OBJECTIVES: We evaluated the effectiveness and implementability of a standardized EHR-integrated handoff report to support intraoperative handoffs. MATERIALS AND METHODS: A pre-post intervention study was used to compare the quality of intraoperative handoffs supported by unstructured notes (pre) to structured, standardized EHR-integrated handoff reports (post). Participants included anesthesia clinicians involved in intraoperative handoffs. A mixed-method approach was followed, supported by general observations, shadowing, surveys, and interviews. RESULTS: One hundred and fifty-one intraoperative permanent handoffs (78 pre, 73 post) were included. One hundred percent of participants in the post-intervention cohort utilized the report. Compared to unstructured, structured handoffs using the EHR-integrated handoff report led to: (1) significant increase in the transfer of information about airway management (55%-78%, P < .001), intraoperative course (63%-86%, P < .001), and potential concerns (64%-88%, P < .001); (2) significant improvement in clinician satisfaction scores, with regards to information clarity and succinctness (4.5-4.7, P = .002), information transfer (3.8-4.2, P = .011), and opportunities for fewer errors reported by senders (3.3-2.5, P < .001) and receivers (3.2-2.4, P < .001); and (3) significant decrease in handoff duration (326.2-262.3 s, P = .016). Clinicians found the report implementation highly acceptable, appropriate, and feasible but noted a few areas for improvement to enhance its usability and integration within the intraoperative workflow. DISCUSSION AND CONCLUSION: A standardized EHR-integrated handoff report ensures the effectiveness and efficiency of intraoperative handoffs with its structured, consistent format that-promotes up-to-date and pertinent intraoperative information transfer; reduces opportunities for errors; and streamlines verbal communication. Handoff standardization can promote safe and high-quality intraoperative care.

OBJECTIVE: An applied problem facing all areas of data science is harmonizing data sources. Joining data from multiple origins with unmapped and only partially overlapping features is a prerequisite to developing and testing robust, generalizable algorithms, especially in healthcare. This integrating is usually resolved using meta-data such as feature names, which may be unavailable or ambiguous. Our goal is to design methods that create a mapping between structured tabular datasets derived from electronic health records independent of meta-data. METHODS: We evaluate methods in the challenging case of numeric features without reliable and distinctive univariate summaries, such as nearly Gaussian and binary features. We assume that a small set of features are a priori mapped between two datasets, which share unknown identical features and possibly many unrelated features. Inter-feature relationships are the main source of identification which we expect. We compare the performance of contrastive learning methods for feature representations, novel partial auto-encoders, mutual-information graph optimizers, and simple statistical baselines on simulated data, public datasets, the MIMIC-III medical-record changeover, and perioperative records from before and after a medical-record system change. Performance was evaluated using both mapping of identical features and reconstruction accuracy of examples in the format of the other dataset. RESULTS: Contrastive learning-based methods overall performed the best, often substantially beating the literature baseline in matching and reconstruction, especially in the more challenging real data experiments. Partial auto-encoder methods showed on-par matching with contrastive methods in all synthetic and some real datasets, along with good reconstruction. However, the statistical method we created performed reasonably well in many cases, with much less dependence on hyperparameter tuning. When validating feature match output in the EHR dataset we found that some mistakes were actually a surrogate or related feature as reviewed by two subject matter experts. CONCLUSION: In simulation studies and real-world examples, we find that inter-feature relationships are effective at identifying matching or closely related features across tabular datasets when meta-data is not available. Decoder architectures are also reasonably effective at imputing features without an exact match.

Major postoperative complications are devastating to surgical patients. Some of these complications are potentially preventable via early predictions based on intraoperative data. However, intraoperative data comprise long and fine-grained multivariate time series, prohibiting the effective learning of accurate models. The large gaps associated with clinical events and protocols are usually ignored. Moreover, deep models generally lack transparency. Nevertheless, the interpretability is crucial to assist clinicians in planning for and delivering postoperative care and timely interventions. Towards this end, we propose a hierarchical model combining the strength of both attention and recurrent models for intraoperative time series. We further develop an explanation module for the hierarchical model to interpret the predictions by providing contributions of intraoperative data in a fine-grained manner. Experiments on a large dataset of 111,888 surgeries with multiple outcomes and an external high-resolution ICU dataset show that our model can achieve strong predictive performance (i.e., high accuracy) and offer robust interpretations (i.e., high transparency) for predicted outcomes based on intraoperative time series.

Given the risks and cost of hospitalization, there has been significant interest in exploiting machine learning models to improve perioperative care. However, due to the high dimensionality and noisiness of perioperative data, it remains a challenge to develop accurate and robust encoding for surgical predictions. Furthermore, it is important for the encoding to be interpretable by perioperative care practitioners to facilitate their decision making process. We proposeclinical variational autoencoder (cVAE), a deep latent variable model that addresses the challenges of surgical applications through two salient features. (1) To overcome performance limitations of traditional VAE, it isprediction-guided with explicit expression of predicted outcome in the latent representation. (2) Itdisentangles the latent space so that it can be interpreted in a clinically meaningful fashion. We apply cVAE to two real-world perioperative datasets to evaluate its efficacy and performance in predicting outcomes that are important to perioperative care, including postoperative complication and surgery duration. To demonstrate the generality and facilitate reproducibility, we also apply cVAE to the open MIMIC-III dataset for predicting ICU duration and mortality. Our results show that the latent representation provided by cVAE leads to superior performance in classification, regression and multi-task predictions. The two features of cVAE are mutually beneficial and eliminate the need of a predictor. We further demonstrate the interpretability of the disentangled representation and its capability to capture intrinsic characteristics of hospitalized patients. While this work is motivated by and evaluated in the context of clinical applications, the proposed approach may be generalized for other fields using high-dimensional and noisy data and valuing interpretable representations.

Medical ontologies are widely used to represent and organize medical terminologies. Examples include ICD-9, ICD-10, UMLS etc. The ontologies are often constructed in hierarchical structures, encoding the multi-level subclass relationships among different medical concepts, allowing very fine distinctions between concepts. Medical ontologies provide a great source for incorporating domain knowledge into a healthcare prediction system, which might alleviate the data insufficiency problem and improve predictive performance with rare categories. To incorporate such domain knowledge, Gram, a recent graph attention model, represents a medical concept as a weighted sum of its ancestors' embeddings in the ontology using an attention mechanism. Although showing improved performance, Gram only considers the unordered ancestors of a concept, which does not fully leverage the hierarchy thus having limited expressibility. In this paper, we propose Hierarchical Attention Propagation (HAP), a novel medical ontology embedding model that hierarchically propagate attention across the entire ontology structure, where a medical concept adaptively learns its embedding from all other concepts in the hierarchy instead of only its ancestors. We prove that HAP learns more expressive medical concept embeddings -- from any medical concept embedding we are able to fully recover the entire ontology structure. Experimental results on two sequential procedure/diagnosis prediction tasks demonstrate HAP's better embedding quality than Gram and other baselines. Furthermore, we find that it is not always best to use the full ontology. Sometimes using only lower levels of the hierarchy outperforms using all levels.