From reporting to relating: Reimagining ePROMs in cancer symptom management

Electronic Patient-Reported Outcome Measures (ePROMs) are digital tools that allow patients to report symptoms using computers, tablets, or smartphones. In cancer care, ePROMs are increasingly used to monitor SPPADE symptoms: sleep disturbance, pain, physical function decline, anxiety, depression, and energy deficit or fatigue. While ePROMs can support timely symptom recognition and clinical response, their influence on how patients and clinicians connect and understand one another during care remains notably underexplored. This study investigates how the integration of digital symptom reporting into clinical routines may shape both the content of symptom reporting and the relational dynamics between patients and providers. We conducted 46 in-depth interviews with cancer patients enrolled in a multi-year symptom management trial (E2C2) at Mayo Clinic. Using a combination of deductive frame analysis and inductive analysis informed by Boundary Object Theory, we first examined how patients with advanced cancer understood and approached the sharing of SPPADE symptoms. We then explored how ePROM technology shaped their experiences and perceptions of symptom reporting. We found that three interrelated factors shaped patient engagement with ePROMs: (1) personal orientations toward perceiving and expressing symptoms, (2) coping styles, including tensions between appropriate self-monitoring and positive self-presentation, and (3) expectations shaped by prior interactions with the care team. While ePROMs helped patients articulate symptoms, they also raised questions about whether responses were reviewed and how they were interpreted by clinicians. Patients described ePROMs as signaling what kinds of symptoms "counted" to providers, subtly reshaping their understanding of illness and their role in managing it. Our findings engage and extend Boundary Object Theory by showing that ePROMs function not only as tools for coordinating information, but also as relational artifacts. Attending to their interpretive and emotional effects highlights new opportunities to design systems that support connection and shared understanding, alongside efficient data flow.