Difference between revisions of "A comparison of a multistate inpatient EHR database to the HCUP Nationwide Inpatient Sample"
(→Related Articles) |
(→Related Articles) |
||
Line 18: | Line 18: | ||
===Related Articles=== | ===Related Articles=== | ||
[[Clinical Research Informatics and Electronic Health Record Data]] | [[Clinical Research Informatics and Electronic Health Record Data]] | ||
+ | |||
[[Measurement]] | [[Measurement]] | ||
Revision as of 20:20, 23 October 2015
This is a review of DeShazo and Hoffman's article "A comparison of a multistate inpatient EHR database to the HCUP Nationwide Inpatient Sample".[1]
Background
Quality measures and research has primarily been used with claims-based data. With the growing adoption of EHR, there is potential in using the clinical data as an alternative for research.The purpose of the work is to compare an EHR database with a claims-based sample data and see if EHR data can be leveraged and used for research and reporting. [1]
Methods
The methodology used was comparing a large EHR database (Cerner Health Facts) to a population sample (Nationwide Inpatient Sample) [[1]]. Differences noted were using t-value. Other things noted were differences in procedures and diagnoses between the two datasets for the year 2010. [1]
Results
They found many similarities between the two databases. The differences generally occurred in diagnoses and procedures as well as psyche and obstetrics/gynecology services. This could be due to a number of factors such as EHR adoption rates in these areas. [1]
Conclusion
EHR databases have the potential to be a great alternative to regular claims-based data for health research purposes. However, different analytical techniques will be required. More research needs to be done to further understand its utilization [1]
Comments
Population health is a new big initiative now in the Health IT space and it is interesting to see what areas EHRs lack in comparison to claims-based data. This study was done using data from 2010 so it is interesting to see what possible barriers explained for differences in data. In particular, the rate of EHR adoption especially in highly sensitive departments such as psyche/behavioral could explain the lack of EHR data in those areas.
Related Articles
Clinical Research Informatics and Electronic Health Record Data