A Taxonomy of Requests by Patients (TORP) A New System for Understanding Clinical Negotiation in Office Practice

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Original Research

A Taxonomy of Requests by Patients (TORP) A New System for Understanding Clinical Negotiation in Office Practice

J Fam Pract. 1999 November;48(11):872-878

References

1. Greenfield S, Kaplan S, Ware JE. Expanding patient involvement in care: effects on patient outcomes. Ann Intern Med 1985;102:520-8.

2. Uhlmann RF, Inui TS, Carter WB. Patient requests and expectations: definitions and clinical applications. Medical Care 1984;22:681-5.

3. Lazare A, Eisenthal S, Wasserman L, Harford TC. Patient requests in a walk-in clinic. Compr Psychiatry 1975;16:467-77.

4. DelVecchio Good MJ, Good BJ, Nassi AJ. Patient requests in primary health care settings: development and validation of a research instrument. J Behavioral Med 1983;6:151-68.

5. Uhlmann RF, Carter WB, Inui TS. Fulfillment of patient requests in a general medicine clinic. Am J Public Health 1984;74:257-8.

6. Like R, Zyzanski SJ. Patient requests in family practice: a focal point for clinical negotiation. Fam Pract 1986;3:216-28.

7. Eisenthal S, Koopman C, Stoeckle JD. The nature of patients’ requests for physicians’ help. Acad Med 1990;65:401-5.

8. Valori R, Woloshynowych M, Bellenger N, Aluvihare V, Salmon P. The Patient Requests Form: a way of measuring what patients want from their general practitioner. J Psychosom Res 1996;40:87-94.

9. Arborelius E, Timpka T. In what way may videotapes be used to get significant information about the patient-physician relationship? Med Teacher 1990;12:197-208.

10. Roter D. The Roter method of interaction process analysis. Internal document, Johns Hopkins University; 1990.

11. Kravitz RL, Callahan EJ, Azari R, Antonius D, Lewis CE. Assessing patients’ expectations in ambulatory practice: does the measurement approach make a difference? J Gen Intern Med 1997;12:67-72.

12. Ware JE, Hays RD. Methods for measuring patient satisfaction with specific medical encounters. Med Care 1988;26:393-402.

13. Stata Corporation. Stata Statistical Software: release 5.0. College Station, Tex: Stata Corporation; 1997.

14. Huber PJ. The behavior of maximum likelihood estimates under nonstandard conditions. In: Proceedings of the fifth Berkeley symposium in mathematical statistics and probability. Berkeley, Calif: University of California, Berkeley Press; 1967.

15. Roter D, Hall JA. Doctors talking with patients/patients talking with doctors: improving communication in medical visits. Westport, Conn: Auburn House; 1992.

16. Levinson W, Roter DL, Mullooly JP, Dull VT, Frankel RM. Physician-patient communication: the relationship with malpractice claims among primary care physicians and surgeons. JAMA 1997;277:553-9.

17. Roter DL, Stewart M, Putnam SM, Lipkin M, Jr, Stiles W, Inui TS. Communication patterns of primary care physicians. JAMA 1997;277:350-6.

18. Hall JA, Irish JT, Roter DL, Ehrlich CM, Miller LH. Satisfaction, gender, and communication in medical visits. Med Care 1994;32:1216-31.

19. Wissow LS, Roter D, Bauman LJ, et al. Patient-provider communication during the emergency department care of children with asthma: the National Cooperative Inner-City Asthma Study, National Institute of Allergy and Infectious Diseases, NIH, Bethesda, Md. Med Care 1998;36:1439-50.

20. Callahan EJ, Bertakis KD. Development and validation of the Davis Observation Code. Fam Med 1991;23:19-24.

21. Eisenthal S, Koopman C, Stoeckle JD. The nature of patients’ requests for physicians’ help. Acad Med 1990;65:401-5.

22. Stange KC, Zyzanski SJ, Smith TF, et al. How valid are medical records and patient questionnaires for physician profiling and health services research? A comparison with direct observation of patient visits. Med Care 1998;36:851-67.

23. Stange KC, Zyzanski SJ, Jaen CR. Illuminating the black box: a description of 4454 patient visits to 138 family physicians. J Fam Pract 1998;46:377-89.

24. Gerbert B, Stone G, Stulbarg M, Gullion DS, Greenfield S. Agreement among physician assessment methods: searching for the truth among fallible methods. Med Care 1988;26:519-35.

25. Scheitel SM, Boland BJ, Wollan PC, Silverstein MD. Patient-physician agreement about medical diagnoses and cardiovascular risk factors in the ambulatory general medical examination. Mayo Clin Proc 1996;71:1131-7.

26. Temple W, Toews J, Fidler H, Lockyer JM, Taenzer P, Parboosingh EJ. Concordance in communication between surgeon and patient. Can J Surg 1998;41:439-45.

Patient Request Fulfillment and Visit Satisfaction

Our second hypothesis was that patients whose requests were more frequently fulfilled would report greater visit satisfaction. We created 2 indicators of request fulfillment (or nonfulfillment) at the patient level according to the coder’s judgment: the number of unfulfilled requests (mean = 0.55; SD = 1.3; median = 0; range = 0-9) and the proportion of unfulfilled requests (mean = 7.5%; median = 0; range = 0%-60%). Mean patient satisfaction with the visit was 4.48 (SD = 0.65) on a scale from 1 to 5 scale (5 = excellent). Patient satisfaction was significantly and inversely correlated with the total number of unfulfilled requests (r = -0.32; P <.001). This relationship appeared to be driven more by action requests (r = -.39; P <.001) than information requests (r = -0.21; P = .015). There were no significant associations between satisfaction and the proportion of unfulfilled requests. Compared with patients without any unfulfilled action requests (n = 112), those with one or more unfulfilled request (n = 23) had lower mean satisfaction (4.21 vs 4.54, P = .03).

In a supsidiary analysis, we compared the 22 visits in which patients and physicians negotiated a request with the 117 visits in which no negotiation occurred. There were no significant differences in patient-reported satisfaction with these 2 types of visits (mean = 4.3 vs 4.5, P = .18), suggesting that the quality of the negotiation process may be more important in influencing patient evaluations than the presence or absence of negotiation.

Patient Requests and Physician Perceptions of the Visit

As a final test of TORP, we hypothesized that visits involving many patient requests would take more time and would be perceived by physicians as more demanding. Using linear regression with adjustment for clustering by physician, more information requests (but not action requests) were associated with increased physician-reported visit duration (P = .017, data not shown). Visits in which patients made more requests were rated by physicians as more demanding (r = 0.40 for total requests; r = 0.35 for information requests; and r = 0.29 for action requests; all P values <.001). Using multiple regression (with adjustment for clustering) to control for patients’ general health perceptions, the number of chronic diseases, physician-reported visit length (in minutes), and visit type (new, follow-up, or urgent care), total requests remained significantly associated with the perceived demands of the visit (regression coefficient = 0.05; P = .006; data not shown).

Discussion

TORP fills an important methodologic void for researchers interested in understanding how patient requests and physician responses influence clinical effectiveness. Our investigation demonstrates that TORP is capable of capturing and categorizing patients’ requests in adult primary care medicine. This coding system exhibits excellent reliability in the hands of trained coders and is relatively easy to apply in real time. TORP also measures meaningful phenomena as demonstrated by the significant associations between patient requests and patient health status, request fulfillment and visit satisfaction, and patients’ request behavior and physicians’ perceptions of the demands of the visit.

To our knowledge, TORP is the first direct-observation system designed to identify, classify, and enumerate patients’ requests and physicians’ responses in office practice. TORP may be usefully compared with 4 other popular coding schemes. The Roter Interactional Analysis System (RIAS) is a major refinement of previous work by Bales.¹⁵ It is a reliable and valid system that has been used with success in several studies^16-19 evaluating the relationship between a clinician’s communication style and health care outcomes. The unit of analysis is the utterance (smallest meaningful unit of speech); the emphasis is on process rather than content; and the raw data consist of audiotapes or videotapes. Unlike TORP, RIAS does not code the content of patients’ requests for information, and it has a single “request for services” code that is used when the patient makes “a direct appeal to the physician’s authority.”

The Davis Observation Code (DOC) is an analysis system designed specifically for primary care.²⁰ The unit of analysis is time (10-second blocks); the emphasis is on content (eg, the proportion of time spent discussing prevention); and data may be acquired either from videotapes or real-time observation. As with RIAS, there is no specific mechanism within the DOC system for extracting and classifying patient requests. RIAS and DOC are validated systems, but neither was specifically intended to examine patients’ requests.

In contrast to RIAS and DOC, the systems developed by Like and Zyzanski⁶ and by Eisenthal and coworkers²¹ provide for a detailed categorization of patients’ wishes. Like and Zyzanski’s Patient Request for Services Scale identified 5 clusters of desires: medical information, psychosocial assistance, therapeutic listening, general health advice, and biomedical treatment. Eisenthal and colleagues distinguished between the desired focus or objective and the desired form or method. For example, a patient might want pain relief (focus) achieved through the prescription of a narcotic analgesic (method). Both systems stress assessment of patients’ self-reported desires. TORP shares the same general objectives as these 2 systems but brings a detailed taxonomy to actual clinical behavior.