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Importance: Posthospital contact with a primary care team is an established pillar of safe transitions. The prevailing model of telephone outreach is usually limited in scope and operationally burdensome.
Objective: To determine whether a 30-day automated texting program to support primary care patients after hospital discharge is associated with reductions in the use of acute care resources.
Design, Setting, And Participants: This cohort study used a difference-in-differences approach at 2 academic primary care practices in Philadelphia from January 27 through August 27, 2021. Established patients of the study practices who were 18 years or older, were discharged from an acute care hospitalization, and received the usual transitional care management telephone call were eligible for the study. At the intervention practice, 604 discharges were eligible and 430 (374 patients, of whom 46 had >1 discharge) were enrolled in the intervention. At the control practice, 953 patients met eligibility criteria. The study period, including before and after the intervention, ran from August 27, 2020, through August 27, 2021.
Exposure: Patients received automated check-in text messages from their primary care practice on a tapering schedule during the 30 days after discharge. Any needs identified by the automated messaging platform were escalated to practice staff for follow-up via an electronic medical record inbox.
Main Outcomes And Measures: The primary study outcome was any emergency department (ED) visit or readmission within 30 days of discharge. Secondary outcomes included any ED visit or any readmission within 30 days, analyzed separately, and 30- and 60-day mortality. Analyses were based on intention to treat.
Results: A total of 1885 patients (mean [SD] age, 63.2 [17.3] years; 1101 women [58.4%]) representing 2617 discharges (447 before and 604 after the intervention at the intervention practice; 613 before and 953 after the intervention at the control practice) were included in the analysis. The adjusted odds ratio (aOR) for any use of acute care resources after implementation of the intervention was 0.59 (95% CI, 0.38-0.92). The aOR for an ED visit was 0.77 (95% CI, 0.45-1.30) and for a readmission was 0.45 (95% CI, 0.23-0.86). The aORs for death within 30 and 60 days of discharge at the intervention practice were 0.92 (95% CI, 0.23-3.61) and 0.63 (95% CI, 0.21-1.85), respectively.
Conclusions And Relevance: The findings of this cohort study suggest that an automated texting program to support primary care patients after hospital discharge was associated with significant reductions in use of acute care resources. This patient-centered approach may serve as a model for improving postdischarge care.
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http://dx.doi.org/10.1001/jamanetworkopen.2022.38293 | DOI Listing |
Am J Emerg Med
September 2025
Department of Surgical Education, Orlando Regional Medical Center, Orlando, FL, USA; Department of Surgery, Division of Trauma and Surgical Critical Care, Orlando Regional Medical Center, Orlando, FL, USA. Electronic address:
Background: There is conflicting literature regarding mortality outcomes associated with REBOA usage in patients with severe thoracic or abdominal trauma. Our study aims to assess the benefits and negative implications of REBOA use in adult trauma patients in hemorrhagic shock with severe thoracic or abdominal injuries.
Methods: This retrospective cohort analysis utilized the American College of Surgeons Trauma Quality Improvement Program Participant Use File (ACS-TQIP-PUF) database from 2017 to 2023 to evaluate adult patients with severe isolated thoracic or abdominal trauma undergoing REBOA placement.
JMIR Hum Factors
September 2025
Media Psychology Lab, Department of Communication Science, KU Leuven, Leuven, Belgium.
Background: Out-of-hospital cardiac arrests (OHCAs) are a leading cause of death worldwide, yet first responder apps can significantly improve outcomes by mobilizing citizens to perform cardiopulmonary resuscitation before professional help arrives. Despite their importance, limited research has examined the psychological and behavioral factors that influence individuals' willingness to adopt these apps.
Objective: Given that first responder app use involves elements of both technology adoption and preventive health behavior, it is essential to examine this behavior from multiple theoretical perspectives.
JCO Glob Oncol
May 2025
Department of Obstetrics and Gynaecology, Stanford University School of Medicine, Stanford, CA.
Purpose: Expanding high-risk human papillomavirus (HPV) vaccine coverage in resource-constrained settings is critical to bridging the cervical cancer gap and achieving the global action plan for elimination. Mobile health (mHealth) technology via short message services (SMS) has the potential to improve HPV vaccination uptake. The mHealth-HPVac study evaluated the effectiveness of mHealth interventions in increasing HPV vaccine uptake among mothers of unvaccinated girls aged 9-14 years in Lagos, Nigeria.
View Article and Find Full Text PDFDiabetes Care
September 2025
Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.
Objective: This study aimed to evaluate the diabetic eye disease screening continuum at two academic centers and identify its barriers.
Research Design And Methods: We analyzed health records from the University of California, San Francisco and University of California, Irvine to identify primary care patients needing diabetic eye screening. We tracked referrals, screenings, diagnoses, and treatments to evaluate predictors and the impact of an automated referral system.
Crit Care Explor
September 2025
Division of Tropical Medicine and Infectious Diseases, Department of Internal Medicine, Dr. Cipto Mangunkusumo National General Hospital, Faculty of Medicine Universitas Indonesia, Jakarta, Indonesia.
Importance: Sepsis remains a leading cause of death in infectious cases. The heterogeneity of immune responses is a major challenge in the management and prognostication of patients with sepsis. Identifying distinct immune response subphenotypes using parsimonious classifiers may improve outcome prediction, particularly in resource-limited settings.
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