2026 Annual Conference

Reshma Gupta, MD, MSHPM, Chief of Population Health and Accountable Care, and Mayu Sasaki, MPH, PMP, Population Health Specialist Supervisor, University of California Davis Health

BE-FAIR (Bias-reduction and Equity Framework for Assessing, Implementing, and Redesigning) framework is a practical, nine-step approach for identifying, evaluating, and mitigating bias in healthcare algorithms. Using implementation data from UC Davis Health, BE-FAIR operationalizes equity across all stages of model design and deployment, from applying an anti-racism lens and engaging community partners to disaggregating data and evaluating model performance. Key findings demonstrate measurable improvements in fairness and sensitivity among high-risk populations following targeted framework interventions. .

Upon completion of this activity, learners should be able to:

1. Identify sources of bias in predictive models and describe how algorithmic inequities can perpetuate disparities in population health management.

2. Apply the nine-step BE-FAIR framework to systematically evaluate, mitigate, and monitor bias in predictive analytics.

3. Implement strategies for organizational accountability, community engagement, and continuous equity-focused improvement in AI-driven care programs.

Intended Audience: Chief Medical Officers, Chief Population Health Officers, Chief Data Officers, Chief Analytics Officers, Chief Information Officers, Chief Equity Officers, Chief Diversity & Inclusion Officers, Chief Executive Officers, Chief Operating Officers, Chief Strategy Officers, senior leaders, directors, and managers involved in population health, data science, informatics, or clinical operations interested in mitigating bias and promoting fairness in predictive analytics
 

Christopher J. Russo MD, MBA, CPE, FAAP, Director of Pediatrics, Women and Children Services and Medical Director for Quality and Innovation, WellSpan Health

This poster presents a systematic approach used by a large healthcare system to design, execute, and monitor outreach campaigns for increasing immunization rates (rotavirus, measles, influenza), improving pediatric lead testing, reducing avoidable emergency department visits, and delivering caregiver education on safe sleep. Notable outcomes include an 18% increase in lead testing requests and a 52% rotavirus vaccination series completion rate. Attendees will see how campaigns are structured—from identifying target populations and key stakeholders, to designing message cadence, tracking metrics, and evaluating results. The framework provides a replicable model for health systems seeking to leverage EHR data for proactive, measurable population health initiatives.

Upon completion of this activity, learners should be able to:

1. Describe a structured framework for designing and executing EHR-based patient outreach campaigns.

2. Utilize data-driven strategies to identify target populations and measure outreach effectiveness.

3. Evaluate the impact of EHR-enabled outreach on care gap closure and patient engagement.

Intended Audience: Chief Medical Officers, Chief Population Health Officers, Chief Quality Officers, Chief Information Officers and Chief Data Officers, Chief Operating Officers and Chief Strategy Officers, senior leaders in population health, clinical informatics, care coordination, or quality improvement seeking practical methods to leverage EHRs for outreach

Mary Jordan, Coordinator, Clinic Operations; Lloyd Speer, Manager, Clinic Operations; and Amy Van Gundy, Operations Administrator, Clinic Operations, Mayo Clinic- Rochester

The Hello World Event Text to Room initiative enhances the outpatient check-in and rooming experience by leveraging Epic’s event-based messaging system. This discreet, patient-friendly notification method replaces overhead paging and name announcements, improving privacy, mobility, and communication. In five pilot areas, the program sent 1,155 messages across 1,650 daily appointments, covering 70% of patient encounters. Early evaluation shows high patient satisfaction (96%), timely care team arrival (86%), and improved workflow efficiency. By fostering quieter, more dignified environments, the initiative reduces patient anxiety, supports staff efficiency, and strengthens overall care quality.

Upon completion of this activity, learners should be able to:

1. Explain the design and implementation of an event-based text notification system for outpatient rooming

2. Apply strategies for improving patient privacy, satisfaction, and workflow efficiency using existing EHR technology

3. Discuss outcomes from patient feedback and operational metrics to optimize outpatient check-in processes

Intended Audience: Chief Operating Officers, Chief Experience Officers, Chief Information Officers, Chief Digital Officers, Chief Nursing Officers, senior leaders in outpatient operations, patient experience, care coordination, or clinical informatics seeking innovative approaches to enhance efficiency, privacy, and satisfaction

Eve Cunningham, MD, Chief Medical Officer, Cadence Health, and Chris Frost, MD, Chief Medical Officer and Chief Quality Officer, LifePoint Health

Lifepoint Health and Cadence’s Advanced Primary Care Management (APCM) program is an AI-powered care model integrated into health system EHRs. APCM proactively identifies care gaps, guides next-best actions, and coordinates a physician- and nurse practitioner-led clinical team to support patients between visits. Since its February 2025 launch, 1,722 patients (mean age 71) with multiple chronic conditions have been enrolled. Early results demonstrate measurable improvements in preventive care engagement, including vaccination and cancer screening closure rates, while reducing clinician burden and enhancing access through 24/7 patient support via text, chat, and phone. 

Upon completion of this activity, learners should be able to:

1. Describe the design and implementation of an AI-powered, proactive primary care program for complex Medicare populations

2. Incorporate strategies to integrate technology, clinical teams, and workflows to improve preventive care and care gap closure

3. Assess outcomes related to patient engagement, clinician workload, and financial sustainability in digitally enabled primary care

Intended Audience: Chief Medical Officers, Chief Population Health Officers, Chief Quality Officers, Chief Operating Officers and Chief Strategy Officers, Chief Information Officers and Chief Data Officers, senior leaders in primary care, population health, care management, or digital health seeking replicable strategies for proactive, high-value care delivery

David Vega, MD, MBA, Senior Vice President and Chief Medical Officer, and Mark Kandrysawtz, Senior Vice President and Chief Innovation Officer, WellSpan Health

WellSpan Health has implemented a large-scale, multilingual generative AI outreach program to improve preventive care and operational efficiency. This poster presents how AI voice agents have engaged more than 34,000 patients due for colorectal cancer (CRC) screening, including historically underserved Spanish-speaking populations. The AI agents conducted outbound calls, verified patient information, and facilitated FIT test completion, achieving a 69% conversion rate among English speakers and an 86% rate among Spanish speakers. Beyond clinical outcomes, the AI system serves as the first contact for the patient call center, offsetting staffing shortages equivalent to 50 FTEs. 

Upon completion of this activity, learners should be able to:

1. Analyze the implementation of generative AI for patient outreach in preventive care

2. Design strategies to leverage AI for engaging multilingual and historically underserved populations

3. Assess the impact of AI-driven workflows on clinical outcomes, patient engagement, and operational efficiency

Intended Audience: Chief Medical Officers, Chief Population Health Officers, Chief Quality Officers, Chief Information Officers and Chief Data Officers, Chief Operating Officers and Chief Experience Officers, senior leaders in population health, care management, digital health, or clinical operations seeking scalable AI solutions to close care gaps and enhance equity

Erica Simon, MBA, CMSC, Director of System Access and Specialty Scheduling, and Kola Ogunrinde, MHA, Director of System Access & Capacity Management | Referral Management Center, Houston Methodist

Houston Methodist has implemented a comprehensive Access Transformation initiative to enhance patient scheduling, referral management, and capacity optimization across a multisite system. Central to this effort are expanded online self-scheduling, AI-driven call automation, proactive outreach, cross-site scheduling, and referral management innovations. Since 2018, same-day clinic visits have increased from 3,000 to more than 50,000 annually, with over half self-scheduled online. AI bots manage millions of routine calls, while large language models assess call quality and support continuous service improvements. The “Fast Pass” automated waitlist and direct referral scheduling have reduced wait times by an average of 18 days and improved patient convenience. Outcomes include a 37% increase in online new patient scheduling and significant adoption of digital tools for specialty care and imaging.

Upon completion of this activity, learners should be able to:

1. Illustrate the implementation of AI-driven and digital scheduling solutions in a multisite health system

2. Formulate strategies to optimize patient access, referral workflows, and cross-site scheduling

3. Interpret operational and patient experience metrics to assess the effectiveness of access transformation initiatives

Intended Audience: Chief Operating Officers and Chief Experience Officers, Chief Medical Officers, Chief Population Health Officers, Chief Quality Officers, Chief Information Officers and Chief Data Officers, senior leaders in operations, patient access, contact center management, or population health seeking actionable strategies to scale technology-driven access improvements.

Monica Abel, MBA, CCMP, FACHDM, Director of Quality Management & PI, and Lori Appedu, LPN, Manager Quality Management & PI, Northwell Health Physician Partners

Conversa’s AI chatbot has enhanced colorectal cancer (CRC) screening rates through scalable, technology-driven patient engagement. The program leveraged interactive digital outreach, including automated reminders and bulk FIT DNA test ordering, to target diverse patient populations. Between 2023 and 2024, CRC screening rates increased by 5.8%, with the largest gains among patients aged 45–49. Digital engagement improved efficiency, reduced clinician workload, and generated cost savings by streamlining administrative tasks. Explore practical strategies to address disparities, optimize digital workflows, and integrate technology to improve preventive care adherence.

Upon completion of this activity, learners should be able to:

1. Examine the use of AI chatbots to improve preventive care engagement and screening adherence

2. Develop strategies to address demographic disparities and barriers in patient outreach

3. Interpret quantitative and operational data to optimize digital health interventions and efficiency

Intended Audience: Chief Medical Officers, Chief Population Health Officers, Chief Quality Officers, Chief Operating Officers and Chief Experience Officers, Chief Information Officers and Chief Data Officers, senior leaders in population health, preventive care, care management, or clinical operations seeking scalable, technology-driven solutions to improve outcomes and reduce disparities

Jessica Cruz, MBA, Clinical Innovation Director, and Sue Graff, MBA, Manager, Mayo Clinic- Rochester

The Department of Cardiovascular Medicine partnered with OpMed AI to optimize staffing, scheduling, and procedure planning across a large, complex department of more than 1,000 team members spanning 40 clinics, procedures, imaging, and hospital units. AI-driven staffing tools have reduced manual scheduling time for 109 sonographers from 25 to 5 hours, extended scheduling visibility from two to four weeks, and increased shift flexibility. Predictive analytics for procedural techs optimize start times and shift lengths, reducing overtime, improving work-life balance, and supporting staff retention. Additionally, procedure length prediction algorithms leverage physician and patient data to better schedule cardiac procedures, decreasing overestimation, enabling earlier case starts, and identifying opportunities to expand weekly procedure volumes.

Upon completion of this activity, learners should be able to:

1. Evaluate AI-driven tools for staffing and scheduling in large, complex healthcare departments

2. Formulate strategies to optimize shift assignments, work-life balance, and resource utilization

3. Apply predictive analytics to enhance procedure planning, efficiency, and departmental throughput

Intended Audience: Chief Operating Officers, Chief Nursing Officers, and Department Chairs responsible for workforce management, procedural operations, and patient access, Chief Information Officers and Chief Data Officers, Chief Medical Officers and Chief Quality Officers, senior leaders in operations, scheduling, or procedural service management seeking scalable AI strategies to improve efficiency, staff satisfaction, and patient throughput