JMIR Publications

ABSTRACT

Background:

Tobacco smoking is an important risk factor for disease, but inaccurate smoking history data in the electronic health record (EHR) limits the reach of lung cancer screening and tobacco cessation interventions. Patient-generated health data is a novel approach to documenting smoking history; however, the comparative effectiveness of different approaches is unclear.

Objective:

We designed a quality improvement intervention to evaluate the effectiveness of portal questionnaires compared to text message-based surveys, to compare message frames, and to evaluate the completeness of patient-generated smoking histories.

Methods:

We evaluated an EHR portal questionnaire and a text survey. The portal questionnaire employed a “helpfulness” message, while the text survey tested frame types informed by behavior economics - “gain”, “loss” and “helpfulness”- and nudge messaging. The primary outcome was response rate for each modality and framing type. Completeness and consistency with documented structured smoking data was also evaluated.

Results:

Participants were more likely to respond to the text survey (19.1%) compared to the portal questionnaire (6.9%). Across all survey rounds, patients were less responsive to the “helpfulness” frame compared to the “gain” frame (OR= 0.29, p < 0.05) and “loss” frame (OR =0.32, p <0.05). Compared to the structured data in the EMR, the patient-generated data was significantly more likely to be complete enough to determine lung cancer screening eligibility.

Conclusions:

We found that a learning health system approach using patient-generated data is a feasible way to engage patients and collect complete smoking histories. Patients are likely to respond to a text survey using “gain” or “loss” framing to report detailed smoking histories. Optimizing a text message approach to collect medical information has implications for preventative and follow-up clinical care beyond smoking histories, lung cancer screening, and smoking cessation therapy.