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BACKGROUND Management of abdominal aortic aneurysms (AAAs) requires serial imaging surveillance to evaluate the aneurysm dimension. Natural language processing (NLP) has been previously developed to retrospectively identify patients with AAA from electronic health records (EHRs). However, there are no reported studies that use NLP to identify patients with AAA in near real-time from radiology reports. OBJECTIVE This study aims to develop and validate a rule-based NLP algorithm for near real-time automatic extraction of AAA diagnosis from radiology reports for case identification. METHODS The AAA-NLP algorithm was developed and deployed to an EHR big data infrastructure for near real-time processing of radiology reports from May 1, 2019, to September 2020. NLP extracted named entities for AAA case identification and classified subjects as cases and controls. The reference standard to assess algorithm performance was a manual review of processed radiology reports by trained physicians following standardized criteria. Reviewers were blinded to the diagnosis of each subject. The AAA-NLP algorithm was refined in 3 successive iterations. For each iteration, the AAA-NLP algorithm was modified based on performance compared to the reference standard. RESULTS A total of 360 reports were reviewed, of which 120 radiology reports were randomly selected for each iteration. At each iteration, the AAA-NLP algorithm performance improved. The algorithm identified AAA cases in near real-time with high positive predictive value (0.98), sensitivity (0.95), specificity (0.98), F1 score (0.97), and accuracy (0.97). CONCLUSIONS Implementation of NLP for accurate identification of AAA cases from radiology reports with high performance in near real time is feasible. This NLP technique will support automated input for patient care and clinical decision support tools for the management of patients with AAA.

Objective To evaluate usability of a quality improvement tool that promotes guideline-based care for patients with peripheral arterial disease (PAD). Patients and Methods The study was conducted from July 19, 2018, to August 21, 2019. We compared the usability of a PAD cohort knowledge solution (CKS) with standard management supported by an electronic health record (EHR). Two scenarios were developed for usability evaluation; the first for the PAD-CKS while the second evaluated standard EHR workflow. Providers were asked to provide opinions about the PAD-CKS tool and to generate a System Usability Scale (SUS) score. Metrics analyzed included time required, number of mouse clicks, and number of keystrokes. Results Usability evaluations were completed by 11 providers. SUS for the PAD-CKS was excellent at 89.6. Time required to complete 21 tasks in the CKS was 4 minutes compared with 12 minutes for standard EHR workflow (median, P = .002). Completion of CKS tasks required 34 clicks compared with 148 clicks for the EHR (median, P = .002). Keystrokes for CKS task completion was 8 compared with 72 for EHR (median, P = .004). Providers indicated that overall they found the tool easy to use and the PAD mortality risk score useful. Conclusions Usability evaluation of the PAD-CKS tool demonstrated time savings, a high SUS score, and a reduction of mouse clicks and keystrokes for task completion compared to standard workflow using the EHR. Provider feedback regarding the strengths and weaknesses also created opportunities for iterative improvement of the PAD-CKS tool.

Background Management of abdominal aortic aneurysms (AAAs) requires serial imaging surveillance to evaluate the aneurysm dimension. Natural language processing (NLP) has been previously developed to retrospectively identify patients with AAA from electronic health records (EHRs). However, there are no reported studies that use NLP to identify patients with AAA in near real-time from radiology reports. Objective This study aims to develop and validate a rule-based NLP algorithm for near real-time automatic extraction of AAA diagnosis from radiology reports for case identification. Methods The AAA-NLP algorithm was developed and deployed to an EHR big data infrastructure for near real-time processing of radiology reports from May 1, 2019, to September 2020. NLP extracted named entities for AAA case identification and classified subjects as cases and controls. The reference standard to assess algorithm performance was a manual review of processed radiology reports by trained physicians following standardized criteria. Reviewers were blinded to the diagnosis of each subject. The AAA-NLP algorithm was refined in 3 successive iterations. For each iteration, the AAA-NLP algorithm was modified based on performance compared to the reference standard. Results A total of 360 reports were reviewed, of which 120 radiology reports were randomly selected for each iteration. At each iteration, the AAA-NLP algorithm performance improved. The algorithm identified AAA cases in near real-time with high positive predictive value (0.98), sensitivity (0.95), specificity (0.98), F1 score (0.97), and accuracy (0.97). Conclusions Implementation of NLP for accurate identification of AAA cases from radiology reports with high performance in near real time is feasible. This NLP technique will support automated input for patient care and clinical decision support tools for the management of patients with AAA.

Hypertrophic cardiomyopathy (HCM) is a heritable, phenotypically diverse, and often asymptomatic heart muscle disease which is a major cause of sudden cardiac death (SCD) in young adults. The gold-standard for the diagnosis of HCM is echocardiography (echo), which is an ultrasound-based cardiac imaging modality. Across all sites of the Mayo Clinic enterprise, echo images and measurement data are reviewed, interpreted, and reported via the Echo Information Management System (EIMS). The objective of this paper is to develop a machine learning model for the identification of HCM from cardiac measurements obtained by the echo. We developed a novel machine learning model on patient demographic information and echo measurements that were retrieved from the EIMS digital data registry and selected by cardiologists. Random forest (RF) was utilized to investigate the predictive performance of these features on the identification of HCM patients. The HCM cohort consists of 3,548 patients with at least one HCM diagnostic billing code (ICD-9 or ICD-10), from 2014 to 2019. The class labels “HCM yes” and “HCM no” were assigned by manual review of medical records as well as the outcomes of the gold standard imaging tests for HCM diagnosis. The developed model performed well in finding HCM patients with an accuracy of 95%, recall of 99%, and precision of 97%. The F1 score was 98 %, while 4% of patients were misclassified. This model will be translated into clinical practice for a clinical decision support system in EIMS to assist providers in the accurate diagnosis of HCM from echo data automatically while ensuring high-quality echo interpretation.

Background Clinical practice guidelines recommend antiplatelet and statin therapies as well as blood pressure control and tobacco cessation for secondary prevention in patients with established atherosclerotic cardiovascular diseases (ASCVDs). However, these strategies for risk modification are underused, especially in rural communities. Moreover, resources to support the delivery of preventive care to rural patients are fewer than those for their urban counterparts. Transformative interventions for the delivery of tailored preventive cardiovascular care to rural patients are needed. Objective A multidisciplinary team developed a rural-specific, team-based model of care intervention assisted by clinical decision support (CDS) technology using participatory design in a sociotechnical conceptual framework. The model of care intervention included redesigned workflows and a novel CDS technology for the coordination and delivery of guideline recommendations by primary care teams in a rural clinic. Methods The design of the model of care intervention comprised 3 phases: problem identification, experimentation, and testing. Input from team members (n=35) required 150 hours, including observations of clinical encounters, provider workshops, and interviews with patients and health care professionals. The intervention was prototyped, iteratively refined, and tested with user feedback. In a 3-month pilot trial, 369 patients with ASCVDs were randomized into the control or intervention arm. Results New workflows and a novel CDS tool were created to identify patients with ASCVDs who had gaps in preventive care and assign the right care team member for delivery of tailored recommendations. During the pilot, the intervention prototype was iteratively refined and tested. The pilot demonstrated feasibility for successful implementation of the sociotechnical intervention as the proportion of patients who had encounters with advanced practice providers (nurse practitioners and physician assistants), pharmacists, or tobacco cessation coaches for the delivery of guideline recommendations in the intervention arm was greater than that in the control arm. Conclusions Participatory design and a sociotechnical conceptual framework enabled the development of a rural-specific, team-based model of care intervention assisted by CDS technology for the transformation of preventive health care delivery for ASCVDs.

Background Automated individualized risk prediction tools linked to electronic health records (EHRs) are not available for management of patients with peripheral arterial disease. The goal of this study was to create a prognostic tool for patients with peripheral arterial disease using data elements automatically extracted from an EHR to enable real‐time and individualized risk prediction at the point of care. Methods and Results A previously validated phenotyping algorithm was deployed to an EHR linked to the Rochester Epidemiology Project to identify peripheral arterial disease cases from Olmsted County, MN, for the years 1998 to 2011. The study cohort was composed of 1676 patients: 593 patients died over 5‐year follow‐up. The c‐statistic for survival in the overall data set was 0.76 (95% confidence interval [CI], 0.74–0.78), and the c‐statistic across 10 cross‐validation data sets was 0.75 (95% CI, 0.73–0.77). Stratification of cases demonstrated increasing mortality risk by subgroup (low: hazard ratio, 0.35 [95% CI, 0.21–0.58]; intermediate‐high: hazard ratio, 2.98 [95% CI, 2.37–3.74]; high: hazard ratio, 8.44 [95% CI, 6.66–10.70], all P, See Editorial by https://10.1161/JAHA.118.011184

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.

Key Points Question Are ankle-brachial indices associated with limb outcomes in community-dwelling patients with peripheral artery disease as identified by computational approaches applied to electronic health records? Findings In this cohort study of 1413 community-dwelling patients, peripheral artery disease was identified and limb outcomes were retrieved by computational approaches applied to electronic health records. Ankle-brachial indices of 1.4 or greater were associated with limb amputation and ankle-brachial indices less than 0.5 were associated with limb revascularization. Meaning Severe disease and poorly compressible arteries detected by ankle-brachial indices were associated with adverse outcomes in community-dwelling patients with peripheral artery disease; ascertainment of peripheral artery disease phenotypes and outcomes by computational approaches applied to electronic health records is feasible., Importance The prevalence and morbidity of peripheral artery disease (PAD) are high, with limb outcomes including revascularization and amputation. In community-dwelling patients with PAD, the role of noninvasive evaluation for risk assessment and rates of limb outcomes have not been established to date. Objective To evaluate whether ankle-brachial indices are associated with limb outcomes in community-dwelling patients with PAD. Design, Setting, and Participants A population-based, observational, test-based cohort study of patients was performed from January 1, 1998, to December 31, 2014. Data analysis was conducted from July 15 to December 15, 2017. Participants included a community-based cohort of 1413 patients with PAD from Olmsted County, Minnesota, identified by validated algorithms deployed to electronic health records. Automated algorithms identified limb outcomes used to build Cox proportional hazards regression models. Ankle-brachial indices and presence of poorly compressible arteries were electronically identified from digital data sets. Guideline-recommended management strategies within 6 months of diagnosis were also electronically retrieved, including therapy with statins, antiplatelet agents, angiotensin-converting enzyme inhibitors or angiotensin-receptor blockers, and smoking abstention. Main Outcomes and Measures Ankle-brachial index (index ≤0.9 indicates PAD, This population-based cohort study evaluates whether ankle-brachial indices identified through electronic health records among community-dwelling patients with peripheral artery disease are associated with limb revascularization or amputation.

Lower extremity peripheral artery disease (PAD) is highly prevalent. Prognostic risk models supported by automated data extraction of electronic health records (EHRs) would be useful for management of PAD patients. The records-linkage system of the Rochester Epidemiology Project was used to

Research objective To develop scalable informatics infrastructure for normalization of both structured and unstructured electronic health record (EHR) data into a unified, concept-based model for high-throughput phenotype extraction. Materials and methods Software tools and applications were developed to extract information from EHRs. Representative and convenience samples of both structured and unstructured data from two EHR systems—Mayo Clinic and Intermountain Healthcare—were used for development and validation. Extracted information was standardized and normalized to meaningful use (MU) conformant terminology and value set standards using Clinical Element Models (CEMs). These resources were used to demonstrate semi-automatic execution of MU clinical-quality measures modeled using the Quality Data Model (QDM) and an open-source rules engine. Results Using CEMs and open-source natural language processing and terminology services engines—namely, Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) and Common Terminology Services (CTS2)—we developed a data-normalization platform that ensures data security, end-to-end connectivity, and reliable data flow within and across institutions. We demonstrated the applicability of this platform by executing a QDM-based MU quality measure that determines the percentage of patients between 18 and 75 years with diabetes whose most recent low-density lipoprotein cholesterol test result during the measurement year was

Colorectal cancer is the second leading cause of cancer-related deaths in the United States. However, 41% of patients do not receive adequate screening, since the surveillance guidelines for colonoscopy are complex and are not easily recalled by health care providers. As a potential solution, we developed a guideline based clinical decision support system (CDSS) that can interpret relevant freetext reports including indications, pathology and procedure notes. The CDSS was evaluated by comparing its recommendations with those of a gastroenterologist for a test set of 53 patients. The CDSS made the optimal recommendation in 48 cases, and helped the gastroenterologist revise the recommendation in 3 cases. We performed an error analysis for the 5 failure cases, and subsequently were able to modify the CDSS to output the correct recommendation for all the test cases. Results indicate that the system has a high potential for clinical deployment, but further evaluation and optimization is required. Limitations of our study are that the study was conducted at a single institution and with a single expert, and the evaluation did not include rare decision scenarios. Overall our work demonstrates the utility of natural language processing to enhance clinical decision support.

Background One challenge in reusing clinical data stored in electronic medical records is that these data are heterogenous. Clinical Natural Language Processing (NLP) plays an important role in transforming information in clinical text to a standard representation that is comparable and interoperable. Information may be processed and shared when a type system specifies the allowable data structures. Therefore, we aim to define a common type system for clinical NLP that enables interoperability between structured and unstructured data generated in different clinical settings. Results We describe a common type system for clinical NLP that has an end target of deep semantics based on Clinical Element Models (CEMs), thus interoperating with structured data and accommodating diverse NLP approaches. The type system has been implemented in UIMA (Unstructured Information Management Architecture) and is fully functional in a popular open-source clinical NLP system, cTAKES (clinical Text Analysis and Knowledge Extraction System) versions 2.0 and later. Conclusions We have created a type system that targets deep semantics, thereby allowing for NLP systems to encapsulate knowledge from text and share it alongside heterogenous clinical data sources. Rather than surface semantics that are typically the end product of NLP algorithms, CEM-based semantics explicitly build in deep clinical semantics as the point of interoperability with more structured data types.

The aim of Area 4 of the Strategic Healthcare IT Advanced Research Project (SHARP 4) is to facilitate secondary use of data stored in Electronic Medical Records (EMR) through high throughput phenotyping. Clinical Natural Language Processing (NLP) plays an important role in transforming information in clinical text to standard representation that is comparable and interoperable. To meet the NLP requirement of different secondary use cases of EMR, accommodate different NLP approaches, enable the interoperability between structured and unstructured data generated in different clinical settings, we define a common type system for clinical NLP that integrates a comprehensive model of clinical semantics with language processing types for SHARP 4. The type system has been implemented in UIMA (Unstructured Information Management Architecture), which allows for flexible passing of input and output data types among NLP components, and is available at the SHARP 4 website.

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.

Health disparities and solutions are heterogeneous within and among racial and ethnic groups, yet existing administrative databases lack the granularity to reflect important sociocultural distinctions. We measured the efficacy of a natural-language–processing algorithm to identify a specific immigrant group. The algorithm demonstrated accuracy and precision in identifying Somali patients from the electronic medical records at a single institution. This technology holds promise to identify and track immigrants and refugees in the United States in local health care settings.