Patient needs and preferences for herb-drug-disease interaction alerts: a structured interview study.

BACKGROUND: While complementary and alternative medicine (CAM) is commonly used in the United States and elsewhere, and hazardous interactions with prescription drugs can occur, patients do not regularly communicate with physicians about their CAM use. The objective of this study was to discover patient information needs and preferences for herb-drug-disease interaction alerts. METHODS: We recruited 50 people from several locations within the University of Utah Hospital to participate in this structured interview study. They were asked to provide their preferences for the herb-drug-disease interaction alerts. Qualitative methods were used to reveal the themes that emerged from the interviews. RESULTS: Most participants reported they had previously used, or they were currently using, CAM therapies. The majority had made the effort to inform their healthcare provider(s) about their CAM usage, although some had not. We found that most respondents were interested in receiving alerts and information about potential interactions. Many preferred to receive the alerts in a variety of ways, both in person and electronically. CONCLUSIONS: In addition to conventional medicine, many patients regularly use complementary and alternative therapies. And yet, communication between patients and providers about CAM use is not consistent. There is a demand for interventions in health care that provide timely, integrative communication support. Delivering the herb-drug-disease alerts through multiple channels could help meet critical patient information needs.

An Evolving Ecosystem for Natural Language Processing in Department of Veterans Affairs.

In an ideal clinical Natural Language Processing (NLP) ecosystem, researchers and developers would be able to collaborate with others, undertake validation of NLP systems, components, and related resources, and disseminate them. We captured requirements and formative evaluation data from the Veterans Affairs (VA) Clinical NLP Ecosystem stakeholders using semi-structured interviews and meeting discussions. We developed a coding rubric to code interviews. We assessed inter-coder reliability using percent agreement and the kappa statistic. We undertook 15 interviews and held two workshop discussions. The main areas of requirements related to; design and functionality, resources, and information. Stakeholders also confirmed the vision of the second generation of the Ecosystem and recommendations included; adding mechanisms to better understand terms, measuring collaboration to demonstrate value, and datasets/tools to navigate spelling errors with consumer language, among others. Stakeholders also recommended capability to: communicate with developers working on the next version of the VA electronic health record (VistA Evolution), provide a mechanism to automatically monitor download of tools and to automatically provide a summary of the downloads to Ecosystem contributors and funders. After three rounds of coding and discussion, we determined the percent agreement of two coders to be 97.2% and the kappa to be 0.7851. The vision of the VA Clinical NLP Ecosystem met stakeholder needs. Interviews and discussion provided key requirements that inform the design of the VA Clinical NLP Ecosystem.

Doodle Health: A Crowdsourcing Game for the Co-design and Testing of Pictographs to Reduce Disparities in Healthcare Communication.

Supplementing patient education content with pictographs can improve the comprehension and recall of information, especially patients with low health literacy. Pictograph design and testing, however, are costly and time consuming. We created a Web-based game, Doodle Health, for crowdsourcing the drawing and validation of pictographs. The objective of this pilot study was to test the usability of the game and its appeal to healthcare consumers. The chief purpose of the game is to involve a diverse population in the co-design and evaluation of pictographs. We conducted a community-based focus group to inform the game design. Game designers, health sciences librarians, informatics researchers, clinicians, and community members participated in two Design Box meetings. The results of the meetings were used to create the Doodle Health crowdsourcing game. The game was presented and tested at two public fairs. Initial testing indicates crowdsourcing is a promising approach to pictograph development and testing for relevancy and comprehension. Over 596 drawings were collected and 1,758 guesses were performed to date with 70-90% accuracies, which are satisfactorily high.

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

BACKGROUND: Shared decision-making can improve patient satisfaction and outcomes. To participate in shared decision-making, patients need information about the potential risks and benefits of treatment options. Our team has developed a novel prototype tool for shared decision-making called hearts like mine (HLM) that leverages EHR data to provide personalized information to patients regarding potential outcomes of different treatments. These potential outcomes are presented through an Icon array and/or simulated narratives for each "person" in the display. In this pilot project we sought to determine whether the inclusion of simulated narratives in the display affects individuals' decision-making. Thirty subjects participated in this block-randomized study in which they used a version of HLM with simulated narratives and a version without (or in the opposite order) to make a hypothetical therapeutic decision. After each decision, participants completed a questionnaire that measured decisional confidence. We used Chi square tests to compare decisions across conditions and Mann-Whitney U tests to examine the effects of narratives on decisional confidence. Finally, we calculated the mean of subjects' post-experiment rating of whether narratives were helpful in their decision-making. RESULTS: In this study, there was no effect of simulated narratives on treatment decisions (decision 1: Chi squared = 0, p = 1.0; decision 2: Chi squared = 0.574, p = 0.44) or Decisional confidence (decision 1, w = 105.5, p = 0.78; decision 2, w = 86.5, p = 0.28). Post-experiment, participants reported that narratives helped them to make decisions (mean = 3.3/4). CONCLUSIONS: We found that simulated narratives had no measurable effect on decisional confidence or decisions and most participants felt that the narratives were helpful to them in making therapeutic decisions. The use of simulated stories holds promise for promoting shared decision-making while minimizing their potential biasing effect.