Nurses use data, too!: Difference between revisions

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The following content was extracted from [[Media:CCMDB Poster 2018 v0.4.pdf]].

[[File:Pie chart - requests by roles.png|thumb]]

[[File:Pie chart - requests by nurses.png|thumb]]

The Critical Care and Medicine Database contains information like interventions and procedures which directly involve nursing care and practice.

Using these data can help establish evidence based nursing practices that will provide a safer environment for patients and improve patient outcomes.

~~{{discussion}} need to upload those two pie charts~~

== How can nurses use the database ==

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====B – Understanding the incidence across patients====

~~{{discussion}} need to upload table picture~~

Rachel, a nurse educator, is currently revising the curriculum of the Regional Critical Care Nursing Education Program and would like to add information about caring for a patient with an overdose. She has no idea what type of overdoses

have been cared for in the regional ICUs. She heard about the Critical Care Database from colleague and got the name of Julie, the Database Statistician. She emails Julie, introduces herself and tells about her need regarding patients with overdoses. Julie replies to her that this information is being collected in the

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===Finding out patterns and correlations – readmission of trach patients===

Tracey, an HSC MICU, discusses with her colleagues that there are some patients who return to the ICU from the ward after they develop complications with their tracheostomies. They wonder why it might be that some of these patients have complications while others don’t, and whether any specific nursing care is part of the cause. The HSC ICU data collector hears them talk and joins the conversation. She knows that the database collects information on when ICU patients are intubated, which diagnoses they have that might cause their intubation, and which

diagnoses are causing a patient to be admitted to an ICU. She also knows that the Internal Medicine wards are included in the database, and that therefore information related to complications on the wards is available. By linking the data together, they will be able to find out if one of the reasons for readmission to ICU is related to tracheostomy complications from the sending ward. They look at the CCMDB wiki to find out that the database indeed already reports on ~~re-admissions~~ and their causes for all patients.

diagnoses are causing a patient to be admitted to an ICU. She also knows that the Internal Medicine wards are included in the database, and that therefore information related to complications on the wards is available. By linking the data together, they will be able to find out if one of the reasons for readmission to ICU is related to tracheostomy complications from the sending ward. They look at the CCMDB wiki to find out that the database indeed already reports on readmissions and their causes for all patients.

Tracey and the data collector email Julie, the statistician for the database, to find out if the information could be filtered to only include patients with tracheostomies. Julie replies that it would be possible to limit the readmission report to only patients with a tracheostomy from the sending ward.

[[File:Cuffed inflated tracheotomy tube.png|thumb]] Tracey and the data collector email Julie, the statistician for the database, to find out if the information could be filtered to only include patients with tracheostomies. Julie replies that it would be possible to limit the readmission report to only patients with a tracheostomy from the sending ward.

After some further conversation, they decide that they will extract the following information from the database:

 List and Frequencies of diagnoses for tracheostomy patients who were ~~re-admitted~~, and for trach patients who were not readmitted for the last 5 years

* List and Frequencies of diagnoses for tracheostomy patients who were readmitted, and for trach patients who were not readmitted for the last 5 years

 A list of patients with a trach who have been ~~re-admitted~~ to ICU after having been discharged in the last 5 years, including

* A list of patients with a trach who have been readmitted to ICU after having been discharged in the last 5 years, including

* The full list of diagnoses, and diagnosis type (admit, complication, comorbid) and the part of the stay where the dx was coded (ie during ICU admission or during med ward stay)

** The full list of diagnoses, and diagnosis type (admit, complication, comorbid) and the part of the stay where the dx was coded (ie during ICU admission or during med ward stay)

* Time between discharge from ICU and ~~re-admission~~

** Time between discharge from ICU and readmission

* Patient age

** Patient age

 counts of patients with tracheostomy from the sending Medicine ward and return

* counts of patients with tracheostomy from the sending Medicine ward and return

 back to ICU for the past 5 years

* back to ICU for the past 5 years

 do the above at STB ICMS and check if the same pattern was observed

* do the above at STB ICMS and check if the same pattern was observed

Two weeks later Tracey receives her data. When she reviews it she notices that, as expected, a lot of the ~~re-admissions~~ had mucus plugs related to trach care on the wards. However, she also finds out that patients with some pre-existing diagnoses are more likely to develop the mucus plugs than others. Tracey presents this finding at her next team meeting, and they decide inform the wards which patients are especially susceptible to trach problems, and need special attention.

Two weeks later Tracey receives her data. When she reviews it she notices that, as expected, a lot of the readmissions had mucus plugs related to trach care on the wards. However, she also finds out that patients with some pre-existing diagnoses are more likely to develop the mucus plugs than others. Tracey presents this finding at her next team meeting, and they decide inform the wards which patients are especially susceptible to trach problems, and need special attention.

The same approach could have been used for

 any phenomenon that need identification of causes and relationships to the outcomes

* any phenomenon that need identification of causes and relationships to the outcomes

===Before and after – introducing new wound care products===

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They also decide on the list of diagnoses that will cause patients to be i cluded in the project data. Julie helps them prepare the data request form, and tells them that she will do a preliminary review of the data to find out how large of a sample would be required to determine if any change is caused by the new product.

The data extract is approved, and Julie determines that, after product introduction, we will need 75 patients who have at least one of the diagnoses we determined to have a large enough sample to know whether the new product has any of the hypothetical effects. Judging by past history, she expects it will take about 4 months to have enough data.

Four months after the introduction of the new product, Julie reviews the data and finds out that enough patients with the right diagnoses come through the unit. She extracts the data from the 75 patients who used the new product. She also extract data from 75 patients with same diagnosis grouping before the introduction of the new product. She compares the before and after implementation of the product using some statistical and analytical tests. She finds that the new product is not causing any negative impact, so Robin and her manager can rest assured that the new products are not harming their patients in the way they were concerned about.

[[File:Not your grandpa's bandaid.png|thumb]] Four months after the introduction of the new product, Julie reviews the data and finds out that enough patients with the right diagnoses come through the unit. She extracts the data from the 75 patients who used the new product. She also extract data from 75 patients with same diagnosis grouping before the introduction of the new product. She compares the before and after implementation of the product using some statistical and analytical tests. She finds that the new product is not causing any negative impact, so Robin and her manager can rest assured that the new products are not harming their patients in the way they were concerned about.

If they had found negative outcomes from the new product, a change back to the old product or to a different product could have been recommended, and the outcomes of this change could have been similarly evaluated in the future.

@@ Line 1: / Line 1: @@
   The following content was extracted from [[Media:CCMDB Poster 2018 v0.4.pdf]].
+[[File:Pie chart - requests by roles.png|thumb]]
+[[File:Pie chart - requests by nurses.png|thumb]]
 The Critical Care and Medicine Database contains information like interventions and procedures which directly involve nursing care and practice.
 Using these data can help establish evidence based nursing practices that will provide a safer environment for patients and improve patient outcomes.
-{{discussion}} need to upload those two pie charts
 == How can nurses use the database ==
@@ Line 25: / Line 24: @@
 ====B – Understanding the incidence across patients====
-{{discussion}} need to upload table picture
 Rachel, a nurse educator, is currently revising the curriculum of the Regional Critical Care Nursing Education Program and would like to add information about caring for a patient with an overdose. She has no idea what type of overdoses
 have been cared for in the regional ICUs. She heard about the Critical Care Database from colleague and got the name of Julie, the Database Statistician. She emails Julie, introduces herself and tells about her need regarding patients with overdoses. Julie replies to her that this information is being collected in the
@@ Line 52: / Line 50: @@
 ===Finding out patterns and correlations – readmission of trach patients===
 Tracey, an HSC MICU, discusses with her colleagues that there are some patients who return to the ICU from the ward after they develop complications with their tracheostomies. They wonder why it might be that some of these patients have complications while others don’t, and whether any specific nursing care is part of the cause. The HSC ICU data collector hears them talk and joins the conversation. She knows that the database collects information on when ICU patients are intubated, which diagnoses they have that might cause their intubation, and which
-diagnoses are causing a patient to be admitted to an ICU. She also knows that the Internal Medicine wards are included in the database, and that therefore information related to complications on the wards is available. By linking the data together, they will be able to find out if one of the reasons for readmission to ICU is related to tracheostomy complications from the sending ward. They look at the CCMDB wiki to find out that the database indeed already reports on re-admissions and their causes for all patients.
+diagnoses are causing a patient to be admitted to an ICU. She also knows that the Internal Medicine wards are included in the database, and that therefore information related to complications on the wards is available. By linking the data together, they will be able to find out if one of the reasons for readmission to ICU is related to tracheostomy complications from the sending ward. They look at the CCMDB wiki to find out that the database indeed already reports on readmissions and their causes for all patients.
-Tracey and the data collector email Julie, the statistician for the database, to find out if the information could be filtered to only include patients with tracheostomies. Julie replies that it would be possible to limit the readmission report to only patients with a tracheostomy from the sending ward.
+[[File:Cuffed inflated tracheotomy tube.png|thumb]] Tracey and the data collector email Julie, the statistician for the database, to find out if the information could be filtered to only include patients with tracheostomies. Julie replies that it would be possible to limit the readmission report to only patients with a tracheostomy from the sending ward.
 After some further conversation, they decide that they will extract the following information from the database:
- List and Frequencies of diagnoses for tracheostomy patients who were re-admitted, and for trach patients who were not readmitted for the last 5 years
+* List and Frequencies of diagnoses for tracheostomy patients who were readmitted, and for trach patients who were not readmitted for the last 5 years
- A list of patients with a trach who have been re-admitted to ICU after having been discharged in the last 5 years, including
+* A list of patients with a trach who have been readmitted to ICU after having been discharged in the last 5 years, including
-* The full list of diagnoses, and diagnosis type (admit, complication, comorbid) and the part of the stay where the dx was coded (ie during ICU admission or during med ward stay)
+** The full list of diagnoses, and diagnosis type (admit, complication, comorbid) and the part of the stay where the dx was coded (ie during ICU admission or during med ward stay)
-* Time between discharge from ICU and re-admission
+** Time between discharge from ICU and readmission
-* Patient age
+** Patient age
- counts of patients with tracheostomy from the sending Medicine ward and return
+* counts of patients with tracheostomy from the sending Medicine ward and return
- back to ICU for the past 5 years
+* back to ICU for the past 5 years
- do the above at STB ICMS and check if the same pattern was observed
+* do the above at STB ICMS and check if the same pattern was observed
-Two weeks later Tracey receives her data. When she reviews it she notices that, as expected, a lot of the re-admissions had mucus plugs related to trach care on the wards. However, she also finds out that patients with some pre-existing diagnoses are more likely to develop the mucus plugs than others. Tracey presents this finding at her next team meeting, and they decide inform the wards which patients are especially susceptible to trach problems, and need special attention.
+Two weeks later Tracey receives her data. When she reviews it she notices that, as expected, a lot of the readmissions had mucus plugs related to trach care on the wards. However, she also finds out that patients with some pre-existing diagnoses are more likely to develop the mucus plugs than others. Tracey presents this finding at her next team meeting, and they decide inform the wards which patients are especially susceptible to trach problems, and need special attention.
 The same approach could have been used for
- any phenomenon that need identification of causes and relationships to the outcomes
+* any phenomenon that need identification of causes and relationships to the outcomes
 ===Before and after – introducing new wound care products===
@@ Line 78: / Line 76: @@
 They also decide on the list of diagnoses that will cause patients to be i cluded in the project data. Julie helps them prepare the data request form, and tells them that she will do a preliminary review of the data to find out how large of a sample would be required to determine if any change is caused by the new product.
 The data extract is approved, and Julie determines that, after product introduction, we will need 75 patients who have at least one of the diagnoses we determined to have a large enough sample to know whether the new product has any of the hypothetical effects. Judging by past history, she expects it will take about 4 months to have enough data.
-Four months after the introduction of the new product, Julie reviews the data and finds out that enough patients with the right diagnoses come through the unit. She extracts the data from the 75 patients who used the new product. She also extract data from 75 patients with same diagnosis grouping before the introduction of the new product. She compares the before and after implementation of the product using some statistical and analytical tests. She finds that the new product is not causing any negative impact, so Robin and her manager can rest assured that the new products are not harming their patients in the way they were concerned about.
+[[File:Not your grandpa's bandaid.png|thumb]] Four months after the introduction of the new product, Julie reviews the data and finds out that enough patients with the right diagnoses come through the unit. She extracts the data from the 75 patients who used the new product. She also extract data from 75 patients with same diagnosis grouping before the introduction of the new product. She compares the before and after implementation of the product using some statistical and analytical tests. She finds that the new product is not causing any negative impact, so Robin and her manager can rest assured that the new products are not harming their patients in the way they were concerned about.
 If they had found negative outcomes from the new product, a change back to the old product or to a different product could have been recommended, and the outcomes of this change could have been similarly evaluated in the future.