Running head: Inferential Research and Statistics Project, Part 1

Inferential Research and Statistics Project, Part 1

PSY/315

Inferential Research and Statistics Project, Part 1

Introduction

Have you ever wanted to know people missed appointment? Did a no call or no show? If a person is sick, need medical attention or a routine check up to remain up to date and current with their health they need to schedule and keep their appointment. The problem that I will be addressing is the missed appointment that the patients are missing in its primary and specialty care clinics. There has been a huge spike in missed appointments and we need a solution to help people get the care they need, ultimately making it to their scheduled appointments. The significance of the research will provide the details needed to understand why appointments are being missed and an attempt to find a solution to curve the spike and keep people healthy.

Background

“A large medical facility is experiencing too many missed appointments in its primary and specialty care clinics. The facility has noticed that not all patients respond well to reminder calls regarding follow-up appointments. Some patients do not answer calls and do not seem to respond to voice mail requesting they call the facility. The result is that many follow up appointments are missed. Management has read articles that people respond very well to text messages and would like to see which method provides the least amount of missed appointments. Missed appointments are tracked in the facility database on a monthly basis.” (University of Phoenix Material n.a.).

What we are going to do in an attempt to lessen the number of missed appointments is send out test messages and mail letters to clients to remind them of their appointment. Text messages will be sent out weekly and upon the week of the appointment we will send out 1 that Sunday and the day before.

Sample

I will pick 60 patients that recently missed appointments. I will place them in 2 groups for method A and method B. Method A is (Text) and Method B is (Mail), I will chose 30 for each group and method. I will be using random sampling. I will randomly pick 60 clients out of about 120 recently missed appointments. This set of research will collect some descriptive demographics, such as gender, age and home location. It will makes sense to collect descriptive data to help us accurate determine why appointments are being missed. It will show us if the majority of the clients that miss appointments are women or men, the age category and the distance they live from the doctor’s office.

Hypothesis

My hypothesis is based upon 2 options that will cause patients to not miss appointments and come in to see the doctor. The two methods will have different effects on lowering the amount of missed appointments. I am going to have scores for the who prefer text messages or mail 2 weeks, 1 week and the day before the scheduled appointment.

I will have the scores for Method A (Text) and Method B (Mail)

Null hypothesis is H0: Method A (Text) = Method B (Mail)

Research hypothesis is H1: Method A (Text) ≠ Method B (Mail)

Dependent variable: Missed appointments changed after Method A (Text) and Method B (Mail) were used.

Independent variable: Text and Mail messages

I will not be stating a direction in the hypotheses (better than or worse than), this will be a two-tailed test. The data will have differences that I will look for in both directions form either way. The data will be set your alpha level of .05 and have a sample for each group of 30 people out of the 60 patients that were randomly selected.

Conclusion

After reading this and looking at the setup of the data you will be able to see why patients missed appointments in its primary and specialty care clinics. You will see a potential solution to help people get the care they need, ultimately making it to their scheduled appointments. The data will show you some demographics and explain the numbers and how receptive the clients are or are not to the appointed solution.

References

The University of Phoenix Material (n.a.). Inferential Research and Statistics Project retrieved from the class materials.