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oncology Oncology

Reducing Avoidable Emergency Visits and Hospitalizations With Patient Risk-Based Prescriptive Analytics: A Quality Improvement Project at an Oncology Care Model Practice.

In JCO oncology practice

PURPOSE : Cancer-related emergency department (ED) visits and hospitalizations that would have been appropriately managed in the outpatient setting are avoidable and detrimental to patients and health systems. This quality improvement (QI) project aimed to leverage patient risk-based prescriptive analytics at a community oncology practice to reduce avoidable acute care use (ACU).

METHODS : Using the Plan-Do-Study-Act (PDSA) methodology, we implemented the Jvion Care Optimization and Recommendation Enhancement augmented intelligence (AI) tool at an Oncology Care Model (OCM) practice, the Center for Cancer and Blood Disorders practice. We applied continuous machine learning to predict risk of preventable harm (avoidable ACU) and generated patient-specific recommendations that nurses implemented to avert it.

RESULTS : Patient-centric interventions included medication/dosage changes, laboratory tests/imaging, physical/occupational/psychologic therapy referral, palliative care/hospice referral, and surveillance/observation. Nurses contacted patients every 1-2 weeks after initial outreach to assess and maintain adherence to recommended interventions. Per 100 unique OCM patients, monthly ED visits dropped from 13.7 to 11.5 (18%), a sustained month-over-month improvement. Quarterly admissions dropped from 19.5 to 17.1 (13%), a sustained quarter-over-quarter improvement. Overall, the practice realized potential annual savings of $2.8 million US dollars (USD) on avoidable ACU.

CONCLUSION : The AI tool has enabled nurse case managers to identify and resolve critical clinical issues and reduce avoidable ACU. Effects on outcomes can be inferred from the reduction; targeting short-term interventions toward patients most at-risk translates to better long-term care and outcomes. QI projects involving predictive modeling of patient risk, prescriptive analytics, and nurse outreach may reduce ACU.

Gajra Ajeet, Jeune-Smith Yolaine, Balanean Alexandrina, Miller Kelly A, Bergman Danielle, Showalter John, Page Ray

2023-Mar-13

General General

For living well, behaviors and circumstances matter just as much as psychological traits.

In Proceedings of the National Academy of Sciences of the United States of America

In 2004 through 2016, three studies in the national Midlife in the United States (MIDUS) project asked participants the open-ended question "What do you do to make life go well?". We use verbatim responses to this question to evaluate the relative importance of psychological traits and circumstances for predicting self-reported, subjective well-being. The use of an open-ended question allows us to test the hypothesis that psychological traits are more strongly associated with self-reported well-being than objective circumstances because psychological traits and well-being are similarly self-rated-meaning that they both ask respondents to decide how to place themselves on provided and unfamiliar survey scales. For this, we use automated zero-shot classification to score statements about well-being without training on existing survey measures, and we evaluate this scoring through subsequent hand-labeling. We then assess associations of this measure and closed-ended measures for health behaviors, socioeconomic circumstances, biomarkers for inflammation and glycemic control, and mortality risk over follow-up. Although the closed-ended measures were far more strongly associated with other multiple-choice self-ratings, including Big 5 personality traits, the closed- and open-ended measures were similarly associated with relatively objective indicators of health, wealth, and social connectedness. The findings suggest that psychological traits, when collected through self-ratings, predict subjective reports of well-being so strongly because of a measurement advantage-and that circumstance matters just as much when assessed using a fairer comparison.

Hobbs William R, Ong Anthony D

2023-Mar-21

health, machine learning, personality, survey design, well-being

General General

Falls caused by balance disorders in the elderly with multiple systems involved: Pathogenic mechanisms and treatment strategies.

In Frontiers in neurology

Falls are the main contributor to both fatal and nonfatal injuries in elderly individuals as well as significant sources of morbidity and mortality, which are mostly induced by impaired balance control. The ability to keep balance is a remarkably complex process that allows for rapid and precise changes to prevent falls with multiple systems involved, such as musculoskeletal system, the central nervous system and sensory system. However, the exact pathogenesis of falls caused by balance disorders in the elderly has eluded researchers to date. In consideration of aging phenomenon aggravation and fall risks in the elderly, there is an urgent need to explore the pathogenesis and treatments of falls caused by balance disorders in the elderly. The present review discusses the epidemiology of falls in the elderly, potential pathogenic mechanisms underlying multiple systems involved in falls caused by balance disorders, including musculoskeletal system, the central nervous system and sensory system. Meanwhile, some common treatment strategies, such as physical exercise, new equipment based on artificial intelligence, pharmacologic treatments and fall prevention education are also reviewed. To fully understand the pathogenesis and treatment of falls caused by balance disorders, a need remains for future large-scale multi-center randomized controlled trials and in-depth mechanism studies.

Xing Liwei, Bao Yi, Wang Binyang, Shi Mingqin, Wei Yuanyuan, Huang Xiaoyi, Dai Youwu, Shi Hongling, Gai Xuesong, Luo Qiu, Yin Yong, Qin Dongdong

2023

balance, elderly, fall, mechanism, pathogenesis, treatments

General General

Computational prediction of interactions between Paxlovid and prescription drugs.

In Proceedings of the National Academy of Sciences of the United States of America

Pfizer's Paxlovid has recently been approved for the emergency use authorization (EUA) from the US Food and Drug Administration (FDA) for the treatment of mild-to-moderate COVID-19. Drug interactions can be a serious medical problem for COVID-19 patients with underlying medical conditions, such as hypertension and diabetes, who have likely been taking other drugs. Here, we use deep learning to predict potential drug-drug interactions between Paxlovid components (nirmatrelvir and ritonavir) and 2,248 prescription drugs for treating various diseases.

Kim Yeji, Ryu Jae Yong, Kim Hyun Uk, Lee Sang Yup

2023-Mar-21

COVID-19, DeepDDI2, Paxlovid, drug interactions

General General

Superhuman artificial intelligence can improve human decision-making by increasing novelty.

In Proceedings of the National Academy of Sciences of the United States of America

How will superhuman artificial intelligence (AI) affect human decision-making? And what will be the mechanisms behind this effect? We address these questions in a domain where AI already exceeds human performance, analyzing more than 5.8 million move decisions made by professional Go players over the past 71 y (1950 to 2021). To address the first question, we use a superhuman AI program to estimate the quality of human decisions across time, generating 58 billion counterfactual game patterns and comparing the win rates of actual human decisions with those of counterfactual AI decisions. We find that humans began to make significantly better decisions following the advent of superhuman AI. We then examine human players' strategies across time and find that novel decisions (i.e., previously unobserved moves) occurred more frequently and became associated with higher decision quality after the advent of superhuman AI. Our findings suggest that the development of superhuman AI programs may have prompted human players to break away from traditional strategies and induced them to explore novel moves, which in turn may have improved their decision-making.

Shin Minkyu, Kim Jin, van Opheusden Bas, Griffiths Thomas L

2023-Mar-21

artificial intelligence, cognitive performance, innovation, judgment and decision-making, novelty

General General

Big data and infectious disease epidemiology: A bibliometric analysis and research agenda.

In Interactive journal of medical research

BACKGROUND : Infectious diseases represent a major challenge for health systems worldwide. With the recent global pandemic of COVID-19, the need to research strategies to treat these health problems has become even more pressing. Although the literature on big data and data science in health has grown rapidly, few studies have synthesized these individual studies, and none has identified the utility of big data in infectious disease surveillance and modeling.

OBJECTIVE : This paper aims to synthesize research and identify hotspots of big data in infectious disease epidemiology.

METHODS : Bibliometric data from 3054 documents that satisfied the inclusion criteria were retrieved from the Web of Science database over 22 years (2000-2022) were analyzed and reviewed. The search retrieval occurred on October 17, 2022. Bibliometric analysis was performed to illustrate the relationships between research constituents, topics, and key terms in the retrieved documents.

RESULTS : The bibliometric analysis revealed internet searches and social media as the most utilized big data sources for infectious disease surveillance or modeling. It also placed the US and Chinese institutions as leaders in this research area. Disease monitoring and surveillance, utility of electronic health (or medical) records, methodology framework for infodemiology tools, and machine/deep learning were identified as the core research themes.

CONCLUSIONS : Proposals for future studies are made based on these findings. This study will provide healthcare informatics scholars with a comprehensive understanding of big data research in infectious disease epidemiology.

Amusa Lateef Babatunde, Twinomurinzi Hossana, Phalane Edith, Phaswana-Mafuya Refilwe Nancy

2022-Nov-29