Artificial Intelligence and Machine Learning in Healthcare: Four Real-World Improvements

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. The data science journey has previously lacked actionable frameworks from implementation and adoption. However, more practical and outcome-driven guidelines, such as the Health CatalystData 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.

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.

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

虽然许多医疗保健组织已经在护理点实现了AI和ML工具，但很少有人成功地将它们应用于高层决策。However, as AI expands from “artificial intelligence” to “augmented intelligence,” it becomes more instrumental in improving healthcare leaders’decision-making. 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.

#2: Overcoming Healthcare Data Security Challenges

Healthcare organizations today face moresecurity threatsthan ever. Some security experts claim that an individual’s medical record can be sold forten timeswhat 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. 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.

#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大型卫生系统每年可能花费数十亿美元，使未偿余额成为其最高成本之一。作为一种解决方案，支付倾向工具通过使用AI来利用外部和内部的财务和社会经济数据，帮助组织锁定未支付账户，并确定人群中患者支付其余额的可能性(支付倾向)。有了人工智能支持的付费倾向洞察，金融团队可以将精力集中在最有可能付费的患者身上，并将无法付费的患者与慈善护理或政府援助联系起来。卫生系统和患者都从中受益，因为患者可以避免坏账，组织可以获得他们提供的服务的补偿。

#4: Improving Patient Flow

Many health systems struggle to effectively manage hospitalpatient 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. 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.

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. 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.

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:

1. AI in Healthcare: Finding the Right Answers Faster
2. Machine Learning Tools Unlock the Most Critical Insights from Unstructured Health Data
3. Meaningful Machine Learning Visualizations for Clinical Users: A Framework
4. Artificial Intelligence in Healthcare: A Change Management Problem
5. Machine Learning and Feature Selection for Population Health

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