Using Civic Service Design Methods to Redevelop a Data Communication Website With a Health Literacy Lens

CONTEXT

Public health agencies routinely publish data in hopes that data influence public health policy and practice. However, data websites can often be difficult to use, posing barriers to people trying to access, understand, and use data. Working to make data websites easier to use can add value to public health data communication work.

PROGRAM

The New York City Department of Health and Mental Hygiene (DOHMH) redesigned its Environment and Health Data Portal, a website used to communicate environmental health data, with the goal of making data more accessible and understandable to a broader audience. The DOHMH used Civic Service Design methods to establish priorities and strategies for the redesign work, to build a data communication website that emphasizes a high level of usability, and content that explains data.

IMPLEMENTATION

By following a Civic Service Design process, the DOHMH synthesized findings from health communications, data visualization and communication, and web usability to create an easy-to-use website with explanations of data and findings alongside datasets. On the new site, automated dataset visualizations are supplemented with narrative content, explanatory content, and custom interactive applications designed to explain data and findings.

EVALUATION

Web analytics showed that, in its first year of operation, the site's web traffic grew substantially, with the last 12 weeks recording weekly page views 150% higher than the first 12 weeks of operation (7185 average weekly page views compared with 2866 average weekly page views). Two-thirds (66.3%) of page views include recorded user engagement. Additional evaluations to measure specific aspects of usability compared with the previous version of the site are planned.

DISCUSSION

By following a Civic Service Design process, the DOHMH redesigned a vital data communication platform to increase its usability and saw significant increase in engagement in its first year of operations. By designing data material with usability in mind, public health departments have the potential to improve public health data communication work.

"R" you getting this? Factors contributing to the public's understanding, evaluation, and use of basic reproduction numbers for infectious diseases

BACKGROUND

We (1) examined the effects of evaluative labels and visual aids on people's understanding, evaluation, and use of the COVID-19 reproduction number (or "r-number"), (2) examined whether people's perceived susceptibility and (intended) adherence to preventive measures changed after being exposed to the r-number, and (3) explored whether these effects and changes depended on people's numeracy skills.

METHODS

In an online experiment, participants from a large Dutch representative sample (N = 1,168) received information about the COVID-19 r-number displayed on the corona dashboard of the Dutch Ministry of Health, Welfare and Sport. The r-number was either presented with or without a categorical line display (i.e., evaluative label) and with or without an icon-based tree diagram (i.e., visual aid) explaining how the number works. Regarding people's use of the statistic, we measured perceived susceptibility to COVID-19 and adherence (intention) to five preventive measures before and after exposure to the r-number. After exposure, we also measured participants' understanding, perceived usefulness, affective and cognitive evaluation, and objective numeracy.

RESULTS

About 56% of participants correctly interpreted the r-number, with highly numerate people having better understanding than less numerate people. Information about the r-number was perceived as more useful when presented with a visual aid. There were no differences across experimental conditions in people's understanding, affective, and cognitive evaluations. Finally, independent of experimental conditions, intention to adhere to preventive measures was higher after seeing the r-number, but only among highly numerate people.

CONCLUSIONS

Although evaluative labels and visual aids did not facilitate people's understanding and evaluation of the r-number, our results show that the statistic is perceived as useful and may be used to stimulate adherence to preventive measures. Policy makers and public health communicators are advised to clearly explain why they are giving these numbers to - especially - the less numerate people, but also how people could use them for behavior change to combat the spread of virus during a pandemic.

Visualizing machine learning-based predictions of postpartum depression risk for lay audiences

OBJECTIVES

To determine if different formats for conveying machine learning (ML)-derived postpartum depression risks impact patient classification of recommended actions (primary outcome) and intention to seek care, perceived risk, trust, and preferences (secondary outcomes).

MATERIALS AND METHODS

We recruited English-speaking females of childbearing age (18-45 years) using an online survey platform. We created 2 exposure variables (presentation format and risk severity), each with 4 levels, manipulated within-subject. Presentation formats consisted of text only, numeric only, gradient number line, and segmented number line. For each format viewed, participants answered questions regarding each outcome.

RESULTS

Five hundred four participants (mean age 31 years) completed the survey. For the risk classification question, performance was high (93%) with no significant differences between presentation formats. There were main effects of risk level (all P < .001) such that participants perceived higher risk, were more likely to agree to treatment, and more trusting in their obstetrics team as the risk level increased, but we found inconsistencies in which presentation format corresponded to the highest perceived risk, trust, or behavioral intention. The gradient number line was the most preferred format (43%).

DISCUSSION AND CONCLUSION

All formats resulted high accuracy related to the classification outcome (primary), but there were nuanced differences in risk perceptions, behavioral intentions, and trust. Investigators should choose health data visualizations based on the primary goal they want lay audiences to accomplish with the ML risk score.

Knowledge exchange sessions on primary health care research findings in public libraries: A qualitative study with citizens in Quebec

Little is known about knowledge transfer with the public. We explored how citizens, physicians, and communication specialists understand knowledge transfer in public spaces such as libraries. The initial study aimed at evaluating the scaling up of a program on disseminating research findings on potentially inappropriate medication. Twenty-two citizen workshops were offered by 16 physicians and facilitated by 6 communication specialists to 322 citizens in libraries during spring 2019. We did secondary analysis using the recorded workshop discussions to explore the type of knowledge participants used. Participants described four kinds of knowledge: biomedical, sociocultural beliefs, value-based reasoning, and institutional knowledge. Biomedical knowledge included scientific evidence, research methods, clinical guidelines, and access to research outcomes. Participants discussed beliefs in scientific progress, innovative clinical practices, and doctors' behaviours. Participants discussed values related to reliability, transparency, respect for patient autonomy and participation in decision-making. All categories of participants used these four kinds of knowledge. However, their descriptions varied particularly for biomedical knowledge which was described by physician-speakers and communication specialists-facilitators as scientific evidence, epidemiological and clinical practice guidelines, and pathophysiological theories. Communication specialists-facilitators also described scientific journalistic sources and scientific journalistic reports as proxies of scientific evidence. Citizens described biomedical knowledge in terms of knowledge to make informed decisions. These findings offer insights for future scientific knowledge exchange interventions with the public.

Beyond mathematics, statistics, and programming: data science, machine learning, and artificial intelligence competencies and curricula for clinicians, informaticians, science journalists, and researchers

Data science, machine learning and artificial intelligence applications impact clinicians, informaticians, science journalists, and researchers. Most biomedical data science training focuses on learning a programming language in addition to higher mathematics and advanced statistics. This approach is appropriate for graduate students but greatly reduces the number of individuals in healthcare who can be involved in data science. To serve these four stakeholder audiences, we describe several curricular strategies focusing on solving real problems of interest to these audiences. Relevant competencies for these audiences include using intuitive programming tools that facilitate data exploration with minimal programming background, creating data models, evaluating results of data analyses, and assessing data science research reports, among others. Offering the curricula described here more broadly could broaden the stakeholder groups knowledgeable about and engaged in data science.

A Scalable Service to Improve Health Care Quality Through Precision Audit and Feedback: Proposal for a Randomized Controlled Trial

BACKGROUND

Health care delivery organizations lack evidence-based strategies for using quality measurement data to improve performance. Audit and feedback (A&F), the delivery of clinical performance summaries to providers, demonstrates the potential for large effects on clinical practice but is currently implemented as a blunt one size fits most intervention. Each provider in a care setting typically receives a performance summary of identical metrics in a common format despite the growing recognition that precisionizing interventions hold significant promise in improving their impact. A precision approach to A&F prioritizes the display of information in a single metric that, for each recipient, carries the highest value for performance improvement, such as when the metric's level drops below a peer benchmark or minimum standard for the first time, thereby revealing an actionable performance gap. Furthermore, precision A&F uses an optimal message format (including framing and visual displays) based on what is known about the recipient and the intended gist meaning being communicated to improve message interpretation while reducing the cognitive processing burden. Well-established psychological principles, frameworks, and theories form a feedback intervention knowledge base to achieve precision A&F. From an informatics perspective, precision A&F requires a knowledge-based system that enables mass customization by representing knowledge configurable at the group and individual levels.

OBJECTIVE

This study aims to implement and evaluate a demonstration system for precision A&F in anesthesia care and to assess the effect of precision feedback emails on care quality and outcomes in a national quality improvement consortium.

METHODS

We propose to achieve our aims by conducting 3 studies: a requirements analysis and preferences elicitation study using human-centered design and conjoint analysis methods, a software service development and implementation study, and a cluster randomized controlled trial of a precision A&F service with a concurrent process evaluation. This study will be conducted with the Multicenter Perioperative Outcomes Group, a national anesthesia quality improvement consortium with >60 member hospitals in >20 US states. This study will extend the Multicenter Perioperative Outcomes Group quality improvement infrastructure by using existing data and performance measurement processes.

RESULTS

The proposal was funded in September 2021 with a 4-year timeline. Data collection for Aim 1 began in March 2022. We plan for a 24-month trial timeline, with the intervention period of the trial beginning in March 2024.

CONCLUSIONS

The proposed aims will collectively demonstrate a precision feedback service developed using an open-source technical infrastructure for computable knowledge management. By implementing and evaluating a demonstration system for precision feedback, we create the potential to observe the conditions under which feedback interventions are effective.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/34990.

The attributes of the images representing the SARS-CoV-2 coronavirus affect people's perception of the virus

BACKGROUND

The recent COVID-19 pandemic has seen an explosion of coronavirus-related information. In many cases, this information was supported by images representing the SARS-CoV-2.

AIM

To evaluate how attributes of images representing the SARS-CoV-2 coronavirus that were used in the initial phase of the coronavirus crisis in 2020 influenced the public's perceptions.

METHODS

We have carried out an in-depth survey using 46 coronavirus images, asking individuals how beautiful, scientific, realistic, infectious, scary and didactic they appeared to be.

RESULTS

We collected 91,908 responses, obtaining 15,315 associations for each category. While the reference image of SARS-CoV-2 used in the media is a three-dimensional, colour, illustration, we found that illustrations of the coronavirus were perceived as beautiful but not very realistic, scientific or didactic. By contrast, black and white coronavirus images are thought to be the opposite. The beauty of coronavirus images was negatively correlated with the perception of scientific realism and didactic value.

CONCLUSION

Given these effects and the consequences on the individual's perception, it is important to evaluate the influence that different images of SARS-CoV-2 may have on the population.

Current Best Practice for Presenting Probabilities in Patient Decision Aids: Fundamental Principles

BACKGROUND

Shared decision making requires evidence to be conveyed to the patient in a way they can easily understand and compare. Patient decision aids facilitate this process. This article reviews the current evidence for how to present numerical probabilities within patient decision aids.

METHODS

Following the 2013 review method, we assembled a group of 9 international experts on risk communication across Australia, Germany, the Netherlands, the United Kingdom, and the United States. We expanded the topics covered in the first review to reflect emerging areas of research. Groups of 2 to 3 authors reviewed the relevant literature based on their expertise and wrote each section before review by the full authorship team.

RESULTS

Of 10 topics identified, we present 5 fundamental issues in this article. Although some topics resulted in clear guidance (presenting the chance an event will occur, addressing numerical skills), other topics (context/evaluative labels, conveying uncertainty, risk over time) continue to have evolving knowledge bases. We recommend presenting numbers over a set time period with a clear denominator, using consistent formats between outcomes and interventions to enable unbiased comparisons, and interpreting the numbers for the reader to meet the needs of varying numeracy.

DISCUSSION

Understanding how different numerical formats can bias risk perception will help decision aid developers communicate risks in a balanced, comprehensible manner and avoid accidental "nudging" toward a particular option. Decisions between probability formats need to consider the available evidence and user skills. The review may be useful for other areas of science communication in which unbiased presentation of probabilities is important.

Judgment and Decision Making

The science of judgment and decision making involves three interrelated forms of research: analysis of the decisions people face, description of their natural responses, and interventions meant to help them do better. After briefly introducing the field's intellectual foundations, we review recent basic research into the three core elements of decision making: judgment, or how people predict the outcomes that will follow possible choices; preference, or how people weigh those outcomes; and choice, or how people combine judgments and preferences to reach a decision. We then review research into two potential sources of behavioral heterogeneity: individual differences in decision-making competence and developmental changes across the life span. Next, we illustrate applications intended to improve individual and organizational decision making in health, public policy, intelligence analysis, and risk management. We emphasize the potential value of coupling analytical and behavioral research and having basic and applied research inform one another.