Plain language summaries: A systematic review of theory, guidelines and empirical research

[User experience with plain language summaries of psychological systematic reviews with meta-analysis ("KLARpsy" texts) - A qualitative study using the think aloud method]

BACKGROUND

Science communication can support informed decision-making. As part of the "PLan Psy" project, a guideline for producing plain language summaries of systematic reviews with meta-analysis on psychological topics ("KLARpsy" texts), was developed. This study aims to investigate the similarities and differences in the user experience with "KLARpsy texts" between laypersons and professionals (science communicators and psychologists).

METHODS

We conducted a qualitative online interview study. Participants read two "KLARpsy" texts presented on a mock-up website and verbalized their impressions and experiences using the think aloud method. The interviews were transcribed verbatim and analyzed with a deductive approach using content analysis.

RESULTS

The study was completed by twelve participants (nine female, three male), including six laypersons, three science communicators, and three psychologists. Both groups found the "KLARpsy" texts to be mainly useful, user-friendly, and trustworthy. Nevertheless, professional users preferred the original studies. Both groups emphasized the need for more detailed descriptions regarding methodology and result presentation. The "KLARsaurus" glossary promoted comprehensibility. The text structure supported usability. However, the structure and transitions between study-specific and general information as well as sentence structure were sometimes seen as non-intuitive. Some opinions on comprehensibility and information density were not consistent within the two groups.

DISCUSSION

The plain language summaries in the form of 'KLARpsy' texts were perceived by interested participants as added value for laypersons. Both laypersons and professionals attach particular importance to a transparent and critical, but also understandable and clear presentation of study results. Individual preferences and differences in the perspectives of both user groups highlight challenges of standardizing such a science communication format.

Using artificial intelligence to expedite and enhance plain language summary abstract writing of scientific content

Objective

To assess the capacity of a bespoke artificial intelligence (AI) process to help medical writers efficiently generate quality plain language summary abstracts (PLSAs).

Materials and Methods

Three independent studies were conducted. In Studies 1 and 3, original scientific abstracts (OSAs; n = 48, n = 2) and corresponding PLSAs written by medical writers versus bespoke AI were assessed using standard readability metrics. Study 2 compared time and effort of medical writers (n = 10) drafting PLSAs starting with an OSA (n = 6) versus the output of 1 bespoke AI (n = 6) and 1 non-bespoke AI (n = 6) process. These PLSAs (n = 72) were assessed by subject matter experts (SMEs; n = 3) for accuracy and physicians (n = 7) for patient suitability. Lastly, in Study 3, medical writers (n = 22) and patients/patient advocates (n = 5) compared quality of medical writer and bespoke AI-generated PLSAs.

Results

In Study 1, bespoke AI PLSAs were easier to read than medical writer PLSAs across all readability metrics (P <.01). In Study 2, bespoke AI output saved medical writers >40% in time for PLSA creation and required less effort than unassisted writing. SME-assessed quality was higher for AI-assisted PLSAs, and physicians preferred bespoke AI-generated outputs for patient use. In Study 3, bespoke AI PLSAs were more readable and rated of higher quality than medical writer PLSAs.

Discussion

The bespoke AI process may enhance access to health information by helping medical writers produce PLSAs of scientific content that are fit for purpose.

Conclusion

The bespoke AI process can more efficiently create better quality, more readable first draft PLSAs versus medical writer-generated PLSAs.

Perspectives of people with chronic illness about plain language summaries: a qualitative analysis

People with chronic health conditions tend to look for information from a range of sources. Although information online is accessible, the quality varies and articles in scientific journals are not written for a general audience. Given this, plain language summaries (PLSs) may be a useful source of information, especially for people with chronic medical conditions. PLSs contain reliable information that is written in plain, easy-to-understand language. We investigated the health information-seeking behaviour of consumers with chronic health conditions and sought to understand what they see as important in a PLS with respect to content, design and structure, and the labels used. We conducted semi-structured focus groups and interviews with 19 participants from 6 countries, all with a chronic medical condition. Using reflexive thematic analysis, we developed four themes: (i) 'Accessing information in a competitive landscape of health information'; (ii) 'I really don't see the patient in this at all'; (iii) 'Co-design should be meaningful, not tokenistic'; and (iv) 'A way forward: Approach PLSs with creativity'. Participants highlighted the importance of PLS labels, the need for actionable content, and the recommendation to vary the reading level depending on audience needs. The results of this study suggest that PLSs produced with consumer input could elicit PLSs that better meet audience needs, focusing on information that is actionable, accessible, and written with useful content that is detailed but still respectful of the reader. Consequently, PLSs could become a more useful source of reliable information, particularly for people with chronic health conditions.

Assessing the Capability of Large Language Model Chatbots in Generating Plain Language Summaries

Background Plain language summaries (PLSs) make scientific research accessible to a broad non-expert audience. However, crafting effective PLS can be challenging, particularly for non-native English-speaking researchers. Large language model (LLM) chatbots have the potential to assist in generating summaries, but their effectiveness compared to human-generated PLS remains underexplored. Methods This cross-sectional study compared 30 human-written PLS with LLM chatbot (viz., ChatGPT (OpenAI, San Francisco, CA), Claude (Anthropic, San Francisco, CA), Copilot (Microsoft Corp., Washington, DC), Gemini (Google, Mountain View, CA), Meta AI (Meta, Menlo Park, CA), and Perplexity (Perplexity AI, Inc., San Francisco, CA)) generated PLS. The readability of the PLS was checked by the Flesch reading (FR) ease score, and understandability was checked by the Flesch-Kincaid (FK) grade level. Three authors rated the text on seven-item predefined criteria, and their average score was used to compare the quality of the PLS. Results In comparison to human-written PLS, chatbots could generate PLS with lower FK grade levels (p-value < 0.0001) and except Copilot, all others had higher FR ease scores. The overall score of human-written PLS was 8.89±0.26. Although there was statistically significant variance among the scores (F = 7.16, p-value = 0.0012), in the post-hoc test, there was no difference between human-generated and individual chatbots-generated PLS (ChatGPT 8.8±0.34, Claude 8.89±0.33, Copilot 8.69±0.4, Gemini 8.56±0.56, Meta AI 8.98±0.23, and Perplexity 8.8±0.3). Conclusion LLM chatbots can generate PLS with better readability and a person with a lower grade of education can understand it. The PLS are of similar quality to those written by human authors. Hence, authors can generate PLS from LLM chatbots and it is particularly beneficial for researchers in developing countries. While LLM chatbots improve readability, they may introduce minor inaccuracies also. Hence, PLS generated by LLM should always checked for accuracy and relevancy.

Interventions for the management of post-COVID-19 condition (long COVID): protocol for a living systematic review and network meta-analysis

BACKGROUND

Up to 15% of survivors of COVID-19 infection experience long-term health effects, including fatigue, myalgia and impaired cognitive function, termed post-COVID-19 condition or long COVID. Several trials that study the benefits and harms of various interventions to manage long COVID have been published and hundreds more are planned or are ongoing. Trustworthy systematic reviews that clarify the benefits and harms of interventions are critical to promote evidence-based practice.

OBJECTIVE

To create and maintain a living systematic review and network meta-analysis addressing the benefits and harms of pharmacologic and non-pharmacologic interventions for the treatment and management of long COVID.

METHODS

Eligible trials will randomise adults with long COVID to pharmacologic or non-pharmacologic interventions, placebo, sham or usual care. We will identify eligible studies by searching MEDLINE, EMBASE, CINAHL, PsycINFO, AMED and CENTRAL from inception, without language restrictions.Reviewers will work independently and in duplicate to screen search records, collect data from eligible trials, including trial and patient characteristics and outcomes of interest and assess risk of bias. Our outcomes of interest will include patient-reported fatigue, pain, postexertional malaise, changes in education or employment status, cognitive function, mental health, dyspnoea, quality of life, physical function, recovery and serious adverse events.For each outcome, when possible, we will perform a frequentist random-effects network meta-analysis. When there are compelling reasons to suspect that certain interventions are only applicable or effective for a subtype of long COVID, we will perform separate network meta-analyses. The Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach will guide our assessment of the certainty of evidence.We will update our living review biannually, on the publication of a seminal trial, or when new evidence emerges that may change clinical practice.

CONCLUSION

This living systematic review and network meta-analysis will provide comprehensive, trustworthy and up-to-date summaries of the evidence addressing the benefits and harms of interventions for the treatment and management of long COVID. We will make our findings available publicly and work with guideline-producing organisations to inform their recommendations.

ETHICS AND DISSEMINATION

The study describes the protocol for a systematic review that uses data from published trial reports. Therefore, the study is exempt from ethics review. We intend to deposit all data in a public repository and publish each iteration of the living review online.

Jargon and Readability in Plain Language Summaries of Health Research: Cross-Sectional Observational Study

BACKGROUND

The idea of making science more accessible to nonscientists has prompted health researchers to involve patients and the public more actively in their research. This sometimes involves writing a plain language summary (PLS), a short summary intended to make research findings accessible to nonspecialists. However, whether PLSs satisfy the basic requirements of accessible language is unclear.

OBJECTIVE

We aimed to assess the readability and level of jargon in the PLSs of research funded by the largest national clinical research funder in Europe, the United Kingdom's National Institute for Health and Care Research (NIHR). We also aimed to assess whether readability and jargon were influenced by internal and external characteristics of research projects.

METHODS

We downloaded the PLSs of all NIHR National Journals Library reports from mid-2014 to mid-2022 (N=1241) and analyzed them using the Flesch Reading Ease (FRE) formula and a jargon calculator (the De-Jargonizer). In our analysis, we included the following study characteristics of each PLS: research topic, funding program, project size, length, publication year, and readability and jargon scores of the original funding proposal.

RESULTS

Readability scores ranged from 1.1 to 70.8, with an average FRE score of 39.0 (95% CI 38.4-39.7). Moreover, 2.8% (35/1241) of the PLSs had an FRE score classified as "plain English" or better; none had readability scores in line with the average reading age of the UK population. Jargon scores ranged from 76.4 to 99.3, with an average score of 91.7 (95% CI 91.5-91.9) and 21.7% (269/1241) of the PLSs had a jargon score suitable for general comprehension. Variables such as research topic, funding program, and project size significantly influenced readability and jargon scores. The biggest differences related to the original proposals: proposals with a PLS in their application that were in the 20% most readable were almost 3 times more likely to have a more readable final PLS (incidence rate ratio 2.88, 95% CI 1.86-4.45). Those with the 20% least jargon in the original application were more than 10 times as likely to have low levels of jargon in the final PLS (incidence rate ratio 13.87, 95% CI 5.17-37.2). There was no observable trend over time.

CONCLUSIONS

Most of the PLSs published in the NIHR's National Journals Library have poor readability due to their complexity and use of jargon. None were readable at a level in keeping with the average reading age of the UK population. There were significant variations in readability and jargon scores depending on the research topic, funding program, and other factors. Notably, the readability of the original funding proposal seemed to significantly impact the final report's readability. Ways of improving the accessibility of PLSs are needed, as is greater clarity over who and what they are for.

Are plain language summaries more readable than scientific abstracts? Evidence from six biomedical and life sciences journals

In recent decades, members of the general public have become increasingly reliant on findings of scientific studies for decision-making. However, scientific writing usually features a heavy use of technical language, which may pose challenges for people outside of the scientific community. To alleviate this issue, plain language summaries were introduced to provide a brief summary of scientific papers in clear and accessible language. Despite increasing attention paid to the research of plain language summaries, little is known about whether these summaries are readable for the intended audiences. Based on a large corpus sampled from six biomedical and life sciences journals, the present study examined the readability and jargon use of plain language summaries and scientific abstracts on a technical level. It was found that (1) plain language summaries were more readable than scientific abstracts, (2) the reading grade levels of plain language summaries were moderately correlated with that of scientific abstracts, (3) researchers used less jargon in plain language summaries than in scientific abstracts, and (4) the readability of and the jargon use in both plain language summaries and scientific abstracts exceeded the recommended threshold for the general public. The findings were discussed with possible explanations. Implications for academic writing and scientific communication were offered.

Literacy and Language Barriers to Overcome in Laboratory Medicine

In the context of laboratory medicine, the authors describe 3 barriers to health communication: access, health communication, and language responsiveness. These barriers are interconnected and present in millions of people in need of equitable access to health communication. Equitable access entails health communication written in plain language and languages other than English to address language and literacy barriers and increase trust by avoiding language discordance and the spread of infodemics. This review includes several options to implement multidisciplinary efforts that lead to measurable improvements in health literacy.

Are plain language summaries published in health journals written according to instructions and health literacy principles? A systematic environmental scan

BACKGROUND

Plain language summaries (PLSs) are easy-to-understand summaries of research articles that should follow principles of plain language and health literacy. PLS author instructions from health journals help guide authors on word count/PLS length, structure and the use of jargon. However, it is unclear whether published PLSs currently adhere to author instructions.

OBJECTIVES

This study aims to determine (1) the degree of compliance of published PLSs against the PLS author instructions in health journals and (2) the extent to which PLSs meet health literacy principles.

STUDY DESIGN

We conducted a three-part systematic environmental scan.

METHODS

We examined 26 health journals identified from a previous review. In part 1, we assessed the inclusion frequency of PLSs in the 26 journals; in part 2, we assessed the level of compliance of PLSs with PLS author instructions; and in part 3, we conducted a health literacy assessment of the PLSs.

RESULTS

Part 1: we found PLSs for 20/26 (76.9%) included journals. Part 2: no journal achieved 100% compliance with PLS author instructions. The highest level was 86% and the lowest was 0%. Part 3: no PLS was written at a readability level suitable for a general audience. The mean reading level was grade 15.8 (range 10.2-21.2and mean percentage of complex words, 31% (range 8.5%-49.8%).

DISCUSSION

PLSs are an important means through which consumers can access research findings. We found a lack of compliance between PLS author instructions and PLSs published in health journals that may impede access and use by consumers. This study highlights the need for better ways to support authors adhere to PLS instructions and improved monitoring by journals.

APPLS: Evaluating Evaluation Metrics for Plain Language Summarization

While there has been significant development of models for Plain Language Summarization (PLS), evaluation remains a challenge. PLS lacks a dedicated assessment metric, and the suitability of text generation evaluation metrics is unclear due to the unique transformations involved (e.g., adding background explanations, removing jargon). To address these questions, our study introduces a granular meta-evaluation testbed, APPLS, designed to evaluate metrics for PLS. We identify four PLS criteria from previous work-informativeness, simplification, coherence, and faithfulness-and define a set of perturbations corresponding to these criteria that sensitive metrics should be able to detect. We apply these perturbations to the texts of two PLS datasets to create our testbed. Using APPLS, we assess performance of 14 metrics, including automated scores, lexical features, and LLM prompt-based evaluations. Our analysis reveals that while some current metrics show sensitivity to specific criteria, no single method captures all four criteria simultaneously. We therefore recommend a suite of automated metrics be used to capture PLS quality along all relevant criteria. This work contributes the first meta-evaluation testbed for PLS and a comprehensive evaluation of existing metrics.

Artificial Intelligence in Medical Affairs: A New Paradigm with Novel Opportunities

The advent of artificial intelligence (AI) revolutionizes the ways of working in many areas of business and life science. In Medical Affairs (MA) departments of the pharmaceutical industry AI holds great potential for positively influencing the medical mission of identifying and addressing unmet medical needs and care gaps, and fostering solutions that improve the egalitarian and unbiased access of patients to treatments worldwide. Given the essential position of MA in corporate interactions with various healthcare stakeholders, AI offers broad possibilities to support strategic decision-making and to pioneer novel approaches in medical stakeholder interactions. By analyzing data derived from the healthcare environment and by streamlining operations in medical content generation, AI advances data-based prioritization and strategy execution. In this review, we discuss promising AI-based solutions in MA that support the effective use of heterogenous information from observations of the healthcare environment, the enhancement of medical education, and the analysis of real-world data. For a successful implementation of such solutions, specific considerations partly unique to healthcare must be taken care of, for example, transparency, data privacy, healthcare regulations, and in predictive applications, explainability.

Practices and Barriers in Developing and Disseminating Plain-Language Resources Reporting Medical Research Information: A Scoping Review

BACKGROUND

The intent of plain-language resources (PLRs) reporting medical research information is to advance health literacy among the general public and enable them to participate in shared decision-making (SDM). Regulatory mandates coupled with academic and industry initiatives have given rise to an increasing volume of PLRs summarizing medical research information. However, there is significant variability in the quality, format, readability, and dissemination channels for PLRs. In this scoping review, we identify current practices, guidance, and barriers in developing and disseminating PLRs reporting medical research information to the general public including patients and caregivers. We also report on the PLR preferences of these intended audiences.

METHODS

A literature search of three bibliographic databases (PubMed, EMBASE, Web of Science) and three clinical trial registries (NIH, EMA, ISRCTN registry) was performed. Snowball searches within reference lists of primary articles were added. Articles with PLRs or reporting topics related to PLRs use and development available between January 2017 and June 2023 were identified. Evidence mapping and synthesis were used to make qualitative observations. Identified PLRs were quantitatively assessed, including temporal annual trends, availability by field of medicine, language, and publisher types.

RESULTS

A total of 9116 PLRs were identified, 9041 from the databases and 75 from clinical trial registries. The final analysis included 6590 PLRs from databases and 72 from registries. Reported barriers to PLR development included ambiguity in guidance, lack of incentives, and concerns of researchers writing for the general public. Available guidance recommendations called for greater dissemination, increased readability, and varied content formats. Patients preferred visual PLRs formats (e.g., videos, comics), which were easy to access on the internet and used short jargon-free text. In some instances, older audiences and more educated readers preferred text-only PLRs. Preferences among the general public were mostly similar to those of patients. Psychology, followed by oncology, showed the highest number of PLRs, predominantly from academia-sponsored research. Text-only PLRs were most commonly available, while graphical, digital, or online formats were less available. Preferred dissemination channels included paywall-free journal websites, indexing on PubMed, third-party websites, via email to research participants, and social media.

CONCLUSIONS

This scoping review maps current practices, recommendations, and patients' and the general public's preferences for PLR development and dissemination. The results suggest that making PLRs available to a wider audience by improving nomenclature, accessibility, and providing translations may contribute to empowerment and SDM. Minimizing variability among available guidance for PLR development may play an important role in amplifying the value and impact of these resources.

Equivalent user experience and improved community augmented meta-analyses knowledge for a new version of a Plain Language Summary guideline

Plain Language Summaries (PLS) offer a promising solution to make meta-analytic psychological research more accessible for non-experts and laypeople. However, existing writing guidelines for this type of publication are seldom grounded in empirical studies. To address this and to test two versions of a new PLS guideline, we investigated the impact of PLSs of psychological meta-analyses on laypeoples' PLS-related knowledge and their user experience (accessibility, understanding, empowerment). In a preregistered online-study, N = 2,041 German-speaking participants read two PLSs. We varied the inclusion of a disclaimer on PLS authorship, a statement on the causality of effects, additional information on community augmented meta-analyses (CAMA) and the PLS guideline version. Results partially confirmed our preregistered hypotheses: Participants answered knowledge items on CAMA more correctly when a PLS contained additional information on CAMA, and there were no user experience differences between the old and the new guideline versions. Unexpectedly, a priori hypotheses regarding improved knowledge via the use of a disclaimer and a causality statement were not confirmed. Reasons for this, as well as general aspects related to science communication via PLSs aimed at educating laypeople, are discussed.

Belief updating when confronted with scientific evidence: Examining the role of trust in science

In one exploratory study (N = 985) and one preregistered study (N = 1100), we investigated whether trust in science influences belief change on a medico-scientific issue when laypersons are confronted with scientific evidence. Moreover, we tested whether individuals with high trust in science trust science "blindly," meaning that their trust in a scientific claim's source prevents them from adequately evaluating the claim itself. Participants read eight fictitious studies on the efficacy of acupuncture, which were experimentally manipulated regarding direction (evidence favoring acupuncture vs diverging evidence) and quality (high vs low; only Study 2). Acupuncture-related beliefs were measured before and after reading. Moderator and mediator analyses showed that the magnitude of belief change indeed depends on trust in science. Furthermore, we found that people with high trust in science are better able to evaluate the quality of scientific studies, which, in turn, protects them from being influenced by low-quality evidence.

Does the format of result presentation and type of conclusion in Cochrane plain language summaries matter? A randomised controlled trial

OBJECTIVES

This study aimed to investigate whether the format and type of conclusion in Cochrane plain language summaries (PLSs) influence readers' perception of treatment benefit and decision-making.

DESIGN

An online parallel group, three-arm randomised controlled trial was conducted.

SETTING

The study was conducted online.

PARTICIPANTS

The participants were physiotherapy students.

INTERVENTIONS

The participants read two Cochrane PLSs, one with a positive conclusion (strong evidence of benefit) and another with a negative conclusion (strong evidence of non-benefit). Each participant read the results of both reviews presented in one of three formats: (1) numerical, (2) textual or (3) numerical and textual.

MAIN OUTCOME MEASURES

The primary outcome measure was the participants' perception of treatment benefit.

RESULTS

All three groups of participants perceived the treatment to have positive effects when the Cochrane PLS had a positive conclusion, regardless of the format of presentation (mean perception of treatment benefit score: textual 7.7 (SD 2.3), numerical 7.9 (SD 1.8), numerical and textual 7.7 (SD 1.7), p=0.362). However, when the Cochrane PLS had a negative conclusion, all three groups of participants failed to perceive a negative effect (mean perception of treatment benefit score: textual 5.5 (SD 3.3), numerical 5.6 (SD 2.7), numerical and textual 5.9 (SD 2.8), p=0.019).

CONCLUSIONS

The format of Cochrane PLSs does not appear to significantly impact physiotherapy students' perception of treatment benefit, understanding of evidence, persuasiveness or confidence in their decision. However, participants' perception of treatment benefit does not align with the conclusion when the Cochrane PLS indicates strong evidence of non-benefit from the intervention.

TRIAL REGISTRATION NUMBER

CTRI/2022/10/046476.

Conclusiveness, readability and textual characteristics of plain language summaries from medical and non-medical organizations: a cross-sectional study

This cross-sectional study compared plain language summaries (PLSs) from medical and non-medical organizations regarding conclusiveness, readability and textual characteristics. All Cochrane (medical PLSs, n = 8638) and Campbell Collaboration and International Initiative for Impact Evaluation (non-medical PLSs, n = 163) PLSs of latest versions of systematic reviews published until 10 November 2022 were analysed. PLSs were classified into three conclusiveness categories (conclusive, inconclusive and unclear) using a machine learning tool for medical PLSs and by two experts for non-medical PLSs. A higher proportion of non-medical PLSs were conclusive (17.79% vs 8.40%, P < 0.0001), they had higher readability (median number of years of education needed to read the text with ease 15.23 (interquartile range (IQR) 14.35 to 15.96) vs 15.51 (IQR 14.31 to 16.77), P = 0.010), used more words (median 603 (IQR 539.50 to 658.50) vs 345 (IQR 202 to 476), P < 0.001). Language analysis showed that medical PLSs scored higher for disgust and fear, and non-medical PLSs scored higher for positive emotions. The reason for the observed differences between medical and non-medical fields may be attributed to the differences in publication methodologies or disciplinary differences. This approach to analysing PLSs is crucial for enhancing the overall quality of PLSs and knowledge translation to the general public.

Publishing clinical trial results in plain language: a clash of ethical principles?

Plain language resources (PLR) are lay summaries of clinical trial results or plain language summaries of publications, in digital/visual/language formats. They aim to provide accurate information in jargon-free, and easy-to-understand language that can meet the health information needs of the general public, especially patients and caregivers. These are typically developed by the study sponsors or investigators, or by national public health bodies, research hospitals, patient organizations, and non-profit organizations. While the usefulness of PLR seems unequivocal, they have never been analyzed from the perspective of ethics. In this commentary, we do so and reflect on whether PLR are categorically advantageous or if they solve certain issues but raise new problems at the same time. Ethical concerns that PLR can potentially address include but are not limited to individual and community level health literacy, patient empowerment and autonomy. We also highlight the ethical issues that PLR may potentially exacerbate, such as fair balanced presentation and interpretation of medical knowledge, positive publication bias, and equitable access to information. PLR are important resources for patients, with promising implications for individual as well as community health. However, they require appropriate oversight and standards to optimize their potential value. Hence, we also highlight recommendations and best practices from our reading of the literature, that aim to minimize these biases.

A powerful partnership: researchers and patients working together to develop a patient-facing summary of clinical trial outcome data

OBJECTIVE

Availability of easy-to-understand patient-reported outcome (PRO) trial data may help individuals make more informed healthcare decisions. Easily interpretable, patient-centric PRO data summaries and visualizations are therefore needed. This three-stage study explored graphical format preferences, understanding, and interpretability of clinical trial PRO data presented to people with prostate cancer (PC).

MATERIALS AND METHODS

A 7-day online survey exploring people with PC's preferences for different PRO data presentations (stage 1; n = 30) informed development of a draft plain-language resource sheet containing PRO data. After refining for clarity during cognitive debriefing interviews (stage 2; n = 18), the final resource sheet was circulated to people with PC for broader feedback (stage 3; n = 45).

RESULTS

Although participants expressed preferences for certain graphical formats (pie charts and bar charts), preference did not always associate with interpretability and overall message clarity. Iterative development (stages 1 and 2) led to a final resource sheet, which 91.1% of participants in stage 3 considered useful and informative, and 88.9% expressed interest in receiving similar resources in the future.

DISCUSSION

Findings demonstrate PRO data are relevant to people with PC and highlights that targeted resource sheets can support patient-clinician discussions. Appropriate graphical formatting and use of plain-language text is essential for conveying interpretable PRO data. Data visualization preferences are context dependent.

CONCLUSION

Resource sheets summarizing clinical trial PRO data can be helpful for decision-making in PC. Researchers and patients can work together to develop clear, relevant, sensitive, and understandable resource sheets, which equally consider patient priorities as well as those of scientists.

Recommended characteristics and processes for writing lay summaries of healthcare evidence: a co-created scoping review and consultation exercise

BACKGROUND

Lay summaries (LSs) of scientific evidence are critical to sharing research with non-specialist audiences. This scoping review with a consultation exercise aimed to (1) Describe features of the available LS resources; (2) Summarize recommended LS characteristics and content; (3) Outline recommended processes to write a LS; and (4) Obtain stakeholder perspectives on LS characteristics and writing processes.

METHODS

This project was a patient and public partner (PPP)-initiated topic co-led by a PPP and a researcher. The team was supported by three additional PPPs and four researchers. A search of peer-reviewed (Ovid MEDLINE, Scopus, Embase, Cochrane libraries, CINAHL, PsycINFO, ERIC and PubMed data bases) and grey literature was conducted using the Joanna Briggs Institute Methodological Guidance for Scoping Reviews to include any resource that described LS characteristics and writing processes. Two reviewers screened and extracted all resources. Resource descriptions and characteristics were organized by frequency, and processes were inductively analyzed. Nine patient and public partners and researchers participated in three consultation exercise sessions to contextualize the review findings.

RESULTS

Of the identified 80 resources, 99% described characteristics of a LS and 13% described processes for writing a LS. About half (51%) of the resources were published in the last two years. The most recommended characteristics were to avoid jargon (78%) and long or complex sentences (60%). The most frequently suggested LS content to include was study findings (79%). The key steps in writing a LS were doing pre-work, preparing for the target audience, writing, reviewing, finalizing, and disseminating knowledge. Consultation exercise participants prioritized some LS characteristics differently compared to the literature and found many characteristics oversimplistic. Consultation exercise participants generally supported the writing processes found in the literature but suggested some refinements.

CONCLUSIONS

Writing LSs is potentially a growing area, however, efforts are needed to enhance our understanding of important LS characteristics, create resources with and for PPPs, and develop optimal writing processes.

Science Communication as a Collective Intelligence Endeavor: A Manifesto and Examples for Implementation

Effective science communication is challenging when scientific messages are informed by a continually updating evidence base and must often compete against misinformation. We argue that we need a new program of science communication as collective intelligence-a collaborative approach, supported by technology. This would have four key advantages over the typical model where scientists communicate as individuals: scientific messages would be informed by (a) a wider base of aggregated knowledge, (b) contributions from a diverse scientific community, (c) participatory input from stakeholders, and (d) better responsiveness to ongoing changes in the state of knowledge.

Generative AI as a Tool for Environmental Health Research Translation

One valuable application for generative artificial intelligence (AI) is summarizing research studies for non-academic readers. We submitted five articles to Chat Generative Pre-trained Transformer (ChatGPT) for summarization, and asked the article's author to rate the summaries. Higher ratings were assigned to more insight-oriented activities, such as the production of eighth-grade reading level summaries, and summaries highlighting the most important findings and real-world applications. The general summary request was rated lower. For the field of environmental health science, no-cost AI technology such as ChatGPT holds the promise to improve research translation, but it must continue to be improved (or improve itself) from its current capability.

Considerations for Effective Communication of Medical Information

An ongoing and increasing shift is occurring in ways in which the healthcare community (healthcare providers, patients, biopharma) disseminates and consumes information. Adoption of digital technologies, accelerated by the virtual environment created by the COVID-19 pandemic, are leading to new and innovative ways in which medical and scientific information and data are communicated. Digital technologies have largely enabled these approaches and led to an explosive increase in availability of information. This article describes considerations in how the healthcare community, and Medical Affairs organizations in biopharma, can effectively harness these channels to communicate effectively, and incorporate changes in behaviors and approaches to redefine what medical information and data look like.