Difference between revisions of "Improving red blood cell orders, utilization, and management with point-of-care clinical decision support"

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== Introduction ==
 
== Introduction ==
Blood management is one of the top five concerns in considering efficiency and cost effectiveness. Interventions, such as [[CDS | clinical decision support (CDS)]] and alerting systems, apart of transfusion policies in hospitals have increased benefits for inpatient outcomes, blood utilization, and lower costs.
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Blood management is one of the top five concerns in considering efficiency and cost effectiveness. Interventions such as [[CDS | clinical decision support (CDS)]] and alerting systems, as a part of transfusion policies in hospitals, have increased benefits for inpatient outcomes, blood utilization, and lower costs.
  
 
== Methods ==
 
== Methods ==
The study takes place in a 455 bed hospital using [[Epic|EPIC]] as the [[EHR|EHR ]]. This study wishes to assess the change in the ratio of 1-unit to 2-unit red blood cell (RBC) orders throughout hospital. Several additions were added during post-implementation:  1) addition of CDS text, 2) addition of last measured Hb, 3) removal of choice for ordering frequency, 4) reordering of existing order elements, 5) modification of order questions, and 6) removal of free-text general comment field.
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The study takes place in a 455 bed hospital using [[Epic|EPIC]] as the Electronic Health Record [[EHR|EHR ]]. This study wishes to assess the change in the ratio of 1-unit to 2-unit red blood cell (RBC) orders throughout hospital. Several additions were added during post-implementation:  1) addition of CDS text, 2) addition of last measured Hb, 3) removal of choice for ordering frequency, 4) reordering of existing order elements, 5) modification of order questions, and 6) removal of free-text general comment field.
  
 
==== Data Collection and Analysis ====
 
==== Data Collection and Analysis ====
For 3 years, standard query language queries extracted information from the HER in order to analyze the data sets. A statistical analysis program was used to conduct chi-square analyses and comparisons of means occurred via analysis of variance for repeated measures.
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For 3 years, standard query language queries extracted information from the EHR in order to analyze the data sets. A statistical analysis program was used to conduct chi-square analyses and comparisons of means occurred via analysis of variance for repeated measures.
  
 
== Results ==
 
== Results ==
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== Comment ==
 
== Comment ==
This is an interesting study because it considers the importance of transfusion policy especially to those making orders. However, it really doesn’t take into count how useful it is towards patients. There are multiple studies that show taht restrictive transfusion may be safer than liberal strategy. I have included a link to such a study. Qaseem A, Humphrey LL, Fitterman N, Starkey M, Shekelle P, for the Clinical Guidelines Committee of the American College of Physicians. Treatment of Anemia in Patients With Heart Disease: A Clinical Practice Guideline From the American College of Physicians. Ann Intern Med. 2013;159:770-779. doi:10.7326/0003-4819-159-11-201312030-00009
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This is an interesting study because it considers the importance of transfusion policy especially to those making orders. However, it really doesn’t take into count how useful it is towards patients. There are multiple studies that show that restrictive transfusion may be safer than liberal transfusion strategy. I have included a link <ref name= "Transfusion">Qaseem A, Humphrey LL, Fitterman N, Starkey M, Shekelle P, for the Clinical Guidelines Committee of the American College of Physicians. Treatment of Anemia in Patients With Heart Disease: A Clinical Practice Guideline From the American College of Physicians. Ann Intern Med. 2013;159:770-779. doi:10.7326/0003-4819-159-11-201312030-00009/</ref> for one such study.
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== Similar Studies ==
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*[[Is there a “magic” hemoglobin number| Is there a "magic" hemoglobin number]] <ref name="Goodnough 2015"> Goodnough, L. T., & Shah, N. (2015). Is there a "magic" hemoglobin number? Clinical decision support promoting restrictive blood transfusion practices. ''American Journal of Hematology, 90(10)'', 927-933. doi:10.1002/ajh.24101 </ref>
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*[[Predicting red blood cell transfusion in hospitalized patients: role of hemoglobin level, comorbidities, and illness severity | Predicting red blood cell transfusion in hospitalized patients: role of hemoglobin level, comorbidities, and illness severity]] <ref name="Roubinian 2014"> Roubinian N.H., Murphy E.L., Swain B.E., Gardner M.N., Liu V., Escobar G.J.. (2014). Predicting red blood cell transfusion in hospitalized patients: role of hemoglobin level, comorbidities, and illness severity. BMC Health Services Research, 14, 213. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4101854/</ref>
  
 
== References ==
 
== References ==
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[[Category: Reviews]]
 
[[Category: Reviews]]
 
[[Category: CDS]]
 
[[Category: CDS]]
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[[Category: HI5313-2015-FALL]]

Latest revision as of 18:52, 4 November 2015

Written by: Zeke J. McKinney, Jessica M. Peters, Jed B. Gorlin, and Elizabeth H. Perry [1]

Introduction

Blood management is one of the top five concerns in considering efficiency and cost effectiveness. Interventions such as clinical decision support (CDS) and alerting systems, as a part of transfusion policies in hospitals, have increased benefits for inpatient outcomes, blood utilization, and lower costs.

Methods

The study takes place in a 455 bed hospital using EPIC as the Electronic Health Record EHR . This study wishes to assess the change in the ratio of 1-unit to 2-unit red blood cell (RBC) orders throughout hospital. Several additions were added during post-implementation: 1) addition of CDS text, 2) addition of last measured Hb, 3) removal of choice for ordering frequency, 4) reordering of existing order elements, 5) modification of order questions, and 6) removal of free-text general comment field.

Data Collection and Analysis

For 3 years, standard query language queries extracted information from the EHR in order to analyze the data sets. A statistical analysis program was used to conduct chi-square analyses and comparisons of means occurred via analysis of variance for repeated measures.

Results

The study considered the ratio for 1-unit:2-unit orders on a sub-period scale and a monthly scale. There was a significant increase in the ratio in all three sub-periods. The monthly scale shows changes within the ratio associated with the changes in RBC orders.

Conclusion

There was a decrease in blood utilization in the hospital but the hemoglobin triggers show mixed results. There was also no change in repeat orders. There was also little change in the workflow in making orders. Overall, the authors of the study felt that the implementation of a more restricted transfusion policy was highly successful.

Comment

This is an interesting study because it considers the importance of transfusion policy especially to those making orders. However, it really doesn’t take into count how useful it is towards patients. There are multiple studies that show that restrictive transfusion may be safer than liberal transfusion strategy. I have included a link [2] for one such study.

Similar Studies

References

  1. McKinney, Z. J., Peters, J. M., Gorlin, J. B. and Perry, E. H. (2015). Improving red blood cell orders, utilization, and management with point-of-care clinical decision support. Transfusion, 55, 2086–2094. doi: 10.1111/trf.13103
  2. Qaseem A, Humphrey LL, Fitterman N, Starkey M, Shekelle P, for the Clinical Guidelines Committee of the American College of Physicians. Treatment of Anemia in Patients With Heart Disease: A Clinical Practice Guideline From the American College of Physicians. Ann Intern Med. 2013;159:770-779. doi:10.7326/0003-4819-159-11-201312030-00009/
  3. Goodnough, L. T., & Shah, N. (2015). Is there a "magic" hemoglobin number? Clinical decision support promoting restrictive blood transfusion practices. American Journal of Hematology, 90(10), 927-933. doi:10.1002/ajh.24101
  4. Roubinian N.H., Murphy E.L., Swain B.E., Gardner M.N., Liu V., Escobar G.J.. (2014). Predicting red blood cell transfusion in hospitalized patients: role of hemoglobin level, comorbidities, and illness severity. BMC Health Services Research, 14, 213. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4101854/