Telemedicine appointments are more likely to be completed than in-person healthcare appointments: a retrospective cohort study.

Objectives: Missed appointments can lead to treatment delays and adverse outcomes. Telemedicine may improve appointment completion because it addresses barriers to in-person visits, such as childcare and transportation. This study compared appointment completion for appointments using telemedicine versus in-person care in a large cohort of patients at an urban academic health sciences center. Materials and Methods: We conducted a retrospective cohort study of electronic health record data to determine whether telemedicine appointments have higher odds of completion compared to in-person care appointments, January 1, 2021, and April 30, 2023. The data were obtained from the University of South Florida (USF), a large academic health sciences center serving Tampa, FL, and surrounding communities. We implemented 1:1 propensity score matching based on age, gender, race, visit type, and Charlson Comorbidity Index (CCI). Results: The matched cohort included 87 376 appointments, with diverse patient demographics. The percentage of completed telemedicine appointments exceeded that of completed in-person care appointments by 9.2 points (73.4% vs 64.2%, P < .001). The adjusted odds ratio for telemedicine versus in-person care in relation to appointment completion was 1.64 (95% CI, 1.59-1.69, P < .001), indicating that telemedicine appointments are associated with 64% higher odds of completion than in-person care appointments when controlling for other factors. Discussion: This cohort study indicated that telemedicine appointments are more likely to be completed than in-person care appointments, regardless of demographics, comorbidity, payment type, or distance. Conclusion: Telemedicine appointments are more likely to be completed than in-person healthcare appointments.

A Reproducible Model Based on Clinical Text for Predicting Suicidal Behavior.

Suicide risk models are critical for prioritizing patients for intervention. We demonstrate a reproducible approach for training text classifiers to identify patients at risk. The models were effective in phenotyping suicidal behavior (F1=.94) and moderately effective in predicting future events (F1=.63).

Development of a 5-Step Electronic Medical Record-Based Algorithm to Identify Patients with Opioid Use Disorder in Pregnancy.

Objectives: This study aimed to develop and validate an algorithm for the identification of opioid use disorder (OUD) in pregnant patients using electronic medical record (EMR) data. Materials and Methods: A cohort of pregnant patients from a single institution was used to develop and validate the algorithm. Five algorithm components were used, and chart reviews were conducted to confirm OUD diagnoses based on established criteria. Positive predictive values (PPV) of each of the algorithm's components were assessed. Results: Of the 334 charts identified by the algorithm, 256 true cases were confirmed. The overall PPV of the algorithm was 76.6%, with 100% accuracy for outpatient medication lists, and high PPVs ranging from 81.3% to 93.4% across other algorithm components. Discussion and Conclusion: The study highlights the significance of a multifaceted approach in identifying OUD among pregnant patients, aiming to improve patient care and target interventions for patients at risk.