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The rapid increase in the use of electronic medical records (EMRs) has led to some unintended consequences that negatively affect physicians and their patients.

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The use of medical scribes may serve as a possible solution to some of the EMR-related concerns.

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Research has demonstrated an overall positive impact of having scribes on both physician and patient well-being, safety, and satisfaction.

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Adaptation of advances in technology, including remote and asynchronous scribing, use of face-mounted devices, voice recognition software, and applications of artificial intelligence may address some of the barriers to more traditional in-person scribes.

Here we examined whether eye movement measures in global-local perceptual processing tasks, where abnormalities were typically found in individuals with schizophrenia, could be used to predict schizotypy through Eye Movement analysis with Hidden Markov Models (EMHMM). Using both multiple regression analysis and Gaussian process classifier to predict schizotypy, we found that in addition to longer response times in contour integration, a less consistent eye fixation to locate a stimulus and a more consistent subsequent fixation to start engaging local processing in the embedded figures task predicted high schizotypy. These effects may be related to reduced top-down attention control due to deficient global processing and enhanced local processing bias respectively. In addition, performance in embedded figures could further enhance classification accuracy when being used in conjunction with the above predictors, suggesting the multifactorial nature of the identification problem. These predictors may be important endophenotype markers for schizotypal personality.

Prospect Theory has been highly influential; however its experimental paradigm lacks higher orders of uncertainty. To introduce this, participants are asked to imagine themselves facing a choice between two bags containing 100,000 blue or red balls in unknown proportions. A red ball wins £500. Participants are shown samples from each bag; e.g., 5 balls from Bag 1 (3 red) and 100 balls from Bag 2 (55 red). The bags can be represented by distributions with Bag 1 having a higher mean probability estimate (60% vs 55%), but more variance (second order uncertainty) in that estimate. By varying observed frequencies and gain vs loss formats, we seek to determine if classic findings remain when higher order uncertainties are present. Results consistent with the four-fold pattern are seen for gains (uncertainty seeking at low probability values, uncertainty aversion at high probability values) but for losses, uncertainty aversion is seen at all values.