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

   https://doi.org/10.1177/0272989X21996328  

   Bonner, Carissa; Trevena, Lyndal J.; Gaissmaier, Wolfgang; Han, Paul K.; Okan, Yasmina; Ozanne, Elissa; Peters, Ellen; Timmermans, Daniëlle; Zikmund-Fisher, Brian J. (October 2021, Medical Decision Making)

   null (Ed.)

   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. 

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2. Communicating Numeric Risk Information to Patients

   https://doi.org/10.1007/s11606-025-09520-8  

   Peters, Ellen; Han, Paul KJ; Lee, Clara N (April 2025, Journal of General Internal Medicine)

   Risk information is increasingly available to health care providers and patients thanks to a growing body of health outcomes research and clinical prediction models. Meanwhile, communicating such information is encouraged for a variety of reasons. Yet clinicians often struggle to communicate risk information—or forego the task altogether due to various challenges. The challenges are real, and this paper briefly discusses six of them: (1) Clinician reliance on verbal risk descriptions, (2) Low patient numeracy; (3) Lack of meaningful numeric evidence; (4) Patient use of heuristics; (5) Uncertain risk information; and (6) The curse of knowledge. Specific strategies exist for clinicians, though, to overcome these complex challenges. In the paper, we present evidence-based best practices with examples of what clinicians can do to effectively communicate risk information to their patients (and what they should not do). The best practices include communicating with numbers, not only words; decreasing cognitive effort for patients; providing the meaning of numeric risk data important to decisions; acknowledging uncertainty; and testing communication with patients through teach-back techniques. We conclude by recommending that clinicians integrate these strategies into their existing scripts for patient encounters. 

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3. ReAct: Synergizing Reasoning and Acting in Language Models

   Yao, Shunyu; Zhao, Jeffrey; Yu, Dian; Du, Nan; Shafran, Izhak; Narasimhan, Karthik; Cao, Yuan (January 2023, International Conference on Learning Representations (ICLR))

   While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation) have primarily been studied as separate topics. In this paper, we explore the use of LLMs to generate both reasoning traces and task-specific actions in an interleaved manner, allowing for greater synergy between the two: reasoning traces help the model induce, track, and update action plans as well as handle exceptions, while actions allow it to interface with external sources, such as knowledge bases or environments, to gather additional information. We apply our approach, named ReAct, to a diverse set of language and decision making tasks and demonstrate its effectiveness over state-of-the-art baselines, as well as improved human interpretability and trustworthiness over methods without reasoning or acting components. Concretely, on question answering (HotpotQA) and fact verification (Fever), ReAct overcomes issues of hallucination and error propagation prevalent in chain-of-thought reasoning by interacting with a simple Wikipedia API, and generates human-like task-solving trajectories that are more interpretable than baselines without reasoning traces. On two interactive decision making benchmarks (ALFWorld and WebShop), ReAct outperforms imitation and reinforcement learning methods by an absolute success rate of 34% and 10% respectively, while being prompted with only one or two in-context examples. 

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4. The Precision-Bias Distinction for Evaluating Visual Decision Aids for Risk Perception

   https://doi.org/10.1177/0272989X20943516  

   Witt, Jessica_K (July 2020, Medical Decision Making)

   Risk communication is critically important, for both patients and providers. However, people struggle to understand risks because there are inherent biases and limitations to reasoning under uncertainty. A common strategy to enhance risk communication is the use of decision aids, such as charts or graphs, that depict the risk visually. A problem with prior research on visual decision aids is that it used a metric of performance that confounds 2 underlying constructs: precision and bias. Precision refers to a person’s sensitivity to the information, whereas bias refers to a general tendency to overestimate (or underestimate) the level of risk. A visual aid is effective for communicating risk only if it enhances precision or, once precision is suitably high, reduces bias. This article proposes a methodology for evaluating the effectiveness of visual decision aids. Empirical data further illustrate how the new methodology is a significant advancement over more traditional research designs. 

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5. Composer—Visual Cohort Analysis of Patient Outcomes

   https://doi.org/10.1055/s-0039-1687862  

   Rogers, Jen; Spina, Nicholas; Neese, Ashley; Hess, Rachel; Brodke, Darrel; Lex, Alexander (March 2019, Applied Clinical Informatics)

   Objective Visual cohort analysis utilizing electronic health record data has become an important tool in clinical assessment of patient outcomes. In this article, we introduce Composer, a visual analysis tool for orthopedic surgeons to compare changes in physical functions of a patient cohort following various spinal procedures. The goal of our project is to help researchers analyze outcomes of procedures and facilitate informed decision-making about treatment options between patient and clinician. Methods In collaboration with orthopedic surgeons and researchers, we defined domain-specific user requirements to inform the design. We developed the tool in an iterative process with our collaborators to develop and refine functionality. With Composer, analysts can dynamically define a patient cohort using demographic information, clinical parameters, and events in patient medical histories and then analyze patient-reported outcome scores for the cohort over time, as well as compare it to other cohorts. Using Composer's current iteration, we provide a usage scenario for use of the tool in a clinical setting. Conclusion We have developed a prototype cohort analysis tool to help clinicians assess patient treatment options by analyzing prior cases with similar characteristics. Although Composer was designed using patient data specific to orthopedic research, we believe the tool is generalizable to other healthcare domains. A long-term goal for Composer is to develop the application into a shared decision-making tool that allows translation of comparison and analysis from a clinician-facing interface into visual representations to communicate treatment options to patients. 

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