ABSTRACT
ABSTRACT OBJECTIVE Clinical encounter data are heterogeneous and vary greatly from institution to institution. These problems of variance affect interpretability and usability of clinical encounter data for analysis. These problems are magnified when multi-site electronic health record data are networked together. This paper presents a novel, generalizable method for resolving encounter heterogeneity for analysis by combining related atomic encounters into composite ‘macrovisits.’ MATERIALS AND METHODS Encounters were composed of data from 75 partner sites harmonized to a common data model as part of the NIH Researching COVID to Enhance Recovery Initiative, a project of the National Covid Cohort Collaborative. Summary statistics were computed for overall and site-level data to assess issues and identify modifications. Two algorithms were developed to refine atomic encounters into cleaner, analyzable longitudinal clinical visits. RESULTS Atomic inpatient encounters data were found to be widely disparate between sites in terms of length-of-stay and numbers of OMOP CDM measurements per encounter. After aggregating encounters to macrovisits, variance of length-of-stay (LOS) and measurement frequency decreased. A subsequent algorithm to identify hospitalized macrovisits further reduced data variability. DISCUSSION Encounters data are a complex and heterogeneous component of EHR data and these issues are not addressed by existing methods. These types of complex and poorly studied issues contribute to the difficulty of deriving value from EHR data, and these types of foundational, large-scale explorations and developments are necessary to realize the full potential of modern real world data. CONCLUSION This paper presents method developments to work with and resolve EHR encounters data in a generalizable way as a foundation for future analyses and research.
ABSTRACT
Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful to assess associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases and the simple removal of these cases may introduce severe bias. For these reasons, several multiple imputation algorithms have been proposed to attempt to recover the missing information. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithms works best in a given scenario. Furthermore, the selection of each algorithm parameters and data-related modelling choices are also both crucial and challenging. In this paper, we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. The experiments presented here show that our approach could effectively highlight the most valid and performant missing-data handling strategy for our case study. Moreover, our methodology allowed us to gain an understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.