Skip to content 
Patient journeys are on the verge of a major shift. As AI becomes embedded across healthcare it will cause some journeys to accelerate, others will become more complex, and in certain therapy areas AI may completely redefine how patients move through the healthcare system.
Pharma companies often map patient journeys for the different therapy areas they work in: from first symptom to diagnosis, initial treatment, through long-term management, switches, relapses, and beyond. The journey map is one of pharma’s most important strategic tools, because every commercial decision (sales force strategy, education programmes, payer engagement, brand campaigns) has to land somewhere on it.
The arrival of the AI-primed patient is the most significant change to that tool in years. Different therapy areas will move in different directions, and to different extents. Some journeys will accelerate. Others will stall, or reroute around the primary care visit altogether. If your journey maps assume that AI will simply add a step at the start, they will mislead the work that depends on them.
This post sets out where the journey is most likely to bend, based on our paired consumer and PCP study and the patient journey work STRAT7 Health does for clients across therapy areas.
Diagnosis: differing impact based on the therapy area
AI is likely to influence diagnosis by reshaping patient behaviour before entering the healthcare system. In some cases, it may encourage earlier action by helping patients to recognise symptoms and seek advice sooner. In others, inconsistent or inaccurate guidance may delay appropriate care-seeking. The impact is unlikely to be the same across all therapy areas.
For common conditions, there is a concern in some instances that AI will offer a false sense of reassurance The Mount Sinai study cited in our first post ‘The AI-primed patient is already in the consultation room. Pharma needs to act now’ highlights exactly this risk, finding that ChatGPT Health failed to direct users to emergency care in a significant proportion of gold-standard medical emergencies.
This dynamic may be reversed in rare diseases. Patients with rare conditions often experience long diagnostic journeys involving multiple consultations, referrals, and misdiagnoses. AI may help connect symptoms, identify patterns, and suggest conditions or referral routes that would not otherwise be considered. The challenge is whether AI can reliably distinguish rare conditions from more common alternatives. Can it spot the zebras amongst the horses?
Referral patterns: Where direct access is possible, expect more of it
In markets where patients can self-refer to specialists (much of the US, parts of continental Europe), AI is anticipated to make that route more common.
In gatekeeper systems like the UK NHS, the primary care physician remains the bottleneck. The conversation in primary care changes though, because the patient may now arrive with a specialist or centre already in mind.
Pharma’s account-based strategies need to factor this in. The current key opinion leaders may not be the ones with the biggest digital footprint. As AI draws more on online content and expert voices, a clinician’s digital presence and influence may become as important as their role within a specific practice or institution.
Treatment choice: Pre-loaded before the consultation
The patient will arrive with an opinion. In our study, 35% of consumers said they would use ChatGPT Health to support a treatment decision.
Many of these patient preferences will be informed by incomplete information, and some will not align with clinical evidence or individual patient circumstances. As a result, the HCP’s job shifts from introducing options to contextualising, validating, or correcting pre-existing beliefs.
Take menopause as an example. The balance between hormonal and non-hormonal treatment approaches remains an active area of clinical discussion, with decisions often depending on a patient’s symptoms, risk profile, and preferences. Patients who have already explored these options through AI tools may arrive with a strong inclination toward a particular approach, making the consultation less about presenting choices and more about evaluating them together.
This has important implications for the resources healthcare professionals rely on to support patient decision-making. Patient educational content and support tools will need to be designed not only for human audiences but also for AI-mediated discovery and interpretation. Yet many pharmaceutical brands continue to produce content in formats that are difficult for AI systems to access, interpret, and surface effectively.
Stakeholder roles: A third party in the room
The most useful reframe from our research is also the simplest: pharma is no longer designing for a two-party consultation. The patient brings AI in with them. Often AI has been part of the conversation before either patient or HCP met.
That influence does not end when the consultation does. Patients leave with a diagnosis, treatment recommendation, or prescription and often continue the conversation elsewhere – turning to AI tools to understand side effects, explore alternative treatments, and anticipate what comes next.
As a result, decisions about treatment initiation, adherence, persistence, and switching may increasingly be shaped by information and perspectives encountered outside the clinical setting. For pharmaceutical companies, this introduces a new layer of influence in the patient journey, one that sits beyond traditional channels of engagement and communication.
Two actions for pharma teams now
From the conversations we are having with insights and brand teams, two things make a measurable difference quickly.
1.
Re-walk your patient journey for each priority therapy area, with AI as a stakeholder. Identify the moments where AI is most likely to disrupt the journey, and the associated opportunities and risks for your brand.
2.
Map the touchpoints where AI has access to authoritative content about your brand. National guidelines, peer-reviewed publications, registered trials, and KOL-authored work are likely to play an important role in how AI systems understand and represent your therapy area and brand.
The AI-primed patient is no longer a future concept. As AI becomes embedded in how patients access, interpret, and act on health information, its influence is already being felt across diagnosis, treatment decisions, and long-term disease management. The priority for pharma is not simply to monitor these changes, but to re-walk the patient journey now and adapt accordingly.
If you need help navigating these shifts and prioritising what your brand should do next, get in touch with the STRAT7 Health team.
About the author
- Dr Sarah Rosen
- Director, STRAT7 Jigsaw
Dr Sarah Rosen is a Director in the Health team at STRAT7 Incite, specialising in oncology, women’s health, and rare diseases. She helps pharmaceutical companies understand evolving patient and healthcare professional behaviours to inform strategy and decision-making. Her recent work focuses on the impact of AI in healthcare, exploring how it is reshaping diagnosis, treatment decisions, and the patient journey.