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Low-grade endometrial stromal sarcoma in the small lady identified soon after resection regarding endometrial polyp-like lesions: A case document.
Addressing patients' social determinants of health via community resource referrals has historically been the primary domain of social workers and information and referral specialists; however, community resource referral platforms have recently entered the market. We lack an account of the process of community resource referrals and the role of technologies within it. Using sociotechnical systems theory, we analyze data from 12 focus groups (n=102) with healthcare providers, and community organization staff and volunteers in Metropolitan Detroit to describe the process of community resource referral. Findings reveal a deeply sociotechnical process including the following steps assessing patients' social needs; choosing appropriate referral sources; and facilitating connections. We characterize the importance of knowledge and skills, personal relationships, interorganizational networks, and data sources such as service directories in the referral process. Findings suggest that digital platforms may augment referral functions, but should not be seen to replace interpersonal work, relationships, and interorganizational networks.Many medical providers employ scribes to manage electronic health record (EHR) documentation. Prior studies have shown the benefits of scribes, but no large-scale study has quantitively assessed scribe impact on documentation workflows. We propose methods that leverage EHR data for identifying scribe presence during an office visit, measuring provider documentation time, and determining how notes are edited and composed. In a case study, we found scribe use was associated with less provider documentation time overall (averaging 2.4 minutes or 39% less time, p less then 0.001), fewer note edits by providers (8.4% less added and 4.2% less deleted text, p less then 0.001), but significantly more documentation time after the visit for four out of seven providers (p less then 0.001) and no change in the amount of copied and imported note text. Our methods could validate prior study results, identify variability for determining best practices, and determine that scribes do not improve all aspects of documentation.Clinicians from different care settings can distort the problem list from conveying a patient's actual health status, affecting quality and patient safety. To measure this effect, a reference standard was built to derive a problem-list based model. Real-world problem lists were used to derive an ideal categorization cutoff score. The model was tested against patient records to categorize problem lists as either having longitudinal inconsistencies or not. The model was able to successfully categorize these events with ~87% accuracy, ~83% sensitivity, and ~89% specificity. This new model can be used to quantify intervention effects, can be reported in problem list studies, and can be used to measure problem list changes based on policy, workflow, or system changes.A longstanding issue with knowledge bases that discuss drug-drug interactions (DDIs) is that they are inconsistent with one another. Computerized support might help experts be more objective in assessing DDI evidence. A requirement for such systems is accurate automatic classification of evidence types. In this pilot study, we developed a hierarchical classifier to classify clinical DDI studies into formally defined evidence types. The area under the ROC curve for sub-classifiers in the ensemble ranged from 0.78 to 0.87. The entire system achieved an F1 of 0.83 and 0.63 on two held-out datasets, the latter consisting focused on completely novel drugs from what the system was trained on. Selleck Blasticidin S The results suggest that it is feasible to accurately automate the classification of a sub-set of DDI evidence types and that the hierarchical approach shows promise. Future work will test more advanced feature engineering techniques while expanding the system to classify a more complex set of evidence types.Online physician review (OPR) websites have been increasingly used by healthcare consumers to make informed decisions in selecting healthcare providers. However, consumer-generated online reviews are often unstructured and contain plural topics with varying degrees of granularity, making it challenging to analyze using conventional topic modeling techniques. In this paper, we designed a novel natural language processing pipeline incorporating qualitative coding and supervised and unsupervised machine learning. Using this method, we were able to identify not only coarse-grained topics (e.g., relationship, clinic management), but also fine-grained details such as diagnosis, timing and access, and financial concerns. We discuss how healthcare providers could improve their ratings based on consumer feedback. We also reflect on the inherent challenges of analyzing user-generated online data, and how our novel pipeline may inform future work on mining consumer-generated online data.We present findings on using natural language processing to classify tobacco-related entries from problem lists found within patient's electronic health records. Problem lists describe health-related issues recorded during a patient's medical visit; these problems are typically followed up upon during subsequent visits and are updated for relevance or accuracy. The mechanics of problem lists vary across different electronic health record systems. In general, they either manifest as pre-generated generic problems that may be selected from a master list or as text boxes where a healthcare professional may enter free text describing the problem. Using commonly-available natural language processing tools, we classified tobacco-related problems into three classes active-user, former-user, and non-user; we further demonstrate that rule-based post-processing may significantly increase precision in identifying these classes (+32%, +22%, +35% respectively). We used these classes to generate tobacco time-spans that reconstruct a patient's tobacco-use history and better support secondary data analysis. We bundle this as an open-source toolkit with flow visualizations indicating how patient tobacco-related behavior changes longitudinally, which can also capture and visualize contradicting information such as smokers being flagged as having never smoked.
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