Risk Stratification and Predictive Modeling of Acute Asthma Exacerbations Using Real World Data of All payers Claims Data in the US
Elnaz Alipour, Ph.D., Head of Data Science, Commercial Analytics, Veeva Systems
Patryk Skowron, Data Scientist, Veeva Business Consulting
Abstract:
With 25 million asthmatics and ~$50-80 billion in health expenditures, the burden of asthma in the US is considerable. Severe and life-threatening asthma exacerbations trigger approximately 1.7 million emergency department visits, with a minority of patients carrying this burden. This burden skews towards populations in zip codes with disadvantaged socioeconomic, racial, and air quality profiles. This analysis quantifies that variance and identifies other risk factors.
Using Veeva Compass, an anonymized longitudinal patient database with ~47 billion prescriptions and claims transactions, we defined the population at risk and identified incidents of exacerbation. We trained Poisson and Cox Regression models to estimate the likelihood of exacerbations per patient within the next n months by modeling demographics, social determinants of health, chronic comorbidities, air quality, and history of exacerbations.
Among 25 M asthma patients, exacerbations requiring clinical encounters were uncommon, with an annual claims distribution of 0.8 % for inpatient care, 3.2% for acute non-inpatient care, and 4.9% for clinic care. These events followed a seasonal trend, with a greater incidence during colder seasons. Exacerbations were treated predominately in the outpatient setting (44.2%), while 35.9% were treated in acute care settings. The most significantly associated comorbidities include dysfunctional breathing (IRR=1.29 [95%CI 1.26-1.31]), bipolar disorder (IRR=1.19, [95%CI 1.17-1.21]), and obesity (IRR=1.16, [95%CI 1.15-1.18]). For exacerbations managed in clinics, the strongest predictor was non-white race (IRR=1.47 [95%CI 1.40-1.54]). Finally, for each setting, the strongest predictor was a history of exacerbation. Predictions were strongest for exacerbations in the past 6 months, and when the predicted and predictive setting were concordant (inpatient IRR=3.11, [95%CI 3.06-3.16], outpatient IRR 1.25, [95%CI 1.22-1.27]).
Patients burdened by exacerbations that require encounters in acute care settings represent an important minority. By identifying subgroups at the greatest risk, we can focus drug development efforts on target populations and enhance our probability of success.
Speaker Bios:
Elnaz brings more than 12 years of experience working in analytics. She has spent most of her career as a PhD academic biophysics researcher, where she worked with chemists, engineers, biologist and clinicians. In her academic career, she worked on data analysis and modeling of metastasis, chromosome structure and Alzheimer's amyloid beta structure. Before joining Veeva, she was a data scientist for ScotiaBank’s treasury group. Since joining Veeva, Elnaz first worked at Andi, Veeva’s “Next Best Action” recommender. She then joined Veeva’s business consulting team. At Business Consulting, she has spearheaded the creation of the data science team. And creating many of the current offerings
related to patient analytics and segmentation and targeting exercises.For her contributions to Veeva Business consulting, she won the Innovator of the Year Award in 2020, and 2022. In addition, her projects were recognized as the Project of the Year in 2020, 2021, and 2022.
Patryk Skowron is a Data scientist at the analytics group at Veeva Business Consulting, where he focuses on patient analytics and real world evidence. He earned his PhD and completed a postdoc at the hospital of Sick Children where he spent most of his time piecing apart the genomics of medulloblastoma, a rare pediatric brain cancer.
Real-Data Simulation On The Impact Of ABO-Compatible Liver Allocation Policy In Ontario
Suting Yang, Data Scientist, Data and Decision Sciences, Ontario Health
Co-authors:
Saba Vahid, Associate Principal, Consulting & Analytics, IQVIA
Shabnam Balamchi, Senior Decision Scientist, Data and Decision Sciences, Ontario Health
Sophie Foxcroft, Director, System and Infrastructure Planning, Ontario Health
Oren Jalon, Senior Analyst, Transplant Services, Ontario Health
Fareed Hameed, Lead, Transplant Services, Ontario Health
Abstract:
This is a retrospective and data-driven study of liver transplant allocation policy in Ontario during 2017/1/1 to 2022/1/1. The objective is to compare different policy scenarios for ABO-compatible liver allocation in Ontario. As per the current policy, livers will only be offered to ABO-compatible recipients if there are no high-priority, ABO-identical, and ABO-incompatible pediatric recipients on the waitlist . As a result, very few ABO-compatible liver transplants were performed each year. Under the proposed candidate policies, recipients with certain blood types and severity scores greater than a threshold are prioritized for ABO-compatible transplantations, with the aim to minimize the wait time disparities & mortality across blood groups. We first build survival models to understand key factors that impact the survival probabilities and predict the expected lifetime of each recipient given the characteristics. We then build Discrete Event Simulation models to represent waitlist changes and recipient-donor matching algorithms for different policies. Our results demonstrate that the optimal policy significantly reduces wait times for B (14%) and AB (24%) type recipients, with a slight increase of wait times for A (0.7%) and O (3%) recipients. It also balances the mortality rates across blood types. Overall, our results have demonstrated that the optimal policy improves access to waitlisted recipients, proving its feasibility and validity.
Speaker Bio:
Suting Yang is a healthcare engineering professional with hands-on experiences in big data analytics, forecasting, and simulation in healthcare. She holds a Master of Applied Science in Industrial Engineering, specializing in healthcare analytics from the University of Toronto. Throughout her career, Suting has worked with multiple healthcare agents and hospitals, collaborating on data analytics projects. Suting is currently a data scientist at Ontario Health, focusing on prescriptive and predictive modelling in collaboration with Ontario Health programs.
Limitless Demand and Finite Space: Leveraging Real-Time Location Systems (RTLS) to Assess Utilization in Healthcare
Laura Younan, Management Consultant, Strategy Department, Mayo Clinic
Abstract:
Rising costs and limited staffing necessitate managers to get the most value possible out of the built environment while leveraging digital technology to improve operations. Real-Time Location Systems (RTLS) offer an evolving source of geospatial data which generate business intelligence around workflow and value-added use of space. Traditionally in healthcare, direct observation and analyses of error-prone medical record timestamps have been used to understand utilization and assess future space needs. Two pilots were conducted in outpatient practices to explore the efficacy of RTLS and refine the methodology for assessing space utilization. We will discuss the results for the study in depth. Overall, the use of RTLS resulted in the following: (1) reduction in turnaround time for completing space utilization assessments, (2) improvement in accuracy of assessments, and (3) discovery of workflow insights.
Laura Younan is a Management Consultant in the Strategy Department at Mayo Clinic in Rochester, MN. She has a B.S. and M.S. in Industrial Engineering, with a focus on Human Factors & Health Systems Engineering, from the University of Wisconsin-Madison. In addition to her work at Mayo Clinic, she has had process improvement experience with Abbvie, UW-Health, and the University of Chicago in her previous roles. By applying industrial and systems engineering tools and approaches to specific healthcare problems, Laura hopes to address the concern for the human component with traditional engineering principles. She is passionate about continuously learning and teaching others as well!
From Descriptive to Prescriptive Analytics for Managing Patient Flow Times in St. Mary’s General Hospital
Mark Fam, MHA, President, St. Mary’s General Hospital
Sarah Farwell, Director of Strategy, Innovation and Communications, St. Mary’s General Hospital
Abstract:
St. Mary’s General Hospital in Kitchener, Ontario, has a vision of Inspiring excellence. Healthier Together. To achieve this vision, in 2021 St. Mary’s implemented business intelligence software to help drive data-informed decisions. Early use of Power BI has enabled insights to manage operational status, program-specific care outcomes, procedural wait times, and health human resource tracking. We will review the journey of the hospital from descriptive analytics, using simple tools like PowerBI that start harnessing operation data for decision making, to prescriptive analytics where we use more advanced analytics and tools to manage patient flow. These tools have informed care planning and delivery, and elevated the quality of care.
Speaker Bio:
Sarah Farwell is a Director at St. Mary’s General Hospital in Kitchener, specializing in strategy, innovation, and communications. She has experience as a healthcare leader and change management specialist in the areas of system, sector and organizational change efforts. Sarah believes that working towards a shared vision with collaboration and transparency can result in a true one-team approach and incredible care for patients. Sarah holds a Masters in Health Studies, a Doctorate in Chiropractic, and a Certificate in Prosci Change Management.
Mark Fam, MHA, is the President of St. Mary’s General Hospital. Previously he was Vice President of Programs at Michael Garron Hospital, a part of the Toronto East Health Network. A Certified Health Executive, Mark has a broad base of industry knowledge based on his close to 20 years working in the health system. His areas of expertise include health system strategy, operations and planning, service integration and partnerships, performance management, facilitation and decision-making support at the regional and local levels. Mark brings a solid understanding of industry trends, supported by experience that ranges across the health care continuum.
In support of his provincial agency, hospital and consulting work, Mark demonstrates an ongoing commitment to the health care industry through his Master of Health Administration, recognition as a Certified Health Executive, and active participation in the Canadian College of Health Services Leaders. Mark also contributes to new learners in the health system through regular coaching and mentoring, and through his work as a Lecturer in both the Rotman School of Management, and the Institute for Health Policy, Management and Evaluation, at the University of Toronto.