OBJECTIVE: Accurate, complete allergy histories are critical for decision-making and medication prescription. However, allergy information is often spread across the electronic health record (EHR); thus, allergy lists are often inaccurate or incomplete. Discrepant allergy information can lead to suboptimal or unsafe clinical care and contribute to alert fatigue. We developed an allergy reconciliation module within Mass General Brigham (MGB)'s EHR to support accurate and intuitive reconciliation of discrepancies in the allergy list, thereby enhancing patient safety. MATERIALS AND METHODS: We combined data-driven methods and knowledge from domain experts to develop 5 mechanisms to compare allergy information across the EHR and designed a user interface to display discrepancies and suggested reconciliation actions, with links to relevant data sources. Qualitative and quantitative analyses were conducted to assess the module's performance and measure user acceptance. RESULTS: We implemented and tested the proposed allergy reconciliation mechanisms and module. A comprehensive integration workflow was developed for the module, which was piloted among 111 primary care physicians at MGB. F1 scores of the reconciliation mechanisms range from 0.86 to 1.0. Qualitative analysis showed majority positive feedback from pilot users. DISCUSSION: Our allergy reconciliation module achieved high performance, and physicians who used it largely accepted its recommendations. However, 56% of the pilot group ultimately did not use the module. User engagement and education are likely needed to increase adoption. CONCLUSION: We built a module to automatically identify discrepancies within patients' allergy records and remind providers to reconcile and update the allergy list. Its high accuracy shows promise for enhancing patient safety and utility of drug allergy alerts.

Reliable automatic radiology report generation is highly desired to reduce the labor-intensive and error-prone workload for healthcare workers. While some multi-modal learning models have been proposed to study on this task, few of them paid attention to the radiology report generation for rare diseases, except for RareGen which solved this problem by enhancing the semantic representations of rare diseases. However, there still exist several open problems to be addressed. The first lies in the low proportion of disease regions in an image, making the visual information redundant or irrelevant to rare diseases to be encoded. The second lies in that correlations modeled in the encoding stage may not be effectively decoded in the decoding stage due to the multi-modal representation. To address these two issues, we propose a few-shot Transformer radiology report generation model, namely TransGen, for rare diseases. It integrates the advantages of Transformer with two key modules assembled. Specifically, in the encoding stage, a Semantic-aware Visual Learning (SVL) module is introduced to capture the regions of rare diseases. Following that, in the decoding stage, a Memory Augmented Semantic Enhancement (MASE) module is proposed to enhance intermediate representations. It could make full use of the semantic information contained in the historical-generated sentences to benefit report generation involving rare diseases. Extensive experiments have been conducted on two public datasets of IU X-Ray and MIMIC-CXR to demonstrate the effectiveness of our proposed model.

OBJECTIVE: Incomplete and static reaction picklists in the allergy module led to free-text and missing entries that inhibit the clinical decision support intended to prevent adverse drug reactions. We developed a novel, data-driven, "dynamic" reaction picklist to improve allergy documentation in the electronic health record (EHR). MATERIALS AND METHODS: We split 3 decades of allergy entries in the EHR of a large Massachusetts healthcare system into development and validation datasets. We consolidated duplicate allergens and those with the same ingredients or allergen groups. We created a reaction value set via expert review of a previously developed value set and then applied natural language processing to reconcile reactions from structured and free-text entries. Three association rule-mining measures were used to develop a comprehensive reaction picklist dynamically ranked by allergen. The dynamic picklist was assessed using recall at top k suggested reactions, comparing performance to the static picklist. RESULTS: The modified reaction value set contained 490 reaction concepts. Among 4 234 327 allergy entries collected, 7463 unique consolidated allergens and 469 unique reactions were identified. Of the 3 dynamic reaction picklists developed, the 1 with the optimal ranking achieved recalls of 0.632, 0.763, and 0.822 at the top 5, 10, and 15, respectively, significantly outperforming the static reaction picklist ranked by reaction frequency. CONCLUSION: The dynamic reaction picklist developed using EHR data and a statistical measure was superior to the static picklist and suggested proper reactions for allergy documentation. Further studies might evaluate the usability and impact on allergy documentation in the EHR.

OBJECTIVE: The study sought to review recent literature regarding use of speech recognition (SR) technology for clinical documentation and to understand the impact of SR on document accuracy, provider efficiency, institutional cost, and more. MATERIALS AND METHODS: We searched 10 scientific and medical literature databases to find articles about clinician use of SR for documentation published between January 1, 1990, and October 15, 2018. We annotated included articles with their research topic(s), medical domain(s), and SR system(s) evaluated and analyzed the results. RESULTS: One hundred twenty-two articles were included. Forty-eight (39.3%) involved the radiology department exclusively and 10 (8.2%) involved emergency medicine; 10 (8.2%) mentioned multiple departments. Forty-eight (39.3%) articles studied productivity; 20 (16.4%) studied the effect of SR on documentation time, with mixed findings. Decreased turnaround time was reported in all 19 (15.6%) studies in which it was evaluated. Twenty-nine (23.8%) studies conducted error analyses, though various evaluation metrics were used. Reported percentage of documents with errors ranged from 4.8% to 71%; reported word error rates ranged from 7.4% to 38.7%. Seven (5.7%) studies assessed documentation-associated costs; 5 reported decreases and 2 reported increases. Many studies (44.3%) used products by Nuance Communications. Other vendors included IBM (9.0%) and Philips (6.6%); 7 (5.7%) used self-developed systems. CONCLUSION: Despite widespread use of SR for clinical documentation, research on this topic remains largely heterogeneous, often using different evaluation metrics with mixed findings. Further, that SR-assisted documentation has become increasingly common in clinical settings beyond radiology warrants further investigation of its use and effectiveness in these settings.

Mutual information clustering is an agglomerative hierarchical clustering method that has been used to group random variables or sets thereof. Some researchers have found that the normalization method used can lead to oddly-sized clusters that do not line up with expected results. We introduce a new normalization parameter to control the size of the clusters, and apply it to food allergy data from a large allergy repository from an electronic health record, treating the distributions of food allergies in our population as random variables. Our method was able to identify previously known food cross-reaction groups (with an adjusted Rand index of 0.971, outperforming alternative clustering algorithms), in addition to proposing possible new groups. Our results demonstrate the viability of mutual information clustering as an approach for discovering possible food cross-reactions.