Appointment Length, Psychiatrists’ Communication Behaviors, and Medication Management Appointment Adherence
Abstract
Objective
The authors explored the relationship between critical elements of medication management appointments (appointment length, patient-centered talk, and positive nonverbal affect among providers) and patient appointment adherence.
Methods
The authors used an exploratory, cross-sectional design employing quantitative analysis of 83 unique audio recordings of split treatment medication management appointments for 46 African-American and 37 white patients with 24 psychiatrists at four ambulatory mental health clinics. All patients had a diagnosis of depression. Data collected included demographic information; Patient Health Questionnaire–9 scores for depression severity; psychiatrist verbal and nonverbal communication behaviors during medication management appointments, identified by the Roter Interaction Analysis System during analysis of audio recordings; and appointment adherence. Bivariate analyses were employed to identify covariates that might influence appointment adherence. Generalized estimating equations (GEEs) were employed to assess the relationship between appointment length, psychiatrist patient-centered talk, and positive voice tone ratings and patient appointment adherence, while adjusting for covariates and the clustering of observations within psychiatrists. Wald chi square analyses were used to test whether all or some variables significantly influenced appointment adherence.
Results
GEE revealed a significant relationship between positive voice tone ratings and appointment adherence (p=.03). Chi square analyses confirmed the hypothesis of a positive and significant relationship between appointment adherence and positive voice tone ratings (p=.03) but not longer visit length and more patient-centered communication.
Conclusions
The nonverbal conveyance of positive affect was associated with greater adherence to medication management appointments by depressed patients. These findings potentially have important implications for communication skills training and adherence research.
Medication management (1) appointments are psychiatric visits used to treat drug-responsive psychiatric disorders (2). They are the principal clinical service provided by U.S. psychiatrists during outpatient care. Medication management appointments last between 15 and 20 minutes, and patients in ongoing care are seen every one to three months. Currently, medication management—provided in the context of split treatment that incorporates psychotherapy by a nonpsychiatrist clinician (3–5)—dominates outpatient psychiatric practice in the United States (6,7).
Stopping recommended treatment before completion (8,9) or before improvement (9,10) is cause for concern. Treatment dropout can signal a decline in medication adherence (11) and an increase in the probability of hospitalization within the next 12 months (12,13).
Most effective treatment adherence interventions target patient behaviors (14,15). Another approach that has received little attention is enhancing verbal and nonverbal communication behaviors among psychiatrists. There is some evidence that appointment length, patient-centered communications, and positive affect by the provider during psychiatric and medical visits are related to greater patient appointment adherence.
Appointment length has special importance in psychiatry, which has been viewed as the most time-intensive medical specialty (16). In one study of a nationally representative sample of visits to office-based psychiatrists between 2001 and 2006, visits by African-American patients were significantly shorter than visits by whites (17). Considering that African-American patients have higher treatment dropout rates than white Americans (9,18,19), shorter visit duration could be one factor that negatively influences patient appointment adherence.
Patient-centered communications focus on eliciting patients’ psychosocial and general medical needs, encouraging them to disclose their concerns, conveying a sense of partnership in treatment, and actively facilitating patient involvement in treatment decision making (20,21). This style of communication has been shown to improve provider and patient satisfaction as well as the continuity of care (22,23) and patient outcomes (24).
A meta-analysis of visit communications during medical appointments found that when the doctor offered more information, asked fewer questions overall but more questions about adherence in particular, and was more positive and less negative (both verbally and nonverbally), subsequent appointment keeping was greater (25). In addition, several measures of conveyed affect in physicians’ voice tone have been shown to be related to patient appointment adherence (26,27).
Our recent studies of psychiatrist communication style during medication management appointments suggest that the interpersonal and communication dynamics of these visits are similar to those of primary care visits. For example, in our earlier work we found wide variation in psychiatrists’ communication styles and expression of affect during routine medication management appointments (28,29). There was a notable difference during these visits between the communication styles of psychiatric residents and attending physicians. Visits by residents were twice as long as and were significantly more patient-centered than visits by attending physicians. Global ratings of affect revealed that residents were perceived as sounding significantly more positive (friendly and sympathetic) and less dominant and rushed.
The objective of this study was to explore the relationship between appointment length and psychiatrists’ communication behaviors in the context of medication management visits and subsequent patient appointment keeping. More specifically, we hypothesized that longer visit length, more patient-centered communication, and more positive conveyed affect would be associated, together or separately, with higher rates of patient appointment keeping.
Methods
Study procedures
Unique psychiatrist-patient dyads were audio-recorded during visits falling between the patient’s first and sixth appointment at four ambulatory mental health clinics located in the Midwest. These clinics were part of a large, urban mental health care system affiliated with a university.
Psychiatrists were eligible if they practiced at a participating study clinic. Patients were eligible if they were 18 to 65 years of age, had a chart-recorded DSM-IV-TR diagnosis of major depressive disorder, depressive disorder not otherwise specified, or dysthymic disorder, were in treatment with a participating psychiatrist, and had been seen by their psychiatrist for their first appointment but had not attended more than five appointments. We limited diagnosis to depression to control diagnostic variation, and we limited patient participation to the second through fifth appointments because patients are more likely to drop out of treatment around the time of these early appointments (9,10,30).
Psychiatrists were recruited through presentations and individual meetings. Patients were either self-referred in response to a flyer or introduced to the study by their therapist or psychiatrist. Eligible patients gave consent and were enrolled in the study prior to a routine medication management visit at the clinic. Patients received a $10 grocery store gift card for study completion. Psychiatrists received no compensation. The study, including the informed consent form, was approved by the University of Pittsburgh Institutional Review Board. Recruitment occurred from March 2005 through May 2008.
Data collection
At the start of the appointment, in front of the psychiatrist and the patient, research staff activated the recorder and left the office. For appointment keeping, we collected information from patients’ health records about patient attendance at the next scheduled appointment.
The appointment recordings were quantitatively analyzed by using the Roter Interaction Analysis System (RIAS) (31). The original deidentified recordings were sent to the RIAS communications laboratory at the Johns Hopkins Bloomberg School of Public Health. Coded analytic data were returned via encrypted electronic mail and merged with all other study data for final analysis.
Measures
Analysis of visit communication.
Visit communication was analyzed by using the RIAS, a system for characterizing medical dialogue that is widely used and that has well-established reliability and predictive validity (31). Each statement by the patient and psychiatrist defined as a complete thought was assigned to one of 41 categories of speech. Statements were coded directly from recordings without transcription, allowing consideration of the emotional context of the visit.
As in many studies using the RIAS, the large number of specific codes was reduced by combining codes into broad common groupings. In this study, seven groupings were created to construct the patient-centered talk variable: biomedically focused questions regarding treatment response, medication side effects, psychiatric history, and other medical problems; psychosocially focused questions regarding social and family relations at work and at home, performance of activities and functions related to daily living, and exchanges related to feelings; biomedical-focused information regarding psychiatric history, symptoms, therapeutic regimen, and other medical problems; psychosocial-focused information regarding social and family relations at work and at home, performance of activities and functions related to daily living, and exchanges related to feelings; rapport-building talk, such as reflecting laughter, compliments, and comments showing agreement or understanding and approval; partnership-building talk, such as asking for opinions and beliefs, asking for clarification of fact, and checking for understanding; and facilitators of speech, such as showing agreement and understanding, paraphrasing, checking for understanding, providing transitions, giving orientation, offering instructions, and making back channel statements, such as “uh-huh.” To adjust for variation in total talk associated with appointment length, we calculated the proportion of total speaker talk for each RIAS communication category.
After the groupings above were constructed, we then calculated the patient-centered talk variable. This variable was calculated, as in other communication research studies using the RIAS, as the ratio of the sum of psychosocial, rapport-building, and facilitative behaviors to the sum of biomedical questions, information giving, and closed-ended questions by clinicians and patients. A score >1 indicates that the visit tended to further the patient’s agenda, and a score <1 means the visit tended to further the clinician’s agenda (32–34).
Appointment length was calculated in minutes from the first to the last statement identified by the RIAS coders during the audio recording of the appointment.
In addition to analyzing verbal behaviors, RIAS coders used a 5-point scale (1–5) to rate the emotional tone conveyed by the psychiatrist or the patient on a variety of affective dimensions. A low value suggests minimal presentation of this affective dimension in the provider’s voice tone, while a high value suggests extensive presentation of this affective dimension. We performed a factor analysis to determine if the variability of the psychiatrist voice tone ratings was a consequence of a few unobserved variables. Factor analyses search for such joint variations in response to unobserved latent variables. Our factor analysis revealed a two factor solution. One factor was comprised of voice tone ratings that have been referred to in the extant literature as conveying positive emotions (dominance-assertiveness, which is reverse-coded, so high dominance expression is coded low; interest-attentiveness; friendliness-warmth; responsiveness-engagement; sympathetic-empathetic; interactivity; and respectfulness). We calculated the mean score for the positive voice tone rating factor and entered it as an independent variable in the main analyses.
Two experienced RIAS coders, unaware of the study hypothesis, analyzed the recordings. Interrater reliability for a random sample of ten recordings ranged from .85 to .97 (Pearson correlation coefficients) across the categories used in this analysis. Coder agreement on ratings of affect within 1 point on the 5-point scale ranged from 80% to 100%.
Patient Health Questionnaire−9.
Because there is some evidence from the psychotherapy literature that depression severity has an inverse relationship with treatment dropout (35), depression severity was measured with the Patient Health Questionnaire−9 (PHQ-9) depression module (36). The PHQ-9 is self-administered, and each DSM-IV criterion for depression is rated from 0, not at all, to 3, nearly every day. Possible PHQ-9 scores range from 0 to 27, with higher scores indicating more severe depression.
Appointment adherence.
Patient attendance of the next scheduled appointment was extracted from patients’ health records. Instances in which the patient rescheduled the appointment and kept it were counted as a kept appointment.
Background and sociodemographic characteristics.
Patient background and sociodemographic characteristics were assessed immediately prior to recording the medication management visit.
Data analysis
The main independent variables for the analyses were appointment length, patient-centered talk ratio, and positive voice tone ratings. The dependent variable was kept or missed appointments.
We performed t test and chi square analyses to assess the relationship between appointment adherence and confounding variables that have been suggested to influence appointment adherence. They were psychiatrists’ gender (37–42); patients’ gender (25,43–45), PHQ-9 score (35), income (35,46–48), health insurance (46,47), and race-ethnicity (17); psychiatrist-patient race concordance (32); and the clinic where services were received (35). Confounding variables with p values <.1 were included in the adjusted multivariate analyses.
We performed logistic regression analyses, both adjusting and not adjusting for the influence of confounding variables, by using generalized estimating equations (GEEs). GEEs were preferred over traditional regression because they accounted for the clustering effects of any within-psychiatrist correlation and the different number of recorded psychiatrist-patient dyads for each psychiatrist (49,50). We assumed an exchangeable correlation structure and employed the Huber-White sandwich estimate of variance, which provides valid and robust standard error (SE) estimates, even if the correlation structure is misspecified (51). Because odds ratios overstate the probability of frequent events, we present estimated unadjusted and adjusted probabilities for logistic regression analyses instead. In our analysis, adjusted probabilities compared patients who kept versus those who missed their next appointment with our main independent variables, while holding all covariates in the model constant, either by using a mean (continuous variables) or the average probability of being in a particular category (dichotomous variables). This allowed us to compare two otherwise equivalent patients on the basis of the available data.
To test that the effect of longer appointments, more patient-centered talk, and more positive voice tone ratings on appointment adherence all equaled 0 (our null hypothesis), we performed a two-tailed multiple coefficient Wald chi square test after fitting our full model. Thereafter, single-coefficient Wald chi square tests were performed on any of our hypothesized variables that were shown by GEE to have a significant relationship with appointment adherence (p<.05). All statistical analyses were performed by using Stata software, version 12.1.
Results
Clinic recruitment
We recruited three community-based and one mood disorder research clinic. The psychiatrists’ role in the participating sites was limited to pharmacotherapy in split treatment appointments. At all clinics, psychotherapy was provided by master’s-level clinicians.
Two clinics provided a majority of the 83 recordings (N=65, 78%) used in the final analysis. One clinic recruited more white (N=15, 65%) than African-American (N=8, 35%) patients. This clinic was staffed with both attending and resident psychiatrists. The other clinic principally recruited African-American patients (N=37, 88%) and was staffed by resident psychiatrists under the supervision of an attending psychiatrist.
The additional two clinics predominantly recruited white Americans (N=8, 89%, and N=9, 100%, respectively) and were staffed by attending psychiatrists.
Psychiatrist and patient recruitment
Fifty-two psychiatrists were approached, 41 (79%) consented to participate, and 24 (46%) were recorded. Eleven psychiatrists who consented were not recorded because they did not have any patients within the study period who met inclusion criteria.
Of the 150 patients approached, 130 (86%) signed consent, and 91 (70%) were recorded. Thirty-nine of the patients who consented did not return. Six recordings were excluded because of inaudibility, one recording was excluded because the patient did not meet diagnostic inclusion criteria, and one recording was excluded because the findings from this recording had an unusually strong influence on the overall study findings (it was a high-leverage point) (51). The final analysis included recordings of appointments for 83 patients, representing 64% of the patients who consented.
Demographic information
On average, psychiatrists were middle-aged (mean age=38, range 27–60 years) (Table 1). A majority were male (63%), white (71%), married (71%), and residents-in-training (67%). The median number of recordings per psychiatrist was three (range 1–11).
| Characteristic | N | % |
|---|---|---|
| Patients | ||
| Gender | ||
| Female | 69 | 83 |
| Male | 14 | 17 |
| Age | ||
| M±SD | 45±10 | |
| Range (years) | 24–65 | |
| Race | ||
| White | 37 | 45 |
| African American | 46 | 55 |
| Marital status | ||
| Married | 9 | 11 |
| Unmarried | 74 | 89 |
| Income | ||
| <$10,000 | 57 | 69 |
| >$10,000–$39,999 | 19 | 23 |
| ≥$40,000 | 7 | 8 |
| Employment status | ||
| Employed | 22 | 26 |
| Unemployed | 13 | 16 |
| Disabled | 48 | 58 |
| Insurance status | ||
| Public | 51 | 61 |
| Private | 15 | 18 |
| None | 17 | 21 |
| Education | ||
| Less than high school | 17 | 21 |
| High school diploma or GED | 31 | 37 |
| Postsecondary | 35 | 42 |
| Psychiatrists | ||
| Gender | ||
| Female | 9 | 37 |
| Male | 15 | 63 |
| Age | ||
| M±SD | 38±9 | |
| Range (years) | 27–60 | |
| Race | ||
| White | 17 | 71 |
| African American | 2 | 8 |
| Asian | 5 | 21 |
| Marital status | ||
| Married | 17 | 71 |
| Unmarried | 7 | 29 |
| Psychiatric status | ||
| Attending | 8 | 33 |
| Resident | 16 | 67 |
The mean age of patients was 45 (range 24–65 years), and most were African American (55%), female (83%), unmarried (89%), and unemployed or disabled because of a mental disorder (74%). The mean±SD PHQ−9 score was 13±7.
Appointment length was 22±12 minutes (range 4–60). Patients’ appointment adherence was 63%±5%. Twenty-five (30%) appointments were for race-concordant white dyads, and five (6%) were for African-American dyads. Scores were 3.17±2.44 for patient-centered talk and 3.93±.47 for positive voice tone rating.
Bivariate analyses
Analyses of independent variables for patients who kept versus missed follow-up appointments were significant for patient race (χ2=12.74, df=1, p<.001), clinic (χ2=15.67, df=3, p=.001), psychiatrist gender (χ2=8.00, df=1, p=.005), psychiatrist-patient race concordance (χ2=11.58, df=1, p=.001), and patient income (χ2=4.73, df=2, p=.09) (Table 2).
| Kept appointment | Missed appointment | ||||
|---|---|---|---|---|---|
| Variable | N | % | N | % | pa |
| PHQ-9 score (M±SD)b | 52 | 12.0±6.6 | 31 | 14.6±7.9 | .12 |
| Psychiatrist-patient dyad | .001 | ||||
| Race concordance | 26 | 87 | 4 | 13 | |
| Race discordance | 26 | 49 | 27 | 51 | |
| Psychiatrist gender | .005 | ||||
| Male | 32 | 53 | 28 | 47 | |
| Female | 20 | 87 | 3 | 13 | |
| Patient race | <.001 | ||||
| White | 31 | 84 | 6 | 16 | |
| African American | 21 | 46 | 25 | 54 | |
| Patient gender | .89 | ||||
| Male | 9 | 64 | 5 | 36 | |
| Female | 43 | 62 | 26 | 38 | |
| Patient insurance | .36 | ||||
| None | 13 | 76 | 4 | 24 | |
| Medicaid or Medicare | 27 | 61 | 17 | 39 | |
| Private | 12 | 55 | 10 | 45 | |
| Patient income | .09 | ||||
| <$10,000 | 33 | 58 | 24 | 42 | |
| $10,000–$39,999 | 12 | 63 | 7 | 37 | |
| ≥$40,000 | 7 | 100 | 0 | — | |
| Clinic | .001 | ||||
| 1 | 7 | 78 | 2 | 22 | |
| 2 | 18 | 78 | 5 | 22 | |
| 3 | 18 | 43 | 24 | 57 | |
| 4 | 9 | 100 | 0 | — | |
a
Means were compared by t test, and proportions were compared by chi square test.
b
Patient Health Questionnaire–9. Possible scores range from 0 to 27, with higher scores indicating more severe depression.
Multivariate analyses
Unadjusted and adjusted GEEs are presented in Table 3. The adjusted analyses showed that positive voice tones conveyed by the psychiatrist were significantly and positively associated with appointment adherence (β=1.83, SE=.87, 95% confidence interval [CI]=.13–3.53, p=.03). Our probability estimator (β) informed us that for a .47 increase (one SD above the mean) in positive voice tone ratings, there was a corresponding increase of 162% (1.83 SDs) in appointment adherence.
| Unadjusted | Adjustedb | |||||||
|---|---|---|---|---|---|---|---|---|
| Characteristic | β | SE | 95% CI | p | β | SE | 95% CI | p |
| Appointment length | –.01 | .02 | –.06 to .03 | .51 | –.04 | .03 | –.10 to .02 | .18 |
| Patient-centered talk | .06 | .06 | –.07 to .18 | .40 | .13 | .10 | –.07 to .34 | .20 |
| Positive voice tones | 1.00 | .45 | .12 to 1.89 | .03 | 1.83 | .87 | .13 to 3.53 | .03 |
a
Generalized estimating equations
b
Adjusted for patient race, psychiatrist gender, clinic, race concordance, and patient income
Wald chi square analysis of independent variables of interest together revealed that the null hypothesis could not be rejected. In tests of single coefficients, the null hypothesis was rejected for positive voice tone ratings (χ2=4.47, df=1, p=.03) but not for appointment length and patient-centered talk. This analysis confirmed the positive and significant relationship between positive voice tone ratings and appointment adherence.
Discussion
Consistent with our hypothesis, positive psychiatrist affect was significantly associated with appointment adherence. Contrary to our hypothesis, logistic regression analyses showed that medication management appointment length and patient-centered talk were not significantly associated with appointment adherence, even after adjustment for patient race and income, psychiatrist gender, race concordance, and the clinic where services were rendered. We believe that the study findings are novel and may be important in understanding how therapeutic alliance influences treatment adherence. A positive therapeutic alliance in mental health care has been shown to be related to retention in treatment for drug (52,53) and alcohol abuse (54) as well as in family therapy (55). Although previous articles detailing core features of the therapeutic alliance have focused specifically on providers’ verbal communicative style, our findings highlight the potential importance of providers’ implicit communication behaviors.
We hypothesize two potential reasons for our negative findings. First, because our study observations consisted of split treatment medication management appointments, the results may have been affected by a misalignment between patient and provider expectations. Patients may have expected the appointment to focus solely on pharmacotherapeutic issues. However, psychiatrists may have expected their role in care to include a discussion of psychosocial issues (a more patient-centered approach), with appointments lasting longer than patients expected. This misalignment of expectations could have resulted in patients’ devaluing the appointment and could have negatively influenced their decision to attend future appointments.
Second, psychiatrists spend more time and utilize more patient-centered talk with problematic patients. Problematic patients are less likely to attend regularly scheduled appointments. Examples of problematic patients are persons with complex biopsychosocial issues, recent histories of suicide attempts (56), or frequent hospitalizations (12,13).
Our study had several strengths. First, this is the only study known to the authors that assessed the relationship between psychiatrist verbal and nonverbal communication behaviors during medication management appointments and patient appointment adherence. Second, our patient sample was made up of persons with diverse racial-ethnic and socioeconomic characteristics. Third, we recruited patients and providers from multiple clinics that are common within the mental health care system.
This study had a few limitations. First, one limitation common to all communications research is observation bias. The presence of recorders in appointments may alter the dynamics of care and affect how providers and patients communicate. In this study, concern about observation bias was tempered for two reasons. Several studies in the field of patient-provider communication have shown that videotaping of clinical encounters, which is more intrusive than making an audio recording, does not systematically alter physician communication or patient satisfaction (57,58). In addition, even if psychiatrist behavior is affected in some way, it would likely reflect best effort. In this circumstance, the study findings would have even greater implications for patient care.
Second, we were unable to assess a possible causal relationship between positive psychiatrist affect and patient appointment adherence because of the cross-sectional nature of our study and the lack of an assessment of the relationship between psychiatrist and patient affect. For example, patients who demonstrated positive nonverbal behaviors during the appointment may have had a greater likelihood of returning for future appointments. Also, their behavior may have caused their provider to demonstrate positive nonverbal behaviors. One approach to addressing the causal question would be to perform a randomized controlled prospective study testing the impact of a psychiatrist-directed positive nonverbal training enhancement intervention on patient appointment adherence. Third, patients were selected via provider referral or self-referral. It is possible that differences in the characteristics of a group selected in this way and of the general population of patients coming to these clinics may be relevant to this research. Last, the null finding related to patient-centered talk could be due to the inherent time constraint of medication management appointments.
Conclusions
Our study found that psychiatrist vocal qualities that convey friendliness, warmth, and empathy—an overlooked factor in the mental health services adherence research literature—may be important to improving appointment adherence among African-American and white patients with depression. This finding has potentially significant implications in terms of our understanding of therapeutic alliance and the development of psychiatrist-directed, evidence-based, positive nonverbal behavior training interventions that could improve outpatient treatment adherence rates.
Acknowledgments and disclosures
This study was supported by grants P30 MH030915, P30 MH07944, R25 MH60473, and K23 MH071520 from the National Institute of Mental Health. The authors thank all of the psychiatrists and patients who participated in this study.
Dr. Roter is the author of the RIAS and holds the copyright for the system. Johns Hopkins University also has rights to enhancements of the system. Dr. Roter and Ms. Larson are coowners of RIASWorks LLC, a company that provides RIAS coding services for projects undertaken outside Johns Hopkins University. RIASWorks may benefit indirectly from dissemination of the current research. Neither author nor Johns Hopkins University collects royalties for use of the system in research. The other authors report no competing interests.
References
1.
Olfson M, Weissman MM, Gottlieb JF: Essential roles for psychiatry in the era of managed care. Archives of General Psychiatry 54:206–208, 1997
2.
Stahl SM: The 7 habits of highly effective psychopharmacologists: overview. Journal of Clinical Psychiatry 61:242–243, 2000
3.
Beitman BD, Chiles J, Carlin A: The pharmacotherapy-psychotherapy triangle: psychiatrist, nonmedical psychotherapist, and patient. Journal of Clinical Psychiatry 45:458–459, 1984
4.
Goldberg RS, Riba M, Tasman A: Psychiatrists’ attitudes toward prescribing medication for patients treated by nonmedical psychotherapists. Hospital and Community Psychiatry 42:276–280, 1991
5.
Goin MK: Split treatment: the psychotherapy role of the prescribing psychiatrist. Psychiatric Services 52:605–606, 609, 2001
6.
Pincus HA, Zarin DA, Tanielian TL, et al.: Psychiatric patients and treatments in 1997: findings from the American Psychiatric Practice Research Network. Archives of General Psychiatry 56:441–449, 1999
7.
Gabbard GO, Kay J: The fate of integrated treatment: whatever happened to the biopsychosocial psychiatrist? American Journal of Psychiatry 158:1956–1963, 2001
8.
Wang J: Mental health treatment dropout and its correlates in a general population sample. Medical Care 45:224–229, 2007
9.
Olfson M, Mojtabai R, Sampson NA, et al.: Dropout from outpatient mental health care in the United States. Psychiatric Services 60:898–907, 2009
10.
Edlund MJ, Wang PS, Berglund PA, et al.: Dropping out of mental health treatment: patterns and predictors among epidemiological survey respondents in the United States and Ontario. American Journal of Psychiatry 159:845–851, 2002
11.
Mitchell A, Selmes T: Why don't patients take their medicine? Reasons and solutions in psychiatry. Advances in Psychiatric Treatment 13:336–346, 2007
12.
Killaspy H, Banerjee S, King M, et al.: Prospective controlled study of psychiatric outpatient nonattendance: characteristics and outcome. British Journal of Psychiatry 176:160–165, 2000
13.
Nelson EA, Maruish ME, Axler JL: Effects of discharge planning and compliance with outpatient appointments on readmission rates. Psychiatric Services 51:885–889, 2000
14.
Hibbard JH, Stockard J, Mahoney ER, et al.: Development of the Patient Activation Measure (PAM): conceptualizing and measuring activation in patients and consumers. Health Services Research 39:1005–1026, 2004
15.
Alegría M, Polo A, Gao S, et al.: Evaluation of a patient activation and empowerment intervention in mental health care. Medical Care 46:247–256, 2008
16.
Hing E, Cherry DK, Woodwell DA: National Ambulatory Medical Care Survey: 2004 summary. Advance Data 374:1–33, 2006
17.
Olfson M, Cherry DK, Lewis-Fernández R: Racial differences in visit duration of outpatient psychiatric visits. Archives of General Psychiatry 66:214–221, 2009
18.
Lê Cook B, McGuire TG, Zuvekas SH: Measuring trends in racial-ethnic health care disparities. Medical Care Research and Review 66:23–48, 2009
19.
Cook BL, McGuire T, Miranda J: Measuring trends in mental health care disparities, 2000–2004. Psychiatric Services 58:1533–1540, 2007
20.
Mead N, Bower P, Hann M: The impact of general practitioners’ patient-centredness on patients’ post-consultation satisfaction and enablement. Social Science and Medicine 55:283–299, 2002
21.
Bensing J: Bridging the gap: the separate worlds of evidence-based medicine and patient-centered medicine. Patient Education and Counseling 39:17–25, 2000
22.
Brown J, Stewart M, Ryan B (eds): Outcomes of Patient Provider Interaction. Mahwah, NJ, Erlbaum, 2003
23.
Levinson W, Roter DL, Mullooly JP, et al.: Physician-patient communication: the relationship with malpractice claims among primary care physicians and surgeons. JAMA 277:553–559, 1997
24.
Street RL, Gordon H, Haidet P: Physicians’ communication and perceptions of patients: is it how they look, how they talk, or is it just the doctor? Social Science and Medicine 65:586–598, 2007
25.
Hall JA, Roter DL, Katz NR: Meta-analysis of correlates of provider behavior in medical encounters. Medical Care 26:657–675, 1988
26.
Hall JA, Roter DL, Rand CS: Communication of affect between patient and physician. Journal of Health and Social Behavior 22:18–30, 1981
27.
DiMatteo MR, Hays RD, Prince LM: Relationship of physicians’ nonverbal communication skill to patient satisfaction, appointment noncompliance, and physician workload. Health Psychology 5:581–594, 1986
28.
Castillo EG, Pincus HA, Wieland M, et al.: Communication profiles of psychiatric residents and attending physicians in medication-management appointments: a quantitative pilot study. Academic Psychiatry 36:96–103, 2012
29.
Cruz M, Roter D, Cruz RF, et al.: Psychiatrist-patient verbal and nonverbal communications during split-treatment appointments. Psychiatric Services 62:1361–1368, 2011
30.
Olfson M, Marcus SC: National patterns in antidepressant medication treatment. Archives of General Psychiatry 66:848–856, 2009
31.
Roter D, Larson S: The Roter Interaction Analysis System (RIAS): utility and flexibility for analysis of medical interactions. Patient Education and Counseling 46:243–251, 2002
32.
Cooper LA, Roter DL, Johnson RL, et al.: Patient-centered communication, ratings of care, and concordance of patient and physician race. Annals of Internal Medicine 139:907–915, 2003
33.
Paasche-Orlow M, Roter D: The communication patterns of internal medicine and family practice physicians. Journal of the American Board of Family Practice 16:485–493, 2003
34.
Johnson RL, Roter D, Powe NR, et al.: Patient race-ethnicity and quality of patient-physician communication during medical visits. American Journal of Public Health 94:2084–2090, 2004
35.
Simon GE, Ludman EJ: Predictors of early dropout from psychotherapy for depression in community practice. Psychiatric Services 61:684–689, 2010
36.
Kroenke K, Spitzer RL, Williams JB: The PHQ-9: validity of a brief depression severity measure. Journal of General Internal Medicine 16:606–613, 2001
37.
Kaplan SH, Gandek B, Greenfield S, et al.: Patient and visit characteristics related to physicians’ participatory decision-making style: results from the Medical Outcomes Study. Medical Care 33:1176–1187, 1995
38.
Roter DL, Hall JA, Aoki Y: Physician gender effects in medical communication: a meta-analytic review. JAMA 288:756–764, 2002
39.
Hall JA, Roter DL: Do patients talk differently to male and female physicians? A meta-analytic review. Patient Education and Counseling 48:217–224, 2002
40.
Roter DL, Hall JA: Physician gender and patient-centered communication: a critical review of empirical research. Annual Review of Public Health 25:497–519, 2004
41.
Roter D, Lipkin M, Korsgaard A: Sex differences in patients’ and physicians’ communication during primary care medical visits. Medical Care 29:1083–1093, 1991
42.
van den Brink-Muinen A, Bensing JM, Kerssens JJ: Gender and communication style in general practice: differences between women’s health care and regular health care. Medical Care 36:100–106, 1998
43.
Hooper EM, Comstock LM, Goodwin JM, et al.: Patient characteristics that influence physician behavior. Medical Care 20:630–638, 1982
44.
Pendleton DA, Bochner S: The communication of medical information in general practice consultations as a function of patients’ social class. Social Science and Medicine, Medical Psychology, and Medical Sociology 14A:669–673, 1980
45.
Verbrugge LM, Steiner RP: Physician treatment of men and women patients: sex bias or appropriate care? Medical Care 19:609–632, 1981
46.
Weissman JS, Stern R, Fielding SL, et al.: Delayed access to health care: risk factors, reasons, and consequences. Annals of Internal Medicine 114:325–331, 1991
47.
Rask KJ, Williams MV, Parker RM, et al.: Obstacles predicting lack of a regular provider and delays in seeking care for patients at an urban public hospital. JAMA 271:1931–1933, 1994
48.
Hayward RA, Bernard AM, Freeman HE, et al.: Regular source of ambulatory care and access to health services. American Journal of Public Health 81:434–438, 1991
49.
Liang KY, Zeger SL: Regression analysis for correlated data. Annual Review of Public Health 14:43–68, 1993
50.
Diggle P, Heagerty P, Liang K-Y, et al.: Analysis of Longitudinal Data. Oxford, Oxford University Press, 2002
51.
Long J, Freese J: Regression Models for Categorical Dependent Variables Using Stata. College Station, Tex, StataCorp, 2006
52.
Barber JP, Luborsky L, Gallop R, et al.: Therapeutic alliance as a predictor of outcome and retention in the National Institute on Drug Abuse Collaborative Cocaine Treatment Study. Journal of Consulting and Clinical Psychology 69:119–124, 2001
53.
Meier PS, Donmall MC, McElduff P, et al.: The role of the early therapeutic alliance in predicting drug treatment dropout. Drug and Alcohol Dependence 83:57–64, 2006
54.
Raytek HS, McGrady BS, Epstein EE, et al.: Therapeutic alliance and the retention of couples in conjoint alcoholism treatment. Addictive Behaviors 24:317–330, 1999
55.
Robbins MS, Liddle HA, Turner CW, et al.: Adolescent and parent therapeutic alliances as predictors of dropout in multidimensional family therapy. Journal of Family Psychology 20:108–116, 2006
56.
Mitchell A, Kingdon D, Cross I: Management of self-harm following hospital discharge: a role for general practitioners in continuity of care. Primary Care and Community Psychiatry 10:149–158, 2006
57.
Campbell LM, Sullivan F, Murray TS: Videotaping of general practice consultations: effect on patient satisfaction. British Medical Journal 311:236, 1995
58.
Pringle M, Stewart-Evans C: Does awareness of being video recorded affect doctors’ consultation behaviour? British Journal of General Practice 40:455–458, 1990
Information & Authors
Information
Published In
Cover: Man, by Ben Shahn, 1946. Tempera on composition board. Gift of Mr. and Mrs. E. Powis Jones, The Museum of Modern Art, New York. © Estate of Ben Shahn/Licensed by VAGA, New York. Digital image © The Museum of Modern Art/Licensed by SCALA/Art Resource, New York.
History
Published in print: September 2013
Published online: 15 October 2014
Authors
Metrics & Citations
Metrics
Citations
Export Citations
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
For more information or tips please see 'Downloading to a citation manager' in the Help menu.
View Options
View options
PDF/EPUB
View PDF/EPUBLogin options
Already a subscriber? Access your subscription through your login credentials or your institution for full access to this article.
Personal login Institutional Login Open Athens loginNot a subscriber?
PsychiatryOnline subscription options offer access to the DSM-5-TR® library, books, journals, CME, and patient resources. This all-in-one virtual library provides psychiatrists and mental health professionals with key resources for diagnosis, treatment, research, and professional development.
Need more help? PsychiatryOnline Customer Service may be reached by emailing [email protected] or by calling 800-368-5777 (in the U.S.) or 703-907-7322 (outside the U.S.).