Measuring the Outcome of Interest - Essay Example

The paper 'Measuring the Outcome of Interest' is a perfect example of outcomes research.
The outcome of interest in my PICO (T) question is lowering the frequency of erroneous surgical operations. This decrease should be measurable because it simply involves lowering the frequency of surgical processes without mistakes and those with mistakes. This outcome takes into account a specific intervention or challenge and contrasts it to a regular intervention or challenge. This contrast often relates to the impact it will have on the hospital’s future surgery patients, especially amputations, over a certain period (Melnyk & Fineout-Overholt, 2015). The intervention of marking surgery patients using markers is a practice that needs greater distinction for surgery nurses and operators to spot errors easily and correct or dismiss them effectively

Article with the Same Outcome of Interest

Nguyen et al conducted a study in late 2013 to find out the effectiveness of information technology when it comes to preventing medication mistakes before operations. The purpose of the study was to build a probabilistic model that can lower medication mistakes by noting unusual or rare “associations” between medicine and illnesses (Nguyen et al., 2013). Nguyen et al collected data for association regulations of extracting methods for 103.5 million prescriptions from Taiwan’s National Health Insurance catalogue. This data collection identified 204.5 million diseases and conditions with ICD9-CM and 347.7 million medicines by employing ATC encryptions (Nguyen et al.). The findings of the study revealed a 96% accuracy for proper and a 45% accuracy for improper prescriptions. The study also revealed a sensitivity rate of 75.9% and a specificity rate of 89.5% (Nguyen et al.).

Nguyen et al defined the outcome a “measurable” consequence of making more accurate, sensitive, and specified medications for patients about to undergo surgery. The authors say that prescription errors mirror the general medical practitioners’ methods of practice, as well as those of their nurses (Nguyen et al.). As a result, knowing that such errors are preventable helps in making them less life-endangering and common than they actually are today. The study proposes that the outcome of interest is achievable through the support of information technology and computerized frameworks that identify and prevent overall nursing errors in hospital surroundings. Nguyen et al measured their outcome of interest using “DM” (Disease-Medication) and “MM” (Medication-Medication) “associations” (Nguyen et al.).

The researchers then automated these variables in line with their co-incidence and associations’ strong points. Second, Nguyen et al measured the automated variables by their levels of interest or “lift values” to nurses and surgeons that were called “Q values” (Nguyen et al.). The researchers used DMQs and MMQs to build the AOP model and project the suitability of a certain prescription. Nguyen et al authenticated this model by contrasting the findings of the assessment conducted by the AOP model and confirmed by human professionals (Nguyen et al.).

Appropriateness of the Article’s Measurement to my PICO (T) Question

The article’s type of measurement for the outcome of interest is appropriate if I were to apply a change in practice based on my PICO (T) question. Even though Nguyen et al essentially used the AOP model on flawed drug prescriptions for operation purposes, it still would be effective for determining scheduling successes made by markers. The AOP is a proficient solution for automatically pinpointing rare and infrequent associations between erroneous operations and flawed marking in scheduling practice by nurses (Nguyen et al.). This type of measurement alerts the surgeon and progresses the patient’s care and general quality of treatment.