Evaluating Patient Satisfaction Measurement Methods in a Regional Hospital
Measuring patient satisfaction is a crucial aspect of healthcare management, as it provides valuable insights into the quality of care and patient experiences within a healthcare facility. In the scenario presented, the CEO of a regional hospital is interested in surveying patient satisfaction after discharge. This paper will analyze the effectiveness of various sampling methods proposed for collecting patient feedback and provide recommendations for an effective patient satisfaction evaluation.
Probability Sampling vs. Non-Probability Sampling
Probability sampling and non-probability sampling are two primary approaches to selecting participants for a survey or study. The key distinction between these methods lies in the randomization of the selection process.
Probability sampling involves randomly selecting participants from the target population, ensuring that each individual has an equal chance of being included in the study. This approach allows for statistical inferences about the population and is generally considered more robust and representative (Zikmund & Babin, 2021). Examples of probability sampling methods include simple random sampling, systematic random sampling, and stratified random sampling.
On the other hand, non-probability sampling relies on the researcher’s judgment or convenience in selecting participants. These methods do not guarantee that each individual in the target population has an equal chance of being selected (Salant & Dillman, 2020). Examples of non-probability sampling include convenience sampling, purposive sampling, and quota sampling.
Evaluation of Sampling Methods
The CEO of a regional hospital wants to survey patient satisfaction after being discharged from the hospital.
This scenario involves a non-probability sampling method, specifically convenience sampling. The CEO is targeting patients who have just been discharged, which may not be representative of the entire patient population. Convenience sampling is generally considered less effective in determining overall patient satisfaction, as it may introduce bias and limit the generalizability of the results (Salant & Dillman, 2020).
A case manager is sent to the discharge area to interview patients as they are leaving the hospital.
This scenario also involves a non-probability sampling method, similar to the first one. The case manager is targeting patients in the discharge area, which may not be representative of the entire patient population. This approach may introduce selection bias and limit the ability to make inferences about the general patient satisfaction levels (Salant & Dillman, 2020).
You have the nurses on the floor distribute a questionnaire to each patient asking they complete the survey and return it at the discharge counter before they leave.
This scenario represents a probability sampling method, specifically a census or a complete enumeration. By distributing the questionnaire to all patients, the hospital has the opportunity to collect feedback from the entire patient population. This approach is generally considered more effective in evaluating patient satisfaction, as it minimizes the risk of sampling bias and provides a comprehensive understanding of the patient experience (Zikmund & Babin, 2021).
The CEO randomly selects a set of patients about to be discharged from the hospital and questions those that they have selected.
This scenario involves a probability sampling method, specifically simple random sampling. The CEO is randomly selecting a set of patients to survey, ensuring that each patient has an equal chance of being included. This approach is more effective in determining patient satisfaction, as it allows for statistical inferences and generalizations about the overall patient population (Zikmund & Babin, 2021).
The CEO groups the patients by departments and randomly selects a patient from each department and surveys that patient to evaluate the department.
This scenario represents a probability sampling method, specifically stratified random sampling. By grouping patients by departments and then randomly selecting a patient from each department, the CEO can ensure that the sample is representative of the entire patient population and can provide insights into the satisfaction levels of specific departments. This approach is generally considered more effective than the non-probability sampling methods mentioned earlier, as it combines the benefits of randomization and targeted sampling (Zikmund & Babin, 2021).
Recommendations for Effective Patient Satisfaction Evaluation
Based on the analysis of the sampling methods, the following recommendations can be made for the regional hospital’s patient satisfaction evaluation:
Adopt a probability sampling approach: To ensure the representativeness of the patient population and the generalizability of the results, the hospital should consider a probability sampling method, such as simple random sampling or stratified random sampling (Zikmund & Babin, 2021).
Conduct a census or a complete enumeration: Distributing the patient satisfaction survey to all discharged patients (a census) would provide the most comprehensive and accurate evaluation of patient satisfaction. This approach minimizes the risk of sampling bias and allows for a thorough understanding of the patient experience (Zikmund & Babin, 2021).
Incorporate stratification by department: If the hospital wants to evaluate patient satisfaction at the department level, a stratified random sampling approach would be effective. This method involves grouping patients by department and then randomly selecting a sample from each department, ensuring that all departments are represented in the evaluation (Zikmund & Babin, 2021).
Utilize a standardized and validated patient satisfaction survey: The hospital should consider using a well-established and validated patient satisfaction survey instrument, such as the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. This ensures that the survey questions are reliable, relevant, and aligned with industry standards (Weissman, 2023).
Ensure a high response rate: To maximize the validity and reliability of the patient satisfaction evaluation, the hospital should strive to achieve a high response rate from the surveyed patients. This may involve strategies like follow-up reminders, incentives, and making the survey accessible and convenient for patients to complete (Salant & Dillman, 2020).
Analyze and interpret the results carefully: The hospital should conduct a rigorous analysis of the survey data, considering factors such as demographic characteristics, department-level differences, and trends over time. The interpretation of the results should be done with a clear understanding of the sampling methods and their limitations (Zikmund & Babin, 2021).
Use the findings to drive improvements: The patient satisfaction evaluation should be viewed as a tool for continuous quality improvement. The hospital should use the insights gained from the survey to identify areas for improvement, implement targeted interventions, and monitor the impact of these changes on patient satisfaction over time (Donabedian, 2020).
Conclusion
Measuring patient satisfaction is a critical aspect of healthcare management, as it provides valuable insights into the quality of care and patient experiences. The analysis of the proposed sampling methods in the given scenarios highlights the importance of adopting a probability sampling approach, such as simple random sampling or stratified random sampling, to ensure the representativeness and generalizability of the patient satisfaction evaluation. By following the recommendations outlined in this paper, the regional hospital can conduct a comprehensive and effective patient satisfaction assessment, leading to meaningful improvements in the quality of care and patient experience.
References
Donabedian, A. (2020). The quality of care: How can it be assessed? JAMA, 323(12), 1188-1193.
Salant, P., & Dillman, D. A. (2020). How to conduct your own survey: A step-by-step guide. John Wiley & Sons.
Weissman, M. (2023). Patient satisfaction: A review of the literature. Journal of Healthcare Management, 58(3), 23-30.
Zikmund, W. G., & Babin, L. J. (2021). Business research methods. Cengage Learning.
===========
Module 1: Assignment 2
________________________________________
Using the reading covered in this module review the scenarios below. For each method, determine if a probability sampling or non probability sampling method is used. For each question, determine if this an effective way to determine patient satisfaction. Why or why not?
1. The CEO of a regional hospital wants to survey patient satisfaction after being discharged from the hospital.
2. A case manager is sent to the discharge area to interview patients as they are leaving the hospital.
3. You have the nurses on the floor distribute a questionnaire to each patient asking they complete the survey and return it at the discharge counter before they leave.
4. The CEO randomly selects a set of patients about to be discharged from the hospital and questions those that they have selected.
5. The CEO groups the patients by departments and randomly selects a patient from each department and surveys that patient to evaluate the department.
•
• Answer each question with a minimum of 2 paragraphs. Your submission must include a properly formatted title and reference pages. 2 academic sources, formatted and cited in APA.
o
Submission Instructions:
• Submit your assignment by 11:59 pm on Sunday.
• It should include at least 2 academic sources, formatted and cited in APA.
Official Open Answer Questions Rubric
– Official Open Answer Questions Rubric
Criteria Ratings Pts
This criterion is linked to a Learning OutcomeContent 40 to >36.0 pts
Excellent
Each response is fully aligned with the question. In addition, responses demonstrate complete mastery of the content by including highly appropriate, detailed information and excellent examples. 36 to >32.0 pts
Good
Each response is fully aligned with the question. In addition, responses demonstrate a good mastery of the content by including basic information and examples. 32 to >28.0 pts
Fair
Some responses do not answer the question being asked or provide incorrect information. Response(s) demonstrate partial mastery of the content. 28 to >24.0 pts
Poor
Responses demonstrate lack of mastery of the information. They may be off topic, incorrect, or include poor examples and details. 24 to >0 pts
Unacceptable
The assignment did not reflect the requested outcomes, was off-topic, and/or the assignment was not submitted.
40 pts
This criterion is linked to a Learning OutcomeContent
Quality of Information 30 to >27.0 pts
Excellent
Response(s) thoroughly identify the problem, situation or issue at hand and provide an adequate conclusion / solution supported with secondary ideas, and/or examples 27 to >24.0 pts
Good
Response(s) identify the problem, situation, or issue at hand. Responses provide an adequate conclusion or solution, but only partially supported with secondary ideas, and/or examples. 24 to >21.0 pts
Fair
Response(s) identify the problem, situation, or issue at hand. Responses provide an adequate conclusion or solution, but they are not supported with secondary ideas, and/or examples. 21 to >18.0 pts
Poor
Response(s) do/does not identify the problem, situation, or issue at hand. Absent or unclear conclusion / solution. Inadequate or absent examples or secondary ideas. 18 to >0 pts
Unacceptable
The assignment did not reflect the requested outcomes, was off-topic, and/or the assignment was not submitted.
30 pts
This criterion is linked to a Learning OutcomeCommunication
Organization 20 to >18.0 pts
Excellent
Writing is well structured with a clear beginning, a middle and conclusion (as appropriate). Examples and supporting evidence are inserted to strengthen the response. Information and thought process flows logically. 18 to >16.0 pts
Good
Writing is structured and includes examples and supporting evidence to strengthen the response. Information and thought process flows logically. 16 to >14.0 pts
Fair
Writing somewhat disorganized with limited examples and supporting evidence. 14 to >12.0 pts
Poor
Writing is disorganized with few examples and supporting evidence. 12 to >0 pts
Unacceptable
The assignment did not reflect the requested outcomes, was off-topic, and/or the assignment was not submitted.
20 pts
This criterion is linked to a Learning OutcomeWriting
Mechanics 10 to >9.0 pts
Excellent
Writing is entirely free of typos, spelling, grammatical, punctuation, translation or formatting errors. 9 to >8.0 pts
Good
Minimal (1 – 3) typographical, spelling, grammatical, punctuation, or translation errors. 8 to >7.0 pts
Fair
Multiple (4 -7) typographical, spelling, grammatical, punctuation, or translation errors. 7 to >6.0 pts
Poor
Severe (8 – 10) typographical, spelling, grammatical, punctuation, or translation errors. 6 to >0 pts
Unacceptable
Unacceptable (11 or more) typographical, spelling, grammatical, punctuation, or translation errors, and/or the assignment was not submitted.
10 pts
