This week’s goal is to evaluate various data collection methods, specifically for patient satisfaction and improvement methods. According to the Agency for Healthcare Research and Quality (2017), there is a significant difference between patient satisfaction and patient experience. Whether or not patients are satisfied with their care does not indicate how their experience was. Variables can be analyzed and included or excluded from all patients’ care for consistency in quality, safety, and to improve overall patient satisfaction using assessments of patient experiences. The purpose of the data collection is to determine the patient’s experience, so the data to be collected is qualitative in nature. For describing the patient’s experiences with health care services, I believe a descriptive phenomenology qualitative design would be best.


The patients of the suburban primary care center are the target population. Because the organization is interested in learning about all of the patients’ points of view, they are essentially heterogeneous, with no one patient’s experience being more important than the others. There are no criteria for inclusion or exclusion. This maximum variation sampling will include a wide range of diversity in the sample, increasing the ability of the results to be generalized (Polit & Beck, 2017). The sample size drawn from the facility’s population of 10,000 people will be determined by how knowledgeable the participants chosen are (Malterud, Siersma, & Guasora, 2016; Polit & Beck, 2017). Typically, data analysis should take place concurrently with the data collection process. At this point, the sample size may be increased if the researchers realize that certain significant aspects of the study have been overlooked, which will improve the study’s validity ( Malterud, Siersma, & Guasora, 2016). Another factor to consider with the sample is how it will be chosen. For this scenario, random sampling would be appropriate. A randomized computer program could be used to select participants at random for inclusion in the study. This will improve the rigor of qualitative designs because non-random sampling has been criticized for selecting bias participants who will have similar or desired opinions (Gray, Grove, & Sutherland, 2017).

Data Gathering

The bias of the researchers conducting the interviews and analyzing the data is another criticized aspect of qualitative designs that tarnishes the study’s rigor and validity. When conducting interviews, the researchers must be reflective. Reflectivity implies that the researcher is aware of their own opinions and beliefs and maintains intellectual honesty while conducting interviews, which is especially important given that they will be analyzing the data (Gray, Grove, & Sutherland, 2017; Polit & Beck, 2017). Intellectual honesty and reflexivity will improve the rigor and validity of a study. Because the scenario is designed to collect data on improvements for access to care, wait times, staff friendliness, and the likelihood of the facility being recommended to others, the interview must be semi-structured, with a list of questions asked of each participant but no prediction of responses (Polit & Beck, 2017). The guidelines would include creating an environment in which information can be freely discussed, questions are asked in a logical order, and the interviewer is attentive (Polit & Beck, 2017). The interviewer may also use probe questions to encourage participants to provide more detailed explanations. The following are some open-ended questions:

1. Could you please walk me through the process of making an appointment at the office?

2. How frequently do you get an appointment within the time frame you’ve requested?

3. When you arrive at the office, can you tell me how long you had to wait?

4. Can you tell me about your interactions with each member of the staff?

5. How would you tell your family or friends about our office