Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies.

Although randomized controlled trials (RCTs) are the gold standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world data has been vital in postapproval monitoring and is being promoted for the regulatory process of experimental therapies. An emerging source of real-world data is electronic health records (EHRs), which contain detailed information on patient care in both structured (eg, diagnosis codes) and unstructured (eg, clinical notes and images) forms. Despite the granularity of the data available in EHRs, the critical variables required to reliably assess the relationship between a treatment and clinical outcome are challenging to extract. To address this fundamental challenge and accelerate the reliable use of EHRs for RWE, we introduce an integrated data curation and modeling pipeline consisting of 4 modules that leverage recent advances in natural language processing, computational phenotyping, and causal modeling techniques with noisy data. Module 1 consists of techniques for data harmonization. We use natural language processing to recognize clinical variables from RCT design documents and map the extracted variables to EHR features with description matching and knowledge networks. Module 2 then develops techniques for cohort construction using advanced phenotyping algorithms to both identify patients with diseases of interest and define the treatment arms. Module 3 introduces methods for variable curation, including a list of existing tools to extract baseline variables from different sources (eg, codified, free text, and medical imaging) and end points of various types (eg, death, binary, temporal, and numerical). Finally, module 4 presents validation and robust modeling methods, and we propose a strategy to create gold-standard labels for EHR variables of interest to validate data curation quality and perform subsequent causal modeling for RWE. In addition to the workflow proposed in our pipeline, we also develop a reporting guideline for RWE that covers the necessary information to facilitate transparent reporting and reproducibility of results. Moreover, our pipeline is highly data driven, enhancing study data with a rich variety of publicly available information and knowledge sources. We also showcase our pipeline and provide guidance on the deployment of relevant tools by revisiting the emulation of the Clinical Outcomes of Surgical Therapy Study Group Trial on laparoscopy-assisted colectomy versus open colectomy in patients with early-stage colon cancer. We also draw on existing literature on EHR emulation of RCTs together with our own studies with the Mass General Brigham EHR.

Enabling phenotypic big data with PheNorm.

Objective: Electronic health record (EHR)-based phenotyping infers whether a patient has a disease based on the information in his or her EHR. A human-annotated training set with gold-standard disease status labels is usually required to build an algorithm for phenotyping based on a set of predictive features. The time intensiveness of annotation and feature curation severely limits the ability to achieve high-throughput phenotyping. While previous studies have successfully automated feature curation, annotation remains a major bottleneck. In this paper, we present PheNorm, a phenotyping algorithm that does not require expert-labeled samples for training. Methods: The most predictive features, such as the number of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes or mentions of the target phenotype, are normalized to resemble a normal mixture distribution with high area under the receiver operating curve (AUC) for prediction. The transformed features are then denoised and combined into a score for accurate disease classification. Results: We validated the accuracy of PheNorm with 4 phenotypes: coronary artery disease, rheumatoid arthritis, Crohn's disease, and ulcerative colitis. The AUCs of the PheNorm score reached 0.90, 0.94, 0.95, and 0.94 for the 4 phenotypes, respectively, which were comparable to the accuracy of supervised algorithms trained with sample sizes of 100-300, with no statistically significant difference. Conclusion: The accuracy of the PheNorm algorithms is on par with algorithms trained with annotated samples. PheNorm fully automates the generation of accurate phenotyping algorithms and demonstrates the capacity for EHR-driven annotations to scale to the next level - phenotypic big data.

Limited Hospital Variation in the Use and Yield of CT for Pulmonary Embolism in Patients Undergoing Total Hip or Total Knee Replacement Surgery.

Purpose To evaluate the variation among U.S. hospitals in overall use and yield of in-hospital computed tomographic (CT) pulmonary angiography (PA) in patients undergoing total hip replacement (THR) or total knee replacement (TKR) surgery. Materials and Methods Patients in the Premier Research Database who underwent elective TKR or THR between 2007 and 2011 were enrolled in this HIPAA-compliant, institutional review board-approved retrospective observational study. The informed consent requirement was waived. Hospitals were categorized into low, medium, and high tertiles of CT PA use to compare baseline patient- and hospital-level characteristics and pulmonary embolism (PE) positivity rates. To further investigate between-hospital variation in CT PA use, a hierarchical logistic regression model that included hospital-specific random effects and fixed patient- and hospital-level effects was used. The intraclass correlation coefficient (ICC) was used to measure the amount of variability in CT PA use attributable to between-hospital variation. Results The cohort included 205 198 patients discharged from 178 hospitals (median of 734.5 patients discharged per hospital; interquartile range, 316-1461 patients) with 3647 CT PA studies (1.8%). The crude frequency of CT PA scans among the hospitals ranged from 0% to 6.2% (median, 1.6%); more than 90% of the hospitals performed CT PA in less than 3% of their patients. The mean hospital-level PE positivity rate was 12.3% (median, 9.1%); there was no significant difference in PE positivity rate across low through high CT PA use tertiles (11.3%, 11.9%, 12.9%, P = .37). After adjustment for hospital- and patient-level factors, the remaining amount of interhospital variation was relatively low (ICC, 9.0%). Conclusion Limited interhospital variation in use and yield of in-hospital CT PA was observed among patients undergoing TKR or THR in the United States. © RSNA, 2016 Online supplemental material is available for this article.