Liu, Hongfang

The concept of optimizing health care by understanding and generating knowledge from previous evidence, ie, the Learning Health-care System (LHS), has gained momentum and now has national prominence. Meanwhile, the rapid adoption of electronic health records (EHRs) enables the data collection required to form the basis for facilitating LHS. A prerequisite for using EHR data within the LHS is an infrastructure that enables access to EHR data longitudinally for health-care analytics and real time for knowledge delivery. Additionally, significant clinical information is embedded in the free text, making natural language processing (NLP) an essential component in implementing an LHS. Herein, we share our institutional implementation of a big data-empowered clinical NLP infrastructure, which not only enables health-care analytics but also has real-time NLP processing capability. The infrastructure has been utilized for multiple institutional projects including the MayoExpertAdvisor, an individualized care recommendation solution for clinical care. We compared the advantages of big data over two other environments. Big data infrastructure significantly outperformed other infrastructure in terms of computing speed, demonstrating its value in making the LHS a possibility in the near future.

Abstract Objective The objective of the Strategic Health IT Advanced Research Project area four (SHARPn) was to develop open-source tools that could be used for the normalization of electronic health record (EHR) data for secondary use—specifically, for high throughput phenotyping. We describe the role of Intermountain Healthcare’s Clinical Element Models ([CEMs] Intermountain Healthcare Health Services, Inc, Salt Lake City, Utah) as normalization “targets” within the project. Materials and Methods Intermountain’s CEMs were either repurposed or created for the SHARPn project. A CEM describes “valid” structure and semantics for a particular kind of clinical data. CEMs are expressed in a computable syntax that can be compiled into implementation artifacts. The modeling team and SHARPn colleagues agilely gathered requirements and developed and refined models. Results Twenty-eight “statement” models (analogous to “classes”) and numerous “component” CEMs and their associated terminology were repurposed or developed to satisfy SHARPn high throughput phenotyping requirements. Model (structural) mappings and terminology (semantic) mappings were also created. Source data instances were normalized to CEM-conformant data and stored in CEM instance databases. A model browser and request site were built to facilitate the development. Discussion The modeling efforts demonstrated the need to address context differences and granularity choices and highlighted the inevitability of iso-semantic models. The need for content expertise and “intelligent” content tooling was also underscored. We discuss scalability and sustainability expectations for a CEM-based approach and describe the place of CEMs relative to other current efforts. Conclusions The SHARPn effort demonstrated the normalization and secondary use of EHR data. CEMs proved capable of capturing data originating from a variety of sources within the normalization pipeline and serving as suitable normalization targets.

Because of the complexity of cervical cancer prevention guidelines, clinicians often fail to follow best-practice recommendations. Moreover, existing clinical decision support (CDS) systems generally recommend a cervical cytology every three years for all female patients, which is inappropriate for patients with abnormal findings that require surveillance at shorter intervals. To address this problem, we developed a decision tree-based CDS system that integrates national guidelines to provide comprehensive guidance to clinicians. Validation was performed in several iterations by comparing recommendations generated by the system with those of clinicians for 333 patients. The CDS system extracted relevant patient information from the electronic health record and applied the guideline model with an overall accuracy of 87%. Providers without CDS assistance needed an average of 1 minute 39 seconds to decide on recommendations for management of abnormal findings. Overall, our work demonstrates the feasibility and potential utility of automated recommendation system for cervical cancer screening and surveillance.

Background: Clinical decision support (CDS) for primary care has been shown to improve delivery of preventive services. However, there is little evidence for efficiency of physicians due to CDS assistance. In this article, we report a pilot study for measuring the impact of CDS on the time spent by physicians for deciding on preventive services and chronic disease management. Methods: We randomly selected 30 patients from a primary care practice, and assigned them to 10 physicians. The physicians were requested to perform chart review to decide on preventive services and chronic disease management for the assigned patients. The patients assignment was done in a randomized crossover design, such that each patient received 2 sets of recommendations—one from a physician with CDS assistance and the other from a different physician without CDS assistance. We compared the physician recommendations made using CDS assistance, with the recommendations made without CDS assistance. Results: The physicians required an average of 1 minute 44 seconds, when they were they had access to the decision support system and 5 minutes when they were unassisted. Hence the CDS assistance resulted in an estimated saving of 3 minutes 16 seconds (65%) of the physicians’ time, which was statistically significant ( P < .0001). There was no statistically significant difference in the number of recommendations. Conclusion: Our findings suggest that CDS assistance significantly reduced the time spent by physicians for deciding on preventive services and chronic disease management. The result needs to be confirmed by performing similar studies at other institutions.

Abstract Objectives Drug repurposing, which finds new indications for existing drugs, has received great attention recently. The goal of our work is to assess the feasibility of using electronic health records (EHRs) and automated informatics methods to efficiently validate a recent drug repurposing association of metformin with reduced cancer mortality. Methods By linking two large EHRs from Vanderbilt University Medical Center and Mayo Clinic to their tumor registries, we constructed a cohort including 32 415 adults with a cancer diagnosis at Vanderbilt and 79 258 cancer patients at Mayo from 1995 to 2010. Using automated informatics methods, we further identified type 2 diabetes patients within the cancer cohort and determined their drug exposure information, as well as other covariates such as smoking status. We then estimated HRs for all-cause mortality and their associated 95% CIs using stratified Cox proportional hazard models. HRs were estimated according to metformin exposure, adjusted for age at diagnosis, sex, race, body mass index, tobacco use, insulin use, cancer type, and non-cancer Charlson comorbidity index. Results Among all Vanderbilt cancer patients, metformin was associated with a 22% decrease in overall mortality compared to other oral hypoglycemic medications (HR 0.78; 95% CI 0.69 to 0.88) and with a 39% decrease compared to type 2 diabetes patients on insulin only (HR 0.61; 95% CI 0.50 to 0.73). Diabetic patients on metformin also had a 23% improved survival compared with non-diabetic patients (HR 0.77; 95% CI 0.71 to 0.85). These associations were replicated using the Mayo Clinic EHR data. Many site-specific cancers including breast, colorectal, lung, and prostate demonstrated reduced mortality with metformin use in at least one EHR. Conclusions EHR data suggested that the use of metformin was associated with decreased mortality after a cancer diagnosis compared with diabetic and non-diabetic cancer patients not on metformin, indicating its potential as a chemotherapeutic regimen. This study serves as a model for robust and inexpensive validation studies for drug repurposing signals using EHR data.

A large amount of medication information resides in the unstructured text found in electronic medical records, which requires advanced techniques to be properly mined. In clinical notes, medication information follows certain semantic patterns (eg, medication, dosage, frequency, and mode). Some medication descriptions contain additional word(s) between medication attributes. Therefore, it is essential to understand the semantic patterns as well as the patterns of the context interspersed among them (ie, context patterns) to effectively extract comprehensive medication information. In this paper we examined both semantic and context patterns, and compared those found in Mayo Clinic and i2b2 challenge data. We found that some variations exist between the institutions but the dominant patterns are common.