Gaissmaier, Wolfgang
0000-0001-6273-178X Berufsbeschreibung
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The Phenomenology of the Diagnostic Process : A Primary Care-Based Survey
Background:
While dichotomous tasks and related cognitive strategies have been extensively researched in cognitive psychology, little is known about how primary care practitioners (general practitioners [GPs]) approach ill-defined or polychotomous tasks and how valid or useful their strategies are.
Objective:
To investigate cognitive strategies used by GPs for making a diagnosis.
Methods:
In a cross-sectional study, we videotaped 282 consultations, irrespective of presenting complaint or final diagnosis. Reflective interviews were performed with GPs after each consultation. Recordings of consultations and GP interviews were transcribed verbatim and analyzed using a coding system that was based on published literature and systematically checked for reliability.
Results:
In total, 134 consultations included 163 diagnostic episodes. Inductive foraging (i.e., the initial, patient-guided search) could be identified in 91% of consultations. It contributed an average 31% of cues obtained by the GP in 1 consultation. Triggered routines and descriptive questions occurred in 38% and 84% of consultations, respectively. GPs resorted to hypothesis testing, the hallmark of the hypothetico-deductive method, in only 39% of consultations.
Limitations:
Video recordings and interviews presumably interfered with GPs’ behavior and accounts. GPs might have pursued more hypotheses and collected more information than usual.
Conclusions:
The testing of specific disease hypotheses seems to play a lesser role than previously thought. Our data from real consultations suggest that GPs organize their search for information in a skillfully adapted way. Inductive foraging, triggered routines, descriptive questions, and hypotheses testing are essential building blocks to make a diagnosis in the generalist setting.
Numbers Can Be Worth a Thousand Pictures : Individual Differences in Understanding Graphical and Numerical Representations of Health-Related Information
Objective
Informed medical decision making requires comprehending statistical information. We aimed to improve the understanding of conveying health-related statistical information with graphical representations compared with numerical representations. First, we investigated whether the iconicity of representations (i.e., their abstractness vs. concreteness) affected comprehension and recall of statistical information. Second, we investigated whether graph literacy helps to identify individuals who comprehend graphical representations better than numerical representations.
Method
Participants (N = 275) were randomly assigned to receive different representations of health-related statistical information, ranging from very low iconicity (numbers) to very high iconicity (icon arrays including photographs). Comprehension and recall of the information were assessed. Additionally, participants rated the accessibility of the information and the attractiveness of the representation. Graph literacy was assessed by means of a recently developed scale.
Results
The only difference between representations that affected comprehension and recall was the difference between graphics and numbers; the actual level of iconicity of graphics did not matter. Individuals with high graph literacy had better comprehension and recall when presented with graphics instead of numbers, and they rated graphical information as more accessible than numerical information, whereas the reverse was true for individuals with low graph literacy, F(4, 185) = 2.60, p = .04, ηp² = .05, and F(4, 245) = 2.71, p = .03, ηp2 = .04, respectively. Both groups judged graphical representations as more attractive than numerical representations.
Conclusion
An assessment of graph literacy distinguished individuals who are best informed with graphical representations of statistical information from those who are better informed with numerical representations.
Smart strategies for doctors and doctors-in-training : heuristics in medicine
CONTEXT How do doctors make sound deci- sions when confronted with probabilistic data, time pressures and a heavy workload? One theory that has been embraced by many researchers is based on optimisation, which emphasises the need to integrate all informa- tion in order to arrive at sound decisions. This notion makes heuristics, which use less than complete information, appear as second-best strategies. In this article, we challenge this pessimistic view of heuristics.
METHODS We introduce two medical problems that involve decision making to the reader: one concerns coronary care issues and the other macrolide prescriptions. In both settings, decision-making tools grounded in the principles of optimisation and heuristics, respectively, have been developed to assist doctors in making decisions. We explain the structure of each of these tools and compare their performance in terms of their facilitation of correct predictions.
RESULTS For decisions concerning both the coronary care unit and the prescribing of macrolides, we demonstrate that sacrificing information does not necessarily imply a forfeiting of predictive accuracy, but can sometimes even lead to better decisions. Sub- sequently, we discuss common misconceptions about heuristics and explain when and why ignoring parts of the available information can lead to the making of more robust predictions.
CONCLUSIONS Heuristics are neither good nor bad per se, but, if applied in situations to which they have been adapted, can be helpful companions for doctors and doctors-in-training. This, however, requires that heuristics in medicine be openly discussed, criticised, refined and then taught to doctors-in-training rather than being simply dismissed as harmful or irrelevant. A more uniform use of explicit and accepted heuristics has the potential to reduce variations in diagnoses and to improve medical care for patients.
Do Physicians Understand Cancer Screening Statistics? : A National Survey of Primary Care Physicians in the United States
Background: Unlike reduced mortality rates, improved survival rates and increased early detection do not prove that cancer screen- ing tests save lives. Nevertheless, these 2 statistics are often used to promote screening.
Objective: To learn whether primary care physicians understand which statistics provide evidence about whether screening saves lives.
Design: Parallel-group, randomized trial (randomization controlled for order effect only), conducted by Internet survey. (ClinicalTrials. gov registration number: NCT00981019)
Setting: National sample of U.S. primary care physicians from a research panel maintained by Harris Interactive (79% cooperation rate).
Participants: 297 physicians who practiced both inpatient and out- patient medicine were surveyed in 2010, and 115 physicians who practiced exclusively outpatient medicine were surveyed in 2011.
Intervention: Physicians received scenarios about the effect of 2 hypothetical screening tests: The effect was described as improved 5-year survival and increased early detection in one scenario and as decreased cancer mortality and increased incidence in the other.
Measurements: Physicians’ recommendation of screening and per- ception of its benefit in the scenarios and general knowledge of screening statistics.
Results: Primary care physicians were more enthusiastic about the screening test supported by irrelevant evidence (5-year survival increased from 68% to 99%) than about the test supported by relevant evidence (cancer mortality reduced from 2 to 1.6 in 1000 persons). When presented with irrelevant evidence, 69% of physi- cians recommended the test, compared with 23% when presented with relevant evidence (P < 0.001). When asked general knowl- edge questions about screening statistics, many physicians did not distinguish between irrelevant and relevant screening evidence; 76% versus 81%, respectively, stated that each of these statistics proves that screening saves lives (P = 0.39). About one half (47%) of the physicians incorrectly said that finding more cases of cancer in screened as opposed to unscreened populations “proves that screening saves lives.”
Limitation: Physicians’ recommendations for screening were based on hypothetical scenarios, not actual practice.
Conclusion: Most primary care physicians mistakenly interpreted improved survival and increased detection with screening as evi- dence that screening saves lives. Few correctly recognized that only reduced mortality in a randomized trial constitutes evidence of the benefit of screening.
Deceiving Numbers : Survival Rates and Their Impact on Doctor' Risk Communication
Background
Increased 5-y survival for screened patients is often inferred to mean that fewer patients die of cancer. However, due to several biases, the 5-y survival rate is a misleading metric for evaluating a screening’s effectiveness. If physicians are not aware of these issues, informed screening counseling cannot take place.
Methods
Two questionnaire versions ("Group" and "time") presented 4 conditions: 5-y survival (5Y), 5-y survival and annual disease-specific mortality (5YM), annual disease-specific mortality (M), and 5-y survival, annual disease-specific mortality, and incidence (5YMI). Questionnaire version "time" presented data as a comparison between 2 time points and version "group" as a comparison between a screened and an unscreened group. All data were based on statistics for the same cancer site (prostate). Outcome variables were the recommendation of screening, reason- ing behind recommendation, judgment of the screening's effectiveness, and, if judged effective, a numerical esti- mate of how many fewer people out of 1000 would die if screened regularly. After randomized allocation, 65 Ger- man physicians in internal medicine and its subspecial- ities completed either of the 2 questionnaire versions.
Results
Across both versions, 66% of the physicians recommended screening when presented with 5Y, but only 8% of the same physicians made the recommenda- tion when presented with M (5YM: 31%; 5YMI: 55%). Also, 5Y made considerably more physicians (78%) judge the screening to be effective than any other condition (5YM: 31%; M: 5%; 5YMI: 49%) and led to the highest overestimations of benefit. Conclusion. A large number of physicians erroneously based their screening recommendation and judgment of screening's effectiveness on the 5-y survival rate. Results show that reporting disease-specific mortality rates can offer a simple solution to phy- sicians' confusion about the real effect of screening.