Discover AI Use Cases in Healthcare
Leveraging AI to address healthcare challenges.
- AI in healthcare has not demonstrated value in analytics in the last ten years, despite rapid growth in investment and development.
- Healthcare organizations are challenged with inefficient business operations and are unsure of how to effectively address them.
- Adopting new technology requires a strategic approach and alignment between IT and healthcare administrators.
Our Advice
Critical Insight
Health organizations should approach AI adoption strategically and responsibly, with a clear understanding of the specific use cases and benefits and a plan for addressing the challenges associated with implementation and ongoing use. Before you start implementing any AI solutions, assess your organization’s readiness maturity level. With a very low maturity level, a new software solution will not improve your operations.
Impact and Result
Healthcare is a highly regulated industry, and many organizations are concerned about the risks associated with AI. It is imperative to approach AI strategically and responsibly. Those that are thriving are digitally mature and recognize that technology empowers people and processes.
Discover AI Use Cases in Healthcare
Leveraging AI to address healthcare challenges.
Analyst Perspective
AI has the potential to create value in your organization.
The potential of artificial intelligence (AI) in healthcare is significant, and health organizations should consider leveraging AI to improve patient outcomes and reduce costs. AI can be used for various purposes, including diagnosis and treatment, drug development, remote patient monitoring, and predictive analytics. By analyzing medical data, AI can help healthcare providers make more accurate diagnoses, develop more effective treatment plans, and monitor patients more effectively.
However, the adoption of AI in healthcare also poses some challenges. One of the main challenges is ensuring that the data used to train AI algorithms is accurate and representative of the population being served. Another challenge is ensuring that AI is used ethically and responsibly, with appropriate safeguards in place to protect patient privacy and prevent bias. Health organizations should approach AI adoption strategically, with a clear understanding of the specific use cases and benefits, and a plan for addressing the challenges associated with implementation and ongoing use. Despite these challenges, the potential benefits of AI in healthcare are significant, and healthcare organizations that invest in AI technology and expertise will foster innovation to improve operational efficiency and patient outcomes.
Sharon Auma-Ebanyat
Research Director, Healthcare
Industry Practice
Info-Tech Research Group
Executive Summary
Your Challenge
- AI in healthcare has not demonstrated value in analytics in the last ten years despite rapid growth in investment and development.
- Healthcare organizations are challenged with inefficient business operations and are uncertain how to effectively address them.
- Adopting new technology requires a strategic approach and alignment between IT and healthcare administrators.
Common Obstacles
- Healthcare leaders have a limited understanding of the benefits of AI, organizational impacts, and how to get started.
- Healthcare organizations are concerned about the risks of AI and compliance with privacy laws, regulations, and policies.
- Health IT leaders must continuously identify and prioritize feasible AI technology trends to foster innovative ways of addressing current operational challenges.
Info-Tech's Approach
- Help healthcare leaders understand and discover AI use cases that can address some of their business challenges.
- Guide healthcare leaders to start their AI journey by identifying and prioritizing AI use cases for their business capabilities through a benefits realization model.
- Leverage the output to gain executive buy-in. The approach will help you determine the most suitable problems (with the greatest value) to solve and meet all business criteria to implement AI responsibly.
Info-Tech Insight
Health organizations should approach AI adoption strategically and responsibly, with a clear understanding of the specific use cases and benefits and a plan for addressing the challenges associated with implementation and ongoing use. Healthcare organizations that invest in AI technology will foster innovation to improve operational efficiency and patient outcomes.
What is artificial intelligence (AI)?
There is no universally accepted definition of artificial intelligence (AI), but it generally refers to the development of computer systems that can perform tasks that would typically require human intelligence, such as learning, reasoning, perception, and problem-solving. AI can be characterized as a set of technologies that enable machines to process and analyze large amounts of data, identify patterns, and make predictions or decisions based on that data.
AI includes subfields such as machine learning, natural language processing, computer vision, and deep learning. Deep learning is a subset of machine learning that involves the use of neural networks to process and analyze large amounts of data. 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.
AI is a rapidly evolving field, and the definition of AI continues to evolve as new technologies and applications emerge. However, at its core, AI is about creating intelligent machines that can perform tasks that would typically require human intelligence.
Source: Dataconomy, 2023; Procedia Computer Science, 2023.
AI is driving automation in healthcare
The application of AI to automate repetitive tasks in healthcare has improved efficiency, accuracy, and productivity. As a result, 90% of healthcare organizations have adopted some form of automation in their operations. AI and automation are related concepts that are often used interchangeably, but they are distinct from each other. Automation refers to the use of technology to perform routine, repetitive tasks without human intervention. This can include tasks such as data entry, data processing, and other rule-based tasks. Robotic process automation (RPA) is a type of automation that uses software robots to automate these tasks.
On the other hand, AI involves the development of computer systems that can perform tasks that would typically require human intelligence, such as learning, reasoning, perception, and problem-solving. And while automation and AI are distinct concepts, they can be used together to create more efficient and effective systems. For example, RPA can be used to automate routine tasks, while AI can be used to analyze data and make predictions or decisions based on that data. This combination of automation and AI can lead to significant productivity gains, cost savings, and improved outcomes in many industries, including healthcare.
Benefits of automation in healthcare
- Better patient experience and satisfaction.
- Easy access to patient data and reduction of administrative errors.
- Lower staffing costs and less overtime.
Areas of healthcare organizations that are benefiting from automation worldwide (N=100)
Source: Statista, 2022.
Info-Tech Insight
RPA is typically the most feared application of automation in healthcare due to the assumption that by automating tasks, computers will replace humans in their jobs. However, it must be stressed that when implementing RPA technologies, it can lead to enhanced job creation, as seen with all disruptive technologies. It can also provide more value to existing jobs. Given the current healthcare staffing shortages and burnout, automation can reduce the administrative burden, and staff can be repurposed to focus on complex aspects of their work.
AI in healthcare is on the rise
Healthcare organizations are investing in AI
- The global AI healthcare market is set to be 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.
AI in healthcare market size worldwide from 2021 to 2030 (in USD billions)
Source: Statista, 2023.
What is the stage of AI adoption in your healthcare organization? (N=373)
Source: Statista, 2021.
The healthcare AI ecosystem is booming
Sources: Marktechpost, 2023; Ampliz, 2023; Omdena, 2023; Healthcare Weekly, 2019; Healthcare IT News, 2023.
Note: This is not an exhaustive list.
AI can create value by addressing specific use cases
|
Sources of Value |
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|---|---|---|---|---|---|
|
Leading AI use case opportunities (Order does not reflect importance.) |
Cost |
Patient Exp. |
Clinician Exp. |
Efficiency |
|
| 1 | Hospital Decision Support: Streamline operations with data insights from forecasted patient volumes, bed availability, staff volumes, and equipment. | ✔ | ✔ | ✔ | ✔ |
| 2 | Call Center Optimization: Reduce call volume and time on calls by providing automated answers to most basic customer questions. | ✔ | ✔ | ✔ | ✔ |
| 3 | Imaging & Diagnostics: Automate image classification to reduce errors and have efficient image processing. | ✔ | ✔ | ✔ | |
| 4 | Preventive Healthcare: Use predictive analytics to analyze patient data and implement system-wide preventive interventions. | ✔ | ✔ | ✔ | ✔ |
| 5 | Remote Patient Monitoring: Manage patient remotely to reduce transfer delays, increase hospital throughput, and reduce emergency visits. | ✔ | ✔ | ✔ | |
| 6 | Clinical Decision Support: Develop personalized treatment plans for complex cases, where clinical guidelines may not present clear options to determine treatment plans with ideal outcomes. | ✔ | ✔ | ✔ | ✔ |
| 7 | Revenue Cycle Management: Reduce errors, reduce costs, and improve efficiency by automating the analysis of the prior authorization process. | ✔ | ✔ | ✔ | |
| 8 | Equipment Monitoring: Trigger maintenance actions via early detection of asset degradation and problems. | ✔ | ✔ | ✔ | |
| 9 | Health Education: Improve patient experience with personalized patient health coaching based on behavior and patterns to support healthy lifestyles. | ✔ | ✔ | ✔ | |
| 10 | Patient Compliance: Improve patient outcomes and increase treatment compliance with an alert messaging and reminder system. | ✔ | ✔ | ✔ | |
| 11 | Infection Control and Monitoring: Reduce infection risk with real-time infection control assessment and feedback. | ✔ | ✔ | ✔ | ✔ |
| 12 | Surgical Procedure Assistance: For improved patient outcome, automatically detect the source of a condition for efficient surgery. | ✔ | ✔ | ✔ | |
| 13 | Virtual Medical Assistant (ChatGPT-enabled): Reduce clinician burnout with virtual scribes and virtual assistants that can automatically respond to patient inquiries. | ✔ | ✔ | ✔ | ✔ |
AI has the potential to optimize clinical practices
|
Use cases |
|
|---|---|
|
1. Clinical Decision Support |
Supervised machine learning can retrospectively review treatment plans of similar patients and predict and recommend the outcomes of various therapeutic treatments, such as chemotherapy for cancer patients. IBM Watson has demonstrated effectiveness in cancer treatment and diagnosis. Physician can develop personalized treatment plans for complex cases, where clinical guidelines may not present clear options to determine treatment plans with ideal outcomes. |
|
2. Imaging & Diagnostics |
AI can analyze and automate the radiology image classification process and provide preliminary predictions, reducing diagnostic errors and increasing the efficiency of radiologists to focus on more complex aspects of their work. |
|
3. Virtual Medical Assistant |
AI can record and transcribe physician-patient conversations, enhancing the physician-patient interaction and limiting administrative documentation. AI can mimic human language and use deep learning algorithms to develop clinical text responses to patient inquiries, addressing low-complexity questions and reducing the administrative burden and burnout clinicians are experiencing. |
|
4. Remote Patient Monitoring |
AI can enable real-time streaming, monitoring, and analysis of patient data through AI-enabled wearable medical technology. For example, with cloud-based AI cardiac care, healthcare providers can detect heart abnormalities from remote electrocardiogram (ECG) readings, reducing transfer delays and increasing hospital throughput. |
|
5. Surgical Procedure Assistance |
Interventional surgeons performing procedures in stroke care can use AI to analyze CT scans to automatically detect the source of a stroke. AI can also help plan and guide endovascular surgeries, increasing accuracy and efficiency. |
|
6. Preventive Healthcare |
AI can analyze large patient data from electronic medical records to identify high-risk patient populations, allowing healthcare organizations to implement systems-level interventions to address quality issues such as reduction of readmissions, fall risks, surgical site infections. This lowers healthcare costs in the long run. |
Preventive Healthcare Case Study
Developing an inpatient electronic medical record phenotype for hospital-acquired pressure injuries: Case study using natural language processing models
INDUSTRY: Healthcare
SOURCE: Alberta Health Services, Canada
Challenge
- Acquired pressure injuries (HAPI) affect 250,000 to 500,000 Canadians annually and are developed during inpatient stays. This condition can significantly extend a patient's hospital stay. However, HAPI is often difficult to detect when captured in free-text fields in the electronic medical record, and when relying on administrative data analysis of International classification of diseases (ICD) codes, there is a risk of under-coding and delayed analysis.
Solution
- In partnership with a local tertiary acute care hospital in Calgary, Alberta Health Services conducted a study to identify HAPI using natural language processing to review free-text clinical notes in electronic medical records. Machine learning AI was also implemented in the study to develop multiple models for analyzing 280 inpatient records for HAPI.
Results
- The study showed that the machine learning models developed using natural language processing had a high sensitivity in timely detection of HAPI patients compared to ICD-based data analysis. This has helped provincial and federal health agencies to monitor HAPI and conduct real-time surveillance to enhance quality patient care and lower the cost of adverse events.
Source: JMIR AI
AI can improve patient experience and healthcare operations
Use cases | |
|---|---|
7. Patient Compliance |
|
8. Revenue Cycle |
|
9. Health Education |
|
10. Equipment Monitoring |
|
11. Infection Control and Monitoring |
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12. Call Center Optimization |
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13. Hospital Decision Support |
|
AI and Call Center Optimization Case Study
How a US hospital implemented AI with existing resources to address some of their operational challenges.
INDUSTRY: Healthcare
SOURCE: Children's Hospital Colorado, USA
Challenge
- Children's Hospital Colorado is a four-hospital system with 618 licensed beds. Due to post-pandemic financial constraints, like many other healthcare organizations, Children's is currently focusing on strategies to maximize revenue and reduce costs.
- Workforce management is a challenge, with growing patient volumes and difficulties in finding people to hire. The goal is to lower the use of travel nurses and radiology technicians, which are driving up labor costs.
- These challenges were a catalyst to make a business case for AI implementation to automate repetitive tasks so that staff can focus on more complex work and be utilized in areas with staffing shortages.
Solution
- The hospital developed a digital health strategy which includes AI as a foundation. They successfully implemented an AI tool by Google into their telephone systems to efficiently triage calls typically handled by phone operators, replacing phone trees.
- They also implemented diagnostic AI machine learning tools to improve care and perform early detection of deterioration.
- Children's Hospital Colorado is working with companies to develop prescription digital therapeutics to treat suicidality across multiple populations, including teens and adolescents, as well as a mental health platform capable of remotely and objectively monitoring mental health, predicting mental health deterioration, and delivering tailored mental healthcare interventions.
Results
- Integrating AI as a part of the organization's digital health strategy is shifting the perception of AI as a business driver in partnership with IT as an enabler.
- The natural language processing AI tool has learned and predicted call patterns, efficiently handling 90 to 95% of all calls. The private branch exchange (PBX) phone operators have been able to expand their duties to aid in other areas of the organization that are short-staffed.
Hospital Decision Support Case Study
Predictive modeling of preventable heart failure 30-day readmission rates, using electronic medical record-wide machine learning
INDUSTRY: Healthcare
SOURCE: Mount Sinai Hospital, NY, USA
Challenge
- Mount Sinai Hospital in New York was experiencing high readmission rates, especially among their heart failure patients, despite providing high-quality healthcare service delivery.
Solution
- To address this issue, they implemented machine learning across their entire system-wide electronic medical records. They conducted a study of 1,068
de-identified inpatient records of heart failure patients using multiple machine learning predictive models to identify patients who were at high risk for readmission and the possible reasons for readmission. Multiple models were designed using naive Bayes algorithms.
Results
- Compared to their existing predictive models, which had an AUC (area under the curve) of 0.6 to 0.7, the new AI models showed an AUC of 0.78 with an accuracy rate of 83%.
- These results were promising, but due to data quality limitations, they needed to continue to train the models to adapt to clinical decision-making systems and further evaluate heart failure readmissions.
Source: Pacific Symposium on Biocomputing
A benefits realization model for AI use case analysis
This approach emphasizes the importance of identifying, quantifying, and developing a framework to prioritize potential use case initiatives across the business capability map. This is based on potential benefits and feasibility, in alignment with business goals to drive operational efficiencies and improve the quality of patient care.
Define your business goals and capabilities
- Define your business goals, supporting initiatives, and aligned business capabilities.
- Select AI use cases that can provide value for business capabilities.
Address these AI challenges
- Privacy and Bias
- AI Governance
- Budget Constraints
- Data Readiness
- Operational Capabilities
- Understanding of AI Role and Business Value
Business criteria to mitigate risk
- Business Alignment
- Responsible AI
- Project Complexity and Feasibility
KEY INSIGHTS
Demonstrating the business value, feasibility, and a responsible approach to AI is vital to effectively mitigate risk to your organization. Empower your team's decision-making process by leveraging Info-tech's approach to accelerate the process and further your organizational goals.
1. Review your business capability map for healthcare
Business capability map defined:
In business architecture, the primary view of an organization is known as a business capability map.
A business capability defines what a business does to enable value creation, rather than how. Business capabilities:
- Represent stable business functions.
- Are unique and independent of each other.
- Typically will have a defined business outcome.
A business capability map provides details that help the business architecture practitioner direct attention to a specific area of the business for further assessment.
Download Info-Tech's Industry Reference Architecture for Healthcare
2. Define your business goals, initiatives, and capabilities
The cascade below reveals that business goals with business initiatives and aligned business capabilities, will drive the effective selection of the appropriate AI use cases to improve and support your business operations.
- Start by developing or listing your organizations business goals.
- List initiatives to support those goals.
- Align those initiatives with the appropriate business capabilities.
- Align the selected business capabilities with the appropriate supporting AI use cases.
Illustrative example
2.1 Align your business goals with AI use cases
Illustrative example
3. Identify any potential AI challenges within your organization
| Category | Challenges | Strategies organizations have adopted to overcome these challenges |
|---|---|---|
| Privacy and Bias |
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| AI Governance |
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| Budget Constraints |
|
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| Data Readiness |
|
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| ML Operations Capabilities |
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| Understanding of AI Role and Its Business Value |
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Download Info-Tech's AI Trends 2023
3.1 Assess your identified use cases for any risks
Healthcare organizations are highly targeted and experience significant and costly data breaches. Therefore, it is important to assess all potential AI use cases against your business criteria to mitigate risk during the implementation process.
4. Develop your use case priority matrix
To effectively gain executive buy-in, it is important to strategically prioritize your organization's AI use cases based on their business value and implementation complexity. While your organization may have many initiatives that can be supported by AI use cases, a prioritization exercise can help decision-makers engage with their teams and business areas to ensure that no contextual information is missed that could impact the selection outcome.
- Common business value drivers
- Impact on the priority value drivers
- Value at stake
- Regulatory compliance
- Impact on critical processes
- Strategic technology foundation
- Financial benefits
- Common business value drivers
- Impact on the priority value drivers
- Value at stake
- Regulatory compliance
- Impact on critical processes
- Strategic technology foundation
- Financial benefits
- Determine a business value and project complexity score for your use cases (score them from 1 to 4).
- Plot initiatives into priority matrix.
- Prioritize high business value and low complexity use cases.
4.1 Prioritize your AI use cases
This prioritization exercise allows decision-makers to collaborate with their teams and business areas, ensuring that no context information is missed, which could potentially impact the selection outcome.
| Color coded No. | Business Capability | AI Use Cases | Business Value (Score 1-4) |
Complexity (Score 1-4) |
| 1 | Medical Imaging | Imaging & Diagnostics | 4 | 3 |
| 2 | Patient Triage |
Virtual Medical Assistant |
4 | 2 |
| 3 | Patient Monitoring | Patient Remote Monitoring | 2 | 3 |
| 4 | Treatment Plan Compliance | Patient Compliance | 2 | 2 |
| 5 | Communication Management | Call Center Optimization | 2 | 4 |
| 6 | Resource and Capacity Management | Hospital Decision Support | 4 | 2 |
Illustrative example
AI Readiness Assessment Framework
|
Assessment Step |
Definition |
Info-Tech Tools |
||
|
5 | Decision Making/ Leadership Capacity + Capability | An assessment of your organization's strategic planning and governance capacity and capability to address challenges and obstacles presented with the access, handling, analytics, and reporting of sensitive health information. | |
| 4 | Unified Analytics Platform Readiness | An assessment of your infrastructure's capability to support a modern architected, single-source platform solution for safe, secure, and governed data ingestion, unified data analytics, and manual (ad hoc), automated, and streaming data sharing and accessibility. |
Public Health Solution Architectures for Enterprise Architects › |
|
| 3 | Social Bias + Equity Assessment for Trustworthy AI | An assessment of your organization's enterprise analytics and reporting capabilities on modeling the relationship between upstream and downstream health outcomes. | ||
| 2 | Interoperability Ecosystem Assessment Data + Standards Readiness | An assessment of your organizations data analytics landscape, capability to provide an information infrastructure that uses technical standards, policies, and protocols to enable seamless and secure capture, discovery, exchange, and utilization of sensitive and aggregated health information. |
Interoperability Primer and Playbook for Public Health and Healthcare Organizations › |
|
| 1 | Capability Gap Assessment (Start here) |
An assessment of business capabilities across three tiers – defining, shared, and enabling – that aligns core functions, essential services, and strategy planning with health information management and health information technology capabilities and capacity. | ||
Insight Summary
|
Insight 1 |
AI is impacting and transforming healthcare |
|---|---|
|
Insight 2 |
AI has various risks and limitations |
|
Insight 3 |
Think big but start small |
|
Insight 4 |
Assess your AI readiness to determine the right path |
More Info-Tech resources to continue your AI journey
Healthcare Services Industry Business Reference Architecture
- Demonstrate the value of IT's role in supporting your hospital organization's capabilities, while highlighting the importance of proper alignment between organizational and IT strategies.
- Apply reference architecture techniques such as strategy maps, value streams, and capability maps to design usable and accurate blueprints of your hospital operations.
- When adopting AI, it is important to have a strong ethical and risk management framework surrounding its use.
- AI governance enables management, monitoring, and control of all AI activities within an organization.
- This blueprint will assist organizations with the assessment, planning, building, and rollout of their AI initiatives, so as not to embark on an AI project with an immature data management practice.
- The success rate of AI initiatives is tightly coupled with data management capabilities and a sound architecture.
Drive Business Value With Off-the-Shelf AI
- To guarantee success of the off-the-shelf AI implementation and deliver value, in addition to formulating a clear definition of the business case and understanding of data, organizations should also:
- Know what questions to ask vendors while evaluating AI-powered products.
- Measure the impact of the project on business and IT processes.
Contributors
Amy Feaster
Chief Digital Office & VP Information Technology
Children's Hospital Colorado
Bill Wong
Principal Research Director
Info-Tech Research Group
Neal Rosenblatt
Principal Research Director
Info-Tech Research Group
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