The growing interest in improving quality through public reporting and pay-for-performance has generated discussion about how best to measure performance of providers who care for clinically complex patients.9,32
In Donabedian’s classic formulation, the quality of health care processes is divided into 2 domains, technical and interpersonal.33
Recent studies that examined the relationship between multimorbidity and technical quality of care unexpectedly found that patients with more chronic conditions have better technical quality of care.34,35
However, few studies have examined the relationship between multimorbidity—one of the key elements thought to contribute to clinical complexity—and patients’ experiences with care, or interpersonal quality.14,36
To our knowledge, this is the first study to assess the relationship between multimorbidity and patients’ ratings of communication using a nationally representative sample and to consider the influence of concordance among conditions when examining the relationship between multimorbidity and quality of care. Our results suggest that a higher number of conditions is associated with worse ratings of communication, even after adjusting for sociodemographic characteristics such as income. However, the magnitude of the decrement is small (a difference of roughly 0.3 points on a 5-to-15 scale for patients with no conditions compared to patients with more than 2 conditions).
What explains this weak relationship between the number of chronic conditions and patients’ ratings of patient–physician communication? In theory, multimorbidity challenges primary care providers and patients, because of competing demands that occur when patients and providers need to address multiple chronic conditions in the context of fixed schedules and resources typical of busy outpatient clinics.37,38
A greater number of conditions may be associated with more medications, diagnostic tests, and specialist visits that in turn need to be discussed.14
Discussion of multiple topics within a short period may encourage providers to employ communication strategies that leave patients feeling like their providers have not listened carefully, explained matters clearly, involved the patient in decision-making, demonstrated respect, or spent enough time with the patient. Yet numerous patient/physician factors influence communication and patients’ ratings of their providers (e.g., severity of the patient’s chronic condition, linguistic and communication styles, patient–provider health belief system concordance).39
Organizational or system-level factors also play a role.39
For example, primary care practices with high patient volume have lower patient ratings of patient–physician communication than lower volume practices.40
The number of chronic conditions, therefore, is just one of a multitude of factors that contribute to communication during a medical encounter and to patients’ ratings of patient–provider communication, and it may be less determinative than some of the above factors. As this study was not able to measure some of the above factors, we do not know how these might relate to each other.
Several study strengths and limitations should be noted. We used a nationally representative sample of the U.S. population, which increases the generalizability of our findings to the U.S. population. Our results may not be generalizable to vulnerable populations such as homeless individuals who may not have a telephone, as we obtained our data through a telephone survey. The purpose of the study did not include focusing on subsets of patients who might be at higher risk for poor patient–provider communication (e.g., frail patients, underserved populations, certain ethnicities), although we adjusted for these characteristics in our analyses. Although our dataset contained information about many prevalent chronic conditions, we did not have data on conditions such as kidney and liver disease and only had limited information about neurological and ophthalmologic conditions. We had limited information about severity of conditions and therefore, used a proxy, self-rated global health, in our analysis. The severity of the conditions is likely to have an effect on communication, because of the expected increase in intensity of treatment and thus volume of information that would be expected with more severe conditions. Furthermore, patients with multimorbidity could be obtaining care from providers with poorer communication skills, which we were unable to measure in our study. We did not directly observe patient–provider communication, because we were interested in measuring the relationship between number of conditions and patients’ ratings of patient–provider communication, which are currently used in public reporting systems and pay-for-performance programs. A third-party observer’s assessment of the quality of patient–provider communication is a different research question warranting a separate study and different methods. Finally, the small interquartile range in our outcome variable likely affected our results. However, our data are typical of patient experience and satisfaction data, which tend to be skewed toward more favorable assessments of providers.41,42
Because it is unlikely that publicly reported performance data or data used for pay-for-performance programs would have normally distributed patient experience ratings data, the relationship between multimorbidity and patient experience ratings that we observed in our analysis would likely be observed in any quality improvement program that relies on patient experience ratings.
In discussions about quality assessment, public-reporting and pay-for-performance programs, some have expressed concern that performance measurement that fails to account for clinical complexity may create performance measure penalties for providers who care for more clinically complex patients. We studied the aspect of clinical complexity most commonly used, multimorbidity, and found that the difference in communication ratings among patients with fewer versus more chronic conditions is numerically small. Whether this small decrement may still have negative consequences in daily practice is unknown. What evidence is available is quite indirect. In New York State, some providers reported less willingness to treat more severely ill patients because of the Cardiac Surgery Reporting System,43
despite data that the risk adjustment model accurately predicted mortality among high-risk patients and that concerns that treating more severe patients would result in worse publicly reported outcomes could not be substantiated.44
By analogy, providers might avoid multimorbid patients if there were a perception that these patients rate their providers lower. Future studies may consider measuring other variables that affect communication such as linguistic skills, nonverbal cues, and organizational characteristics such as visit length and physical setting.39,45
The effects of other elements of clinical and nonclinical complexity, such as the presence of a newly diagnosed condition (e.g., cancer), severity of conditions, and homelessness, should also be investigated.25,46