Background Successful integrations of machine learning into routine clinical care are exceedingly rare, and barriers to its adoption are poorly characterized in the literature. Objective This study aims to report a quality improvement effort to integrate a deep learning sepsis detection and management platform, Sepsis Watch, into routine clinical care. Methods In 2016, a multidisciplinary team consisting of statisticians, data scientists, data engineers, and clinicians was assembled by the leadership of an academic health system to radically improve the detection and treatment of sepsis. This report of the quality improvement effort follows the learning health system framework to describe the problem assessment, design, development, implementation, and evaluation plan of Sepsis Watch. Results Sepsis Watch was successfully integrated into routine clinical care and reshaped how local machine learning projects are executed. Frontline clinical staff were highly engaged in the design and development of the workflow, machine learning model, and application. Novel machine learning methods were developed to detect sepsis early, and implementation of the model required robust infrastructure. Significant investment was required to align stakeholders, develop trusting relationships, define roles and responsibilities, and to train frontline staff, leading to the establishment of 3 partnerships with internal and external research groups to evaluate Sepsis Watch. Conclusions Machine learning models are commonly developed to enhance clinical decision making, but successful integrations of machine learning into routine clinical care are rare. Although there is no playbook for integrating deep learning into clinical care, learnings from the Sepsis Watch integration can inform efforts to develop machine learning technologies at other health care delivery systems.

Social prescribing is an approach that aims to improve health and well-being. It connects individuals to non-clinical services and supports that address social needs, such as those related to loneliness, housing instability and mental health. At the person level, social prescribing can give individuals the knowledge, skills, motivation and confidence to manage their own health and well-being. At the society level, it can facilitate greater collaboration across health, social, and community sectors to promote integrated care and move beyond the traditional biomedical model of health. While the term social prescribing was first popularised in the UK, this practice has become more prevalent and widely publicised internationally over the last decade. This paper aims to illuminate the ways social prescribing has been conceptualised and implemented across 17 countries in Europe, Asia, Australia and North America. We draw from the ‘Beyond the Building Blocks’ framework to describe the essential inputs for adopting social prescribing into policy and practice, related to service delivery; social determinants and household production of health; workforce; leadership and governance; financing, community organisations and societal partnerships; health technology; and information, learning and accountability. Cross-cutting lessons can inform country and regional efforts to tailor social prescribing models to best support local needs.

Background Machine learning models have the potential to improve diagnostic accuracy and management of acute conditions. Despite growing efforts to evaluate and validate such models, little is known about how to best translate and implement these products as part of routine clinical care. Objective This study aims to explore the factors influencing the integration of a machine learning sepsis early warning system (Sepsis Watch) into clinical workflows. Methods We conducted semistructured interviews with 15 frontline emergency department physicians and rapid response team nurses who participated in the Sepsis Watch quality improvement initiative. Interviews were audio recorded and transcribed. We used a modified grounded theory approach to identify key themes and analyze qualitative data. Results A total of 3 dominant themes emerged: perceived utility and trust, implementation of Sepsis Watch processes, and workforce considerations. Participants described their unfamiliarity with machine learning models. As a result, clinician trust was influenced by the perceived accuracy and utility of the model from personal program experience. Implementation of Sepsis Watch was facilitated by the easy-to-use tablet application and communication strategies that were developed by nurses to share model outputs with physicians. Barriers included the flow of information among clinicians and gaps in knowledge about the model itself and broader workflow processes. Conclusions This study generated insights into how frontline clinicians perceived machine learning models and the barriers to integrating them into clinical workflows. These findings can inform future efforts to implement machine learning interventions in real-world settings and maximize the adoption of these interventions.

PURPOSE: Electronic patient-reported outcomes (ePROs) can help clinicians proactively assess and manage their patients’ symptoms. Despite known benefits, there is limited adoption of ePROs into routine clinical care as a result of workflow and technologic challenges. This study identifies oncologists’ perspectives on factors that affect integration of ePROs into clinical workflows. METHODS: We conducted semistructured qualitative interviews with 16 oncologists from a large academic medical center, across diverse subspecialties and cancer types. Oncologists were asked how they currently use or could imagine using ePROs before, during, and after a patient visit. We used an inductive approach to thematically analyze these qualitative data. RESULTS: Results were categorized into the following three main themes: (1) selection and development of ePRO tool, (2) contextual drivers of adoption, and (3) patient-facing concerns. Respondents preferred diagnosis-based ePRO tools over more general symptom screeners. Although they noted information overload as a potential barrier, respondents described strong data visualization and ease of use as facilitators. Contextual drivers of oncologist adoption include identifying target early adopters, incentivizing uptake through use of ePRO data to support billing and documentation, and emphasizing benefits for patient care and efficiency. Respondents also indicated the need to focus on patient-facing issues, such as patient response rate, timing of survey distribution, and validity and reliability of responses. DISCUSSION: Respondents identified several barriers and facilitators to successful uptake of ePROs. Understanding oncologists’ perspectives is essential to inform both practice-level implementation strategies and policy-level decisions to include ePROs in alternative payment models for cancer care.