Leveraging Advances in Artificial Intelligence to Improve the Quality and Timing of Palliative Care.
Ontology highlight
ABSTRACT:
SUBMITTER: Windisch P
PROVIDER: S-EPMC7281519 | biostudies-literature | 2020 May
REPOSITORIES: biostudies-literature
Publications
Leveraging Advances in Artificial Intelligence to Improve the Quality and Timing of Palliative Care.
Windisch Paul P Hertler Caroline C Blum David D Zwahlen Daniel D Förster Robert R
Cancers 20200503 5
In recent years, research on artificial intelligence (AI) in medicine has seen great advances, especially with regards to the detection of diseases [...]. ...[more]
Similar Datasets
Project description:SignificanceMany bad decisions and their devastating consequences could be avoided if people used optimal decision strategies. Here, we introduce a principled computational approach to improving human decision making. The basic idea is to give people feedback on how they reach their decisions. We develop a method that leverages artificial intelligence to generate this feedback in such a way that people quickly discover the best possible decision strategies. Our empirical findings suggest that a principled computational approach leads to improvements in decision-making competence that transfer to more difficult decisions in more complex environments. In the long run, this line of work might lead to apps that teach people clever strategies for decision making, reasoning, goal setting, planning, and goal achievement.
| S-EPMC8944825 | biostudies-literature
Project description:ObjectivesArtificial intelligence (AI), including predictive analytics, has great potential to improve the care of common chronic conditions with high morbidity and mortality. However, there are still many challenges to achieving this vision. The goal of this project was to develop and apply methods for enhancing chronic disease care using AI.MethodsUsing a dataset of 27,904 patients with diabetes, an analytical method was developed and validated for generating a treatment pathway graph which consists of models that predict the likelihood of alternate treatment strategies achieving care goals. An AI-driven clinical decision support system (CDSS) integrated with the electronic health record (EHR) was developed by encapsulating the prediction models in an OpenCDS Web service module and delivering the model outputs through a SMART on FHIR (Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources) web-based dashboard. This CDSS enables clinicians and patients to review relevant patient parameters, select treatment goals, and review alternate treatment strategies based on prediction results.ResultsThe proposed analytical method outperformed previous machine-learning algorithms on prediction accuracy. The CDSS was successfully integrated with the Epic EHR at the University of Utah.ConclusionA predictive analytics-based CDSS was developed and successfully integrated with the EHR through standards-based interoperability frameworks. The approach used could potentially be applied to many other chronic conditions to bring AI-driven CDSS to the point of care.
| S-EPMC8294941 | biostudies-literature
Project description:Peripheral artery disease is an atherosclerotic disorder which, when present, portends poor patient outcomes. Low diagnosis rates perpetuate poor management, leading to limb loss and excess rates of cardiovascular morbidity and death. Machine learning algorithms and artificially intelligent systems have shown great promise in application to many areas in health care, such as accurately detecting disease, predicting patient outcomes, and automating image interpretation. Although the application of these technologies to peripheral artery disease are in their infancy, their promises are tremendous. In this review, we provide an introduction to important concepts in the fields of machine learning and artificial intelligence, detail the current state of how these technologies have been applied to peripheral artery disease, and discuss potential areas for future care enhancement with advanced analytics.
| S-EPMC8285054 | biostudies-literature
Project description: Not available
| S-EPMC1118445 | biostudies-literature
Project description:ObjectiveTo develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches.MethodsWe extracted data on alerts generated from January 1, 2019 to December 31, 2020, at Vanderbilt University Medical Center. We developed machine learning models to predict user responses to alerts. We applied XAI techniques to generate global explanations and local explanations. We evaluated the generated suggestions by comparing with alert's historical change logs and stakeholder interviews. Suggestions that either matched (or partially matched) changes already made to the alert or were considered clinically correct were classified as helpful.ResultsThe final dataset included 2 991 823 firings with 2689 features. Among the 5 machine learning models, the LightGBM model achieved the highest Area under the ROC Curve: 0.919 [0.918, 0.920]. We identified 96 helpful suggestions. A total of 278 807 firings (9.3%) could have been eliminated. Some of the suggestions also revealed workflow and education issues.ConclusionWe developed a data-driven process to generate suggestions for improving alert criteria using XAI techniques. Our approach could identify improvements regarding clinical decision support (CDS) that might be overlooked or delayed in manual reviews. It also unveils a secondary purpose for the XAI: to improve quality by discovering scenarios where CDS alerts are not accepted due to workflow, education, or staffing issues.
| S-EPMC10990514 | biostudies-literature
Project description:ImportanceDespite research supporting the benefits of early palliative care, timely initiation by gynecologic oncology patients is reportedly low, which may limit the effectiveness of palliative care.ObjectiveTo investigate the association of the timing of palliative care initiation with the aggressiveness of end-of-life care using established quality indicators among patients with ovarian cancer.Design, setting, and participantsThis population-based retrospective cohort study of ovarian cancer decedents used linked administrative health care data to identify palliative care provision across all health care sectors and health care professionals (specialist and nonspecialist) and end-of-life quality indicators in Ontario, Canada, from 2006 to 2018. Data analyses were performed July 12, 2024.Main outcomes and measuresThe primary outcome was the associations between the timing of palliative care and end-of-life quality indicators, including emergency department use, hospital or intensive care unit admission in the last 30 days of life, chemotherapy in last 14 days of life, death in the hospital, and a composite measure of aggressive care. Late palliative care was defined as 3 months or less prior to death.ResultsThere were 8297 ovarian cancer decedents. Their mean (SD) age at death was 69.6 (13.1) years, and their mean (SD) oncologic survival was 2.8 (3.9) years. Among 3958 patients with known cancer stage, 3495 (88.3%) presented with stage III or IV disease. One-third of patients (2667 [32.1%]) received late palliative care in the final 3 months of life. Results of multivariable regression analysis indicated that any palliative care initiated earlier than 3 months before death was associated with lower rates of aggressive end-of-life care (odds ratio [OR], 0.47 [95% CI, 0.37-0.60]), death in hospital (OR, 0.54 [95% CI, 0.45-0.65]), and intensive care unit admission (OR, 0.46 [95% CI, 0.27-0.76]). Specialist palliative consultation from 3 months up to 6 monts before death was associated with decreased likelihood of late chemotherapy (OR, 0.46 [95% CI, 0.24-0.88]).ConclusionsFindings from this cohort study suggested that early palliative care may be associated with less-aggressive end-of-life care than late palliative care. Implementation strategies for early palliative care initiation are needed to optimize care quality and health resource utilization at the end of life.
| S-EPMC11519754 | biostudies-literature
Project description:BackgroundElectronic health records (EHRs) have contributed to increased workloads for clinicians. Ambient artificial intelligence (AI) tools offer potential solutions, aiming to streamline clinical documentation and alleviate cognitive strain on healthcare providers.ObjectiveTo assess the clinical utility of an ambient AI tool in enhancing consultation experience and the completion of clinical documentation.MethodsOutpatient consultations were simulated with actors and clinicians, comparing the AI tool against standard EHR practices. Documentation was assessed by the Sheffield Assessment Instrument for Letters (SAIL). Clinician experience was measured through questionnaires and the NASA Task Load Index.ResultsAI-produced documentation achieved higher SAIL scores, with consultations 26.3% shorter on average, without impacting patient interaction time. Clinicians reported an enhanced experience and reduced task load.ConclusionsThe AI tool significantly improved documentation quality and operational efficiency in simulated consultations. Clinicians recognised its potential to improve note-taking processes, indicating promise for integration into healthcare practices.
| S-EPMC11452835 | biostudies-literature
Project description:We modeled profiles of ovary gene expression and their relationship to egg quality, evaluated as production of viable mid-blastula stage embryos, in striped bass (Morone saxatilis) using artificial neural networks and supervised machine learning. Collective changes in expression of a limited suite of genes (233) representing only 2% of the queried ovary transcriptome explained >90% of the eventual variance in embryo survival. Egg quality related to minor changes in expression (≤0.2-fold), with most gene transcripts making minor contribution (<1%) to the overall prediction of egg quality. Correlation analyses of this suite of candidate genes indicated that collective dysfunction of the ubiquitin-26S proteasome, COP9 signalosome, and subsequent control of the cell cycle engenders embryonic developmental incompetence in striped bass. Our results show that the transcriptomic signature evidencing this dysfunction is of, and therefore likely to influence, egg quality, a biologically complex trait that is crucial to reproductive fitness.
2012-12-10 | GSE42804 | GEO
Project description:IntroductionNasopharyngeal carcinoma (NPC) typically presents as advanced disease due to the lack of significant symptoms in the early stages. Accurate prognostication is therefore challenging as current methods based on anatomical staging often lack the granularity to differentiate between patients with differing prognoses. This study investigates the potential of radiomics to improve the prediction of locoregional recurrence (LRR) and overall survival in patients with NPC.MethodsRadiomic features were extracted from radiotherapy planning CT scans for 294 NPC patients divided into training (n = 147) and validation (n = 147) sets. A feature selection step utilising feature clustering and mutual information classifier to select six key radiomic features was employed to reduce redundancy and improve interpretability. Models were trained using clinical data, radiomic features, and these in combination to predict 2-year LRR, with performance assessed on the left-out independent validation set.ResultsCombining radiomic features with clinical data resulted in the best performance for predicting 2-year LRR (Area Under the Curve, AUC 0.76) compared to prediction using clinical or radiomic features alone (mean AUC 0.56 and 0.57, respectively). Risk stratification based on the combined model was significant for LRR-free survival and overall survival (p < 0.01). Key radiomic features included tumour size, intensity distribution, overall textural patterns, and distribution of fine and coarse textured regions.DiscussionRadiomics holds promise for improving NPC risk stratification, potentially allowing for personalised treatment strategies. The most important radiomics feature, maximum 2D diameter, suggests a need to reconsider tumour size as a prognostic criterion despite its current exclusion from TNM staging. Larger prospective studies are needed to validate these findings.
| S-EPMC11921138 | biostudies-literature
Project description:We modeled profiles of ovary gene expression and their relationship to egg quality, evaluated as production of viable mid-blastula stage embryos, in striped bass (Morone saxatilis) using artificial neural networks and supervised machine learning. Collective changes in expression of a limited suite of genes (233) representing only 2% of the queried ovary transcriptome explained >90% of the eventual variance in embryo survival. Egg quality related to minor changes in expression (M-bM-^IM-$0.2-fold), with most gene transcripts making minor contribution (<1%) to the overall prediction of egg quality. Correlation analyses of this suite of candidate genes indicated that collective dysfunction of the ubiquitin-26S proteasome, COP9 signalosome, and subsequent control of the cell cycle engenders embryonic developmental incompetence in striped bass. Our results show that the transcriptomic signature evidencing this dysfunction is of, and therefore likely to influence, egg quality, a biologically complex trait that is crucial to reproductive fitness. Female striped bass were sorted into groups (N=8 each) producing M-bM-^@M-^Xhigh qualityM-bM-^@M-^Y or M-bM-^@M-^Xlow qualityM-bM-^@M-^Y eggs (spawns) based upon the percentage of eggs bearing viable 4 h embryos. Spawns with >50% of eggs producing 4 h embryos were considered to be of high quality and spawns with <30% of eggs producing 4 h embryos were considered to be of low quality.
2012-12-10 | E-GEOD-42804 | biostudies-arrayexpress