Can Text Messages Improve Attendance to Primary Care Appointments in Underserved Populations?

We explored whether text message (TM) reminders could be used at a community health center (CHC) to improve primary care appointment attendance in adult patients. Over six months, we allocated 8,425 appointments to intervention and 2,679 to control. The proportion of no-shows in the intervention was 18.

A Novel Model for Predicting Rehospitalization Risk Incorporating Physical Function, Cognitive Status, and Psychosocial Support Using Natural Language Processing.

Background: With the increasing focus on reducing hospital readmissions in the United States, numerous readmissions risk prediction models have been proposed, mostly developed through analyses of structured data fields in electronic medical records and administrative databases. Three areas that may have an impact on readmission but are poorly captured using structured data sources are patients' physical function, cognitive status, and psychosocial environment and support.

Objective Of The Study: The objective of the study was to build a discriminative model using information germane to these 3 areas to identify hospitalized patients' risk for 30-day all cause readmissions.

Patient navigation based on predictive modeling decreases no-show rates in cancer care.

Background: Patient adherence to appointments is key to improving outcomes in health care. "No-show" appointments contribute to suboptimal resource use. Patient navigation and telephone reminders have been shown to improve cancer care and adherence, particularly in disadvantaged populations, but may not be cost-effective if not targeted at the appropriate patients.

Success of automated algorithmic scheduling in an outpatient setting.

Objective: To determine if algorithmically generated double-booking recommendations could increase patient volume per clinical session without increasing the burden on physicians.

Study Design: A randomized controlled trial was conducted with 519 clinical sessions for 13 dermatologists from December 1, 2011, through March 31, 2012.

Methods: Sessions were randomly assigned to "Smart-Booking," an algorithm that generates double-booking recommendations using a missed appointment (no-shows + same-day cancella- tions) predictive model (c-statistic 0.

Development and implementation of a real-time 30-day readmission predictive model.

Hospitals are under great pressure to reduce readmissions of patients. Being able to reliably predict patients at increased risk for rehospitalization would allow for tailored interventions to be offered to them. This requires the creation of a functional predictive model specifically designed to support real-time clinical operations.