Responsible AI Primer and Playbook for Public Health and Healthcare Organizations
Leverage the power of AI to improve individual and population health outcomes.
AI is disrupting all industries and providing opportunities for organization-wide advantages. Understand this disruptive technology and its trends to properly develop a strategy for leveraging AI technology.
- In public health and healthcare practice, AI must be aligned to a business strategy and outcomes. As an AI enabler, IT must align with and support business stakeholders including programs and core public health and healthcare functions.
- Public health and healthcare organizations need to adopt a data-driven culture.
- All public health and healthcare organizations should be planning the responsible leveraging and implementation of this innovative and exponential technology.
- Public health and healthcare business stakeholders, including programs and policy developers, must:
- Cut through the hype to optimize and leverage AI to address core functions and drive business outcomes focused on essential services delivery.
- Understand the public health and healthcare landscape and benefits and risks associated with AI.
- Plan for responsible AI.
- Understand the gaps the organization needs to address to fully leverage AI.
Without a proper strategy and responsible AI guiding principles, the risks to deploying AI technology could negatively impact service delivery and outcomes.
Our Advice
Critical Insight
- Build your responsible AI roadmap to guide investments and deployment of these solutions in alignment with public health and healthcare core functions and essential services.
- Assemble leadership to make them aware of the benefits and risks of adopting responsible AI–based solutions.
- Establish responsible AI guiding principles to govern the development and deployment of AI applications.
- Assemble key stakeholders and subject matter experts to assess the challenges and tasks required to implement responsible AI applications.
- Assess current level of AI maturity, skills, and resources.
- Identify desired AI maturity level and challenges to enable deployment of candidate use cases.
- Assess candidate business capabilities targeted for responsible AI implementation to see if they align with the organization’s business criteria, responsible AI guiding principles, and capabilities for delivering the project.
- Develop prioritized list of candidate use cases.
- Develop policies for AI usage.
- Identify the gaps that must be addressed to deploy AI responsibly and successfully.
- Identify organizational impact and requirements for deploying responsible AI applications.
Impact and Result
This playbook provides a list of activities and deliverables required for the successful deployment of AI solutions in public health and healthcare practice.Info-Tech’s human-centric, value-based approach is a guide for deploying AI applications and covers:
- Establishing responsible AI guiding principles
- Using the AI Maturity Model
- Prioritizing candidate AI–based use cases
- Developing policies for usage
- Getting started with AI value–based initiatives
Responsible AI Primer and Playbook for Public Health and Healthcare Organizations
Leverage the power of AI to improve individual and population health outcomes.
Analyst perspective
Although increased AI adoption unlocks new value, it also introduces new risks. To achieve the full benefits of AI, risks must be mitigated by understanding and adhering to principles that foster trust at each stage of AI development and deployment.
The integration of AI in public health and healthcare practice has opened exciting opportunities to address complex health challenges more effectively. However, as AI becomes more deeply embedded in public health and healthcare systems, ensuring responsible AI practices becomes paramount – not merely as a choice but as an ethical obligation.
Striking the right balance between harnessing AI's potential for innovation and safeguarding human values and well-being is critical. By adhering to ethical principles, prioritizing transparency and accountability, and continuously evaluating AI systems, we can harness the power of AI to address public health and healthcare challenges while ensuring that no one is left behind. Responsible AI in public health and healthcare practice is not just a trend; it is the path to a more equitable and effective healthcare system.
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Neal Rosenblatt |
Executive summary
Your Challenge |
Common Obstacles |
Solution |
|---|---|---|
AI is disrupting all industries and providing opportunities for organization-wide advantages. Understand this disruptive technology and its trends to develop a successful strategy for leveraging AI.
All public health and healthcare organizations should be planning the responsible leveraging and implementation of this innovative and exponential technology. |
Public health and healthcare business stakeholders, including programs and policy developers, must cut through the hype to optimize and leverage AI to address core functions and drive business outcomes focused on essential services delivery.
Without a proper strategy and responsible AI guiding principles, the risks to deploying AI technology could negatively impact service delivery and outcomes. |
Info-Tech’s human-centric, value-based approach is a guide for deploying AI applications and covers:
This playbook will provide the list of activities and deliverables required for the successful deployment of AI solutions in public health and healthcare practice. |
Info-Tech Insight
Make public health and healthcare leadership aware of the potential benefits and risks of transforming core function and essential services delivery with responsible AI solutions.
Your challenge
This research is designed to help public health and healthcare organizations that seek to:
- Establish responsible AI guiding principles to address human-based requirements and to govern the development and deployment of AI applications.
- Identify new AI-enabled opportunities to transform the work environment to increase efficiencies, drive innovation, and reduce risk.
- Prioritize candidate use cases and develop responsible AI policies for usage.
- Measure the progress and success of AI initiatives with clear metrics.
- Build a roadmap to implement candidate use cases.
Common obstacles
Barriers that make outcomes-focused goals, objectives, and strategies challenging for many organizations:
- Getting the right business stakeholders together to develop the organization’s AI strategy, vision, and objectives.
- Establishing responsible AI guiding principles to guide AI investments and deployments.
- Advancing the AI maturity of the organization to meet requirements of data and AI governance as well as human-based requirements such as fairness and bias detection, transparency, and accountability.
- Assessing AI opportunities and developing policies for use.
Info-Tech’s definition of an AI-enabled business strategy
An effective AI strategy is driven by the business stakeholders of the organization and focused on delivering improved business outcomes.
- A high-level plan that provides guiding principles for applications that are fully driven by the business needs and capabilities that are essential to the organization.
- A strategy that tightly weaves business needs and the applications required to support them. It covers AI architecture, adoption, development, and maintenance.
- A way to ensure that the necessary people, processes, and technology are in place at the right time to sufficiently support business goals.
- A visionary roadmap to communicate how strategic initiatives will address business concerns.
This playbook in context
Info-Tech’s guidance covers how to create a tactical roadmap for executing responsible AI initiatives across your organization
Scope
- This playbook is not a proxy for a fully formed AI strategy. Step 1 of our framework necessitates alignment of your AI and business strategies. Note: Creation of your AI strategy is not within the scope of this approach.
- This approach sets the foundations for building and applying responsible AI principles and policies aligned to enterprise governance and key regulatory obligations (e.g. privacy). Both steps are foundational components of how you should develop, manage, and govern your AI program but are not substitutes for implementing broader AI governance.
Guidance on how to implement AI governance can be found in the blueprint linked below:
Download Info-Tech’s AI Governance blueprint
Measure the value of this playbook
Leverage this playbook’s approach to ensure your AI initiatives align with and support your key business drivers
This playbook will guide you to drive and improve business outcomes. Key business drivers in public health and healthcare practice will often focus on:
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This playbook will help you identify the key AI strategy initiatives that align with your organization’s goals. Value to the organization is often measured by the estimated impact on population health improvement via core function/essential services delivery, operational efficiencies, innovation, or risk mitigation. The playbook will also help you develop a plan and a roadmap for addressing any gaps and introducing the relevant responsible AI capabilities that drive value to the organization based on defined business metrics. |
Once you implement your 12-month roadmap, track the metrics below over the next fiscal year (FY) to assess the effectiveness of measures:
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Info-Tech offers various levels of support to best suit your needs
DIY Toolkit |
Guided Implementation |
Workshop |
Consulting |
|---|---|---|---|
| “Our team has already made this critical project a priority, and we have the time and capability, but some guidance along the way would be helpful.” | “Our team knows that we need to fix a process, but we need assistance to determine where to focus. Some check-ins along the way would help keep us on track.” | “We need to hit the ground running and get this project kicked off immediately. Our team has the ability to take this over once we get a framework and strategy in place.” | “Our team does not have the time or the knowledge to take this project on. We need assistance through the entirety of this project.” |
Diagnostics and consistent frameworks used throughout all four options
Guided Implementation (GI)
What does a typical GI on this topic look like?
| Phase 1 | Phase 2 | Phase 3 | Phase 4 |
|---|---|---|---|
Call #1: Scope requirements, objectives, and your specific challenges. Call #2: Identify AI strategy, vision, and objectives. |
Call #3: Define responsible AI guiding principles to adopt and identify current AI maturity level. |
Call #4: Assess and prioritize AI initiatives and draft policies for usage. |
Call #5: Build proof of concept (PoC) implementation plan and establish metrics for PoC success. Call #6: Build and deliver leadership-level AI presentation. |
A Guided Implementation (GI) is a series of calls with an Info-Tech analyst to help implement our best practices in your organization.
A typical GI is between 5 to 8 calls over the course of 1 to 2 months.
Four plays to building your responsible AI roadmap
| Play 1 | Play 2 | Play 3 | Play 4 | |
|---|---|---|---|---|
Establish Responsible AI Guiding Principles |
Assess AI Maturity |
Prioritize Opportunities and Develop Policies |
Building Your Roadmap |
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| Trends | Consumer groups, organizations, and governments around the world demand that AI applications adhere to human-based values and take into consideration possible impacts of the technology on society. |
Leading organizations build AI models guided by responsible AI guiding principles. |
Organizations delivering new applications without developing policies for use will produce negative business outcomes. |
Developing a roadmap to address human-based values is challenging. This process introduces new tools, processes, and organizational change. |
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Insight summary
Overarching Insight
Build your responsible AI roadmap to guide investments and deployment of these solutions in alignment with public health and healthcare core functions and essential services.
Responsible AI
Assemble leadership to make them aware of the benefits and risks of adopting responsible AI–based solutions.
- Establish responsible AI guiding principles to govern the development and deployment of AI applications.
AI Maturity Model
Assemble key stakeholders and subject matter experts (SME) to assess the challenges and tasks required to implement responsible AI applications.
- Assess current level of AI maturity, skills, and resources.
- Identify desired AI maturity level and challenges to enable deployment of candidate use cases.
Opportunity Prioritization
Assess candidate business capabilities targeted for responsible AI implementation to see if they align with the organization’s business criteria, responsible AI guiding principles, and capabilities for delivering the project.
- Develop prioritized list of candidate use cases.
- Develop policies for AI usage.
Tactical Insight
Identify the gaps that must be addressed to deploy AI responsibly and successfully.
Tactical Insight
Identify organizational impact and requirements for deploying responsible AI applications.
Key takeaways for developing an effective outcomes-focused, business-driven responsible AI roadmap
Align the AI strategy with the digital business strategy |
Create responsible AI guiding principles, which are a critical success factor |
Evolve AI maturity level by focusing on principle-based requirements |
Develop criteria to assess AI initiatives |
Develop responsible AI policies for use |
Playbook deliverables
Each step of this playbook is accompanied by supporting deliverables to help you accomplish your goals:
Responsible AI Maturity Assessment and Roadmap Planning Tool |
Responsible AI Checklist |
Implementing Responsible AI Leadership Presentation |
7-Step Guide to Getting Started with Responsible AI |
|---|---|---|---|
Use our best-of-breed AI maturity framework to analyze the gap between your current and target states and develop a roadmap aligned with your value stream to close the gap. |
Present your AI roadmap with assurance using Info-Tech’s Responsible AI Checklist to ensure your organization has the resources in place to successfully create a responsible AI solution. |
Present your AI roadmap in a prepopulated document that summarizes this playbook’s key findings and provides an outcomes-focused view of AI challenges and your plan of action to meet them. |
Use this guide to elevate your organization's AI maturity level by taking gradual steps toward responsible AI implementation. |
Info-Tech Insight
Info-Tech’s Responsible AI Maturity Assessment and Roadmap Planning Tool, Responsible AI Checklist, Implementing Responsible AI Leadership Presentation, and 7-Step Guide to Getting Started with Responsible AI enable you to shape your AI roadmap and communicate these deliverables to your stakeholders and sponsors effectively and comprehensively.
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 software or hardware component that enables machines to process and analyze large amounts of data, identify patterns within it, and make predictions or decisions based on it.
There are several sub-categories of AI including machine learning (ML), 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. Mitigate questions of ethics, bias, and equity by carefully curating the data used to train these models and keeping humans in the loop, especially when model outputs involve individual and population health.
Sources: Techopedia, 2023; Dataconomy, 2023; McKinsey, 2022, 2023; Google, 2023; IBM, n.d., 2020; Kavlakoglu, 2020.
Where are we today?
We are hereArtificial narrow intelligence (ANI) “The present AI”
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vs. |
Artificial general intelligence (AGI) “The future of AI”
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Info-Tech Insight
ANI has made significant strides to enhance life through specialized AI applications, and AGI represents the next frontier in AI research and development. Balancing innovation and responsible development is essential to continue exploring the potential of AI and harnessing its transformative power for the betterment of society.
Mythbusting AI
Breaking down the common misconceptions so you can focus on what is real
Myth: Only big tech uses AI
What AI uses exist in your organizational function today? Where else could AI be used? |
Myth: AI will replace our jobs
How will AI impact what your team does? How might you use the capacity that AI could generate? |
Myth: AI is self-sufficient
Have you thought about the impacts of tacit biases and the biases within your data sets? |
Source: Anand, 2023
Rapid advancements in AI are improving the future of public health and healthcare 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 well-being.
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 the preservation of human-centric care must be at the forefront of AI deployment.
Info-Tech Insight
Harness the power of AI to pave the way to a future of enhanced population health outcomes and accessible and efficient healthcare.
Key concepts
AI |
AI Maturity Model |
Responsible AI |
About Info-Tech
Info-Tech Research Group is the world’s fastest-growing information technology research and advisory company, proudly serving over 30,000 IT professionals.
We produce unbiased and highly relevant research to help CIOs and IT leaders make strategic, timely, and well-informed decisions. We partner closely with IT teams to provide everything they need, from actionable tools to analyst guidance, ensuring they deliver measurable results for their organizations.
What Is a Blueprint?
A blueprint is designed to be a roadmap, containing a methodology and the tools and templates you need to solve your IT problems.
Each blueprint can be accompanied by a Guided Implementation that provides you access to our world-class analysts to help you get through the project.
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Speak With An Analyst
Get the help you need in this 4-phase advisory process. You'll receive 6 touchpoints with our researchers, all included in your membership.
Guided Implementation 1: Establish Responsible AI Guiding Principles
- Call 1: Scope requirements, objectives, and your specific challenges.
- Call 2: Identify AI strategy, vision, and objectives
Guided Implementation 2: Assess AI Maturity
- Call 1: Define responsible AI Guiding principles to adopt and identify current AI maturity level.
Guided Implementation 3: Prioritize Opportunities and Develop Policies
- Call 1: Assess and prioritize AI initiatives and draft policies for usage.
Guided Implementation 4: Building Your Roadmap
- Call 1: Build proof of concept (PoC) implementation plan and establish metrics for PoC success.
- Call 2: Build and deliver leadership-level AI presentation.