An online experiment to assess bias in professional medical coding.

BackgroundMultiple studies have documented bias in medical decision making, but no studies have examined whether this bias extends to medical coding practices. Medical coding is foundational to the US health care enterprise. We evaluate whether bias based on patient characteristics influences specific coding practices of professional medical coders.MethodsThis is an online experimental study of members of a national professional medical coding organization. Participants were randomly assigned a set of six clinical scenarios reflecting common medical conditions and asked to report encounter level of service codes for these clinical scenarios. Clinical scenarios differed by patient demographics (race, age, gender, ability) or social context (food insecurity, housing security) but were otherwise identical. We estimated Ordinary Least Squares regression models to evaluate differences in outcome average visit level of service by patient demographic characteristics described in the clinical scenarios; we adjusted for coders' age, gender, race, and years of coding experience.ResultsThe final analytic sample included 586 respondents who coded at least one clinical scenario. Higher mean level of service was assigned to clinical scenarios describing seniors compared to middle-aged patients in two otherwise identical scenarios, one a patient with type II diabetes mellitus (Coef: 0.28, SE: 0.15) and the other with rheumatoid arthritis (Coef: 0.30, SE: 0.13). Charts describing women were assigned lower level of service than men in patients with asthma exacerbation (Coef: -0.25, SE: 0.13) and rheumatoid arthritis (Coef: -0.20, SE: 0.12). There were no other significant differences in mean complexity score by patient demographics or social needs.ConclusionWe found limited evidence of bias in professional medical coding practice by patient age and gender, though findings were inconsistent across medical conditions. Low levels of observed bias may reflect medical coding workflow and training practices. Future research is needed to better understand bias in coding and to identify effective and generalizable bias prevention practices

Women and men with coronary heart disease in three countries : are they treated differently?

Non-medical determinants of medical decision making were investigated in an international research project in the US, in the UK and in Germany. The key question in this paper is whether and to what extent doctors' diagnostic and therapeutic decisions in coronary heart disease (CHD) are influenced by the patients' gender. A factorial experiment with a videotaped patient consultation was conducted. Professional actors played the role of patients with symptoms of CHD. Several alternative versions were taped featuring the same script with patient-actors of different sex, age, race and socio-economic status. The videotapes were presented to a randomly selected sample of 128 primary care physicians in each country. Using an interview with standardized and open-ended questions, physicians were asked how they would diagnose and treat such a patient after they had seen the video. Results show gender differences in the diagnostic strategies of the doctors. Women were asked different questions, a CHD was mentioned more often as a possible diagnosis for men than for women, and physicians were less certain about their diagnosis with female patients. Moreover, results indicate that gender differences in management decisions (therapy and lifestyle advice) are less pronounced and less consistent than in diagnostic decisions. Magnitude of gender effect on doctors' decisions varies between countries with smaller influences in the US. Although patients with identical symptoms were presented, primary care doctors’ behavior differed by patients' gender in all three countries under study. These gender differences suggest that women may be less likely to receive an accurate diagnosis and appropriate treatment than men

The influence of patient's age on clinical decision-making about coronary heart disease in the USA and the UK

This paper examines UK and US primary care doctors' decision-making about older (aged 75 years) and midlife (aged 55 years) patients presenting with coronary heart disease (CHD). Using an analytic approach based on conceptualising clinical decision-making as a classification process, it explores the ways in which doctors' cognitive processes contribute to ageism in health-care at three key decision points during consultations. In each country, 56 randomly selected doctors were shown videotaped vignettes of actors portraying patients with CHD. The patients' ages (55 or 75 years), gender, ethnicity and social class were varied systematically. During the interviews, doctors gave free-recall accounts of their decision-making. The results do not establish that there was substantial ageism in the doctors' decisions, but rather suggest that diagnostic processes pay insufficient attention to the significance of older patients' age and its association with the likelihood of co-morbidity and atypical disease presentations. The doctors also demonstrated more limited use of ‘knowledge structures’ when diagnosing older than midlife patients. With respect to interventions, differences in the national health-care systems rather than patients' age accounted for the differences in doctors' decisions. US doctors were significantly more concerned about the potential for adverse outcomes if important diagnoses were untreated, while UK general practitioners cited greater difficulty in accessing diagnostic tests

Citizen science as a new tool in dog cognition research

The work of Á.M. was supported by the Hungarian Academy of Sciences (MTA 01 031).Family dogs and dog owners offer a potentially powerful way to conduct citizen science to answer questions about animal behavior that are difficult to answer with more conventional approaches. Here we evaluate the quality of the first data on dog cognition collected by citizen scientists using the Dognition. com website. We conducted analyses to understand if data generated by over 500 citizen scientists replicates internally and in comparison to previously published findings. Half of participants participated for free while the other half paid for access. The website provided each participant a temperament questionnaire and instructions on how to conduct a series of ten cognitive tests. Participation required internet access, a dog and some common household items. Participants could record their responses on any PC, tablet or smartphone from anywhere in the world and data were retained on servers. Results from citizen scientists and their dogs replicated a number of previously described phenomena from conventional lab-based research. There was little evidence that citizen scientists manipulated their results. To illustrate the potential uses of relatively large samples of citizen science data, we then used factor analysis to examine individual differences across the cognitive tasks. The data were best explained by multiple factors in support of the hypothesis that nonhumans, including dogs, can evolve multiple cognitive domains that vary independently. This analysis suggests that in the future, citizen scientists will generate useful datasets that test hypotheses and answer questions as a complement to conventional laboratory techniques used to study dog psychology.Publisher PDFPeer reviewe

Gender in Engineering Departments: Are There Gender Differences in Interruptions of Academic Job Talks?

We use a case study of job talks in five engineering departments to analyze the under-studied area of gendered barriers to finalists for faculty positions. We focus on one segment of the interview day of short-listed candidates invited to campus: the “job talk”, when candidates present their original research to the academic department. We analyze video recordings of 119 job talks across five engineering departments at two Research 1 universities. Specifically, we analyze whether there are differences by gender or by years of post-Ph.D. experience in the number of interruptions, follow-up questions, and total questions that job candidates receive. We find that, compared to men, women receive more follow-up questions and more total questions. Moreover, a higher proportion of women’s talk time is taken up by the audience asking questions. Further, the number of questions is correlated with the job candidate’s statements and actions that reveal he or she is rushing to present their slides and complete the talk. We argue that women candidates face more interruptions and often have less time to bring their talk to a compelling conclusion, which is connected to the phenomenon of “stricter standards” of competence demanded by evaluators of short-listed women applying for a masculine-typed job. We conclude with policy recommendations

Feeling the future: A meta-analysis of 90 experiments on the anomalous anticipation of random future events

In 2011, one of the authors (DJB) published a report of nine experiments in the Journal of Personality and Social Psychology purporting to demonstrate that an individual\u2019s cognitive and affective responses can be influenced by randomly selected stimulus events that do not occur until after his or her responses have already been made and recorded, a generalized variant of the phenomenon traditionally denoted by the term precognition. To encourage replications, all materials needed to conduct them were made available on request. We here report a meta-analysis of 90 experiments from 33 laboratories in 14 countries which yielded an overall effect greater than 6 sigma, z = 6.40, p = 1.2 7 10 with an effect size (Hedges\u2019 g) of 0.09. A Bayesian analysis yielded a Bayes Factor of 5.1 7 10 , greatly exceeding the criterion value of 100 for \u201cdecisive evidence\u201d in support of the experimental hypothesis. When DJB\u2019s original experiments are excluded, the combined effect size for replications by independent investigators is 0.06, z = 4.16, p = 1.1 7 10 , and the BF value is 3,853, again exceeding the criterion for \u201cdecisive evidence.\u201d The number of potentially unretrieved experiments required to reduce the overall effect size of the complete database to a trivial value of 0.01 is 544, and seven of eight additional statistical tests support the conclusion that the database is not significantly compromised by either selection bias or by intense \u201cp -hacking\u201d\u2014the selective suppression of findings or analyses that failed to yield statistical significance. P-curve analysis, a recently introduced statistical technique, estimates the true effect size of the experiments to be 0.20 for the complete database and 0.24 for the independent replications, virtually identical to the effect size of DJB\u2019s original experiments (0.22) and the closely related \u201cpresentiment\u201d experiments (0.21). We discuss the controversial status of precognition and other anomalous effects collectively known as psi

The effects of change decomposition on code review -- a controlled experiment

Background: Code review is a cognitively demanding and time-consuming process. Previous qualitative studies hinted at how decomposing change sets into multiple yet internally coherent ones would improve the reviewing process. So far, literature provided no quantitative analysis of this hypothesis. Aims: (1) Quantitatively measure the effects of change decomposition on the outcome of code review (in terms of number of found defects, wrongly reported issues, suggested improvements, time, and understanding); (2) Qualitatively analyze how subjects approach the review and navigate the code, building knowledge and addressing existing issues, in large vs. decomposed changes. Method: Controlled experiment using the pull-based development model involving 28 software developers among professionals and graduate students. Results: Change decomposition leads to fewer wrongly reported issues, influences how subjects approach and conduct the review activity (by increasing context-seeking), yet impacts neither understanding the change rationale nor the number of found defects. Conclusions: Change decomposition reduces the noise for subsequent data analyses but also significantly supports the tasks of the developers in charge of reviewing the changes. As such, commits belonging to different concepts should be separated, adopting this as a best practice in software engineering

Preparatory study for the revaluation of the EQ-5D tariff: methodology report.

BACKGROUND: EQ-5D is a widely used generic measure of health with a 'tariff', or preference weights, obtained from the general population, using time trade-off (TTO). PRET (Preparatory study for the Re-valuation of the EQ-5D Tariff project) contributes towards the methodology for its revaluation. METHODS: Stage 1 examined key assumptions typically involved in health-state valuations through a series of binary choice exercises, namely that health-state preferences are independent of (1) duration of the state; (2) whose health it is (i.e. perspective); (3) length of 'lead time' (a mechanism to value all states on the same scale, including those who are worse than being dead); (4) when health events take place (time preference); and (5) satisfaction associated with the state. Further topics addressed were (6) exhaustion of lead time in the worst state; (7) health-state valuation using discrete choice experiments (DCEs) with a duration attribute; and (8) binary choice administration of lead time - time trade-off (LT-TTO). Stage 1 consisted of an online survey with 6000 respondents. Stage 2 compared the results above to those of an identical survey conducted in 200 face-to-face computer-assisted personal interviews (CAPIs), covering topics (1) to (7). Stages 3 and 4 examined - in more detail and depth - issues taken from stage 1. Stage 3 consisted of CAPI surveys of a representative UK sample of 300, using examples of TTO, LT-TTO, and DCE with duration, each followed by extensive feedback questions. Stage 4 was a more intensive exercise involving a qualitative analysis of people's thought processes during both binary choice and iterative health-state valuation exercises. Data were collected through 'think-aloud' methods in 30 interviews of a convenience sample. RESULTS: Stage 1 found that health-state values are not independent of (1) duration of the state but there is no clear pattern; (2) whose health it is; (3) the duration of 'lead time' but there was no clear pattern; (4) when health events take place; or (5) satisfaction associated with the state. Furthermore, (6) exhaustion of lead time in the worst state was subject to substantial framing effects; (7) the five-level version of the EQ-5D (EQ-5D-5L) can be valued using DCE with duration as an attribute; and (8) binary choice LT-TTO can be administered in an online environment. Stage 2 found that although online surveys and CAPI surveys resulted in different compositions of respondents, at the aggregate, their responses to the experimental questions covering (1) to (7) above were not statistically significantly different from each other. Stages 3 and 4 found that TTO and LT-TTO were easier than DCE with duration; respondents did not necessarily trade across all attributes of EQ-5D; some respondents found it difficult to distinguish between the two worst levels of EQ-5D-5L, and some respondents may be thinking about the impact of their ill health on their family. CONCLUSIONS: In order for the National Institute for Health and Care Excellence to make the most appropriate decisions, the EQ-5D tariff needs to incorporate the latest understanding of health-state preferences. PRET contributed to the knowledge base on the conduct of health-state valuation studies. FUNDING: The Medical Research Council (MRC)-National Institute for Health Research (NIHR) Methodology Research Programme funded the PRET project (MRC ref. G0901500), and the EuroQol Group funded the PRET-AS project (Preparatory study for the Re-valuation of the EQ-5D Tariff project - Additional Sample) as an extension to the PRET project with formal agreement from the MRC