Relationship between biomarkers and pain and falls in middle aged and older adults - An analysis of the Canadian Longitudinal Study on Aging.

Background: Pain is highly prevalent in older adults, and is associated with an increased risk of falls. There has been growing attention on the role of biomarkers. However, most studies have focused on a smaller number of biomarkers, and few have investigated the interrelationship with other biopsychosocial outcomes and the interrelationship between pain and falls simultaneously.

Machine learning driven diabetes care using predictive-prescriptive analytics for personalized medication prescription.

The increasing prevalence of type 2 diabetes (T2D) is a significant health concern worldwide. Effective and personalized treatment strategies are essential for improving patient outcomes and reducing healthcare costs. Machine learning (ML) has the potential to create clinical decision support systems (CDSS) that assist clinicians in making prediction-informed treatment decisions.

Data-driven insights into interhospital care fragmentation: Implications for health policy and equity among older adults.

Objective: To determine factors leading to interhospital care fragmentation (ICF); evaluate how ICF affects rehospitalization costs, length of stays (LOS), and delayed discharge; and analyze ICF disparity among equity-seeking groups.

Materials And Methods: We used a 13-year retrospective cohort of older adults (65+) in Ontario, Canada. Utilizing multivariable logistic regression, we identified characteristics associated with ICF and determined its association with outcomes.

Setting the balance of care for older adults at risk of hospitalization and delayed discharge: A mixed-methods research protocol.

Introduction: Delayed hospital discharge is a persistent care quality issue experienced across health systems worldwide and remains a priority area to be addressed in Canada. Often associated with a decrease in services while waiting to leave the hospital, delayed discharge from hospital can lead to increased frailty, physical and cognitive decline, and caregiver burnout. Optimizing availability of and timely access to community-based health and social care are avenues that could reduce initial admissions to the hospital and length of hospital stay, and facilitate hospital discharges.

Decoding the persistence of delayed hospital discharge: An in-depth scoping review and insights from two decades.

Objective: This article addresses the persistent challenge of Delayed Hospital Discharge (DHD) and aims to provide a comprehensive overview, synthesis, and actionable, sustainable plan based on the synthesis of the systematic review articles spanning the past 24 years. Our research aims to comprehensively examine DHD, identifying its primary causes and emphasizing the significance of effective communication and management in healthcare settings.

Methods: We conducted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) method for synthesizing findings from 23 review papers published over the last two decades, encompassing over 700 studies.

Climate crisis risks to elderly health: strategies for effective promotion and response.

The climate crisis significantly impacts the health and well-being of older adults, both directly and indirectly. This issue is of growing concern in Canada due to the country's rapidly accelerating warming trend and expanding elderly population. This article serves a threefold purpose: (i) outlining the impacts of the climate crisis on older adults, (ii) providing a descriptive review of existing policies with a specific focus on the Canadian context, and (iii) promoting actionable recommendations.

Exploring trajectories of functional decline and recovery among older adults: a data-driven approach.

Independently performing activities of daily living (ADLs) is vital for maintaining one's quality of life. Losing this ability can significantly impact an individual's overall health status, including their mental health and social well-being. Aging is an important factor contributing to the loss of ADL abilities, and our study focuses on investigating the trajectories of functional decline and recovery in older adults.

Prehospital prediction of hospital admission for emergent acuity patients transported by paramedics: A population-based cohort study using machine learning.

Introduction: The closest emergency department (ED) may not always be the optimal hospital for certain stable high acuity patients if further distanced ED's can provide specialized care or are less overcrowded. Machine learning (ML) predictions may support paramedic decision-making to transport a subgroup of emergent patients to a more suitable, albeit more distanced, ED if hospital admission is unlikely. We examined whether characteristics known to paramedics in the prehospital setting were predictive of hospital admission in emergent acuity patients.

A resilient, robust transformation of healthcare systems to cope with COVID-19 through alternative resources.

The COVID-19 pandemic - as a massive disruption - has significantly increased the need for medical services putting an unprecedented strain on health systems. This study presents a robust location-allocation model under uncertainty to increase the resiliency of health systems by applying alternative resources, such as backup and field hospitals and student nurses. A multi-objective optimization model is developed to minimize the system's costs and maximize the satisfaction rate among medical staff and COVID-19 patients.

Impact of multimorbidity and frailty on adverse outcomes among older delayed discharge patients: Implications for healthcare policy.

Objective: To assess the impacts of multiple chronic conditions (MCC) and frailty on 30-day post-discharge readmission and mortality among older patients with delayed discharge.

Data Source/extraction: We used a retrospective cohort of older patients in the Discharge Abstract Database (DAD) between 2004 and 2017 in Ontario, Canada. We extracted data on patients aged ≥ 65 who experienced delayed discharge during hospitalization (N = 353,106).

The impact of recency and adequacy of historical information on sepsis predictions using machine learning.

Sepsis is a major public and global health concern. Every hour of delay in detecting sepsis significantly increases the risk of death, highlighting the importance of accurately predicting sepsis in a timely manner. A growing body of literature has examined developing new or improving the existing machine learning (ML) approaches for timely and accurate predictions of sepsis.

Examining the predictability and prognostication of multimorbidity among older Delayed-Discharge Patients: A Machine learning analytics.

Background: Patient complexity among older delayed-discharge patients complicates discharge planning, resulting in a higher rate of adverse outcomes, such as readmission and mortality. Early prediction of multimorbidity, as a common indicator of patient complexity, can support proactive discharge planning by prioritizing complex patients and reducing healthcare inefficiencies.

Objective: We set out to accomplish the following two objectives: 1) to examine the predictability of three common multimorbidity indices, including Charlson-Deyo Comorbidity Index (CDCI), the Elixhauser Comorbidity Index (ECI), and the Functional Comorbidity Index (FCI) using machine learning (ML), and 2) to assess the prognostic power of these indices in predicting 30-day readmission and mortality.

Predicting hospital admission for older emergency department patients: Insights from machine learning.

Background: Emergency departments (ED) are a portal of entry into the hospital and are uniquely positioned to influence the health care trajectories of older adults seeking medical attention. Older adults present to the ED with distinct needs and complex medical histories, which can make disposition planning more challenging. Machine learning (ML) approaches have been previously used to inform decision-making surrounding ED disposition in the general population.

Using observed sequence to orient causal networks.

In learning causal networks, typically cross-sectional data are used and the sequence among the network nodes is learned through conditional independence. Sequence is inherently a longitudinal concept. We propose to learn sequence of events in longitudinal data and use it to orient arc directions in a network learned from cross-sectional data.

Sequence of Functional Loss and Recovery in Nursing Homes.

Purpose Of The Study: This study provides benchmarks for likelihood, number of days until, and sequence of functional decline and recovery.

Design And Methods: We analyzed activities of daily living (ADLs) of 296,051 residents in Veteran Affairs nursing homes between January 1, 2000 and October 9, 2012. ADLs were extracted from standard minimum data set assessments.

Causal Analysis of Emergency Department Delays.

Background: Improvement teams make causal inferences, but the methods they use are based on statistical associations. This article shows how data and statistical models can be used to help improvement teams make causal inferences and find the root causes of problems.

Methods: This article uses attribution data, competing risk survival analysis, and Bayesian network probabilities to analyze excessive emergency department (ED) stays within one hospital.

Comparative effectiveness of hypoglycemic medications among veterans.

Background: The efficacy of diabetic medications among patients with multiple comorbidities is not tested in randomized clinical studies. It is important to monitor the performance of these medications after marketing approvals.

Objective: To investigate the risk of all-cause mortality associated with prescription of hypoglycemic agents.

A psychological approach to learning causal networks.

We examine the role of a common cognitive heuristic in unsupervised learning of Bayesian probability networks from data. Human beings perceive a larger association between causal than diagnostic relationships. This psychological principal can be used to orient the arcs within Bayesian networks by prohibiting the direction that is less predictive.

Genetic markers anticipate response to citalopram in a majority of patients.

Objective: Scientists have concluded that genetic profiles cannot predict a large percentage of variation in response to citalopram, a common antidepressant. Using the same data, we examined if a different conclusion can be arrived at when the results are personalized to fit specific patients.

Methods: We used data available through the Sequenced Treatment Alternatives to Relieve Depression database.

A Simple Method for Causal Analysis of Return on IT Investment.

This paper proposes a method for examining the causal relationship among investment in information technology (IT) and the organization's productivity. In this method, first a strong relationship among (1) investment in IT, (2) use of IT and (3) organization's productivity is verified using correlations. Second, the assumption that IT investment preceded improved productivity is tested using partial correlation.