The effect of simulated narratives that leverage EMR data on shared decision-making: a pilot study

OpenNotes and shared decision making: a growing practice in clinical transparency and how it can support patient-centered care

Objective

Prior studies suggest inviting patients to read their visit notes (OpenNotes) has important benefits for patient engagement. We utilized survey data to investigate our hypothesis that patients who read more notes would report greater shared decision making (SDM).

Materials and Methods

Our survey focused on the safety and quality implications of OpenNotes. 24 722 patients at an urban healthcare organization were invited to complete the survey, which included an item assessing the number of notes read and the CollaboRATE scale to measure SDM. We used log-binomial regression to estimate the relative probability of top CollaboRATE scores across number of notes read while controlling for several covariates.

Results

6913 patients responded (28% response rate). Patients reading 4+ clinical notes in the past 12 months were 17% more likely to have top CollaboRATE scores when compared to patients who had not read a note in the previous 12 months (RR: 1.17, 95%CI: 1.04-1.32).

Discussion

There is a clear relationship between what SDM requires and the transparency OpenNotes provides. Access to clinicians' notes can support the SDM model, which relies on efficient information exchange between clinicians and well-informed patients.

Conclusion

Our study showed evidence of a relationship between note reading and perceived SDM. Implementation of SDM is likely to expand, given its association with improved patient satisfaction, adherence, and medical decision making. Findings from this study highlight OpenNotes as a policy that institutions can implement as a facilitator of SDM and a manifestation of their commitment to patient autonomy and transparency.

Veterans Like Me: Formative evaluation of a patient decision aid design

Patient decision aids are tools intended to facilitate shared decision-making. Currently development of a patient decision aid is resource intensive: it requires a decision-specific review of the scientific literature by experts to ascertain the potential outcomes under different treatments. The goal of this project was to conduct a formative evaluation of a generalizable, scalable decision aid component we call Veterans Like Me (VLme). VLme mines EHR data to present the outcomes of individuals "like you" on different treatments to the user. These outcome are presented through a combination of an icon array and simulated narratives. Twenty-six patients participated in semi-structured interviews intended to elicit feedback on the tool's functional and interface design. The interview focused on the filters users desired with which to make cases similar to them, the kinds of outcomes they wanted presented, and their envisioned use of the tool. The interview also elicited participants information needs and salient factors related to the therapeutic decision. The interview transcripts were analyzed using an iteratively refined coding schema and content analysis. . Participants generally expressed enthusiasm for the tool's design and functionality. Our analysis identified desired filters for users to view patients like themselves, outcome types that should be included in future iterations of the tool (e.g. patient reported outcomes), and information needs that need to be addressed for patients to effectively participate in shared decision making. Implications for the integration of our findings into the design of patient decision aids are discussed.