Exploring trends and autonomy levels of adaptive business intelligence in healthcare: A systematic review.

OBJECTIVE: In order to comprehensively understand the characteristics of Adaptive Business Intelligence (ABI) in Healthcare, this study is structured to provide insights into the common features and evolving patterns within this domain. Applying the Sheridan's Classification as a framework, we aim to assess the degree of autonomy exhibited by various ABI components. Together, these objectives will contribute to a deeper understanding of ABI implementation and its implications within the Healthcare context. METHODS: A comprehensive search of academic databases was conducted to identify relevant studies, selecting AIS e-library (AISel), Decision Support Systems Journal (DSSJ), Nature, The Lancet Digital Health (TLDH), PubMed, Expert Systems with Application (ESWA) and npj Digital Medicine as information sources. Studies from 2006 to 2022 were included based on predefined eligibility criteria. PRISMA statements were used to report this study. RESULTS: The outcomes showed that ABI systems present distinct levels of development, autonomy and practical deployment. The high levels of autonomy were essentially associated with predictive components. However, the possibility of completely autonomous decisions by these systems is totally excluded. Lower levels of autonomy are also observed, particularly in connection with prescriptive components, granting users responsibility in the generation of decisions. CONCLUSION: The study presented emphasizes the vital connection between desired outcomes and the inherent autonomy of these solutions, highlighting the critical need for additional research on the consequences of ABI systems and their constituent elements. Organizations should deploy these systems in a way consistent with their objectives and values, while also being mindful of potential adverse effects. Providing valuable insights for researchers, practitioners, and policymakers aiming to comprehend the diverse levels of ABI systems implementation, it contributes to well-informed decision-making in this dynamic field.

An Overview of Omnichannel Interaction in Health Care Services.

The adoption of omnichannel interaction services in health care can bring significant benefits to both health care institutions and their patients. The ongoing health pandemic caused by coronavirus disease has further emphasized the need for health care providers to implement an omnichannel strategy to provide seamless personalized experiences to their patients through multiple access channels. This study aimed to examine the current state of research on omnichannel interaction services in health care with a focus on the benefits, challenges, and issues that health care institutions may encounter when adopting this strategy. A systematic literature review was conducted to synthesize the current state of research and provide a comprehensive overview of the field. The results of the review were used to perform a strengths, weaknesses, opportunities, and threats analysis of omnichannel services in health care and identify 5 key criteria that health care institutions should consider when implementing an omnichannel strategy. This study contributes to the field by offering an updated and comprehensive understanding of omnichannel interaction services in health care and provides valuable insights for health care providers considering this strategy. The ultimate goal of an omnichannel strategy in health care is to improve patient engagement, increase access to care, and reduce costs while improving communication and collaboration among health care providers. The successful implementation of this strategy requires a well-defined plan, robust technology, infrastructure, data analytics, capabilities, trained professionals, and a basic understanding of the communication channels among patients. The adoption of an omnichannel strategy in health care can lead to new business growth and increased patient engagement, but health care institutions must be properly aligned and patients must be prepared for its implementation.

Data mining techniques in psychotherapy: applications for studying therapeutic alliance.

Therapeutic Alliance (TA) has been consistently reported as a robust predictor of therapy outcomes and is one of the most investigated therapy relational factors. Research on therapists' and clients' contributions to the alliance development and the alliance-outcome relationship had shown mixed results. The relation of the therapist's and client's biological markers with the alliance is an important and under-investigated topic. Taking advantage of data mining techniques, this exploratory study aimed to investigate the role of different therapist and client factors, including heart rate (HR) and electrodermal activity (EDA), in relation to TA. Twenty-two dyads with 6 therapists and 22 clients participated in the study. The Working Alliance Inventory (WAI) was used to evaluate the client's and therapist's perception of the alliance at the end of each session and through the therapy processes. The Cross-Industry Standard Process for Data Mining (CRISP-DM) was used to explore patterns that may contribute to TA. Machine Learning (ML) models have been employed to provide insights into the predictors and correlates of TA. Our results showed that Linear Regression (LR) was the best technique for predicting the therapist's TA, with client "Diagnostic" and therapy "Termination" being identified as significant predictors of the therapist's TA. In addition, for clients' TA, the Random Forest (RF) was shown to have the best performance. The therapist's TA and therapy "Outcome" were observed as the most influential predictors for the client's TA. In addition, while the Heart Rate (therapist) was negatively associated with the therapist's TA, EDA in the client was a physiological indicator related to the client's TA. Overall, these findings can assist in identifying key factors that therapists should focus on to enhance the quality of therapeutic alliance. Results are discussed in terms of their consistency with empirical literature, innovative and interdisciplinary research on the therapeutic alliance field, and, in particular, the use of the Data Mining approach in a psychotherapy context.

Health Professional's Decision-Making Based on Multichannel Interaction Services.

The COVID-19 pandemic has reinforced the importance and impact of telemedicine and multichannel interactions in healthcare services provided to patients. Health professionals are in turn increasingly dependent on patient data collected through multichannel interactions to make their clinical decisions. This article intends to present a brief analysis from the viewpoint of health professionals regarding the use of technologies in telemedicine and multichannel interactions to support decision making, basing on the analysis of clinical data of patients collected in a telemedicine environment. These technologies have numerous advantages for healthcare professionals and patients, but there are also some obstacles and gaps inherent that need to be overcome. Furthermore, health professionals can perform a more detailed analysis of patient data before taking any decision, as this practice promotes data collection to facilitate the decision-making process of health professionals.

OpenEHR modeling: improving clinical records during the COVID-19 pandemic.

The COVID-19 pandemic had put pressure on various national healthcare systems, due to the lack of health professionals and exhaustion of those avaliable, as well as lack of interoperability and inability to restructure their IT systems. Therefore, the restructuring of institutions at all levels is essential, especially at the level of their information systems. Furthermore, the COVID-19 pandemic had arrived in Portugal at March 2020, with a breakout on the northern region. In order to quickly respond to the pandemic, the CHUP healthcare institution, known as a research center, has embraced the challenge of developing and integrating a new approach based on the openEHR standard to interoperate with the institution's existing information and its systems. An openEHR clinical modelling methodology was outlined and adopted, followed by a survey of daily clinical and technical requirements. With the arrival of the virus in Portugal, the CHUP institution has undergone through constant changes in their working methodologies as well as their openEHR modelling. As a result, an openEHR patient care workflow for COVID-19 was developed.

Anti-Remodeling Effects of Xanthohumol-Fortified Beer in Pulmonary Arterial Hypertension Mediated by ERK and AKT Inhibition.

Polyphenols present in some alcoholic beverages have been linked to beneficial effects in preventing cardiovascular diseases. Polyphenols found in beer with anti-proliferative and anti-cancer properties are appealing in the context of the quasi-malignant phenotype of pulmonary arterial hypertension (PAH). Our purpose was to evaluate if the chronic ingestion of a xanthohumol-fortified beer (FB) would be able to modulate the pathophysiology of experimental PAH. Male Wistar rats with monocrotaline (MCT)-induced PAH (60 mg/kg) were allowed to drink either xanthohumol-fortified beer (MCT + FB) or 5.2% ethanol (MCT + SHAM) for a period 4 weeks. At the end of the protocol, cardiopulmonary exercise testing and hemodynamic recordings were performed, followed by sample collection for further analysis. FB intake resulted in a significant attenuation of the pulmonary vascular remodeling in MCT + FB animals. This improvement was paralleled with the downregulation in expression of proteins responsible for proliferation (ERK1/2), cell viability (AKT), and apoptosis (BCL-XL). Moreover, MCT + FB animals presented improved right ventricle (RV) function and remodeling accompanied by VEGFR-2 pathway downregulation. The present study demonstrates that a regular consumption of xanthohumol through FB modulates major remodeling pathways activated in experimental PAH.

Huge interparietal posterior fontanel meningohydroencephalocele.

Congenital encephalocele is a neural tube defect characterized by a sac-like protrusion of the brain, meninges, and other intracranial structures through the skull, which is caused by an embryonic development abnormality. The most common location is at the occipital bone, and its incidence varies according to different world regions. We report a case of an 1-month and 7-day-old male child with a huge interparietal-posterior fontanel meningohydroencephalocele, a rare occurrence. Physical examination and volumetric computed tomography were diagnostic. The encephalocele was surgically resected. Intradural and extradural approaches were performed; the bone defect was not primarily closed. Two days after surgery, the patient developed hydrocephaly requiring ventriculoperitoneal shunting. The surgical treatment of the meningohydroencephalocele of the interparietal-posterior fontanel may be accompanied by technical challenges and followed by complications due to the presence of large blood vessels under the overlying skin. In these cases, huge sacs herniate through large bone defects including meninges, brain, and blood vessels. The latter present communication with the superior sagittal sinus and ventricular system. A favorable surgical outcome generally follows an accurate strategy taking into account individual features of the lesion.

Rating organ failure via adverse events using data mining in the intensive care unit.

OBJECTIVE: The main intensive care unit (ICU) goal is to avoid or reverse the organ failure process by adopting a timely intervention. Within this context, early identification of organ impairment is a key issue. The sequential organ failure assessment (SOFA) is an expert-driven score that is widely used in European ICUs to quantify organ disorder. This work proposes a complementary data-driven approach based on adverse events, defined from commonly monitored biometrics. The aim is to study the impact of these events when predicting the risk of ICU organ failure. MATERIALS AND METHODS: A large database was considered, with a total of 25,215 daily records taken from 4425 patients and 42 European ICUs. The input variables include the case mix (i.e. age, diagnosis, admission type and admission from) and adverse events defined from four bedside physiologic variables (i.e. systolic blood pressure, heart rate, pulse oximeter oxygen saturation and urine output). The output target is the organ status (i.e. normal, dysfunction or failure) of six organ systems (respiratory, coagulation, hepatic, cardiovascular, neurological and renal), as measured by the SOFA score. Two data mining (DM) methods were compared: multinomial logistic regression (MLR) and artificial neural networks (ANNs). These methods were tested in the R statistical environment, using 20 runs of a 5-fold cross-validation scheme. The area under the receiver operator characteristic (ROC) curve and Brier score were used as the discrimination and calibration measures. RESULTS: The best performance was obtained by the ANNs, outperforming the MLR in both discrimination and calibration criteria. The ANNs obtained an average (over all organs) area under the ROC curve of 64, 69 and 74% and Brier scores of 0.18, 0.16 and 0.09 for the dysfunction, normal and failure organ conditions, respectively. In particular, very good results were achieved when predicting renal failure (ROC curve area of 76% and Brier score of 0.06). CONCLUSION: Adverse events, taken from bedside monitored data, are important intermediate outcomes, contributing to a timely recognition of organ dysfunction and failure during ICU length of stay. The obtained results show that it is possible to use DM methods to get knowledge from easy obtainable data, thus making room for the development of intelligent clinical alarm monitoring.

Mortality assessment in intensive care units via adverse events using artificial neural networks.

OBJECTIVE: This work presents a novel approach for the prediction of mortality in intensive care units (ICUs) based on the use of adverse events, which are defined from four bedside alarms, and artificial neural networks (ANNs). This approach is compared with two logistic regression (LR) models: the prognostic model used in most of the European ICUs, based on the simplified acute physiology score (SAPS II), and a LR that uses the same input variables of the ANN model. MATERIALS AND METHODS: A large dataset was considered, encompassing forty two ICUs of nine European countries. The recorded features of each patient include the final outcome, the case mix (e.g. age) and the intermediate outcomes, defined as the daily averages of the out of range values of four biometrics (e.g. heart rate). The SAPS II score requires 17 static variables (e.g. serum sodium), which are collected within the first day of the patient's admission. A nonlinear least squares method was used to calibrate the LR models while the ANNs are made up of multilayer perceptrons trained by the RPROP algorithm. A total of 13,164 adult patients were randomly divided into training (66%) and test (33%) sets. The two methods were evaluated in terms of receiver operator characteristic (ROC) curves. RESULTS: The event based models predicted the outcome more accurately than the currently used SAPS II model (P<0.05), with ROC areas within the ranges 83.9-87.1% (ANN) and 82.6-85.2% (LR) versus 80% (LR SAPS II). When using the same inputs, the ANNs outperform the LR (improvement of 1.3-2%). CONCLUSION: Better prognostic models can be achieved by adopting low cost and real-time intermediate outcomes rather than static data.