Hospital Sepsis Program : Core Elements: 2023

Who is the Hospital Sepsis Program Core Elements guidance for?Clinicians, hospitals, and health systems leading efforts to improve the hospital management and outcomes of sepsis.Effective leadership is required to engage the multidisciplinary expertise required to support the care of patients with sepsis, as detailed later in this document.The Core Elements are intended to build upon the work of a number of initiatives related to sepsis that have been developed over the years. To find the most updated links to some practical resources that can help hospitals improve specific aspects of their sepsis programs, please visit https://www.cdc.gov/sepsis/core-elements/resources.html.Suggested citation: CDC. Hospital Sepsis Program Core Elements. Atlanta, GA: US Department of Health and Human Services, CDC; 2023. Available at https://www.cdc.gov/sepsis/core-elements.htmlCS341364-Asepsis-core-elements-H.pd

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. Health care providers, nurses, and computer scientists collaboratively designed and developed mHealth family self-management intervention (myFAMI), a smartphone-based intervention app to promote a family self-management intervention for pediatric transplant patients’ families. Objective This paper presents outcomes of the design stages and development actions of the myFAMI app framework, along with key challenges, limitations, and strengths. Methods The myFAMI app framework is built upon a theory-based intervention for pediatric transplant patients, with aid from the action research (AR) methodology. Based on initially defined design motivation, the team of researchers collaboratively explored 4 research stages (research discussions, feedback and motivations, alpha testing, and deployment and release improvements) and developed features required for successful inauguration of the app in the real-world setting. Results Deriving from app users and their functionalities, the myFAMI app framework is built with 2 primary components: the web app (for nurses’ and superadmin usage) and the smartphone app (for participant/family member usage). The web app stores survey responses and triggers alerts to nurses, when required, based on the family members’ response. The smartphone app presents the notifications sent from the server to the participants and captures survey responses. Both the web app and the smartphone app were built upon industry-standard software development frameworks and demonstrate great performance when deployed and used by study participants. Conclusions The paper summarizes a successful and efficient mHealth app-building process using a theory-based intervention in nursing and the AR methodology in computer science. Focusing on factors to improve efficiency enabled easy navigation of the app and collection of data. This work lays the foundation for researchers to carefully integrate necessary information (from the literature or experienced clinicians) to provide a robust and efficient solution and evaluate the acceptability, utility, and usability for similar studies in the future

Patient participation in postoperative care activities: Improving the patient experience

The findings from this research provide evidence that a bedside, multimedia, facilitated intervention, designed to increase the capability and opportunity for patients to participate in achieving their goals of recovery in the immediate postoperative period after Total Knee Replacement surgery, enhanced patient participation in their care. This enhanced participation resulted in improved patient outcomes.<br /

Design and Evaluation of User-Centered Explanations for Machine Learning Model Predictions in Healthcare

Challenges in interpreting some high-performing models present complications in applying machine learning (ML) techniques to healthcare problems. Recently, there has been rapid growth in research on model interpretability; however, approaches to explaining complex ML models are rarely informed by end-user needs and user evaluations of model interpretability are lacking, especially in healthcare. This makes it challenging to determine what explanation approaches might enable providers to understand model predictions in a comprehensible and useful way. Therefore, I aimed to utilize clinician perspectives to inform the design of explanations for ML-based prediction tools and improve the adoption of these systems in practice. In this dissertation, I proposed a new theoretical framework for designing user-centered explanations for ML-based systems. I then utilized the framework to propose explanation designs for predictions from a pediatric in-hospital mortality risk model. I conducted focus groups with healthcare providers to obtain feedback on the proposed designs, which was used to inform the design of a user-centered explanation. The user-centered explanation was evaluated in a laboratory study to assess its effect on healthcare provider perceptions of the model and decision-making processes. The results demonstrated that the user-centered explanation design improved provider perceptions of utilizing the predictive model in practice, but exhibited no significant effect on provider accuracy, confidence, or efficiency in making decisions. Limitations of the evaluation study design, including a small sample size, may have affected the ability to detect an impact on decision-making. Nonetheless, the predictive model with the user-centered explanation was positively received by healthcare providers, and demonstrated a viable approach to explaining ML model predictions in healthcare. Future work is required to address the limitations of this study and further explore the potential benefits of user-centered explanation designs for predictive models in healthcare. This work contributes a new theoretical framework for user-centered explanation design for ML-based systems that is generalizable outside the domain of healthcare. Moreover, the work provides meaningful insights into the role of model interpretability and explanation in healthcare while advancing the discussion on how to effectively communicate ML model information to healthcare providers

Fusion, 2023

https://hsrc.himmelfarb.gwu.edu/smhs_fusion/1015/thumbnail.jp