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

Authors

  • Pascal Geldsetzer M.B.Ch.B.,

    Corresponding author
    1. Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA
    • Address correspondence to Pascal Geldsetzer, M.B.Ch.B., Department of Global Health and Population, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA 02115; e-mail: pgeldsetzer@mail.harvard.edu.

    Search for more papers by this author
  • Günther Fink Ph.D.,

    1. Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA
    Search for more papers by this author
  • Maria Vaikath M.Sc.,

    1. Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA
    Search for more papers by this author
  • Till Bärnighausen M.D., Sc.D.

    1. Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA
    2. Institute of Public Health, Heidelberg University, Heidelberg, Germany
    3. Africa Health Research Institute, KwaZulu-Natal, South Africa
    Search for more papers by this author

Abstract

Objective

(1) To evaluate the operational efficiency of various sampling methods for patient exit interviews; (2) to discuss under what circumstances each method yields an unbiased sample; and (3) to propose a new, operationally efficient, and unbiased sampling method.

Study Design

Literature review, mathematical derivation, and Monte Carlo simulations.

Principal Findings

Our simulations show that in patient exit interviews it is most operationally efficient if the interviewer, after completing an interview, selects the next patient exiting the clinical consultation. We demonstrate mathematically that this method yields a biased sample: patients who spend a longer time with the clinician are overrepresented. This bias can be removed by selecting the next patient who enters, rather than exits, the consultation room. We show that this sampling method is operationally more efficient than alternative methods (systematic and simple random sampling) in most primary health care settings.

Conclusion

Under the assumption that the order in which patients enter the consultation room is unrelated to the length of time spent with the clinician and the interviewer, selecting the next patient entering the consultation room tends to be the operationally most efficient unbiased sampling method for patient exit interviews.

Ancillary