Using Artificial Intelligence and Machine Learning to Improve Population Health Outcomes

A high-level AI primer for public health practice.

Analyst Perspective

The intersection of artificial intelligence (AI), machine learning (ML), and analytics techniques with public health practice holds great promise for transforming the field.

The integration of AI, ML, and analytics techniques into public health practice holds immense potential for revolutionizing health and healthcare systems worldwide. From disease surveillance and diagnosis to precision public health and behavior change, these technologies offer new avenues for improving population health outcomes.

AI and ML algorithms can revolutionize disease surveillance by efficiently analyzing vast amounts of data from various sources.

AI and ML algorithms have the potential to enhance diagnosis and treatment strategies in public health.

AI and analytics techniques enable the practice of precision public health, where interventions are tailored to specific population segments.

AI and ML techniques can also play a crucial role in promoting healthy behaviors and facilitating behavior change.

Analytics techniques, including data mining and predictive modeling, can support evidence-based decision making in public health policy and planning.

However, it is essential to address ethical considerations, privacy concerns, and disparities in data access to ensure that the benefits of AI in public health are equitably distributed. By embracing these technologies responsibly, we can harness their power to advance public health and create a healthier future for all.

Neal Rosenblatt
Principal Research Director
Info-Tech Research Group

“Our future is a race between the growing power of technology and the wisdom with which we use it.”

— Stephen Hawking

Executive Summary

Info-Tech Insight

The widespread adoption of digital open-source surveillance and AI technology is needed for a future where population health outcomes are enhanced and serious epidemics going forward are prevented.

Putting AI and ML into public health practice

Using AI/ML tools and technology to improve health status assessment and preparedness core functions and enhance capacity and capability to improve population health management practice and health outcomes.

What is AI?

Artificial intelligence (AI) is not new. In fact, AI has been with us as an academic discipline since the 1950s. However, today, there remains no universally accepted definition.

Generally, AI refers to the development of computer systems that can perform tasks that would typically require human intelligence, such as learning, reasoning, perception, interacting with an environment, problem-solving, and even exercising creativity.

AI is not a single technology. Rather, it can be characterized as a set of technologies that includes any type of software or hardware component that enables machines to process and analyze large amounts of data, identify patterns, and make predictions or decisions based on that data.

There are several subcategories of AI including machine learning, federated learning, and deep learning; generative AI; natural language processing including both natural language generation and natural language understanding; speech recognition; computer vision; and expert systems. Intelligent automation, including robotic process automation (RPA), is not technically a form of AI. Instead, it works in conjunction with AI by automating repetitive processes in a quicker, more efficient way. The critical difference is that RPA is process-driven, whereas AI is data-driven. These subfields each focus on different aspects of AI, but they are all united by the goal of developing intelligent machines that can perform tasks without human intervention.

In recent years, the use of AI has been expanding. In fact, since 2017, the adoption of AI models in some industries has more than doubled, and investment has increased apace. With rapid evolution, there are inherent risks – some known and some unknown. Questions of ethical use, bias, and equity can be mitigated if initial data used to train these models are carefully selected and curated and humans are kept in the loop, especially when model outputs involve individual and population health and overall human welfare.

— Sources: Techopedia, 2023; Dataconomy, 2023; McKinsey, 2022, 2023; Google, 2023; IBM, n.d., 2020.

AI at a glance

Subcategories of AI

AI subcategory definitions

There are distinct differences between the terms AI and ML

Artificial Intelligence (AI) – An umbrella concept wherein machines are taught to perform tasks normally associated with human intelligence, such as decision-making and language interaction.

Machine Learning (ML) – A subset of AI dedicated to taking data and training algorithms to create models that can perform highly complex tasks without being explicitly programmed.

• Deep Learning – A subset of ML that uses artificial neural networks to mimic the learning process of the human brain to solve complex problems.
  • Generative AI – A subset of deep learning that focuses on creating computer systems capable of generating new and original content, such as images, music, videos, or text.
  • Computer Vision – A subset of ML and deep learning that uses deep neural networks to interpret the meaning of images and videos to apply them to predictive or decision-making tasks.
• Federated Learning – A subset of ML enabling an organization to train AI models on decentralized data without the need to centralize or share that data.
• Natural Language Processing (NLP) – A subset of ML that enables computers to understand, analyze, and generate human language.
  • Natural Language Understanding (NLU) – A subset of NLP that focuses on understanding the meaning of text or speech.
  • Natural Language Generation (NLG) – A subset of NLP that focuses on the production of written or spoken language.
• Speech Recognition – A subset of ML that uses algorithms to identify, understand, and process patterns in human speech.
• Expert Systems – A subset of ML designed to emulate and mimic human intelligence, expert skills, or behavior in a particular field or topic.

Intelligent Automation (IA) – Technically not a form of AI or ML, IA uses automation technologies to perform repetitive process tasks with predictable inputs. It is used in combination with ML and NLP.

• Robotic Process Automation (RPA) – A software technology and subset of IA that automates repetitive high-volume tasks by mimicking human behavior and speeding up processing time.
• Business Process Management (BPM) – A management discipline that focuses on optimizing business processes using automation technologies.

AI classification

Generally, the degree to which an AI system can replicate human capabilities is used as the criterion to determine AI type.

AI and AI-enabled machines that can perform more human-like functions with equivalent or better levels of proficiency are considered a more evolved type of AI, while an AI that has limited functionality and performance is considered a simpler and less evolved type.

Based on this criterion, there are two ways in which AI is generally classified. One type is considered human-centric, while the other type is said to be technology-centric.

Artificial intelligence, machine learning, and other analytics techniques

Likelihood to be used in AI applications and level of automation.

As artificial intelligence technologies advance, so does the definition of which techniques constitute AI and which techniques are used in analytics for machine learning preprocessing.

The intersection of AI, ML, and analytics techniques with public health practice holds great promise for transforming the field.

These cutting-edge technologies offer innovative solutions to complex challenges in disease surveillance, diagnosis, treatment, and health promotion.

— Source: “Notes From the AI Frontier,” McKinsey, 2018.

Where are we today?

Artificial narrow intelligence (ANI) vs. artificial general intelligence (AGI)

Artificial Narrow Intelligence (ANI)

“Where we are today with AI”

• AKA applied artificial intelligence (AAI)
• Capable of performing only a limited set of predetermined functions based on mathematics and algorithms
• ANI technologies include machine learning, generative AI, computer vision, natural language processing, and robotic process automation

vs.

Artificial General Intelligence (AGI)

“The future of AI”

• The next level of AI
• Equals the human mind’s ability to function autonomously
• Capable of understanding, learning, reasoning, planning, problem-solving, and applying knowledge across a broad range of tasks and domains

Info-Tech Insight

Today, we find ourselves at an intersection where ANI has made significant strides, enhancing our lives through specialized AI applications. AGI represents the next frontier in AI research and development. As we continue to explore the potential of AI, striking a balance between innovation and responsible development will be essential to navigate the path forward and harness the transformative power of AI for the betterment of society.

Myth-busting artificial intelligence

Breaking down the common misconceptions so you can focus on what is real

Myth: Only Big Tech uses AI

AI is present in many aspects of our everyday lives. Although large tech companies are usually the ones exploring AI research, we all interact with AI technology in our daily routines – even if we don’t always realize it. The hype and seeming explosion of generative AI has pushed AI to the front of corporate consciousness and is increasing the regularity of conscious AI usage.

What uses of AI exist in your organizational function today? Where else could AI be used?

Myth: AI will replace our jobs

Artificial intelligence is meant to work with humans, not instead of humans. AI is expected to augment human abilities, allowing us to work more efficiently and effectively. AI can take over repetitive and mundane tasks, allowing humans to focus on the creative, strategic, and interpersonal aspects of their work.

How will AI impact what your team does? How might you use the capacity that AI could generate?

Myth: AI is self-sufficient

Many people believe that all AI programs can exist and run entirely on their own. In fact, some work in AI still requires the human touch. AI algorithms are developed by humans, who inevitably have their own biases and preferences and, therefore, the data sets used by models may be biased as a result. Having the “human in the loop” helps to eliminate biases in the design process and strive to create diverse and inclusive data sets.

Have you thought about the impacts of tacit biases and the biases within your data sets?

— Sources: Mashable, 2022; Anand, 2023.

Rapid advancements in AI are improving the future of public health outcomes

The rapid advancements in AI have opened new horizons in health and human services, revolutionizing the way we approach population health outcomes. AI adoption in these sectors holds immense potential for transforming healthcare delivery, improving public health interventions, and empowering individuals to take control of their wellbeing.

Enhancing Disease Surveillance and Early Detection
AI adoption has significantly improved disease surveillance and early detection mechanisms. By analyzing vast amounts of data from multiple sources, including electronic health records, social media, and environmental sensors, AI algorithms can identify patterns and trends that signify potential health threats.

Precision Public Health
Precision public health enables public health authorities to detect outbreaks early, develop targeted interventions, and prevent the spread of diseases. AI-powered predictive models help allocate resources efficiently and guide public health strategies to mitigate risks and improve population health outcomes.

Ethical Considerations and the Future
While the benefits of AI adoption are evident, ethical considerations remain crucial. Protecting privacy, ensuring algorithmic transparency, and addressing biases within AI systems are paramount concerns. Striking the right balance between technology-driven innovations and preserving human-centric care must be at the forefront of AI deployment.

— Source: CDC, 2022.

Info-Tech Insight

By harnessing the power of AI, we pave the way for a future where population health outcomes are enhanced, and healthcare becomes more accessible and efficient.

AI adoption globally is on the rise

The AI adoption rate and the number of AI capabilities organizations use have both doubled.

AI adoption rate is expected to rapidly grow worldwide
AI adoption globally is 2.5 times higher today than in 2017.1 Among senior technology leaders worldwide, by 2025, 50% say that AI will be crucial to their organizational function.[1]

AI capabilities adoption rate is also increasing
The average number of AI capabilities that organizations use in their daily operations, such as natural-language generation and computer vision, has also doubled, from 1.9 in 2018 to 3.8 in 2022.[1]

[1] “The State of AI in 2022–and a Half Decade in Review." McKinsey & Company, 2022.

AI in Healthcare is also on the rise

Source: Statista, 2023.

Source: Statista, 2021.

Healthcare organizations are investing in AI

• The global AI healthcare market is set to be US$45.2 billion by 2026. In 2020, the US AI healthcare market had a value of over $1.15 billion and is expected to have a compounded annual growth rate of 44% by 2027.

AI is revolutionizing patient care

• For example, robotic-assisted surgery has revolutionized minimally invasive surgeries with limited variability between cases. These types of surgeries in the US have increased by 19% from 2012 to 2018 and are set to have a market size value of US$119.7 billion by 2030.

AI is helping address clinician burnout and administrative waste

• According to a 2020 McKinsey study, 70% of a clinician’s time is spent on routine administrative duties, of which 35% is being automated.
• Generative AI ChatGPT has the potential in healthcare to take on repetitive low-complexity tasks to reduce clinician burnout from administrative tasks.
• Steward Health, the largest for-profit hospital in the US, implemented AI to optimize staffing and realized a 1% reduction in nurses' hours paid per patient, saving the organization $2 million per year.
• According to a 2020 HIMSS report, inefficient administrative processes cost healthcare organizations $91 billion in wasted spending annually.

— Source: Statista, 2021; McKinsey & Company, 2022; Statista, 2023; HIMSS, 2020.