A Learning Health Sciences Approach to Understanding Clinical Documentation in Pediatric Rehabilitation Settings

The work presented in this dissertation provides an analysis of clinical documentation that challenges the concepts and thinking surrounding missingness of data from clinical settings and the factors that influence why data are missing. It also foregrounds the critical role of clinical documentation as infrastructure for creating learning health systems (LHS) for pediatric rehabilitation settings. Although completeness of discrete data is limited, the results presented do not reflect the quality of care or the extent of unstructured data that providers document in other locations of the electronic health record (EHR) interface. While some may view imputation and natural language processing as means to address missingness of clinical data, these practices carry biases in their interpretations and issues of validity in results. The factors that influence missingness of discrete clinical data are rooted not just in technical structures, but larger professional, system level and unobservable phenomena that shape provider practices of clinical documentation. This work has implications for how we view clinical documentation as critical infrastructure for LHS, future studies of data quality and health outcomes research, and EHR design and implementation. The overall research questions for this dissertation are: 1) To what extent can data networks be leveraged to build classifiers of patient functional performance and physical disability? 2) How can discrete clinical data on gross motor function be used to draw conclusions about clinical documentation practices in the EHR for cerebral palsy? 3) Why does missingness of discrete data in the EHR occur? To address these questions, a three-pronged approach is used to examine data completeness and the factors that influence missingness of discrete clinical data in an exemplar pediatric data learning network will be used. As a use-case, evaluation of EHR data completeness of gross motor function related data, populated by providers from 2015-2019 for children with cerebral palsy (CP), will be completed. Mixed methods research strategies will be used to achieve the dissertation objectives, including developing an expert-informed and standards-based phenotype model of gross motor function data as a task-based mechanism, conducting quantitative descriptive analyses of completeness of discrete data in the EHR, and performing qualitative thematic analyses to elicit and interpret the latent concepts that contribute to missingness of discrete data in the EHR. The clinical data for this dissertation are sourced from the Shriners Hospitals for Children (SHC) Health Outcomes Network (SHOnet), while qualitative data were collected through interviews and field observations of clinical providers across three care sites in the SHC system.PHDHlth Infrastr & Lrng Systs PhDUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162994/1/njkoscie_1.pd

PCORnet's Collaborative Research Groups.

The Patient-Centered Outcomes Research Institute (PCORI) launched a multi-institutional "network of networks" in 2013 - Patient-Centered Clinical Research Network (PCORnet) - that is designed to conduct clinical research that is faster, less expensive, and more responsive to the information needs of patients and clinicians. To enhance cross-network and cross-institutional collaboration and catalyze the use of PCORnet, PCORI has supported formation of 11 Collaborative Research Groups focusing on specific disease types (e.g., cardiovascular health and cancer) or particular patient populations (e.g., pediatrics and health disparities). PCORnet's Collaborative Research Groups are establishing research priorities within these focus areas, establishing relationships with potential funders, and supporting development of specific research projects that will use PCORnet resources. PCORnet remains a complex, multilevel, and heterogeneous network that is still maturing and building a diverse portfolio of observational and interventional people-centered research; engaging with PCORnet can be daunting, particularly for outside investigators. We believe the Collaborative Research Groups are stimulating interest and helping investigators navigate the complexity, but only time will tell if these efforts will bear fruit in terms of funded multicenter PCORnet projects

Generating Reliable and Responsive Observational Evidence: Reducing Pre-analysis Bias

A growing body of evidence generated from observational data has demonstrated the potential to influence decision-making and improve patient outcomes. For observational evidence to be actionable, however, it must be generated reliably and in a timely manner. Large distributed observational data networks enable research on diverse patient populations at scale and develop new sound methods to improve reproducibility and robustness of real-world evidence. Nevertheless, the problems of generalizability, portability and scalability persist and compound. As analytical methods only partially address bias, reliable observational research (especially in networks) must address the bias at the design stage (i.e., pre-analysis bias) including the strategies for identifying patients of interest and defining comparators. This thesis synthesizes and enumerates a set of challenges to addressing pre-analysis bias in observational studies and presents mixed-methods approaches and informatics solutions for overcoming a number of those obstacles. We develop frameworks, methods and tools for scalable and reliable phenotyping including data source granularity estimation, comprehensive concept set selection, index date specification, and structured data-based patient review for phenotype evaluation. We cover the research on potential bias in the unexposed comparator definition including systematic background rates estimation and interpretation, and definition and evaluation of the unexposed comparator. We propose that the use of standardized approaches and methods as described in this thesis not only improves reliability but also increases responsiveness of observational evidence. To test this hypothesis, we designed and piloted a Data Consult Service - a service that generates new on-demand evidence at the bedside. We demonstrate that it is feasible to generate reliable evidence to address clinicians’ information needs in a robust and timely fashion and provide our analysis of the current limitations and future steps needed to scale such a service

Electronic medical records in paediatric ophthalmology: a study of potential users and uses to inform design

Electronic medical records are at the core of an advancing movement toward information-driven healthcare. By enhancing abilities to capture, store, and analyse vast amounts of health data, the routine use of electronic medical records is advocated as a means to improve the efficiency and quality of care provision, advance population health, empower patients, and reduce healthcare costs. However, the delivery of any benefits is threatened by a failure to understand the unique care environments of different clinical specialties, and to appropriately customise system design. This has prompted a move to the user-centred design process of health information technology. Paediatric ophthalmology is a unique field that faces particular challenges in electronic medical record adoption. As with other ophthalmic specialties, the heavy use of imaging and diagrammatic documentation is difficult to replicate electronically. As is the flexibility required to meet the demands incurred by the varying ages, developmental stages, and visual needs of each patient, reflecting a unique interface between the ophthalmic and paediatric requirements. The consideration of such requirements is essential throughout the user-centred design of effective health information technology systems. However, paucity in the evidence base surrounding electronic medical record design methodologies and system usage hinders technological development and application within paediatric ophthalmology. This research was centred on a user-centred design process, to provide an understanding of the users of electronic medical records in paediatric ophthalmology, and their requirements. Taking a mixed methods approach, this research initially explored the landscape of medical record use – gathering user- centred requirements – and concluded with the development and testing of three prototype data collection forms, for specific use cases within paediatric ophthalmology. Overall, this work articulates the specific challenges and requirements in this area, and provides the foundation for future design and adoption strategies of electronic medical record systems within paediatric ophthalmology