Many studies have reported associations between higher nurse‐to‐patient ratios and decreased mortality and complications. These studies coupled with increasing concern about patient safety, nursing shortages, and nurse burnout have spurred many state legislatures to discuss mandating minimum nurse staffing ratios.15 The California legislature passed law AB394 in 1999, mandating minimum nurse staffing ratios in order to improve patient safety and the nurse work environment. The original implementation date, January 1, 2001, was delayed to allow the California Department of Health Services more time to develop minimum nurse ratios for each unit type.6, 7 California implemented a ratio of at least 1 licensed nurse (RN+LVN) for every 6 patients on general adult medical‐surgical floors on January 1, 2004. This was subsequently increased, on January 1, 2005, to at least 1 licensed nurse for every 5 patients, a ratio that was upheld by the California Supreme Court on March 14, 2005.8

Additional laws regarding nurse staffing are being considered in at least 25 states.9 States have taken 3 main approaches to legislation: mandating nurse staffing ratios for each hospital unit type, requiring hospitals to establish and report nurse staffing plans that typically include ratios, or a combination of mandated ratios and staffing plans.10 This type of legislation would have a major impact on hospitalists, nurses, other health care personnel, hospital administrators, and patients. However, little is known about trends in nurse staffing, how staffing levels vary among hospitals overall, in different markets, and by ownership type and location, and consequently how implementing nurse staffing ratios will affect different types of hospitals, including those that make up the safety net.11

California nurse staffing data are better than many other sources because the state provides nurse staffing hours by unit types in hospitals as opposed to aggregate numbers of nurse hours across an entire hospital or medical center.12 California is also at the forefront of mandated minimum nurse staffing legislation, as it is the only state to have enacted nurse staffing ratio legislation. Examining nurse staffing trends and hospital types currently under mandated or proposed nurse staffing ratios is integral to informing the debate on nurse staffing legislation and its effect on hospitalists. We hypothesized that nurse staffing would increase in California after the legislation was passed in 1999 but that safety‐net hospitals such as those that are urban, government owned, and serving a high percentage of Medicaid and uninsured patients would be more likely to be below minimum ratios.13

MATERIALS AND METHODS

We used hospital financial panel data for 1993 through 2004, the most recent year with complete data, from California's Office of Statewide Health Planning and Development (OSHPD). We included only short‐term acute‐care general hospitals and excluded other hospital types such as long‐term care, children's, and psychiatric hospitals. We investigated staffing of adult general medical‐surgical units and not of other types of units such as intensive care units. The numerator of the staffing variables for each hospital was the combined medical‐surgical productive hours for registered nurses (RNs) and licensed vocational nurses (LVNs), as California allows up to 50% of staffing hours to be LVN hours. Staffing hours of the adult general medical‐surgical units of each hospital are reported on an annual basis. The denominator was total patient days on the acute adult medical‐surgical units of each hospital in a given year. We calculated the number of patients per one nurse by dividing 24 by the nurse hours per patient day (eg, 4.0 nurse hours per patient day is equivalent to a nurse‐to‐patient ratio of 1:6). We did not adjust staffing ratios by the hospital case mix or other factors because the ratio legislation did not take these factors into account.

We further evaluated staffing ratios in 2003 and 2004 based on 5 hospital characteristics: hospital ownership, market competitiveness, teaching status, urban versus rural location, and safety‐net hospitals, using 2 common definitions for the latter. The Institute of Medicine report defines safety‐net providers as those with a substantial share of their patient mix from uninsured and Medicaid populations.13 Safety‐net hospitals have been more specifically defined as short‐term general hospitals whose percentage of Medicaid and uninsured patients is greater than 1 standard deviation above the mean.14 Using this definition, hospitals in California where more than 36% of patients had Medicaid or no insurance in 2004 would be considered safety‐net hospitals. A more comprehensive definition of the hospital safety net that has been used includes urban nonprofit and government hospitals and hospitals with a high percentage of Medicaid/uninsured patients.10, 11, 15 We analyzed nurse staffing ratios using both these definitions. Hospital ownership was designated as for profit, nonprofit, or government owned. Hospital competitiveness was measured using the Hirschman‐Herfindahl Index (HHI), or the sum of squared market shares, a standard approach to defining hospital market competition. Market boundaries were defined as those zip codes from which each hospital draws most of its patients.16 We then dichotomized hospitals into a high‐ or low‐competition category based on the approximate median HHI cut point of 0.34. Teaching status was based on intern/resident‐to‐bed ratio (ie, 0 = nonteaching, 0.010.25 = minor teaching, and >0.25 = major teaching). Location was defined by county location as either urban or nonurban medical service area.

We then analyzed the percentage of hospitals in 2003 and 2004 below the mandated minimum ratios of (1) at least 1 licensed nurse (RN+LVN) per 6 patients effective in 2004, (2) the ratio of 1 (RN+LVN) nurse per 5 patients to be implemented in 2005, (3) the ratio of at least 1 registered nurse (RN only) per 5 patients, and (4) at least 1 nurse (RN+LVN) per 4 patients, as these ratios are under consideration in other states.9, 17 Finally, we examined the trend in nurse staffing ratios from 2003, the pre‐implementation year, to 2004, the post‐implementation year. Data analysis was performed using STATA SE 9.1 (College Station, TX).

RESULTS

Nurse Staffing Trends

The trend in nurse staffing ratios based on licensed nurses (RN + LVN) from 1993 to 2004 is shown in Figure 1, with lines representing the 10th, 25th, 50th (median), and 75th percentiles of hospital nurse staffing ratios. The nurse staffing ratios were essentially flat from 1993 to 1999 without any significant trend. After nurse staffing legislation was passed in 1999, median nurse‐to‐patient ratio rose, with the largest increase from 2003 to the implementation year for staffing ratios, 2004. From 2003 to 2004, the median hospital staffing ratio increased from fewer than 1 nurse per 4 patients to a ratio of more than 1 nurse per 4 patients. The first year that fewer than 25% of hospitals were below the minimum of at least 1 nurse per 5 patients was 2003.

Figure 1

DISCUSSION

These data demonstrate that nurse staffing ratios in California were relatively stable from 1993 to 1999. In 1999, law AB 394 with its focus on nurse staffing levels passed, and subsequently, from 1999 to 2004, nurse staffing levels increased significantly, with the largest increase in 2004, the year of implementation. Although multiple factors could account for this trend, a likely cause for the statewide increase in nurse staffing was the anticipation and then implementation of legislation to achieve minimum ratios.

This study had several limitations. The OSHPD data capture nurse staffing on an annual basis, but the California legislation mandated minimum nurse staffing ratios be kept at all times; these data do not capture how often a given hospital was below the minimum ratio on a monthly or shift‐by‐shift basis. These data may overreport nurse staffing hours if they include hours not spent in direct patient care, or they could misrepresent nurse staffing ratios because of poor reporting.

Certain hospitals are more likely to be below mandated ratios. These hospitals are often government owned, in urban areas, and serve a high percentage of Medicaid/uninsured patients. Hospitals with these characteristics are typically considered part of the safety net. These are the hospitals that serve our nation's most vulnerable populations and are likely to struggle disproportionately to meet minimum mandated ratios. As evidence of these precarious finances, 67% of hospitals defined as safety‐net hospitals based on a high percentage of Medicaid/uninsured patients in 2004 had a negative operating margin versus 40% of hospitals not considered to be safety‐net hospitals (P < .001).18 The question remains how hospitals will meet minimum nurse staffing ratios given these tenuous operating margins, as some of the approaches might result in restricted access, reduced services, reduced expenditures on new equipment or technology, or other decisions that might adversely affect quality. These potential tradeoffs will directly affect hospitalists, nurses, and other health care personnel working in hospitals. Because legislation generally does not provide funds or mechanisms to help hospitals meet proposed staffing ratios and there is a national nursing shortage, hospitals may struggle to meet minimum ratios. Cross‐sectional studies have demonstrated a potential link between increased nurse staffing and better patient outcomes,15 but if a financially constrained hospital makes tradeoffs by restricting access to care and services or by diverting funds from other beneficial uses, on balance, mandated nurse staffing ratios may not be beneficial to patients. The potential for unintended but serious negative consequences exists if hospitals in the safety net are mandated to meet minimum nurse staffing ratios without adequate resources.

At all types of hospitals, hospitalists are increasingly becoming responsible for quality improvement programs and outcomes measurement. However, the outcomes of these programs may be strongly influenced by nurse staffing. For example, cross‐sectional studies have demonstrated that increased nurse staffing was associated with decreased mortality, length of stay, failure to rescue from complications, catheter‐associated bloodstream infections, catheter‐associated urinary tract infections, gastrointestinal bleeding, ventilator‐acquired pneumonia, and shock or cardiac arrest.1, 4, 19 These types of quality and patient safety outcomes are likely to be the focus of many hospitalist‐led quality improvement programs and may even be linked to hospitalist compensation. Therefore, hospitals and their hospitalists must take into account the effect that inadequate nurse staffing could have on their patient outcomes while balancing the investment in nurse staffing with other quality improvement investments. An interaction between nurse staffing level and hospitalist staffing may exist, but we are unaware of any published studies investigating this interaction. The nurse burnout documented to be associated with inadequate nurse staffing certainly could affect hospitalists if it increases nurse turnover or inhibits effective communication.1 Additional research is needed to better delineate the effects of nurse staffing, particularly in regard to hospitalists and hospital‐based quality and safety initiatives.

Finally, these data highlight the need for policymakers and hospital administrators to consider whether the aim is to establish a minimal floor or an optimal ratio. California first opted for what many would consider a minimal floor of at least 1 nurse per 6 patients, as only 5% of hospitals were below this ratio in 2003. California then increased the ratio to a 1:5 nurse‐to‐patient ratio, which affected a larger percentage of hospitals, presumably because of a belief that this higher ratio would lead to better outcomes. In addition, some states such as Massachusetts have considered a minimum ratio of 1:4.17 A ratio of 1:4 would require a significant proportion of hospitals to hire more nurses if staffing levels are similar to California. Only a few studies have estimated the cost effectiveness of staffing changes. Based on cross‐sectional data, Needleman et al. estimated that it would cost $8.5 billion nationally to raise all hospitals to the 75th percentile of RN and overall nurse staffing but that this would prevent 70,000 adverse patient outcomes (eg, hospital‐acquired pneumonia). Rothberg et al. estimated that the incremental cost per life saved as a hospital moved from 1 nurse per 8 patients to 1 nurse per 5 patients was $48,100. However, these estimates based on cross‐sectional data fail to inform the debate on optimal nurse staffing ratios. The effect on patient outcomes when hospitals move from 1:6 to 1:5 or 1:4 nurse staffing levels needs to be determined in a longitudinal study. Thus, legislators and hospitals have little to guide them in establishing optimal nurse staffing ratios, and consideration of specific mandated minimum ratios would benefit greatly from comparative information on the cost and quality tradeoffs.

Hospitals, policy makers, health care providers, and researchers are struggling to improve the health care delivered in our hospitals; fortunately, there has been an increased focus on the importance of nurses who deliver medical care on the front lines and are responsible for many aspects of quality. Mandating minimum nurse staffing ratios may seem like an easy fix of the problem; however, we must consider how these ratios can be met, the potential difficulty for hospitals to meet these ratios in the fraying safety net20, and possible unintended negative consequences. Without a mechanism for hospitals to meet ratios, simply mandating a minimum ratio will not necessarily improve care. Hospitalists should be leaders in better understanding the effects of nurse staffing on patient outcomes and quality initiatives in hospitals.

Many studies have reported associations between higher nurse‐to‐patient ratios and decreased mortality and complications. These studies coupled with increasing concern about patient safety, nursing shortages, and nurse burnout have spurred many state legislatures to discuss mandating minimum nurse staffing ratios.15 The California legislature passed law AB394 in 1999, mandating minimum nurse staffing ratios in order to improve patient safety and the nurse work environment. The original implementation date, January 1, 2001, was delayed to allow the California Department of Health Services more time to develop minimum nurse ratios for each unit type.6, 7 California implemented a ratio of at least 1 licensed nurse (RN+LVN) for every 6 patients on general adult medical‐surgical floors on January 1, 2004. This was subsequently increased, on January 1, 2005, to at least 1 licensed nurse for every 5 patients, a ratio that was upheld by the California Supreme Court on March 14, 2005.8

Additional laws regarding nurse staffing are being considered in at least 25 states.9 States have taken 3 main approaches to legislation: mandating nurse staffing ratios for each hospital unit type, requiring hospitals to establish and report nurse staffing plans that typically include ratios, or a combination of mandated ratios and staffing plans.10 This type of legislation would have a major impact on hospitalists, nurses, other health care personnel, hospital administrators, and patients. However, little is known about trends in nurse staffing, how staffing levels vary among hospitals overall, in different markets, and by ownership type and location, and consequently how implementing nurse staffing ratios will affect different types of hospitals, including those that make up the safety net.11

California nurse staffing data are better than many other sources because the state provides nurse staffing hours by unit types in hospitals as opposed to aggregate numbers of nurse hours across an entire hospital or medical center.12 California is also at the forefront of mandated minimum nurse staffing legislation, as it is the only state to have enacted nurse staffing ratio legislation. Examining nurse staffing trends and hospital types currently under mandated or proposed nurse staffing ratios is integral to informing the debate on nurse staffing legislation and its effect on hospitalists. We hypothesized that nurse staffing would increase in California after the legislation was passed in 1999 but that safety‐net hospitals such as those that are urban, government owned, and serving a high percentage of Medicaid and uninsured patients would be more likely to be below minimum ratios.13

MATERIALS AND METHODS

We used hospital financial panel data for 1993 through 2004, the most recent year with complete data, from California's Office of Statewide Health Planning and Development (OSHPD). We included only short‐term acute‐care general hospitals and excluded other hospital types such as long‐term care, children's, and psychiatric hospitals. We investigated staffing of adult general medical‐surgical units and not of other types of units such as intensive care units. The numerator of the staffing variables for each hospital was the combined medical‐surgical productive hours for registered nurses (RNs) and licensed vocational nurses (LVNs), as California allows up to 50% of staffing hours to be LVN hours. Staffing hours of the adult general medical‐surgical units of each hospital are reported on an annual basis. The denominator was total patient days on the acute adult medical‐surgical units of each hospital in a given year. We calculated the number of patients per one nurse by dividing 24 by the nurse hours per patient day (eg, 4.0 nurse hours per patient day is equivalent to a nurse‐to‐patient ratio of 1:6). We did not adjust staffing ratios by the hospital case mix or other factors because the ratio legislation did not take these factors into account.

We further evaluated staffing ratios in 2003 and 2004 based on 5 hospital characteristics: hospital ownership, market competitiveness, teaching status, urban versus rural location, and safety‐net hospitals, using 2 common definitions for the latter. The Institute of Medicine report defines safety‐net providers as those with a substantial share of their patient mix from uninsured and Medicaid populations.13 Safety‐net hospitals have been more specifically defined as short‐term general hospitals whose percentage of Medicaid and uninsured patients is greater than 1 standard deviation above the mean.14 Using this definition, hospitals in California where more than 36% of patients had Medicaid or no insurance in 2004 would be considered safety‐net hospitals. A more comprehensive definition of the hospital safety net that has been used includes urban nonprofit and government hospitals and hospitals with a high percentage of Medicaid/uninsured patients.10, 11, 15 We analyzed nurse staffing ratios using both these definitions. Hospital ownership was designated as for profit, nonprofit, or government owned. Hospital competitiveness was measured using the Hirschman‐Herfindahl Index (HHI), or the sum of squared market shares, a standard approach to defining hospital market competition. Market boundaries were defined as those zip codes from which each hospital draws most of its patients.16 We then dichotomized hospitals into a high‐ or low‐competition category based on the approximate median HHI cut point of 0.34. Teaching status was based on intern/resident‐to‐bed ratio (ie, 0 = nonteaching, 0.010.25 = minor teaching, and >0.25 = major teaching). Location was defined by county location as either urban or nonurban medical service area.

We then analyzed the percentage of hospitals in 2003 and 2004 below the mandated minimum ratios of (1) at least 1 licensed nurse (RN+LVN) per 6 patients effective in 2004, (2) the ratio of 1 (RN+LVN) nurse per 5 patients to be implemented in 2005, (3) the ratio of at least 1 registered nurse (RN only) per 5 patients, and (4) at least 1 nurse (RN+LVN) per 4 patients, as these ratios are under consideration in other states.9, 17 Finally, we examined the trend in nurse staffing ratios from 2003, the pre‐implementation year, to 2004, the post‐implementation year. Data analysis was performed using STATA SE 9.1 (College Station, TX).

RESULTS

Nurse Staffing Trends

The trend in nurse staffing ratios based on licensed nurses (RN + LVN) from 1993 to 2004 is shown in Figure 1, with lines representing the 10th, 25th, 50th (median), and 75th percentiles of hospital nurse staffing ratios. The nurse staffing ratios were essentially flat from 1993 to 1999 without any significant trend. After nurse staffing legislation was passed in 1999, median nurse‐to‐patient ratio rose, with the largest increase from 2003 to the implementation year for staffing ratios, 2004. From 2003 to 2004, the median hospital staffing ratio increased from fewer than 1 nurse per 4 patients to a ratio of more than 1 nurse per 4 patients. The first year that fewer than 25% of hospitals were below the minimum of at least 1 nurse per 5 patients was 2003.

Figure 1

DISCUSSION

These data demonstrate that nurse staffing ratios in California were relatively stable from 1993 to 1999. In 1999, law AB 394 with its focus on nurse staffing levels passed, and subsequently, from 1999 to 2004, nurse staffing levels increased significantly, with the largest increase in 2004, the year of implementation. Although multiple factors could account for this trend, a likely cause for the statewide increase in nurse staffing was the anticipation and then implementation of legislation to achieve minimum ratios.

This study had several limitations. The OSHPD data capture nurse staffing on an annual basis, but the California legislation mandated minimum nurse staffing ratios be kept at all times; these data do not capture how often a given hospital was below the minimum ratio on a monthly or shift‐by‐shift basis. These data may overreport nurse staffing hours if they include hours not spent in direct patient care, or they could misrepresent nurse staffing ratios because of poor reporting.

Certain hospitals are more likely to be below mandated ratios. These hospitals are often government owned, in urban areas, and serve a high percentage of Medicaid/uninsured patients. Hospitals with these characteristics are typically considered part of the safety net. These are the hospitals that serve our nation's most vulnerable populations and are likely to struggle disproportionately to meet minimum mandated ratios. As evidence of these precarious finances, 67% of hospitals defined as safety‐net hospitals based on a high percentage of Medicaid/uninsured patients in 2004 had a negative operating margin versus 40% of hospitals not considered to be safety‐net hospitals (P < .001).18 The question remains how hospitals will meet minimum nurse staffing ratios given these tenuous operating margins, as some of the approaches might result in restricted access, reduced services, reduced expenditures on new equipment or technology, or other decisions that might adversely affect quality. These potential tradeoffs will directly affect hospitalists, nurses, and other health care personnel working in hospitals. Because legislation generally does not provide funds or mechanisms to help hospitals meet proposed staffing ratios and there is a national nursing shortage, hospitals may struggle to meet minimum ratios. Cross‐sectional studies have demonstrated a potential link between increased nurse staffing and better patient outcomes,15 but if a financially constrained hospital makes tradeoffs by restricting access to care and services or by diverting funds from other beneficial uses, on balance, mandated nurse staffing ratios may not be beneficial to patients. The potential for unintended but serious negative consequences exists if hospitals in the safety net are mandated to meet minimum nurse staffing ratios without adequate resources.

At all types of hospitals, hospitalists are increasingly becoming responsible for quality improvement programs and outcomes measurement. However, the outcomes of these programs may be strongly influenced by nurse staffing. For example, cross‐sectional studies have demonstrated that increased nurse staffing was associated with decreased mortality, length of stay, failure to rescue from complications, catheter‐associated bloodstream infections, catheter‐associated urinary tract infections, gastrointestinal bleeding, ventilator‐acquired pneumonia, and shock or cardiac arrest.1, 4, 19 These types of quality and patient safety outcomes are likely to be the focus of many hospitalist‐led quality improvement programs and may even be linked to hospitalist compensation. Therefore, hospitals and their hospitalists must take into account the effect that inadequate nurse staffing could have on their patient outcomes while balancing the investment in nurse staffing with other quality improvement investments. An interaction between nurse staffing level and hospitalist staffing may exist, but we are unaware of any published studies investigating this interaction. The nurse burnout documented to be associated with inadequate nurse staffing certainly could affect hospitalists if it increases nurse turnover or inhibits effective communication.1 Additional research is needed to better delineate the effects of nurse staffing, particularly in regard to hospitalists and hospital‐based quality and safety initiatives.

Finally, these data highlight the need for policymakers and hospital administrators to consider whether the aim is to establish a minimal floor or an optimal ratio. California first opted for what many would consider a minimal floor of at least 1 nurse per 6 patients, as only 5% of hospitals were below this ratio in 2003. California then increased the ratio to a 1:5 nurse‐to‐patient ratio, which affected a larger percentage of hospitals, presumably because of a belief that this higher ratio would lead to better outcomes. In addition, some states such as Massachusetts have considered a minimum ratio of 1:4.17 A ratio of 1:4 would require a significant proportion of hospitals to hire more nurses if staffing levels are similar to California. Only a few studies have estimated the cost effectiveness of staffing changes. Based on cross‐sectional data, Needleman et al. estimated that it would cost $8.5 billion nationally to raise all hospitals to the 75th percentile of RN and overall nurse staffing but that this would prevent 70,000 adverse patient outcomes (eg, hospital‐acquired pneumonia). Rothberg et al. estimated that the incremental cost per life saved as a hospital moved from 1 nurse per 8 patients to 1 nurse per 5 patients was $48,100. However, these estimates based on cross‐sectional data fail to inform the debate on optimal nurse staffing ratios. The effect on patient outcomes when hospitals move from 1:6 to 1:5 or 1:4 nurse staffing levels needs to be determined in a longitudinal study. Thus, legislators and hospitals have little to guide them in establishing optimal nurse staffing ratios, and consideration of specific mandated minimum ratios would benefit greatly from comparative information on the cost and quality tradeoffs.

Hospitals, policy makers, health care providers, and researchers are struggling to improve the health care delivered in our hospitals; fortunately, there has been an increased focus on the importance of nurses who deliver medical care on the front lines and are responsible for many aspects of quality. Mandating minimum nurse staffing ratios may seem like an easy fix of the problem; however, we must consider how these ratios can be met, the potential difficulty for hospitals to meet these ratios in the fraying safety net20, and possible unintended negative consequences. Without a mechanism for hospitals to meet ratios, simply mandating a minimum ratio will not necessarily improve care. Hospitalists should be leaders in better understanding the effects of nurse staffing on patient outcomes and quality initiatives in hospitals.

Many studies have reported associations between higher nurse‐to‐patient ratios and decreased mortality and complications. These studies coupled with increasing concern about patient safety, nursing shortages, and nurse burnout have spurred many state legislatures to discuss mandating minimum nurse staffing ratios.15 The California legislature passed law AB394 in 1999, mandating minimum nurse staffing ratios in order to improve patient safety and the nurse work environment. The original implementation date, January 1, 2001, was delayed to allow the California Department of Health Services more time to develop minimum nurse ratios for each unit type.6, 7 California implemented a ratio of at least 1 licensed nurse (RN+LVN) for every 6 patients on general adult medical‐surgical floors on January 1, 2004. This was subsequently increased, on January 1, 2005, to at least 1 licensed nurse for every 5 patients, a ratio that was upheld by the California Supreme Court on March 14, 2005.8

Additional laws regarding nurse staffing are being considered in at least 25 states.9 States have taken 3 main approaches to legislation: mandating nurse staffing ratios for each hospital unit type, requiring hospitals to establish and report nurse staffing plans that typically include ratios, or a combination of mandated ratios and staffing plans.10 This type of legislation would have a major impact on hospitalists, nurses, other health care personnel, hospital administrators, and patients. However, little is known about trends in nurse staffing, how staffing levels vary among hospitals overall, in different markets, and by ownership type and location, and consequently how implementing nurse staffing ratios will affect different types of hospitals, including those that make up the safety net.11

California nurse staffing data are better than many other sources because the state provides nurse staffing hours by unit types in hospitals as opposed to aggregate numbers of nurse hours across an entire hospital or medical center.12 California is also at the forefront of mandated minimum nurse staffing legislation, as it is the only state to have enacted nurse staffing ratio legislation. Examining nurse staffing trends and hospital types currently under mandated or proposed nurse staffing ratios is integral to informing the debate on nurse staffing legislation and its effect on hospitalists. We hypothesized that nurse staffing would increase in California after the legislation was passed in 1999 but that safety‐net hospitals such as those that are urban, government owned, and serving a high percentage of Medicaid and uninsured patients would be more likely to be below minimum ratios.13

MATERIALS AND METHODS

We used hospital financial panel data for 1993 through 2004, the most recent year with complete data, from California's Office of Statewide Health Planning and Development (OSHPD). We included only short‐term acute‐care general hospitals and excluded other hospital types such as long‐term care, children's, and psychiatric hospitals. We investigated staffing of adult general medical‐surgical units and not of other types of units such as intensive care units. The numerator of the staffing variables for each hospital was the combined medical‐surgical productive hours for registered nurses (RNs) and licensed vocational nurses (LVNs), as California allows up to 50% of staffing hours to be LVN hours. Staffing hours of the adult general medical‐surgical units of each hospital are reported on an annual basis. The denominator was total patient days on the acute adult medical‐surgical units of each hospital in a given year. We calculated the number of patients per one nurse by dividing 24 by the nurse hours per patient day (eg, 4.0 nurse hours per patient day is equivalent to a nurse‐to‐patient ratio of 1:6). We did not adjust staffing ratios by the hospital case mix or other factors because the ratio legislation did not take these factors into account.

We further evaluated staffing ratios in 2003 and 2004 based on 5 hospital characteristics: hospital ownership, market competitiveness, teaching status, urban versus rural location, and safety‐net hospitals, using 2 common definitions for the latter. The Institute of Medicine report defines safety‐net providers as those with a substantial share of their patient mix from uninsured and Medicaid populations.13 Safety‐net hospitals have been more specifically defined as short‐term general hospitals whose percentage of Medicaid and uninsured patients is greater than 1 standard deviation above the mean.14 Using this definition, hospitals in California where more than 36% of patients had Medicaid or no insurance in 2004 would be considered safety‐net hospitals. A more comprehensive definition of the hospital safety net that has been used includes urban nonprofit and government hospitals and hospitals with a high percentage of Medicaid/uninsured patients.10, 11, 15 We analyzed nurse staffing ratios using both these definitions. Hospital ownership was designated as for profit, nonprofit, or government owned. Hospital competitiveness was measured using the Hirschman‐Herfindahl Index (HHI), or the sum of squared market shares, a standard approach to defining hospital market competition. Market boundaries were defined as those zip codes from which each hospital draws most of its patients.16 We then dichotomized hospitals into a high‐ or low‐competition category based on the approximate median HHI cut point of 0.34. Teaching status was based on intern/resident‐to‐bed ratio (ie, 0 = nonteaching, 0.010.25 = minor teaching, and >0.25 = major teaching). Location was defined by county location as either urban or nonurban medical service area.

We then analyzed the percentage of hospitals in 2003 and 2004 below the mandated minimum ratios of (1) at least 1 licensed nurse (RN+LVN) per 6 patients effective in 2004, (2) the ratio of 1 (RN+LVN) nurse per 5 patients to be implemented in 2005, (3) the ratio of at least 1 registered nurse (RN only) per 5 patients, and (4) at least 1 nurse (RN+LVN) per 4 patients, as these ratios are under consideration in other states.9, 17 Finally, we examined the trend in nurse staffing ratios from 2003, the pre‐implementation year, to 2004, the post‐implementation year. Data analysis was performed using STATA SE 9.1 (College Station, TX).

RESULTS

Nurse Staffing Trends

The trend in nurse staffing ratios based on licensed nurses (RN + LVN) from 1993 to 2004 is shown in Figure 1, with lines representing the 10th, 25th, 50th (median), and 75th percentiles of hospital nurse staffing ratios. The nurse staffing ratios were essentially flat from 1993 to 1999 without any significant trend. After nurse staffing legislation was passed in 1999, median nurse‐to‐patient ratio rose, with the largest increase from 2003 to the implementation year for staffing ratios, 2004. From 2003 to 2004, the median hospital staffing ratio increased from fewer than 1 nurse per 4 patients to a ratio of more than 1 nurse per 4 patients. The first year that fewer than 25% of hospitals were below the minimum of at least 1 nurse per 5 patients was 2003.

Figure 1

DISCUSSION

These data demonstrate that nurse staffing ratios in California were relatively stable from 1993 to 1999. In 1999, law AB 394 with its focus on nurse staffing levels passed, and subsequently, from 1999 to 2004, nurse staffing levels increased significantly, with the largest increase in 2004, the year of implementation. Although multiple factors could account for this trend, a likely cause for the statewide increase in nurse staffing was the anticipation and then implementation of legislation to achieve minimum ratios.

This study had several limitations. The OSHPD data capture nurse staffing on an annual basis, but the California legislation mandated minimum nurse staffing ratios be kept at all times; these data do not capture how often a given hospital was below the minimum ratio on a monthly or shift‐by‐shift basis. These data may overreport nurse staffing hours if they include hours not spent in direct patient care, or they could misrepresent nurse staffing ratios because of poor reporting.

Certain hospitals are more likely to be below mandated ratios. These hospitals are often government owned, in urban areas, and serve a high percentage of Medicaid/uninsured patients. Hospitals with these characteristics are typically considered part of the safety net. These are the hospitals that serve our nation's most vulnerable populations and are likely to struggle disproportionately to meet minimum mandated ratios. As evidence of these precarious finances, 67% of hospitals defined as safety‐net hospitals based on a high percentage of Medicaid/uninsured patients in 2004 had a negative operating margin versus 40% of hospitals not considered to be safety‐net hospitals (P < .001).18 The question remains how hospitals will meet minimum nurse staffing ratios given these tenuous operating margins, as some of the approaches might result in restricted access, reduced services, reduced expenditures on new equipment or technology, or other decisions that might adversely affect quality. These potential tradeoffs will directly affect hospitalists, nurses, and other health care personnel working in hospitals. Because legislation generally does not provide funds or mechanisms to help hospitals meet proposed staffing ratios and there is a national nursing shortage, hospitals may struggle to meet minimum ratios. Cross‐sectional studies have demonstrated a potential link between increased nurse staffing and better patient outcomes,15 but if a financially constrained hospital makes tradeoffs by restricting access to care and services or by diverting funds from other beneficial uses, on balance, mandated nurse staffing ratios may not be beneficial to patients. The potential for unintended but serious negative consequences exists if hospitals in the safety net are mandated to meet minimum nurse staffing ratios without adequate resources.

At all types of hospitals, hospitalists are increasingly becoming responsible for quality improvement programs and outcomes measurement. However, the outcomes of these programs may be strongly influenced by nurse staffing. For example, cross‐sectional studies have demonstrated that increased nurse staffing was associated with decreased mortality, length of stay, failure to rescue from complications, catheter‐associated bloodstream infections, catheter‐associated urinary tract infections, gastrointestinal bleeding, ventilator‐acquired pneumonia, and shock or cardiac arrest.1, 4, 19 These types of quality and patient safety outcomes are likely to be the focus of many hospitalist‐led quality improvement programs and may even be linked to hospitalist compensation. Therefore, hospitals and their hospitalists must take into account the effect that inadequate nurse staffing could have on their patient outcomes while balancing the investment in nurse staffing with other quality improvement investments. An interaction between nurse staffing level and hospitalist staffing may exist, but we are unaware of any published studies investigating this interaction. The nurse burnout documented to be associated with inadequate nurse staffing certainly could affect hospitalists if it increases nurse turnover or inhibits effective communication.1 Additional research is needed to better delineate the effects of nurse staffing, particularly in regard to hospitalists and hospital‐based quality and safety initiatives.

Finally, these data highlight the need for policymakers and hospital administrators to consider whether the aim is to establish a minimal floor or an optimal ratio. California first opted for what many would consider a minimal floor of at least 1 nurse per 6 patients, as only 5% of hospitals were below this ratio in 2003. California then increased the ratio to a 1:5 nurse‐to‐patient ratio, which affected a larger percentage of hospitals, presumably because of a belief that this higher ratio would lead to better outcomes. In addition, some states such as Massachusetts have considered a minimum ratio of 1:4.17 A ratio of 1:4 would require a significant proportion of hospitals to hire more nurses if staffing levels are similar to California. Only a few studies have estimated the cost effectiveness of staffing changes. Based on cross‐sectional data, Needleman et al. estimated that it would cost $8.5 billion nationally to raise all hospitals to the 75th percentile of RN and overall nurse staffing but that this would prevent 70,000 adverse patient outcomes (eg, hospital‐acquired pneumonia). Rothberg et al. estimated that the incremental cost per life saved as a hospital moved from 1 nurse per 8 patients to 1 nurse per 5 patients was $48,100. However, these estimates based on cross‐sectional data fail to inform the debate on optimal nurse staffing ratios. The effect on patient outcomes when hospitals move from 1:6 to 1:5 or 1:4 nurse staffing levels needs to be determined in a longitudinal study. Thus, legislators and hospitals have little to guide them in establishing optimal nurse staffing ratios, and consideration of specific mandated minimum ratios would benefit greatly from comparative information on the cost and quality tradeoffs.

Hospitals, policy makers, health care providers, and researchers are struggling to improve the health care delivered in our hospitals; fortunately, there has been an increased focus on the importance of nurses who deliver medical care on the front lines and are responsible for many aspects of quality. Mandating minimum nurse staffing ratios may seem like an easy fix of the problem; however, we must consider how these ratios can be met, the potential difficulty for hospitals to meet these ratios in the fraying safety net20, and possible unintended negative consequences. Without a mechanism for hospitals to meet ratios, simply mandating a minimum ratio will not necessarily improve care. Hospitalists should be leaders in better understanding the effects of nurse staffing on patient outcomes and quality initiatives in hospitals.

Pneumonia complicated by lung necrosis and pleural disease consisting of parapneumonic effusion or empyema is a cause of significant morbidity among pediatric inpatients. Current practice in caring for these patients is highly variable, even within single institutions. Medical management of hospitalized children with complex pneumonias includes an attempt to isolate the offending organism, tailored antibiotic therapy, and adequate pain management in association with pleural catheter drainage of large effusions. Thrombolytic agents are frequently trialed in an attempt to lyse loculated effusions without surgical intervention. Surgical drainage or decortication of walled‐off infections is employed when there is poor response to more conservative treatment with pleural catheter drainage. The major therapeutic goal for this patient population is promotion of clinical recovery despite residual pleural abnormality at time of hospital discharge, with the knowledge that complete disease resolution is almost universal.

Variability in management as well as unpredictable patient response to differing therapeutic modalities has hindered the development of clear practice guidelines. Additionally, studies have suggested a shift in bacterial causative pathogens since the early 1990s, particularly after the heptavalent pneumococcal vaccine was added to routine childhood immunization schedules in 2000, and have warned of the growing prevalence of methicillin‐resistant Staphylococcus aureus (MRSA).1

This article reviews the management of pediatric patients hospitalized with complex parapneumonic effusions and summarizes current diagnostic and therapeutic modalities to offer an updated approach to clinical practice.

METHODS

This review was constructed after careful appraisal of data from recent pediatric studies on parapneumonic effusions. The subheadings in the Results section summarize the findings from these published studies and address the changing epidemiology, diagnostic techniques, and management options for this patient population. Finally, the author's impressions of management challenges related to the existing variation in clinical practice and the absence of strong evidenced‐based guidelines are presented.

RESULTS

Disease Management

The initial management of complicated parapneumonic effusions is summarized in Table 3 and includes oxygen delivery for hypoxia, intravenous fluid hydration, and empirical antibiotic therapy, as well as consultation with an interventional radiology or surgical team to discuss possible drainage methods. A management algorithm is also provided in Figure 1.

Figure 1

Table 3.Initial Management of Parapneumonic Effusion

Oxygen delivery as indicated
Empiric antibiotic therapy
Intravenous fluid therapy as indicated
Analgesia
Antipyretics
Consultation with service to perform pleural drainage

CONCLUSIONS

Children with complicated parapneumonic effusions raise a challenge to pediatric hospitalists in choosing an initial management plan that is likely to be successful for their pleural disease stage on admission and to prevent the need for unnecessary intervention. Ultrasound is generally sufficient in diagnosing the stage of pleural disease and avoids both sedation and radiation exposure. It can also be used for guidance to access the pleural space effectively and position pleural catheters in the optimal location for maximum fluid drainage.

Clinicians must appreciate the degree of inflammation possible leading to pleural disease and the length of time necessary for complete disease resolution. Measures to keep patients comfortable and as mobile as possible during hospitalization, especially with pleural drains in place, coupled with proactive assessment of clinical response to initial therapy, are essential management goals.

Studies to compare hospital outcomes among patients receiving conservative medical management with antibiotics and pleural catheter drainage versus those undergoing early surgical debridement and drainage should be interpreted cautiously until larger studies are performed with attention to initial disease staging. Until that time, there is likely to be continued variability in practice even in an individual center because of factors related to hospitalist staff and surgical consultant staff impressions of initial illness and institutional resources to perform various procedures under conscious sedation versus general anesthesia at the time of admission.

Finally, it should be emphasized that disease pathogens should be restudied nationally to guide empirical antibiotic therapy because the treatment duration is longer than in most pediatric illnesses and patients may be at higher risk for adverse events related to prolonged antibiotic exposure. Further studies using large numbers of subjects from geographically diverse regions with more current epidemiological data are our best chance at defining the present picture of bacterial pathogens causing complicated pediatric pneumonia.

Pneumonia complicated by lung necrosis and pleural disease consisting of parapneumonic effusion or empyema is a cause of significant morbidity among pediatric inpatients. Current practice in caring for these patients is highly variable, even within single institutions. Medical management of hospitalized children with complex pneumonias includes an attempt to isolate the offending organism, tailored antibiotic therapy, and adequate pain management in association with pleural catheter drainage of large effusions. Thrombolytic agents are frequently trialed in an attempt to lyse loculated effusions without surgical intervention. Surgical drainage or decortication of walled‐off infections is employed when there is poor response to more conservative treatment with pleural catheter drainage. The major therapeutic goal for this patient population is promotion of clinical recovery despite residual pleural abnormality at time of hospital discharge, with the knowledge that complete disease resolution is almost universal.

Variability in management as well as unpredictable patient response to differing therapeutic modalities has hindered the development of clear practice guidelines. Additionally, studies have suggested a shift in bacterial causative pathogens since the early 1990s, particularly after the heptavalent pneumococcal vaccine was added to routine childhood immunization schedules in 2000, and have warned of the growing prevalence of methicillin‐resistant Staphylococcus aureus (MRSA).1

This article reviews the management of pediatric patients hospitalized with complex parapneumonic effusions and summarizes current diagnostic and therapeutic modalities to offer an updated approach to clinical practice.

METHODS

This review was constructed after careful appraisal of data from recent pediatric studies on parapneumonic effusions. The subheadings in the Results section summarize the findings from these published studies and address the changing epidemiology, diagnostic techniques, and management options for this patient population. Finally, the author's impressions of management challenges related to the existing variation in clinical practice and the absence of strong evidenced‐based guidelines are presented.

RESULTS

Disease Management

The initial management of complicated parapneumonic effusions is summarized in Table 3 and includes oxygen delivery for hypoxia, intravenous fluid hydration, and empirical antibiotic therapy, as well as consultation with an interventional radiology or surgical team to discuss possible drainage methods. A management algorithm is also provided in Figure 1.

Figure 1

Table 3.Initial Management of Parapneumonic Effusion

Oxygen delivery as indicated
Empiric antibiotic therapy
Intravenous fluid therapy as indicated
Analgesia
Antipyretics
Consultation with service to perform pleural drainage

CONCLUSIONS

Children with complicated parapneumonic effusions raise a challenge to pediatric hospitalists in choosing an initial management plan that is likely to be successful for their pleural disease stage on admission and to prevent the need for unnecessary intervention. Ultrasound is generally sufficient in diagnosing the stage of pleural disease and avoids both sedation and radiation exposure. It can also be used for guidance to access the pleural space effectively and position pleural catheters in the optimal location for maximum fluid drainage.

Clinicians must appreciate the degree of inflammation possible leading to pleural disease and the length of time necessary for complete disease resolution. Measures to keep patients comfortable and as mobile as possible during hospitalization, especially with pleural drains in place, coupled with proactive assessment of clinical response to initial therapy, are essential management goals.

Studies to compare hospital outcomes among patients receiving conservative medical management with antibiotics and pleural catheter drainage versus those undergoing early surgical debridement and drainage should be interpreted cautiously until larger studies are performed with attention to initial disease staging. Until that time, there is likely to be continued variability in practice even in an individual center because of factors related to hospitalist staff and surgical consultant staff impressions of initial illness and institutional resources to perform various procedures under conscious sedation versus general anesthesia at the time of admission.

Finally, it should be emphasized that disease pathogens should be restudied nationally to guide empirical antibiotic therapy because the treatment duration is longer than in most pediatric illnesses and patients may be at higher risk for adverse events related to prolonged antibiotic exposure. Further studies using large numbers of subjects from geographically diverse regions with more current epidemiological data are our best chance at defining the present picture of bacterial pathogens causing complicated pediatric pneumonia.

Pneumonia complicated by lung necrosis and pleural disease consisting of parapneumonic effusion or empyema is a cause of significant morbidity among pediatric inpatients. Current practice in caring for these patients is highly variable, even within single institutions. Medical management of hospitalized children with complex pneumonias includes an attempt to isolate the offending organism, tailored antibiotic therapy, and adequate pain management in association with pleural catheter drainage of large effusions. Thrombolytic agents are frequently trialed in an attempt to lyse loculated effusions without surgical intervention. Surgical drainage or decortication of walled‐off infections is employed when there is poor response to more conservative treatment with pleural catheter drainage. The major therapeutic goal for this patient population is promotion of clinical recovery despite residual pleural abnormality at time of hospital discharge, with the knowledge that complete disease resolution is almost universal.

Variability in management as well as unpredictable patient response to differing therapeutic modalities has hindered the development of clear practice guidelines. Additionally, studies have suggested a shift in bacterial causative pathogens since the early 1990s, particularly after the heptavalent pneumococcal vaccine was added to routine childhood immunization schedules in 2000, and have warned of the growing prevalence of methicillin‐resistant Staphylococcus aureus (MRSA).1

This article reviews the management of pediatric patients hospitalized with complex parapneumonic effusions and summarizes current diagnostic and therapeutic modalities to offer an updated approach to clinical practice.

METHODS

This review was constructed after careful appraisal of data from recent pediatric studies on parapneumonic effusions. The subheadings in the Results section summarize the findings from these published studies and address the changing epidemiology, diagnostic techniques, and management options for this patient population. Finally, the author's impressions of management challenges related to the existing variation in clinical practice and the absence of strong evidenced‐based guidelines are presented.

RESULTS

Disease Management

The initial management of complicated parapneumonic effusions is summarized in Table 3 and includes oxygen delivery for hypoxia, intravenous fluid hydration, and empirical antibiotic therapy, as well as consultation with an interventional radiology or surgical team to discuss possible drainage methods. A management algorithm is also provided in Figure 1.

Figure 1

Table 3.Initial Management of Parapneumonic Effusion

Oxygen delivery as indicated
Empiric antibiotic therapy
Intravenous fluid therapy as indicated
Analgesia
Antipyretics
Consultation with service to perform pleural drainage

CONCLUSIONS

Children with complicated parapneumonic effusions raise a challenge to pediatric hospitalists in choosing an initial management plan that is likely to be successful for their pleural disease stage on admission and to prevent the need for unnecessary intervention. Ultrasound is generally sufficient in diagnosing the stage of pleural disease and avoids both sedation and radiation exposure. It can also be used for guidance to access the pleural space effectively and position pleural catheters in the optimal location for maximum fluid drainage.

Clinicians must appreciate the degree of inflammation possible leading to pleural disease and the length of time necessary for complete disease resolution. Measures to keep patients comfortable and as mobile as possible during hospitalization, especially with pleural drains in place, coupled with proactive assessment of clinical response to initial therapy, are essential management goals.

Studies to compare hospital outcomes among patients receiving conservative medical management with antibiotics and pleural catheter drainage versus those undergoing early surgical debridement and drainage should be interpreted cautiously until larger studies are performed with attention to initial disease staging. Until that time, there is likely to be continued variability in practice even in an individual center because of factors related to hospitalist staff and surgical consultant staff impressions of initial illness and institutional resources to perform various procedures under conscious sedation versus general anesthesia at the time of admission.

Finally, it should be emphasized that disease pathogens should be restudied nationally to guide empirical antibiotic therapy because the treatment duration is longer than in most pediatric illnesses and patients may be at higher risk for adverse events related to prolonged antibiotic exposure. Further studies using large numbers of subjects from geographically diverse regions with more current epidemiological data are our best chance at defining the present picture of bacterial pathogens causing complicated pediatric pneumonia.

Patient safety can be understood in terms of the Swiss cheese model of systems accidents. This model implies that many holes must align before an adverse event occurs.1 The limitations on work hours instituted by the Accreditation Council for Graduate Medical Education (ACGME)2 sought to close one hole by reducing fatigue in residents. As programs comply with these regulations, new interventions are being implemented to limit resident hours. This has resulted in more handoffs of care and therefore less continuity. The ultimate result may be to increase patient care errors by opening up new holes, the opposite of the stated goal of this reform.

Some residency programs have reported on their experience with hour reductions, giving insight into residents' perceptions on the benefits and drawbacks of such interventions. Residents have reported concern about continuity of care after such interventions.37 However, some residents believed they provided better patient care after the interventions to reduce hours.8, 9 Few studies have actually documented changes in the incidence of adverse events or errors as a result of work hour limitations.10 One study conducted prior to implementation of the ACGME work hour rules demonstrated more complications in internal medicine patients after New York's Code 405 (a state regulation that limited resident work hours, similar to the ACGME rules) was implemented.11 In contrast, another study showed that errors committed by interns were reduced with scheduling changes that resulted in shorter shifts and reduced hours.12

Because residents are on the front lines of patient care, they are uniquely positioned to provide insight into the impact of the work hour rules on patient safety. We conducted this study to more fully understand the effect of the ACGME work hour limitations and other possible factors on patient care errors from the perspectives of internal medicine residents.

METHODS

Design

We conducted 4 focus groups in total (Appendix 1). The first 3 focus groups followed the same focus group guide, developed after a literature review. Focus groups 1 and 2 were conducted at the university‐based program. Focus group 3 was conducted at the community teaching hospitalaffiliated program. The first 3 focus groups were analyzed before the fourth focus group was conducted. A new focus group guide was developed for the fourth focus group to further explore themes identified in the first 3 focus groups (Fig. 1 and Appendix 2). The fourth focus group was conducted at the program affiliated with a freestanding medical college. All focus groups were audiotaped and transcribed verbatim. Each lasted approximately 90‐120 minutes.

Figure 1

RESULTS

Twenty‐eight residents participated. Some of these residents had experience in the prework hour era, and some did not. Average age was 28 years (range 26‐33 years); 18 were women, and 11 were interns (Table 2). The focus groups ranged in size from 5 to 9. A sample of the codes and their definitions can be found in Table 3.

The Model

The model (Fig. 2) illustrates resident‐perceived contributors to patient care mistakes related to the ACGME work hour rules. These contributors are in the center circle. They include fatigue, inexperience, sign‐out, not knowing their own patients well enough, entropy (which we defined as the amount of chaos in the system), and workload. They are not listed in order of importance. The boxes outside the circle are consequences of the ACGME work hour rules and their perceived impact on the contributors to patient care mistakes. At the top are the intended consequences, that is the specific goals of the ACGME: less resident time in the hospital (ie, reduced hours) and improved well‐being.17 At the bottom are the unintended consequences: more patient care discontinuity and compliance with the work hour rules becoming a goal equally important to providing high‐quality patient care. Of these 4 consequences, only improved well‐being was viewed by the residents as decreasing patient care mistakes. The other consequences were cited by residents as sometimes increasing patient care errors. Because of the complexity of the model, several factors not directly related to resident work hours were identified in the analysis but are not shown in the model. They include faculty involvement and team work (usually positive influences), nurses and information technology (could be positive or negative), and late‐night/early‐morning hours (negative).

Figure 2

The quotations below illustrate the relationships between the consequences of the work hour rules, resident‐perceived contributors to patient care mistakes, and actual patient care.

DISCUSSION

We conducted this study to better understand why internal medicine residents thought patient care mistakes occurred; we were particularly interested in how they perceived the impact of certain aspects of the ACGME work hour rules on patient care mistakes. Designing systems that achieve compliance with the work hour rules while minimizing patient risk can best be accomplished by fully understanding why errors occur.

Our study revealed that in the opinion of some interns and residents, the work hour rules had consequences for patient care. Like any intervention, this one had both intended and unintended consequences.18 The ACGME has stated that improvement in residents' quality of life was an intended consequence,17 and the participants in our study reported that this had occurred. Despite uncertainty about the overall impact on patient outcomes, residents were glad to have more time away from the hospital.

Our respondents reported that not knowing patients well was a factor that contributed to patient care errors. It is intuitive that working fewer hours often results in more handoffs of care,19 a situation characterized by not knowing patients well. However, residents also identified not getting to know their own patients well as a factor that led to patient care mistakes because of (1) incomplete knowledge of a patient's status, (2) delays in diagnosis, and (3) errors in management. They also described feelings of professional disappointment and frustration at not being able to perform certain aspects of patient care (eg, family meetings) because of the hour limits and the inflexibility of the rules. As we strive to redefine professionalism in the setting of reduced work hours,20 this phenomenon should be addressed.

Sign‐out was identified as another contributor to patient care errors. The effectiveness of sign‐outs is a concern across medicine, and the Joint Commission on Accreditation of Healthcare Organizations made sign‐out procedures one of its priority areas in 2006.21 Much has been written about resident sign‐out, emphasizing the relationship between poor‐quality sign‐outs and patient safety.19, 22 However, barriers to effective sign‐out processes persist,23 even though standardized sign‐out strategies have been described.24, 25 Even in a rigorous study of work hours and patient safety, the computerized sign‐out template for the residents was rarely used.12 Cross‐coverage, or the patient care that occurs after sign‐out is complete, has also been linked to a greater likelihood of adverse events.26

Several factors not related to resident work hours were noted to often mitigate patient care mistakes. Physician teamwork, nursing, information technology (eg, computerized medical records), and faculty supervision were the most prominent. For example, the information technology available at the VA hospitals often helped to facilitate patient care, but it also provided an overwhelming amount of information to sift through. It was clear that the influence of some of these factors varied from institution to institution, reflecting the cultures of different programs.

Our results are consistent with those reported from previous studies. Striking a balance between preventing resident fatigue and preserving continuity of care has been debated since the ACGME announced changes to resident work hour limits.27 Resident quality of life generally improves and fatigue decreases with work hour limits in place,28 but patient safety remains a concern.10 Our findings corroborate the benefits of improved resident well‐being and the persistent concerns about patient safety, identified in a recently published study at a different institution.29 However, our findings expand on those reported in the literature by offering additional empirical evidence, albeit qualitative, about the way that residents see the relationships among the consequences of work hour rules, resident‐reported contributors to patient care mistakes, and the mistakes themselves.

Our study should be interpreted in the context of several limitations. First, the use of qualitative methods did not allow us to generalize or quantify our findings. However, we purposely included 3 diverse institutions with differing responses to the work hour rules to enhance the external validity of our findings. Second, the last focus group was conducted a year after the first 3; by that point, the work hour rules had been in place for 20 months. We believe that this was both a strength and a limitation because it allowed us to gain a perspective after some of the initial growing pains were over. This time lag also allowed for analysis of the first 3 transcripts so we could revise the focus group guide and ultimately determine that thematic saturation had occurred. In addition, few of our questions were phrased to evaluate the ACGME rules; instead, they asked about links among discontinuity, scheduling, fatigue, and patient care. We therefore believe that even residents who were not in the programs before the work hour rules began were still able to knowledgeably participate in the conversation. One question directly referable to the ACGME rules asked residents to reflect on problems arising from them. This could have led residents to only reflect on the problems associated with the rules. However, in all 4 focus groups, residents commented on the positive impact of improved well‐being resulting from the work hour rules. This led us to believe the respondents felt they could voice their favorable feelings as well as their unfavorable feelings about the rules. An additional limitation is that the agreement between coders was only 45%. It is important to realize that assessing coding agreement in qualitative work is quite difficult because it is often difficult to assign a single code to a section of text. When the coders discussed a disagreement, it was almost always the case that the difference was subtle and that the coding of either investigator would made sense for that text. Finally, our results are based on the participation of 28 residents. To be certain we were not representing the opinions of only a few people, we presented iterations of this model to faculty and resident groups for their feedback. Importantly, the residents offered no substantial changes or criticisms of the model.

Limitations notwithstanding, we believe our findings have important policy implications. First, despite work hours successfully being reduced, residents perceived no decrease in the amount of work they did. This resulted in higher workload and more entropy, suggesting that residency programs may need to carefully evaluate the patient care responsibility carried by residents. Second, continued effort to educate residents to provide effective sign‐out is needed. As one participant pointed out, residency offers a unique opportunity to learn to manage discontinuity in a controlled setting. Another educational opportunity is the chance to teach physician teamwork. Participants believed that effective teamwork could ameliorate some of the discontinuity in patient care. This teamwork training should include faculty as well, although further work is needed to define how faculty can best add to patient continuity while still fostering resident autonomy. Finally, the impact of work hour rules on the professional development of residents should be further explored.

In conclusion, we have proposed a model to explain the major resident‐reported contributors to patient care mistakes with respect to resident work hour rules. Our results help to clarify the next steps needed: testing the proposed relationships between the factors and patient care mistakes and rigorously evaluating solutions that minimize the impact of these factors. Returning to the Swiss cheese framework for describing systems accidents, our results suggest that although resident work hour reductions may have sufficiently filled the hole caused by resident fatigue, other gaps may have actually widened as a result of the systems put into place to achieve compliance. Continued vigilance is therefore necessary to both identify the additional holes likely to appear and, more importantly, effectively close those holes before patient harm occurs.

FOCUS GROUP GUIDE (4TH FOCUS GROUP)

The IOM definition of a medical error is the failure of a planned action to be completed as intended or the use of a wrong plan to achieve an aim.

• 1

  Please describe the call structure at each institution where you do ward months (eg, non‐ICU months).

• 2

  What are some settings at the hospitals where you work where you have seen medical errors, mistakes, or adverse outcomes?

• 3

  How do you think that other nurses influence the occurrence of medical errors, mistakes, or adverse outcomes?

   

  • a

    Clerks?

  • b

    Other ancillary staff?

  • 4

    How would you describe the responsibilities of a cross‐covering resident or intern?

  • 5

    How do you think continuity of care impacts patient care in terms of medical errors, mistakes, or adverse outcomes?

     

    • a

      What role do sign‐outs have?

    • 6

      How do you think that fatigue impacts patient care in terms of medical errors, mistakes, or adverse outcomes?

    • 7

      How do you think that technology such as computerized physician order entry impacts patient care in terms of medical errors, mistakes, or adverse outcomes?

       

      • a

        Electronic medical records?

      • b

        Palm pilots?

      • c

        Is there such a thing as too much information?

      • 8

        How do you think that experience (or inexperience) impacts patient care in terms of medical errors, mistakes, or adverse outcomes?

      • 9

        Please describe how attendings supervise you when you are on a ward team. How do you think that attending supervision impacts patient care in terms of medical errors, mistakes, or adverse outcomes?

         

        • a

          What about resident supervision of interns?

        • b

          What is the ideal role of an attending on a team?

        • c

          Can you think of a time when having attending input changed the plans or the course of a patient in a major way, good, bad, or neutral?

        • 10

          How do you think that time of day impacts patient care in terms of in terms of medical errors, mistakes, or adverse outcomes?

           

          • a

            How comfortable do you feel calling for help at night? What makes you more or less likely to do it (personal attributes of person to be called, situation, etc.)?

          • 11

            What do you think is an ideal workload? (eg, How many complex patients are typical of your hospitals?) Does that vary from the VA to St. Joe's to Froedtert? How many patients should be admitted in 1 night by an intern? How many should an intern have ongoing responsibility for? Is there such a thing as too few patients?

          • 12

            If one of your family members were to admitted to your hospital at night with a life‐threatening condition, which situation would you prefer for their care (all other things being equal): admission by night float with handoff to a new but well‐rested resident or admission by a resident who then continues to care for that family member the next day but has been awake for 24 hours, admitting and cross‐covering other patients? Why?

          • 13

            What do you think was the intent of the ACGME rules? Do you think that those goals have been accomplished? Why or why not? How have they affected you as residents? How do you think that the ACGME work hour rules have influenced patient care?

           

Patient safety can be understood in terms of the Swiss cheese model of systems accidents. This model implies that many holes must align before an adverse event occurs.1 The limitations on work hours instituted by the Accreditation Council for Graduate Medical Education (ACGME)2 sought to close one hole by reducing fatigue in residents. As programs comply with these regulations, new interventions are being implemented to limit resident hours. This has resulted in more handoffs of care and therefore less continuity. The ultimate result may be to increase patient care errors by opening up new holes, the opposite of the stated goal of this reform.

Some residency programs have reported on their experience with hour reductions, giving insight into residents' perceptions on the benefits and drawbacks of such interventions. Residents have reported concern about continuity of care after such interventions.37 However, some residents believed they provided better patient care after the interventions to reduce hours.8, 9 Few studies have actually documented changes in the incidence of adverse events or errors as a result of work hour limitations.10 One study conducted prior to implementation of the ACGME work hour rules demonstrated more complications in internal medicine patients after New York's Code 405 (a state regulation that limited resident work hours, similar to the ACGME rules) was implemented.11 In contrast, another study showed that errors committed by interns were reduced with scheduling changes that resulted in shorter shifts and reduced hours.12

Because residents are on the front lines of patient care, they are uniquely positioned to provide insight into the impact of the work hour rules on patient safety. We conducted this study to more fully understand the effect of the ACGME work hour limitations and other possible factors on patient care errors from the perspectives of internal medicine residents.

METHODS

Design

We conducted 4 focus groups in total (Appendix 1). The first 3 focus groups followed the same focus group guide, developed after a literature review. Focus groups 1 and 2 were conducted at the university‐based program. Focus group 3 was conducted at the community teaching hospitalaffiliated program. The first 3 focus groups were analyzed before the fourth focus group was conducted. A new focus group guide was developed for the fourth focus group to further explore themes identified in the first 3 focus groups (Fig. 1 and Appendix 2). The fourth focus group was conducted at the program affiliated with a freestanding medical college. All focus groups were audiotaped and transcribed verbatim. Each lasted approximately 90‐120 minutes.

Figure 1

RESULTS

Twenty‐eight residents participated. Some of these residents had experience in the prework hour era, and some did not. Average age was 28 years (range 26‐33 years); 18 were women, and 11 were interns (Table 2). The focus groups ranged in size from 5 to 9. A sample of the codes and their definitions can be found in Table 3.

The Model

The model (Fig. 2) illustrates resident‐perceived contributors to patient care mistakes related to the ACGME work hour rules. These contributors are in the center circle. They include fatigue, inexperience, sign‐out, not knowing their own patients well enough, entropy (which we defined as the amount of chaos in the system), and workload. They are not listed in order of importance. The boxes outside the circle are consequences of the ACGME work hour rules and their perceived impact on the contributors to patient care mistakes. At the top are the intended consequences, that is the specific goals of the ACGME: less resident time in the hospital (ie, reduced hours) and improved well‐being.17 At the bottom are the unintended consequences: more patient care discontinuity and compliance with the work hour rules becoming a goal equally important to providing high‐quality patient care. Of these 4 consequences, only improved well‐being was viewed by the residents as decreasing patient care mistakes. The other consequences were cited by residents as sometimes increasing patient care errors. Because of the complexity of the model, several factors not directly related to resident work hours were identified in the analysis but are not shown in the model. They include faculty involvement and team work (usually positive influences), nurses and information technology (could be positive or negative), and late‐night/early‐morning hours (negative).

Figure 2

The quotations below illustrate the relationships between the consequences of the work hour rules, resident‐perceived contributors to patient care mistakes, and actual patient care.

DISCUSSION

We conducted this study to better understand why internal medicine residents thought patient care mistakes occurred; we were particularly interested in how they perceived the impact of certain aspects of the ACGME work hour rules on patient care mistakes. Designing systems that achieve compliance with the work hour rules while minimizing patient risk can best be accomplished by fully understanding why errors occur.

Our study revealed that in the opinion of some interns and residents, the work hour rules had consequences for patient care. Like any intervention, this one had both intended and unintended consequences.18 The ACGME has stated that improvement in residents' quality of life was an intended consequence,17 and the participants in our study reported that this had occurred. Despite uncertainty about the overall impact on patient outcomes, residents were glad to have more time away from the hospital.

Our respondents reported that not knowing patients well was a factor that contributed to patient care errors. It is intuitive that working fewer hours often results in more handoffs of care,19 a situation characterized by not knowing patients well. However, residents also identified not getting to know their own patients well as a factor that led to patient care mistakes because of (1) incomplete knowledge of a patient's status, (2) delays in diagnosis, and (3) errors in management. They also described feelings of professional disappointment and frustration at not being able to perform certain aspects of patient care (eg, family meetings) because of the hour limits and the inflexibility of the rules. As we strive to redefine professionalism in the setting of reduced work hours,20 this phenomenon should be addressed.

Sign‐out was identified as another contributor to patient care errors. The effectiveness of sign‐outs is a concern across medicine, and the Joint Commission on Accreditation of Healthcare Organizations made sign‐out procedures one of its priority areas in 2006.21 Much has been written about resident sign‐out, emphasizing the relationship between poor‐quality sign‐outs and patient safety.19, 22 However, barriers to effective sign‐out processes persist,23 even though standardized sign‐out strategies have been described.24, 25 Even in a rigorous study of work hours and patient safety, the computerized sign‐out template for the residents was rarely used.12 Cross‐coverage, or the patient care that occurs after sign‐out is complete, has also been linked to a greater likelihood of adverse events.26

Several factors not related to resident work hours were noted to often mitigate patient care mistakes. Physician teamwork, nursing, information technology (eg, computerized medical records), and faculty supervision were the most prominent. For example, the information technology available at the VA hospitals often helped to facilitate patient care, but it also provided an overwhelming amount of information to sift through. It was clear that the influence of some of these factors varied from institution to institution, reflecting the cultures of different programs.

Our results are consistent with those reported from previous studies. Striking a balance between preventing resident fatigue and preserving continuity of care has been debated since the ACGME announced changes to resident work hour limits.27 Resident quality of life generally improves and fatigue decreases with work hour limits in place,28 but patient safety remains a concern.10 Our findings corroborate the benefits of improved resident well‐being and the persistent concerns about patient safety, identified in a recently published study at a different institution.29 However, our findings expand on those reported in the literature by offering additional empirical evidence, albeit qualitative, about the way that residents see the relationships among the consequences of work hour rules, resident‐reported contributors to patient care mistakes, and the mistakes themselves.

Our study should be interpreted in the context of several limitations. First, the use of qualitative methods did not allow us to generalize or quantify our findings. However, we purposely included 3 diverse institutions with differing responses to the work hour rules to enhance the external validity of our findings. Second, the last focus group was conducted a year after the first 3; by that point, the work hour rules had been in place for 20 months. We believe that this was both a strength and a limitation because it allowed us to gain a perspective after some of the initial growing pains were over. This time lag also allowed for analysis of the first 3 transcripts so we could revise the focus group guide and ultimately determine that thematic saturation had occurred. In addition, few of our questions were phrased to evaluate the ACGME rules; instead, they asked about links among discontinuity, scheduling, fatigue, and patient care. We therefore believe that even residents who were not in the programs before the work hour rules began were still able to knowledgeably participate in the conversation. One question directly referable to the ACGME rules asked residents to reflect on problems arising from them. This could have led residents to only reflect on the problems associated with the rules. However, in all 4 focus groups, residents commented on the positive impact of improved well‐being resulting from the work hour rules. This led us to believe the respondents felt they could voice their favorable feelings as well as their unfavorable feelings about the rules. An additional limitation is that the agreement between coders was only 45%. It is important to realize that assessing coding agreement in qualitative work is quite difficult because it is often difficult to assign a single code to a section of text. When the coders discussed a disagreement, it was almost always the case that the difference was subtle and that the coding of either investigator would made sense for that text. Finally, our results are based on the participation of 28 residents. To be certain we were not representing the opinions of only a few people, we presented iterations of this model to faculty and resident groups for their feedback. Importantly, the residents offered no substantial changes or criticisms of the model.

Limitations notwithstanding, we believe our findings have important policy implications. First, despite work hours successfully being reduced, residents perceived no decrease in the amount of work they did. This resulted in higher workload and more entropy, suggesting that residency programs may need to carefully evaluate the patient care responsibility carried by residents. Second, continued effort to educate residents to provide effective sign‐out is needed. As one participant pointed out, residency offers a unique opportunity to learn to manage discontinuity in a controlled setting. Another educational opportunity is the chance to teach physician teamwork. Participants believed that effective teamwork could ameliorate some of the discontinuity in patient care. This teamwork training should include faculty as well, although further work is needed to define how faculty can best add to patient continuity while still fostering resident autonomy. Finally, the impact of work hour rules on the professional development of residents should be further explored.

In conclusion, we have proposed a model to explain the major resident‐reported contributors to patient care mistakes with respect to resident work hour rules. Our results help to clarify the next steps needed: testing the proposed relationships between the factors and patient care mistakes and rigorously evaluating solutions that minimize the impact of these factors. Returning to the Swiss cheese framework for describing systems accidents, our results suggest that although resident work hour reductions may have sufficiently filled the hole caused by resident fatigue, other gaps may have actually widened as a result of the systems put into place to achieve compliance. Continued vigilance is therefore necessary to both identify the additional holes likely to appear and, more importantly, effectively close those holes before patient harm occurs.

FOCUS GROUP GUIDE (4TH FOCUS GROUP)

The IOM definition of a medical error is the failure of a planned action to be completed as intended or the use of a wrong plan to achieve an aim.

• 1

  Please describe the call structure at each institution where you do ward months (eg, non‐ICU months).

• 2

  What are some settings at the hospitals where you work where you have seen medical errors, mistakes, or adverse outcomes?

• 3

  How do you think that other nurses influence the occurrence of medical errors, mistakes, or adverse outcomes?

   

  • a

    Clerks?

  • b

    Other ancillary staff?

  • 4

    How would you describe the responsibilities of a cross‐covering resident or intern?

  • 5

    How do you think continuity of care impacts patient care in terms of medical errors, mistakes, or adverse outcomes?

     

    • a

      What role do sign‐outs have?

    • 6

      How do you think that fatigue impacts patient care in terms of medical errors, mistakes, or adverse outcomes?

    • 7

      How do you think that technology such as computerized physician order entry impacts patient care in terms of medical errors, mistakes, or adverse outcomes?

       

      • a

        Electronic medical records?

      • b

        Palm pilots?

      • c

        Is there such a thing as too much information?

      • 8

        How do you think that experience (or inexperience) impacts patient care in terms of medical errors, mistakes, or adverse outcomes?

      • 9

        Please describe how attendings supervise you when you are on a ward team. How do you think that attending supervision impacts patient care in terms of medical errors, mistakes, or adverse outcomes?

         

        • a

          What about resident supervision of interns?

        • b

          What is the ideal role of an attending on a team?

        • c

          Can you think of a time when having attending input changed the plans or the course of a patient in a major way, good, bad, or neutral?

        • 10

          How do you think that time of day impacts patient care in terms of in terms of medical errors, mistakes, or adverse outcomes?

           

          • a

            How comfortable do you feel calling for help at night? What makes you more or less likely to do it (personal attributes of person to be called, situation, etc.)?

          • 11

            What do you think is an ideal workload? (eg, How many complex patients are typical of your hospitals?) Does that vary from the VA to St. Joe's to Froedtert? How many patients should be admitted in 1 night by an intern? How many should an intern have ongoing responsibility for? Is there such a thing as too few patients?

          • 12

            If one of your family members were to admitted to your hospital at night with a life‐threatening condition, which situation would you prefer for their care (all other things being equal): admission by night float with handoff to a new but well‐rested resident or admission by a resident who then continues to care for that family member the next day but has been awake for 24 hours, admitting and cross‐covering other patients? Why?

          • 13

            What do you think was the intent of the ACGME rules? Do you think that those goals have been accomplished? Why or why not? How have they affected you as residents? How do you think that the ACGME work hour rules have influenced patient care?

           

Patient safety can be understood in terms of the Swiss cheese model of systems accidents. This model implies that many holes must align before an adverse event occurs.1 The limitations on work hours instituted by the Accreditation Council for Graduate Medical Education (ACGME)2 sought to close one hole by reducing fatigue in residents. As programs comply with these regulations, new interventions are being implemented to limit resident hours. This has resulted in more handoffs of care and therefore less continuity. The ultimate result may be to increase patient care errors by opening up new holes, the opposite of the stated goal of this reform.

Some residency programs have reported on their experience with hour reductions, giving insight into residents' perceptions on the benefits and drawbacks of such interventions. Residents have reported concern about continuity of care after such interventions.37 However, some residents believed they provided better patient care after the interventions to reduce hours.8, 9 Few studies have actually documented changes in the incidence of adverse events or errors as a result of work hour limitations.10 One study conducted prior to implementation of the ACGME work hour rules demonstrated more complications in internal medicine patients after New York's Code 405 (a state regulation that limited resident work hours, similar to the ACGME rules) was implemented.11 In contrast, another study showed that errors committed by interns were reduced with scheduling changes that resulted in shorter shifts and reduced hours.12

Because residents are on the front lines of patient care, they are uniquely positioned to provide insight into the impact of the work hour rules on patient safety. We conducted this study to more fully understand the effect of the ACGME work hour limitations and other possible factors on patient care errors from the perspectives of internal medicine residents.

METHODS

Design

We conducted 4 focus groups in total (Appendix 1). The first 3 focus groups followed the same focus group guide, developed after a literature review. Focus groups 1 and 2 were conducted at the university‐based program. Focus group 3 was conducted at the community teaching hospitalaffiliated program. The first 3 focus groups were analyzed before the fourth focus group was conducted. A new focus group guide was developed for the fourth focus group to further explore themes identified in the first 3 focus groups (Fig. 1 and Appendix 2). The fourth focus group was conducted at the program affiliated with a freestanding medical college. All focus groups were audiotaped and transcribed verbatim. Each lasted approximately 90‐120 minutes.

Figure 1

RESULTS

Twenty‐eight residents participated. Some of these residents had experience in the prework hour era, and some did not. Average age was 28 years (range 26‐33 years); 18 were women, and 11 were interns (Table 2). The focus groups ranged in size from 5 to 9. A sample of the codes and their definitions can be found in Table 3.

The Model

The model (Fig. 2) illustrates resident‐perceived contributors to patient care mistakes related to the ACGME work hour rules. These contributors are in the center circle. They include fatigue, inexperience, sign‐out, not knowing their own patients well enough, entropy (which we defined as the amount of chaos in the system), and workload. They are not listed in order of importance. The boxes outside the circle are consequences of the ACGME work hour rules and their perceived impact on the contributors to patient care mistakes. At the top are the intended consequences, that is the specific goals of the ACGME: less resident time in the hospital (ie, reduced hours) and improved well‐being.17 At the bottom are the unintended consequences: more patient care discontinuity and compliance with the work hour rules becoming a goal equally important to providing high‐quality patient care. Of these 4 consequences, only improved well‐being was viewed by the residents as decreasing patient care mistakes. The other consequences were cited by residents as sometimes increasing patient care errors. Because of the complexity of the model, several factors not directly related to resident work hours were identified in the analysis but are not shown in the model. They include faculty involvement and team work (usually positive influences), nurses and information technology (could be positive or negative), and late‐night/early‐morning hours (negative).

Figure 2

The quotations below illustrate the relationships between the consequences of the work hour rules, resident‐perceived contributors to patient care mistakes, and actual patient care.

DISCUSSION

We conducted this study to better understand why internal medicine residents thought patient care mistakes occurred; we were particularly interested in how they perceived the impact of certain aspects of the ACGME work hour rules on patient care mistakes. Designing systems that achieve compliance with the work hour rules while minimizing patient risk can best be accomplished by fully understanding why errors occur.

Our study revealed that in the opinion of some interns and residents, the work hour rules had consequences for patient care. Like any intervention, this one had both intended and unintended consequences.18 The ACGME has stated that improvement in residents' quality of life was an intended consequence,17 and the participants in our study reported that this had occurred. Despite uncertainty about the overall impact on patient outcomes, residents were glad to have more time away from the hospital.

Our respondents reported that not knowing patients well was a factor that contributed to patient care errors. It is intuitive that working fewer hours often results in more handoffs of care,19 a situation characterized by not knowing patients well. However, residents also identified not getting to know their own patients well as a factor that led to patient care mistakes because of (1) incomplete knowledge of a patient's status, (2) delays in diagnosis, and (3) errors in management. They also described feelings of professional disappointment and frustration at not being able to perform certain aspects of patient care (eg, family meetings) because of the hour limits and the inflexibility of the rules. As we strive to redefine professionalism in the setting of reduced work hours,20 this phenomenon should be addressed.

Sign‐out was identified as another contributor to patient care errors. The effectiveness of sign‐outs is a concern across medicine, and the Joint Commission on Accreditation of Healthcare Organizations made sign‐out procedures one of its priority areas in 2006.21 Much has been written about resident sign‐out, emphasizing the relationship between poor‐quality sign‐outs and patient safety.19, 22 However, barriers to effective sign‐out processes persist,23 even though standardized sign‐out strategies have been described.24, 25 Even in a rigorous study of work hours and patient safety, the computerized sign‐out template for the residents was rarely used.12 Cross‐coverage, or the patient care that occurs after sign‐out is complete, has also been linked to a greater likelihood of adverse events.26

Several factors not related to resident work hours were noted to often mitigate patient care mistakes. Physician teamwork, nursing, information technology (eg, computerized medical records), and faculty supervision were the most prominent. For example, the information technology available at the VA hospitals often helped to facilitate patient care, but it also provided an overwhelming amount of information to sift through. It was clear that the influence of some of these factors varied from institution to institution, reflecting the cultures of different programs.

Our results are consistent with those reported from previous studies. Striking a balance between preventing resident fatigue and preserving continuity of care has been debated since the ACGME announced changes to resident work hour limits.27 Resident quality of life generally improves and fatigue decreases with work hour limits in place,28 but patient safety remains a concern.10 Our findings corroborate the benefits of improved resident well‐being and the persistent concerns about patient safety, identified in a recently published study at a different institution.29 However, our findings expand on those reported in the literature by offering additional empirical evidence, albeit qualitative, about the way that residents see the relationships among the consequences of work hour rules, resident‐reported contributors to patient care mistakes, and the mistakes themselves.

Our study should be interpreted in the context of several limitations. First, the use of qualitative methods did not allow us to generalize or quantify our findings. However, we purposely included 3 diverse institutions with differing responses to the work hour rules to enhance the external validity of our findings. Second, the last focus group was conducted a year after the first 3; by that point, the work hour rules had been in place for 20 months. We believe that this was both a strength and a limitation because it allowed us to gain a perspective after some of the initial growing pains were over. This time lag also allowed for analysis of the first 3 transcripts so we could revise the focus group guide and ultimately determine that thematic saturation had occurred. In addition, few of our questions were phrased to evaluate the ACGME rules; instead, they asked about links among discontinuity, scheduling, fatigue, and patient care. We therefore believe that even residents who were not in the programs before the work hour rules began were still able to knowledgeably participate in the conversation. One question directly referable to the ACGME rules asked residents to reflect on problems arising from them. This could have led residents to only reflect on the problems associated with the rules. However, in all 4 focus groups, residents commented on the positive impact of improved well‐being resulting from the work hour rules. This led us to believe the respondents felt they could voice their favorable feelings as well as their unfavorable feelings about the rules. An additional limitation is that the agreement between coders was only 45%. It is important to realize that assessing coding agreement in qualitative work is quite difficult because it is often difficult to assign a single code to a section of text. When the coders discussed a disagreement, it was almost always the case that the difference was subtle and that the coding of either investigator would made sense for that text. Finally, our results are based on the participation of 28 residents. To be certain we were not representing the opinions of only a few people, we presented iterations of this model to faculty and resident groups for their feedback. Importantly, the residents offered no substantial changes or criticisms of the model.

Limitations notwithstanding, we believe our findings have important policy implications. First, despite work hours successfully being reduced, residents perceived no decrease in the amount of work they did. This resulted in higher workload and more entropy, suggesting that residency programs may need to carefully evaluate the patient care responsibility carried by residents. Second, continued effort to educate residents to provide effective sign‐out is needed. As one participant pointed out, residency offers a unique opportunity to learn to manage discontinuity in a controlled setting. Another educational opportunity is the chance to teach physician teamwork. Participants believed that effective teamwork could ameliorate some of the discontinuity in patient care. This teamwork training should include faculty as well, although further work is needed to define how faculty can best add to patient continuity while still fostering resident autonomy. Finally, the impact of work hour rules on the professional development of residents should be further explored.

In conclusion, we have proposed a model to explain the major resident‐reported contributors to patient care mistakes with respect to resident work hour rules. Our results help to clarify the next steps needed: testing the proposed relationships between the factors and patient care mistakes and rigorously evaluating solutions that minimize the impact of these factors. Returning to the Swiss cheese framework for describing systems accidents, our results suggest that although resident work hour reductions may have sufficiently filled the hole caused by resident fatigue, other gaps may have actually widened as a result of the systems put into place to achieve compliance. Continued vigilance is therefore necessary to both identify the additional holes likely to appear and, more importantly, effectively close those holes before patient harm occurs.

FOCUS GROUP GUIDE (4TH FOCUS GROUP)

The IOM definition of a medical error is the failure of a planned action to be completed as intended or the use of a wrong plan to achieve an aim.

• 1

  Please describe the call structure at each institution where you do ward months (eg, non‐ICU months).

• 2

  What are some settings at the hospitals where you work where you have seen medical errors, mistakes, or adverse outcomes?

• 3

  How do you think that other nurses influence the occurrence of medical errors, mistakes, or adverse outcomes?

   

  • a

    Clerks?

  • b

    Other ancillary staff?

  • 4

    How would you describe the responsibilities of a cross‐covering resident or intern?

  • 5

    How do you think continuity of care impacts patient care in terms of medical errors, mistakes, or adverse outcomes?

     

    • a

      What role do sign‐outs have?

    • 6

      How do you think that fatigue impacts patient care in terms of medical errors, mistakes, or adverse outcomes?

    • 7

      How do you think that technology such as computerized physician order entry impacts patient care in terms of medical errors, mistakes, or adverse outcomes?

       

      • a

        Electronic medical records?

      • b

        Palm pilots?

      • c

        Is there such a thing as too much information?

      • 8

        How do you think that experience (or inexperience) impacts patient care in terms of medical errors, mistakes, or adverse outcomes?

      • 9

        Please describe how attendings supervise you when you are on a ward team. How do you think that attending supervision impacts patient care in terms of medical errors, mistakes, or adverse outcomes?

         

        • a

          What about resident supervision of interns?

        • b

          What is the ideal role of an attending on a team?

        • c

          Can you think of a time when having attending input changed the plans or the course of a patient in a major way, good, bad, or neutral?

        • 10

          How do you think that time of day impacts patient care in terms of in terms of medical errors, mistakes, or adverse outcomes?

           

          • a

            How comfortable do you feel calling for help at night? What makes you more or less likely to do it (personal attributes of person to be called, situation, etc.)?

          • 11

            What do you think is an ideal workload? (eg, How many complex patients are typical of your hospitals?) Does that vary from the VA to St. Joe's to Froedtert? How many patients should be admitted in 1 night by an intern? How many should an intern have ongoing responsibility for? Is there such a thing as too few patients?

          • 12

            If one of your family members were to admitted to your hospital at night with a life‐threatening condition, which situation would you prefer for their care (all other things being equal): admission by night float with handoff to a new but well‐rested resident or admission by a resident who then continues to care for that family member the next day but has been awake for 24 hours, admitting and cross‐covering other patients? Why?

          • 13

            What do you think was the intent of the ACGME rules? Do you think that those goals have been accomplished? Why or why not? How have they affected you as residents? How do you think that the ACGME work hour rules have influenced patient care?

           

Specialist or generalist? The question of which physicians are best suited to treat patients with a single condition or in a particular care setting has been the subject of study and debate for decades.13 Investigators have asked whether cardiologists provide better care for patients with acute myocardial infarction1 or whether intensivists achieve superior outcomes in critical care settings.2 One implication of these studies is that a hospital or health plan armed with this knowledge would be capable of improving outcomes by directing a greater proportion of patients to the superior physician group. In fact, much of the literature reporting on the effect of hospitalists is simply a new variation on this old theme.48 Of course, to realize any potential gains, there must be an adequate number of specialists or the ability to increase the supply quickly. Neither option tends to be especially realistic. Further, these studies have a tendency to create false dilemmas because consultation and comanagement are more common than single‐handed care.

Because studies comparing the outcomes of physician groups are generally not randomized trials, minimizing the threat of selection bias (ie, patient prognosis influencing treatment assignment) is of paramount importance. For example, one can imagine how patients with a particularly poor prognosis in the setting of acute myocardial infarction (perhaps related to age or the presence of multiple comorbidities) might be preferentially directed toward a general medicine service, especially when remunerative cardiac intervention is unlikely. In such instances, comparing simple mortality rates would erroneously lead to the conclusion that patients cared for by cardiologists had better outcomes.

Multivariable modeling techniques like logistic and liner regression and more recently, propensity‐based methods, are the standard approaches used to adjust for differences in patient characteristics stemming from nonrandom assignment. When propensity methods are used, a multivariable model is created to predict the likelihood, or propensity, of a patient receiving treatment. Because it is not necessary to be parsimonious in the development of propensity models, they can include many factors and interaction terms that might be left out of a standard multivariable logistic regression. Then, the outcomes of patients with a similar treatment propensity who did receive the intervention can be compared to the outcomes of those who did not. Some have gone so far as to use the term pseudorandomized trial to describe this approach because it is often capable balancing covariates between the treated and nontreated patients. However, as sophisticated as this form of modeling may be, these techniques at best are only capable of reducing bias related to measured confounders. Residual bias from confounders that go unmeasured remains a threatand is particularly common when relying on administrative data sources.

In this issue of the Journal of Hospital Medicine, Gillum and Johnston9 apply a version of instrumental variable analysis, a technique borrowed from econometrics, to address the issue of unmeasured confounding head‐on. The approach, called group‐treatment analysis, is based on the relatively simple notion that if neurologist care is superior to that provided by generalists, all other things being equal, hospitals that admit a large proportion of their patients to neurologists should have better outcomes than those admitting a smaller proportion. This approach has theoretical advantages over propensity adjustment because it does not attempt to control for differences between treated and untreated patients at the individual hospital level, where, presumably, the problem of selection bias is more potent. Although their standard multivariable models suggested that patients admitted to a neurologist were 40% less likely to die while hospitalized than patients admitted to generalists, Gillum and Johnston found that after adjusting for the institutional rate of neurologist admission, any apparent benefit had disappeared. Similar results were observed in their analyses of length of stay and cost.

In some ways, the findings of this study are more startling for the questions they raise about the presence of residual bias in observational studies using conventional multivariable methods than for the fact that generalist care was found to be as safe as neurologist care and add to a growing body of evidence suggesting that stronger methods are required to deal with residual bias in observational studies.10

Although the results largely speak for themselves and should be reassuring given that most patients with ischemic stroke in the United States are and will continue to be cared for by generalists, a number of important questions remain unanswered. First, the focus of this study was on short‐term outcomes. Because functional status and quality of life probably matter as much or more to stroke patients than in‐hospital mortality and certainly length of stay or cost, we can only hope that it is safe to extrapolate from the authors' mortality findings. Second, this study relied on data from the late 1990s, before the widespread availability of hospitalists. How generalizable the findings would be in today's environment is uncertain. On a more practical level, the authors were unable to assess the impact of formal or informal consultation by a neurologist. If this played a significant role (a reasonable assumption, I think), this would have blurred any distinction between the 2 physician groups. For this reason one cannot draw any conclusions about a more pragmatic questionthe necessity or benefit of neurologist consultation in patients with ischemic stroke.

Looking ahead, researchers hoping to improve the outcomes of patients with acute ischemic stroke should focus on developing novel models of collaboration between hospitalists and neurologists, instead of simply trying to prove that a neurologist should take care of a patient suffering a stroke alone versus a hospitalist without help from a neurologist. We also should recognize that the use of protocols and checklists or leveraging information technology investments may provide clinical decision support that improves care more than just consulting a specialist or having them care for the patient.

Specialist or generalist? The question of which physicians are best suited to treat patients with a single condition or in a particular care setting has been the subject of study and debate for decades.13 Investigators have asked whether cardiologists provide better care for patients with acute myocardial infarction1 or whether intensivists achieve superior outcomes in critical care settings.2 One implication of these studies is that a hospital or health plan armed with this knowledge would be capable of improving outcomes by directing a greater proportion of patients to the superior physician group. In fact, much of the literature reporting on the effect of hospitalists is simply a new variation on this old theme.48 Of course, to realize any potential gains, there must be an adequate number of specialists or the ability to increase the supply quickly. Neither option tends to be especially realistic. Further, these studies have a tendency to create false dilemmas because consultation and comanagement are more common than single‐handed care.

Because studies comparing the outcomes of physician groups are generally not randomized trials, minimizing the threat of selection bias (ie, patient prognosis influencing treatment assignment) is of paramount importance. For example, one can imagine how patients with a particularly poor prognosis in the setting of acute myocardial infarction (perhaps related to age or the presence of multiple comorbidities) might be preferentially directed toward a general medicine service, especially when remunerative cardiac intervention is unlikely. In such instances, comparing simple mortality rates would erroneously lead to the conclusion that patients cared for by cardiologists had better outcomes.

Multivariable modeling techniques like logistic and liner regression and more recently, propensity‐based methods, are the standard approaches used to adjust for differences in patient characteristics stemming from nonrandom assignment. When propensity methods are used, a multivariable model is created to predict the likelihood, or propensity, of a patient receiving treatment. Because it is not necessary to be parsimonious in the development of propensity models, they can include many factors and interaction terms that might be left out of a standard multivariable logistic regression. Then, the outcomes of patients with a similar treatment propensity who did receive the intervention can be compared to the outcomes of those who did not. Some have gone so far as to use the term pseudorandomized trial to describe this approach because it is often capable balancing covariates between the treated and nontreated patients. However, as sophisticated as this form of modeling may be, these techniques at best are only capable of reducing bias related to measured confounders. Residual bias from confounders that go unmeasured remains a threatand is particularly common when relying on administrative data sources.

In this issue of the Journal of Hospital Medicine, Gillum and Johnston9 apply a version of instrumental variable analysis, a technique borrowed from econometrics, to address the issue of unmeasured confounding head‐on. The approach, called group‐treatment analysis, is based on the relatively simple notion that if neurologist care is superior to that provided by generalists, all other things being equal, hospitals that admit a large proportion of their patients to neurologists should have better outcomes than those admitting a smaller proportion. This approach has theoretical advantages over propensity adjustment because it does not attempt to control for differences between treated and untreated patients at the individual hospital level, where, presumably, the problem of selection bias is more potent. Although their standard multivariable models suggested that patients admitted to a neurologist were 40% less likely to die while hospitalized than patients admitted to generalists, Gillum and Johnston found that after adjusting for the institutional rate of neurologist admission, any apparent benefit had disappeared. Similar results were observed in their analyses of length of stay and cost.

In some ways, the findings of this study are more startling for the questions they raise about the presence of residual bias in observational studies using conventional multivariable methods than for the fact that generalist care was found to be as safe as neurologist care and add to a growing body of evidence suggesting that stronger methods are required to deal with residual bias in observational studies.10

Although the results largely speak for themselves and should be reassuring given that most patients with ischemic stroke in the United States are and will continue to be cared for by generalists, a number of important questions remain unanswered. First, the focus of this study was on short‐term outcomes. Because functional status and quality of life probably matter as much or more to stroke patients than in‐hospital mortality and certainly length of stay or cost, we can only hope that it is safe to extrapolate from the authors' mortality findings. Second, this study relied on data from the late 1990s, before the widespread availability of hospitalists. How generalizable the findings would be in today's environment is uncertain. On a more practical level, the authors were unable to assess the impact of formal or informal consultation by a neurologist. If this played a significant role (a reasonable assumption, I think), this would have blurred any distinction between the 2 physician groups. For this reason one cannot draw any conclusions about a more pragmatic questionthe necessity or benefit of neurologist consultation in patients with ischemic stroke.

Looking ahead, researchers hoping to improve the outcomes of patients with acute ischemic stroke should focus on developing novel models of collaboration between hospitalists and neurologists, instead of simply trying to prove that a neurologist should take care of a patient suffering a stroke alone versus a hospitalist without help from a neurologist. We also should recognize that the use of protocols and checklists or leveraging information technology investments may provide clinical decision support that improves care more than just consulting a specialist or having them care for the patient.

Specialist or generalist? The question of which physicians are best suited to treat patients with a single condition or in a particular care setting has been the subject of study and debate for decades.13 Investigators have asked whether cardiologists provide better care for patients with acute myocardial infarction1 or whether intensivists achieve superior outcomes in critical care settings.2 One implication of these studies is that a hospital or health plan armed with this knowledge would be capable of improving outcomes by directing a greater proportion of patients to the superior physician group. In fact, much of the literature reporting on the effect of hospitalists is simply a new variation on this old theme.48 Of course, to realize any potential gains, there must be an adequate number of specialists or the ability to increase the supply quickly. Neither option tends to be especially realistic. Further, these studies have a tendency to create false dilemmas because consultation and comanagement are more common than single‐handed care.

Because studies comparing the outcomes of physician groups are generally not randomized trials, minimizing the threat of selection bias (ie, patient prognosis influencing treatment assignment) is of paramount importance. For example, one can imagine how patients with a particularly poor prognosis in the setting of acute myocardial infarction (perhaps related to age or the presence of multiple comorbidities) might be preferentially directed toward a general medicine service, especially when remunerative cardiac intervention is unlikely. In such instances, comparing simple mortality rates would erroneously lead to the conclusion that patients cared for by cardiologists had better outcomes.

Multivariable modeling techniques like logistic and liner regression and more recently, propensity‐based methods, are the standard approaches used to adjust for differences in patient characteristics stemming from nonrandom assignment. When propensity methods are used, a multivariable model is created to predict the likelihood, or propensity, of a patient receiving treatment. Because it is not necessary to be parsimonious in the development of propensity models, they can include many factors and interaction terms that might be left out of a standard multivariable logistic regression. Then, the outcomes of patients with a similar treatment propensity who did receive the intervention can be compared to the outcomes of those who did not. Some have gone so far as to use the term pseudorandomized trial to describe this approach because it is often capable balancing covariates between the treated and nontreated patients. However, as sophisticated as this form of modeling may be, these techniques at best are only capable of reducing bias related to measured confounders. Residual bias from confounders that go unmeasured remains a threatand is particularly common when relying on administrative data sources.

In this issue of the Journal of Hospital Medicine, Gillum and Johnston9 apply a version of instrumental variable analysis, a technique borrowed from econometrics, to address the issue of unmeasured confounding head‐on. The approach, called group‐treatment analysis, is based on the relatively simple notion that if neurologist care is superior to that provided by generalists, all other things being equal, hospitals that admit a large proportion of their patients to neurologists should have better outcomes than those admitting a smaller proportion. This approach has theoretical advantages over propensity adjustment because it does not attempt to control for differences between treated and untreated patients at the individual hospital level, where, presumably, the problem of selection bias is more potent. Although their standard multivariable models suggested that patients admitted to a neurologist were 40% less likely to die while hospitalized than patients admitted to generalists, Gillum and Johnston found that after adjusting for the institutional rate of neurologist admission, any apparent benefit had disappeared. Similar results were observed in their analyses of length of stay and cost.

In some ways, the findings of this study are more startling for the questions they raise about the presence of residual bias in observational studies using conventional multivariable methods than for the fact that generalist care was found to be as safe as neurologist care and add to a growing body of evidence suggesting that stronger methods are required to deal with residual bias in observational studies.10

Although the results largely speak for themselves and should be reassuring given that most patients with ischemic stroke in the United States are and will continue to be cared for by generalists, a number of important questions remain unanswered. First, the focus of this study was on short‐term outcomes. Because functional status and quality of life probably matter as much or more to stroke patients than in‐hospital mortality and certainly length of stay or cost, we can only hope that it is safe to extrapolate from the authors' mortality findings. Second, this study relied on data from the late 1990s, before the widespread availability of hospitalists. How generalizable the findings would be in today's environment is uncertain. On a more practical level, the authors were unable to assess the impact of formal or informal consultation by a neurologist. If this played a significant role (a reasonable assumption, I think), this would have blurred any distinction between the 2 physician groups. For this reason one cannot draw any conclusions about a more pragmatic questionthe necessity or benefit of neurologist consultation in patients with ischemic stroke.

Looking ahead, researchers hoping to improve the outcomes of patients with acute ischemic stroke should focus on developing novel models of collaboration between hospitalists and neurologists, instead of simply trying to prove that a neurologist should take care of a patient suffering a stroke alone versus a hospitalist without help from a neurologist. We also should recognize that the use of protocols and checklists or leveraging information technology investments may provide clinical decision support that improves care more than just consulting a specialist or having them care for the patient.

A 26‐year‐old woman presented with a 1‐week history of epigastric and left upper quadrant pain associated with nausea and vomiting. She also described 3 weeks of constant substernal chest pain, dyspnea, and decreased exercise tolerance.

Her medical history was significant for a pituitary macroadenoma diagnosed 6 years previously that had been treated with cabergoline. She had a miscarriage 7 years ago but gave birth to a healthy child 5 months prior to admission. She had smoked 2 cigarettes per day for the last 7 years. She denied alcohol or illicit drug use. Her mother had sickle cell trait.

On admission, her heart rate was 112 beats/minute, blood pressure was 110/80 mm Hg, and respiratory rate was 26 per minute. Jugular venous distension was not appreciated. She had decreased breath sounds over the right lung base. The apical impulse was palpated in the left sixth intercostal space 1 cm lateral to the midclavicular line, and a 2/6 holosystolic murmur was auscultated at the left lower sternal border. No other murmurs or S3 or S4 gallop could be appreciated. There were no vascular or immunological phenomena suggestive of infective endocarditis. She had abdominal tenderness in the epigastrium and bilateral upper quadrants. There was no lower extremity edema, and the extremities were well perfused.

Complete blood count, electrolytes, and liver, renal, and coagulation profiles were normal. Her chest x‐ray revealed cardiomegaly and bilateral pleural effusions. EKG showed sinus tachycardia and nonspecific T‐wave changes. To further evaluate her abdominal pain, a CT scan of the abdomen and pelvis (Fig. 1) was ordered. This revealed a 3 by 1.8 cm splenic infarct. Because of her respiratory symptoms and tachycardia, a pulmonary embolism was suspected but was ruled out with a CT angiogram of the chest.

Figure 1

She was diagnosed with new‐onset heart failure and a splenic infarct. However, it was unclear if the 2 problems were linked. Possible etiologies of the splenic infarct included thrombus from hypercoagulable state (given her prior miscarriage, postpartum state), infarct from hemoglobinopathy (given her family history), septic emboli from infective endocarditis, and peripartum cardiomyopathy associated with embolism to the spleen.

Pain control, empiric antibiotics, and intravenous diuretics were started. Twelve hours later, the patient's dyspnea and chest pain resolved. Her blood culture results were negative, and hemoglobin electrophoresis was normal. Results of a hypercoagulable workup for an arterial thrombus that included lupus anticoagulant, anticardiolipin antibodies, and antibodies to 2‐glycoprotein‐I were negative. The echocardiogram (Fig. 2) showed a dilated left ventricle with an ejection fraction (EF) of 10%15%, normal valvular morphology without vegetations, moderate mitral and tricuspid regurgitation, and a 1‐cm left ventricular thrombus and 3 small adjacent thrombi.

Figure 2

Based on the echocardiographic data, recent pregnancy, and absence of other risk factors for heart failure, a diagnosis was made of peripartum cardiomyopathy with left ventricular thrombi and subsequent embolization to the spleen.

Standard heart failure therapy including diuretics, beta‐blockers, and angiotensin‐converting enzyme inhibitors and anticoagulation with warfarin were started. Within 24 hours, the patient was asymptomatic except for minimal abdominal pain. The patient was discharged home in a stable condition the following day. At her outpatient follow‐up 3 months later, she was well compensated and asymptomatic.

DISCUSSION

Using the search terms peripartum cardiomyopathy, cardiomyopathy, thromboembolism, and postpartum period, we performed a MEDLINE search of the English literature from 1950 to 2007. We did not find any reported cases of splenic infarction complicating peripartum cardiomyopathy.

Peripartum cardiomyopathy (PPCM) is a form of dilated cardiomyopathy that occurs as a complication of pregnancy. It can present with heart failure in the last month of pregnancy or within 5 months after delivery.1, 2 The incidence of PPCM is unknown but has been estimated at 1 in 30004000 live births.3

Our patient met the criteria for PPCM as set forth by the National Heart, Lung, and Blood Institute (NHLBI), in conjunction with the Office of Rare Disease of the National Institutes of Health in April 1997.3 To establish a diagnosis of PPCM, 4 criteria have to be met:

• 1

  Development of heart failure in the last month of pregnancy or within 5 months after delivery;

• 2

  Absence of an identifiable cause of heart failure;

• 3

  Absence of recognizable heart disease prior to the last month of pregnancy; and

• 4

  Left ventricular systolic dysfunction demonstrated by echocardiographic variables such as depressed shortening fraction or left ventricular ejection fraction < 45%.

Thromboembolism has been reported with an incidence of 4% to 30% in peripartum cardiomyopathy.4 In our literature review, we found several case reports of thromboembolic phenomena complicating peripartum cardiomyopathy. These included lower extremity arterial thromboembolism with compromised circulation,5 cerebral embolism,6 and acute myocardial infarction secondary to coronary artery embolism.7 This is the first reported case of splenic artery embolization leading to splenic infarct as a complication of peripartum cardiomyopathy.

Because of the high risk of thromboembolism, the NHLBI recommends that anticoagulation be added to the standard heart failure treatment of PPCM patients with an ejection fraction of less than 35%,3 although there are no prospective randomized clinical trial data to support this recommendation. For anticoagulation, heparin is generally used in the antepartum period and warfarin in the postpartum period. It has been recommended that anticoagulation be continued for as long as the cardiomegaly persists.1

In addition to anticoagulation for PPCM patients with an EF < 35%, standard heart failure therapy includes salt restriction, diuretics, and beta‐blockers. Angiotensin‐converting enzyme inhibitors can be teratogenic during pregnancy but can be used after delivery. Hydralazine can be used safely during pregnancy as an alternative to angiotensin‐converting enzyme inhibitors. Patients failing maximal medical management may be candidates for cardiac transplantation.

Recommendations regarding subsequent pregnancies seem related to the return of ventricular size and function. Patients whose heart size does not return to normal should be strongly advised to avoid future pregnancies.2, 3 Patients who recover ventricular function may have deterioration of left ventricular function with future pregnancies.8 They should be counseled about the risk and closely monitored for development of heart failure if they become pregnant again.

A 26‐year‐old woman presented with a 1‐week history of epigastric and left upper quadrant pain associated with nausea and vomiting. She also described 3 weeks of constant substernal chest pain, dyspnea, and decreased exercise tolerance.

Her medical history was significant for a pituitary macroadenoma diagnosed 6 years previously that had been treated with cabergoline. She had a miscarriage 7 years ago but gave birth to a healthy child 5 months prior to admission. She had smoked 2 cigarettes per day for the last 7 years. She denied alcohol or illicit drug use. Her mother had sickle cell trait.

On admission, her heart rate was 112 beats/minute, blood pressure was 110/80 mm Hg, and respiratory rate was 26 per minute. Jugular venous distension was not appreciated. She had decreased breath sounds over the right lung base. The apical impulse was palpated in the left sixth intercostal space 1 cm lateral to the midclavicular line, and a 2/6 holosystolic murmur was auscultated at the left lower sternal border. No other murmurs or S3 or S4 gallop could be appreciated. There were no vascular or immunological phenomena suggestive of infective endocarditis. She had abdominal tenderness in the epigastrium and bilateral upper quadrants. There was no lower extremity edema, and the extremities were well perfused.

Complete blood count, electrolytes, and liver, renal, and coagulation profiles were normal. Her chest x‐ray revealed cardiomegaly and bilateral pleural effusions. EKG showed sinus tachycardia and nonspecific T‐wave changes. To further evaluate her abdominal pain, a CT scan of the abdomen and pelvis (Fig. 1) was ordered. This revealed a 3 by 1.8 cm splenic infarct. Because of her respiratory symptoms and tachycardia, a pulmonary embolism was suspected but was ruled out with a CT angiogram of the chest.

Figure 1

She was diagnosed with new‐onset heart failure and a splenic infarct. However, it was unclear if the 2 problems were linked. Possible etiologies of the splenic infarct included thrombus from hypercoagulable state (given her prior miscarriage, postpartum state), infarct from hemoglobinopathy (given her family history), septic emboli from infective endocarditis, and peripartum cardiomyopathy associated with embolism to the spleen.

Pain control, empiric antibiotics, and intravenous diuretics were started. Twelve hours later, the patient's dyspnea and chest pain resolved. Her blood culture results were negative, and hemoglobin electrophoresis was normal. Results of a hypercoagulable workup for an arterial thrombus that included lupus anticoagulant, anticardiolipin antibodies, and antibodies to 2‐glycoprotein‐I were negative. The echocardiogram (Fig. 2) showed a dilated left ventricle with an ejection fraction (EF) of 10%15%, normal valvular morphology without vegetations, moderate mitral and tricuspid regurgitation, and a 1‐cm left ventricular thrombus and 3 small adjacent thrombi.

Figure 2

Based on the echocardiographic data, recent pregnancy, and absence of other risk factors for heart failure, a diagnosis was made of peripartum cardiomyopathy with left ventricular thrombi and subsequent embolization to the spleen.

Standard heart failure therapy including diuretics, beta‐blockers, and angiotensin‐converting enzyme inhibitors and anticoagulation with warfarin were started. Within 24 hours, the patient was asymptomatic except for minimal abdominal pain. The patient was discharged home in a stable condition the following day. At her outpatient follow‐up 3 months later, she was well compensated and asymptomatic.

DISCUSSION

Using the search terms peripartum cardiomyopathy, cardiomyopathy, thromboembolism, and postpartum period, we performed a MEDLINE search of the English literature from 1950 to 2007. We did not find any reported cases of splenic infarction complicating peripartum cardiomyopathy.

Peripartum cardiomyopathy (PPCM) is a form of dilated cardiomyopathy that occurs as a complication of pregnancy. It can present with heart failure in the last month of pregnancy or within 5 months after delivery.1, 2 The incidence of PPCM is unknown but has been estimated at 1 in 30004000 live births.3

Our patient met the criteria for PPCM as set forth by the National Heart, Lung, and Blood Institute (NHLBI), in conjunction with the Office of Rare Disease of the National Institutes of Health in April 1997.3 To establish a diagnosis of PPCM, 4 criteria have to be met:

• 1

  Development of heart failure in the last month of pregnancy or within 5 months after delivery;

• 2

  Absence of an identifiable cause of heart failure;

• 3

  Absence of recognizable heart disease prior to the last month of pregnancy; and

• 4

  Left ventricular systolic dysfunction demonstrated by echocardiographic variables such as depressed shortening fraction or left ventricular ejection fraction < 45%.

Thromboembolism has been reported with an incidence of 4% to 30% in peripartum cardiomyopathy.4 In our literature review, we found several case reports of thromboembolic phenomena complicating peripartum cardiomyopathy. These included lower extremity arterial thromboembolism with compromised circulation,5 cerebral embolism,6 and acute myocardial infarction secondary to coronary artery embolism.7 This is the first reported case of splenic artery embolization leading to splenic infarct as a complication of peripartum cardiomyopathy.

Because of the high risk of thromboembolism, the NHLBI recommends that anticoagulation be added to the standard heart failure treatment of PPCM patients with an ejection fraction of less than 35%,3 although there are no prospective randomized clinical trial data to support this recommendation. For anticoagulation, heparin is generally used in the antepartum period and warfarin in the postpartum period. It has been recommended that anticoagulation be continued for as long as the cardiomegaly persists.1

In addition to anticoagulation for PPCM patients with an EF < 35%, standard heart failure therapy includes salt restriction, diuretics, and beta‐blockers. Angiotensin‐converting enzyme inhibitors can be teratogenic during pregnancy but can be used after delivery. Hydralazine can be used safely during pregnancy as an alternative to angiotensin‐converting enzyme inhibitors. Patients failing maximal medical management may be candidates for cardiac transplantation.

Recommendations regarding subsequent pregnancies seem related to the return of ventricular size and function. Patients whose heart size does not return to normal should be strongly advised to avoid future pregnancies.2, 3 Patients who recover ventricular function may have deterioration of left ventricular function with future pregnancies.8 They should be counseled about the risk and closely monitored for development of heart failure if they become pregnant again.

A 26‐year‐old woman presented with a 1‐week history of epigastric and left upper quadrant pain associated with nausea and vomiting. She also described 3 weeks of constant substernal chest pain, dyspnea, and decreased exercise tolerance.

Her medical history was significant for a pituitary macroadenoma diagnosed 6 years previously that had been treated with cabergoline. She had a miscarriage 7 years ago but gave birth to a healthy child 5 months prior to admission. She had smoked 2 cigarettes per day for the last 7 years. She denied alcohol or illicit drug use. Her mother had sickle cell trait.

On admission, her heart rate was 112 beats/minute, blood pressure was 110/80 mm Hg, and respiratory rate was 26 per minute. Jugular venous distension was not appreciated. She had decreased breath sounds over the right lung base. The apical impulse was palpated in the left sixth intercostal space 1 cm lateral to the midclavicular line, and a 2/6 holosystolic murmur was auscultated at the left lower sternal border. No other murmurs or S3 or S4 gallop could be appreciated. There were no vascular or immunological phenomena suggestive of infective endocarditis. She had abdominal tenderness in the epigastrium and bilateral upper quadrants. There was no lower extremity edema, and the extremities were well perfused.

Complete blood count, electrolytes, and liver, renal, and coagulation profiles were normal. Her chest x‐ray revealed cardiomegaly and bilateral pleural effusions. EKG showed sinus tachycardia and nonspecific T‐wave changes. To further evaluate her abdominal pain, a CT scan of the abdomen and pelvis (Fig. 1) was ordered. This revealed a 3 by 1.8 cm splenic infarct. Because of her respiratory symptoms and tachycardia, a pulmonary embolism was suspected but was ruled out with a CT angiogram of the chest.

Figure 1

She was diagnosed with new‐onset heart failure and a splenic infarct. However, it was unclear if the 2 problems were linked. Possible etiologies of the splenic infarct included thrombus from hypercoagulable state (given her prior miscarriage, postpartum state), infarct from hemoglobinopathy (given her family history), septic emboli from infective endocarditis, and peripartum cardiomyopathy associated with embolism to the spleen.

Pain control, empiric antibiotics, and intravenous diuretics were started. Twelve hours later, the patient's dyspnea and chest pain resolved. Her blood culture results were negative, and hemoglobin electrophoresis was normal. Results of a hypercoagulable workup for an arterial thrombus that included lupus anticoagulant, anticardiolipin antibodies, and antibodies to 2‐glycoprotein‐I were negative. The echocardiogram (Fig. 2) showed a dilated left ventricle with an ejection fraction (EF) of 10%15%, normal valvular morphology without vegetations, moderate mitral and tricuspid regurgitation, and a 1‐cm left ventricular thrombus and 3 small adjacent thrombi.

Figure 2

Based on the echocardiographic data, recent pregnancy, and absence of other risk factors for heart failure, a diagnosis was made of peripartum cardiomyopathy with left ventricular thrombi and subsequent embolization to the spleen.

Standard heart failure therapy including diuretics, beta‐blockers, and angiotensin‐converting enzyme inhibitors and anticoagulation with warfarin were started. Within 24 hours, the patient was asymptomatic except for minimal abdominal pain. The patient was discharged home in a stable condition the following day. At her outpatient follow‐up 3 months later, she was well compensated and asymptomatic.

DISCUSSION

Using the search terms peripartum cardiomyopathy, cardiomyopathy, thromboembolism, and postpartum period, we performed a MEDLINE search of the English literature from 1950 to 2007. We did not find any reported cases of splenic infarction complicating peripartum cardiomyopathy.

Peripartum cardiomyopathy (PPCM) is a form of dilated cardiomyopathy that occurs as a complication of pregnancy. It can present with heart failure in the last month of pregnancy or within 5 months after delivery.1, 2 The incidence of PPCM is unknown but has been estimated at 1 in 30004000 live births.3

Our patient met the criteria for PPCM as set forth by the National Heart, Lung, and Blood Institute (NHLBI), in conjunction with the Office of Rare Disease of the National Institutes of Health in April 1997.3 To establish a diagnosis of PPCM, 4 criteria have to be met:

• 1

  Development of heart failure in the last month of pregnancy or within 5 months after delivery;

• 2

  Absence of an identifiable cause of heart failure;

• 3

  Absence of recognizable heart disease prior to the last month of pregnancy; and

• 4

  Left ventricular systolic dysfunction demonstrated by echocardiographic variables such as depressed shortening fraction or left ventricular ejection fraction < 45%.

Thromboembolism has been reported with an incidence of 4% to 30% in peripartum cardiomyopathy.4 In our literature review, we found several case reports of thromboembolic phenomena complicating peripartum cardiomyopathy. These included lower extremity arterial thromboembolism with compromised circulation,5 cerebral embolism,6 and acute myocardial infarction secondary to coronary artery embolism.7 This is the first reported case of splenic artery embolization leading to splenic infarct as a complication of peripartum cardiomyopathy.

Because of the high risk of thromboembolism, the NHLBI recommends that anticoagulation be added to the standard heart failure treatment of PPCM patients with an ejection fraction of less than 35%,3 although there are no prospective randomized clinical trial data to support this recommendation. For anticoagulation, heparin is generally used in the antepartum period and warfarin in the postpartum period. It has been recommended that anticoagulation be continued for as long as the cardiomegaly persists.1

In addition to anticoagulation for PPCM patients with an EF < 35%, standard heart failure therapy includes salt restriction, diuretics, and beta‐blockers. Angiotensin‐converting enzyme inhibitors can be teratogenic during pregnancy but can be used after delivery. Hydralazine can be used safely during pregnancy as an alternative to angiotensin‐converting enzyme inhibitors. Patients failing maximal medical management may be candidates for cardiac transplantation.

Recommendations regarding subsequent pregnancies seem related to the return of ventricular size and function. Patients whose heart size does not return to normal should be strongly advised to avoid future pregnancies.2, 3 Patients who recover ventricular function may have deterioration of left ventricular function with future pregnancies.8 They should be counseled about the risk and closely monitored for development of heart failure if they become pregnant again.

Status asthmaticus, although a relatively infrequent cause of admission to the intensive care unit, carries a significant risk of mortality and complications of critical care.1 Asthma prevalence has risen,2 and recent data have suggested an improvement in overall mortality.3 Yet there may remain a subgroup of patients with the most severe asthma in whom this outcome benefit may not be seen. Asthma severity and mortality may be concentrated in certain urban areas, and there may even be disparities within cities. One recent study found a trend toward fewer and less severe presentations of ICU patients with status asthmaticus.4 Our clinical experience in an urban hospital suggested otherwise, and we undertook an examination of status asthmaticus and compared these data with those of our previously published experience at this center.5

MATERIALS AND METHODS

A retrospective review was performed of all patients with status asthmaticus admitted to the medical intensive care unit (MICU) of St. Luke's Hospital during the 5‐year period January 2002 through December 2006. St. Luke's Hospital is a university‐affiliated hospital in New York City. Patients were identified by discharge diagnosis of status asthmaticus through a computerized medical record database. Demographic data, initial presentation data, MICU course, and outcome were collected. Results were compared to our previous study during the 5‐year period 19951999 at this institution.5 Data are presented as means standard deviations.

The means of the groups were compared using the Student t test.

RESULTS

There were 89 MICU admissions for status asthmaticus; the records of 84 patients were available for review. The hospital admission rate for asthma remained stable at 1.6% of admissions during the period 20022006, compared with 1.4% of hospital admissions during the previous study period of 19951999. In the current study, 3% of asthma admissions required MICU care compared with 5% in the prior era.

Between the 2 study periods, there were no changes in MICU admission criteria or new protocols for management of status asthmaticus in the emergency department. The only difference in ICU management of intubated patients is that in the most recent study period there was an emphasis on earlier identification of patients for extubation. A new sedative, propofol, was available for ICU sedation during the current study period.

Two patients were admitted to the MICU 4 times, and 9 patients were admitted twice. Each presentation was counted as a separate admission and was analyzed individually. Seven patients (8%) had sustained a cardiopulmonary arrest prior to MICU admission. All were intubated in the field by emergency medical services. Characteristics of the patients are shown in Table 1. African American and Hispanic patients constituted 96% of the group. Half the patients were current cigarette smokers, and 30% admitted to current use of illicit drug. Fifty‐five percent of patients reported allergies (dust, pollen, pets), and 59% had previously been intubated for asthma.

Status asthmaticus was associated with an upper respiratory tract infection in 54%, illicit drug use in 15%, allergies in 12%, and a recent corticosteroid taper in 8% of exacerbations. Almost all patients had used a short‐acting beta‐2 agonist, and 78% had been prescribed inhaled corticosteroids either alone or in combination. Thirty‐six percent had used oral prednisone. Nonadherence was self‐reported by 45% of patients (Table 2).

Emergency department management for all patients included inhaled beta‐2 agonist therapy administered continuously, intravenous corticosteroid therapy (methylprednisolone 125 mg once), and magnesium sulfate (2 g intravenously).

Noninvasive ventilation was initiated in 10 patients (Table 2).

DISCUSSION

We identified greater severity of status asthmaticus among patients requiring admission to our urban intensive care unit. Despite reports of improvement in outcome3 and reduction in the severity and number of MICU admissions by other investigators4 in New York City, patients with status asthmaticus admitted to the MICU suffered significant mortality and morbidity. During the recent 5‐year period, compared with the period reported in our previous report,5 these patients had greater respiratory acidosis, more frequent need for neuromuscular blockade, longer duration of mechanical ventilation, increased complications, and higher unadjusted mortality.

There remain few large series of status asthmaticus. Episodes of life‐threatening asthma occur more frequently in specific high‐risk areas. We had the benefit of a prior study in our institution in order to compare trends in status asthmaticus. With greater attention to asthma severity, treatment, access to information, and medical care, a change in demographic features may have been expected. Yet we found that noncompliance with medications and smoking and illicit drug usage increased in this recent 5‐year period compared with the prior study period. Minority populations are also at particular risk for severe asthma.6

Noninvasive ventilation has been shown to be effective in acute hypercapnic respiratory failure in patients with chronic obstructive lung disease.7, 8 A small study of asthma found that NIV was associated with a reduction in PaCO₂ during the early hours of use and that mortality and complications were not increased in those who subsequently required intubation.9 However, in another study of 27 patients managed with NIV, 2 of the 5 patients requiring intubation died.10 In the 1 randomized controlled study of NIV in severe asthma, Soroksky et al. found that NIV significantly improved lung function and decreased hospitalization rate compared with the use of conventional therapy alone. The average PaCO₂ and pH of these patients were 33.59 mm Hg and 7.41, respectively.11 Meduri et al. reported a small series of 17 patients with severe asthma treated with NIV, 2 of whom were subsequently intubated. The initial pH and PaCO₂ of these patients averaged 7.25 and 65 mm Hg, respectively.12 In our series, NIV was used in 10 patients, 4 of whom were subsequently intubated. The average time on NIV before intubation was 2 hours, and there were no deaths in this group. Patients who were intubated after NIV had a statistically significant lower pH (7.17) and higher PaCO₂ (76 mm Hg) on admission than those who were successfully managed with NIV, with the pH and PaCO₂ of the latter group 7.32 and 50 mm Hg, respectively.

Improvement in mortality of status asthmaticus over the past decades has been attributed to improved ventilatory strategy using permissive hypercapnia. This approach has been credited with a decrease in barotrauma, hemodynamic instability, and mortality.5, 13 The latter complications were mainly a result of the dynamic hyperinflation found in patients with severe asthma. Decreasing the respiratory rate and tidal volume as well as increasing the inspiratory flow rate will lead to an increase in expiratory time and will subsequently decrease the dynamic hyperinflation. With this approach, hypercapnia may occur. Hypercapnia (PaCO₂ level up to 90 mm Hg) is generally well tolerated when oxygenation is maintained.14 Sedation is crucial to achieving optimal ventilation. Because of its short duration of action and bronchodilator effects,15, 16 propofol was the main sedative used in our MICU. Additional sedatives were required for half our patients. A prolonged sedative effect was noted in several cases, which prompted additional neurologic evaluation. It is conceivable that higher doses of sedatives are required for ventilatory control of young patients with a strong respiratory drive.

The administration of therapeutic paralysis is generally avoided in patients with status asthmaticus treated concurrently with corticosteroids. Myopathy may develop in the setting of neuromuscular blockade and corticosteroid administration and prolong ventilatory failure.17 In our earlier series, only 1 patient received a paralytic agent; in the current series, neuromuscular paralysis was needed in 6 episodes despite maximum sedative infusion. Patients requiring neuromuscular blockade were younger and had a significantly lower pH and higher PaCO₂ than did those not receiving neuromuscular blockade. These patients developed more complications, including prolonged weakness, supporting the general approach of avoiding paralytic use unless absolutely necessary. It is noteworthy that despite this greater degree of respiratory failure and subsequent ICU complications, no patients in this group died.

The median duration of mechanical ventilation was 4.4 days. Complications included ventilator‐associated pneumonia, catheter‐related infection, excessive sedation, and prolonged weakness. These events occurred primarily in patients who received paralytics and patients whose mechanical ventilation was prolonged. The average duration of mechanical ventilation for patients who had ventilator‐associated pneumonia and catheter‐related infection was 22 and 31 days, respectively.

Status asthmaticus in pregnancy deserves special attention, and its course has not been well described in the literature. We report finding that in the current study period there were 5 pregnant patients requiring ICU management for status asthmaticus, all with dramatic degrees of hypercapnia and acidosis during controlled mechanical ventilation; the highest PaCO₂ and lowest pH averaged 101 mm Hg and 7.06, respectively. Management of status asthmaticus in pregnancy is no different than in nonpregnant individuals, but there are concerns about the effects of hypercapnia and acidosis on the fetus.18 In all 4 patients who delivered, the pregnancies resulted in healthy babies. In the 1 patient who suffered a pneumomediastinum during early labor, the decision was made for cesarean delivery because of concerns about potential worsening of the barotrauma and maternal cardiopulmonary condition. This patient did not require intubation prior to or during the cesarean delivery. Collaboration with the obstetrician is essential in the management of these cases.

Despite advances in ventilator management and critical care, there remains a mortality risk in patients with status asthmaticus.17, 19, 20 In our study, 6 patients (7%) died; 3 patients died after suffering pre‐MICU cardiac arrest, and 3 patients died of multiorgan failure. Regular asthma clinic follow‐up, to include counseling about smoking cessation and illicit drugs, is essential. Unfortunately, only 45% of our patients had specialty clinic referral on discharge. Lack of patient understanding of their illness may also complicate their care, as demonstrated by nonadherence to medication and medical appointments. Five of our patients left against medical advice, 4 of them within a day of extubation.

Our study had several limitations. Patients were identified based on admission diagnosis by the attending physician; the coexistence of chronic obstructive pulmonary disease could not always be definitely excluded. However, all patients had a prior diagnosis of asthma and had been treated for asthma. The young age of the patient group is consistent with that reported in the literature.

It is difficult to compare studies of status asthmaticus, given the dynamic nature of the airways disease and individual clinician judgments about intubation and extubation. We believe that longer duration of ventilation reflects more severe asthma, especially in this time when clinicians attempt noninvasive ventilation and daily trials of spontaneous breathing for earlier extubation.

In conclusion, this report describes an increase in the severity of status asthmaticus in patients admitted to an urban MICU. The reason for the increase in severity compared to our previous study is uncertain. Possible factors include: cigarette and substance use, refractoriness to therapy because of environment or smoking, inadequate medical care, poor understanding of illness, and adherence to therapy. As the ICU management is supportive, the best approach is prevention, targeting at‐risk minority populations with education, counseling for smoking and drug cessation, and specialty care. Once status asthmaticus has developed, a careful, limited trial of NIV in selected patients may offer benefits in the management of ventilatory failure and avoidance of ICU complications.

Status asthmaticus, although a relatively infrequent cause of admission to the intensive care unit, carries a significant risk of mortality and complications of critical care.1 Asthma prevalence has risen,2 and recent data have suggested an improvement in overall mortality.3 Yet there may remain a subgroup of patients with the most severe asthma in whom this outcome benefit may not be seen. Asthma severity and mortality may be concentrated in certain urban areas, and there may even be disparities within cities. One recent study found a trend toward fewer and less severe presentations of ICU patients with status asthmaticus.4 Our clinical experience in an urban hospital suggested otherwise, and we undertook an examination of status asthmaticus and compared these data with those of our previously published experience at this center.5

MATERIALS AND METHODS

A retrospective review was performed of all patients with status asthmaticus admitted to the medical intensive care unit (MICU) of St. Luke's Hospital during the 5‐year period January 2002 through December 2006. St. Luke's Hospital is a university‐affiliated hospital in New York City. Patients were identified by discharge diagnosis of status asthmaticus through a computerized medical record database. Demographic data, initial presentation data, MICU course, and outcome were collected. Results were compared to our previous study during the 5‐year period 19951999 at this institution.5 Data are presented as means standard deviations.

The means of the groups were compared using the Student t test.

RESULTS

There were 89 MICU admissions for status asthmaticus; the records of 84 patients were available for review. The hospital admission rate for asthma remained stable at 1.6% of admissions during the period 20022006, compared with 1.4% of hospital admissions during the previous study period of 19951999. In the current study, 3% of asthma admissions required MICU care compared with 5% in the prior era.

Between the 2 study periods, there were no changes in MICU admission criteria or new protocols for management of status asthmaticus in the emergency department. The only difference in ICU management of intubated patients is that in the most recent study period there was an emphasis on earlier identification of patients for extubation. A new sedative, propofol, was available for ICU sedation during the current study period.

Two patients were admitted to the MICU 4 times, and 9 patients were admitted twice. Each presentation was counted as a separate admission and was analyzed individually. Seven patients (8%) had sustained a cardiopulmonary arrest prior to MICU admission. All were intubated in the field by emergency medical services. Characteristics of the patients are shown in Table 1. African American and Hispanic patients constituted 96% of the group. Half the patients were current cigarette smokers, and 30% admitted to current use of illicit drug. Fifty‐five percent of patients reported allergies (dust, pollen, pets), and 59% had previously been intubated for asthma.