Week 3: Problem Solving in Today's Healthcare World ALL SOLUTION FALL-2022 LATEST 100% CORRECT GUARANTEED GRADE A+

Identify a current healthcare policy in your current role that you would like to see revised. Why? What would be the projected outcomes? I would like to see a uniform policy for lab specimen collection and labeling at my current practice. Currently we do not have an efficient policy in place on the laboratory collection process and mislabeling errors have increased since opening the centers. The process of laboratory testing plays a significant role in the patient-centered care approach in healthcare. The process starts with the patient and ends with the patient. When an error occurs in the process, the patient’s treatment or management could be delayed and the patient could be subjected to unnecessary retesting (Kaushik, & Green, 2014). Developing a policy that encompasses the requirements of the pre-analytical phase of the blood testing process could help decrease the number of errors in specimen collection in current practice. Analyzing policy problems is a complex activity therefore using a process such as Bardach’s eightfold path will allow for an easy practical step-by-step process to follow and analyze the policy in a more organized manner (Bardach, 2000). The steps are as follow: 1. Define the problem: When defining the problem, the DNP student should consider the deficits and excesses when it is appropriate. The definition should be quantitative and evaluative (Bardach, 2000). Pre-analytical blood testing errors are a significant problem in healthcare. A pre-analytical error is defined as an error in the process of ordering the lab, patient and specimen identification, specimen collection, transport, accessioning and processing. This error can affect the care of the patient. May cause misdiagnosing and improper treatment. Pre-analytical errors account for 46%-68.2% of errors compared to the total testing process. These errors cost on average up to 1.2% of total hospital operating cost (Kaushik & Green, 2014). 2. Assemble some evidence: There is a plethora of evidence on pre-analytical errors in healthcare that will support a policy change. Doing a literature search on the topic and collecting the evidence that is the most relevant, up to date, the best practiced, and valuable to the problem early in the process is key for the evidence assemble step (Bardach, 2000). 3. Construct the alternatives: Early in the analysis process it is best to evaluate multiple alternatives to policy options and other courses of action to implement changes (Bardach, 2000). In this case assessing alternatives to decreasing specimen collection errors would be appropriate. In this stage I think it would be helpful to engage the stakeholders in the process to help with alternative approaches. 4. Select the criteria: This step focuses on the selection of appropriate criteria against which the proposed alternatives will be evaluated criteria to support the policy outcomes. This part can be confusing because it is easy to judge the alternatives instead of evaluating the outcomes of the alternative. If the DNP student select an alternative approach to the initial intervention and it looks like the alternative will lead to the desired outcome, then that intervention will be considered to be the best. The main practical criteria are legality, political acceptability, robustness, and improvability (Bardach, 2000). 5. Project the outcomes: Being realistic in creating policy alternatives and keeping in mind the total aspects of changing policy can affect everyone involved. Using empirical evidence to support policy alternatives and avoid wishful thinking. Policy alternatives ahould be cost and effective. According to Bardach, this step is omitted in policy analysis because it is the most difficult step in the process (Bardach, 2000). In this case, I could project a magnitude estimate that the new policy and procedures on specimen collection to decrease the pre- analytical lab errors by 90%-95% over the next six weeks. I could look at the cost of the errors and use that data to discuss the cost of not having those errors.