Every measurement, scoring method, and pedagogical design decision in ReasonDx is grounded in peer-reviewed research from medical education, cognitive psychology, and communication science.
ReasonDx is built on four intersecting bodies of research: how clinicians reason diagnostically, how cognitive biases cause diagnostic errors, how clinical communication is taught and assessed, and how expert knowledge is structured. Each framework directly shapes a platform feature.
Clinicians generate a small set of hypotheses early and selectively gather data to test them. ReasonDx's 10-phase structure mirrors this empirically-validated process.
System 1 (fast, intuitive) and System 2 (deliberate, analytical) thinking processes. ReasonDx requires explicit justification to activate System 2 and detect when students anchor prematurely on System 1 responses.
The gold-standard frameworks for medical interviewing and communication assessment, defining open-to-closed question sequencing, empathy behaviors, and the essential elements of every clinical encounter.
Expert clinicians organize diagnostic knowledge into illness scripts (enabling conditions, fault, consequences). Richer, contrastive language (semantic qualifiers) predicts diagnostic accuracy. ReasonDx scores articulation quality using these markers.
Cognitive factors contribute to 74% of diagnostic errors. Premature closure is the single most common cause. ReasonDx detects anchoring bias and premature closure using operationalizations grounded in this taxonomy.
Provider communication complexity is a modifiable upstream cause of poor health outcomes. The CDC recommends patient materials at 6th–8th grade level. ReasonDx measures reading level of student-patient communication using validated formulas.
Self-assessment is systematically inaccurate in medical trainees. Overconfidence is associated with diagnostic error. ReasonDx calibrates student-rated confidence against objective differential accuracy across sessions.
Spaced practice produces superior retention compared to massed practice. ReasonDx generates spaced repetition review cards from each student's identified reasoning gaps — personalized to the specific concepts each student missed.
ReasonDx passively collects behavioral data during normal simulation activity — no additional student effort required. Every construct measured maps to a validated theoretical framework.
| Construct | Domain | Grounding |
|---|---|---|
Differential breadth & accuracy Phases 1, 4, 6–7 |
Reasoning | Hypothetico-deductive model (Elstein et al., 1978); illness script theory (Schmidt & Rikers, 2007) |
Anchoring bias Cross-phase differential comparison |
Reasoning | Croskerry (2009); Graber et al. (2005); Kunitomo et al. (2022) |
Premature closure Post-labs differential change |
Reasoning | Graber et al. (2005) — single most common cognitive error; Berner & Graber (2008) |
Evidence integration Phase 4 → Final differential |
Reasoning | Croskerry (2009) debiasing framework — System 2 hypothesis updating |
Reasoning articulation quality 0–4 scale per justification response |
Reasoning | Semantic qualifier framework (Bordage & Lemieux, 1991); elaborated knowledge (Bordage, 1994) |
History-taking completeness Critical history elicitation |
Reasoning | Faulty context generation (Graber et al., 2005 taxonomy) |
Language complexity (readability) Flesch-Kincaid, SMOG, ARI |
Communication | Kincaid et al. (1975); McLaughlin (1969); CDC 6th–8th grade benchmark |
Language adaptation for patient Patient vs. attending register |
Communication | SEGUE Framework (Makoul, 2001); Calgary-Cambridge Guide (Silverman et al., 2005) |
Question type sequencing Open/closed/leading/clarifying |
Communication | Calgary-Cambridge open-to-closed cone; Langewitz et al. (2002) spontaneous talking time |
Empathy & rapport behaviors 7 behavioral signals per turn |
Communication | Kalamazoo Consensus Statement (Makoul et al., 2001); CARE Measure (Mercer et al., 2004) |
Implicit confidence language Hedging vs. commitment markers |
Epistemic markers in clinical reasoning (Lingard et al., 2003) | |
Confidence calibration 5-point Likert vs. accuracy |
Eva & Regehr (2005, 2008); Berner & Graber (2008); Sætrevik et al. (2024) | |
Guideline-grounded debrief RAG over open-access guidelines |
Evidence | Retrieval-Augmented Generation (Lewis et al., 2020); open-access clinical guidelines only |
ReasonDx is an educational platform, not a clinical assessment tool. We are committed to transparency about what the platform can and cannot measure.
The following are primary citations for the platform's core theoretical frameworks. Full evidence base documentation — including operationalizations, thresholds, and limitations for every construct — is maintained in the platform's research documentation.
Croskerry, P. (2009). A universal model of diagnostic reasoning. Academic Medicine, 84(8), 1022–1028. doi:10.1097/ACM.0b013e3181ace703
Elstein, A. S., Shulman, L. S., & Sprafka, S. A. (1978). Medical Problem Solving: An Analysis of Clinical Reasoning. Harvard University Press.
Graber, M. L., Franklin, N., & Gordon, R. (2005). Diagnostic error in internal medicine. Archives of Internal Medicine, 165(13), 1493–1499. doi:10.1001/archinte.165.13.1493
Berner, E. S., & Graber, M. L. (2008). Overconfidence as a cause of diagnostic error in medicine. American Journal of Medicine, 121(5 Suppl), S2–S23.
Kunitomo, K., Harada, T., & Watari, T. (2022). Cognitive biases encountered by physicians in the emergency room. BMC Emergency Medicine, 22, 148. doi:10.1186/s12873-022-00708-3
Sætrevik, B., Seeligmann, V. T., Frotvedt, T. F., & Bondevik, Ø. K. (2024). Anchoring, confirmation and confidence bias among medical decision-makers. Collabra: Psychology, 10(1), 126223. doi:10.1525/collabra.126223
Schmidt, H. G., & Rikers, R. M. J. P. (2007). How expertise develops in medicine: knowledge encapsulation and illness script formation. Medical Education, 41(12), 1133–1139.
Bordage, G., & Lemieux, M. (1991). Semantic structures and diagnostic thinking of experts and novices. Academic Medicine, 66(9 Suppl), S70–S72.
Bordage, G. (1994). Elaborated knowledge: a key to successful diagnostic thinking. Academic Medicine, 69(11), 883–885.
Makoul, G. et al. (2001). Essential elements of communication in medical encounters: the Kalamazoo Consensus Statement. Academic Medicine, 76(4), 390–393.
Silverman, J., Kurtz, S., & Draper, J. (2005). Skills for Communicating with Patients (2nd ed.). Radcliffe Publishing.
Mercer, S. W. et al. (2004). The CARE Measure. Family Practice, 21(6), 699–705.
Levinson, W., Gorawara-Bhat, R., & Lamb, J. (2000). A study of patient clues and physician responses. JAMA, 284(8), 1021–1027.
Paasche-Orlow, M. K., & Wolf, M. S. (2007). The causal pathways linking health literacy to health outcomes. American Journal of Health Behavior, 31(S1), S19–S26.
Kincaid, J. P. et al. (1975). Derivation of new readability formulas. Naval Air Station Memphis.
McLaughlin, G. H. (1969). SMOG grading: a new readability formula. Journal of Reading, 12(8), 639–646.
Centers for Disease Control and Prevention. (2016). Health Literacy. cdc.gov/healthliteracy
Eva, K. W., & Regehr, G. (2005). Self-assessment in the health professions: a reformulation and research agenda. Academic Medicine, 80(10 Suppl), S46–S54.
Wolpaw, T. M., Wolpaw, D. R., & Papp, K. K. (2003). SNAPPS: a learner-centered model for outpatient education. Academic Medicine, 78(9), 893–898.
Moulton, C. A. et al. (2007). Slowing down when you should: a new model of expert judgment. Academic Medicine, 82(S10), S109–S116.