Future Health Index 2025

Table of Contents

Introduction

Healthcare systems are at a critical juncture, facing challenges such as staff shortages, rising costs, and system inefficiencies. The 2025 Future Health Index, a global healthcare survey now in its 10th year, highlights that in over half of the surveyed countries, patients face waiting times of approximately two months or more for specialist appointments. Without decisive action, a projected shortage of 10 million healthcare professionals by 2030 could leave millions unable to access timely care.

Artificial Intelligence (AI) is emerging as a powerful enabler and a promising solution to address the growing demand for healthcare, particularly with an aging population. Technology has advanced rapidly in the past five years and is expected to continue its trajectory. It is anticipated that by 2030, AI will automate many of the administrative tasks performed by healthcare professionals, significantly enhancing their clinical capabilities without extending their working hours.

Our research indicates that healthcare professionals recognize the potential of AI. They believe it can free up time spent on administrative tasks, improve diagnostic accuracy, reduce avoidable readmissions, and ultimately improve patient outcomes. Furthermore, studies suggest that wider adoption of current AI technologies could save the US healthcare system between $200 billion and $360 billion annually.

However, while AI is evolving rapidly, public trust has not kept pace. The 2025 Future Health Index reveals a significant gap: while most healthcare professionals are optimistic about AI's role in improving healthcare, many patients remain skeptical, especially when it concerns their health. Even among healthcare professionals, while optimistic, there are significant concerns about AI bias and reliability. Without trust, the full potential of AI in healthcare cannot be realized.

Building trust requires a responsible, human-centered approach that places "collaboration" at the heart of AI innovation. AI should deepen, not detract from, the trust between patients and healthcare professionals. It must deliver tangible benefits, be supported by robust safety measures, and operate within a clear and consistent regulatory framework. Only then can AI gain the trust necessary to drive meaningful transformation in healthcare.

This means accelerating AI innovation in the right direction, not slowing it down. We must work together across sectors, organizations, and borders to ensure that life-saving AI solutions reach more people, more quickly, while fostering trust. This report provides crucial insights to help drive that collaboration, urging healthcare leaders worldwide to act on these findings and build a future where technology and trust go hand in hand to deliver better care for all.

Chapter 1: Healthcare Transformation Powered by AI

Japan is experiencing the world's fastest aging population, with 28% of its population aged 65 and over. The nation's healthcare system is struggling to cope with increased demand, rising costs, and staff shortages. Our research focuses on how AI can help address these challenges by improving administrative efficiency, enhancing operational productivity, and ultimately improving patient outcomes.

Lost Time, Lost Care: The Burden on Healthcare Professionals

Staff shortages are a critical issue across Japan. In the 2024 Future Health Index, 80% of Japanese healthcare leaders reported an increase in burnout, stress, low morale, and poor work-life balance among staff. The government has implemented limits on doctors' working hours to improve work-life balance, but this has exacerbated staff shortages in rural areas and certain specialties like surgery.

Our survey reveals that healthcare professionals, despite their dissatisfaction, are losing valuable time to inefficient tasks, moving them further away from their original motivation: supporting patient recovery and providing care (cited as sources of job satisfaction by 59% and 56% of respondents, respectively). Data-driven insights are crucial for timely, high-quality care, reducing wait times, and addressing staff shortages. However, data inefficiencies are a significant stressor, consuming valuable time and energy that could be spent on patient care. Two-thirds of healthcare professionals report that incomplete or inaccessible patient data is impacting their consultation times, with 22% losing over 45 minutes per shift due to these inefficiencies. This lost time equates to over four weeks of working days per healthcare professional annually, highlighting the urgent need for simplified data management through AI and digital technologies.

Healthcare Professionals' Time is Consumed by Administrative Tasks

While healthcare professionals still struggle to access necessary data, the burden of administrative tasks is increasing. Our survey indicates that one in five healthcare professionals reports spending less time with patients and more time on paperwork compared to five years ago. Only 9% reported spending more time with patients, significantly lower than the global average of 20%. Addressing administrative inefficiencies is crucial for retaining staff and maintaining care quality, especially as healthcare staff shortages are projected to grow.

Time Constraints: Patients Experience Delays in Care

Patients also feel the impact of time constraints on their care. Last year's Future Health Index revealed that over three-quarters of healthcare leaders reported care delays due to staff shortages. This year's findings confirm that patient care delays remain a significant issue. While Japan has one of the shortest average wait times, nearly half of patients report waiting for specialist appointments.

AI for Better Care for More People

Japanese healthcare professionals recognize the significant potential of AI in their departments. They believe AI can improve access to clinical research and automate repetitive tasks. AI is also seen as beneficial for staffing, triage, and enhancing capabilities, leading to faster access to care for patients and reduced wait times and overtime.

Healthcare professionals are urging for the adoption of AI, warning of the consequences of delay. They fear missing opportunities to save time (47%), delaying early intervention (36%), and compromising patient care quality (35%). There is also concern about increased burnout among healthcare professionals due to non-clinical tasks (32%).

AI can also play a crucial role in improving responses to natural disasters. Japan has seen a 3.5-fold increase in natural disasters over the past 40 years. AI can facilitate rapid patient triage, optimize resource allocation (staff, equipment), and aid in planning complex disaster scenarios, ultimately saving more lives and improving national emergency response.

From Treatment to Prevention: AI's Potential to Transform Healthcare

While AI is already improving healthcare efficiency, its greatest impact may lie in preventing the need for hospitalization altogether. With the rise of chronic diseases and escalating healthcare costs, the Japanese government aims to shift from a treatment-focused model to a prevention-focused one.

Efforts are underway to expand the scope of patient care and detect health issues or worsening conditions early. The 2024 Future Health Index found that healthcare leaders are investing in remote monitoring solutions to support various clinical areas. Our findings indicate that healthcare professionals welcome this shift, believing that AI-powered predictive analytics and remote systems can reduce avoidable hospitalizations and save lives through early intervention. However, they also recognize that patients need to embrace AI and digital technologies for these new care models to succeed, which may present a challenge.

AI and Preventive Care to Ease the Burden on Japan's Healthcare System: Japan faces a growing elderly population and an increasing prevalence of chronic diseases, placing further strain on its healthcare system. AI can play a crucial role by predicting the progression of chronic diseases and optimizing patient care plans. Data from wearables, sensors, and medical devices can identify worsening health conditions, enabling early intervention and reducing hospitalizations. In care facilities and medical settings, AI can predict accidents and optimize staff schedules, further improving efficiency and patient outcomes.

Chapter 2: The Trust Gap in Healthcare AI

For AI to be widely adopted in healthcare, trust is essential. While many healthcare professionals are optimistic about AI, it will take time for patients to feel comfortable with it. Healthcare professionals also have concerns, and addressing these trust gaps is crucial to unlocking AI's full potential.

Patient Trust in Healthcare AI is Lagging

Despite AI's rapid advancements and potential, its adoption and effectiveness in healthcare depend not only on technological progress but also on building trust and acceptance among healthcare professionals and, most importantly, patients. Our survey shows that 60% of healthcare professionals are optimistic about AI's potential to improve patient outcomes. While this is a relatively high figure, it is lower than the global average of 79% and below the US figure of 63%, making it the lowest among the surveyed countries.

Patients, on the other hand, are more cautious about AI's potential in healthcare. Only one-third of patients are optimistic that AI will improve patient outcomes, significantly lower than the global average of 59%. The proportion of healthcare professionals optimistic about AI's potential is nearly double that of patients, indicating a significant challenge for Japanese healthcare leaders, policymakers, and industry stakeholders to maximize AI's benefits while increasing patient trust and acceptance.

Patient Comfort with AI in Healthcare is Low

Healthcare professionals are confident in AI's ability to support various aspects of healthcare, from administrative tasks to diagnostic decision-making and treatment planning. However, patients express lower comfort levels across almost all applications of AI in healthcare, falling below global averages in most areas. While comfort is higher for administrative tasks like appointment scheduling, it decreases as AI applications move into clinical areas and involve higher health risks, widening the gap between patients and healthcare professionals.

Japanese Patients are Less Receptive to Technology in Their Care

Patient concerns about technology are not limited to AI; many patients also worry about the broader impact of digital technologies, fearing that healthcare may become less human. While 73% of patients globally welcome technological advancements, only 56% in Japan do. This suggests that Japan is one of the more conservative countries among those surveyed regarding the adoption of technology in healthcare.

When AI Makes a Mistake, Who is Responsible?

While generally positive about AI's role in improving care, Japanese healthcare professionals also have concerns. They worry about who would be responsible if AI systems make errors in diagnosis or treatment. With issues like hallucinations in generative AI systems impacting accuracy and reliability, the survey results indicate that uncertainty about legal liability remains a barrier to adoption. Indeed, nearly nine out of ten healthcare professionals in Japan are concerned or unsure about whether AI developers, healthcare institutions, or individual practitioners would be held accountable for AI errors.

Chapter 3: Bridging the Trust Gap

What is needed to strengthen patient and healthcare professional trust in AI? Our survey findings provide clear guidance on integrating AI more effectively and reliably into healthcare, ultimately improving patient outcomes and the overall care experience.

Patients Seek Greater Benefits from AI

To understand what is needed for patients to feel more positive about AI in healthcare, we asked them directly. Their responses were clear: patients want AI to function safely and effectively, reduce costs, improve their health, and minimize errors. Furthermore, amidst concerns that technology may lead to less human interaction, patients are more receptive if AI can free up doctors' time, allowing for more face-to-face interactions. If used correctly, AI has the potential to make healthcare more human, not less. This is precisely what patients are looking for.

The Paradox of Knowledge: Patients with More AI Knowledge Seek Greater Assurance

As expected, our survey found that patients who perceive themselves as more knowledgeable about AI tend to feel more comfortable with it. However, these patients also seek greater assurance, such as confirmation that AI technologies have been tested for safety and effectiveness, and that their data is securely stored. This suggests that greater AI knowledge does not necessarily reduce anxiety but may, in fact, increase it.

Knowledgeable patients, while understanding AI's potential benefits, are also more aware of its risks and limitations, leading to a stronger demand for transparency and control.

Healthcare Professionals' Support is Key to Patient Trust

Who do patients trust regarding healthcare AI? Our survey results indicate that regardless of their AI knowledge, patients want information and reassurance from doctors, nurses, and the healthcare system. This trend clearly shows that trusted healthcare professionals play a vital role in building patient trust in AI. By leveraging established relationships and credibility, healthcare professionals can guide the integration of AI into patient care, alleviate concerns, and foster comfort with this technology.

Recommendations

These measures should contribute to building trust in digital health technologies and AI:

1. Human-Centered AI Design

   AI should be designed to meet the needs of both patients and healthcare professionals. Engaging the right stakeholders from the outset and involving them throughout the process is crucial for building trust and promoting acceptance. AI solutions should seamlessly support patients' daily health management and integrate smoothly into the healthcare workflow and IT infrastructure. This will lead to a less stressful experience for healthcare professionals and improved patient outcomes.

2. Strengthening Collaboration Between Humans and AI

   The true potential of AI lies in augmenting the capabilities of healthcare professionals and empowering patients and caregivers to manage their own health and well-being. While AI agents may perform certain tasks autonomously, human oversight is essential when health is involved. Healthcare professionals play a critical role in building patient trust by clearly and frankly explaining AI's role. This requires comprehensive training starting from the early stages of education.

3. Demonstrating Efficacy and Fairness

   Both healthcare professionals and patients want assurance that AI functions as intended, and regulatory bodies require evidence that AI meets safety and performance standards. Consistent performance across target patient populations and clinical scenarios is essential, as are measures to mitigate bias and ensure fair outcomes. Using representative and high-quality datasets during development and validation can help reduce bias and ensure fair results for all patients.

4. Enabling Innovation Through Clear Guidance

   To bring life-saving AI solutions to patients more quickly, regulations need to evolve to balance the speed of innovation with safety measures that protect patients and build trust. Harmonizing regulatory frameworks internationally can reduce complexity and accelerate access to innovation without compromising patient safety. Approaches like regulatory sandboxes can enable the responsible development and monitoring of AI while maintaining consistency in medical device regulations.

5. Building Strong, Cross-Sector Partnerships

   Transformation in healthcare cannot be achieved in isolation. Close collaboration among all stakeholders—healthcare providers, professionals, patients, insurers, policymakers, regulators, researchers, and the medtech industry—is essential to drive innovation, meet stakeholder needs, and build trust. Aligning goals and incentives, including payment models, is also important. This will ensure a focus on what matters most: improving the health and well-being of patients and healthcare professionals.

Appendix

Methodology

Two quantitative surveys were conducted by Accenture Song, a global creative group, using online survey methods (CAWI). The surveys were administered in 16 countries from December 2024 to March 2025.

Survey 1: Healthcare Professionals

• Participants: 1,926 healthcare professionals
• Details: Included doctors (including surgeons), nurses, and other related healthcare professionals from various departments in both public and private healthcare institutions.

Survey 2: Patients

• Participants: 16,144 patients aged 18 and over
• Details: Participants' age and gender were representative of the population in each country. 99% of respondents had seen a doctor within the past two years.

The surveys were translated into local languages and conducted with minor adjustments to questions where necessary to reflect country-specific contexts, while maintaining the original meaning of the English text.

To ensure global representativeness, sample sizes for healthcare professionals and patients were weighted.

Weighting: A statistical method used to adjust sample data to accurately reflect the population. This is particularly important when certain groups are over- or under-represented compared to the population.

• Improved Accuracy: Weighting corrects for potential biases arising from differing sample sizes across countries.
• Ensuring Representativeness: Findings are more accurately reflective of the overall population's demographics and characteristics.
• Enabling Comparisons: Weighted data allows for fair comparisons between countries, leading to more reliable conclusions.

The table below shows the sample sizes before and after weighting, along with the margin of error (***) at a 95% confidence level.

***The margin of error is derived from the sample size for each country.

Glossary

Artificial Intelligence (AI)

An AI system is a machine-based system that can, for a given set of human-defined objectives, make predictions, explanations, recommendations, or decisions with respect to the information and data that it inputs. AI systems vary in their level of autonomy and adaptability after implementation.

AI Algorithm

An AI algorithm is a set of rules or instructions that tells a computer how to make decisions or perform functions.

AI Hallucination

A response generated by an AI system that is presented as fact but is misleading, inaccurate, or nonsensical.

Automation

The use of technology and software solutions to perform tasks and processes with limited human intervention. This includes digital tools and systems that streamline and optimize various aspects of healthcare delivery, operations, and management.

Data

In this context, refers to the various types of clinical and/or operational information collected from numerous sources, such as electronic health records (EMRs), medical devices, and workflow management tools.

Data Bias

A flaw that occurs when specific elements of a dataset are missing, underrepresented, or overrepresented.

Digital Health Technology

Various technologies used to transmit, share, and/or analyze health data. This can take many forms, including home health monitors, digital health records, medical equipment in hospitals/clinics, and health/fitness trackers.

Generative AI

A type of AI system that can create original content based on user prompts or requests.

Healthcare Leader

A senior executive or manager working in a hospital, clinic, imaging center, emergency department, or other healthcare facility who makes or influences final decisions.

Healthcare Provider

An individual directly involved in providing healthcare services to patients, such as doctors, nurses, surgeons, specialists, and technicians.

Hospitality

The efficiency of the patient's journey from arrival at a healthcare facility to discharge.

In-Person Care

Care provided outside of a traditional hospital setting, such as in a home, clinic, outpatient facility, or other community-based setting, either directly or virtually.

Medical Institution

A hospital or healthcare facility where healthcare professionals work.

Outpatient Care

Care provided outside of a traditional hospital setting, such as in a home, clinic, outpatient facility, or other community-based setting, either directly or virtually.

Patient Throughput

The efficiency of a series of patient care processes from the time a patient arrives at a healthcare facility until discharge.

Predictive Analytics

A branch of advanced analytics that involves forecasting future events, behaviors, or outcomes.

Remote Physiological Monitoring

Technology used to track and diagnose a patient's health status remotely.

Specialist

A physician or other healthcare professional trained and licensed in a specific area of medicine. Examples include oncologists (cancer specialists) and cardiologists (heart specialists).

Staff

Refers to all employees within a healthcare institution, including healthcare professionals, IT staff, finance personnel, administrative support, and facility workers.

Workflow

A process that includes a series of tasks performed by individuals and teams within a work environment to achieve specific goals. Each task may require individual action, interpersonal interaction, or organizational coordination.