Discover Artificial Intelligence and Machine Learning Thought Leadership at HIMSS21

The 2021 HIMSS Global Health Conference & Exhibition will present highly vetted education sessions within 17 topic categories, including Applied Artificial Intelligence and Machine Learning.

HIMSS21 AI and ML sessions will explore how solutions provide the promise, and more recently, the reality of revolutionizing the way healthcare data is analyzed and delivered.

By leveraging the power of reasoning, knowledge representation, planning, learning, natural language processing and other methods, AI and ML can positively enhance efficiencies, reduce risk, increase value, improve outcomes and reduce clinical variation.

AI & ML Education Sessions

In-person programming includes highly vetted sessions from industry leaders and specialty, niche programs to meet distinct needs. HIMSS is at the forefront covering cutting edge topics impacting the health IT ecosystem.

Tuesday, August 10

Ethical Machine Learning: 11:30 a.m. - 12:30 p.m.

This session explores how to build ethical considerations into machine learning projects and tools. Having an ethical framework will help to provide transparency and build public trust. The speakers will draw upon the experience of New Zealand, where they have implemented a national algorithm hub in response to the COVID-19 pandemic.

Applying Food Nutrition Apps in a Heart Failure Clinic: 12 p.m. - 3:30 p.m.

This poster will be displayed in conjunction with the HIMSS21 Career Fair. The current digital age, with advanced technology and Artificial intelligence (AI) such as smartphone applications and wearable devices, could bring new solutions to the table with a tailored approach. This poster introduces an app, which is named “MyMenu.”

Comparing Percent Babesiosis Model Performance: Development vs. Clinical Validation: 12 p.m. - 3:30 p.m.

This poster will be displayed in conjunction with the HIMSS21 Career Fair. Machine learning-based methods for automated measurement of percent parasitemia in digital microscopic images of peripheral blood smears could improve clinical efficiency, as compared to the current reference standard.

Increasing Access to Patient Counseling Information with Artificial Intelligence: 12 p.m. - 3:30 p.m.

This poster will be displayed in conjunction with the HIMSS21 Career Fair. This project was designed to build an artificial intelligence tool that contains medication counseling information and make it easily accessible to patients.

Individualized Medicine: How to Minimize Health Disparity for Colorectal Cancer Patients in Minnesota?: 12 p.m. - 3:30 p.m.

This poster will be displayed in conjunction with the HIMSS21 Career Fair. The overall preliminary goal of this project is to formulate a bio-psycho-social and clinical algorithm by gathering and analyzing retrospective patient’s clinic-epidemiological data to assess the scope of CRC prevention and improve survival for minority patients treated at different Minnesota hospitals in the United States of America.

Making Sense of Health Data to Accelerate the Shift From a Reactive to Proactive Healthcare System: 2:30 p.m. - 3:30 p.m.

Hear from global healthcare leaders about how and why they are focused on solving the problem of organizing and structuring their data to make better patient support decisions, design better clinical trials, operate more efficiently, understand population health trends and share data more securely. Examine examples of how to use AI and prebuilt machine learning models to analyze and understand relationships in data, identify trends and make predictions to improve patient care.

Biases in Datasets, Ethical Mindsets and Designing Fair AI: 2:45 p.m. - 3:45 p.m.

This session will address ethical dilemmas, implications, and challenges of using sophisticated AI algorithms to diagnose and develop treatment plans for complex medical diseases, prescribing medicines, defining clinical guidelines, and risk screening.

Wednesday, August 11

At-Risk Identification Using AI and Social Determinants: 11:30 a.m. - 12:30 p.m.

RTI International is developing an “artificially intelligent” approach to risk adjustment for SRFs using random forests to understand life expectancy variances at the census tract level. More than just maps, the approach involves using SRFs to explain variation in population health status and outcomes.

The Implementation of AI to Predict Ventilator Utilization: 11:45 a.m. - 12:45 p.m.

In the study presented, University Hospitals, an integrated health network with over 150 locations in the greater Cleveland, Ohio, area, assesses whether clinical artificial intelligence (AI) can more accurately predict ventilator utilization in the ICU, and how using clinical AI in the ICU setting impacts patient outcomes. This session will also review the data science behind implementation, and the strategies needed to implement AI successfully.

Healthcare Workflow Automation: Industry Learnings and Best Practices: 2:45 p.m. - 3:45 p.m.

Learn best practices and pitfalls to avoid from leading health systems that have effectively implemented automation across patient engagement, clinical, and revenue cycle workflows. 

Using EHR Metadata to Determine Relative Value of Care: 2:45 p.m. - 3:45 p.m.

In this research, we are using machine learning techniques over EHR data and metadata found in audit logs to automatically and accurately determine the appropriate level of service provided, as an alternative to documentation-based calculations or subjective decision-making criteria or time-based calculations that have the potential for fraud.

Forecasting Influenza and COVID-19 With AI: 4 p.m. - 4:30 p.m.

Leveraging artificial intelligence and diverse datasets, the infectious disease platform’s insights allow healthcare leaders to predict local disease risk and better mitigate the annual health and financial burden of flu.

Applying Clinical AI to Reduce Readmissions by More Than 20%: 4:15 p.m. - 5:15 p.m.

In this discussion, clinicians from Northwell Health, the largest healthcare provider in New York State, will share how they applied clinical artificial intelligence (AI) to augment their post-discharge workflows and reduce readmissions by 23.6%. This session will also share how they plan to scale the clinical AI application to a wider patient population and new use cases to prevent avoidable utilization.

Thursday, August 12

Machine Learning Algorithm to Predict Documentation Quality: 10:15 a.m. - 11:15 a.m.

In this session, we will present the development of a machine learning algorithm for feedback on clinical reasoning documentation, including an overview of a shared mental model of high-quality clinical reasoning documentation; the development of a predictive model that generates output on quality of clinical reasoning documentation; and an interactive, dynamically updated dashboard embedded in EPIC to provide feedback to residents

Avoiding an AI Winter Through Industry Self-Governance: 11:30 a.m. - 12:30 p.m.

This session will detail a framework and approach to promote and maintain trust in AI solutions. The speakers will describe how this framework could be used by AI developers and implementers to assess AI risks and implement risk-mitigation efforts in their organizations.

Friday, August 13

Safeguarding Trust in Deploying AI in Healthcare: 10:30 a.m. - 11:30 a.m.

This session examines the root causes of AI's potential disparate treatment of patients based on nonmedical factors. It also examines how healthcare organizations can safeguard their patients from such effects when implementing AI to support clinical decision-making.

Social Media Food Outbreak Signal Detection Using Natural Language Processing: 12 p.m. - 12:30 p.m.

Natural language processing (NLP), a subset of artificial intelligence (AI), enables faster detection of food outbreak signals and analyzes possible causes. Further, it processes real-time social media postings and detects the food outbreak signals and location to provide public health and food safety authorities with warnings and information for further investigation.

Preconference Forum

The week kicks off Monday, Aug. 9, with topic-focused or audience-specific events before general programming and exhibition opens. Each full-day program is immersive in actionable sessions and networking. Events are optional. Additional registration required.

This year’s preconference Machine Learning & AI for Healthcare Forum will dig deeper than ever into using machine learning and AI to improve care. The program will explore building data and analytics competency and move into real-world applications of machine learning and AI with a focus on best practices for implementation and integration into clinical workflows.