Luke V. Rasmussen

Timothy M. Herr, Therese A. Nelson, Luke V. Rasmussen et al.

OBJECTIVE: To design and evaluate new pharmacogenomic (PGx) clinical decision support (CDS) alerts, built to adhere to PGx CDS design principles developed through socio-technical approaches. MATERIALS AND METHODS: Based on previously identified design principles, we created 11 new PGx CDS alert designs and developed an interactive web application containing realistic clinical scenarios and user workflows that mimicked a real-world EHR system. We recruited General Internal Medicine and Cardiology clinicians from Northwestern Medicine and recorded their interactions with the original and new designs. We measured clinician response, satisfaction, speed, and confidence through questionnaires and analysis of the recordings. RESULTS: The study included 12 clinicians. Participants were significantly more satisfied (p=0.0000001), faster (p=0.009), and more confident (p<.05) with the new designs than the original ones. The study lacked statistical power to determine whether prescribing accuracy was improved, but participants were no less accurate, and clinical actions were more concordant with alert interactions (p=0.004) with the new designs. We found a significant learning curve associated with the original designs, which was eliminated with the new designs. DISCUSSION: This study successfully demonstrates that socio-technical and user-centered design techniques can improve PGx CDS alert designs. Best practices for PGx CDS design are limited in the literature, with few effectiveness studies available. These results can help guide future PGx CDS implementations to be more clinician friendly and less time-consuming. CONCLUSION: The results of this study support the PGx CDS design principles we proposed in previous work. As a next step, the new designs should be implemented in a live setting for further validation.

Zhenxing Xu, Jingyuan Chou, Xi Sheryl Zhang et al.

Acute Kidney Injury (AKI) is a common clinical syndrome characterized by the rapid loss of kidney excretory function, which aggravates the clinical severity of other diseases in a large number of hospitalized patients. Accurate early prediction of AKI can enable in-time interventions and treatments. However, AKI is highly heterogeneous, thus identification of AKI sub-phenotypes can lead to an improved understanding of the disease pathophysiology and development of more targeted clinical interventions. This study used a memory network-based deep learning approach to discover AKI sub-phenotypes using structured and unstructured electronic health record (EHR) data of patients before AKI diagnosis. We leveraged a real world critical care EHR corpus including 37,486 ICU stays. Our approach identified three distinct sub-phenotypes: sub-phenotype I is with an average age of 63.03$ \pm 17.25 $ years, and is characterized by mild loss of kidney excretory function (Serum Creatinine (SCr) $1.55\pm 0.34$ mg/dL, estimated Glomerular Filtration Rate Test (eGFR) $107.65\pm 54.98$ mL/min/1.73$m^2$). These patients are more likely to develop stage I AKI. Sub-phenotype II is with average age 66.81$ \pm 10.43 $ years, and was characterized by severe loss of kidney excretory function (SCr $1.96\pm 0.49$ mg/dL, eGFR $82.19\pm 55.92$ mL/min/1.73$m^2$). These patients are more likely to develop stage III AKI. Sub-phenotype III is with average age 65.07$ \pm 11.32 $ years, and was characterized moderate loss of kidney excretory function and thus more likely to develop stage II AKI (SCr $1.69\pm 0.32$ mg/dL, eGFR $93.97\pm 56.53$ mL/min/1.73$m^2$). Both SCr and eGFR are significantly different across the three sub-phenotypes with statistical testing plus postdoc analysis, and the conclusion still holds after age adjustment.

Prakash Adekkanattu, Guoqian Jiang, Yuan Luo et al.

While natural language processing (NLP) of unstructured clinical narratives holds the potential for patient care and clinical research, portability of NLP approaches across multiple sites remains a major challenge. This study investigated the portability of an NLP system developed initially at the Department of Veterans Affairs (VA) to extract 27 key cardiac concepts from free-text or semi-structured echocardiograms from three academic medical centers: Weill Cornell Medicine, Mayo Clinic and Northwestern Medicine. While the NLP system showed high precision and recall measurements for four target concepts (aortic valve regurgitation, left atrium size at end systole, mitral valve regurgitation, tricuspid valve regurgitation) across all sites, we found moderate or poor results for the remaining concepts and the NLP system performance varied between individual sites.