55 research outputs found
Evaluating openEHR for storing computable representations of electronic health record phenotyping algorithms
Electronic Health Records (EHR) are data generated during routine clinical
care. EHR offer researchers unprecedented phenotypic breadth and depth and have
the potential to accelerate the pace of precision medicine at scale. A main EHR
use-case is creating phenotyping algorithms to define disease status, onset and
severity. Currently, no common machine-readable standard exists for defining
phenotyping algorithms which often are stored in human-readable formats. As a
result, the translation of algorithms to implementation code is challenging and
sharing across the scientific community is problematic. In this paper, we
evaluate openEHR, a formal EHR data specification, for computable
representations of EHR phenotyping algorithms.Comment: 30th IEEE International Symposium on Computer-Based Medical Systems -
IEEE CBMS 201
Evaluation of Semantic Web Technologies for Storing Computable Definitions of Electronic Health Records Phenotyping Algorithms
Electronic Health Records are electronic data generated during or as a
byproduct of routine patient care. Structured, semi-structured and unstructured
EHR offer researchers unprecedented phenotypic breadth and depth and have the
potential to accelerate the development of precision medicine approaches at
scale. A main EHR use-case is defining phenotyping algorithms that identify
disease status, onset and severity. Phenotyping algorithms utilize diagnoses,
prescriptions, laboratory tests, symptoms and other elements in order to
identify patients with or without a specific trait. No common standardized,
structured, computable format exists for storing phenotyping algorithms. The
majority of algorithms are stored as human-readable descriptive text documents
making their translation to code challenging due to their inherent complexity
and hinders their sharing and re-use across the community. In this paper, we
evaluate the two key Semantic Web Technologies, the Web Ontology Language and
the Resource Description Framework, for enabling computable representations of
EHR-driven phenotyping algorithms.Comment: Accepted American Medical Informatics Association Annual Symposium
201
Desiderata for the development of next-generation electronic health record phenotype libraries
Background
High-quality phenotype definitions are desirable to enable the extraction of patient cohorts from large electronic health record repositories and are characterized by properties such as portability, reproducibility, and validity. Phenotype libraries, where definitions are stored, have the potential to contribute significantly to the quality of the definitions they host. In this work, we present a set of desiderata for the design of a next-generation phenotype library that is able to ensure the quality of hosted definitions by combining the functionality currently offered by disparate tooling.
Methods
A group of researchers examined work to date on phenotype models, implementation, and validation, as well as contemporary phenotype libraries developed as a part of their own phenomics communities. Existing phenotype frameworks were also examined. This work was translated and refined by all the authors into a set of best practices.
Results
We present 14 library desiderata that promote high-quality phenotype definitions, in the areas of modelling, logging, validation, and sharing and warehousing.
Conclusions
There are a number of choices to be made when constructing phenotype libraries. Our considerations distil the best practices in the field and include pointers towards their further development to support portable, reproducible, and clinically valid phenotype design. The provision of high-quality phenotype definitions enables electronic health record data to be more effectively used in medical domains
Desiderata for the development of next-generation electronic health record phenotype libraries
BackgroundHigh-quality phenotype definitions are desirable to enable the extraction of patient cohorts from large electronic health record repositories and are characterized by properties such as portability, reproducibility, and validity. Phenotype libraries, where definitions are stored, have the potential to contribute significantly to the quality of the definitions they host. In this work, we present a set of desiderata for the design of a next-generation phenotype library that is able to ensure the quality of hosted definitions by combining the functionality currently offered by disparate tooling.MethodsA group of researchers examined work to date on phenotype models, implementation, and validation, as well as contemporary phenotype libraries developed as a part of their own phenomics communities. Existing phenotype frameworks were also examined. This work was translated and refined by all the authors into a set of best practices.ResultsWe present 14 library desiderata that promote high-quality phenotype definitions, in the areas of modelling, logging, validation, and sharing and warehousing.ConclusionsThere are a number of choices to be made when constructing phenotype libraries. Our considerations distil the best practices in the field and include pointers towards their further development to support portable, reproducible, and clinically valid phenotype design. The provision of high-quality phenotype definitions enables electronic health record data to be more effectively used in medical domains
Computable Records : The Next Generation of the EMR Conversation
In 2016 and onward, computable medical records will fuel the next generation of EHRs, as the quest for interoperable, portable, and comprehensive health data continues. Computable medical records, readable by both human and machine, will house a patient’s entire record from conception to death. Importantly, such records will declare their fidelity level — their degree of completeness and accuracy — so that users can not only identify what data is there, but also what’s missing. The computable medical record will be unique, enabling users to find the right record for the right person; will support a health status scoring system; and will ideally be open source to drive adoption across software vendors, hospital systems, and government
Analyzing the heterogeneity of rule-based EHR phenotyping algorithms in CALIBER and the UK Biobank
Electronic Health Records (EHR) are data
generated during routine interactions across
healthcare settings and contain rich, longitudinal
information on diagnoses, symptoms, medications,
investigations and tests. A primary use-case for
EHR is the creation of phenotyping algorithms
used to identify disease status, onset and
progression or extraction of information on risk
factors or biomarkers. Phenotyping however is
challenging since EHR are collected for different
purposes, have variable data quality and often
require significant harmonization. While
considerable effort goes into the phenotyping
process, no consistent methodology for
representing algorithms exists in the UK. Creating
a national repository of curated algorithms can
potentially enable algorithm dissemination and
reuse by the wider community. A critical first step
is the creation of a robust minimum information
standard for phenotyping algorithm components
(metadata, implementation logic, validation
evidence) which involves identifying and
reviewing the complexity and heterogeneity of
current UK EHR algorithms. In this study, we
analyzed all available EHR phenotyping algorithms
(n=70) from two large-scale contemporary EHR
resources in the UK (CALIBER and UK Biobank).
We documented EHR sources, controlled clinical
terminologies, evidence of algorithm validation,
representation and implementation logic patterns.
Understanding the heterogeneity of UK EHR
algorithms and identifying common implementation patterns will facilitate the design of
a minimum information standard for representing
and curating algorithms nationally and
internationally
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
Towards an Ontology-Based Phenotypic Query Model
Clinical research based on data from patient or study data management systems plays an
important role in transferring basic findings into the daily practices of physicians. To support study
recruitment, diagnostic processes, and risk factor evaluation, search queries for such management
systems can be used. Typically, the query syntax as well as the underlying data structure vary
greatly between different data management systems. This makes it difficult for domain experts (e.g.,
clinicians) to build and execute search queries. In this work, the Core Ontology of Phenotypes is used
as a general model for phenotypic knowledge. This knowledge is required to create search queries
that determine and classify individuals (e.g., patients or study participants) whose morphology,
function, behaviour, or biochemical and physiological properties meet specific phenotype classes. A
specific model describing a set of particular phenotype classes is called a Phenotype Specification
Ontology. Such an ontology can be automatically converted to search queries on data management
systems. The methods described have already been used successfully in several projects. Using
ontologies to model phenotypic knowledge on patient or study data management systems is a viable
approach. It allows clinicians to model from a domain perspective without knowing the actual data
structure or query language
UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER
Objective: Electronic health records (EHRs) are a rich source of information on human diseases, but the information is variably structured, fragmented, curated using different coding systems, and collected for purposes
other than medical research. We describe an approach for developing, validating, and sharing reproducible
phenotypes from national structured EHR in the United Kingdom with applications for translational research.
Materials and Methods: We implemented a rule-based phenotyping framework, with up to 6 approaches of
validation. We applied our framework to a sample of 15 million individuals in a national EHR data source (population-based primary care, all ages) linked to hospitalization and death records in England. Data comprised continuous measurements (for example, blood pressure; medication information; coded diagnoses, symptoms,
procedures, and referrals), recorded using 5 controlled clinical terminologies: (1) read (primary care, subset of
SNOMED-CT [Systematized Nomenclature of Medicine Clinical Terms]), (2) International Classification of
Diseases–Ninth Revision and Tenth Revision (secondary care diagnoses and cause of mortality), (3) Office of
Population Censuses and Surveys Classification of Surgical Operations and Procedures, Fourth Revision (hospital surgical procedures), and (4) DMþD prescription codes.
Results: Using the CALIBER phenotyping framework, we created algorithms for 51 diseases, syndromes, biomarkers, and lifestyle risk factors and provide up to 6 validation approaches. The EHR phenotypes are curated
in the open-access CALIBER Portal (https://www.caliberresearch.org/portal) and have been used by 40 national
and international research groups in 60 peer-reviewed publications.
Conclusions: We describe a UK EHR phenomics approach within the CALIBER EHR data platform with initial evidence of validity and use, as an important step toward international use of UK EHR data for health research
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