Evidence map›Paper›PMID 35486432›Full record

ArticleJMIR human factors2022

Effective Communication of Personalized Risks and Patient Preferences During Surgical Informed Consent Using Data Visualization: Qualitative Semistructured Interview Study With Patients After Surgery.

Undina Gisladottir, Drashko Nakikj, Rashi Jhunjhunwala, Jasmine Panton, Gabriel Brat, Nils Gehlenborg

Abstract read
In one paragraph

Article in JMIR human factors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Bedside preoperative predictors of difficult laryngeal exposure in microlaryngeal surgery.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024
    Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Undina GisladottirDepartment of Biomedical Informatics, Harvard Medical School, Harvard University, Boston, MA, United States.ORCID https://orcid.org/0000-0001-5892-0852
Drashko NakikjDepartment of Biomedical Informatics, Harvard Medical School, Harvard University, Boston, MA, United States.ORCID https://orcid.org/0000-0001-9723-9138
Rashi JhunjhunwalaDepartment of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, United States.ORCID https://orcid.org/0000-0002-0970-8369
Jasmine PantonDepartment of Surgery, Beth Israel Deaconess Medical Center, Boston, MA, United States.ORCID https://orcid.org/0000-0002-3396-045X
Gabriel Brat *Department of Biomedical Informatics, Harvard Medical School, Harvard University, Boston, MA, United States.ORCID https://orcid.org/0000-0003-3928-5931
Nils Gehlenborg *Department of Biomedical Informatics, Harvard Medical School, Harvard University, Boston, MA, United States.ORCID https://orcid.org/0000-0003-0327-8297

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere is no consensus on which risks to communicate to a prospective surgical patient during informed consent or how. Complicating the process, patient preferences may diverge from clinical assumptions and are often not considered for discussion. Such discrepancies can lead to confusion and resentment, raising the potential for legal action. To overcome these issues, we propose a visual consent tool that incorporates patient preferences and communicates personalized risks to patients using data visualization. We used this platform to identify key effective visual elements to communicate personalized surgical risks.

objectiveOur main focus is to understand how to best communicate personalized risks using data visualization. To contextualize patient responses to the main question, we examine how patients perceive risks before surgery (research question 1), how suitably the visual consent tool is able to present personalized surgical risks (research question 2), how well our visualizations convey those personalized surgical risks (research question 3), and how the visual consent tool could improve the informed consent process and how it can be used (research question 4).

methodsWe designed a visual consent tool to meet the objectives of our study. To calculate and list personalized surgical risks, we used the American College of Surgeons risk calculator. We created multiple visualization mock-ups using visual elements previously determined to be well-received for risk communication. Semistructured interviews were conducted with patients after surgery, and each of the mock-ups was presented and evaluated independently and in the context of our visual consent tool design. The interviews were transcribed, and thematic analysis was performed to identify major themes. We also applied a quantitative approach to the analysis to assess the prevalence of different perceptions of the visualizations presented in our tool.

resultsIn total, 20 patients were interviewed, with a median age of 59 (range 29-87) years. Thematic analysis revealed factors that influenced the perception of risk (the surgical procedure, the cognitive capacity of the patient, and the timing of consent; research question 1); factors that influenced the perceived value of risk visualizations (preference for rare event communication, preference for risk visualization, and usefulness of comparison with the average; research question 3); and perceived usefulness and use cases of the visual consent tool (research questions 2 and 4). Most importantly, we found that patients preferred the visual consent tool to current text-based documents and had no unified preferences for risk visualization. Furthermore, our findings suggest that patient concerns were not often represented in existing risk calculators.

conclusionsWe identified key elements that influence effective visual risk communication in the perioperative setting and pointed out the limitations of the existing calculators in addressing patient concerns. Patient preference is highly variable and should influence choices regarding risk presentation and visualization.

Indexed as

biomedical informaticsdata visualizationshared decision-makingsurgical informed consent

Identifiers

PMID35486432
PMCPMC9107059

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.