"Learning healthcare system"

BACKGROUND: Multimodal analgesic strategies that reduce perioperative opioid consumption are well-supported in Enhanced Recovery After Surgery (ERAS) literature. However, the optimal analgesic regimen has not been established, as the contributions of each individual agent to the overall analgesic efficacy with opioid reduction remains unknown. Perioperative ketamine infusions can decrease opioid consumption and opioid-related side effects. However, as opioid requirements are drastically minimized within ERAS models, the differential effects of ketamine within an ERAS pathway remain unknown...

Multiple Sclerosis is a neurodegenerative disease which shows different phenotypes making difficult for clinicians to make short-term decisions related with treatment and prognosis. Diagnosis is usually retrospective. Learning Healthcare Systems (LHS) can support clinical practice as they are devised as constantly improving modules. LHS can identify insights which allow evidence-based clinical decisions and more accurate prognosis. We are developing a LHS with the aim of reducing uncertainty. We are using ReDCAP to collect patients' data, both from Clinical Reported Outcomes (CRO) and from Patients Reported Outcomes (PRO)...

BACKGROUND: Pregnant people have been overlooked or excluded from clinical research, resulting in a lack of scientific knowledge on medication safety and efficacy during pregnancy. Thus far, both the opportunities to generate evidence-based knowledge beyond clinical trials and the role of pregnant people in changing their status quo have not been discussed. Some scholars have argued that for rare disease patients, for whom, just like pregnant people, a poor evidence base exists regarding treatments, solidarity has played an important role in addressing the evidence gap...

The explosion of clinical textual data has drawn the attention of researchers. Owing to the abundance of clinical data, it is becoming difficult for healthcare professionals to take real-time measures. The tools and methods are lacking when compared to the amount of clinical data generated every day. This review aims to survey the text processing pipeline with deep learning methods such as CNN, RNN, LSTM, and GRU in the healthcare domain and discuss various applications such as clinical concept detection and extraction, medically aware dialogue systems, sentiment analysis of drug reviews shared online, clinical trial matching, and pharmacovigilance...

N-of-1 strategies can provide high-quality evidence of treatment efficacy at the individual level and optimize evidence-based selection of off-label treatments for patients with rare diseases. Given their design characteristics, n-of-1 strategies are considered to sit at the intersection between medical research and clinical care. Therefore, whether n-of-1 strategies should be governed by research or care regulations remains a debated issue. Here, we delineate differences between medical research and optimized clinical care, and distinguish the regulations which apply to either...

INTRODUCTION: Suicide prevention research is a national priority, and national guidance includes the development of suicide risk management protocols (SRMPs) for the assessment and management of suicidal ideation and behavior in research trials. Few published studies describe how researchers develop and implement SRMPs or articulate what constitutes an acceptable and effective SRMP. METHODS: The Texas Youth Depression and Suicide Research Network (TX-YDSRN) was developed with the goal of evaluating screening and measurement-based care in Texas youth with depression or suicidality (i...

BACKGROUND: One benefit of pragmatic clinical trials is reduction of the burden on patients and clinical staff while facilitating a learning healthcare system. One way to decrease the work of clinical staff is through decentralized telephone consent. METHODS: The Diuretic Comparison Project (DCP) was a nationwide Point of Care pragmatic clinical trial conducted by the VA Cooperative Studies Program. The purpose of the trial was to compare the clinical effectiveness on major CV outcomes of two commonly used diuretics, hydrochlorothiazide and chlorthalidone, in an elderly patient population...

Outcome research that supports guideline recommendations for primary and secondary preventions largely depends on the data obtained from clinical trials or selected hospital populations. The exponentially growing amount of real-world medical data could enable fundamental improvements in cardiovascular disease (CVD) prediction, prevention, and care. In this review we summarize how data from health insurance claims (HIC) may improve our understanding of current health provision and identify challenges of patient care by implementing the perspective of patients (providing data and contributing to society), physicians (identifying at-risk patients, optimizing diagnosis and therapy), health insurers (preventive education and economic aspects), and policy makers (data-driven legislation)...

BACKGROUND: The Utrecht Cardiovascular Cohort - CardioVascular Risk Management (UCC-CVRM) was set up as a learning healthcare system (LHS), aiming at guideline based cardiovascular risk factor measurement in all patients in routine clinical care. However, not all patients provided informed consent, which may lead to participation bias. We aimed to study participation bias in a LHS by assessing differences in and completeness of cardiovascular risk management (CVRM) indicators in electronic health records (EHRs) of consenting, non-consenting, and non-responding patients, using the UCC-CVRM as an example...

The Program of Education through Work - Health (PET-Health) Interprofessionality is one of the strategic actions of the "Plan for the Strengthening of Interprofessionality" in healthcare in Brazil. Based on the experience of the program, this paperexamines the aspects that impact the adoption and strengthening of interprofessional education and collaborative practices, and issues recommendations for the strengthening of interprofessionality as a guiding principle of training and working in healthcare. This is a document analysis of partial reports from the six- and 12-months of execution of 120 PET-Health Interprofessionality projects in Brazil...

Several principles have been proposed to improve use of artificial intelligence (AI) in healthcare, but the need for AI to improve longstanding healthcare challenges has not been sufficiently emphasized. We propose that AI should be designed to alleviate health disparities, report clinically meaningful outcomes, reduce overdiagnosis and overtreatment, have high healthcare value, consider biographical drivers of health, be easily tailored to the local population, promote a learning healthcare system, and facilitate shared decision-making...

BACKGROUND: Multimodal analgesic strategies that reduce perioperative opioid consumption are well-supported in Enhanced Recovery After Surgery (ERAS) literature. However, the optimal analgesic regimen has not been established, as the contributions of each individual agent to the overall analgesic efficacy with opioid reduction remains unknown. Perioperative ketamine infusions can decrease opioid consumption and opioid-related side effects. However, as opioid requirements are drastically minimized within ERAS models, the differential effects of ketamine within an ERAS pathway remain unknown...

OBJECTIVE: Millions of people have been affected by coronavirus disease 2019 (COVID-19), which has caused millions of deaths around the world. Artificial intelligence (AI) plays an increasing role in all areas of patient care, including prognostics. This paper proposes a novel predictive model based on one dimensional convolutional neural networks (1D CNN) to use clinical variables in predicting the survival outcome of COVID-19 patients. METHODS AND PROCEDURES: We have considered two scenarios for survival analysis, 1) uni-variate analysis using the Log-rank test and Kaplan-Meier estimator and 2) combining all clinical variables ([Formula: see text]=44) for predicting the short-term from long-term survival...

OBJECTIVES: Self-affirmation may be a promising treatment strategy for improving clinical outcomes. This study examined the association between self-affirmation and self-reported health status among people with chronic pain. METHODS: In this cross-sectional study, 768 treatment seeking people (female 67.2%, mean age=50.4 years with SD of 17.1, White/Caucasian 59.9%) completed surveys using a learning healthcare system. Measures included spontaneous self-affirmation (SSA) items, PROMIS® outcome measures, and Pain Catastrophizing Scale (PCS)...

INTRODUCTION: Antibiotics are time-critical in the management of sepsis. When infectious organisms are unknown, patients are treated with empiric antibiotics to include coverage for gram-negative organisms, such as antipseudomonal cephalosporins and penicillins. However, in observational studies, some antipseudomonal cephalosporins (eg, cefepime) are associated with neurologic dysfunction while the most common antipseudomonal penicillin (piperacillin-tazobactam) is associated with acute kidney injury (AKI)...

Current methods of emergency-room-based syndromic surveillance were insufficient to detect early community spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) in the United States, which slowed the infection prevention and control response to the novel pathogen. Emerging technologies and automated infection surveillance have the potential to improve upon current practice standards and to revolutionize the practice of infection detection, prevention and control both inside and outside of healthcare settings...

Since 2015 we organized a uniform, structured collection of a fixed set of cardiovascular risk factors according the (inter)national guidelines on cardiovascular risk management. We evaluated the current state of a developing cardiovascular towards learning healthcare system-the Utrecht Cardiovascular Cohort Cardiovascular Risk Management (UCC-CVRM)-and its potential effect on guideline adherence in cardiovascular risk management. We conducted a before-after study comparing data from patients included in UCC-CVRM (2015-2018) and patients treated in our center before UCC-CVRM (2013-2015) who would have been eligible for UCC-CVRM using the Utrecht Patient Oriented Database (UPOD)...

Rural-dwelling individuals with dementia and their caregivers face unique challenges compared to urban-dwelling peers. Barriers to accessing services and supports are common, and individual resources and informal networks available to support rural families can be difficult to track for providers and healthcare systems outside of the local community. This study uses qualitative data from rural-dwelling dyads, individuals with dementia ( n = 12) and informal caregivers ( n = 18), to demonstrate how rural patients' daily life needs can be summarized through life-space map visualizations...

The feasibility and value of linking electrocardiogram (ECG) data to longitudinal population-level administrative health data to facilitate the development of a learning healthcare system has not been fully explored. We developed ECG-based machine learning models to predict risk of mortality among patients presenting to an emergency department or hospital for any reason. Using the 12-lead ECG traces and measurements from 1,605,268 ECGs from 748,773 healthcare episodes of 244,077 patients (2007-2020) in Alberta, Canada, we developed and validated ResNet-based Deep Learning (DL) and gradient boosting-based XGBoost (XGB) models to predict 30-day, 1-year, and 5-year mortality...

Machine learning (ML) is a sub-field of artificial intelligence that uses computer algorithms to extract patterns from raw data, acquire knowledge without human input, and apply this knowledge for various tasks. Traditional statistical methods that classify or regress data have limited capacity to handle large datasets that have a low signal-to-noise ratio. In contrast to traditional models, ML relies on fewer assumptions, can handle larger and more complex datasets, and does not require predictors or interactions to be pre-specified, allowing for novel relationships to be detected...