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.

ABSTRACT:Cardiac arrest in patients on mechanical support is a new phenomenon brought about by the increased use of this therapy in patients with end-stage heart failure. This American Heart Association scientific statement highlights the recognition and treatment of cardiovascular collapse or cardiopulmonary arrest in an adult or pediatric patient who has a ventricular assist device or total artificial heart. Specific, expert consensus recommendations are provided for the role of external chest compressions in such patients. Mechanical circulatory support (MCS) has evolved from a rarely used therapy reserved for the most critically ill hospitalized patients to an accepted longterm outpatient therapy for treating patients with advanced heart failure. This growth is attributable to improved technology, improved survival, reduced adverse event profiles, greater reliability and mechanical durability, and limited numbers of organs available for donation. With the number of patients supported by durable MCS systems increasing in the community, so too is the need for emergency care providers to receive specific guidance on how to assess and treat a patient with MCS who is unresponsive or hypotensive.No evidence-based or consensus recommendations currently exist for the evaluation and treatment of cardiovascular emergencies in patients with MCS. Because of the unique characteristics of mechanical support, these patients have physical findings that cannot be interpreted the same as for patients without MCS. For example, stable patients supported by a durable, continuous-flow ventricular assist device (VAD) often do not have a palpable pulse. Unfortunately, different and sometimes conflicting instructions are given by hospital providers and emergency medical services (EMS) directors to EMS and other healthcare personnel on core resuscitative practices such as the role of external chest compressions in such a patient who suddenly becomes or is found unresponsive. PURPOSEThe purpose of this scientific statement is to describe the common types of MCS devices that emergency healthcare providers may encounter and to present expert, consensus-based recommendations for the evaluation and resuscitation of adult and pediatric patients with MCS with suspected cardiovascular collapse or cardiac arrest. These recommendations focus initially on emergency first-response providers, whether outside or inside the hospital, with additional sections on advanced care that may be provided in the emergency department or in-hospital settings. CONSENSUS PROCESSThe need for standardized recommendations for the emergency treatment of acutely unstable patients with MCS was identified during the 2014 meeting of the American Heart Association (AHA) Science Subcommittee. A writing group was commissioned to review the current literature and to develop consensus-derived recommendations for the initial treatment of these patients. Members of the writing group were chosen for their combined expertise in MCS, cardiopulmonary resuscitation (CPR), emergenc...

As a way to make medical decisions, Evidence-Based Medicine (EBM) has failed. EBM's failure arises from not being founded on real-world decision-making. EBM aspires to a scientific standard for the best way to treat a disease and determine its cause, but it fails to recognise that the scientific method is inapplicable to medical and other real-world decision-making. EBM also wrongly assumes that evidence can be marshaled and applied according to an hierarchy that is determined in an argument by authority to the method by which it has been obtained. If EBM had valid theoretical, practical or empirical foundations, there would be no hierarchy of evidence. In all real-world decision-making, evidence stands or falls on its inherent reliability. This has to be and can only be assessed on a case-by-case basis applying understanding and wisdom against the background of all available facts-the "factual matrix." EBM's failure is structural and was inevitable from its inception. EBM confuses the inherent reliability and probative value of evidence with the means by which it is obtained. EBM is therefore an ad hoc construct and is not a valid basis for medical decision-making. This is further demonstrated by its exclusion of relevant scientific and probative real-world decision-making evidence and processes. It draws upon a narrow evidence base that is itself inherently unreliable. It fails to take adequate account of the nature of causation, the full range of evidence relevant to its determination, and differing approaches to determining cause and effect in real-world decision-making. EBM also makes a muddled attempt to emulate the scientific method and it does not acknowledge the role of experience, understanding and wisdom in making medical decisions.

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