Scalable digital solution connecting hypertensive patients and nursing teams in a managed care environment.
Health-tech startup focused on safe, human-centered communication between healthcare companies, physicians and patients.
Special Patient Monitoring Team (MOPE in Portuguese). Nursing team dedicated to remotely monitoring patients from the health insurance company São Cristóvão Saúde (based in Brazil).
The MOPE Team needed to increase patient coverage without increasing nurse workload or clinical risk using a digital solution as a way to expand and enhance its reach, capacity and quality of care.
Align product scope with clinical constraints, operational capacity, and measurable outcomes for remote hypertension programs.
Nurses needed workflows that reduced cognitive load during remote follow-ups while keeping patients engaged between in-person visits.
Clear, timely communication channels and lightweight tasks patients could complete without friction on common devices.
Why was this important?
As patient volume increased, the monitoring team could not scale manual follow-ups without compromising care quality and nurse workload.
Provide safe, scalable, and engaged care for nursing teams monitoring hypertensive patients beyond traditional follow-ups.
To understand the context, exploratory conversations were conducted with the monitoring team (team leaders, nurses, and attendants) to learn how patient monitoring was currently performed, the challenges faced, and unmet needs.
For patient context, the MOPE team helped to select patients that could be part of the pilot solution.
Research focused on understanding existing monitoring workflows, patient engagement, tracked health indicators, data organization, and communication during follow-up. In parallel, we reviewed the hypertension care protocol to identify the key events and indicators that would guide the solution.
Health Indicators
The solution combines structured indicator tracking, multi-channel patient communication, and a nurse-first monitoring workspace—so teams can act on the right signal at the right time.
Based on the type, relevance, and frequency of each health indicator, I pushed for defining the most appropriate patient communication channels—chat, alerts, tasks, orientation messages, and phone calls—ensuring data was exchanged in the right context and could be acted on effectively by nurses and the system.
Nurses get a prioritized view of patients, recent indicators, and open conversations in one place—reducing tab-hopping between informal tools during follow-up.
Because the number of required indicators and explanations directly affected patient adherence, onboarding was intentionally distributed across multiple moments during the first week of app use. This approach reduced cognitive load while introducing information in context with each monitoring event.
For the MVP to be effective, the monitoring team needed immediate access to patient-entered indicators, as well as timely alerts when values fell outside defined clinical thresholds (for example, blood pressure readings that were too high or too low). To support this, I advocated for contextual alerts and data triggers that provided nurses with a clear, objective view of patient data throughout the monitoring period, enabling faster recognition of risk and appropriate clinical response.
To support different monitoring needs, individual flows were defined for each health indicator, combining the most appropriate communication channels and content types. Together, these flows form a modular system structure that allows monitoring logic, frequency, and responses to adapt to each indicator’s context.
Example flows show how a single indicator triggers the right sequence of tasks, messages, and escalations—so patients and nurses stay aligned on what happens next.
This approach delivered the following benefits:
The end-to-end flow connects patient tasks, nurse chat, and escalation paths so teams can follow a single journey from first contact through resolution.
The MOPE web workspace brings together patient lists, real-time messaging, and clinical context in one place—aligned with how nurses monitor cases today.
To maximize learning speed during early validation, patient-facing screens were implemented using Web Views instead of native components. This decision significantly reduced deployment friction, allowing the team to iterate on UX, content, and interaction patterns without repeated app store releases and enabling faster response to insights from testing.
I collaborated closely on the definition of the backend data model, advocating for a generic and extensible structure. This approach allowed indicators, flows, and clinical protocols to evolve over time without major structural changes, supporting both scalability and long-term product adaptability.
Rather than automating processes prematurely, early testing relied on manual triggering of tasks and alerts, with the monitoring team acting as test patients. This deliberate choice prioritized understanding real workflows and refining UX and system logic before committing to automation, reducing downstream rework and risk.
Modeling for evolution
Advocating for a generic backend model involved close collaboration with engineering to balance short-term delivery pressure with long-term flexibility. The indicator-based flow model established a reusable foundation that could support additional conditions beyond hypertension without redesigning the system.
Two complementary surfaces
Indicator-specific subflows connect these two components through patient data entry and system events. Together, automated responses and nurse interpretation form a unified monitoring flow that supports continuous, safe patient follow-up.
The solution can be understood as two complementary components:
