Quality of Outpatient Care for Diabetes Mellitus in a National Electronic Health Record Network

2006 ◽  
Vol 21 (1) ◽  
pp. 13-17 ◽  
Author(s):  
James M. Gill ◽  
Andrew J. Foy ◽  
Yu Ling
Author(s):  
Amos Otieno Olwendo ◽  
George Ochieng ◽  
Kenneth Rucha

This research aims to determine the applicability of routine healthcare in clinical informatics research.  One of the key areas of research in precision medicine is computational phenotyping from longitudinal Electronic Health Record (EHR) data. The objective of this research was to determine how the interplay of EHR software design, the use of a data dictionary, the process of data collection, and the training and motivation of the human resource involved in the collection and entry of data into the EHR affect the quality of EHR data thus the suitability of such data for utility in computational phenotyping of diabetes mellitus. This research employed a prospective/retrospective study design at the diabetes clinic in Nairobi Hospital. The first source of data was from interviews with 32 staff; nurses, doctors, and health record officers using a referenced peer-reviewed usability questionnaire. Thereafter, a sample of EHR data collected during routine care between January 2012 and December 2016 was also analyzed by looking into the quality of clusters identified in the data using a density-based clustering algorithm and Statistical Package for Social Sciences (SPSS) version 21. Regression analysis shows that software design and the utility of a data dictionary explained 50.7% and 32.3% respectively in the improvement of the suitability of EHR data for computational phenotyping of diabetes mellitus. Also, EHR software was rated useful (82%) in accomplishing users’ daily tasks. However, EHR data were found to be unsuitable for utility in computational phenotyping of diabetes.   Despite the fact that 88% of EHR data were clustered as noise, the clustering algorithm identified a total of 23 clusters from the diabetes dataset. However, with improved quality of EHR data, sub-phenotyping tasks would be achievable. This research concludes that the poor quality of EHR data is a result of employees’ unmet intrinsic factors of motivation.  


SLEEP ◽  
2018 ◽  
Vol 41 (suppl_1) ◽  
pp. A402-A402 ◽  
Author(s):  
B Staley ◽  
B T Keenan ◽  
S Simonsen ◽  
R Warrell ◽  
R Schwab ◽  
...  

2014 ◽  
Vol 05 (03) ◽  
pp. 757-772 ◽  
Author(s):  
R. Benkert ◽  
P. Dennehy ◽  
J. White ◽  
A. Hamilton ◽  
C. Tanner ◽  
...  

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


2009 ◽  
Vol 16 (4) ◽  
pp. 457-464 ◽  
Author(s):  
L. Zhou ◽  
C. S. Soran ◽  
C. A. Jenter ◽  
L. A. Volk ◽  
E. J. Orav ◽  
...  

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

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