Being patient-centered is necessary but insufficient for adoption. Technology in healthcare must be seamlessly embedded into a physician's existing, time-constrained workflow. Great tech that adds friction will be ignored, regardless of its potential patient benefit.

To drive adoption of new medical software, move beyond traditional IT support. Identify clinical influencers, elevate them to "super user" status, and empower them to be the first point of contact for their peers. This peer-led model is more effective, builds community, and scales support organically.

Despite industry rhetoric, healthcare technology development overwhelmingly prioritizes physicians over patients. This creates a significant gap, as the ultimate end-user's needs are often an afterthought in solution design.

Product stickiness in health systems is achieved through deep workflow integration. By embedding a solution into the daily processes of every stakeholder—from medical assistants to billing coordinators—it becomes entrenched and difficult to replace, mirroring the zero-churn model of EMR giant Epic.

To get adopted, a technology must speak three languages. It needs to provide excellent clinical outcomes for physicians, operational efficiency for staff, and a positive economic impact for hospital administrators. Excelling in only one area is not enough.

Founders often define "integration" as connecting software via APIs. However, true integration means embedding a product seamlessly into the clinician's and patient's existing daily workflow. Any deviation, no matter how small, creates friction that kills adoption rates among busy healthcare professionals.

The successful early adoption of AI in healthcare was brilliant because it first targeted the administrative burdens that clinicians hate, such as documentation (scribes) and billing. By winning the hearts and minds of powerful incumbents with immediate quality-of-life improvements, the industry built momentum for more complex clinical applications.

AI adoption in healthcare has accelerated by sidestepping slow enterprise sales cycles. Companies like Open Evidence offer free, consumer-like apps directly to doctors (prosumers). This bottom-up approach creates widespread use, forcing organizations to adopt the technology once a critical mass of their staff is already using it.

Unlike the top-down, regulated rollout of EHRs, the rapid uptake of AI in healthcare is an organic, bottom-up movement. It's driven by frontline workers like pharmacists who face critical staffing shortages and need tools to manage overwhelming workloads, pulling technology in out of necessity.