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| Mapping Markets |
In-depth healthcare technology insights from our team of expert researchers |
| Patient Marketing Automation: Engaging the Right Patients at the Right Time |
|  | Recent studies indicate a decline in patient loyalty, particularly among younger demographics. A past patient might not automatically return for follow-up care, and new patients are more likely to search online—for a provider, to Google their symptoms, or to learn more about treatments or diagnostic services available to them—rather than visiting their PCP. In this environment, patient marketing automation platforms offer an opportunity for healthcare organizations to proactively engage and retain patients.
What Are Patient Marketing Automation Platforms?Patient marketing automation tools automate targeted outreach to prospective and existing patients across multiple channels—email, SMS, search engines, and social media. They aim to drive patient acquisition, retention, and engagement by delivering personalized health information, screening reminders, and promotional campaigns for relevant services.
Unlike traditional appointment reminders or scheduling systems, these platforms focus on patient acquisition or net-new appointments and services. They are also distinct from push-message or email marketing platforms; to be included within our category, vendors must use personalization and automated targeting, as opposed to mass messaging across an entire patient population or basic transactional emails.
The Evolution of Patient Marketing AutomationIn the past, hospitals and clinics relied on traditional marketing methods—billboards, TV ads, and community outreach (tactics that may hit the wrong note with consumers in the current atmosphere). As digital communications have become the norm, some practices have also adopted scheduling or email marketing platforms that include features like appointment confirmation via SMS, annual check up reminders, or promotional offers for add-on services.
However, Google and social media have permanently shifted how patients find and choose care. Today, patients actively search for symptoms, treatments, and specialists online, allowing digital advertisers to target patients who have expressed intent. Crucially, advertisers must navigate HIPAA and patient privacy concerns, especially in an environment where inadvertent patient data tracking remains a significant risk. Advancements in AI and automation have further accelerated this shift. Early digital marketing tools focused on static ads and email blasts, but modern platforms now aim to predict patient needs, personalize outreach, and automate follow-up communication. (This mirrors similar changes that we’ve seen in the patient payments category, where vendors have begun incorporating machine learning to optimize messaging, delivery time, and message channels.)
How Patient Marketing Automation WorksThese platforms combine data-driven targeting, AI-driven engagement, and automated workflows to optimize patient outreach: Data Sourcing & Targeting: Platforms use SEO data and paid search ads or rely on population health data to identify at-risk patients who are good candidates for targeting. Many integrate with EHRs and past patient records to re-engage individuals who may have lapsed in their care.
Multi-Channel Outreach: Personalized email, SMS messages, social media, and digital ads engage potential patients, often leveraging retargeting based on past browsing behavior. Some platforms integrate conversational AI to answer questions, guide users through decision-making, and encourage scheduling. (There is overlap here with AI symptom checkers, the difference being that marketing automation platforms take a more macro view, including top of the funnel targeting before patients reach the chatbot and potentially retargeting afterward.)
Conversion & Tracking: Many tools integrate with scheduling systems, allowing seamless transitions from marketing to care access. Once a patient engages, the platform should track lead conversion, monitoring which outreach strategies drive actual appointments.
Vendor Landscape: Key Players and DifferentiationVendors in the patient marketing automation space differentiate based on data sources, campaign automation features, and integration capabilities. AWS Pinpoint, Braze, Customer.io, and Fertu are essentially traditional email marketing platforms with transactional and customer journey SMS and email tools, but each offer some additional features that merit inclusion within this category. For example, deeper integration into healthcare workflows (Fertu has automations for VBC enrollment and patient referrals), HIPAA compliance, or (in the case of Braze) automations to trigger retargeting ads.
Brado, Cured, Pulsepoint, and Upfront dive deeper into healthcare-specific use-cases. Cured uses APIs to integrate healthcare data and machine learning for campaign targeting; it also has workflows for optimizing health system relationships with providers and driving referrals. Upfront uses signals like patient-reported outcomes and psychographic segmentation to target automations for appointment scheduling, payment, and more. Brado creates ad campaigns and AI chatbots for specific patient concerns. Finally, Pulsepoint’s HIPAA-compliant platform can leverage patient data to target patient populations in a disease-specific or behavior-based way.
Liine focuses on call-based workflows, integrating with platforms like Luma and Clearwave to serve retargeting ads to patients who have contacted the practice but haven’t completed booking, and it automates followup calls with new leads.
Where Patient Marketing Automation is HeadedWe’re eager to see how these tools play out alongside population health data and analytics solutions, particularly in value-based arrangements. VBC analytics technologies are already driving toward being able to predict patient needs; direct mail campaigns are common here, but we’d like to see vendors who’ve specialized in targeted, multi-channel outreach partner with these VBC organizations to deliver more effective digital communication strategies. |
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| Executive Insights |
A glimpse into what’s top of mind for healthcare IT executives |
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Role: Former SVP of Product
Organization: Oscar |
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| This is part two of our conversation with Duncan. You can see the full interview here.
Has vendor evaluation changed in this AI-driven environment? I would say it’s 80% the same, 20% different. You still assess vendor risk and product quality, ideally using competitive bake-offs. But with GenAI: Outputs are non-deterministic, so even in setting up and running a pilot, there’s more effort required on the client’s part to determine whether the outputs are up to snuff, whether the team using the outputs is succumbing to automation bias, whether the occasional errors you get are tolerable or highly problematic, etc.
Pricing models are shifting; a good number of AI startups now charge based on performance rather than fixed fees, aligning cost with measurable impact, which is a positive development but sometimes it can take time to agree on the attribution methodology that will be used in this case.
There’s heightened sensitivity to data privacy and breach liability in the contracting and BAA process, especially if client data is being used to train a model, since in theory, if someone was really careless, a version of your data could show up in another client’s model outputs.
New vendors emerge frequently, forcing constant reassessment. Related to this, long-term contracts are risky. The AI landscape is evolving too fast to commit to multi-year deals.
What AI trends are you excited about in 2025? There are a couple things I’m really intrigued by: Voice Agents: This year is shaping up to be the year of the AI voice agent. While inbound call centers are an obvious use case, there are also significant opportunities for voice AI in outbound patient engagement and B2B use cases, such as provider-to-provider or provider-to-pharmacy interactions.
Self-Learning AI Models: Today’s LLM agents don’t learn from past interactions in real time. But new reinforcement learning techniques (like those used to incentivize reasoning in DeepSeek) could eventually allow AI models to improve continuously based on their success or failure in completing a task. This will be especially impactful in areas like call centers where AI agents can learn based on whether the patient’s question was resolved or the patient scheduled an appointment, if that was the goal. These are examples of single interaction conversion but you could imagine extending this concept to more clinically complex scenarios with lagging outcomes as well. [Editor’s note: We’ve previously shared some conjecture around AI model learning in the context of RCM in our State of AI in RCM 2024 Report.]
Are there any learnings you can share based on your experience implementing AI within the products you were building? One is that it’s significantly harder than people assume to measure, validate, and iterate on results on the path to production. LLM-based products in particular require subject matter experts—doctors, nurses, billing analysts—folks who may have to be pulled away from patient care or daily task queues to review the outputs for accuracy, adding significant time and cost to the development process. Many companies underestimate this.
Another is that people often hold AI to a higher standard than human performance. For example, self-driving cars get scrutinized for rare accidents, even though human drivers crash far more frequently. AI in healthcare faces a similar perception challenge—companies need to define what’s "good enough" at the outset, long before you get to the point of pushing something to production to avoid delays. Ideally the threshold you set corrects for the tendency to hold AI to a super-human standard. Also, for consistency you should try to align AI evaluation with existing quality control processes (e.g., chart reviews, call note audits) rather than reinventing quality benchmarks and sampling procedures. |
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| Market Pulse |
Roundup of the most important movements in health IT |
| Cedars-Sinai + Aiva: The academic medical center announced a pilot with the AI scribe for nurses. (more)
John Muir Health + Ambience: The CA health system rolled out the ambient solution for documentation, CDI & coding, and care coordination enterprise-wide. (more)
CHC + CarePilot: The hospital management company announced it would be rolling out the AI scribe across its managed and affiliated hospitals nationwide. (more)
Yardi + OneStep: The senior living community management organization added the app-based motion analysis solution for predicting falls to its vendor platform. (more)
Apple + Brigham and Women’s Hospital: The iPhone developer is launching a new study in partnership with the hospital exploring, “relationships between various areas of health, such as mental health’s impact on heart rate, or how sleep can influence exercise,” leveraging device data from smartphones, watches, AirPods, and more. (more)
Altera Paragon Denali: The EHR designed for rural and community hospitals was selected by four hospitals: Columbus Community Hospital, Pipeline Health System, Sioux Falls Specialty Hospital, and West Calcasieu Cameron Hospital. (more)
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| | Until next week! Bobby & Team
P.S. Have partnership or product news to share with the community? Submit a news tip. |
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