Reducing Readmission Rates Within 30 Days of Discharge by Increasing Frequency of Calls by the East Bay Transitions Care Department

Abstract Background: Within the organization, the estimated cost of readmissions is $10,107.00 per day. Managing readmission rates within the hospitals has been challenging due to multiple factors. Readmissions cause financial and emotional stress on patients and their families, along with the stresses experienced during hospitalization. Problem: The yearly readmission goal for the Transitions Care Department is below 14% per month. In 2022 the Transitions Care Department has been above 14% for most of the year, averaging 17%. Hospital readmissions impose a substantial financial and resource burden on the healthcare system. Interventions: Staff training will ensure their effectiveness in carrying out responsibilities. They will make at least two weekly calls to medium and high-risk patients, with daily call tracking. Standardized documentation for CHF and sepsis diagnoses will be implemented. Transitions Care Managers will actively participate in readmission calls and engage in discussions. CHF patients will receive scales for weight monitoring, and medication reviews will be conducted during calls. Transitions Care Nurses will interview readmitted patients to understand contributing factors. Outcome Measures: The specific aim of this project is increasing call frequencies to high and medium risk patients to reduce avoidable readmissions below 14% per month from current baseline of 17%, by June 2023 in the East Bay Transitions Care Department. Results: In April and June, the main outcome results achieved were below the targeted threshold of 14%. Overall, staff feedback on the project has been positive, noting the increased call frequency\u27s remarkable impact on trust and patient relationships. While the project alone may not be the sole cause of the positive impact on readmission rates, it undoubtedly contributed significantly. Concurrently introduced initiatives have also played a role in improving outcomes. Conclusion: The Transitions Care staff\u27s commitment to calling patients twice weekly has reduced readmissions and improved patient outcomes. Continuing with these calls and complementary initiatives promises further progress. This proactive approach enhances care quality and patient satisfaction while addressing readmission challenges. Expanding the initiative to other Transitions Care Departments can extend its success. Keywords: Transitions Care Department, readmission

Mitigating Overexposure in Viral Marketing

In traditional models for word-of-mouth recommendations and viral marketing, the objective function has generally been based on reaching as many people as possible. However, a number of studies have shown that the indiscriminate spread of a product by word-of-mouth can result in overexposure, reaching people who evaluate it negatively. This can lead to an effect in which the over-promotion of a product can produce negative reputational effects, by reaching a part of the audience that is not receptive to it. How should one make use of social influence when there is a risk of overexposure? In this paper, we develop and analyze a theoretical model for this process; we show how it captures a number of the qualitative phenomena associated with overexposure, and for the main formulation of our model, we provide a polynomial-time algorithm to find the optimal marketing strategy. We also present simulations of the model on real network topologies, quantifying the extent to which our optimal strategies outperform natural baselinesComment: In AAAI-1

Using Search Queries to Understand Health Information Needs in Africa

The lack of comprehensive, high-quality health data in developing nations creates a roadblock for combating the impacts of disease. One key challenge is understanding the health information needs of people in these nations. Without understanding people's everyday needs, concerns, and misconceptions, health organizations and policymakers lack the ability to effectively target education and programming efforts. In this paper, we propose a bottom-up approach that uses search data from individuals to uncover and gain insight into health information needs in Africa. We analyze Bing searches related to HIV/AIDS, malaria, and tuberculosis from all 54 African nations. For each disease, we automatically derive a set of common search themes or topics, revealing a wide-spread interest in various types of information, including disease symptoms, drugs, concerns about breastfeeding, as well as stigma, beliefs in natural cures, and other topics that may be hard to uncover through traditional surveys. We expose the different patterns that emerge in health information needs by demographic groups (age and sex) and country. We also uncover discrepancies in the quality of content returned by search engines to users by topic. Combined, our results suggest that search data can help illuminate health information needs in Africa and inform discussions on health policy and targeted education efforts both on- and offline.Comment: Extended version of an ICWSM 2019 pape

Opinion dynamics with varying susceptibility to persuasion

A long line of work in social psychology has studied variations in people's susceptibility to persuasion -- the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation by interacting parties in a network: in addition to considering interventions that directly modify people's intrinsic opinions, it is also natural to consider interventions that modify people's susceptibility to persuasion. In this work, we adopt a popular model for social opinion dynamics, and we formalize the opinion maximization and minimization problems where interventions happen at the level of susceptibility. We show that modeling interventions at the level of susceptibility lead to an interesting family of new questions in network opinion dynamics. We find that the questions are quite different depending on whether there is an overall budget constraining the number of agents we can target or not. We give a polynomial-time algorithm for finding the optimal target-set to optimize the sum of opinions when there are no budget constraints on the size of the target-set. We show that this problem is NP-hard when there is a budget, and that the objective function is neither submodular nor supermodular. Finally, we propose a heuristic for the budgeted opinion optimization and show its efficacy at finding target-sets that optimize the sum of opinions compared on real world networks, including a Twitter network with real opinion estimates

Difficult Lessons on Social Prediction from Wisconsin Public Schools

Early warning systems (EWS) are prediction algorithms that have recently taken a central role in efforts to improve graduation rates in public schools across the US. These systems assist in targeting interventions at individual students by predicting which students are at risk of dropping out. Despite significant investments and adoption, there remain significant gaps in our understanding of the efficacy of EWS. In this work, we draw on nearly a decade's worth of data from a system used throughout Wisconsin to provide the first large-scale evaluation of the long-term impact of EWS on graduation outcomes. We present evidence that risk assessments made by the prediction system are highly accurate, including for students from marginalized backgrounds. Despite the system's accuracy and widespread use, we find no evidence that it has led to improved graduation rates. We surface a robust statistical pattern that can explain why these seemingly contradictory insights hold. Namely, environmental features, measured at the level of schools, contain significant signal about dropout risk. Within each school, however, academic outcomes are essentially independent of individual student performance. This empirical observation indicates that assigning all students within the same school the same probability of graduation is a nearly optimal prediction. Our work provides an empirical backbone for the robust, qualitative understanding among education researchers and policy-makers that dropout is structurally determined. The primary barrier to improving outcomes lies not in identifying students at risk of dropping out within specific schools, but rather in overcoming structural differences across different school districts. Our findings indicate that we should carefully evaluate the decision to fund early warning systems without also devoting resources to interventions tackling structural barriers

Permanent Draft Genome Sequence of Frankia sp. Strain ACN1ag, a Nitrogen-Fixing Actinobacterium Isolated from the Root Nodules of Alnus glutinosa

Frankia strain ACN1ag is a member of Frankia lineage Ia, which are able to re-infect plants of the Betulaceae and Myricaceae families. Here, we report a 7.5-Mbp draft genome sequence with a G+C content of 72.35% and 5,687 candidate protein-encoding genes

Permanent draft genome sequence of Frankia sp. strain AvcI1, a nitrogen-fixing actinobacterium isolated from the root nodules of Alnus viridis subsp. crispa grown in Canada

Frankia strain AvcI1, isolated from root nodules of Alnus viridis subsp. crispa, is a member of Frankia lineage Ia, which is able to reinfect plants of the Betulaceae and Myricaceae families. Here, we report a 7.7-Mbp draft genome sequence with a G+C content of 72.41% and 6,470 candidate protein-encoding genes