scholarly journals Can Linked Electronic Medical Record and Administrative Data Help Us Identify Those Living with Frailty?

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Sabrina Wong ◽  
Alan Katz ◽  
Tyler Williamson ◽  
Alexander Singer ◽  
Sandra Peterson ◽  
...  
Keyword(s):  
Medical Record ◽  
Electronic Medical Record ◽  
Administrative Data ◽  
Population Data ◽  
Sentinel Surveillance ◽  
Structured Data ◽  
Data Sources ◽  
Sociodemographic Characteristics ◽  
Surveillance Network ◽  
Functional Deficits

IntroductionFrailty is a complex condition that affects many aspects of a patients’ wellbeing and health outcomes. ObjectivesWe used available Electronic Medical Record (EMR) and administrative data to determine definitionsof frailty. We also examined whether there were differences in demographics or health conditionsamong those identified as frail in either the EMR or administrative data. MethodsEMR and administrative data were linked in British Columbia (BC) and Manitoba (MB) to identifythose aged 65 years and older who were frail. The EMR data were obtained from the CanadianPrimary Care Sentinel Surveillance Network (CPCSSN) and the administrative data (e.g. billing,hospitalizations) was obtained from Population Data BC and the Manitoba Population ResearchData Repository. Sociodemographic characteristics, risk factors, prescribed medications, use andcosts of healthcare are described for those identified as frail. ResultsSociodemographic and utilization differences were found among those identified as frail from theEMR compared to those in the administrative data. Among those who were >65 years, who hada record in both EMR and administrative data, 5%-8% (n=191 of 3,553, BC; n=2,396 of 29,382,MB) were identified as frail. There was a higher likelihood of being frail with increasing age andbeing a woman. In BC and MB, those identified as frail in both data sources have approximatelytwice the number of contacts with primary care (n=20 vs. n=10) and more days in hospital (n=7.2vs. n=1.9 in BC; n=9.8 vs. n=2.8 in MB) compared to those who are not frail; 27% (BC) and 14%(MB) of those identified as frail in 2014 died in 2015. ConclusionsIdentifying frailty using EMR data is particularly challenging because many functional deficits arenot routinely recorded in structured data fields. Our results suggest frailty can be captured along acontinuum using both EMR and administrative data.

scholarly journals Can Linked Electronic Medical Record and Administrative Data Help Us Identify Those Living With Frailty?

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Sabrina Wong ◽  
Alan Katz ◽  
Tyler Williamson ◽  
Sandra Peterson ◽  
Carole Taylor ◽  
...  
Keyword(s):  
Primary Care ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Administrative Data ◽  
Functional Decline ◽  
Improve Patient Care ◽  
Population Data ◽  
Population Level ◽  
Surveillance Network ◽  
Clinical Indicators

IntroductionFrailty is a combination of factors that increase vulnerability to functional decline, dependence and/or death. Frailty cannot easily be defined by comorbidities or medical treatment alone. Accurate detection of frailty in practice and at a population level is needed. This may be achieved using a combination of data sources. Objectives and ApproachWe construct algorithms that can identify frailty using electronic medical record (EMR) and administrative data. We linked EMR data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) nodes and the administrative (e.g. billings, hospitalizations) from Population Data BC and the Manitoba Health Policy Centre. Frailty was defined as individuals 65+ who were receiving home services, had specific chronic conditions, received specific diagnoses, and/or had specific lab or other clinical indicators. We describe sociodemographic characteristics, risk factors, prescribed medications, use and costs of healthcare for those identified as frail. ResultsPeople were identified as frail in 2014 and all analysis was completed with 2015 data. Among those who were > 65 years, who had a record in both EMR and administrative data, 5\%-8\% (n=191 of 3,553, BC; n=2,396 of 29,382, MB) were identified as frail. There was a higher likelihood of being frail with increasing age and being a woman. In BC, those identified as frail have higher contacts with primary care (n=20 vs. n=10) and more days in hospital (n=7.4 vs. n=2.0) compared to those who are not frail. Twenty two percent of those identified as frail in 2014 died in 2015, compared to a mortality rate of 2\% among those who are not frail. Conclusion/ImplicationsIdentifying and reporting on those who are frail in primary care as well as in communities could enable targeted communications with patients and families and community based resources in order to improve patient care, patients’ and caregivers’ quality of life and better use of the healthcare system.

Validity of early-onset dementia diagnoses in VA electronic medical record administrative data

2019 ◽  
Vol 34 (6) ◽  
pp. 1175-1189 ◽  
Author(s):  
Janice C. Marceaux ◽  
Jason R. Soble ◽  
Justin J. F. O’Rourke ◽  
Alicia A. Swan ◽  
Margaret Wells ◽  
...  
Keyword(s):  
Medical Record ◽  
Electronic Medical Record ◽  
Administrative Data ◽  
Early Onset ◽  
Early Onset Dementia

Automated Extraction of Structured Data from Text Notes in the Electronic Medical Record

2020 ◽  
Author(s):  
Alexander C. Flint ◽  
Ronald B. Melles ◽  
Jeff G. Klingman ◽  
Sheila L. Chan ◽  
Vivek A. Rao ◽  
...  
Keyword(s):  
Medical Record ◽  
Electronic Medical Record ◽  
Structured Data ◽  
Automated Extraction

scholarly journals The Establishment and Refinement of the National Basketball Association Player Injury and Illness Database

2019 ◽  
Vol 54 (5) ◽  
pp. 466-471 ◽  
Author(s):  
Christina D. Mack ◽  
Peter Meisel ◽  
Mackenzie M. Herzog ◽  
Lisa Callahan ◽  
Eva E. Oakkar ◽  
...  
Keyword(s):  
Medical Record ◽  
Electronic Medical Record ◽  
Health And Safety ◽  
Electronic Medical Record System ◽  
National Basketball Association ◽  
Data Sources ◽  
Record System ◽  
Additional Information ◽  
External Data ◽  
Data Linkages

The National Basketball Association (NBA; also referred to as “the league”) has established a centralized, audited electronic medical record system that has been linked with external sources to provide a platform for robust research and to allow the NBA to conduct player health and safety reviews. The system is customized and maintained by the NBA and individual teams as part of the employment records for each player and is deployed uniformly across all 30 teams in the league, thereby allowing for standardized data on injuries, illnesses, and player participation in NBA games and practices. The electronic medical record data are enriched by linkage with other league external data sources that provide additional information about injuries, players, game and practice participation, and movement. These data linkages allow for the assessment of potential injury trends, development of injury-prevention programs, and rule changes, with the ultimate goal of improving player health and wellness. The purpose of this article is to describe this NBA injury database, including the details of data collection, data linkages with external data sources, and activities related to reporter training and data quality improvement.

scholarly journals Models for Combining Aggregate-Level Administrative Data in the Absence of a Traditional Census

2015 ◽  
Vol 31 (3) ◽  
pp. 431-451 ◽  
Author(s):  
Dilek Yildiz ◽  
Peter W.F. Smith
Keyword(s):  
Administrative Data ◽  
Census Data ◽  
Geographical Area ◽  
Population Data ◽  
Population Characteristics ◽  
Data Sources ◽  
Aggregate Level ◽  
Administrative Data Source ◽  
Data Source ◽  
Population Counts

Abstract Administrative data sources are an important component of population data collection and they have been used in census data production in the Nordic countries since the 1960s. A large amount of information about the population is already collected in administrative data sources by governments. However, there are some challenges to using administrative data sources to estimate population counts by age, sex, and geographical area as well as population characteristics. The main limitation with the administrative data sources is that they only collect information from a subset of the population about specific events, and this may result in either undercoverage or overcoverage of the population. Another issue with the administrative data sources is that the information may not have the same quality for all population groups. This research aims to correct an inaccurate administrative data source by combining aggregate-level administrative data with more accurate marginal distributions or two-way marginal information from an auxiliary data source and produce accurate population estimates in the absence of a traditional census. The methodology developed is applied to estimate population counts by age, sex, and local authority area in England and Wales. The administrative data source used is the Patient Register which suffers from overcoverage, particularly for people between the ages of 20 and 50.

scholarly journals Linkage of whole genome sequencing with administrative health, and electronic medical record data for the study of autism spectrum disorder: Feasibility, Opportunities and Challenges

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Jennifer Brooks ◽  
Evdokia Anagnostou ◽  
Farah Rahman ◽  
Karen Tu ◽  
Lavnaya Uruthiramoorthy ◽  
...  
Keyword(s):  
Health System ◽  
Whole Genome Sequencing ◽  
Medical Record ◽  
Electronic Medical Record ◽  
Administrative Data ◽  
Genome Sequencing ◽  
Health Data ◽  
Spectrum Disorder ◽  
Whole Genome ◽  
Administrative Health Data

IntroductionAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder (NDD) that presents with a high degree of heterogeneity (e.g., co-occurrence of other NDDs and other co-morbid conditions), contributing to differential health system needs. Genetics are known to play an important role in ASD and may be associated with different disease trajectories. Objectives and ApproachIn this proof of principle project, our objective is to link >2,200 children with a confirmed diagnosis of a NDD from the Province of Ontario Neurodevelopmental (POND) Study to administrative health data and electronic medical record (EMR) data in order to identify subgroups of ASD with unique health system trajectories. POND includes detailed phenotype and whole genome sequencing (WGS) data. Identified subgroups will be characterized based on clinical phenotype and genetics. To meet this goal, consideration of WGS-specific privacy and data issues is needed to implement processes which are above and beyond traditional requirements for analyzing individual-level administrative health data. ResultsLinkage of WGS data with administrative health data is an emerging area of research. As such it has presented a number of initial challenges for our study of ASD. Privacy concerns surrounding the use of WGS data and rare-variant analysis are of particular importance. Practical issues required the need for analysts with expertise in administrative data, EMR data and genetic analyses, and specialized software and sufficient processing power to analyze WGS data. Transdisciplinary discussions of the scope and significance of research questions addressed through this linkage were crucial. The identification of genetic determinants of phenotypes and trajectories in ASD could support targeted early interventions; EMR linkage may inform algorithms to identify ASD in broader populations. These approaches could improve both patient outcome and family experience. Conclusion/ImplicationsAs the cost of genetic sequencing decreases, WGS data will become part of the routine clinical management of patients. Linkage of WGS, EMR and administrative data has tremendous potential that has largely not been realized; including population-level ASD research to improve our ability to predict long-term outcomes associated with ASD.

Sign in / Sign up

Export Citation Format

Share Document