Design, Creation, and 13-Month Performance of a Novel, Web-Based Activity for Education in Primary Cardiology Palliative Care.

Cardiovascular disease (CVD) clinicians who care for seriously ill patients frequently report that they do not feel confident nor adequately prepared to manage patients' palliative care (PC) needs. With the goal, therefore, of increasing PC knowledge and skills amongst interprofessional clinicians providing CVD care, the ACC's PC Workgroup designed, developed, and implemented a comprehensive PC online educational activity. This paper describes the process and 13-month performance of this free, online activity for clinicians across disciplines and levels of training, "Palliative Care for the Cardiovascular Clinician" (PCCVC). A key component of PCCVC is that it is tailored to the lifelong learner; users can choose and receive credit for the activities that meet their individual learning needs. This webinar series was well-subscribed, and upon completion of the modules, learners reported better self-perceived abilities related to palliative care competencies. We propose PCCVC as a model for primary PC education for clinicians caring for individuals with other serious or life-shortening illnesses.

Palliative Care Education in Cardiovascular Disease Fellowships: A National Survey of Program Directors.

BACKGROUND: Palliative care (PC) is an essential component of high-quality care for people with cardiovascular disease (CVD). However, little is known about the current state of PC education in CVD training, including attitudes toward integration of PC into training and implementation of PC by the program's leadership. METHODS: We developed a nationwide, cross-sectional survey that queried education approaches, perspectives and barriers to PC education in general CVD fellowship training. The survey was distributed to 392 members of the American College of Cardiology Program Director (PD) listserv, representing 290 general CVD fellowships between 1/2023 and 4/2023. We performed descriptive and ꭕ2 analyses of survey data. RESULTS: Of the program's representatives, 56 completed the survey (response rate = 19.3%). Respondents identified themselves as current PDs (89%), associate PDs (8.9%) or former PDs (1.8%), representing a diverse range of program sizes and types and regions of the country. Respondents reported the use of informal bedside teaching (88%), formal didactics (59%), online or self-paced modules (13%), in-person simulation (11%), and clinical rotations (16%) to teach PC content. Most programs covered PC topics at least annually, although there was variability by topic. We found no associations between program demographics and type or frequency of PC education. Most respondents reported dissatisfaction with the quantity (62%) or quality (59%) of the PC education provided. Barriers to PC education included an overabundance of other content to cover (36%) and perceived lack of fellow (20%) or faculty (18%) interest. Comments demonstrated the importance of PC education in fellowship, the lack of a requirement to provide PC education, difficulty in covering all topics, and suggestions of how PC skills should be taught. CONCLUSIONS: In a national survey of CVD educational leadership concerning approaches to PC education in CVD training, respondents highlighted both challenges to implementation of formal PC curricula in cardiology training and opportunities for comprehensive PC education.

Prediction of odor complaints at a large composite reservoir in a highly urbanized area: A machine learning approach.

Odorous compound emissions and odor complaints from the public are rising concerns for agricultural, industrial, and water resource recovery facilities (WRRFs) near urban areas. Many facilities are deploying sensors that measure malodorous compounds and other factors related to odor creation and dispersion. Focusing on the Metropolitan Water Reclamation District of Greater Chicago's (MWRDGCs) Thornton Composite Reservoir (7.9 billion gallon capacity), we used meteorological, operational, and H2S sensor data to train a 3-day advance-warning predictor of local odor complaints, so as to implement targeted odor prevention measures. Using a machine learning approach, we bypassed difficulties in modeling both physical dispersion and human perception of odors. Utilizing random forest algorithms with varied settings and input attributes, we find that a small network of H2S sensors, meteorological data, and operational data are able to predict odor complaints three days in advance with greater than 60% accuracy and less than 25% false-positive rates, exceeding MWRDGC's standards required for full-scale deployment. PRACTITIONER POINTS: A random forest algorithm trained on H2 S, weather, and operations data successfully predicted odor complaints surrounding a large composite reservoir. Thirty-two data attribute combinations were tested. It was found that H2 S sensor data alone are insufficient for predicting odor complaints. The best predictor was a Random Forest Classifier trained on weather, operational, and H2 S readings from the reservoir corner locations. This study demonstrates odor complaint prediction capability utilizing a limited set of data sources and open-source machine learning techniques. Given a small network of H2 S sensors and organized data management, WRRFs and similar facilities can conduct advance-warning odor complaint prediction.