Original Research

Racial and ethnic disparities in health care have been catalogued across numerous diseases and care settings.¹ By clarifying the causes of these disparities, we can develop solutions. In a seminal study, Shulman found in patient simulations that identical presentations for heart disease received different recommendations for care based on the patient’s race and gender, thus pinpointing the patient-provider relationship as a potential source of disparities.² Other research suggests interactions with non-physician health care personnel might also be a source of negative experiences with care.³

Research is beginning to identify how the health care encounter might relate to disparities in use of services and quality of care. For example, race concordance between the physician and patient, at least for blacks, is associated with higher patient satisfaction and greater participatory decision-making. This in turn can impact compliance and possibly outcomes.^4-6 While black patients who have black physicians are more likely to report receipt of counseling about preventive care and cancer screening,⁷ race concordance does not appear to be independently associated with different patterns of utilization.⁸

Perceived discrimination has also been associated with lower levels of satisfaction with the health care system.⁹ In one survey, two thirds of respondents reported feeling discriminated against in their interactions with health care providers due to their race or socioeconomic status.¹⁰ How perceived discrimination influences quality and outcomes of care has not been fully explored.

We hypothesized that minority patients and those who do not speak English perceive negative experiences with the health care encounter more often than whites or English-speakers. We further hypothesized that patients who report such negative experiences are less likely to seek care initially or return for follow-up care. We tested these hypotheses using data from the Commonwealth Fund 2001 Health Care Quality Survey.

Methods

Sample

Respondents were from a nationally representative sample of 6722 adults, aged 18 years and older, living in the continental United States, and who speak English, Spanish, Mandarin, Cantonese, Vietnamese, or Korean.

The sampling frame was based on random-digit dialing; telephone exchanges with higher-than-average numbers of minority households were oversampled. In addition to the oversampling based on telephone exchanges, we interviewed members of 394 households identified from a nationwide demographic tracking survey as having an Asian/Asian American or African American family member. Interviews were conducted in English, Spanish, Mandarin, Cantonese, Vietnamese, or Korean, depending on the respondent preference. The response rate for the entire sample was 53.1%.

The final sample was weighted to correct for the disproportionate sample design and to ensure the sample was representative of all adults aged 18 years and older based on the March 2001 Current Population Survey (CPS). The final weighted sample is therefore reflective of the 193 million adults in the United States who have telephones. A more detailed description of the sampling and weighting methods can be found else-where.¹¹ Data were collected between April 30 and November 5, 2001.

We focus on the subset of questions addressing respondent’s experience with the health care encounter and their use of certain health care services. We used STATA Version 6.0¹² to conduct statistical analyses using the weighted sample.

Dependent variables

Negative perceptions of the patient-provider relationship. We identified factors that define the patient-provider relationship, and formulated questions based on these factors that would elicit meaningful responses. Specific questions included:

• “Did the doctor treat you with a great deal of respect and dignity, a fair amount, not too much, or none at all?” (4-point scale)
• Please tell me if you strongly agree, somewhat agree, somewhat disagree, or strongly disagree with the statement, “I often feel as if my doctor looks down on me and the way I live my life.” (4-point scale)
• “Thinking about all of the experiences you have had with health care visits in the last two years, have you ever felt that the doctor or medical staff you saw judged you unfairly or treated you with disrespect because of how well you speak English?” (yes/no)
• “Thinking about all of the experiences you have had with health care visits in the last two years, have you ever felt that the doctor or medical staff you saw judged you unfairly or treated you with disrespect because of your race or ethnic background?” (yes/no)
• “Do you think there was ever a time when you would have gotten better medical care if you had belonged to a different race or ethnic group?” (yes/no)

Those who said they did not have a doctor were unable to answer these questions and were excluded from our analysis. We combined the first 2 questions into a single dichotomous variable, characterized as “being treated with disrespect,” because both questions described negative perceptions of the health care encounter and because doing so preserved sample size for our analyses.

Utilization and optimal care

We examined self-reported use of specific services, including whether respondents had a physical exam within the past year. For cancer screening and chronic disease care, we created variables designed to represent optimal care. For example, optimal cancer screening was defined as being up to date on all tests for which the individual was eligible, based on age and gender.

These included:

• fecal occult blood testing for colon cancer screening within the prior year (both female and male respondents aged >50 years )
• cervical cancer screening within the prior 3 years (all women over the age of the 18)
• mammography within the prior year (women over the age of 50).

We excluded men younger than 50 years since colon cancer screening is not routinely recommended.

We considered respondents to have optimal chronic disease testing if they reported receiving all appropriate testing relevant for their particular condition. For persons with diabetes, this included having a hemoglobin A1c level checked within the past 6 months, a blood pressure check and foot and eye exams within the year, and cholesterol testing within 5 years. For those with heart disease or hypertension, it included having had blood pressure checked within the prior year and cholesterol testing within the prior 5 years. This approach is consistent with that of McBean et al, who have shown that a combination of appropriate tests is more predictive of glycemic control for diabetes.¹³

Because we were interested in different aspects of patient-initiated care seeking, we also evaluated delay in seeking care and adherence to physician recommendations as further measures of outcome. Specific questions were:

• “During the last 12 months, was there any time when you had a medical problem but put off, postponed, or did not seek medical care when you needed to?”
• “Has there been a time in the last two years when you didn’t follow the doctor’s advice or treatment plan, get a recommended test, or see a referred doctor?” (asked of respondents who had visited a doctor or clinic or had been admitted to the hospital in the last 2 years.)

Analysis

To test our first hypothesis—that persons of racial/ethnic minorities perceive negative experiences with the health care encounter more often than whites or English speakers—we examined

associations between demographic characteristics, utilization variables, and negative perceptions of the health care encounter using chisquared tests and multivariate logistic regression. In these analyses, we dichotomized education into high school graduate or less, and some technical school/college and more. We dichotomized the primary language spoken at home into non-English and English, and we used federal poverty level groupings (<100%, 100%–200% and >200%) to categorize household income. Almost 19% percent of respondents did not report incomes, so we created a dummy variable to account for those with unreported incomes.

We classified insurance status as none or any (either public or private); race/ethnicity as white, black, Hispanic, Asian, and other (Native American, mixed race, or other). We examined the effect of these variables alone and in concert. For example, we calculated predicted percentages to evaluate the combined effects of race and gender, as well as race and education, in relationship to our outcome variables.

Finally, we used multivariate logistic regression to test the relationship between negative perceptions of the patient-provider relationship and our utilization variables. In these analyses, perceptions were the covariate of interest; we controlled for patient characteristics that could also influence utilization, including education, income, insurance status, presence of a primary physician and existence of a comorbid condition (in this case, hypertension, diabetes, heart disease, asthma, and cancer.) This last variable was, by necessity, excluded from the analysis involving optimal chronic disease testing.

Results

Table 1 describes demographic characteristics and utilization measures for our sample. Consistent with prior literature, blacks and Hispanics had lower incomes and higher rates of non-insurance than both whites and Asians. Hispanics responded most frequently that English was not their primary language.

Hispanics and Asians were less likely than whites to have received optimal chronic disease care, while blacks and Hispanics were more likely than whites to have received optimal cancer screening. There were no differences between racial/ethnic groups in not following the doctor’s advice or in putting off care.

TABLE 1
Demographics/characteristics and health care utilization of study participants

Negative perceptions of the patient-provider relationship

Race. Over 14% of blacks, 19% of Hispanics, and 20% of Asians reported they had been treated with disrespect by their doctor. Members of these groups were also more likely than whites to report that they were treated unfairly because of their race or their language, and that they would have received better care had they belonged to a different race (Table 2).

Language. Persons for whom English was not the primary language were also more likely to say they had been treated with disrespect, and to report they would have received better care had they been of a different race. For each racial/ethnic group, bivariate relationships persisted after controlling for other respondent characteristics, including education and income (Table 2).

Sex. Men were significantly more likely than women to perceive being treated with disrespect by the doctor (15.9% vs 11.6%), and the percentage varied by race/ethnicity. Using our model to predict the combined effects of race and gender, we found that Asian and Hispanic men (24% and 23%, respectively) were more likely than black men (17%) or white men (11%) to perceive being treated with disrespect.

Education. Education was similarly associated with perceptions of disrespect. Almost 18% of persons without a college education believed they had been treated with disrespect, versus only 10% of those with a college education. Minorities with lower education were more likely to have this perception. Twenty-nine percent of Asians, 22% of Hispanics, and 19% of blacks without a college education reported being treated with disrespect or being looked down upon, versus 13% of whites.

TABLE 2
Relationship of demographic variables to measures of negative perceptions

Impact on care

Respondents who reported being treated with disrespect were significantly less likely to have had a physical exam within the prior year; those with diabetes, hypertension, or heart disease were less likely to have received optimal care. These respondents were also more likely to report not following the doctor’s advice and putting off needed care (Table 3). This relationship was not seen for optimal cancer screening.

Persons who believed they had been treated unfairly due to their race and who thought they would have received better care had they been of a different race were more likely to ignore the doctor’s advice and put off care when medically needed. Those who believed they would have received better care had they been of a different race were also less likely to receive optimal chronic disease care. In analyses not shown, we examined the independent effects of income and education, as well as interactions between these variables and insurance, and found the results basically unchanged.

TABLE 3
Relationship of negative perceptions to health care outcomes

Discussion

We hypothesized that patients who have negative perceptions of the patient-provider relationship would be less likely to seek needed care, and that reports of such feelings would be more prevalent among minority patients. As anticipated, large proportions of blacks, Hispanics, and Asians reported that they were treated with disrespect, were treated unfairly, or would have received better care if their race had been different. Male gender and lower educational attainment were also associated with perceived disrespect, particularly among minorities.

Negative experiences lead to suboptimal care

The finding of greater likelihood of perceived disrespect among minority groups, men, and those with lower levels of education is particularly important in light of the strong relationship between such reports and the quality of care that patients receive. Those who reported that they were treated unfairly because of race were less likely to get a routine physical exam, follow a doctor’s advice, or receive appropriate secondary preventive care for diabetes, heart disease, and hypertension. In other words, negative experiences within the health care environment may jeopardize care for medically needy patients. Receipt of suboptimal care, particularly in the context of chronic disease, is likely to be associated with worse health outcomes, and may contribute to disparities.

Cancer screening the exception

While the relationships between negative perceptions and receipt of care for chronic disease and receipt of a routine physical examination were strong, the correlation did not persist for cancer screening. Black and Hispanic respondents were more likely than whites to receive optimal cancer screening, a finding that has been reported elsewhere.^{14, 15}

We hypothesize that this is in part because a wide array of community programs make special outreach efforts allowing patients to “bypass” the traditional office environment.¹⁶ These settings may be more likely to use culturally sensitive approaches or may be so transient that negative perceptions based on race or income may be less likely to form.

However, based on our finding that care requiring follow-up (eg, diabetes management) is less likely to occur with individuals who report negative perceptions of the patient-provider relationship, we would hypothesize that individuals who receive initial cancer screening might be less likely to follow up on abnormal results once screened. It may be that in situations requiring long term relationships, such as chronic disease care, perceptions of discrimination and disrespect may take the greatest toll. This hypothesis is supported by previous literature consistently reporting excess mortality despite higher cancer screening rates among blacks.^17-20

Limitations

Our study has several limitations. Because we are relying on self-report, we could not assess which specific aspects of the patient-provider relationship may have influenced the reports of disrespect. Responses may have been affected by experiences completely outside of this relation ship, or outside of the health care system, that independently could have impacted health care utilization. We cannot disentangle how general life experience influences perceptions of the health care encounter or care-seeking; for example respondents who perceive racial bias in other environments such as the workplace may also be more likely to perceive it in the healthcare setting. In addition, self-reported utilization measures may not always be accurate, particularly regarding cancer screening.^21,22

Despite the deliberate oversampling of major racial/ethnic groups, we remain limited in our ability to examine important subgroups within them, whether related to ethnicity (eg, Cuban, Vietnamese) or chronic condition (eg, asthma, diabetes), even though some groups may differ dramatically from others.

We also had insufficient numbers of Native Americans to analyze separately.

We excluded respondents who did not have a regular doctor because they were unable to answer key questions about the health care encounter.

Finally, there is no agreement on the definition of age-appropriate breast and cervical cancer screening.^{23, 24} We conducted additional analyses varying the age criteria for testing, including starting the required age for screening at age 40 (for breast cancer) as well as setting the age cut off for required screening at 65 (for both breast and cervical cancer screening), and found that the results were essentially unchanged from those presented. Similarly, adding prostate cancer screening to our models for men over age 50 did not alter our results significantly.

Research should focus on improving perceptions of care

Although it is difficult to quantify or measure negative responses objectively, the strong relationship between patient perceptions of the encounter and utilization suggests an important area for further attention. These findings suggest there may still be a substantial core of individuals who will actively avoid care, perhaps based on previous negative interpersonal experiences in getting care. Interventions aimed at both doctors and potential patients will be required to address this. Research is needed to focus on what approaches can best improve perceptions of care within the patient-provider relationship and how such interventions can reduce racial disparities in health care.

Acknowledgments

This research was done in part by a grant from The Commonwealth Fund.

Corresponding author
Nicole Lurie, MD, MSPH, RAND Corporation, 1200 S. Hayes Street, Arlington, VA 22202. E-mail: [email protected]

Racial and ethnic disparities in health care have been catalogued across numerous diseases and care settings.¹ By clarifying the causes of these disparities, we can develop solutions. In a seminal study, Shulman found in patient simulations that identical presentations for heart disease received different recommendations for care based on the patient’s race and gender, thus pinpointing the patient-provider relationship as a potential source of disparities.² Other research suggests interactions with non-physician health care personnel might also be a source of negative experiences with care.³

Research is beginning to identify how the health care encounter might relate to disparities in use of services and quality of care. For example, race concordance between the physician and patient, at least for blacks, is associated with higher patient satisfaction and greater participatory decision-making. This in turn can impact compliance and possibly outcomes.^4-6 While black patients who have black physicians are more likely to report receipt of counseling about preventive care and cancer screening,⁷ race concordance does not appear to be independently associated with different patterns of utilization.⁸

Perceived discrimination has also been associated with lower levels of satisfaction with the health care system.⁹ In one survey, two thirds of respondents reported feeling discriminated against in their interactions with health care providers due to their race or socioeconomic status.¹⁰ How perceived discrimination influences quality and outcomes of care has not been fully explored.

We hypothesized that minority patients and those who do not speak English perceive negative experiences with the health care encounter more often than whites or English-speakers. We further hypothesized that patients who report such negative experiences are less likely to seek care initially or return for follow-up care. We tested these hypotheses using data from the Commonwealth Fund 2001 Health Care Quality Survey.

Methods

Sample

Respondents were from a nationally representative sample of 6722 adults, aged 18 years and older, living in the continental United States, and who speak English, Spanish, Mandarin, Cantonese, Vietnamese, or Korean.

The sampling frame was based on random-digit dialing; telephone exchanges with higher-than-average numbers of minority households were oversampled. In addition to the oversampling based on telephone exchanges, we interviewed members of 394 households identified from a nationwide demographic tracking survey as having an Asian/Asian American or African American family member. Interviews were conducted in English, Spanish, Mandarin, Cantonese, Vietnamese, or Korean, depending on the respondent preference. The response rate for the entire sample was 53.1%.

The final sample was weighted to correct for the disproportionate sample design and to ensure the sample was representative of all adults aged 18 years and older based on the March 2001 Current Population Survey (CPS). The final weighted sample is therefore reflective of the 193 million adults in the United States who have telephones. A more detailed description of the sampling and weighting methods can be found else-where.¹¹ Data were collected between April 30 and November 5, 2001.

We focus on the subset of questions addressing respondent’s experience with the health care encounter and their use of certain health care services. We used STATA Version 6.0¹² to conduct statistical analyses using the weighted sample.

Dependent variables

Negative perceptions of the patient-provider relationship. We identified factors that define the patient-provider relationship, and formulated questions based on these factors that would elicit meaningful responses. Specific questions included:

• “Did the doctor treat you with a great deal of respect and dignity, a fair amount, not too much, or none at all?” (4-point scale)
• Please tell me if you strongly agree, somewhat agree, somewhat disagree, or strongly disagree with the statement, “I often feel as if my doctor looks down on me and the way I live my life.” (4-point scale)
• “Thinking about all of the experiences you have had with health care visits in the last two years, have you ever felt that the doctor or medical staff you saw judged you unfairly or treated you with disrespect because of how well you speak English?” (yes/no)
• “Thinking about all of the experiences you have had with health care visits in the last two years, have you ever felt that the doctor or medical staff you saw judged you unfairly or treated you with disrespect because of your race or ethnic background?” (yes/no)
• “Do you think there was ever a time when you would have gotten better medical care if you had belonged to a different race or ethnic group?” (yes/no)

Those who said they did not have a doctor were unable to answer these questions and were excluded from our analysis. We combined the first 2 questions into a single dichotomous variable, characterized as “being treated with disrespect,” because both questions described negative perceptions of the health care encounter and because doing so preserved sample size for our analyses.

Utilization and optimal care

We examined self-reported use of specific services, including whether respondents had a physical exam within the past year. For cancer screening and chronic disease care, we created variables designed to represent optimal care. For example, optimal cancer screening was defined as being up to date on all tests for which the individual was eligible, based on age and gender.

These included:

• fecal occult blood testing for colon cancer screening within the prior year (both female and male respondents aged >50 years )
• cervical cancer screening within the prior 3 years (all women over the age of the 18)
• mammography within the prior year (women over the age of 50).

We excluded men younger than 50 years since colon cancer screening is not routinely recommended.

We considered respondents to have optimal chronic disease testing if they reported receiving all appropriate testing relevant for their particular condition. For persons with diabetes, this included having a hemoglobin A1c level checked within the past 6 months, a blood pressure check and foot and eye exams within the year, and cholesterol testing within 5 years. For those with heart disease or hypertension, it included having had blood pressure checked within the prior year and cholesterol testing within the prior 5 years. This approach is consistent with that of McBean et al, who have shown that a combination of appropriate tests is more predictive of glycemic control for diabetes.¹³

Because we were interested in different aspects of patient-initiated care seeking, we also evaluated delay in seeking care and adherence to physician recommendations as further measures of outcome. Specific questions were:

• “During the last 12 months, was there any time when you had a medical problem but put off, postponed, or did not seek medical care when you needed to?”
• “Has there been a time in the last two years when you didn’t follow the doctor’s advice or treatment plan, get a recommended test, or see a referred doctor?” (asked of respondents who had visited a doctor or clinic or had been admitted to the hospital in the last 2 years.)

Analysis

To test our first hypothesis—that persons of racial/ethnic minorities perceive negative experiences with the health care encounter more often than whites or English speakers—we examined

associations between demographic characteristics, utilization variables, and negative perceptions of the health care encounter using chisquared tests and multivariate logistic regression. In these analyses, we dichotomized education into high school graduate or less, and some technical school/college and more. We dichotomized the primary language spoken at home into non-English and English, and we used federal poverty level groupings (<100%, 100%–200% and >200%) to categorize household income. Almost 19% percent of respondents did not report incomes, so we created a dummy variable to account for those with unreported incomes.

We classified insurance status as none or any (either public or private); race/ethnicity as white, black, Hispanic, Asian, and other (Native American, mixed race, or other). We examined the effect of these variables alone and in concert. For example, we calculated predicted percentages to evaluate the combined effects of race and gender, as well as race and education, in relationship to our outcome variables.

Finally, we used multivariate logistic regression to test the relationship between negative perceptions of the patient-provider relationship and our utilization variables. In these analyses, perceptions were the covariate of interest; we controlled for patient characteristics that could also influence utilization, including education, income, insurance status, presence of a primary physician and existence of a comorbid condition (in this case, hypertension, diabetes, heart disease, asthma, and cancer.) This last variable was, by necessity, excluded from the analysis involving optimal chronic disease testing.

Results

Table 1 describes demographic characteristics and utilization measures for our sample. Consistent with prior literature, blacks and Hispanics had lower incomes and higher rates of non-insurance than both whites and Asians. Hispanics responded most frequently that English was not their primary language.

Hispanics and Asians were less likely than whites to have received optimal chronic disease care, while blacks and Hispanics were more likely than whites to have received optimal cancer screening. There were no differences between racial/ethnic groups in not following the doctor’s advice or in putting off care.

TABLE 1
Demographics/characteristics and health care utilization of study participants

Negative perceptions of the patient-provider relationship

Race. Over 14% of blacks, 19% of Hispanics, and 20% of Asians reported they had been treated with disrespect by their doctor. Members of these groups were also more likely than whites to report that they were treated unfairly because of their race or their language, and that they would have received better care had they belonged to a different race (Table 2).

Language. Persons for whom English was not the primary language were also more likely to say they had been treated with disrespect, and to report they would have received better care had they been of a different race. For each racial/ethnic group, bivariate relationships persisted after controlling for other respondent characteristics, including education and income (Table 2).

Sex. Men were significantly more likely than women to perceive being treated with disrespect by the doctor (15.9% vs 11.6%), and the percentage varied by race/ethnicity. Using our model to predict the combined effects of race and gender, we found that Asian and Hispanic men (24% and 23%, respectively) were more likely than black men (17%) or white men (11%) to perceive being treated with disrespect.

Education. Education was similarly associated with perceptions of disrespect. Almost 18% of persons without a college education believed they had been treated with disrespect, versus only 10% of those with a college education. Minorities with lower education were more likely to have this perception. Twenty-nine percent of Asians, 22% of Hispanics, and 19% of blacks without a college education reported being treated with disrespect or being looked down upon, versus 13% of whites.

TABLE 2
Relationship of demographic variables to measures of negative perceptions

Impact on care

Respondents who reported being treated with disrespect were significantly less likely to have had a physical exam within the prior year; those with diabetes, hypertension, or heart disease were less likely to have received optimal care. These respondents were also more likely to report not following the doctor’s advice and putting off needed care (Table 3). This relationship was not seen for optimal cancer screening.

Persons who believed they had been treated unfairly due to their race and who thought they would have received better care had they been of a different race were more likely to ignore the doctor’s advice and put off care when medically needed. Those who believed they would have received better care had they been of a different race were also less likely to receive optimal chronic disease care. In analyses not shown, we examined the independent effects of income and education, as well as interactions between these variables and insurance, and found the results basically unchanged.

TABLE 3
Relationship of negative perceptions to health care outcomes

Discussion

We hypothesized that patients who have negative perceptions of the patient-provider relationship would be less likely to seek needed care, and that reports of such feelings would be more prevalent among minority patients. As anticipated, large proportions of blacks, Hispanics, and Asians reported that they were treated with disrespect, were treated unfairly, or would have received better care if their race had been different. Male gender and lower educational attainment were also associated with perceived disrespect, particularly among minorities.

Negative experiences lead to suboptimal care

The finding of greater likelihood of perceived disrespect among minority groups, men, and those with lower levels of education is particularly important in light of the strong relationship between such reports and the quality of care that patients receive. Those who reported that they were treated unfairly because of race were less likely to get a routine physical exam, follow a doctor’s advice, or receive appropriate secondary preventive care for diabetes, heart disease, and hypertension. In other words, negative experiences within the health care environment may jeopardize care for medically needy patients. Receipt of suboptimal care, particularly in the context of chronic disease, is likely to be associated with worse health outcomes, and may contribute to disparities.

Cancer screening the exception

While the relationships between negative perceptions and receipt of care for chronic disease and receipt of a routine physical examination were strong, the correlation did not persist for cancer screening. Black and Hispanic respondents were more likely than whites to receive optimal cancer screening, a finding that has been reported elsewhere.^{14, 15}

We hypothesize that this is in part because a wide array of community programs make special outreach efforts allowing patients to “bypass” the traditional office environment.¹⁶ These settings may be more likely to use culturally sensitive approaches or may be so transient that negative perceptions based on race or income may be less likely to form.

However, based on our finding that care requiring follow-up (eg, diabetes management) is less likely to occur with individuals who report negative perceptions of the patient-provider relationship, we would hypothesize that individuals who receive initial cancer screening might be less likely to follow up on abnormal results once screened. It may be that in situations requiring long term relationships, such as chronic disease care, perceptions of discrimination and disrespect may take the greatest toll. This hypothesis is supported by previous literature consistently reporting excess mortality despite higher cancer screening rates among blacks.^17-20

Limitations

Our study has several limitations. Because we are relying on self-report, we could not assess which specific aspects of the patient-provider relationship may have influenced the reports of disrespect. Responses may have been affected by experiences completely outside of this relation ship, or outside of the health care system, that independently could have impacted health care utilization. We cannot disentangle how general life experience influences perceptions of the health care encounter or care-seeking; for example respondents who perceive racial bias in other environments such as the workplace may also be more likely to perceive it in the healthcare setting. In addition, self-reported utilization measures may not always be accurate, particularly regarding cancer screening.^21,22

Despite the deliberate oversampling of major racial/ethnic groups, we remain limited in our ability to examine important subgroups within them, whether related to ethnicity (eg, Cuban, Vietnamese) or chronic condition (eg, asthma, diabetes), even though some groups may differ dramatically from others.

We also had insufficient numbers of Native Americans to analyze separately.

We excluded respondents who did not have a regular doctor because they were unable to answer key questions about the health care encounter.

Finally, there is no agreement on the definition of age-appropriate breast and cervical cancer screening.^{23, 24} We conducted additional analyses varying the age criteria for testing, including starting the required age for screening at age 40 (for breast cancer) as well as setting the age cut off for required screening at 65 (for both breast and cervical cancer screening), and found that the results were essentially unchanged from those presented. Similarly, adding prostate cancer screening to our models for men over age 50 did not alter our results significantly.

Research should focus on improving perceptions of care

Although it is difficult to quantify or measure negative responses objectively, the strong relationship between patient perceptions of the encounter and utilization suggests an important area for further attention. These findings suggest there may still be a substantial core of individuals who will actively avoid care, perhaps based on previous negative interpersonal experiences in getting care. Interventions aimed at both doctors and potential patients will be required to address this. Research is needed to focus on what approaches can best improve perceptions of care within the patient-provider relationship and how such interventions can reduce racial disparities in health care.

Acknowledgments

This research was done in part by a grant from The Commonwealth Fund.

Corresponding author
Nicole Lurie, MD, MSPH, RAND Corporation, 1200 S. Hayes Street, Arlington, VA 22202. E-mail: [email protected]

Racial and ethnic disparities in health care have been catalogued across numerous diseases and care settings.¹ By clarifying the causes of these disparities, we can develop solutions. In a seminal study, Shulman found in patient simulations that identical presentations for heart disease received different recommendations for care based on the patient’s race and gender, thus pinpointing the patient-provider relationship as a potential source of disparities.² Other research suggests interactions with non-physician health care personnel might also be a source of negative experiences with care.³

Research is beginning to identify how the health care encounter might relate to disparities in use of services and quality of care. For example, race concordance between the physician and patient, at least for blacks, is associated with higher patient satisfaction and greater participatory decision-making. This in turn can impact compliance and possibly outcomes.^4-6 While black patients who have black physicians are more likely to report receipt of counseling about preventive care and cancer screening,⁷ race concordance does not appear to be independently associated with different patterns of utilization.⁸

Perceived discrimination has also been associated with lower levels of satisfaction with the health care system.⁹ In one survey, two thirds of respondents reported feeling discriminated against in their interactions with health care providers due to their race or socioeconomic status.¹⁰ How perceived discrimination influences quality and outcomes of care has not been fully explored.

We hypothesized that minority patients and those who do not speak English perceive negative experiences with the health care encounter more often than whites or English-speakers. We further hypothesized that patients who report such negative experiences are less likely to seek care initially or return for follow-up care. We tested these hypotheses using data from the Commonwealth Fund 2001 Health Care Quality Survey.

Methods

Sample

Respondents were from a nationally representative sample of 6722 adults, aged 18 years and older, living in the continental United States, and who speak English, Spanish, Mandarin, Cantonese, Vietnamese, or Korean.

The sampling frame was based on random-digit dialing; telephone exchanges with higher-than-average numbers of minority households were oversampled. In addition to the oversampling based on telephone exchanges, we interviewed members of 394 households identified from a nationwide demographic tracking survey as having an Asian/Asian American or African American family member. Interviews were conducted in English, Spanish, Mandarin, Cantonese, Vietnamese, or Korean, depending on the respondent preference. The response rate for the entire sample was 53.1%.

The final sample was weighted to correct for the disproportionate sample design and to ensure the sample was representative of all adults aged 18 years and older based on the March 2001 Current Population Survey (CPS). The final weighted sample is therefore reflective of the 193 million adults in the United States who have telephones. A more detailed description of the sampling and weighting methods can be found else-where.¹¹ Data were collected between April 30 and November 5, 2001.

We focus on the subset of questions addressing respondent’s experience with the health care encounter and their use of certain health care services. We used STATA Version 6.0¹² to conduct statistical analyses using the weighted sample.

Dependent variables

Negative perceptions of the patient-provider relationship. We identified factors that define the patient-provider relationship, and formulated questions based on these factors that would elicit meaningful responses. Specific questions included:

• “Did the doctor treat you with a great deal of respect and dignity, a fair amount, not too much, or none at all?” (4-point scale)
• Please tell me if you strongly agree, somewhat agree, somewhat disagree, or strongly disagree with the statement, “I often feel as if my doctor looks down on me and the way I live my life.” (4-point scale)
• “Thinking about all of the experiences you have had with health care visits in the last two years, have you ever felt that the doctor or medical staff you saw judged you unfairly or treated you with disrespect because of how well you speak English?” (yes/no)
• “Thinking about all of the experiences you have had with health care visits in the last two years, have you ever felt that the doctor or medical staff you saw judged you unfairly or treated you with disrespect because of your race or ethnic background?” (yes/no)
• “Do you think there was ever a time when you would have gotten better medical care if you had belonged to a different race or ethnic group?” (yes/no)

Those who said they did not have a doctor were unable to answer these questions and were excluded from our analysis. We combined the first 2 questions into a single dichotomous variable, characterized as “being treated with disrespect,” because both questions described negative perceptions of the health care encounter and because doing so preserved sample size for our analyses.

Utilization and optimal care

We examined self-reported use of specific services, including whether respondents had a physical exam within the past year. For cancer screening and chronic disease care, we created variables designed to represent optimal care. For example, optimal cancer screening was defined as being up to date on all tests for which the individual was eligible, based on age and gender.

These included:

• fecal occult blood testing for colon cancer screening within the prior year (both female and male respondents aged >50 years )
• cervical cancer screening within the prior 3 years (all women over the age of the 18)
• mammography within the prior year (women over the age of 50).

We excluded men younger than 50 years since colon cancer screening is not routinely recommended.

We considered respondents to have optimal chronic disease testing if they reported receiving all appropriate testing relevant for their particular condition. For persons with diabetes, this included having a hemoglobin A1c level checked within the past 6 months, a blood pressure check and foot and eye exams within the year, and cholesterol testing within 5 years. For those with heart disease or hypertension, it included having had blood pressure checked within the prior year and cholesterol testing within the prior 5 years. This approach is consistent with that of McBean et al, who have shown that a combination of appropriate tests is more predictive of glycemic control for diabetes.¹³

Because we were interested in different aspects of patient-initiated care seeking, we also evaluated delay in seeking care and adherence to physician recommendations as further measures of outcome. Specific questions were:

• “During the last 12 months, was there any time when you had a medical problem but put off, postponed, or did not seek medical care when you needed to?”
• “Has there been a time in the last two years when you didn’t follow the doctor’s advice or treatment plan, get a recommended test, or see a referred doctor?” (asked of respondents who had visited a doctor or clinic or had been admitted to the hospital in the last 2 years.)

Analysis

To test our first hypothesis—that persons of racial/ethnic minorities perceive negative experiences with the health care encounter more often than whites or English speakers—we examined

associations between demographic characteristics, utilization variables, and negative perceptions of the health care encounter using chisquared tests and multivariate logistic regression. In these analyses, we dichotomized education into high school graduate or less, and some technical school/college and more. We dichotomized the primary language spoken at home into non-English and English, and we used federal poverty level groupings (<100%, 100%–200% and >200%) to categorize household income. Almost 19% percent of respondents did not report incomes, so we created a dummy variable to account for those with unreported incomes.

We classified insurance status as none or any (either public or private); race/ethnicity as white, black, Hispanic, Asian, and other (Native American, mixed race, or other). We examined the effect of these variables alone and in concert. For example, we calculated predicted percentages to evaluate the combined effects of race and gender, as well as race and education, in relationship to our outcome variables.

Finally, we used multivariate logistic regression to test the relationship between negative perceptions of the patient-provider relationship and our utilization variables. In these analyses, perceptions were the covariate of interest; we controlled for patient characteristics that could also influence utilization, including education, income, insurance status, presence of a primary physician and existence of a comorbid condition (in this case, hypertension, diabetes, heart disease, asthma, and cancer.) This last variable was, by necessity, excluded from the analysis involving optimal chronic disease testing.

Results

Table 1 describes demographic characteristics and utilization measures for our sample. Consistent with prior literature, blacks and Hispanics had lower incomes and higher rates of non-insurance than both whites and Asians. Hispanics responded most frequently that English was not their primary language.

Hispanics and Asians were less likely than whites to have received optimal chronic disease care, while blacks and Hispanics were more likely than whites to have received optimal cancer screening. There were no differences between racial/ethnic groups in not following the doctor’s advice or in putting off care.

TABLE 1
Demographics/characteristics and health care utilization of study participants

Negative perceptions of the patient-provider relationship

Race. Over 14% of blacks, 19% of Hispanics, and 20% of Asians reported they had been treated with disrespect by their doctor. Members of these groups were also more likely than whites to report that they were treated unfairly because of their race or their language, and that they would have received better care had they belonged to a different race (Table 2).

Language. Persons for whom English was not the primary language were also more likely to say they had been treated with disrespect, and to report they would have received better care had they been of a different race. For each racial/ethnic group, bivariate relationships persisted after controlling for other respondent characteristics, including education and income (Table 2).

Sex. Men were significantly more likely than women to perceive being treated with disrespect by the doctor (15.9% vs 11.6%), and the percentage varied by race/ethnicity. Using our model to predict the combined effects of race and gender, we found that Asian and Hispanic men (24% and 23%, respectively) were more likely than black men (17%) or white men (11%) to perceive being treated with disrespect.

Education. Education was similarly associated with perceptions of disrespect. Almost 18% of persons without a college education believed they had been treated with disrespect, versus only 10% of those with a college education. Minorities with lower education were more likely to have this perception. Twenty-nine percent of Asians, 22% of Hispanics, and 19% of blacks without a college education reported being treated with disrespect or being looked down upon, versus 13% of whites.

TABLE 2
Relationship of demographic variables to measures of negative perceptions

Impact on care

Respondents who reported being treated with disrespect were significantly less likely to have had a physical exam within the prior year; those with diabetes, hypertension, or heart disease were less likely to have received optimal care. These respondents were also more likely to report not following the doctor’s advice and putting off needed care (Table 3). This relationship was not seen for optimal cancer screening.

Persons who believed they had been treated unfairly due to their race and who thought they would have received better care had they been of a different race were more likely to ignore the doctor’s advice and put off care when medically needed. Those who believed they would have received better care had they been of a different race were also less likely to receive optimal chronic disease care. In analyses not shown, we examined the independent effects of income and education, as well as interactions between these variables and insurance, and found the results basically unchanged.

TABLE 3
Relationship of negative perceptions to health care outcomes

Discussion

We hypothesized that patients who have negative perceptions of the patient-provider relationship would be less likely to seek needed care, and that reports of such feelings would be more prevalent among minority patients. As anticipated, large proportions of blacks, Hispanics, and Asians reported that they were treated with disrespect, were treated unfairly, or would have received better care if their race had been different. Male gender and lower educational attainment were also associated with perceived disrespect, particularly among minorities.

Negative experiences lead to suboptimal care

The finding of greater likelihood of perceived disrespect among minority groups, men, and those with lower levels of education is particularly important in light of the strong relationship between such reports and the quality of care that patients receive. Those who reported that they were treated unfairly because of race were less likely to get a routine physical exam, follow a doctor’s advice, or receive appropriate secondary preventive care for diabetes, heart disease, and hypertension. In other words, negative experiences within the health care environment may jeopardize care for medically needy patients. Receipt of suboptimal care, particularly in the context of chronic disease, is likely to be associated with worse health outcomes, and may contribute to disparities.

Cancer screening the exception

While the relationships between negative perceptions and receipt of care for chronic disease and receipt of a routine physical examination were strong, the correlation did not persist for cancer screening. Black and Hispanic respondents were more likely than whites to receive optimal cancer screening, a finding that has been reported elsewhere.^{14, 15}

We hypothesize that this is in part because a wide array of community programs make special outreach efforts allowing patients to “bypass” the traditional office environment.¹⁶ These settings may be more likely to use culturally sensitive approaches or may be so transient that negative perceptions based on race or income may be less likely to form.

However, based on our finding that care requiring follow-up (eg, diabetes management) is less likely to occur with individuals who report negative perceptions of the patient-provider relationship, we would hypothesize that individuals who receive initial cancer screening might be less likely to follow up on abnormal results once screened. It may be that in situations requiring long term relationships, such as chronic disease care, perceptions of discrimination and disrespect may take the greatest toll. This hypothesis is supported by previous literature consistently reporting excess mortality despite higher cancer screening rates among blacks.^17-20

Limitations

Our study has several limitations. Because we are relying on self-report, we could not assess which specific aspects of the patient-provider relationship may have influenced the reports of disrespect. Responses may have been affected by experiences completely outside of this relation ship, or outside of the health care system, that independently could have impacted health care utilization. We cannot disentangle how general life experience influences perceptions of the health care encounter or care-seeking; for example respondents who perceive racial bias in other environments such as the workplace may also be more likely to perceive it in the healthcare setting. In addition, self-reported utilization measures may not always be accurate, particularly regarding cancer screening.^21,22

Despite the deliberate oversampling of major racial/ethnic groups, we remain limited in our ability to examine important subgroups within them, whether related to ethnicity (eg, Cuban, Vietnamese) or chronic condition (eg, asthma, diabetes), even though some groups may differ dramatically from others.

We also had insufficient numbers of Native Americans to analyze separately.

We excluded respondents who did not have a regular doctor because they were unable to answer key questions about the health care encounter.

Finally, there is no agreement on the definition of age-appropriate breast and cervical cancer screening.^{23, 24} We conducted additional analyses varying the age criteria for testing, including starting the required age for screening at age 40 (for breast cancer) as well as setting the age cut off for required screening at 65 (for both breast and cervical cancer screening), and found that the results were essentially unchanged from those presented. Similarly, adding prostate cancer screening to our models for men over age 50 did not alter our results significantly.

Research should focus on improving perceptions of care

Although it is difficult to quantify or measure negative responses objectively, the strong relationship between patient perceptions of the encounter and utilization suggests an important area for further attention. These findings suggest there may still be a substantial core of individuals who will actively avoid care, perhaps based on previous negative interpersonal experiences in getting care. Interventions aimed at both doctors and potential patients will be required to address this. Research is needed to focus on what approaches can best improve perceptions of care within the patient-provider relationship and how such interventions can reduce racial disparities in health care.

Acknowledgments

This research was done in part by a grant from The Commonwealth Fund.

Corresponding author
Nicole Lurie, MD, MSPH, RAND Corporation, 1200 S. Hayes Street, Arlington, VA 22202. E-mail: [email protected]

Survival curves illustrate prognosis. The percentage of patients reaching an endpoint (eg, death, recurrence of disease, or cure) is plotted on the y(vertical) axis against time on the x(horizontal) axis.

Plotting a survival curve

Two common plotting methods are used. With the actuarial method, the x axis is divided into regular intervals (eg, by month) and percent survival is calculated for each interval. With the Kaplan-Meier method, percent survival is recalculated each time a patient dies (or reaches a different endpoint). Consider the example here (Figure).¹

Time zerois when each patient entered the trial. Survival is the percentage of patients still alive thereafter. Median survivalis found by extending a horizontal line from the 50% survival point until it intersects the curve (24 months in this case).

FIGURE
Sample survival curve

Survival curves have limitations. Consider a study that enrolls patients between 1996 and 2002 and ends in 2005. All that is known about a patient enrolled in 2002 who survived until 2005 is that he or she survived 3 years. Some patients also drop out of the study early or are lost to follow-up. Some patients die from causes other than the one under study.

Censoringis the process of excluding data from survival curves when information about survival is unknown. For a patient who drops out early, for example, only data obtained when the patient was followed would be included. The result is a more accurate picture of survival for the patients under study.

Correspondence
Stephen A. Wilson, MD, UPMC Family Praxctice Residency, 200 Lothrop St., Pittsburgh, PA 15213-2582. E-mail: [email protected].

Survival curves illustrate prognosis. The percentage of patients reaching an endpoint (eg, death, recurrence of disease, or cure) is plotted on the y(vertical) axis against time on the x(horizontal) axis.

Plotting a survival curve

Two common plotting methods are used. With the actuarial method, the x axis is divided into regular intervals (eg, by month) and percent survival is calculated for each interval. With the Kaplan-Meier method, percent survival is recalculated each time a patient dies (or reaches a different endpoint). Consider the example here (Figure).¹

Time zerois when each patient entered the trial. Survival is the percentage of patients still alive thereafter. Median survivalis found by extending a horizontal line from the 50% survival point until it intersects the curve (24 months in this case).

FIGURE
Sample survival curve

Survival curves have limitations. Consider a study that enrolls patients between 1996 and 2002 and ends in 2005. All that is known about a patient enrolled in 2002 who survived until 2005 is that he or she survived 3 years. Some patients also drop out of the study early or are lost to follow-up. Some patients die from causes other than the one under study.

Censoringis the process of excluding data from survival curves when information about survival is unknown. For a patient who drops out early, for example, only data obtained when the patient was followed would be included. The result is a more accurate picture of survival for the patients under study.

Correspondence
Stephen A. Wilson, MD, UPMC Family Praxctice Residency, 200 Lothrop St., Pittsburgh, PA 15213-2582. E-mail: [email protected].

Survival curves illustrate prognosis. The percentage of patients reaching an endpoint (eg, death, recurrence of disease, or cure) is plotted on the y(vertical) axis against time on the x(horizontal) axis.

Plotting a survival curve

Two common plotting methods are used. With the actuarial method, the x axis is divided into regular intervals (eg, by month) and percent survival is calculated for each interval. With the Kaplan-Meier method, percent survival is recalculated each time a patient dies (or reaches a different endpoint). Consider the example here (Figure).¹

Time zerois when each patient entered the trial. Survival is the percentage of patients still alive thereafter. Median survivalis found by extending a horizontal line from the 50% survival point until it intersects the curve (24 months in this case).

FIGURE
Sample survival curve

Survival curves have limitations. Consider a study that enrolls patients between 1996 and 2002 and ends in 2005. All that is known about a patient enrolled in 2002 who survived until 2005 is that he or she survived 3 years. Some patients also drop out of the study early or are lost to follow-up. Some patients die from causes other than the one under study.

Censoringis the process of excluding data from survival curves when information about survival is unknown. For a patient who drops out early, for example, only data obtained when the patient was followed would be included. The result is a more accurate picture of survival for the patients under study.

Correspondence
Stephen A. Wilson, MD, UPMC Family Praxctice Residency, 200 Lothrop St., Pittsburgh, PA 15213-2582. E-mail: [email protected].

Randomization is a crucial part of most clinical trials. The purpose of randomization in a trial comparing 2 groups is to ensure that the groups differ only with respect to the interventions being compared. Randomization determines not only which treatment subjects receive (eg, drug vs placebo), but also how the results are analyzed at the end of the trial.

Intention to treat prevents biased outcomes

The intention-to-treat principle states that all subjects must be analyzed with respect to the group to which they were randomized. Consider a recent study by Spector et al¹ in which patients with dementia were randomized to receive an intervention known as cognitive stimulation therapy (CST) or control (equivalent to placebo in a drug trial). The investigators used an intention-to-treat analysis. The intervention was a complex 14-session training program. Many patients did not complete all the sessions. Whether they had completed all the sessions or not, patients initially randomized to the intervention were still considered to have received the intervention when the results were analyzed.

At first, intention to treat doesn’t seem logical. If we are testing an intervention, doesn’t it make sense to evaluate its effect among patients who complied with it fully, and then compare them with patients who were not assigned to the intervention or failed to comply? The problem is, patients who fail to comply with an intervention for whatever reason (not attending all training sessions in the example above) may differ in an important way from those who do.

In the Spector study, for example, it is possible that patients who attended very few of the sessions were more likely to have some subtle cognitive deficits that limited their participation. If placed in the control group, the 2 groups would these patients were excluded from the analysis or differ not only with respect to the intervention, but also with respect to these subtle factors. The value of randomization, therefore, would be compromised. The treatment arm may even appear to be more effective than it really is.

Correspondence
Kiame J. Mahaniah, MD, Greater Lawrence Family Health Center, 34 Haverhill Street, Lawrence, MA 01841. E-mail: [email protected] .

Randomization is a crucial part of most clinical trials. The purpose of randomization in a trial comparing 2 groups is to ensure that the groups differ only with respect to the interventions being compared. Randomization determines not only which treatment subjects receive (eg, drug vs placebo), but also how the results are analyzed at the end of the trial.

Intention to treat prevents biased outcomes

The intention-to-treat principle states that all subjects must be analyzed with respect to the group to which they were randomized. Consider a recent study by Spector et al¹ in which patients with dementia were randomized to receive an intervention known as cognitive stimulation therapy (CST) or control (equivalent to placebo in a drug trial). The investigators used an intention-to-treat analysis. The intervention was a complex 14-session training program. Many patients did not complete all the sessions. Whether they had completed all the sessions or not, patients initially randomized to the intervention were still considered to have received the intervention when the results were analyzed.

At first, intention to treat doesn’t seem logical. If we are testing an intervention, doesn’t it make sense to evaluate its effect among patients who complied with it fully, and then compare them with patients who were not assigned to the intervention or failed to comply? The problem is, patients who fail to comply with an intervention for whatever reason (not attending all training sessions in the example above) may differ in an important way from those who do.

In the Spector study, for example, it is possible that patients who attended very few of the sessions were more likely to have some subtle cognitive deficits that limited their participation. If placed in the control group, the 2 groups would these patients were excluded from the analysis or differ not only with respect to the intervention, but also with respect to these subtle factors. The value of randomization, therefore, would be compromised. The treatment arm may even appear to be more effective than it really is.

Correspondence
Kiame J. Mahaniah, MD, Greater Lawrence Family Health Center, 34 Haverhill Street, Lawrence, MA 01841. E-mail: [email protected] .

Randomization is a crucial part of most clinical trials. The purpose of randomization in a trial comparing 2 groups is to ensure that the groups differ only with respect to the interventions being compared. Randomization determines not only which treatment subjects receive (eg, drug vs placebo), but also how the results are analyzed at the end of the trial.

Intention to treat prevents biased outcomes

The intention-to-treat principle states that all subjects must be analyzed with respect to the group to which they were randomized. Consider a recent study by Spector et al¹ in which patients with dementia were randomized to receive an intervention known as cognitive stimulation therapy (CST) or control (equivalent to placebo in a drug trial). The investigators used an intention-to-treat analysis. The intervention was a complex 14-session training program. Many patients did not complete all the sessions. Whether they had completed all the sessions or not, patients initially randomized to the intervention were still considered to have received the intervention when the results were analyzed.

At first, intention to treat doesn’t seem logical. If we are testing an intervention, doesn’t it make sense to evaluate its effect among patients who complied with it fully, and then compare them with patients who were not assigned to the intervention or failed to comply? The problem is, patients who fail to comply with an intervention for whatever reason (not attending all training sessions in the example above) may differ in an important way from those who do.

In the Spector study, for example, it is possible that patients who attended very few of the sessions were more likely to have some subtle cognitive deficits that limited their participation. If placed in the control group, the 2 groups would these patients were excluded from the analysis or differ not only with respect to the intervention, but also with respect to these subtle factors. The value of randomization, therefore, would be compromised. The treatment arm may even appear to be more effective than it really is.

Correspondence
Kiame J. Mahaniah, MD, Greater Lawrence Family Health Center, 34 Haverhill Street, Lawrence, MA 01841. E-mail: [email protected] .

Clinical practice guidelines have become a common aspect of clinical care.¹ Guidelines have been defined as “systematically developed statements to assist practitioner and patient decisions about appropriate health care.”² Clinical practice guidelines have been seen as the remedy to at least 3 problems facing healthcare systems³ : wide variation in the health care people receive⁴ ; rising health care costs⁵ ; and health professionals’ difficulty in keeping abreast of research evidence.⁶ Despite increasing numbers of clinical practice guidelines, clinicians often do not change their practice accordingly.⁷ The reasons for this have not been fully explained.¹

At least 45 different depression guidelines have been published for use in primary care since 1991. However, a review concluded that they all make essentially the same recommendations.⁸ Thus, whichever guidelines GPs used, the recommendations were similar and were based on the 1992 joint consensus statement,⁹ which advises that that 4 depressive symptoms must have been present for at least 2 weeks before prescribing antidepressants. In this study, in-depth interviews explored GPs’ views on guidelines for the management of depression, how they used these in practice, barriers to using the guidelines, and how best to implement guidelines.

Barriers to effective treatment

Successful implementation of a depression guideline (by the US Agency for Healthcare Research and Quality) increases the quality of care and improves clinical outcomes.¹⁰ However, a widely acknowledged gap exists between research findings and their clinical implementation.¹¹ In the UK, GPs tend to overprescribe relative to recommendations¹² —antidepressant prescribing has increased for all age and sex groups over the last 20 years¹³ ; prescribing no drugs is rare.¹⁴ Nevertheless, depressed persons are often under-diagnosed and undertreated^13,15 ; only about 10% receive appropriate treatment.¹⁶

Barriers preventing effective treatment for depression include service provision¹⁷ ; patients’ attitudes and beliefs about depression and its care¹⁸ ; lack of access to care; treatment preference; and concerns about confidentiality and stigma.^19-21 Physicians have sometimes overruled guidelines when patients have complex illness patterns.¹⁸ Physician factors, including lack of time²² and poor awareness of guidelines,^22,23 may also contribute.

Asking questions about guidelines in practice

This study sought GPs’ views about the gap between depression guideline recommendations and practice, and examined how best to implement clinical guidelines from the GPs’ perspective. Specifically, the following research questions were addressed:

1. Do GPs agree with the recommendations made by depression guidelines?
2. Do GPs feel that guidelines are flexible enough to manage depression in all patients?
3. What barriers do GPs perceive to following the recommendations?
4. What do GPs perceive to be the most fruitful method to promote guideline use?

Methods

Participants’ characteristics

GPs eligible for this study (n=102) participated in 1 of 2 postal questionnaires, wherein they were asked to make treatment decisions in 20 systematically varied case vignettes of patients with symptoms that might indicate depression. Fifteen GPs were invited to participate, of whom 11 (73%) agreed to be interviewed.

Potential participants were sampled to reflect the range of compliance in response to the previous study’s vignettes (5 exhibited high levels of compliance, 3 medium, and 3 low), and to ensure the sample included GPs from different-sized practices (5 GPs worked in small practices, 5 in medium, and 1 in a large practice) and different locations (7 from the Scottish Grampian region, 4 from the Northeast of England). Eight GPs were male, 3 were female. GPs were interviewed during April 2002 at their practice premises by LS. Previous questionnaires did not reveal that analyses took guideline compliance into account; thus it was deemed that participants would not be affected by social desirability characteristics.

Interview procedure

A topic guide was designed to guide interviews and included the research design showing who was to be interviewed and key questions to be addressed. Questions were open-ended, semi-structured, and followed research questions. GPs’ permission was sought to record interviews, and confidentiality was assured. GPs were encouraged to talk freely. Interviews lasted between 45 and 75 minutes; they were tape-recorded and transcribed with all identifying text removed.

Data analysis

Two researchers (LS & AW) analyzed transcripts using the Framework Technique,²⁴ chosen because it is grounded in and driven by participating GPs’ original accounts and observations. Abstraction began after the full data set was reviewed. Emergent themes and issues were noted and given a code, and an index was constructed. This was revised several times as new issues emerged and was systematically reapplied to all the interview transcripts. Interviews were analyzed independently and any differences of interpretation were resolved through discussion.

Results

Of the 7 GPs who knew which was their latest depression guideline, 2 had no problems with recommendations. However, several GPs disagreed with some recommendations, possibly explaining variable compliance.¹²

Disagreements

One area of disagreement was the recommendation to refer patients, as specialists were not always available or waiting times were too long. Criteria for referring patients to secondary care include diagnostic uncertainty, treatment failure, suicidal tendencies, and psychotic or disturbed behavior. (This recurring issue of referral is discussed below.)

Another area of disagreement was the dura-tion-of-symptoms criterion, as heard in the follow-ing observation:

It stipulates they have to have these features and for at least 2 weeks … and if they only have them for a week why should I wait … why should they be miserable for a week, when I am pretty certain they are depressed? (GP3)

Guidelines’ flexibility

Evidence-based recommendations are usually expressed in terms of typical clinical situations. Perhaps such recommendations are particularly difficult to apply to individuals who can present with varying combinations of pre-existing illness, beliefs about depression, treatment preferences, concerns about confidentiality and stigma, as well as varying degrees of access to care. We therefore asked GPs whether they believed the available depression guidelines are sufficiently flexible to use with all their patients in managing depression.

Many of the GPs thought the guidelines were not flexible. For instance, GP4 said he worried about lawyers becoming involved in guideline compliance, which could result in defensive practice rather than the best treatment for patients. Similarly GP2 said that guidelines should not be used in all situations because they vary so much. GP7 reported that depression guidelines made invalid assumptions about patients presenting with only one illness (and GPs having plenty of time), resulting in the guidelines not being useful for some patients with certain illness combinations.

Barriers to following guidelines

Number of guidelines. The most common perceived barrier preventing these GPs from follow-ing guidelines was the volume of guidelines they receive. They thought they received too many guidelines and had too little time to read them all. The GPs sometimes felt confused about which one to follow. Although they could not quantify how many new guidelines they received in a month, or from how many sources, GPs appeared to feel overwhelmed and despondent.

…There’s a bit of numbing as well: oh no, not another guideline. (GP11)

We get flooded with stuff.… With a lot of stuff I bin it or file it. (GP5)

Time constraints. Lack of time was consistently viewed by participating GPs as a major barrier to guideline use. This is not surprising considering patients are booked in every 5–10 minutes,²⁵ with GPs seeing around 140 patients a week.²⁶ Furthermore, GPs viewed guideline accessibility, style, and presentation as barriers.

SIGN guidelines are always very good because they come on clear to follow laminated cards which are kind of summary versions of them. Many other guidelines are not so good … much longer and difficult to follow.… (GP6)

Lack of resources. Lack of resources re-emerged as a major barrier to following guideline recommendations. Problems of patient referral included having no specialist to refer them to, patients being misled about specialists’ qualifications, and patient confidentiality issues. Several GPs reported that by the time patients received appointments, they reported their problems had disappeared and they no longer wanted appointments.

…a guideline might come through and I’ve followed the protocol … and arranged a referral … then the reply has come back from the hospital that they don’t have the resources for this at the moment. So it [the guideline] has fallen flat on its face and that is extremely disappointing when we in primary care are trying our best. (GP2)

Waiting times reported were between 2 to 26 weeks for psychiatrists or community psychiatric nurses and 9 to 12 months for psychologists. Perceived delays or deficiencies in specialist services may partially explain GPs’ tendency to over prescribe relative to recommendations.¹²

Increasing guideline use

For guideline use to increase, GPs in this study thought that more resources needed to be put in place (particularly mental health professionals); the number of guidelines issued should be reduced; and guidelines should be produced and sent from a central body with a multidisciplinary team including some GPs, to reduce problems of perceived unrealistic assumptions. Incorporation of guideline recommendations onto computer systems with prompts and flow charts was also suggested by several GPs as method to promote guideline use. The majority of interviewed GPs also said they would like some form of audit and feedback.

We really need some kind of measure.… We’re all meant to audit our work, but again its time and we audit what we have to. If someone could demonstrate that I’m not managing depression well, then I might sit up and think I need that guideline there. We need all the feedback we can get really. (GP9)

Discussion

In this study, GPs perceived barriers to implementation of current depression guidelines matched other research findings on this subject—eg, lack of time,²² lack of resources,¹⁷ variability among patients,^19-21 lack of awareness,^22,23,27 lack of agreement with guideline recommendations,²⁸ and poor accessibility to guidelines.¹¹

The relatively small group of participants in this study cannot be generalized to all GPs. Additionally, there are always difficulties with self-reporting—participants may not do what they say they do. However, “purposive” sampling is consistent with qualitative approaches and allows a wide range of GPs’ views to be explored in depth. This study could be replicated elsewhere to assess how representative these views are.

Interviewed GPs did not always agree with depression guidelines. To address disagreement, some sort of educational intervention may be useful. Previous research has shown educational interventions to enable guideline implementation: an educational program was reportedly one of the most important elements in the successful implementation of cervical screening guidelines²⁸ ; and large group meetings were effective in modifying drug use in coronary artery disease.²⁹

An important theme in this study was the issue of referring patients and the availability of specialist services. GPs disagreed with the recommendations about referring, and saw lack of mental health professionals as a main barrier to following depression guidelines. This problem needs to be addressed, and interviewed GPs believed certain recommendations would be followed if resources were put into place. Their views have important implications for clinical guideline development. Resources must be considered before recommendations are made. Alternatively, those involved in guideline production may be demonstrating the case for more mental health professionals.

The volume of guidelines and lack of time and accessibility to guidelines were also perceived barriers. Both barriers could be addressed by introducing computerized decision support systems. Indeed, several GPs suggested the incorporation of guidelines onto computer systems as a way of promoting guideline use. However, the effect of computerized evidence-based guidelines has been variable,^1,30 and further study is needed.

The GPs thought depression guidelines were insufficiently flexible to use with the spectrum of depressed patients they see. However, some expected this, believing there would always be certain patients to whom guidelines do not apply. Greater involvement of GPs in guideline development was seen as a means to addressing this problem as well as reducing unrealistic assumptions made about general practice.

Audit and feedback emerged as a potential method for assessing and improving compliance. This matches the evidence. A review of 12 studies using audit and feedback as implementation strategies concluded these activities change behavior modestly, but all studies reported improvements in the process of care.¹

If we are serious about closing the gap between research evidence and practice, possibly a new system of guideline development is needed, with a national clearinghouse for guidelines. Here a multidisciplinary team including some GPs would be responsible for evaluating guidelines, incorporating them onto computer systems, auditing performance, and giving feedback to GPs. This study has opened up possibilities for further exploration.

Acknowledgments

This study was carried out from the Health Services Research Unit, which is funded by the Chief Scientist Office of the Scottish Executive Health Department. LS was supported by the Medical Research Council Health Services Research Collaboration. We would like to thank the GPs who participated in this study, and Vikki Entwistle and Steve Ratcliff for their helpful comments on earlier drafts of this paper. The opinions expressed in this paper are those of the authors, and may not be shared by the funding bodies.

Corresponding author
Liz Smith, MA, PhD, Manchester Centre for Healthcare Management, University of Manchester, Devonshire House, University Precinct Centre, Oxford Road, Manchester, UK, M13 9PL. E-mail: [email protected].

Clinical practice guidelines have become a common aspect of clinical care.¹ Guidelines have been defined as “systematically developed statements to assist practitioner and patient decisions about appropriate health care.”² Clinical practice guidelines have been seen as the remedy to at least 3 problems facing healthcare systems³ : wide variation in the health care people receive⁴ ; rising health care costs⁵ ; and health professionals’ difficulty in keeping abreast of research evidence.⁶ Despite increasing numbers of clinical practice guidelines, clinicians often do not change their practice accordingly.⁷ The reasons for this have not been fully explained.¹

At least 45 different depression guidelines have been published for use in primary care since 1991. However, a review concluded that they all make essentially the same recommendations.⁸ Thus, whichever guidelines GPs used, the recommendations were similar and were based on the 1992 joint consensus statement,⁹ which advises that that 4 depressive symptoms must have been present for at least 2 weeks before prescribing antidepressants. In this study, in-depth interviews explored GPs’ views on guidelines for the management of depression, how they used these in practice, barriers to using the guidelines, and how best to implement guidelines.

Barriers to effective treatment

Successful implementation of a depression guideline (by the US Agency for Healthcare Research and Quality) increases the quality of care and improves clinical outcomes.¹⁰ However, a widely acknowledged gap exists between research findings and their clinical implementation.¹¹ In the UK, GPs tend to overprescribe relative to recommendations¹² —antidepressant prescribing has increased for all age and sex groups over the last 20 years¹³ ; prescribing no drugs is rare.¹⁴ Nevertheless, depressed persons are often under-diagnosed and undertreated^13,15 ; only about 10% receive appropriate treatment.¹⁶

Barriers preventing effective treatment for depression include service provision¹⁷ ; patients’ attitudes and beliefs about depression and its care¹⁸ ; lack of access to care; treatment preference; and concerns about confidentiality and stigma.^19-21 Physicians have sometimes overruled guidelines when patients have complex illness patterns.¹⁸ Physician factors, including lack of time²² and poor awareness of guidelines,^22,23 may also contribute.

Asking questions about guidelines in practice

This study sought GPs’ views about the gap between depression guideline recommendations and practice, and examined how best to implement clinical guidelines from the GPs’ perspective. Specifically, the following research questions were addressed:

1. Do GPs agree with the recommendations made by depression guidelines?
2. Do GPs feel that guidelines are flexible enough to manage depression in all patients?
3. What barriers do GPs perceive to following the recommendations?
4. What do GPs perceive to be the most fruitful method to promote guideline use?

Methods

Participants’ characteristics

GPs eligible for this study (n=102) participated in 1 of 2 postal questionnaires, wherein they were asked to make treatment decisions in 20 systematically varied case vignettes of patients with symptoms that might indicate depression. Fifteen GPs were invited to participate, of whom 11 (73%) agreed to be interviewed.

Potential participants were sampled to reflect the range of compliance in response to the previous study’s vignettes (5 exhibited high levels of compliance, 3 medium, and 3 low), and to ensure the sample included GPs from different-sized practices (5 GPs worked in small practices, 5 in medium, and 1 in a large practice) and different locations (7 from the Scottish Grampian region, 4 from the Northeast of England). Eight GPs were male, 3 were female. GPs were interviewed during April 2002 at their practice premises by LS. Previous questionnaires did not reveal that analyses took guideline compliance into account; thus it was deemed that participants would not be affected by social desirability characteristics.

Interview procedure

A topic guide was designed to guide interviews and included the research design showing who was to be interviewed and key questions to be addressed. Questions were open-ended, semi-structured, and followed research questions. GPs’ permission was sought to record interviews, and confidentiality was assured. GPs were encouraged to talk freely. Interviews lasted between 45 and 75 minutes; they were tape-recorded and transcribed with all identifying text removed.

Data analysis

Two researchers (LS & AW) analyzed transcripts using the Framework Technique,²⁴ chosen because it is grounded in and driven by participating GPs’ original accounts and observations. Abstraction began after the full data set was reviewed. Emergent themes and issues were noted and given a code, and an index was constructed. This was revised several times as new issues emerged and was systematically reapplied to all the interview transcripts. Interviews were analyzed independently and any differences of interpretation were resolved through discussion.

Results

Of the 7 GPs who knew which was their latest depression guideline, 2 had no problems with recommendations. However, several GPs disagreed with some recommendations, possibly explaining variable compliance.¹²

Disagreements

One area of disagreement was the recommendation to refer patients, as specialists were not always available or waiting times were too long. Criteria for referring patients to secondary care include diagnostic uncertainty, treatment failure, suicidal tendencies, and psychotic or disturbed behavior. (This recurring issue of referral is discussed below.)

Another area of disagreement was the dura-tion-of-symptoms criterion, as heard in the follow-ing observation:

It stipulates they have to have these features and for at least 2 weeks … and if they only have them for a week why should I wait … why should they be miserable for a week, when I am pretty certain they are depressed? (GP3)

Guidelines’ flexibility

Evidence-based recommendations are usually expressed in terms of typical clinical situations. Perhaps such recommendations are particularly difficult to apply to individuals who can present with varying combinations of pre-existing illness, beliefs about depression, treatment preferences, concerns about confidentiality and stigma, as well as varying degrees of access to care. We therefore asked GPs whether they believed the available depression guidelines are sufficiently flexible to use with all their patients in managing depression.

Many of the GPs thought the guidelines were not flexible. For instance, GP4 said he worried about lawyers becoming involved in guideline compliance, which could result in defensive practice rather than the best treatment for patients. Similarly GP2 said that guidelines should not be used in all situations because they vary so much. GP7 reported that depression guidelines made invalid assumptions about patients presenting with only one illness (and GPs having plenty of time), resulting in the guidelines not being useful for some patients with certain illness combinations.

Barriers to following guidelines

Number of guidelines. The most common perceived barrier preventing these GPs from follow-ing guidelines was the volume of guidelines they receive. They thought they received too many guidelines and had too little time to read them all. The GPs sometimes felt confused about which one to follow. Although they could not quantify how many new guidelines they received in a month, or from how many sources, GPs appeared to feel overwhelmed and despondent.

…There’s a bit of numbing as well: oh no, not another guideline. (GP11)

We get flooded with stuff.… With a lot of stuff I bin it or file it. (GP5)

Time constraints. Lack of time was consistently viewed by participating GPs as a major barrier to guideline use. This is not surprising considering patients are booked in every 5–10 minutes,²⁵ with GPs seeing around 140 patients a week.²⁶ Furthermore, GPs viewed guideline accessibility, style, and presentation as barriers.

SIGN guidelines are always very good because they come on clear to follow laminated cards which are kind of summary versions of them. Many other guidelines are not so good … much longer and difficult to follow.… (GP6)

Lack of resources. Lack of resources re-emerged as a major barrier to following guideline recommendations. Problems of patient referral included having no specialist to refer them to, patients being misled about specialists’ qualifications, and patient confidentiality issues. Several GPs reported that by the time patients received appointments, they reported their problems had disappeared and they no longer wanted appointments.

…a guideline might come through and I’ve followed the protocol … and arranged a referral … then the reply has come back from the hospital that they don’t have the resources for this at the moment. So it [the guideline] has fallen flat on its face and that is extremely disappointing when we in primary care are trying our best. (GP2)

Waiting times reported were between 2 to 26 weeks for psychiatrists or community psychiatric nurses and 9 to 12 months for psychologists. Perceived delays or deficiencies in specialist services may partially explain GPs’ tendency to over prescribe relative to recommendations.¹²

Increasing guideline use

For guideline use to increase, GPs in this study thought that more resources needed to be put in place (particularly mental health professionals); the number of guidelines issued should be reduced; and guidelines should be produced and sent from a central body with a multidisciplinary team including some GPs, to reduce problems of perceived unrealistic assumptions. Incorporation of guideline recommendations onto computer systems with prompts and flow charts was also suggested by several GPs as method to promote guideline use. The majority of interviewed GPs also said they would like some form of audit and feedback.

We really need some kind of measure.… We’re all meant to audit our work, but again its time and we audit what we have to. If someone could demonstrate that I’m not managing depression well, then I might sit up and think I need that guideline there. We need all the feedback we can get really. (GP9)

Discussion

In this study, GPs perceived barriers to implementation of current depression guidelines matched other research findings on this subject—eg, lack of time,²² lack of resources,¹⁷ variability among patients,^19-21 lack of awareness,^22,23,27 lack of agreement with guideline recommendations,²⁸ and poor accessibility to guidelines.¹¹

The relatively small group of participants in this study cannot be generalized to all GPs. Additionally, there are always difficulties with self-reporting—participants may not do what they say they do. However, “purposive” sampling is consistent with qualitative approaches and allows a wide range of GPs’ views to be explored in depth. This study could be replicated elsewhere to assess how representative these views are.

Interviewed GPs did not always agree with depression guidelines. To address disagreement, some sort of educational intervention may be useful. Previous research has shown educational interventions to enable guideline implementation: an educational program was reportedly one of the most important elements in the successful implementation of cervical screening guidelines²⁸ ; and large group meetings were effective in modifying drug use in coronary artery disease.²⁹

An important theme in this study was the issue of referring patients and the availability of specialist services. GPs disagreed with the recommendations about referring, and saw lack of mental health professionals as a main barrier to following depression guidelines. This problem needs to be addressed, and interviewed GPs believed certain recommendations would be followed if resources were put into place. Their views have important implications for clinical guideline development. Resources must be considered before recommendations are made. Alternatively, those involved in guideline production may be demonstrating the case for more mental health professionals.

The volume of guidelines and lack of time and accessibility to guidelines were also perceived barriers. Both barriers could be addressed by introducing computerized decision support systems. Indeed, several GPs suggested the incorporation of guidelines onto computer systems as a way of promoting guideline use. However, the effect of computerized evidence-based guidelines has been variable,^1,30 and further study is needed.

The GPs thought depression guidelines were insufficiently flexible to use with the spectrum of depressed patients they see. However, some expected this, believing there would always be certain patients to whom guidelines do not apply. Greater involvement of GPs in guideline development was seen as a means to addressing this problem as well as reducing unrealistic assumptions made about general practice.

Audit and feedback emerged as a potential method for assessing and improving compliance. This matches the evidence. A review of 12 studies using audit and feedback as implementation strategies concluded these activities change behavior modestly, but all studies reported improvements in the process of care.¹

If we are serious about closing the gap between research evidence and practice, possibly a new system of guideline development is needed, with a national clearinghouse for guidelines. Here a multidisciplinary team including some GPs would be responsible for evaluating guidelines, incorporating them onto computer systems, auditing performance, and giving feedback to GPs. This study has opened up possibilities for further exploration.

Acknowledgments

This study was carried out from the Health Services Research Unit, which is funded by the Chief Scientist Office of the Scottish Executive Health Department. LS was supported by the Medical Research Council Health Services Research Collaboration. We would like to thank the GPs who participated in this study, and Vikki Entwistle and Steve Ratcliff for their helpful comments on earlier drafts of this paper. The opinions expressed in this paper are those of the authors, and may not be shared by the funding bodies.

Corresponding author
Liz Smith, MA, PhD, Manchester Centre for Healthcare Management, University of Manchester, Devonshire House, University Precinct Centre, Oxford Road, Manchester, UK, M13 9PL. E-mail: [email protected].

Clinical practice guidelines have become a common aspect of clinical care.¹ Guidelines have been defined as “systematically developed statements to assist practitioner and patient decisions about appropriate health care.”² Clinical practice guidelines have been seen as the remedy to at least 3 problems facing healthcare systems³ : wide variation in the health care people receive⁴ ; rising health care costs⁵ ; and health professionals’ difficulty in keeping abreast of research evidence.⁶ Despite increasing numbers of clinical practice guidelines, clinicians often do not change their practice accordingly.⁷ The reasons for this have not been fully explained.¹

At least 45 different depression guidelines have been published for use in primary care since 1991. However, a review concluded that they all make essentially the same recommendations.⁸ Thus, whichever guidelines GPs used, the recommendations were similar and were based on the 1992 joint consensus statement,⁹ which advises that that 4 depressive symptoms must have been present for at least 2 weeks before prescribing antidepressants. In this study, in-depth interviews explored GPs’ views on guidelines for the management of depression, how they used these in practice, barriers to using the guidelines, and how best to implement guidelines.

Barriers to effective treatment

Successful implementation of a depression guideline (by the US Agency for Healthcare Research and Quality) increases the quality of care and improves clinical outcomes.¹⁰ However, a widely acknowledged gap exists between research findings and their clinical implementation.¹¹ In the UK, GPs tend to overprescribe relative to recommendations¹² —antidepressant prescribing has increased for all age and sex groups over the last 20 years¹³ ; prescribing no drugs is rare.¹⁴ Nevertheless, depressed persons are often under-diagnosed and undertreated^13,15 ; only about 10% receive appropriate treatment.¹⁶

Barriers preventing effective treatment for depression include service provision¹⁷ ; patients’ attitudes and beliefs about depression and its care¹⁸ ; lack of access to care; treatment preference; and concerns about confidentiality and stigma.^19-21 Physicians have sometimes overruled guidelines when patients have complex illness patterns.¹⁸ Physician factors, including lack of time²² and poor awareness of guidelines,^22,23 may also contribute.

Asking questions about guidelines in practice

This study sought GPs’ views about the gap between depression guideline recommendations and practice, and examined how best to implement clinical guidelines from the GPs’ perspective. Specifically, the following research questions were addressed:

1. Do GPs agree with the recommendations made by depression guidelines?
2. Do GPs feel that guidelines are flexible enough to manage depression in all patients?
3. What barriers do GPs perceive to following the recommendations?
4. What do GPs perceive to be the most fruitful method to promote guideline use?

Methods

Participants’ characteristics

GPs eligible for this study (n=102) participated in 1 of 2 postal questionnaires, wherein they were asked to make treatment decisions in 20 systematically varied case vignettes of patients with symptoms that might indicate depression. Fifteen GPs were invited to participate, of whom 11 (73%) agreed to be interviewed.

Potential participants were sampled to reflect the range of compliance in response to the previous study’s vignettes (5 exhibited high levels of compliance, 3 medium, and 3 low), and to ensure the sample included GPs from different-sized practices (5 GPs worked in small practices, 5 in medium, and 1 in a large practice) and different locations (7 from the Scottish Grampian region, 4 from the Northeast of England). Eight GPs were male, 3 were female. GPs were interviewed during April 2002 at their practice premises by LS. Previous questionnaires did not reveal that analyses took guideline compliance into account; thus it was deemed that participants would not be affected by social desirability characteristics.

Interview procedure

A topic guide was designed to guide interviews and included the research design showing who was to be interviewed and key questions to be addressed. Questions were open-ended, semi-structured, and followed research questions. GPs’ permission was sought to record interviews, and confidentiality was assured. GPs were encouraged to talk freely. Interviews lasted between 45 and 75 minutes; they were tape-recorded and transcribed with all identifying text removed.

Data analysis

Two researchers (LS & AW) analyzed transcripts using the Framework Technique,²⁴ chosen because it is grounded in and driven by participating GPs’ original accounts and observations. Abstraction began after the full data set was reviewed. Emergent themes and issues were noted and given a code, and an index was constructed. This was revised several times as new issues emerged and was systematically reapplied to all the interview transcripts. Interviews were analyzed independently and any differences of interpretation were resolved through discussion.

Results

Of the 7 GPs who knew which was their latest depression guideline, 2 had no problems with recommendations. However, several GPs disagreed with some recommendations, possibly explaining variable compliance.¹²

Disagreements

One area of disagreement was the recommendation to refer patients, as specialists were not always available or waiting times were too long. Criteria for referring patients to secondary care include diagnostic uncertainty, treatment failure, suicidal tendencies, and psychotic or disturbed behavior. (This recurring issue of referral is discussed below.)

Another area of disagreement was the dura-tion-of-symptoms criterion, as heard in the follow-ing observation:

It stipulates they have to have these features and for at least 2 weeks … and if they only have them for a week why should I wait … why should they be miserable for a week, when I am pretty certain they are depressed? (GP3)

Guidelines’ flexibility

Evidence-based recommendations are usually expressed in terms of typical clinical situations. Perhaps such recommendations are particularly difficult to apply to individuals who can present with varying combinations of pre-existing illness, beliefs about depression, treatment preferences, concerns about confidentiality and stigma, as well as varying degrees of access to care. We therefore asked GPs whether they believed the available depression guidelines are sufficiently flexible to use with all their patients in managing depression.

Many of the GPs thought the guidelines were not flexible. For instance, GP4 said he worried about lawyers becoming involved in guideline compliance, which could result in defensive practice rather than the best treatment for patients. Similarly GP2 said that guidelines should not be used in all situations because they vary so much. GP7 reported that depression guidelines made invalid assumptions about patients presenting with only one illness (and GPs having plenty of time), resulting in the guidelines not being useful for some patients with certain illness combinations.

Barriers to following guidelines

Number of guidelines. The most common perceived barrier preventing these GPs from follow-ing guidelines was the volume of guidelines they receive. They thought they received too many guidelines and had too little time to read them all. The GPs sometimes felt confused about which one to follow. Although they could not quantify how many new guidelines they received in a month, or from how many sources, GPs appeared to feel overwhelmed and despondent.

…There’s a bit of numbing as well: oh no, not another guideline. (GP11)

We get flooded with stuff.… With a lot of stuff I bin it or file it. (GP5)

Time constraints. Lack of time was consistently viewed by participating GPs as a major barrier to guideline use. This is not surprising considering patients are booked in every 5–10 minutes,²⁵ with GPs seeing around 140 patients a week.²⁶ Furthermore, GPs viewed guideline accessibility, style, and presentation as barriers.

SIGN guidelines are always very good because they come on clear to follow laminated cards which are kind of summary versions of them. Many other guidelines are not so good … much longer and difficult to follow.… (GP6)

Lack of resources. Lack of resources re-emerged as a major barrier to following guideline recommendations. Problems of patient referral included having no specialist to refer them to, patients being misled about specialists’ qualifications, and patient confidentiality issues. Several GPs reported that by the time patients received appointments, they reported their problems had disappeared and they no longer wanted appointments.

…a guideline might come through and I’ve followed the protocol … and arranged a referral … then the reply has come back from the hospital that they don’t have the resources for this at the moment. So it [the guideline] has fallen flat on its face and that is extremely disappointing when we in primary care are trying our best. (GP2)

Waiting times reported were between 2 to 26 weeks for psychiatrists or community psychiatric nurses and 9 to 12 months for psychologists. Perceived delays or deficiencies in specialist services may partially explain GPs’ tendency to over prescribe relative to recommendations.¹²

Increasing guideline use

For guideline use to increase, GPs in this study thought that more resources needed to be put in place (particularly mental health professionals); the number of guidelines issued should be reduced; and guidelines should be produced and sent from a central body with a multidisciplinary team including some GPs, to reduce problems of perceived unrealistic assumptions. Incorporation of guideline recommendations onto computer systems with prompts and flow charts was also suggested by several GPs as method to promote guideline use. The majority of interviewed GPs also said they would like some form of audit and feedback.

We really need some kind of measure.… We’re all meant to audit our work, but again its time and we audit what we have to. If someone could demonstrate that I’m not managing depression well, then I might sit up and think I need that guideline there. We need all the feedback we can get really. (GP9)

Discussion

In this study, GPs perceived barriers to implementation of current depression guidelines matched other research findings on this subject—eg, lack of time,²² lack of resources,¹⁷ variability among patients,^19-21 lack of awareness,^22,23,27 lack of agreement with guideline recommendations,²⁸ and poor accessibility to guidelines.¹¹

The relatively small group of participants in this study cannot be generalized to all GPs. Additionally, there are always difficulties with self-reporting—participants may not do what they say they do. However, “purposive” sampling is consistent with qualitative approaches and allows a wide range of GPs’ views to be explored in depth. This study could be replicated elsewhere to assess how representative these views are.

Interviewed GPs did not always agree with depression guidelines. To address disagreement, some sort of educational intervention may be useful. Previous research has shown educational interventions to enable guideline implementation: an educational program was reportedly one of the most important elements in the successful implementation of cervical screening guidelines²⁸ ; and large group meetings were effective in modifying drug use in coronary artery disease.²⁹

An important theme in this study was the issue of referring patients and the availability of specialist services. GPs disagreed with the recommendations about referring, and saw lack of mental health professionals as a main barrier to following depression guidelines. This problem needs to be addressed, and interviewed GPs believed certain recommendations would be followed if resources were put into place. Their views have important implications for clinical guideline development. Resources must be considered before recommendations are made. Alternatively, those involved in guideline production may be demonstrating the case for more mental health professionals.

The volume of guidelines and lack of time and accessibility to guidelines were also perceived barriers. Both barriers could be addressed by introducing computerized decision support systems. Indeed, several GPs suggested the incorporation of guidelines onto computer systems as a way of promoting guideline use. However, the effect of computerized evidence-based guidelines has been variable,^1,30 and further study is needed.

The GPs thought depression guidelines were insufficiently flexible to use with the spectrum of depressed patients they see. However, some expected this, believing there would always be certain patients to whom guidelines do not apply. Greater involvement of GPs in guideline development was seen as a means to addressing this problem as well as reducing unrealistic assumptions made about general practice.

Audit and feedback emerged as a potential method for assessing and improving compliance. This matches the evidence. A review of 12 studies using audit and feedback as implementation strategies concluded these activities change behavior modestly, but all studies reported improvements in the process of care.¹

If we are serious about closing the gap between research evidence and practice, possibly a new system of guideline development is needed, with a national clearinghouse for guidelines. Here a multidisciplinary team including some GPs would be responsible for evaluating guidelines, incorporating them onto computer systems, auditing performance, and giving feedback to GPs. This study has opened up possibilities for further exploration.

Acknowledgments

This study was carried out from the Health Services Research Unit, which is funded by the Chief Scientist Office of the Scottish Executive Health Department. LS was supported by the Medical Research Council Health Services Research Collaboration. We would like to thank the GPs who participated in this study, and Vikki Entwistle and Steve Ratcliff for their helpful comments on earlier drafts of this paper. The opinions expressed in this paper are those of the authors, and may not be shared by the funding bodies.

Corresponding author
Liz Smith, MA, PhD, Manchester Centre for Healthcare Management, University of Manchester, Devonshire House, University Precinct Centre, Oxford Road, Manchester, UK, M13 9PL. E-mail: [email protected].

The first trial of beta-blockers in myocardial infarction was entitled “The lack of prophylactic effect of propranolol in myocardial infarction”¹—a conclusion inconsistent with our current understanding of beta-blocker therapy. The reason has to do with “statistical power”—a statistic that tells us the chance of finding a significant difference between treatments.²

Type 1 and type 2 errors

We draw conclusions based on the results of clinical trials. No trial is perfect. Trials are designed with the knowledge that there is a probability of drawing a conclusion based on the results that does not represent the truth about 2 or more therapies.

If we conclude from the results of a trial that 2 therapies are of different effectiveness, when in reality they are the same, we have committed what is known as a type 1 error. The probability of making a type 1 error is termed the alpha. Trials are usually designed with an a of 0.05 (5%).

On the other hand, if we conclude that the 2 therapies are the same when they are actually different, we have committed a type 2 error. The probability of making a type 2 error is known as the beta.

Perhaps a bit more intuitively, we are often interested in knowing the probability of finding a difference when there really is one. This probability is called power and may be expressed as 1-β .

Power in study design

In designing a study, the power of a study to detect differences between 2 groups depends upon the number of subjects in each group, whether the groups are equal in size, the variability of responses among subjects, the magnitude of difference one is trying to detect, and the probability of making a type 1 error.³ Researchers can make some educated assumptions to determine the number of subjects to include in a study to assure that clinically relevant differences are found between 2 groups if they exist.

Practicing clinicians should use power to determine the impact of a negative study. For example, the propranolol study¹ was designed with a power of only 23%, meaning that there was only a 23% chance of detecting a difference. Drawing conclusions about the lack of effectiveness of propranolol based on this study, therefore, would be a mistake. In clinical trials of an active drug vs a placebo, 100 subjects in each group or more are often needed to detect clinically relevant results—so beware of negative results with small numbers of patients.

Correspondence
Goutham Rao, MD, 3518 Fifth Avenue, Pittsburgh, PA 15261. E-mail: [email protected].

The first trial of beta-blockers in myocardial infarction was entitled “The lack of prophylactic effect of propranolol in myocardial infarction”¹—a conclusion inconsistent with our current understanding of beta-blocker therapy. The reason has to do with “statistical power”—a statistic that tells us the chance of finding a significant difference between treatments.²

Type 1 and type 2 errors

We draw conclusions based on the results of clinical trials. No trial is perfect. Trials are designed with the knowledge that there is a probability of drawing a conclusion based on the results that does not represent the truth about 2 or more therapies.

If we conclude from the results of a trial that 2 therapies are of different effectiveness, when in reality they are the same, we have committed what is known as a type 1 error. The probability of making a type 1 error is termed the alpha. Trials are usually designed with an a of 0.05 (5%).

On the other hand, if we conclude that the 2 therapies are the same when they are actually different, we have committed a type 2 error. The probability of making a type 2 error is known as the beta.

Perhaps a bit more intuitively, we are often interested in knowing the probability of finding a difference when there really is one. This probability is called power and may be expressed as 1-β .

Power in study design

In designing a study, the power of a study to detect differences between 2 groups depends upon the number of subjects in each group, whether the groups are equal in size, the variability of responses among subjects, the magnitude of difference one is trying to detect, and the probability of making a type 1 error.³ Researchers can make some educated assumptions to determine the number of subjects to include in a study to assure that clinically relevant differences are found between 2 groups if they exist.

Practicing clinicians should use power to determine the impact of a negative study. For example, the propranolol study¹ was designed with a power of only 23%, meaning that there was only a 23% chance of detecting a difference. Drawing conclusions about the lack of effectiveness of propranolol based on this study, therefore, would be a mistake. In clinical trials of an active drug vs a placebo, 100 subjects in each group or more are often needed to detect clinically relevant results—so beware of negative results with small numbers of patients.

Correspondence
Goutham Rao, MD, 3518 Fifth Avenue, Pittsburgh, PA 15261. E-mail: [email protected].

The first trial of beta-blockers in myocardial infarction was entitled “The lack of prophylactic effect of propranolol in myocardial infarction”¹—a conclusion inconsistent with our current understanding of beta-blocker therapy. The reason has to do with “statistical power”—a statistic that tells us the chance of finding a significant difference between treatments.²

Type 1 and type 2 errors

We draw conclusions based on the results of clinical trials. No trial is perfect. Trials are designed with the knowledge that there is a probability of drawing a conclusion based on the results that does not represent the truth about 2 or more therapies.

If we conclude from the results of a trial that 2 therapies are of different effectiveness, when in reality they are the same, we have committed what is known as a type 1 error. The probability of making a type 1 error is termed the alpha. Trials are usually designed with an a of 0.05 (5%).

On the other hand, if we conclude that the 2 therapies are the same when they are actually different, we have committed a type 2 error. The probability of making a type 2 error is known as the beta.

Perhaps a bit more intuitively, we are often interested in knowing the probability of finding a difference when there really is one. This probability is called power and may be expressed as 1-β .

Power in study design

In designing a study, the power of a study to detect differences between 2 groups depends upon the number of subjects in each group, whether the groups are equal in size, the variability of responses among subjects, the magnitude of difference one is trying to detect, and the probability of making a type 1 error.³ Researchers can make some educated assumptions to determine the number of subjects to include in a study to assure that clinically relevant differences are found between 2 groups if they exist.

Practicing clinicians should use power to determine the impact of a negative study. For example, the propranolol study¹ was designed with a power of only 23%, meaning that there was only a 23% chance of detecting a difference. Drawing conclusions about the lack of effectiveness of propranolol based on this study, therefore, would be a mistake. In clinical trials of an active drug vs a placebo, 100 subjects in each group or more are often needed to detect clinically relevant results—so beware of negative results with small numbers of patients.

Correspondence
Goutham Rao, MD, 3518 Fifth Avenue, Pittsburgh, PA 15261. E-mail: [email protected].

The hospitalist model has spread relatively rapidly throughout hospitals in the United States. Family physicians can proactively work with this new patient care model by developing standardized and efficient ways to communicate and to partner with hospitalists.

Advances in electronic data exchange can help facilitate these communications, and can reduce the risks associated with discontinuity of care inherent in the hospitalist model. Developing communications protocols involving transfer of patient information and maintaining contact with hospitalists while patients are under their care can help family physicians best serve the needs of their patients and ensure continuity of care and compliance with patient wishes.

Hospitalists in the US

Rarely in medicine does the opportunity arise to examine a newly developed area of medical specialization and its effect on other providers. The emergence of the hospitalist, a specialist in inpatient medicine, provides this opportunity. Although dedicated inpatient physicians have been in practice in Canada and overseas for some time,^1-6 attention to, and experimentation with, this role in the US has been relatively new.

Hospitalists were first described in 1996 by Robert Wachter and Lee Goldman,⁷ who coined the term and have widely studied and promoted the model. Presently, approximately 6000 US hospitalists are practicing inpatient medicine in diverse organizations, including adult and children’s hospitals and skilled nursing facilities. The number of hospitalists in practice in the US has been projected to increase to around 19,000 within the next 10 years, making the size of hospitalist physician practice similar to that of the specialty of cardiology,¹ but far smaller than that of family practice.

Yet the introduction and spread of hospitalists throughout the US has not occurred without controversy. Given substantial debate about the changing role of family practitioners with respect to such issues as scope of practice, professional identity, and care and service to patients, the emergence of hospitalists has been perceived by many as a potential threat on all fronts.

Responses to the hospitalist movement

Responses to the hospitalist movement vary. To many, a specialty in hospital medicine appears to threaten the role of generalists in health care practice, and risks such as a reduced practice scope or the loss of hospital privileges are real concerns.^8-11 For others, the introduction of hospitalists has increased flexibility for family practitioners who are interested in working with or becoming hospitalists themselves.

As of 2001, 1 in 5 members of the American Academy of Family Physicians reported using hospitalists. Further, reasons such as economics, lifestyle choices, and concern about maintaining competence in caring for hospitalized patients have contributed to the decision of as many as 1 in 5 family practitioners who have chosen not to be involved in hospital care.¹² Yet, as noted by Edsall,¹³ for family practitioners who choose not to practice inpatient medicine, the philosophical, professional, and financial risks of that decision should not be trivialized.

Despite the debate in the literature and the media, it appears this inpatient care model is here to stay.^1,13,16 Major medical organizations, including the American Academy of Family Physicians and the American College of Physicians–American Society of Internal Medicine, now note that hospitalist programs are acceptable as long as they are well designed and implemented voluntarily, and this consensus has helped spark program growth.¹⁷

However, the increasing presence of hospitalists in hospitals and academic medical centers is forcing many family physicians to choose how involved they want to be in inpatient medicine. The goal of this study was to synthesize available information in the literature regarding the practice of hospitalists and their effect on family physicians, and to provide a discussion about future research opportunities to further evaluate the hospitalist model and its influence on family practice.

Methods

A comprehensive review of the literature was conducted by database searches, by hand, and the Internet. Medline, Lexis-Nexis, and Academic Universe were used as the primary databases for the literature search. Key words such as hospitalists, inpatient physicians, hospital medicine, primary care physicians, and family practice were used to focus a search. Furthermore, references in each article were reviewed to find related literature.

Literature was largely concentrated within the past 5 years and included both peer-reviewed and descriptive articles on hospitalists and their effect. Internet searches used Google as the primary search engine; results supplemented findings in other published material.

This literature review continued until saturation was achieved with respect to considering the possible issues and implications of the expansion of hospitalists, with special attention paid to the risks and opportunities to family physicians.

Findings

This integrative literature review revealed 3 major themes of interest to family physicians regarding the emergence and expansion of hospitalists in the US: descriptions of the hospitalist role and responsibilities; hypothesized benefits and risks of the hospitalist model; and reported research results evaluating the effect of the hospitalist model. Synthesis of this literature also uncovered 2 major opportunities related to hospitalist practice: opportunities to conduct future research to study the impact of hospitalists on family physicians; and opportunities to leverage relationships with these new practitioners.

Hospitalist roles and responsibilities

A hospitalist physician is a new type of medical specialist who combines the roles of acute care subspecialist and medical generalist in the hospital care setting.¹⁸ Hospitalists do not replace primary care physicians, surgeons, or specialists, but, instead, are concerned with managing hospital inpatients, from admission until discharge. They act somewhat as a case manager for a patient’s hospital stay, working and communicating closely with other physicians involved in the patient’s care.

Patients are assigned to hospitalists upon admission, either when an outpatient provider such as a family practitioner transfers inpatient care responsibilities to the hospitalist, or when patients arrive at the hospital unassigned to any other provider. The clinical and organizational responsibilities of hospitalists are in Table 1.

TABLE 1
Typical responsibilities of hospitalist physicians

Hypothesized benefits and risks of the hospitalist model

Persuasive arguments have been raised about the advantages and disadvantages of the hospitalist model.^18,19,21,22 A variety of these potential advantages and disadvantages are summarized in Table 2, representing perspectives of 3 different stakeholder groups: hospitals, patients and families, and hospitalist physicians. Each of the listed advantages or disadvantages was discussed in 3 or more independent articles that were reviewed.

For family physicians specifically, the introduction of a hospitalist program at a local hospital has numerous associated potential benefits and risks. Table 3 presents a summary of the issues that were raised in 3 or more articles or studies.

Benefit: focus on ambulatory care. One widely discussed advantage in using hospitalists is the option for family practitioners, who so desire, to limit practice to outpatient medicine because of their interest in ambulatory care or because they feel overtaxed by the demands of the health care system.^12,21 Willing family physicians can relinquish care of their hospitalized patients to a hospitalist so they do not have to travel to the hospital for daily rounds or more frequent patient contact; upon hospital discharge, family practitioners subsequently resume care for their patients.

Given the pressures of managed care to increase office productivity,⁴⁸ this delegation of responsibilities can create an important practice advantage.¹⁵ Even for those family physicians who choose to visit their hospitalized patients, shifting overall responsibility for inpatient care to hospitalists can make hospital visits more efficient and thereby free office time for outpatient practices.⁴⁹

Risk: lack of patient familiarity. Research has shown that a lack of familiarity with patients can increase the risk of errors and poor outcomes in medicine, and the use of a hospitalist as a new provider indeed introduces this risk.^50,51

Without dedicated effort on the part of the family physician, the treating hospitalist may have limited appreciation of a patient’s situation. Hospitalists focused only on inpatient care may not know where patients come from or where they return to, and are less likely to be knowledgeable about needs for psychosocial support or for such patient preferences as end-of-life care.^14,21

Risk: reduced political leverage. In addition, a political issue for family physicians may arise if hospitalists become providers of choice for inpatient internal medicine, thereby defining a smaller role for community-based family practitioners.²¹

Risk: communication problems. Another major risk of hospitalist programs is poor communication, an issue raised in nearly every article discussing the hospitalist model. The involvement of a new physician provider and the process of patient care transfers between outpatient family physicians and inpatient hospitalists can lead to missed information, gaps in communication, and misunderstandings.^19,22,35,37

Recent studies of discontinuity of care when patients are hospitalized reported that inpatients specifically wanted both contact with their primary care physicians and good communication between their established primary care physician and hospital-based physicians.⁴⁹ Guidelines created by the American Academy of Family Physicians (www.aafp.org/x6873.xml) support communication and interaction between community-based physicians and hospitalists for excellent patient care,¹² but the burden may fall on family physicians to ensure communication.

TABLE 2
Stakeholder perspectives of hospitalist model: Advantages and disadvantages

TABLE 3
Potential benefits and risks of the hospitalist model for family physicians

Assessing the effect of the hospitalist model

Research evaluating the impact of hospitalists has largely focused on hospital-based outcomes. Recently, Wachter and Goldman’s review of 19 published studies showed that hospital costs decreased 13.4% on average and hospital lengths of stay decreased 16.6% on average after a hospitalist program was initiated.²³ These efficiency improvements were apparently gained while patient satisfaction was preserved.

However, results indicating improved outcomes, such as mortality and readmissions, were reportedly inconsistent among the studies evaluated.²³ Additional studies^3,24,52 of hospitalist programs have shown similar reductions in hospital costs and lengths of stay, and have also reported preservation or improvement of quality of care as measured by reductions in mortality^3,24 and constancy of readmission rates.⁵²

Study of the effect of hospitalists specifically on family practice has been limited. As noted by Smith and colleagues,⁵³ methodologic constraints limit the reliability of many reported results, and the focus of most studies does not extend beyond the hospital setting.

This study additionally questioned whether hospitalist care is truly of better quality and lowers costs. Findings of higher costs associated with subspecialist vs generalist hospitalist care also warrant further investigation in larger studies. Also, because many recent studies have examined only length of stay and in-hospital costs, it is still unknown whether the hospitalist model produces costs savings for the health system overall.¹²

Opportunities to further study hospitalists and their impact

Research has focused largely on quantitative values related to hospitalist care. Yet the emergence of this new provider type introduces issues to be studied that encompass more than effects on length of stay and mortality.

In particular, questions remain about issues surrounding the patient–physician relationship, including patient perceptions of how hospitalists affect communication, continuity of care, and trust.¹⁶ Similarly, studies have investigated primary care physicians’ attitudes regarding desired communication with hospitalists,¹⁴ but none have studied the changing role of primary care hysicians who no longer perform inpatient care, or have questioned family physicians about career satisfaction.

Further, published studies have not been large enough to consider the influence of multiple independent variables such as hospital type, hospital location, or patient factors such as insurance status, disease classification, or psychosocial issues. Table 4 shows some of the many opportunities to formally study the effect of hospitalists on family practice, considering both the areas of existing research focus and new areas that can be explored.

TABLE 4
Opportunities to study impact of hospitalists on family practice

Conclusions

Given that the goal of hospitalists is to affect the hospital sector of the US market—associated with around $430 billion in expenditures for 2000^54,55 —the potential to decrease costs while preserving quality of care is undeniably attractive. However, research evidence does not show uniformly positive results from the introduction of hospitalist programs.

A primary concern is that the purposeful discontinuity of care introduced by the hospitalist can affect quality of care, resulting in medical errors and poor outcomes for patients.³² In addition, more attention must be given to compensation and reimbursement so that family physicians are not discouraged from providing inpatient care for purely financial reasons.

Although a number of publications have discussed the implications of hospitalists, the specific effect of the hospitalist model on family practice remains largely unknown. Knowledge of such effects can be increased by performing well-designed research involving family physicians and by including both qualitative and quantitative approaches. Answers to clinical and managerial questions such as how to best manage communications, how to facilitate the crucial transitions between outpatient and inpatient care, and how to maintain clinical relationships given the introduction of a new provider type can help family physicians preserve and enhance relationships with hospitals, inpatient providers, and patients.

Acknowledgments

The author is very grateful to Kelly Kelleher, MD, MPH, and to the editors of this Journal for thoughtful review and suggestions to improve this report. The author has no conflict of interest to report.

Correspondence
Ann Scheck McAlearney, ScD, Division of Health Services Management and Policy, Ohio State University, School of Public Health, 1583 Perry Street, Atwell Hall 246, Columbus, OH 43210-1234. E-mail:[email protected].

The hospitalist model has spread relatively rapidly throughout hospitals in the United States. Family physicians can proactively work with this new patient care model by developing standardized and efficient ways to communicate and to partner with hospitalists.

Advances in electronic data exchange can help facilitate these communications, and can reduce the risks associated with discontinuity of care inherent in the hospitalist model. Developing communications protocols involving transfer of patient information and maintaining contact with hospitalists while patients are under their care can help family physicians best serve the needs of their patients and ensure continuity of care and compliance with patient wishes.

Hospitalists in the US

Rarely in medicine does the opportunity arise to examine a newly developed area of medical specialization and its effect on other providers. The emergence of the hospitalist, a specialist in inpatient medicine, provides this opportunity. Although dedicated inpatient physicians have been in practice in Canada and overseas for some time,^1-6 attention to, and experimentation with, this role in the US has been relatively new.

Hospitalists were first described in 1996 by Robert Wachter and Lee Goldman,⁷ who coined the term and have widely studied and promoted the model. Presently, approximately 6000 US hospitalists are practicing inpatient medicine in diverse organizations, including adult and children’s hospitals and skilled nursing facilities. The number of hospitalists in practice in the US has been projected to increase to around 19,000 within the next 10 years, making the size of hospitalist physician practice similar to that of the specialty of cardiology,¹ but far smaller than that of family practice.

Yet the introduction and spread of hospitalists throughout the US has not occurred without controversy. Given substantial debate about the changing role of family practitioners with respect to such issues as scope of practice, professional identity, and care and service to patients, the emergence of hospitalists has been perceived by many as a potential threat on all fronts.

Responses to the hospitalist movement

Responses to the hospitalist movement vary. To many, a specialty in hospital medicine appears to threaten the role of generalists in health care practice, and risks such as a reduced practice scope or the loss of hospital privileges are real concerns.^8-11 For others, the introduction of hospitalists has increased flexibility for family practitioners who are interested in working with or becoming hospitalists themselves.

As of 2001, 1 in 5 members of the American Academy of Family Physicians reported using hospitalists. Further, reasons such as economics, lifestyle choices, and concern about maintaining competence in caring for hospitalized patients have contributed to the decision of as many as 1 in 5 family practitioners who have chosen not to be involved in hospital care.¹² Yet, as noted by Edsall,¹³ for family practitioners who choose not to practice inpatient medicine, the philosophical, professional, and financial risks of that decision should not be trivialized.

Despite the debate in the literature and the media, it appears this inpatient care model is here to stay.^1,13,16 Major medical organizations, including the American Academy of Family Physicians and the American College of Physicians–American Society of Internal Medicine, now note that hospitalist programs are acceptable as long as they are well designed and implemented voluntarily, and this consensus has helped spark program growth.¹⁷

However, the increasing presence of hospitalists in hospitals and academic medical centers is forcing many family physicians to choose how involved they want to be in inpatient medicine. The goal of this study was to synthesize available information in the literature regarding the practice of hospitalists and their effect on family physicians, and to provide a discussion about future research opportunities to further evaluate the hospitalist model and its influence on family practice.

Methods

A comprehensive review of the literature was conducted by database searches, by hand, and the Internet. Medline, Lexis-Nexis, and Academic Universe were used as the primary databases for the literature search. Key words such as hospitalists, inpatient physicians, hospital medicine, primary care physicians, and family practice were used to focus a search. Furthermore, references in each article were reviewed to find related literature.

Literature was largely concentrated within the past 5 years and included both peer-reviewed and descriptive articles on hospitalists and their effect. Internet searches used Google as the primary search engine; results supplemented findings in other published material.

This literature review continued until saturation was achieved with respect to considering the possible issues and implications of the expansion of hospitalists, with special attention paid to the risks and opportunities to family physicians.

Findings

This integrative literature review revealed 3 major themes of interest to family physicians regarding the emergence and expansion of hospitalists in the US: descriptions of the hospitalist role and responsibilities; hypothesized benefits and risks of the hospitalist model; and reported research results evaluating the effect of the hospitalist model. Synthesis of this literature also uncovered 2 major opportunities related to hospitalist practice: opportunities to conduct future research to study the impact of hospitalists on family physicians; and opportunities to leverage relationships with these new practitioners.

Hospitalist roles and responsibilities

A hospitalist physician is a new type of medical specialist who combines the roles of acute care subspecialist and medical generalist in the hospital care setting.¹⁸ Hospitalists do not replace primary care physicians, surgeons, or specialists, but, instead, are concerned with managing hospital inpatients, from admission until discharge. They act somewhat as a case manager for a patient’s hospital stay, working and communicating closely with other physicians involved in the patient’s care.

Patients are assigned to hospitalists upon admission, either when an outpatient provider such as a family practitioner transfers inpatient care responsibilities to the hospitalist, or when patients arrive at the hospital unassigned to any other provider. The clinical and organizational responsibilities of hospitalists are in Table 1.

TABLE 1
Typical responsibilities of hospitalist physicians

Hypothesized benefits and risks of the hospitalist model

Persuasive arguments have been raised about the advantages and disadvantages of the hospitalist model.^18,19,21,22 A variety of these potential advantages and disadvantages are summarized in Table 2, representing perspectives of 3 different stakeholder groups: hospitals, patients and families, and hospitalist physicians. Each of the listed advantages or disadvantages was discussed in 3 or more independent articles that were reviewed.

For family physicians specifically, the introduction of a hospitalist program at a local hospital has numerous associated potential benefits and risks. Table 3 presents a summary of the issues that were raised in 3 or more articles or studies.

Benefit: focus on ambulatory care. One widely discussed advantage in using hospitalists is the option for family practitioners, who so desire, to limit practice to outpatient medicine because of their interest in ambulatory care or because they feel overtaxed by the demands of the health care system.^12,21 Willing family physicians can relinquish care of their hospitalized patients to a hospitalist so they do not have to travel to the hospital for daily rounds or more frequent patient contact; upon hospital discharge, family practitioners subsequently resume care for their patients.

Given the pressures of managed care to increase office productivity,⁴⁸ this delegation of responsibilities can create an important practice advantage.¹⁵ Even for those family physicians who choose to visit their hospitalized patients, shifting overall responsibility for inpatient care to hospitalists can make hospital visits more efficient and thereby free office time for outpatient practices.⁴⁹

Risk: lack of patient familiarity. Research has shown that a lack of familiarity with patients can increase the risk of errors and poor outcomes in medicine, and the use of a hospitalist as a new provider indeed introduces this risk.^50,51

Without dedicated effort on the part of the family physician, the treating hospitalist may have limited appreciation of a patient’s situation. Hospitalists focused only on inpatient care may not know where patients come from or where they return to, and are less likely to be knowledgeable about needs for psychosocial support or for such patient preferences as end-of-life care.^14,21

Risk: reduced political leverage. In addition, a political issue for family physicians may arise if hospitalists become providers of choice for inpatient internal medicine, thereby defining a smaller role for community-based family practitioners.²¹

Risk: communication problems. Another major risk of hospitalist programs is poor communication, an issue raised in nearly every article discussing the hospitalist model. The involvement of a new physician provider and the process of patient care transfers between outpatient family physicians and inpatient hospitalists can lead to missed information, gaps in communication, and misunderstandings.^19,22,35,37

Recent studies of discontinuity of care when patients are hospitalized reported that inpatients specifically wanted both contact with their primary care physicians and good communication between their established primary care physician and hospital-based physicians.⁴⁹ Guidelines created by the American Academy of Family Physicians (www.aafp.org/x6873.xml) support communication and interaction between community-based physicians and hospitalists for excellent patient care,¹² but the burden may fall on family physicians to ensure communication.

TABLE 2
Stakeholder perspectives of hospitalist model: Advantages and disadvantages

TABLE 3
Potential benefits and risks of the hospitalist model for family physicians

Assessing the effect of the hospitalist model

Research evaluating the impact of hospitalists has largely focused on hospital-based outcomes. Recently, Wachter and Goldman’s review of 19 published studies showed that hospital costs decreased 13.4% on average and hospital lengths of stay decreased 16.6% on average after a hospitalist program was initiated.²³ These efficiency improvements were apparently gained while patient satisfaction was preserved.

However, results indicating improved outcomes, such as mortality and readmissions, were reportedly inconsistent among the studies evaluated.²³ Additional studies^3,24,52 of hospitalist programs have shown similar reductions in hospital costs and lengths of stay, and have also reported preservation or improvement of quality of care as measured by reductions in mortality^3,24 and constancy of readmission rates.⁵²

Study of the effect of hospitalists specifically on family practice has been limited. As noted by Smith and colleagues,⁵³ methodologic constraints limit the reliability of many reported results, and the focus of most studies does not extend beyond the hospital setting.

This study additionally questioned whether hospitalist care is truly of better quality and lowers costs. Findings of higher costs associated with subspecialist vs generalist hospitalist care also warrant further investigation in larger studies. Also, because many recent studies have examined only length of stay and in-hospital costs, it is still unknown whether the hospitalist model produces costs savings for the health system overall.¹²

Opportunities to further study hospitalists and their impact

Research has focused largely on quantitative values related to hospitalist care. Yet the emergence of this new provider type introduces issues to be studied that encompass more than effects on length of stay and mortality.

In particular, questions remain about issues surrounding the patient–physician relationship, including patient perceptions of how hospitalists affect communication, continuity of care, and trust.¹⁶ Similarly, studies have investigated primary care physicians’ attitudes regarding desired communication with hospitalists,¹⁴ but none have studied the changing role of primary care hysicians who no longer perform inpatient care, or have questioned family physicians about career satisfaction.

Further, published studies have not been large enough to consider the influence of multiple independent variables such as hospital type, hospital location, or patient factors such as insurance status, disease classification, or psychosocial issues. Table 4 shows some of the many opportunities to formally study the effect of hospitalists on family practice, considering both the areas of existing research focus and new areas that can be explored.

TABLE 4
Opportunities to study impact of hospitalists on family practice

Conclusions

Given that the goal of hospitalists is to affect the hospital sector of the US market—associated with around $430 billion in expenditures for 2000^54,55 —the potential to decrease costs while preserving quality of care is undeniably attractive. However, research evidence does not show uniformly positive results from the introduction of hospitalist programs.

A primary concern is that the purposeful discontinuity of care introduced by the hospitalist can affect quality of care, resulting in medical errors and poor outcomes for patients.³² In addition, more attention must be given to compensation and reimbursement so that family physicians are not discouraged from providing inpatient care for purely financial reasons.

Although a number of publications have discussed the implications of hospitalists, the specific effect of the hospitalist model on family practice remains largely unknown. Knowledge of such effects can be increased by performing well-designed research involving family physicians and by including both qualitative and quantitative approaches. Answers to clinical and managerial questions such as how to best manage communications, how to facilitate the crucial transitions between outpatient and inpatient care, and how to maintain clinical relationships given the introduction of a new provider type can help family physicians preserve and enhance relationships with hospitals, inpatient providers, and patients.

Acknowledgments

The author is very grateful to Kelly Kelleher, MD, MPH, and to the editors of this Journal for thoughtful review and suggestions to improve this report. The author has no conflict of interest to report.

Correspondence
Ann Scheck McAlearney, ScD, Division of Health Services Management and Policy, Ohio State University, School of Public Health, 1583 Perry Street, Atwell Hall 246, Columbus, OH 43210-1234. E-mail:[email protected].

The hospitalist model has spread relatively rapidly throughout hospitals in the United States. Family physicians can proactively work with this new patient care model by developing standardized and efficient ways to communicate and to partner with hospitalists.

Advances in electronic data exchange can help facilitate these communications, and can reduce the risks associated with discontinuity of care inherent in the hospitalist model. Developing communications protocols involving transfer of patient information and maintaining contact with hospitalists while patients are under their care can help family physicians best serve the needs of their patients and ensure continuity of care and compliance with patient wishes.

Hospitalists in the US

Rarely in medicine does the opportunity arise to examine a newly developed area of medical specialization and its effect on other providers. The emergence of the hospitalist, a specialist in inpatient medicine, provides this opportunity. Although dedicated inpatient physicians have been in practice in Canada and overseas for some time,^1-6 attention to, and experimentation with, this role in the US has been relatively new.

Hospitalists were first described in 1996 by Robert Wachter and Lee Goldman,⁷ who coined the term and have widely studied and promoted the model. Presently, approximately 6000 US hospitalists are practicing inpatient medicine in diverse organizations, including adult and children’s hospitals and skilled nursing facilities. The number of hospitalists in practice in the US has been projected to increase to around 19,000 within the next 10 years, making the size of hospitalist physician practice similar to that of the specialty of cardiology,¹ but far smaller than that of family practice.

Yet the introduction and spread of hospitalists throughout the US has not occurred without controversy. Given substantial debate about the changing role of family practitioners with respect to such issues as scope of practice, professional identity, and care and service to patients, the emergence of hospitalists has been perceived by many as a potential threat on all fronts.

Responses to the hospitalist movement

Responses to the hospitalist movement vary. To many, a specialty in hospital medicine appears to threaten the role of generalists in health care practice, and risks such as a reduced practice scope or the loss of hospital privileges are real concerns.^8-11 For others, the introduction of hospitalists has increased flexibility for family practitioners who are interested in working with or becoming hospitalists themselves.

As of 2001, 1 in 5 members of the American Academy of Family Physicians reported using hospitalists. Further, reasons such as economics, lifestyle choices, and concern about maintaining competence in caring for hospitalized patients have contributed to the decision of as many as 1 in 5 family practitioners who have chosen not to be involved in hospital care.¹² Yet, as noted by Edsall,¹³ for family practitioners who choose not to practice inpatient medicine, the philosophical, professional, and financial risks of that decision should not be trivialized.

Despite the debate in the literature and the media, it appears this inpatient care model is here to stay.^1,13,16 Major medical organizations, including the American Academy of Family Physicians and the American College of Physicians–American Society of Internal Medicine, now note that hospitalist programs are acceptable as long as they are well designed and implemented voluntarily, and this consensus has helped spark program growth.¹⁷

However, the increasing presence of hospitalists in hospitals and academic medical centers is forcing many family physicians to choose how involved they want to be in inpatient medicine. The goal of this study was to synthesize available information in the literature regarding the practice of hospitalists and their effect on family physicians, and to provide a discussion about future research opportunities to further evaluate the hospitalist model and its influence on family practice.

Methods

A comprehensive review of the literature was conducted by database searches, by hand, and the Internet. Medline, Lexis-Nexis, and Academic Universe were used as the primary databases for the literature search. Key words such as hospitalists, inpatient physicians, hospital medicine, primary care physicians, and family practice were used to focus a search. Furthermore, references in each article were reviewed to find related literature.

Literature was largely concentrated within the past 5 years and included both peer-reviewed and descriptive articles on hospitalists and their effect. Internet searches used Google as the primary search engine; results supplemented findings in other published material.

This literature review continued until saturation was achieved with respect to considering the possible issues and implications of the expansion of hospitalists, with special attention paid to the risks and opportunities to family physicians.

Findings

This integrative literature review revealed 3 major themes of interest to family physicians regarding the emergence and expansion of hospitalists in the US: descriptions of the hospitalist role and responsibilities; hypothesized benefits and risks of the hospitalist model; and reported research results evaluating the effect of the hospitalist model. Synthesis of this literature also uncovered 2 major opportunities related to hospitalist practice: opportunities to conduct future research to study the impact of hospitalists on family physicians; and opportunities to leverage relationships with these new practitioners.

Hospitalist roles and responsibilities

A hospitalist physician is a new type of medical specialist who combines the roles of acute care subspecialist and medical generalist in the hospital care setting.¹⁸ Hospitalists do not replace primary care physicians, surgeons, or specialists, but, instead, are concerned with managing hospital inpatients, from admission until discharge. They act somewhat as a case manager for a patient’s hospital stay, working and communicating closely with other physicians involved in the patient’s care.

Patients are assigned to hospitalists upon admission, either when an outpatient provider such as a family practitioner transfers inpatient care responsibilities to the hospitalist, or when patients arrive at the hospital unassigned to any other provider. The clinical and organizational responsibilities of hospitalists are in Table 1.

TABLE 1
Typical responsibilities of hospitalist physicians

Hypothesized benefits and risks of the hospitalist model

Persuasive arguments have been raised about the advantages and disadvantages of the hospitalist model.^18,19,21,22 A variety of these potential advantages and disadvantages are summarized in Table 2, representing perspectives of 3 different stakeholder groups: hospitals, patients and families, and hospitalist physicians. Each of the listed advantages or disadvantages was discussed in 3 or more independent articles that were reviewed.

For family physicians specifically, the introduction of a hospitalist program at a local hospital has numerous associated potential benefits and risks. Table 3 presents a summary of the issues that were raised in 3 or more articles or studies.

Benefit: focus on ambulatory care. One widely discussed advantage in using hospitalists is the option for family practitioners, who so desire, to limit practice to outpatient medicine because of their interest in ambulatory care or because they feel overtaxed by the demands of the health care system.^12,21 Willing family physicians can relinquish care of their hospitalized patients to a hospitalist so they do not have to travel to the hospital for daily rounds or more frequent patient contact; upon hospital discharge, family practitioners subsequently resume care for their patients.

Given the pressures of managed care to increase office productivity,⁴⁸ this delegation of responsibilities can create an important practice advantage.¹⁵ Even for those family physicians who choose to visit their hospitalized patients, shifting overall responsibility for inpatient care to hospitalists can make hospital visits more efficient and thereby free office time for outpatient practices.⁴⁹

Risk: lack of patient familiarity. Research has shown that a lack of familiarity with patients can increase the risk of errors and poor outcomes in medicine, and the use of a hospitalist as a new provider indeed introduces this risk.^50,51

Without dedicated effort on the part of the family physician, the treating hospitalist may have limited appreciation of a patient’s situation. Hospitalists focused only on inpatient care may not know where patients come from or where they return to, and are less likely to be knowledgeable about needs for psychosocial support or for such patient preferences as end-of-life care.^14,21

Risk: reduced political leverage. In addition, a political issue for family physicians may arise if hospitalists become providers of choice for inpatient internal medicine, thereby defining a smaller role for community-based family practitioners.²¹

Risk: communication problems. Another major risk of hospitalist programs is poor communication, an issue raised in nearly every article discussing the hospitalist model. The involvement of a new physician provider and the process of patient care transfers between outpatient family physicians and inpatient hospitalists can lead to missed information, gaps in communication, and misunderstandings.^19,22,35,37

Recent studies of discontinuity of care when patients are hospitalized reported that inpatients specifically wanted both contact with their primary care physicians and good communication between their established primary care physician and hospital-based physicians.⁴⁹ Guidelines created by the American Academy of Family Physicians (www.aafp.org/x6873.xml) support communication and interaction between community-based physicians and hospitalists for excellent patient care,¹² but the burden may fall on family physicians to ensure communication.

TABLE 2
Stakeholder perspectives of hospitalist model: Advantages and disadvantages

TABLE 3
Potential benefits and risks of the hospitalist model for family physicians

Assessing the effect of the hospitalist model

Research evaluating the impact of hospitalists has largely focused on hospital-based outcomes. Recently, Wachter and Goldman’s review of 19 published studies showed that hospital costs decreased 13.4% on average and hospital lengths of stay decreased 16.6% on average after a hospitalist program was initiated.²³ These efficiency improvements were apparently gained while patient satisfaction was preserved.

However, results indicating improved outcomes, such as mortality and readmissions, were reportedly inconsistent among the studies evaluated.²³ Additional studies^3,24,52 of hospitalist programs have shown similar reductions in hospital costs and lengths of stay, and have also reported preservation or improvement of quality of care as measured by reductions in mortality^3,24 and constancy of readmission rates.⁵²

Study of the effect of hospitalists specifically on family practice has been limited. As noted by Smith and colleagues,⁵³ methodologic constraints limit the reliability of many reported results, and the focus of most studies does not extend beyond the hospital setting.

This study additionally questioned whether hospitalist care is truly of better quality and lowers costs. Findings of higher costs associated with subspecialist vs generalist hospitalist care also warrant further investigation in larger studies. Also, because many recent studies have examined only length of stay and in-hospital costs, it is still unknown whether the hospitalist model produces costs savings for the health system overall.¹²

Opportunities to further study hospitalists and their impact

Research has focused largely on quantitative values related to hospitalist care. Yet the emergence of this new provider type introduces issues to be studied that encompass more than effects on length of stay and mortality.

In particular, questions remain about issues surrounding the patient–physician relationship, including patient perceptions of how hospitalists affect communication, continuity of care, and trust.¹⁶ Similarly, studies have investigated primary care physicians’ attitudes regarding desired communication with hospitalists,¹⁴ but none have studied the changing role of primary care hysicians who no longer perform inpatient care, or have questioned family physicians about career satisfaction.

Further, published studies have not been large enough to consider the influence of multiple independent variables such as hospital type, hospital location, or patient factors such as insurance status, disease classification, or psychosocial issues. Table 4 shows some of the many opportunities to formally study the effect of hospitalists on family practice, considering both the areas of existing research focus and new areas that can be explored.

TABLE 4
Opportunities to study impact of hospitalists on family practice

Conclusions

Given that the goal of hospitalists is to affect the hospital sector of the US market—associated with around $430 billion in expenditures for 2000^54,55 —the potential to decrease costs while preserving quality of care is undeniably attractive. However, research evidence does not show uniformly positive results from the introduction of hospitalist programs.

A primary concern is that the purposeful discontinuity of care introduced by the hospitalist can affect quality of care, resulting in medical errors and poor outcomes for patients.³² In addition, more attention must be given to compensation and reimbursement so that family physicians are not discouraged from providing inpatient care for purely financial reasons.

Although a number of publications have discussed the implications of hospitalists, the specific effect of the hospitalist model on family practice remains largely unknown. Knowledge of such effects can be increased by performing well-designed research involving family physicians and by including both qualitative and quantitative approaches. Answers to clinical and managerial questions such as how to best manage communications, how to facilitate the crucial transitions between outpatient and inpatient care, and how to maintain clinical relationships given the introduction of a new provider type can help family physicians preserve and enhance relationships with hospitals, inpatient providers, and patients.

Acknowledgments

The author is very grateful to Kelly Kelleher, MD, MPH, and to the editors of this Journal for thoughtful review and suggestions to improve this report. The author has no conflict of interest to report.

Correspondence
Ann Scheck McAlearney, ScD, Division of Health Services Management and Policy, Ohio State University, School of Public Health, 1583 Perry Street, Atwell Hall 246, Columbus, OH 43210-1234. E-mail:[email protected].