AI Insights

The GLP-1 revolution has outpaced the behavioural science

Thu, 12 Mar 2026 07:57:27 GMT

Why digital health platforms may now be running the largest behavioural experiment in obesity care

GLP-1 medications have transformed the treatment landscape for obesity.

In a remarkably short period of time, pharmacological therapies such as semaglutide and tirzepatide have demonstrated levels of weight loss that were previously difficult to achieve outside of bariatric surgery. Demand has surged globally as both clinicians and patients recognise the potential of these treatments.

But an important detail is often overlooked in the public conversation around these medications.

In the clinical trials that led to their approval, GLP-1 therapies were almost never studied in isolation. They were typically delivered alongside structured lifestyle and behavioural interventions.

That was the treatment model.

Medication plus behavioural support.

Yet despite the rapid growth of pharmacological treatments for obesity, there is a surprising gap in the evidence base. We still have relatively limited high quality evidence about the most effective behavioural interventions to combine with these medications.

In other words, drug innovation has moved faster than the behavioural science.

A new real world laboratory

This creates a fascinating and important situation.

Digital health platforms delivering GLP-1 treatments are now operating at a scale that was rarely possible in traditional clinical research.

Hundreds of thousands of patients are interacting with these services.

Millions of digital touchpoints are being generated across patient journeys.

Within those interactions sit a wide range of behavioural signals:

• how patients respond to early side effects

• what happens during plateau periods

• when motivation drops

• what helps patients stay engaged long term

• how patients transition into long term maintenance

These are not purely clinical questions.

They are behavioural questions.

And the answers are embedded within the real world data generated by digital care platforms.

Obesity is a chronic disease

Another key point emphasised in the latest guidance is that obesity should be understood as a chronic disease requiring sustained management rather than a short term intervention.

This has important implications for how treatment systems are designed.

Pharmacotherapy may initiate weight loss, but long term outcomes depend heavily on behavioural factors such as adherence, nutrition, physical activity, and psychological support.

Digital health platforms therefore face a challenge that is partly clinical but deeply behavioural.

How do you design systems that support patients not just for weeks or months, but potentially for years?

Increasingly, the answer involves combining clinical care with behavioural science and AI enabled support systems that can respond to patients at scale.

From product data to behavioural insight

At the same time, many organisations now hold large volumes of patient interaction data but have limited ways of translating that information into meaningful behavioural insight.

The opportunity is significant.

If digital health platforms are effectively running one of the largest real world behavioural experiments in obesity care, the next question becomes whether we are learning from it.

At Sacher AI, much of our work sits at the intersection of industry and research.

Alongside building and evaluating AI systems for healthcare, our team conducts applied behavioural and clinical research designed to turn real world platform data into actionable insight. This includes identifying behavioural patterns in patient journeys, testing intervention strategies, and generating credible evidence that can inform both product development and clinical practice.

Because the next phase of innovation in obesity care may not come solely from new medications.

It may come from understanding behaviour better.

Why GLP-1 weight loss platforms struggle to scale without behavioural support

Tue, 10 Mar 2026 16:41:39 GMT

Lessons from real world digital health platforms on why behaviour change support becomes critical as GLP-1 services scale.

Most people building GLP-1 services pour their energy into the clinical and operational infrastructure. The prescribing, the compliance, the customer support, getting the medical side to scale. That is genuinely hard work, and it is where most of the pressure concentrates.

Which means the lifestyle and behaviour change layer often gets less focus than it deserves. Not because people think it does not matter, but because there is always something more urgent competing for attention.

Last year my team and I were working with the senior leadership of a very large weight management company. The business was growing fast, with demand accelerating month after month. The operational load had spread across the whole system at once: prescribers reviewing eligibility, patients asked for additional medical information, questions about side effects, dose increases, delivery issues, lifestyle support, and people wanting reassurance that what they were experiencing was normal.

In some of the weight management providers we have worked with across the UK and USA, inbound support volumes have run into the hundreds of thousands of contacts per month. At that scale, pressure stops being an inconvenience and starts becoming a clinical risk.

A question came up in those leadership discussions that I have now heard in several companies.

How much does the surrounding support actually matter?

The medication clearly works. That is why demand exists. But everything around it adds complexity, more teams, more systems, more cost. The argument goes: patients are losing weight, so why invest heavily in lifestyle support now? Focus on medication adherence and side effect management first. Deal with the lifestyle piece later, perhaps when patients start tapering or coming off the drug.

It is a reasonable question, and in the short term the numbers can seem to support it.

The evidence tells a different story. Lifestyle support is not just about behaviour change. It is about preventing muscle and bone loss, avoiding nutritional deficiencies, supporting mental health, and building the habits that sustain weight loss when patients eventually reduce or stop the medication. Without it, much of what the drug achieves can unwind quickly.

The question is not whether to offer lifestyle support. It is when and how to deliver it without overwhelming patients or services.

The real issue is rarely information. It is uncertainty.

People disengage from treatment for many reasons: cost, life circumstances, plateaus, or simply reaching their goal weight. Some take temporary breaks from treatment entirely, sometimes called drug holidays, a period where patients step back from medication, often losing contact with the service at exactly the moment they most need support.

But there is a particular pattern that comes up again and again. Uncertainty builds, patients feel unsupported, and there is no way to resolve that quickly at scale.

In GLP-1 services, that matters more than most platforms realise. Leaders we have worked with have told us that even a one or two percentage point improvement in retention can be worth more than almost any other intervention. The economics compound quickly. When uncertainty goes unaddressed, patient engagement drops and operational pressure rises together.

What we found, working across several of these services, is that many of those moments are predictable. They sit at specific points in the treatment journey: anxiety around the first medication dose, plateau periods, side effect spikes. They are not random. Across the services we have analysed, a significant proportion of inbound support contacts, in some cases over a third, relate to situations that proactive, well timed communication could have addressed before the patient ever needed to reach out.

When the system responds at those moments, sometimes with human support, sometimes automated, patients stay engaged longer and the support burden falls.

The companies getting this right are asking different questions. Not just what support to offer, but when it needs to reach patients, and what it needs to do at that specific point in the treatment journey.

This is the work we do at Sacher AI. We sit at the intersection of behavioural science, applied research, and clinical AI safety, helping health and weight management platforms build AI that is not just technically sound, but behaviourally safe and clinically trustworthy. Designing systems that adapt and respond in the right way at the right moment, from starting treatment through to long term maintenance, is where most of the real work sits.

If any of this reflects what your team is working through, I am happy to have a conversation.

When does wellness AI become a medical device?

Mon, 09 Mar 2026 18:17:12 GMT

Reflections from the MHRA AI Airlock simulation workshop

Last week, representatives from across the healthcare and AI ecosystem gathered for the first of three Phase 2 Simulation Workshops as part of the UK Medicines and Healthcare products Regulatory Agency (MHRA) AI Airlock programme.

The session brought together regulators, developers, clinicians, academics, and public and patient representatives to explore one of the most complex issues emerging in digital health: where the boundary sits between wellness AI and AI that becomes a regulated medical device.

Dr Paul Sacher, Founder of Sacher AI, was invited to participate in the workshop as an expert contributor based on his experience developing and evaluating patient facing conversational AI systems. His work spans both academic research and real world deployment of AI in healthcare settings, with a particular focus on clinical and behavioural safety in human AI interactions.

The AI Airlock is the MHRA’s first regulatory sandbox. It is designed to work alongside developers to better understand emerging AI as a Medical Device (AIaMD) technologies and help address regulatory challenges in a responsible, patient centred way.

The programme is currently in its second phase, working with seven AIaMD developers.

Two companies from the cohort took part in the workshop. TORTUS demonstrated its clinician facing AI system designed to support clinical documentation, while Numan presented its digital health platform delivering remote patient care. These demonstrations helped ground the discussion in real product scenarios and practical regulatory questions.

A central theme of the discussion was intended use . In medical device regulation, intended use determines whether a system qualifies as a medical device, how it is classified, what evidence is required, and how it must be monitored once deployed.

For AI systems, particularly large language model based conversational systems, intended use is rarely static.

Products evolve. Features expand. Systems become integrated into clinical workflows. Outputs begin to influence users, clinicians, or treatment decisions in new ways.

A system that initially sits within the wellness category can gradually cross into medical device territory, sometimes without a single clear moment where that shift occurs.

Another major focus of the workshop was post market surveillance .

Regulators increasingly expect companies to demonstrate how they monitor their AI systems once deployed and how they identify, track, and respond to risks over time. This responsibility extends across the full product lifecycle.

For teams deploying conversational AI at scale, this creates practical challenges.

Risks in large language model systems often emerge gradually across many interactions rather than appearing as a single obvious failure. In many cases, relying solely on manual review of conversations or retrospective audits is unlikely to be sustainable.

Monitoring systems therefore need to detect patterns, trends, and behavioural signals across thousands or millions of interactions.

This challenge is becoming increasingly visible across healthcare AI more broadly.

As AI systems become embedded into care pathways, even subtle product changes can shift the regulatory position of a system. Expanding features, integrating with clinical services, or increasing the influence of system outputs can all move a product closer to the regulatory definition of a medical device.

One of the most valuable aspects of the session was the level of practical nuance in the conversation.

Participants discussed how seemingly small design choices or product updates can affect regulatory classification, and how difficult it can be to detect early signals that a system may be drifting across regulatory boundaries.

The workshop also highlighted the growing diversity of AI technologies entering healthcare, including ambient voice technologies and conversational AI systems.

Overall, the workshop offered a thoughtful and grounded look at the real challenges facing teams building AI systems for healthcare today.

The boundary between wellness products and regulated medical devices is becoming increasingly complex. Understanding where that line sits, and how systems may gradually move across it, will be critical for companies developing AI in health and clinical contexts.

At Sacher AI, we work with organisations developing AI systems that interact directly with patients, clinicians, and health consumers. Our focus is on helping teams design, evaluate, and monitor these systems so that safety, behavioural impact, and regulatory expectations are addressed from the earliest stages of development.

As regulatory expectations evolve, building systems that are not only innovative but also demonstrably safe, transparent, and monitorable will become essential.

If your organisation is developing AI systems that interact directly with people in healthcare settings, you can learn more about Sacher AI’s work supporting safe and responsible AI deployment here

The missing discipline in AI: why behavioural science must shape responsible AI

Mon, 09 Mar 2026 14:30:14 GMT

Artificial intelligence systems are increasingly interacting directly with people.

They guide health decisions. They answer personal questions. They offer advice, reassurance, and encouragement. In many cases they are now embedded in products people use repeatedly over months or even years.

Yet most conversations about AI safety still focus almost entirely on technical performance. Accuracy, bias, privacy, and security dominate the discussion.

These are essential considerations. But they are not the whole story.

What is still missing in many AI systems is systematic evaluation of behavioural impact.

A global call from behavioural scientists

Recently, Dr Paul Sacher, Founder of Sacher AI and Research Director at the Behavioral AI Institute, led an international group of behavioural scientists to address this issue.

Their open letter, now published in Wellcome Open Research, argues that artificial intelligence systems inevitably influence how people think, feel, decide, and act.

Despite this, behavioural effects are rarely treated as a core requirement in AI development, evaluation, or governance.

The paper brings together researchers from institutions including Imperial College London, Harvard University, Duke University, the University of Exeter, and the Alan Turing Institute. It outlines where behavioural risks arise in real world AI systems and why these risks deserve far greater attention.

Behavioural risks are not edge cases

When organisations think about AI risk, they often focus on incorrect outputs or technical failures.

However, behavioural risks can emerge even when systems are technically accurate.

AI systems influence behaviour through repeated interaction. They shape decision making, motivation, confidence, and emotional responses over time.

For example, conversational systems can unintentionally:

· encourage over reliance on automated advice

· reinforce existing beliefs through personalisation

· influence emotional regulation through tone and framing

· alter motivation and goal setting behaviour

· reduce appropriate help seeking in certain contexts

These effects arise through well documented behavioural mechanisms such as automation bias, trust calibration, anthropomorphism, and reinforcement learning from user feedback.

Yet most AI evaluation frameworks still prioritise task success and user satisfaction rather than behavioural outcomes.

The growing gap between technical success and real world impact

This creates a gap between technical performance and real world impact.

A system can perform well on benchmarks and still shape behaviour in ways that undermine user wellbeing, decision quality, or long term outcomes.

The risk becomes particularly important in domains where AI systems interact with people repeatedly and at scale. Healthcare, education, financial guidance, and emotional support tools are clear examples.

In these environments, small behavioural effects can accumulate over time and remain invisible to standard performance metrics.

Behavioural science is often missing from the AI lifecycle

Behavioural science offers decades of research into how people think, feel, and act. It provides practical tools for understanding trust, influence, decision making, and motivation.

However, behavioural expertise is still rarely embedded systematically across the AI lifecycle.

In many projects behavioural scientists are not involved in system design, consulted only during ethics review, or brought in late in development when major design choices are already fixed.

This often means behavioural risks are identified too late or not assessed at all.

What responsible AI should look like

The open letter argues that responsible AI must go beyond technical safeguards.

Systems that interact directly with people must demonstrate what the authors describe as psychological competence. In practice, this means responding in ways that are emotionally appropriate, behaviourally responsible, and aligned with the user’s needs and context.

Achieving this requires several shifts in how AI systems are designed and evaluated.

Behavioural assumptions should be made explicit during design.

Behavioural expertise should be embedded early in development.

Evaluation should assess behavioural outcomes alongside technical performance.

Monitoring should continue after deployment as systems evolve and users adapt.

These are not theoretical concerns. They are practical requirements for building AI systems that work safely in real world environments.

Why this matters for digital health and AI companies

At Sacher AI we see this challenge frequently when working with digital health companies building patient facing AI systems.

Teams often focus heavily on model performance, prompt design, or system architecture. These are important elements. But once systems begin interacting with people, behavioural dynamics quickly become central to product safety and effectiveness.

Tone influences trust. Feedback influences motivation. Personalisation influences decision making.

Without deliberate behavioural evaluation, systems can unintentionally nudge users in directions that product teams never intended.

For companies operating in regulated environments such as healthcare, this also has implications for governance, compliance, and long term product risk.

Bringing behavioural safety into AI development

Addressing behavioural risk does not require reinventing AI governance. It requires integrating behavioural science into existing development processes.

In practice this can include structured behavioural evaluation during development, adversarial testing of conversational agents, governance frameworks for human facing AI, and ongoing monitoring of behavioural outcomes once systems are deployed.

Many organisations are beginning to recognise this gap and are looking for ways to assess behavioural safety earlier in their development pipeline.

A broader shift in how we think about AI safety

The central message of the open letter is simple.

If AI systems influence human behaviour, behavioural science must be treated as foundational infrastructure for responsible AI.

Technical safety alone is not enough. Understanding how people interpret, trust, and respond to AI systems is essential for building technology that works safely and effectively in the real world.

Read the open letter

The full paper is available in Wellcome Open Research .

About Sacher AI

Sacher AI works with digital health and AI companies to design, test, and deploy human facing AI systems safely and effectively.

Our work combines behavioural science, AI engineering, and real world healthcare experience to help organisations build AI systems that are not only technically strong but also clinically and behaviourally safe.

If your organisation is developing AI systems that interact directly with patients or users, we are always happy to start a conversation.

More information can be found at https://sacher.ai

Sacher PM, Michie S, Hauser OP et al. The missing discipline in AI: a call for behavioural science . Wellcome Open Res 2026, 11:152 (https://doi.org/10.12688/wellcomeopenres.25922.1)

The real challenge with AI in healthcare is not intelligence. It is safety

Fri, 06 Mar 2026 13:29:20 GMT

Over the last two years, generative AI has rapidly entered healthcare.

Startups are building AI health coaches. Pharmaceutical companies are experimenting with patient support agents. Digital health platforms are deploying conversational AI to guide patients through treatment journeys.

The opportunity is clear. AI can provide personalised support, expand access to care, and improve patient engagement.

But there is a fundamental challenge that is still poorly understood.

Most AI systems were not designed to interact safely with humans.

And that gap becomes obvious the moment AI starts speaking directly to patients.

Human facing AI creates a new class of risk

When large language models are used internally, mistakes can often be caught before they reach users.

When AI interacts directly with patients, the situation changes.

An AI system that speaks to patients can provide incorrect health information, reinforce harmful behaviours, misunderstand emotional context, respond inappropriately to distress, or fail to escalate serious clinical signals.

These are not rare edge cases. They are predictable failure modes of generative AI.

The issue is not that the models are unintelligent. The issue is that they are probabilistic systems generating language without true understanding. That makes them powerful, but also unpredictable.

For organisations deploying conversational AI in healthcare, safety therefore becomes a design challenge.

Why traditional AI evaluation is not enough

Most AI systems today are evaluated using benchmarks that measure reasoning ability, coding performance, knowledge retrieval, or accuracy on test datasets.

These metrics are useful. But they say very little about how an AI system behaves in a real conversation with a human.

When AI interacts with patients, the most important questions are different.

Does the AI respond appropriately to vulnerable users?
Does it avoid giving unsafe health advice?
Does it communicate in a supportive and non judgemental tone?
Does it recognise when it should escalate to a clinician?
Does it stay aligned with clinical guidance?

These are behavioural and clinical safety questions. Standard AI benchmarks do not answer them.

Designing safe conversational AI

At Sacher AI, our work focuses on a simple principle. If AI is going to interact with humans, it needs to be designed with human behaviour in mind.

That means combining expertise from several fields including behavioural science, clinical safety, conversational design, and AI engineering.

Together these disciplines help teams understand how users interpret what an AI says, how behaviour can be influenced by language, and when systems must escalate to human support.

This perspective is often missing in early AI development. Many teams only start thinking about safety after an AI system is already live. By then, fixing the underlying issues becomes much harder.

Stress testing AI systems before they reach patients

One of the most effective ways to improve safety is to stress test AI systems before deployment.

This means simulating large numbers of conversations that reflect the kinds of interactions real users might have.

For example a patient feeling anxious about medication, a parent asking for advice about a child’s weight, a user frustrated by slow progress, or someone describing symptoms that may require clinical attention.

By exposing AI systems to diverse scenarios, teams can identify unsafe behaviours early and improve the system before it reaches real users.

At Sacher AI, this approach led us to build PromptSafe, a platform designed to evaluate conversational AI used in health and behavioural contexts.

PromptSafe enables teams to simulate interactions with synthetic patient personas, define behavioural and clinical safety metrics, test AI systems across thousands of conversations, and track safety improvements over time.

Monitoring AI once it is deployed

Testing before launch is important, but it is not enough.

AI systems continue to evolve once they are live. Models are updated, prompts change, and user behaviour shifts.

This creates a second challenge which is monitoring safety during real world use.

To address this, we are developing OVRSI, a system designed to monitor AI interactions and detect potential safety signals in real time.

This includes identifying things like unsafe advice, emotional distress signals, guideline deviations, or escalation scenarios.

When risks are detected, organisations can intervene quickly and improve the system.

The future of AI in healthcare will depend on trust

AI has the potential to transform healthcare by providing scalable and personalised support.

But this future depends on trust.

Patients, clinicians, and regulators will increasingly expect AI systems to demonstrate that they behave safely and responsibly.

Organisations building human facing AI must therefore design for safety from the start. This means stress testing systems, embedding behavioural and clinical expertise into development teams, and monitoring how AI behaves in the real world.

The companies that succeed will not just build powerful AI. They will build safe AI that people can trust.

Book a discovery call

If you are building patient facing conversational AI and want to ensure it is safe, reliable, and ready for real world deployment, we can help.

At Sacher AI we work with digital health companies and AI teams to design and evaluate safe human facing AI systems.

If you would like to explore how we can support your team, book a discovery call.

Transforming Behavioural Science with Generative AI: Beyond Content Creation

Fri, 13 Dec 2024 11:34:48 GMT

In the rapidly evolving landscape of artificial intelligence, generative AI has become synonymous with creating images, text and video. However, its potential extends far beyond content generation—it's revolutionising the field of behavioural science, opening up unprecedented opportunities for understanding and influencing human behaviour.

The Convergence of Gen AI and Behavioural Science

By combining the creative capabilities of generative AI with deep behavioural insights, we're pioneering innovative approaches to human behaviour analysis and intervention. This fusion is enabling more sophisticated, personalised and effective solutions than ever before.

Innovative Applications in Practice

Advanced Chatbot Testing and Monitoring

Our approach to chatbot development incorporates extensive automated testing using synthetic personas. These carefully crafted personas mirror the complex characteristics of real users, including:

Cognitive and behavioural patterns
Psychosocial factors
Health conditions
Demographic variables

Human reviewers evaluate these interactions, ensuring thorough validation of language models before deployment. Additionally, we've implemented a continuous quality and safety monitoring system that provides real-time oversight of production chatbots, maintaining high standards of accuracy and safety.

AI-Powered Behavioural Interventions

We're leveraging generative AI to create interactive agents that deliver sophisticated behavioural support. These systems provide:

Human-like, empathetic interactions supporting mental health
Personalised micro-interventions based on cognitive behavioural phenotypes
Integration with wearable data for real-time behavioural insights
Tailored psychoeducation and behavioural change techniques

Quality Assurance and Transparency

Our commitment to responsible AI development is reflected in our approach to explainability and quality control. We've developed:

A custom generative AI quality framework
Human-readable outputs for thorough review
Continuous feedback loops for prompt optimisation
Enhanced RAG systems and fine-tuning processes

B eyond Automation: A Human-Centred Approach

Generative AI isn't merely an automation tool—it's catalysing a transformation in behavioural science. By integrating behavioural insights into AI systems, we're creating solutions that are:

More personalised to individual needs
Better at building trust with users
More effective at delivering equitable outcomes

Continuous Innovation at Sacher AI

Our success is driven by our interdisciplinary team of experts, including:

AI product owners
Machine learning researchers and engineers
Data scientists
Behavioural scientists

Together, we're constantly refining and improving our systems through rigorous testing and monitoring.

Looking Forward

The potential of generative AI to transform behavioural science is immense. If you're interested in exploring how our custom AI-powered behaviour change tools can benefit your patients, customers, employees or users, we invite you to connect with us.

Our solutions can help you deliver more effective, personalised support whilst maintaining the highest standards of quality and ethical practice. Let's explore how we can work together to create positive behavioural change at scale.

The Transformative Power of AI and Behavioural Science: Meeting Industry Needs with Precision and Privacy

Fri, 04 Oct 2024 13:55:44 GMT

In today’s fast-evolving landscape, blending AI with behavioural science is transforming industries—from healthcare to finance and legal—by creating solutions that not only advance organisational goals but also resonate deeply with individual users. At Sacher AI, our approach combines cutting-edge AI with insights from behavioural science, leading to tailored, effective, and responsible solutions.

Personalising Patient Journeys in Healthcare

In healthcare, personalised experiences improve patient engagement and outcomes. By using data-driven AI to understand which combinations of support yield the best outcomes for various patient profiles, we can personalise every aspect of the patient journey. This doesn’t just improve health outcomes; it transforms the care experience. At the same time, rigorous compliance with data privacy regulations ensures that personal health information remains protected, building trust and ensuring safety across AI-enabled health solutions.

Driving Financial Planning with Future-Focused Tools

For the financial sector, our approach includes interactive AI-driven tools that help customers envision their financial futures, encouraging positive behaviours around saving and planning. By employing behavioural insights, these solutions foster habits aligned with users’ financial goals, making saving feel both actionable and relevant. Privacy and regulatory compliance remain top priorities here, too, with our solutions designed to meet FCA and data protection standards while guiding users through personalised experiences.

Supporting Technology Adoption in Legal

Legal settings benefit greatly from AI-enhanced employee onboarding solutions that identify and address barriers to technology adoption. By using behavioural insights to create diagnostic tools and recommendations, we help new employees adapt more seamlessly. Our approach aligns with industry-specific privacy requirements, ensuring that data collection remains both secure and compliant with regulatory standards, supporting a frictionless transition for legal professionals.

Commitment to Privacy and Compliance

Across all sectors, our dedication to data privacy and regulatory compliance underpins everything we do. As AI grows, so does the responsibility to protect and respect personal data. We rigorously ensure that all solutions align with the highest standards of data security and ethical practice.

A Future Focused on People

AI’s potential is limitless, yet its true power is unlocked only when combined with a nuanced understanding of human behaviour. At Sacher AI, our commitment to behavioural science and user-centric design drives every solution, creating responsible, effective applications of AI that deliver real-world impact across industries.

Behavioural Science and AI: The Emerging Frontier of Digital Health

Sun, 01 Sep 2024 12:33:16 GMT

Behavioural science and AI are increasingly converging, driving significant advancements in digital health. Here’s why this combination represents the next frontier

1. AI-Driven Personalisation

AI’s ability to analyse vast amounts of behavioural data enables the creation of highly personalised user experiences. This personalisation is not just about preferences but also about predicting and influencing future behaviours.

2. Scalable Behavioural Interventions

Traditionally, behavioural interventions required significant human resources, making them difficult to scale. AI-driven tools like chatbots and digital coaches can deliver these interventions at scale, maintaining effectiveness while reaching broader audiences.

3. Enhanced User Engagement

AI can optimise user engagement by adapting interactions based on real-time data. Techniques like Just in Time Adaptive Interventions (JITAI), sentiment analysis, and culturally appropriate communication can be effective in maintaining user engagement and encouraging sustained behaviour change.

4. Improving Health Outcomes

In digital health, AI-powered systems are being used to influence behaviours that lead to better health and wellness. These systems can offer real-time feedback and psychological support, which are critical for behaviour change.

5. Augmenting Human Coaching with AI

AI has the potential to significantly enhance human coaching by providing on demand support around the clock, making coaching more accessible and affordable. By augmenting traditional coaching methods, AI can help deliver high quality coaching to a broader audience, ensuring that personalised guidance and support are available to many more people.

Call to Action

Explore how AI can transform your approach to digital health. By using AI to automate aspects of behaviour change, organisations across all sectors can reap significant benefits, enabling them to support their patients, users, or customers in making positive and sustainable changes to their lifestyles, habits, financial planning, and more. To learn more about how AI can revolutionise your approach, connect with Sacher AI for expert insights and solutions.

CTL Communications has made a strategic investment in Sacher AI

Sun, 14 Jul 2024 13:45:57 GMT

Automating behaviour change in healthcare using AI - news from one of Europe's fastest-growing healthcare communications companies

CTL Communications made a strategic investment in Sacher AI in July 2024 to jointly create and deploy safe and effective AI-powered solutions to increase engagement and product impact. Sacher AI, a leader in development of AI-enabled behaviour change solutions, will offer healthcare industry clients AI solutions that not only improve engagement with campaigns, but also drive behaviour change and outcomes.

Multi-awarding CTL Communications has teamed up with Sacher AI which has picked up multiple leading digital health and wellness clients in the US and Europe.

Catherine Turner, Founder and CEO of CTL Communications, said: “The number one priority of our healthcare clients is behaviour change. They want campaigns that not only raise awareness but that also move the needle on creating actual changes in how people act. Our communications-inspired work will now be powered by AI-driven insights to create technology-powered solutions and outcomes. This partnership is a great example of two fast-growing entrepreneurial cultures combining to provide healthcare clients with innovative solutions at the cutting-edge of AI and behavioural science.

The Founder and CEO of Sacher AI, Dr Paul Sacher, is a clinician scientist with decades of expertise developing and evaluating some of the most effective health behaviour change products and services globally. He said: “Since launching our combined AI and behavioural science product development and evaluation service, we have been overwhelmed by the number of companies seeking to engage our services. This demonstrates how big the demand is for AI-driven health behaviour change solutions. With CTL Communications behind us we will now have the resources to further scale up, develop safe and impactful AI products, and create innovative and impactful solutions for our combined clients.”

CTL Communications has grown its team tenfold in recent years and this announcement marks the start of the next phase of its expansion. Its global client reach and holistic communications offering together with its deep knowledge and experience in healthcare will now be combined with new AI technology-powered solutions. The scale and strength of CTL together with the insight of Sacher AI will accelerate the progress of these two fast-growing businesses and greatly benefit their clients.

Top 10 Takeaways from Stanford University's Human Centred AI Report

Mon, 22 Apr 2024 13:57:00 GMT

AI beats humans on some tasks, but not on all. AI has surpassed human performance on several benchmarks, including some in image classification, visual reasoning, and English understanding. Yet it trails behind on more complex tasks like competition-level mathematics, visual commonsense reasoning and planning.
Industry continues to dominate frontier AI research. In 2023, industry produced 51 notable ML models, while academia contributed only 15. There were also 21 notable models resulting from industry-academia collaborations in 2023, a new high.
Frontier models get a lot more expensive. According to AI Index estimates, the training costs of state-of-the-art AI models have reached unprecedented levels.
The United States leads China, the EU, and the U.K. as the leading source of top AI models.
Robust and standardised evaluations for LLM responsibility are seriously lacking. New research from the AI Index reveals a significant lack of standardisation in responsible AI reporting. Leading developers, including OpenAI, Google, and Anthropic, primarily test their models against different responsible AI benchmarks. This practice complicates efforts to systematically compare the risks and limitations of top AI models.
Generative AI investment skyrockets. Despite a decline in overall AI private investment last year, funding for generative AI surged.
The data is in: AI makes workers more productive and leads to higher quality work. In 2023, several studies assessed AI’s impact on labor, suggesting that AI enables workers to complete tasks more quickly and to improve the quality of their output. These studies also demonstrated AI’s potential to bridge the skill gap between low- and high-skilled workers. Other studies caution that using AI without proper oversight can lead to diminished performance.
Scientific progress accelerates even further, thanks to AI. In 2022, AI began to advance scientific discovery. 2023, however, saw the launch of even more significant science-related AI applications.
The number of AI regulations in the United States sharply increases. The number of AI regulations in the U.S. has risen significantly in the past year and over the last five years.
People across the globe are more cognisant of AI’s potential impact—and more nervous. A survey from Ipsos shows that, over the last year, the proportion of those who think AI will dramatically affect their lives in the next 3-5 years has increased from 60% to 66%. 52% express nervousness toward AI products and services, marking a 13% rise from 2022.

The most staggering part of this report is that it doesn’t even cover 2024, which has already seen major model advances like Claude 3, Llama 3, and more to come. As quickly as things are moving, each year is likely going to be magnitudes crazier — whether the world is ready or not.

Read the full report here

Virtual Health Coaches: An Emerging Role for AI Agents in Patient Care

Wed, 03 Apr 2024 13:12:20 GMT

In the rapidly evolving world of healthcare, AI-powered health coach agents have emerged as innovative allies for individuals on a health journey.

Offering a blend of personalised support, round the clock accessibility, and data-driven insights, these virtual coaches are carving a niche in patient care that extends beyond traditional methods. This post delves into the transformative potential of AI health coaches, exploring how they're set to redefine our approach to wellness and healthcare management.

Examples of the Role of AI Health Coaches in Patient Support

- 24/7 Availability : Unlike humans, AI health coaches are available round the clock

- Emotional Support : Dealing with health issues can be isolating. AI health coaches offer unlimited empathetic support, reducing feelings of loneliness and anxiety and providing a sense of companionship.

- Symptom Assessment : AI health coaches evaluate symptoms through advanced prompt engineering and safety protocols, suggesting symptom management strategies or urging care when necessary.

- Lifestyle Tailoring : Based on patient profiles, AI health coaches offer personalised lifestyle advice, from physical activity suggestions to dietary plans, fostering healthier life choices.

Navigating Challenges with AI Health Coaches:
- Ensuring Accuracy and Trust : The accuracy of information provided by AI agents is crucial. Regular updates and safety checks are essential to maintain trust. Conversation evaluation, safety monitoring and ongoing fine-tuning are necessary.

- Upholding Privacy and Security : The confidentiality of patient data is critical. Implementing robust encryption and adhering to privacy laws are non-negotiable standards.

- Integrating Human Support : AI health coaches are invaluable for routine inquiries, yet human intervention remains crucial for addressing complex health concerns, ensuring a balanced approach to patient care.

Looking Ahead: The Future of Healthcare with AI Chatbots
- Advancements in Natural Language Understanding : By improving understanding of context and behavioural nuances, AI health coaches will offer even more nuanced support.

- Insightful Behavioural Analytics : Through analysing self-monitoring data and other behavioural patterns, AI health coaches will seamlessly deliver just in time adaptive interventions (JITAI), helping healthcare providers by making patient care more proactive and targeted

Engage with the Future of Healthcare
AI health coaches are more than just code—they represent a significant leap towards bridging the gap between patients and comprehensive healthcare.
If you're intrigued by the potential of AI health coaches to transform patient care, connect with us. Let's explore together how this technology can be integrated into your health management strategy.

The Science of AI in Weight Management: An Evolving Evidence Base

Thu, 21 Mar 2024 17:42:48 GMT

At Doctor Sacher & Co. we're constantly reviewing and assimilating the latest evidence for the application of AI in behaviour change, coaching and weight management.

Given that most systematic reviews include studies conducted before the launch of Gen AI chatbots like ChatGPT, the present research landscape understandably presents a growing yet still emerging perspective on this dynamic field.

Key Insights:

AI Agents for Weight Management:
Chew's review (2022) emphasises AI chatbots ability to provide personalised, engaging, and 24/7 support for people aiming for weight loss. By integrating user data, including health metrics and personal preferences, AI chatbots offer tailored advice and motivational support, potentially enhancing weight loss outcomes.

AI Chatbots for Behaviour Change:
A review by Aggarwal et al. (2023) assessed AI chatbots' feasibility, efficacy, and intervention characteristics in promoting health behaviour change. Their findings underscored success of chatbots in encouraging healthy lifestyles. However, they also pointed out the need for more rigorous studies to confirm these preliminary results conclusively.

Personalised Approach:
Studies, such as those by Terblanche et al. (2022) and Chew (2022), demonstrate AI's capability to offer personalised coaching. AI chatbots have have shown efficacy in goal attainment, providing users with tailored support and guidance.

Accessibility and Scalability:
One of AI's most significant advantages is its potential to make coaching more accessible and affordable. By leveraging AI, coaching services can reach a broader audience, overcoming traditional barriers like cost and availability.

Evidence of Effectiveness:
While initial findings on goal attainment are encouraging, results in areas such as psychological well-being and resilience are still inconclusive. This highlights the infancy of AI in weight management and the need for further research to understand its full impact.

Looking Ahead:
This body of research highlights the nuanced capabilities and challenges of AI in health coaching and weight management. As we advance, there's a clear horizon where AI chatbots could democratise access to personalised health coaching, making significant strides in public health and individual wellness.
As health professionals and innovators, we must continue exploring these technologies, ensuring they're leveraged to their fullest potential, ethically and effectively. The journey towards integrating AI into health and wellness is just beginning, and its future looks promising.

At Doctor Sacher & Co., we are committed to staying informed about the latest research and integrating evidence-based AI solutions into our practice.

Follow us on LinkedIn us as we navigate the evolving landscape of AI in healthcare!

Incorporating AI and Chatbots: A Must for Digital Health Companies

Thu, 14 Mar 2024 08:18:32 GMT

In an era where digital health is evolving at lightning speed, Dr. Sacher & Co. stands at the forefront of innovation. Our recent discussion with Dr. Sacher shed light on the indispensable role of Generative AI and chatbots in the digital health sector.

�� The Competitive Edge of AI & Chatbots
In today's digital health landscape, consumer expectations are soaring. The demand for instant, 24/7 support is more pronounced than ever, highlighting the necessity for digital health companies, regardless of their stage, to adopt a Gen AI and chatbot strategy. Without this, companies risk falling behind in a fiercely competitive market.

�� Tailored AI & Chatbot Integration Packages
At Dr. Sacher & Co., we understand that integrating AI and chatbots can seem daunting. That's why we offer specialized packages of support designed to navigate companies of all sizes through their AI journey. From early-stage startups to established health giants, our tailored solutions ensure that every digital health business can harness the power of AI and chatbots safely, efficiently and effectively.

�� Enhancing Patient Experience with AI
Gen AI and chatbots are more than just technological advancements; they are transformational tools that enhance patient care. By providing real-time, personalised support, they not only improve patient engagement but can also boost outcomes. Dr. Sacher emphasises that integrating these technologies is not an option but a necessity for staying relevant and delivering unparalleled patient experiences.

�� Preparing for the Future of Digital Health
The integration of AI and chatbots is just the beginning. As digital health continues to evolve, staying ahead means continuously adapting and innovating. Dr. Sacher & Co. is committed to leading this charge, helping digital health companies not only meet but exceed the ever-changing expectations of their consumers.

�� A Vision for Tomorrow
Under Dr. Sacher's guidance, we're not just preparing companies for the current market demands; we're equipping them for the future of digital health. Our comprehensive support packages ensure that every digital health company can leverage Gen AI and chatbots to their fullest potential, setting a new standard for patient care and interaction.

�� Join us in revolutionising digital health. With Dr. Sacher & Co.'s expertise, your company can lead the way in AI and chatbot integration, delivering exceptional care and support to patients around the globe.

Unlock the potential of AI and chatbot strategies for your digital health offerings. Connect us for a discussion on how we can assist your team in substantially boosting your product's impact and outcomes.

The Pros and Cons of Building vs. Buying an AI Health Chatbot Solution

Mon, 04 Mar 2024 16:48:56 GMT

Businesses face a crucial choice: build a bespoke AI Chatbot or purchase a ready-made solution.

Dr. Sacher & Co., your AI chatbot experts, are here to help you navigate this decision with insights that fit your strategic vision.

�� Building a Custom AI Health Chatbot

�� Pros:
Tailored Focus and Integration : Designed specifically to meet the needs of your patients and human coaches, a tailored Dr Sacher & Co. solution seamlessly integrates with your existing digital infrastructure, offering unlimited customisation.

Competitive Edge : Opens doors to innovation and unique solutions, potentially creating new revenue streams.

Regulatory Compliance : Our solutions facilitate easier compliance with industry regulations, vital for organisations not wanting to enter into the medical device or software as a medical device (SaMD) regulatory process.

�� Cons:
Cost : Building a custom solution demands substantial investment in tech and hiring skilled professionals. Engaging with specialist consultancy expertise at critical stages can streamline processes, mitigate mistakes, and ultimately lead to savings.

Time : The process of developing and deploying can be extensive, potentially delaying realisation of benefits. However, this timeline can be optimised by the strategic injection of expert insights at key stages.

Expertise : While a robust in-house team is valuable, the incorporation of niche experts can significantly enhance outcomes, offering specialised knowledge and experience that complements and elevates the capabilities of your existing team.

�� Buying an Off the Shelf AI Health Chatbot

�� Pros:
Cost-Effective : Often more affordable with subscription models, including R&D, maintenance, and support.

Quick Deployment : Enables businesses to leverage AI capabilities sooner, crucial in a competitive market.

Simplicity : Vendor takes care of updates, security, and compliance, easing the internal burden.

�� Cons :
Limited Options and Regulatory Risks : Most off the shelf chatbots are designed for customer support. Though useful, they are not equipped or safe for supporting patients without being defined as therapeutic and therefore potentially falling under any medical device or SaMD regulations.

Integration Challenges : May require adjustments to fit perfectly with your systems, although high-quality solutions tend to minimise this issue.

Customisation Limits : Might not fully meet specific business needs or offer a unique competitive advantage.

We understand the strategic implications of choosing between building or buying an AI Chatbot solution. Whether you're looking to build or buy, we're here to support your journey.

Ready to explore the potential of AI chatbots for your business? Contact us today .

Boosting Uptake & Engagement in Digital Health Interventions: A Strategic Blueprint

Tue, 27 Feb 2024 17:04:45 GMT

In today's rapidly evolving digital landscape, the potential for digital health interventions to transform patient care and outcomes is immense. However, a key challenge remains: how do we significantly boost the uptake and engagement of these interventions?

Drawing insights from the latest industry research, here's a strategic blueprint to elevate digital health engagement:

�� Understand Your Audience Deeply
The first step in increasing engagement is to deeply understand who your users are, what motivates them, and the barriers they face in adopting digital health solutions. Leveraging product intelligence tools can provide unparalleled insights into user behaviour, allowing for a more tailored approach to meet their needs.

�� Make It Personal
Customization is no longer a luxury but a necessity. Users expect interventions that are not just relevant but also personalized to their health journey. Implementing AI and machine learning can help tailor the digital health experience at an individual level, increasing relevance and engagement. Methods such as archetyping to inform personalisation will sustain engagement and improve outcomes.

�� Enhance Accessibility
Digital health solutions must be accessible to all, regardless of age, tech-savvy, or socio-economic status. Simplifying user interfaces and ensuring interventions are accessible on multiple devices can drastically improve uptake.

�� Leverage Behavioural Science
Incorporating principles of behavioural science into digital health interventions will significantly improve user engagement and adherence. Designing for behaviour change requires integrated research, data and product expertise.

�� Build Trust and Transparency
For digital health interventions to be embraced, users must trust them. Ensuring data privacy, security, and transparency about how data are used can build this trust. Engaging with users via a human-centred approach and incorporating their feedback into the development process will foster a sense of ownership and trust.

�� Promote Digital Literacy
Increasing digital literacy among users can help them feel more comfortable and confident in using digital health tools. Providing easy-to-understand resources and support can empower users to take an active role in their health care through digital means.

�� The Path Forward
As digital health continues to evolve, so must our strategies for engaging users. By leveraging deep insights into user behaviour, embracing personalisation, ensuring accessibility, and building trust, we can significantly boost the uptake and engagement of digital health interventions, ultimately leading to better health outcomes.

�� Let's Connect
If you're working on innovative digital health solutions or have insights to share, we would love to hear from you.

Navigating the Future of Digital Health: Building a Non-Medical Device AI Health Coach Chatbot

Mon, 26 Feb 2024 17:00:47 GMT

In the rapidly evolving landscape of digital health, the intersection of technology and wellness presents both opportunities and regulatory challenges

As we develop innovative solutions like generative AI chatbots to support patients on their behaviour change journeys, it's crucial to design these tools carefully to ensure they provide value without inadvertently crossing into medical device territory.

Here are 6 key principles we're following at Dr Sacher & Co. to achieve this balance:

1. Focus on Health Coaching: Our chatbots provide general advice on behaviour change, motivation, accountability, and share information on health, nutrition, and wellness, offering 24/7 support, enabling just in time adaptive interventions (JITAI) and support rather than offering personalised medical advice.

2. Transparency and Disclaimers: We emphasise that our chatbots are a supplementary tool for support, motivation and information, not a replacement for professional healthcare advice. Patients tell us they value the ability to communicate anonymously with a chatbot who replies immediately, but also knows when to handover to a human healthcare professional.

3. Engagement Without Diagnosis: Our chatbots are crafted to motivate and monitor progress towards healthier habits, without offering diagnoses or treating conditions. Through establishment of stringent safeguards and continuous testing, we ensure that chatbots refrain from delivering personalised diagnoses or medical advice.

4. Limiting Claims about Health Outcomes: We're cautious about making claims on the efficacy of the chatbots in improving health outcomes. Avoiding suggestions of medical benefits helps us steer clear of regulatory scrutiny. Focus instead on qualitative feedback and quantitative benchmarking of quality, safety and accuracy.

5. Privacy as a Priority: We commit to the highest data privacy standards, safeguarding patient information and upholding their trust along with regulatory demands. By securing proper consents, we guarantee that patients are fully informed and consent to interactions with an AI chatbot, including the sharing of necessary information with their healthcare providers

6. Staying Informed: We commit to continuous learning and adaptation, ensuring our solutions align with evolving health tech regulations and best practices.

By establishing these guardrails, we provide a supportive, motivational tool for those seeking to improve their health and wellness. This approach allows us to navigate the regulatory landscape responsibly while innovating in the digital health space.

We're excited about the possibilities this brings to the digital health and wellness community.

Discover How Our Expert Team Can Guide You Through the Fast-Changing World of Chatbots !

Advancing Product Development with AI and Behavioral Science

Mon, 22 Jan 2024 13:57:40 GMT

Products that bridge the gap between AI and behavioural science are far more likely to deliver sustained outcomes

The fusion of AI and Behavioural Science can significantly push the envelope of healthcare solutions. “Products that bridge the gap between AI and behavioural science are far more likely to deliver sustained outcomes”, says Dr Sacher, a global leader in weight management, behavioural science and digital health.

We asked Dr Sacher why this blend is revolutionary for digital health products? Let's dive in!

�� Personalisation at its Best:
Imagine a world where healthcare is not just a one-size-fits-all solution but tailored specifically to individual behavioural patterns and psychological profiles. By leveraging AI's data processing capabilities with behavioral science's nuanced understanding of human behaviour we create highly personalized health interventions. This means better engagement, compliance, and ultimately, health outcomes.

�� Predictive Health Becomes a Reality:
By understanding the subtle cues and patterns in how individuals interact with digital health tools, AI can predict potential health issues before they become serious. Coupled with insights from behavioural science, interventions can be more timely and effective, leading to preventive health care, not just reactive care.

�� Enhanced User Experience:
It's all about the user journey in adapting digital health tools. By understanding the psychological motivators and barriers, Dr. Sacher and his team craft user experiences that not only attract but retain engagement. Think of apps that users love to interact with, transforming mundane health tasks into rewarding experiences. Dr Sacher shares that they are figuring out how to replicate the engagement and stickiness seen in highly engaging products such as gaming and personal chatbots.

�� Behavior Change for Good:
The holy grail of most health interventions is achieving lasting behaviour change. Merging AI with behavioural science offers a powerful toolkit for designing solutions that adapt to evolving personal goals and emerging scientific evidence, making healthier choices easier and more intuitive.
Dr Sacher is at the forefront, turning these possibilities into realities for digital health companies. Whether you're a start-up eager to make your mark or an established player looking to innovate, you can have greater impact by integrating behavioral analysis and design into your product development process.

Let's pioneer a future where health technology is as adaptive, intuitive, and personal as the individuals it serves. ��

�� Follow our journey and stay tuned for more insights on how we're transforming the digital health landscape, one innovation at a time.

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