Artificial Intelligence and Machine Learning in Healthcare: Four Real-World Improvements
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This document discusses how artificial intelligence (AI) and machine learning (ML) are improving healthcare outcomes in four key areas: 1) Augmenting leadership decisions by helping identify issues and make future-oriented decisions 2) Overcoming data security challenges by detecting potential privacy violations or attacks 3) Resolving uncompensated care costs by using propensity-to-pay tools to target unpaid accounts 4) Improving patient flow by reducing wait times and avoiding delays through predictive models
Artificial Intelligence and Machine Learning in Healthcare: Four Real-World Improvements
- 1. Artificial Intelligence and Machine Learning in Healthcare: Four Real-World Improvements Health Catalyst Editors
- 2. © 2021 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. AI and Machine Learning in Healthcare The healthcare industry has been progressing towards more widespread data science adoption for some time, but COVID-19-driven data and analytics demands have further motivated organizations to advance their artificial intelligence (AI) and machine learning (ML) capabilities.
- 3. © 2021 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. AI and Machine Learning in Healthcare The data science journey has previously lacked actionable frameworks from implementation and adoption. However, more practical and outcome-driven guidelines, such as the Health Catalyst Data Science Adoption Model™, are helping organizations leverage AI and ML in support of their strategic goals. As a result, data science has meaningful impacts on leadership decision making, information security, revenue, operational outcomes, patient experience, and more.
- 4. © 2021 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Healthcare Data Science Adoption Guidelines Advance Artificial Intelligence and Machine Learning An actionable data science adoption framework guides organizations through critical levels of analytics capabilities. For example, the Data Science Adoption Model (Figure 1) provides steps to help data science practitioners and leaders direct their analytic investments and deliver real value. As a result, decision makers bridge the gap between interest in data science and its real-world application, achieving measurable data-driven improvement across the health system.
- 5. © 2021 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Healthcare Data Science Adoption Guidelines Advance Artificial Intelligence and Machine Learning Figure 1: The Data Science Adoption Model.
- 6. © 2021 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Four Ways Artificial Intelligence and Machine Learning Improve Healthcare Outcomes As healthcare increasingly adopts advanced data science capabilities, such as AI and ML, organizations are improving outcomes across the continuum of care, including the following four examples: 1. Augmenting Healthcare Leadership Decisions 2. Overcoming Healthcare Data Security Challenges 3. Resolving One of Healthcare’s Biggest Costs— Uncompensated Care 4. Improving Patient Flow
- 7. © 2021 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Four Ways Artificial Intelligence and Machine Learning Improve Healthcare Outcomes #1: Augmenting Healthcare Leadership Decisions While many healthcare organizations have implemented AI and ML tools at the point of care, few have successfully applied them to high-level decision making. However, as AI expands from “artificial intelligence” to “augmented intelligence,” it becomes more instrumental in improving healthcare leaders’ decision-making.
- 8. © 2021 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Four Ways Artificial Intelligence and Machine Learning Improve Healthcare Outcomes #1: Augmenting Healthcare Leadership Decisions Augmented intelligence can help leadership identify urgent issues, make future-oriented decisions, and navigate some of healthcare’s most complex problems, such as solving healthcare inequality.
- 9. © 2021 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Four Ways Artificial Intelligence and Machine Learning Improve Healthcare Outcomes #2: Overcoming Healthcare Data Security Challenges Healthcare organizations today face more security threats than ever. Some security experts claim that an individual’s medical record can be sold for ten times what their credit card goes for on the black market, making it a common target for hackers. Fortunately, combining AI with human judgment is emerging as an effective healthcare data security strategy.
- 10. © 2021 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Four Ways Artificial Intelligence and Machine Learning Improve Healthcare Outcomes #2: Overcoming Healthcare Data Security Challenges Together, both resources power a highly accurate privacy analytics model that allows organizations to review access points to patient data and detect when a system’s EHR is potentially exposed to a privacy violation, attack, or breach. With specific techniques, including supervised and unsupervised ML and transparent AI methods, health systems can advance toward more predictive, analytics-based, collaborative privacy analytics infrastructures that safeguard patient privacy.
- 11. © 2021 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Four Ways Artificial Intelligence and Machine Learning Improve Healthcare Outcomes #3: Resolving One of Healthcare’s Biggest Costs—Uncompensated Care A health system can significantly improve its bottom line by collecting unpaid balances from patients for healthcare services. Uncompensated care can cost large health systems billions of dollars annually, making outstanding balances one of their highest costs. As a solution, propensity-to-pay tools help organizations target unpaid accounts by using AI to leverage external and internal financial and socioeconomic data and identify the likelihood that patients in a population will pay their balances (propensity to pay).
- 12. © 2021 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Four Ways Artificial Intelligence and Machine Learning Improve Healthcare Outcomes #3: Resolving One of Healthcare’s Biggest Costs—Uncompensated Care With AI-powered propensity-to-pay insight, financial teams can focus their efforts on the patients most likely to pay and connect patients who cannot pay with charity care or government assistance. Both health systems and patients benefit, as patients can avoid bad debt and organi- zations receive compensation for the care they’ve delivered.
- 13. © 2021 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Four Ways Artificial Intelligence and Machine Learning Improve Healthcare Outcomes #4: Improving Patient Flow Many health systems struggle to effectively manage hospital patient flow—the movement of patients through the hospital from entry to discharge. Machine learning-powered tools and predictive models can help organizations improve patient flow for departments throughout the system.
- 14. © 2021 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Four Ways Artificial Intelligence and Machine Learning Improve Healthcare Outcomes #4: Improving Patient Flow As a result, organizations can reduce patient wait times and staff overtime and improve patient outcomes and patient and clinician satisfaction while avoiding common challenges, including surgery delays or cancellations, clinician and overload and burnout, emergency department overcrowding, and more.
- 15. © 2021 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Artificial Intelligence and Machine Learning Are Pivotal in New-Normal Decision Making As post-COVID-19 healthcare continues to rely on advanced data science to better understand diseases and health conditions, patient populations, operational and financial challenges, and more, AI and ML will continue to play pivotal roles in new- normal decision making.
- 16. © 2021 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Artificial Intelligence and Machine Learning Are Pivotal in New-Normal Decision Making Health systems with an actionable data science strategy will be able to leverage advanced predictive capabilities and deeper operational and environmental understanding to better care for their patients and prepare for future challenges and crises.
- 17. © 2021 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. For more information: “This book is a fantastic piece of work” – Robert Lindeman MD, FAAP, Chief Physician Quality Officer
- 18. © 2021 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. More about this topic Link to original article for a more in-depth discussion. Artificial Intelligence and Machine Learning in Healthcare: Four Real-World Improvements AI in Healthcare: Finding the Right Answers Faster Health Catalyst Editors Machine Learning Tools Unlock the Most Critical Insights from Unstructured Health Data Health Catalyst Editors Meaningful Machine Learning Visualizations for Clinical Users: A Framework Valere Lemon, MBA, RN Senior Subject Matter Expert; Alejo Jumat, User Experience Designer, Sr. Achieve Data-Informed Healthcare in Eight Steps Health Catalyst Editors Machine Learning and Feature Selection for Population Health Health Catalyst Success Story
- 19. © 2021 Health Catalyst Proprietary. Feel free to share but we would appreciate a Health Catalyst citation. Other Clinical Quality Improvement Resources Click to read additional information at www.healthcatalyst.com Health Catalyst is a mission-driven data warehousing, analytics and outcomes-improvement company that helps healthcare organizations of all sizes improve clinical, financial, and operational outcomes needed to improve population health and accountable care. Our proven enterprise data warehouse (EDW) and analytics platform helps improve quality, add efficiency and lower costs in support of more than 65 million patients for organizations ranging from the largest US health system to forward-thinking physician practices. Health Catalyst was recently named as the leader in the enterprise healthcare BI market in improvement by KLAS, and has received numerous best-place-to work awards including Modern Healthcare in 2013, 2014, and 2015, as well as other recognitions such as “Best Place to work for Millenials, and a “Best Perks for Women.”