Healthcare Analytics Leadership: Clinical & Business Intelligence Plan Development

Future healthcare leaders require expert knowledge and practical capabilities in the evaluation, selection, application and ongoing oversight of the best types of analytics to create continuous learning healthcare systems. These systems may result in continuously improving the demonstrable quality, safety and efficiency of healthcare organizations. Data is an asset for organizations. However, many companies do not know how to establish analytical road maps for future action. Population Health Intelligence describes a new discipline whose role is to collect, organize, harmonize, analyze, disseminate and act upon the data available to clinicians, health system leaders, the pharmaceutical and biotechnology industry, and healthcare payers. This webinar on Analytics Leadership will demonstrate how to create and implement Clinical & Business Intelligence Plans that transform data into actionable organizational insights. Agenda Introduction Healthcare Analytics Leadership: Clinical & Business Intelligence Plan Development Population Health Intelligence Presentation: 53:3

Population Health Intelligence: Turning Data into Action

Today, health systems receive a significant portion of their revenue through value-based contracts that focus on lowering costs while improving health outcomes. True population health improvement is driven by insights derived and distilled from interpreting vast amounts of heterogeneous and distinct data. Professionals with expertise in health data analytics are in high demand. Population Health Intelligence® is a new discipline that equips practitioners with the skills to curate, organize, harmonize, and analyze disparate data sets. The insights gained enable population health professionals to turn data into action. For more information, contact [email protected]. Agenda Population Health Population Health Intelligence The MS-PHI Program Presentation: 35:3

What is Health Data Science? An Introduction for Health Care Professionals and Clinicians

Health Data Science is a dynamic multi-disciplinary field that uses statistics, algorithms, and technology to construct insights from available data to address complex healthcare problems. This expanding field of practice requires diverse interdisciplinary teams that combine their strengths in clinical, technical, programming and operations. This webinar features a clinician and a data scientist who will share their insights about career opportunities in this expanding field of practice. Presentation: 53:1

Navigating Through the COVID-19 Pandemic Utilizing Data & Analytics

The onset of the COVID-19 pandemic brought to light unique challenges in acquiring key metrics across a large multi- campus health system. This presentation will highlight the unique partnership between the COVID-19 Incident Command Center and the Enterprise Analytics team in the journey to develop powerful analytics to support the daily management of the COVID 19 pandemic. Presentation: 55:0

Women in Health Data Science and Statistics

Opportunities are expanding for women interested in health data science. This panel of leading professionals from academia and industry will highlight the accomplishments, perspectives and varied roles available for data professionals in the healthcare sector. Learn how to leverage your skills and talents into this expanding and dynamic field. Presentation: 58:4

Phyllodes Tumor of the Breast: Association Between Age, Surgical Treatment and Survival

Background: Phyllodes tumor of the breast (PT) is a rare malignancy among women worldwide accounting for less than 1% of all primary breast neoplasms (Chao et al., 2019). Little evidence exists describing the survival in PT in younger and older age women. The objective of this study was to evaluate the association of age on initial surgical treatment and outcomes in adults including older age patients with PT of the breast Methods: A retrospective, cohort study was conducted using data from the Surveillance Epidemiology and End Results Program (SEER) for women ≥18 years old and diagnosed with PT from 1988-2015. The main exposure of interest was age at presentation (18-49 vs. 50-64 vs. ≥65). The main outcomes of interest were initial surgical treatment (breast conserving surgery vs. mastectomy) and survival (OS and CSS). The outcomes were adjusted for race/ethnicity, marital status, time period of diagnosis, tumor grade, tumor size, and initial surgical treatment. Descriptive statistics were used to describe the relationship between age and other demographic/clinical variables. Multivariate logistic regression was used to assess the association of age on initial surgical treatment. Kaplan-Meier analysis was used to estimate overall survival and cause-specific survival estimates. The log-rank test was used to compare survival estimates between age groups. Risk for PT survival at five years we identified using univariate and multivariable Cox analysis. Results: A total of 2052 women aged ≥18 year and older with PT were identified. The majority of patients were 18-49 years old, white, married, diagnosed between 2006-2015, had an unknown tumor grade, tumor size ≤5cm, and underwent breast conserving surgery (BCS). Roughly 48.2% (n=988) and 51.8% (n=1064) of patients underwent mastectomy and breast conserving surgery (BCS), respectively. Younger aged patients (18-49 years old) with PT (45.0%, n=923) exhibited better (OS;=38.411,pp 5cm exhibited worse (OS;149.83,p ppp. Conclusion: In this sample of PT patients it was determined that there is significant evidence on the association of survival at five years based on patient age, tumor size and type of initial surgical treatment. These findings have important implications for practice and policy and suggests we need to explore ways of increasing earlier diagnosis among younger and older aged PT patients

The Prevalence of Microalbuminuria in Adolescents with Obesity in United States Using the National Health and Nutrition Examination Survey (NHANES) Data 2011-2016TA 2011-2016

The prevalence of obesity in adolescents between 12-19 years old is 20.6%. Studies have shown that there is a relationship between albuminuria and obesity in pediatrics. Presence of microalbuminuria is an early predictor for renal and cardiovascular disease. A previous study using NHANES 1999-2004 has shown that the prevalence of microalbuminuria in adolescents is 8.9%. The primary objective of this study evaluated the prevalence of microalbuminuria in adolescents with or without obesity using the NHANES 2011-2016. The secondary objective is to determine the cardiovascular risk factors associated with microalbuminuria. This is a retrospective secondary analysis of NHANES 2011-2016 data of patients. Inclusion criteria were patients between the age of 12-19 years. Patients were excluded if they had type 2 diabetes mellitus, chronic kidney disease, urinary albumin to creatinine ratio ≥ 300mg/g, fasting glucose ≥ 126 mg/dL, HbA1c ≥ 6.5%, fasting time \u3c 8 hours and incomplete data. Means and proportions of clinical characteristics were compared using t-test and Chi-square. The study included 1316 adolescents. 22.1% (n=303) of adolescents were obese and 77.89% (n=1013) were not obese, p0.05). Elevated triglycerides, TG/HDL and HbA1c were associated with microalbuminuria along with metabolic syndrome and low LDL in adolescents with obesity. Prevalence of microalbuminuria was lower in adolescents with obesity compared to those without obesity

Evaluating a Population Health Intervention: Insights from a Diabetes Management Program

Understanding the return on investment of population health initiatives is very complex. Before calculating financial outcomes, it\u27s important to understand clinicaloutcomes, and if a program or intervention is impacting care as intended. Join us for a look at the evaluation of Blessing Health System\u27s Be Well with Diabetes (BWWD) program for its diabetic patients This webinar will present program highlights and share insights from the exploratory analysis of the BWWD program at Blessing and how it affects patient outcomes

Impact of Palliative Care Consultation on End of Life Care Measures: A Retrospective Analysis of Patients in the Oncology Care Model

Introduction In 2016 ASCO recommended that patients with advanced cancer receive dedicated palliative care (PC) services1. Early PC involvement is associated with lower spending, fewer 30-day readmission rates, decreased chemotherapy administration at the end of life (EOL) and increased hospice referrals2. Many patients are not referred and continue to receive chemotherapy and utilize high-acuity services near the EOL. The Oncology Care Model (OCM) is a CMS episode-based alternative payment model promoting high-value care. We evaluated the effect of PC visits on EOL outcomes including code status (CS) and spending in the last 30 days of life.https://jdc.jefferson.edu/medoncposters/1010/thumbnail.jp