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Diagnosis is a complex and ambiguous process and yet, it is the critical hinge point for all subsequent clinical reasoning and decision-making. Tracking the quality of the patient diagnostic process has the potential to provide valuable insights in improving the diagnostic accuracy and to reduce downstream errors but needs to be informative, timely, and efficient at scale. However, due to the rate at which healthcare data are captured on a daily basis, manually reviewing the diagnostic history of each patient would be a severely taxing process without efficient data reduction and representation. Application of data visualization and visual analytics to healthcare data is one promising approach for addressing these challenges. This paper presents a novel flexible visualization and analysis framework for exploring the patient diagnostic process over time (i.e., patient diagnosis paths). Our framework allows users to select a specific set of patients, events and/or conditions, filter data based on different attributes, and view further details on the selected patient cohort while providing an interactive view of the resulting patient diagnosis paths. A practical demonstration of our system is presented with a case study exploring infection-based patient diagnosis paths., Competing Interests: Declaration of Competing Interest One or more of the authors of this paper have disclosed potential or pertinent conflicts of interest, which may include receipt of payment, either direct or indirect, institutional support, or association with an entity in the biomedical field which may be perceived to have potential conflict of interest with this work. For full disclosure statements refer to https://doi.org/10.1016/j.jbi.2022.104178. Wathsala Widanagamaachchi reports financial support was provided by Gordon and Betty Moore Foundation., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Housing stability is an important determinant of health. The US Department of Veterans Affairs (VA) administers several programs to assist Veterans experiencing unstable housing. Measuring long-term housing stability of Veterans who receive assistance from VA is difficult due to a lack of standardized structured documentation in the Electronic Health Record (EHR). However, the text of clinical notes often contains detailed information about Veterans' housing situations that may be extracted using natural language processing (NLP). We present a novel NLP-based measurement of Veteran housing stability: Relative Housing Stability in Electronic Documentation (ReHouSED). We first develop and evaluate a system for classifying documents containing information about Veterans' housing situations. Next, we aggregate information from multiple documents to derive a patient-level measurement of housing stability. Finally, we demonstrate this method's ability to differentiate between Veterans who are stably and unstably housed. Thus, ReHouSED provides an important methodological framework for the study of long-term housing stability among Veterans receiving housing assistance., (Copyright © 2021. Published by Elsevier Inc.)

Background: Understanding complexity in healthcare has the potential to reduce decision and treatment uncertainty. Therefore, identifying both patient and task complexity may offer better task allocation and design recommendation for next-generation health information technology system design., Objective: To identify specific complexity-contributing factors in the infectious disease domain and the relationship with the complexity perceived by clinicians., Method: We observed and audio recorded clinical rounds of three infectious disease teams. Thirty cases were observed for a period of four consecutive days. Transcripts were coded based on clinical complexity-contributing factors from the clinical complexity model. Ratings of complexity on day 1 for each case were collected. We then used statistical methods to identify complexity-contributing factors in relationship to perceived complexity of clinicians., Results: A factor analysis (principal component extraction with varimax rotation) of specific items revealed three factors (eigenvalues>2.0) explaining 47% of total variance, namely task interaction and goals (10 items, 26%, Cronbach's Alpha=0.87), urgency and acuity (6 items, 11%, Cronbach's Alpha=0.67), and psychosocial behavior (4 items, 10%, Cronbach's alpha=0.55). A linear regression analysis showed no statistically significant association between complexity perceived by the physicians and objective complexity, which was measured from coded transcripts by three clinicians (Multiple R-squared=0.13, p=0.61). There were no physician effects on the rating of perceived complexity., Conclusion: Task complexity contributes significantly to overall complexity in the infectious diseases domain. The different complexity-contributing factors found in this study can guide health information technology system designers and researchers for intuitive design. Thus, decision support tools can help reduce the specific complexity-contributing factors. Future studies aimed at understanding clinical domain-specific complexity-contributing factors can ultimately improve task allocation and design for intuitive clinical reasoning., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Objectives: To understand clinicians' impressions of and decision-making processes regarding an informatics-supported antibiotic timeout program to re-evaluate the appropriateness of continuing vancomycin and piperacillin/tazobactam., Methods: We implemented a multi-pronged informatics intervention, based on Dual Process Theory, to prompt discontinuation of unwarranted vancomycin and piperacillin/tazobactam on or after day three in a large Veterans Affairs Medical Center. Two workflow changes were introduced to facilitate cognitive deliberation about continuing antibiotics at day three: (1) teams completed an electronic template note, and (2) a paper summary of clinical and antibiotic-related information was provided to clinical teams. Shortly after starting the intervention, six focus groups were conducted with users or potential users. Interviews were recorded and transcribed. Iterative thematic analysis identified recurrent themes from feedback., Results: Themes that emerged are represented by the following quotations: (1) captures and controls attention ("it reminds us to think about it"), (2) enhances informed and deliberative reasoning ("it makes you think twice"), (3) redirects decision direction ("…because [there was no indication] I just [discontinued] it without even trying"), (4) fosters autonomy and improves team empowerment ("the template… forces the team to really discuss it"), and (5) limits use of emotion-based heuristics ("my clinical concern is high enough I think they need more aggressive therapy…")., Conclusions: Requiring template completion to continue antibiotics nudged clinicians to re-assess the appropriateness of specified antibiotics. Antibiotic timeouts can encourage deliberation on overprescribed antibiotics without substantially curtailing autonomy. An effective nudge should take into account clinician's time, workflow, and thought processes., (Published by Elsevier Inc.)