Nurses use data, too!: Difference between revisions

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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.

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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.