Advanced electronic health record use and quality of asthma care in hospitals that care for children

2015 ◽  
Vol 15 (1) ◽  
pp. 22 ◽  
Author(s):  
Abby Swanson Kazley ◽  
Ronald J. Teufel <suffix>II</suffix>
Keyword(s):  
Electronic Health Record ◽  
Health Record ◽  
Asthma Care ◽  
Electronic Health

scholarly journals Evaluation of the quality of antenatal care using electronic health record information in family medicine clinics of Mexico City

2014 ◽  
Vol 14 (1) ◽  
Author(s):  
Svetlana V Doubova ◽  
Ricardo Pérez-Cuevas ◽  
Eduardo Ortiz-Panozo ◽  
Bernardo Hernández-Prado
Keyword(s):  
Family Medicine ◽  
Antenatal Care ◽  
Electronic Health Record ◽  
Mexico City ◽  
Health Record ◽  
Electronic Health

scholarly journals 1082 Using an Electronic Health Record (EHR) to Collect and Use Quality-Of-Life Data for AASM Process and Outcomes Quality Measures

SLEEP ◽  
2018 ◽  
Vol 41 (suppl_1) ◽  
pp. A402-A402 ◽  
Author(s):  
B Staley ◽  
B T Keenan ◽  
S Simonsen ◽  
R Warrell ◽  
R Schwab ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Electronic Health Record ◽  
Quality Measures ◽  
Health Record ◽  
Life Data ◽  
Electronic Health

scholarly journals Diabetes and Hypertension Quality Measurement in Four Safety-Net Sites

2014 ◽  
Vol 05 (03) ◽  
pp. 757-772 ◽  
Author(s):  
R. Benkert ◽  
P. Dennehy ◽  
J. White ◽  
A. Hamilton ◽  
C. Tanner ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Quality Measurement ◽  
Safety Net ◽  
Lessons Learned ◽  
Health Record ◽  
Partnership Model ◽  
Safety Net Clinics ◽  
Electronic Health ◽  
Query Algorithms

SummaryBackground: In this new era after the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, the literature on lessons learned with electronic health record (EHR) implementation needs to be revisited.Objectives: Our objective was to describe what implementation of a commercially available EHR with built-in quality query algorithms showed us about our care for diabetes and hypertension populations in four safety net clinics, specifically feasibility of data retrieval, measurements over time, quality of data, and how our teams used this data.Methods: A cross-sectional study was conducted from October 2008 to October 2012 in four safety-net clinics located in the Midwest and Western United States. A data warehouse that stores data from across the U.S was utilized for data extraction from patients with diabetes or hypertension diagnoses and at least two office visits per year. Standard quality measures were collected over a period of two to four years. All sites were engaged in a partnership model with the IT staff and a shared learning process to enhance the use of the quality metrics.Results: While use of the algorithms was feasible across sites, challenges occurred when attempting to use the query results for research purposes. There was wide variation of both process and outcome results by individual centers. Composite calculations balanced out the differences seen in the individual measures. Despite using consistent quality definitions, the differences across centers had an impact on numerators and denominators. All sites agreed to a partnership model of EHR implementation, and each center utilized the available resources of the partnership for Center-specific quality initiatives.Conclusions: Utilizing a shared EHR, a Regional Extension Center-like partnership model, and similar quality query algorithms allowed safety-net clinics to benchmark and improve the quality of care across differing patient populations and health care delivery models.Citation: Benkert R, Dennehy P, White J, Hamilton A, Tanner C, Pohl JM. Diabetes and hypertension quality measurement in four safety-net sites: Lessons learned after implementation of the same commercial electronic health record. Appl Clin Inf 2014; 5: 757–772http://dx.doi.org/10.4338/ACI-2014-03-RA-0019

scholarly journals The Relationship between Electronic Health Record Use and Quality of Care over Time

2009 ◽  
Vol 16 (4) ◽  
pp. 457-464 ◽  
Author(s):  
L. Zhou ◽  
C. S. Soran ◽  
C. A. Jenter ◽  
L. A. Volk ◽  
E. J. Orav ◽  
...  
Keyword(s):  
Quality Of Care ◽  
Electronic Health Record ◽  
Health Record ◽  
Electronic Health ◽  
The Relationship ◽  
Over Time

The Challenge of Measuring Quality of Care From the Electronic Health Record

2009 ◽  
Vol 24 (5) ◽  
pp. 385-394 ◽  
Author(s):  
Carol P. Roth ◽  
Yee-Wei Lim ◽  
Joshua M. Pevnick ◽  
Steven M. Asch ◽  
Elizabeth A. McGlynn
Keyword(s):  
Quality Of Care ◽  
Electronic Health Record ◽  
Health Record ◽  
Electronic Health

scholarly journals Health behaviors and quality of life predictors for risk of hospitalization in an electronic health record-linked biobank

2015 ◽  
pp. 247 ◽  
Author(s):  
Paul Takahashi ◽  
James Cerhan ◽  
Euijung Ryu ◽  
Erin Winkler ◽  
Matthew Hathcock ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Health Behaviors ◽  
Electronic Health Record ◽  
Health Record ◽  
Electronic Health

scholarly journals Desiderata for the development of next-generation electronic health record phenotype libraries

GigaScience ◽  
2021 ◽  
Vol 10 (9) ◽  
Author(s):  
Martin Chapman ◽  
Shahzad Mumtaz ◽  
Luke V Rasmussen ◽  
Andreas Karwath ◽  
Georgios V Gkoutos ◽  
...  
Keyword(s):  
Best Practices ◽  
Electronic Health Record ◽  
Health Record ◽  
Next Generation ◽  
Electronic Health Record Data ◽  
High Quality ◽  
Record Data ◽  
Electronic Health ◽  
Further Development

Abstract 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.

Sign in / Sign up

Export Citation Format

Share Document