View source for Effectiveness of Evidence-Based Congestive Heart Failure (CHF) CPOE Order Sets Measured by Health Outcomes

Article Review

Krive, J., Shoolin, J. S., & Zink, S. D. (2014). Effectiveness of Evidence-Based Congestive Heart Failure (CHF) CPOE Order Sets Measured by Health Outcomes. AMIA Annual Symposium Proceedings, 2014, 815–824. <ref name= "OrderSets">Krive, J., Shoolin, J. S., & Zink, S. D. (2014). Effectiveness of Evidence-Based Congestive Heart Failure (CHF) CPOE Order Sets Measured by Health Outcomes http://www-ncbi-nlm-nih-gov.ezproxyhost.library.tmc.edu/pmc/articles/PMC4419927/</ref>

== Introduction ==

Evidence-based order sets for treatment of patients with common conditions promise ordering efficiency and more consistent health outcomes.
Computerized Physician Order Entry ([[CPOE|CPOE]]) is now linked to reducing variability in treating well-researched conditions by sharpening diagnoses as well as tracking treatment plans throughout the continuum of patient care.
Early studies in rating [http://www.mayoclinic.org/diseases-conditions/heart-failure/basics/definition/con-20029801// chronic heart failure (CHF)] found [[Order Set|order sets]] effective in reducing mortality, re-admissions, and length of hospital stay (LOS).  This study examines effectiveness of CHF order sets at a major community integrated healthcare delivery network using patient care history from five Advocate Health Care hospitals over five years.

== Methods ==

Mortality and re-admissions were analyzed by comparing "order set" and "free text" groups of adult patients using logistic regression.

Guidelines:
* Patient data between 2007 and 2011
* Adults over the age of 18
* Patients with primary or secondary diagnosis of CHF
* Psych and OB patients were excluded

Data was loaded into SPSS statistical analysis software to conduct statistical analysis.  Binary logistic regression with chi-squared option was employed to compare groups in measuring mortality and re-admissions outcomes.

== Results ==

''' Mortality ''' 

The [http://www.statisticssolutions.com/logistic-regression-assumptions/ binary logistic regression method] revealed that 1.8% of patients in the "order set" group died versus 3.2% in the "free text".

''' Re-admissions Rate ''' 

Patients returning to the hospital in less than 30 days; there was insufficient information on re-admissions available in the enterprise data warehouse for this study, and results were not statistically significant.

''' Length of Stay ''' 

The mean length of stay among the "order set" group was 4.75 days vs. 5.46 days for patients in the "free text" group, indicating that CHF patients who received orders via sets stayed in the hospital almost a day less compared to patients whose orders were placed manually.

== Conclusion ==

This study reports improvement in patient mortality and decrease in the number of days a patient stays in the hospital when treated for CHF utilizing evidence-based prescribing practices.  These results will help in the efforts of acceptance of CPOE order sets when outcomes could lead to patient safety and cost efficiency gains.

== Remarks ==

The utilization of order sets created in CPOE for this study had a low utilization rate about 7%.  This is an issue that many hospitals are facing.  Nowadays, physicians are allowed to customize their order sets to take in consideration not only evidence-based data but also physician's historical practice.

Order sets have the potential to make a difference in patient safety, quality of care, efficiency, the challenge is to obtain the requesting physician's buy in to ensure these positive effects.

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== References ==
<references/>

[[Category: Reviews]]
[[Category: CPOE]]
[[Category: Order Sets]]
[[Category: HI5313-2015-FALL]]
[[Category: Evidence Based Medicine (EBM)]]
[[Category: EMR]]
[[Category: Technologies]]
[[Category: Terminology and Coding]]
[[Category: Usability]]
[[Category: EHR standard]]