Association of Age, Gender, and Health-Insurance Category as Nonmodifiable Parameters on Patient Satisfaction Scores in an Academic Urology Clinic

Over the past six years, CMS has shifted physician reimbursement from a simple fee-for-service schedule to a complex fee-for-performance system, the Merit-Based Incentive Payment System (MIPS).(1) A major aspect of MIPS is an emphasis on quality measures, which accounted for 60% of the physician performance evaluation during the first year of rollout. The other 40% consisted of three measurements: 1) advancing care information; 2) clinical practice improvement activities; and 3) resource use.(2)

A key component of the MIPS system is its budget neutrality. The bonuses awarded to physicians for optimal performance are funded by penalties incurred by physicians with suboptimal performance. The maximum percentage adjustments were rolled out over several years, cumulating in performance year 2020 at ±9%. For the foreseeable future, the maximum bonus adjustment to CMS reimbursement will remain at +9%, and the maximum penalty will remain as a reduction of –9%.(3) Beginning in performance year 2022, the threshold that determines whether a physician will receive a bonus or penalty will be either the mean or median score of all physicians from the previous year.(4)

The emphasis placed on quality measures by Medicare, the largest payer in healthcare, and subsequent commercial insurer implementation,(5) warrants investigation to determine equitability of the MIPS system as currently constituted. At this time, there only limited data have been published examining the potential impact of age, gender, and insurance coverage on patient satisfaction scores in urology clinics. After previously publishing our data on insurance coverage,(6) the question was raised about age and gender impact on satisfaction within our patient population. This study aims to expand on previously published data and examine the impact of all three nonmodifiable patient characteristics (age, gender, insurance category) on satisfaction surveys, such as Clinician and Group Consumer Assessment of Healthcare Providers (CG-CAHPS), that contribute to reimbursement alterations.

Methods

Self-reported, de-identified patient satisfaction surveys completed from the urology clinic at Texas Tech University Health Sciences Center (TTUHSC) provided the data for this study. Six physicians staffed the urology clinic. Inclusion criteria consisted of completion of a Press Ganey (PG) survey, either electronic or paper, between January 1st, 2013, and December 31st, 2020. All patients seen at the TTUHSC urology clinic received a PG survey for each clinical and surgical encounter. Paper surveys were sent to one out of five patient encounters via random selection, and all other patients received an electronic survey through email. Any patient carrying dual coverage was categorized by their primary plan. Each plan was stratified into six payer segments: Medicare; Medicaid; other government coverage; commercial insurance; Workers’ Compensation; and self-pay/uncompensated. Age groups in years were divided as follows: 1–17, 18–34, 35–49, 50–64, 65–79, and 80+. Gender was divided into female and male as self-reported on the surveys.

A weighted mean ± standard deviation and 95% confidence interval were calculated for each payer category, age group, and gender. One-way analysis of variance (ANOVA) determined whether statistically significant differences were present in each data category. After obtaining ANOVA results, post-hoc analysis was performed using the Tukey-Kramer honestly significant difference (HSD) test. This analysis allowed all possible head-to-head comparisons of weighted means between groups. Finally, 95% confidence intervals were also calculated for each Tukey-Kramer result. An alpha level of 0.05 was used for all statistical tests.

Results

A total of 20,773 surveys met inclusion criteria for this study. Complete descriptive statistics are tabulated in Table 1 and a graphical comparison of mean satisfaction scores between groups is found in Figure 1. Payer class, age, and gender results are presented in the following paragraphs.

Figure 1. Average patient satisfaction score of each category and group.

Payer Class

The largest segment of patients carried commercial insurance (40%, n = 9,479), followed by Medicare (37%, n = 7,741), Medicaid (9%, n = 1,916), other government insurance (5%, n = 1,041), self-pay (2%, n = 322), and Workers’ Compensation (1%, n = 274). In total, 108 coverage plans were represented in this study. The weighted average satisfaction scores out of 100 for each payer class were Medicare (93.04), other government (91.53), commercial (91.34), Workers’ Compensation (90.56), Medicaid (89.37), and self-pay/uncompensated (89.04). ANOVA analysis resulted in an F value of 37.57 (p <.001). Tukey-Kramer HSD analysis demonstrated statistically significant differences between the following intergroup comparisons: commercial vs. Medicaid (p <.001); commercial vs. Medicare (p <.001); commercial vs. self-pay/uncompensated (p <.01); Medicaid vs. other government (p <.001); Medicare vs. other government (p <.01); Medicare vs. Medicaid (p <.001); Medicare vs. self-pay/uncompensated (p <.001); Medicare vs. Workers’ Compensation (p <.05); and other government vs. self-pay/uncompensated (p <.05). No statistically significant difference was noted in the following comparisons: commercial vs. other government; commercial vs. Workers’ Compensation; other government vs. Workers’ Compensation; Medicaid vs. self-pay/uncompensated; Medicare vs. Workers’ Compensation; and self-pay/uncompensated vs. Workers’ Compensation (Table 2).

Age

The largest age group was 65–79 (31%, n = 6,345) followed by 50–64 (23%, n = 4,841), 0–17 (16%, n = 3,256), 18–34 (11%, n = 2,379), 35-49 (11%, n = 2,257), and 80+ (8%, n = 1,695). Satisfaction scores for each age group were 65-79 (93.28), 80+ (92.66), 50-64 (92.08), 35-49 (90.30), 0-17 (90.67), and 18-34 (89.24). Statistically significant differences were observed in the following Tukey-Kramer HSD comparisons: 0–17 vs. 18–34 (p <.001); 0–17 vs. 50–49 (p <.001); 0–17 vs. 65–79 (p <.001); 0–17 vs. 80+ (p <.001); 18–34 vs. 35–49 (p <.05); 18–34 vs. 50–64 (p <.001); 18–34 vs. 65–79 (p <.001); 18–34 vs. 80+ (p <.001); 35–49 vs. 50–64 (p <.001); 35–49 vs. 65–79 (p <.001); 35–49 vs. 80+ (p <.001); 50–64 vs. 65–79 (p <.001). Insignificant differences were observed in the following comparisons: 0–17 vs. 35–49; 50–64 vs. 80+; 65–79 vs. 80+ (Table 3).

Gender

Of the surveys returned, 12,371 were completed by females (60%) and 8,402 were completed by males (40%). The female average score was 91.40 (95% CI: 91.18-91.62) and the male average score was 92.27 (95% CI: 92.01-92.52) There was statistically significant difference in the male vs. female Tukey-Kramer HSD comparison (p <.001) with a difference of 0.87 (95% CI 0.53-1.21).

Discussion

Patient satisfaction has the potential to significantly impact physician reimbursement. CMS initiated this paradigm shift with the MIPS system, and large commercial insurance companies, such as Anthem, UnitedHealth Group, Aetna, and Cigna, have adopted similar policies.(5) Although an emphasis on care quality over quantity is worthwhile, careful consideration must be taken into account for certain variables that adjust the patient’s perception of his or her care delivery. Furthermore, it has been documented that high patient satisfaction scores are not well correlated with improved outcomes.(7,8)

Our data agree with current literature that male patients report greater satisfaction and female patients tend to express greater dissatisfaction.

Within the literature, the impact of patient age on satisfaction has some variability. A study of 950 patients with psoriasis found no difference in satisfaction between age strata,(9) whereas a study conducted in 93 Canadian hospitals found patients over the age of 60 to be more satisfied than younger patients.(10) Another study found age to be a statistically significant driver of satisfaction scores in adults undergoing total knee replacement.(11) However, published data on the effect of age on patient satisfaction in urology is very limited.(12) Our data suggest that once patients reach adulthood, they gradually turn more satisfied as they get older until the age of 80. The decrease in satisfaction in the most elderly population is likely influenced by factors such as complex medication regimens, near–end-of-life complications, and the need to balance multidisciplinary care for geriatric ailments. Few stressors of elderly patients are likely a direct result of their interaction with their urologist. Future geriatric research may explore the potential confounding factors of geriatric satisfaction scores. Overall, the general trend is that patient satisfaction scores increase with age until the average life expectancy age(13) has been surpassed.

Our data agree with current literature that male patients report greater satisfaction and female patients tend to express greater dissatisfaction.(14,15) At TTUHSC, approximately 75% of staff in the urology department, including resident physicians, are male. This imbalance could lead to an assumption that our female patient dissatisfaction rates are a product of patient–physician gender discordance. However, recently published data showed no statistical significance in patient satisfaction for gender-concordant physician encounters compared with gender-discordant encounters.(16) Current literature shows gender may play a role in non-response rate, with females responding to surveys more often than males.(17) The gender difference in response rates in conjunction with lower scores from female patients may be of particular importance for urologists who see a large number of female patients.

Patients who are more polarized in their satisfaction or dissatisfaction than the patient population as a whole may respond to surveys more often.

Due to limitations in our institution’s coding software, we were unable to delineate patients with dual coverage, paper versus electronic surveys, or patients who submitted more than one survey. It is possible that some patients completed more than one survey. The number of our patients who carry both Medicaid and Medicare coverage may falsely reduce the extent of true differences between these two groups. This limitation warrants future studies by institutions with more robust coding software to further assess the difference between payer classes. Other potential limitations of this study include low survey response rates, which is a common limitation of patient satisfaction studies.(18) Furthermore, patients who are more polarized in their satisfaction or dissatisfaction than the patient population as a whole may respond to surveys more often.(19) Lastly, these data were collected from a geographically distinct location in West Texas; however, several other publications have reported similar results from other regions of the United States.(20-23) Nonetheless, further investigation of any regional variables that may impact patient satisfaction still is needed.

The results of our study show patient bias based on gender, age, and health insurance category. Due to limited data currently available on potential patient bias in survey scores, we recommend additional studies, because surveys have an important impact on physician’s reimbursement. If our data are confirmed, then 1) physicians should monitor the case mix of their practice and its impact on their “fee-for-performance”; and 2) policymakers should modify reimbursement algorithms to account for the “mix” of the patient population of each physician.

Conclusion

Our data suggest that nonmodifiable patient characteristics, such as insurance status, age, and gender, create bias in the satisfaction scores of an academic urology practice. We recommend further studies to confirm these results. For the time being, physicians should be aware of this reimbursement issue and monitor the patient mix of their practice. If future data should confirm the presented results, policymakers must consider modifying reimbursement algorithms in order to account for the unique patient mix of each individual physician.

Acknowledgments: The authors thank Dennis Lamb in Texas Tech University HSC Patient Experience and Brett Swett in Texas Tech University HSC Business Offices for their expertise and contributions to this study.

References

1. Mullins A. Medicare payment reform: making sense of MACRA. Family Practice Management. 2016;23(2):12-15.

2. Hirsch JA, Rosenkrantz AB, Ansari SA, Manchikanti L, Nicola GN. MACRA 2.0: Are you ready for MIPS? Journal of NeuroInterventional Surgery. 2017;9:713-715.

3. Medicare Program; Merit-Based Incentive Payment System (MIPS) and Alternative Payment Model (APM) Incentive Under the Physician Fee Schedule, and Criteria for Physician-Focused Payment Models. Final rule with comment period. Fed. Regist. 81, 77008–77831. November 4, 2016. www.govinfo.gov/app/details/FR-2016-11-04/2016-25240 .

4. Social Security Administration. Payment for physicians’ services. 2018. https://www.ssa.gov/OP_Home/ssact/title18/1848.htm#ft390

5. Japsen B. More doctor pay tied to patient satisfaction and outcomes. Forbes. June 18, 2018. www.forbes.com/sites/brucejapsen/2018/06/18/more-doctor-pay-tied-to-patient-satisfaction-and-outcomes/#6cfac48f504a .

6. Hayward D, Bui S, de Riese C, de Riese W. Association between payer-class and patient satisfaction scores in an academic urology outpatient clinic. Urol Pract. 2021;8:454-459.

7. Xiang X, Xu WY, Foraker RE. Is higher patient satisfaction associated with better stroke outcomes? Am J Manag Care. 2017;23:e316–e322.

8. Prabhu KL, Cleghorn MC, Elnahas A, et al. Is quality important to our patients? The relationship between surgical outcomes and patient satisfaction. BMJ Qual Saf. 2018;27:48-52.

9. van Winden MEC, Ter Haar ELM, Groenewoud JMM, van de Kerkhof PCM, de Jong EMGJ, Lubeek SFK. Quality of life, treatment goals, preferences and satisfaction in older adults with psoriasis: a patient survey comparing age groups. Br J Dermatol. 2021;184:759-762.

10. Kemp KA, Santana MJ, Southern DA, McCormack B, Quan H. Association of inpatient hospital experience with patient safety indicators: a cross-sectional, Canadian study. BMJ Open. 2016;6(7):e011242.

11. Eftekhary N, Feng JE, Anoushiravani AA, et al. Hospital consumer assessment of healthcare providers and systems: do patient demographics affect outcomes in total knee arthroplasty? J Arthroplasty. 2019;34:1570-1574.

12. Schoenfelder T, Schaal T, Klewer J, Kugler J. Patient satisfaction in urology: effects of hospital characteristics, demographic data and patients’ perceptions of received care. Urol J. 2014;11:1834-1840.

13. Life expectancy. National Center for Health Statistics. https://www.cdc.gov/nchs/fastats/life-expectancy.htm

14. Woods SE, Heidari Z. The influence of gender on patient satisfaction. J Gend Specif Med. 2003;6:30-35.

15. Okunrintemi V, Valero-Elizondo J, Patrick B, et al. Gender differences in patient-reported outcomes among adults with atherosclerotic cardiovascular disease. J Am Heart Assoc. 2018;7:e010498. DOI: 10.1161/JAHA.118.010498

16. Takeshita J, Wang S, Loren AW, et al. Association of racial/ethnic and gender concordance between patients and physicians with patient experience ratings. JAMA Netw Open. 2020; 3(11):e2024583.

17. Smith WG. Does gender influence online survey participation? A record-linkage analysis of university faculty online survey response behavior. San Jose University. 2008. https://files.eric.ed.gov/fulltext/ED501717.pdf

18. Hayward D, de Riese W, de Riese C. Potential bias of patient payer category on CG-CAHPS scores and its impact on physician reimbursement. Urol Pract. 2021;8:183-188.

19. Perneger TV, Chamot E, Bovier PA. Nonresponse bias in a survey of patient perceptions of hospital care. Medical Care. 2005;43:374-380.

20. Abtahi AM, Presson AP, Zhang C, Saltzman CL, Tyser AR. Association between orthopaedic outpatient satisfaction and non-modifiable patient factors. J Bone Jt Surg Am. 2014;97:1041-1048.

21. Tisano BK, Nakonezny PA, Gross BS, Martinez JR, Wells JE. Depression and non-modifiable patient factors associated with patient satisfaction in an academic orthopaedic outpatient clinic: is it more than a provider issue? Clin Orthop Relat Res. 2019;477:2653-2661.

22. Rane AA, Tyser AR, Kazmers NH. Evaluating the impact of wait time on orthopaedic outpatient satisfaction using the Press Ganey Survey. JBJS Open Access. 2019;4(4):e0014.

23. Pines JM, Penninti P, Alfaraj S, et al. Measurement under the microscope: high variability and limited construct validity in emergency department patient-experience scores. Ann Emerg Med. 2018;71:545-554.