and lack a systematic view. In order to address the research gap, the two essays investigate: (a) the impact of organization’s core information processing mechanisms and its impact on analytical capabilities, (b) the sequential approach to integration of IT-enabled business processes and its impact on analytical capabilities, and (c) network position and its impact on analytical capabilities. Drawing upon the Information Processing Theory (IPT), the first essay investigates the relationship between organization’s core information processing mechanisms–i.e., electronic health record (EHRs), clinical information standards (CIS), and collaborative information exchange (CIE)–and its impact on analytical capabilities. We use data from two sources (HIMSS Analytics 2013 and AHA IT Survey 2013) to test the theorized relationships in the healthcare context empirically. Using the competitive progression theory, the second essay investigates whether organizations sequential approach to the integration of IT-enabled business processes is associated with increased analytical capabilities. We use data from three sources (HIMSS Analytics 2013, AHA IT Survey 2013, and CMS 2014) to test if sequential integration of EHRs –i.e., reflecting the unique organizational path of integration–has a significant impact on hospital’s analytical capability. Together the two essays advance our understanding of the factors that underlie enabling of firm’s analytical capabilities. We discuss in detail the theoretical and practical implications of the findings and the opportunities for future research

Aprendizaje automático e inteligencia artificial aplicado a modelos de clasificación y regresión

Las técnicas de aprendizaje automático e inteligencia artificial son herramientas basadas en el análisis de datos para poder calcular la probabilidad de que sucedan determinados hechos o resultados, o para identificar la pertenencia a un determinado grupo basándose en sus propiedades. Mediante el uso del aprendizaje supervisado, en el cual se conocen previamente los resultados, se han realizado predicciones gracias a los datos obtenidos de los departamentos de administración y de atención primaria de un hospital, aunque el uso de estas mismas herramientas se puede extrapolar a otras áreas de conocimiento. Concretamente se ha estudiado los días que permanecen ingresados los pacientes debido a la causa que originó su ingreso a nivel hospitalario, donde se innova al no tratar de forma independiente los departamentos del hospital, y también se estudia las tasas de readmisión hospitalaria producidas por los pacientes al volver a ingresar en el hospital por motivos relacionados con la admisión previa, donde se mejoran las tasas predictivas gracias al uso de las técnicas más recientes y al empleo de redes neuronales combinadas con series temporales. Gracias al presente trabajo y a las técnicas utilizadas se conoce el comportamiento actual y futuro de los casos de uso sobre salud analizados, permitiendo incluso aprender con los datos analizados para adaptarse a los nuevos datos que puedan llegar en un futuro, potenciando así su uso

Three analytics-based essays examining the use and impact of Intelligent Voice Assistants (IVA) and Health Information Technologies (HIT) in service contexts

Recent advancements in information technology (IT) innovation, such as artificial intelligence (AI) and machine learning (ML), are changing the dynamics in the service sector by driving smart reinvention of service tasks and processes. Additionally, organisations are leveraging the capabilities of emerging information systems (IS) to make their services more efficient and customer centric. However, the decision to use recent advancements in IT can be challenging for organizations since the required initial investment for implementation is often high and the economic value and impact on service performance cannot be gauged with certainty (Kwon et al. 2015). This forces many organizations to prioritise which IT functionalities may best be suited for their needs. To support the decision making process of organizations, regarding the adoption and use of innovative IT, scholars in the information systems (IS) and related fields are called to improve knowledge and understanding about various IT components and functionalities as well as their corresponding impact on individual users and organizations. Scholars are also expected to provide the means by which businesses can meaningfully predict the potential impact and economic value of innovative IT (Ravichandran 2018). In this three essay dissertation, we investigate how the use of various components and functionalities of innovative information systems can individually (or together) impact the quality of service delivered to end consumers. The essays are broadly based on the intersection of artificial intelligence (AI), machine learning(ML) and services. In the first study, we found that during encounters between eService consumers and Intelligent Voice Assistants (IVAs), typically powered by artificial intelligence, machine learning and natural language processing, the following dimensions are important for the perceived quality of service: IVA interactivity, IVA personalization, IVA flexibility, IVA assurance and IVA reliability. Among the five dimensions of IVA encounter, we found that IVA interactivity, IVA personalization and IVA reliability had positive impacts on the effective use of IVAs. In study 2, we investigated performance of hospitals in the health service sector. We proposed a smart decision support system (DSS) for predicting the performance of hospitals based on the Health Information Technology (HIT) functionalities as applied and used in these hospitals for patient care and in improving hospital performance. We found that the predictive performance of our proposed smart DSS was most accurate when HIT functionalities were used in certain bundles than in isolation. In study 3, we investigated the effect of hospital heterogeneity on the accuracy of prediction of our proposed smart DSS as we recognize that not all hospitals have the same set of context, opportunity, location and constraints. We found that the following sources of variations in hospitals had significant moderator effects on the accurate prediction of our smart DSS: hospital size, ownership, region, location (urban/rural) and complexity of cases treated. In summary, this dissertation contributes to the IS literature by providing insight into the emergent use of artificial intelligence and machine learning technologies as part of IS/IT solutions in both consumer-oriented services and the healthcare sector