An exploratory, retrospective evaluation of Ambulatory Integration of the Medical and Social (AIMS), a care coordination model designed to integrate medical and non-medical needs of patients and delivered exclusively by social workers was conducted to examine mean utilization of costly health care services for older adult patients. Results reveal mean utilization of 30-day hospital readmissions, emergency department (ED) visits, and hospital admissions are significantly lower for the study sample compared to the larger patient population. Comparisons with national population statistics reveal significantly lower mean utilization of 30-day admissions and ED visits for the study sample. The findings offer preliminary support regarding the value of AIMS.
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http://dx.doi.org/10.1080/00981389.2016.1164269 | DOI Listing |
JMIR Hum Factors
January 2025
Nursing Research, Department of Nursing, Memorial Sloan Kettering Cancer Center, New York, NY, United States.
Background: Remote patient monitoring (RPM) aims to improve patient access to care and communication with clinical providers. Overall, understanding the usability of RPM applications and their influence on clinical care workflows is limited from the perspectives of clinician end users at a cancer center in the Northeastern United States.
Objective: This study aims to explore the usability and functionality of RPM and elicit the perceptions and experiences of oncology clinicians using RPM for oncology patients after hospital discharge.
J Affect Disord
January 2025
Department of Anesthesiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Ambulatory Surgery Center, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China. Electronic address:
Background: Individuals with metabolic syndrome (MetS) are at a higher risk of developing depressive symptoms, with inflammation hypothesized to mediate this association. This study used data from the National Health and Nutrition Examination Survey (NHANES) (2015-2020) to investigate the relationship between MetS and depression and assess the mediating role of inflammatory markers.
Methods: This cross-sectional study included 20,520 participants.
J Frailty Aging
February 2025
Division of Geriatrics and Osher Center for Integrative Health, University of California, San Francisco, San Francisco, CA, USA.
Background: Pre-frailty is highly prevalent and multimodal lifestyle interventions are effective for preventing transition to frailty. However, little is known about the potential for medical group visits (MGV) to prevent frailty progression.
Objectives: To assess the feasibility and acceptability of the MGV Age Self Care-Resilience.
J Urol
January 2025
Department of Population Health, NYU Grossman School of Medicine, New York, New York.
Purpose: We aimed to determine whether implementation of clinical decision support (CDS) tool integrated into the electronic health record (EHR) of a multi-site academic medical center increased the proportion of patients with American Urological Association (AUA) "high risk" microscopic hematuria (MH) who receive guideline concordant evaluations.
Materials And Methods: We conducted a two-arm cluster randomized quality improvement project in which 202 ambulatory sites from a large health system were randomized to either have their physicians receive at time of test results an automated CDS alert for patients with 'high-risk' MH with associated recommendations for imaging and cystoscopy (intervention) or usual care (control). Primary outcome was met if a patient underwent both imaging and cystoscopy within 180 days from MH result.
ACS Appl Mater Interfaces
January 2025
Center for Wearable Intelligent Systems and Healthcare, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Recognizing human body motions opens possibilities for real-time observation of users' daily activities, revolutionizing continuous human healthcare and rehabilitation. While some wearable sensors show their capabilities in detecting movements, no prior work could detect full-body motions with wireless devices. Here, we introduce a soft electronic textile-integrated system, including nanomaterials and flexible sensors, which enables real-time detection of various full-body movements using the combination of a wireless sensor suit and deep-learning-based cloud computing.
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