Sampling for Patient Exit Interviews: Assessment of Methods Using Mathematical Derivation and Computer Simulations

Area of Science:

• Healthcare research methodology
• Clinical trial sampling techniques
• Patient experience data collection

Background:

• Patient exit interviews are crucial for gathering feedback on clinical encounters.
• Existing sampling methods for these interviews may suffer from operational inefficiencies or bias.
• Optimizing sampling is essential for reliable healthcare quality assessment.

Purpose of the Study:

• To assess the operational efficiency of different patient exit interview sampling strategies.
• To identify conditions under which sampling methods produce unbiased results.
• To introduce a novel sampling method that is both operationally efficient and unbiased.

Main Methods:

• A comprehensive literature review was conducted.
• Mathematical derivations were employed to analyze sampling biases.
• Monte Carlo simulations were utilized to evaluate method performance.

Main Results:

• Selecting the next exiting patient is operationally efficient but introduces bias, overrepresenting longer patient visits.
• Selecting the next entering patient eliminates this bias.
• The entering patient method is more operationally efficient than systematic or simple random sampling in most primary care settings.

Conclusions:

• The most operationally efficient and unbiased sampling method for patient exit interviews involves selecting the next patient entering the consultation room.
• This method assumes patient entry order is independent of consultation duration.
• This approach enhances the reliability of patient feedback in primary healthcare.