Structural causal model with expert augmented knowledge to estimate the effect of oxygen therapy on mortality in the ICU.

Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identifying causal effects from observational data in cases where the causal graph is identifiable, i.e., the data generation mechanism can be recovered from the joint distribution.

An mHealth App-Based Self-management Intervention for Family Members of Pediatric Transplant Recipients (myFAMI): Framework Design and Development Study.

Background: Solid-organ transplantation is the treatment of choice for children with end-stage organ failure. Ongoing recovery and medical management at home after transplant are important for recovery and transition to daily life. Smartphones are widely used and hold the potential for aiding in the establishment of mobile health (mHealth) protocols.

Feasibility and Acceptability of a mHealth Self-Management Intervention for Pediatric Transplant Families.

Families of pediatric solid organ transplant recipients need ongoing education and support in the first 30 days following hospital discharge for the transplantation. The purpose of this report is to describe the feasibility, acceptability, and preliminary efficacy of a mHealth family-self management intervention, (), designed to improve post-discharge outcomes of coping, family quality of life, self-efficacy, family self-management, and utilization of health care resources. We enrolled 46 primary family members.

One Size Does Not Fit All: Discharge Teaching and Child Challenging Behaviors.

This study compares quality of discharge teaching and care coordination for parents of children with challenging behaviors participating in a nursing implementation project, which used an interactive iPad application, to usual discharge care. Unlike parents in the larger quasi-experimental longitudinal project, parents of children with challenging behaviors receiving the discharge teaching application (n = 14) reported lower mean scores on the quality of discharge teaching scale-delivery subscale ( = 8.2,  = 3.

Engaging Parents in Education for Discharge (ePED): Evaluating the Reach, Adoption & Implementation of an Innovative Discharge Teaching Method.

Purpose: This paper describes the evaluation of the implementation of an innovative teaching method, the "Engaging Parents in Education for Discharge" (ePED) iPad application (app), at a pediatric hospital.

Design And Methods: The Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework was used to guide the evaluation. Three of the five RE-AIM elements are addressed in this study: Reach, Adoption, and Implementation.

Using the Engaging Parents in Education for Discharge (ePED) iPad Application to Improve Parent Discharge Experience.

Purpose: The purpose of this study was to evaluate the use of the Engaging Parents in Education for Discharge (ePED) iPad application on parent experiences of hospital discharge teaching and care coordination. Hypotheses were: parents exposed to discharge teaching using ePED will have 1) higher quality of discharge teaching and 2) better care coordination than parents exposed to usual discharge teaching. The secondary purpose examined group differences in the discharge teaching, care coordination, and 30-day readmissions for parents of children with and without a chronic condition.

Pilot study protocol of a mHealth self-management intervention for family members of pediatric transplant recipients.

Solid-organ transplantation is the treatment of choice for end-stage organ failure. Parents of pediatric transplant recipients who reported a lack of readiness for discharge had more difficulty coping and managing their child's medically complex care at home. In this paper, we describe the protocol for the pilot study of a mHealth intervention (myFAMI).

SmartHeLP: Smartphone-based Hemoglobin Level Prediction Using an Artificial Neural Network.

Blood hemoglobin level (Hgb) measurement has a vital role in the diagnosis, evaluation, and management of numerous diseases. We describe the use of smartphone video imaging and an artificial neural network (ANN) system to estimate Hgb levels non-invasively. We recorded 10 second-300 frame fingertip videos using a smartphone in 75 adults.