One financial incentive to improve quality of care is the Centers for Medicare and Medicaid Services' (CMS) policy to not pay additionally for certain adverse events that are classified as hospital‐acquired conditions (HACs).[1, 2, 3] HACs are specific conditions that occur during the hospital stay and presumably could have been prevented.[4, 5, 6] Under the CMS policy, if an HAC occurs during a patient's stay, that condition is not included in the Medicare Severity Diagnosis‐Related Group (MS‐DRG) assignment.

The MS‐DRG assigned to a patient discharge determines reimbursement. Each MS‐DRG is assigned a weight, which is used to adjust for the fact that the treatment of different conditions consume different resources and have difference costs. Groups of patients who are expected to require above‐average resources have a higher weight than those who require fewer resources, and higher‐weighted MS‐DRG assignment results in a higher payment. In some cases, the inclusion of the diagnosis code of an HAC in the determination of the MS‐DRG results in a higher complexity level and higher DRG weight. The policy is designed to shift the incremental costs associated with treating the HAC to the hospital. As of October 2009, there were 10 HACs included in the CMS nonpayment program (see Supporting Table 1 in the online version of this article). CMS expanded the list of HACs to include 13 conditions in 2013.

Withholding additional reimbursement for an HAC has been controversial. One area of debate is that the assignment of an HAC may be imprecise, in part due to the variation in how physicians document in the medical record.[1, 2, 6, 7, 8, 9] Coding is derived from documentation in physician notes and is the primary mechanism for assigning International Classification of Diseases, 9th Revision, Clinical Modification (ICD‐9) diagnosis codes to the patient's encounter. The coding process begins with health information technicians (ie, medical record coders) reviewing all medical record documentation to assign diagnosis and procedure codes using the ICD‐9 codes.[10] Primary and secondary diagnoses are determined by certain definitions in the hospital setting. Secondary diagnoses can be further separated into complications or comorbidities in the MS‐DRG system, which can affect reimbursement. The MS‐DRG is then determined using these diagnosis and procedure codes. Physician documentation is the principal source of data for hospital billing, because health information technicians (ie, medical record coders) must assign a code based on what is documented in the chart. If key medical detail is missing or language is ambiguous, then coding can be inaccurate, which may lead to inappropriate compensation.[11]

Accurate and complete ICD‐9 diagnosis and procedure coding is essential for correct MS‐DRG assignment and reimbursement.[12] Physicians may influence coding prioritization by either over‐emphasizing a patient diagnosis or by downplaying the significance of new findings. In addition, unless the physician uses specific, accurate, and accepted terminology, the diagnosis may not even appear in the list of diagnosis codes. Medical records with nonstandard abbreviations may result in coder‐omission of key diagnoses. Finally, when clinicians use qualified diagnoses such as rule‐out or probable, the final diagnosis coded may not be accurate.[10]

Although the CMS policy creates a financial incentive for hospitals to improve quality, the extent to which the policy actually impacts reimbursement across multiple HACs has not been quantified. Additionally, if HACsas a policy initiativereflect actual quality of care, then the position of the ICD‐9 code should not affect MS‐DRG assignment. In this study we evaluated the extent to which MS‐DRG assignment would have been influenced by the presence of an HAC and tested the association of the position of an HAC in the list of ICD‐9 diagnosis codes with changes in MS‐DRG assignment.

METHODS

RESULTS

Of the 954,946 discharges from UHC academic medical centers, 7027 patients (0.7%) had an HAC. Of the patients with an HAC, 6047 did not change MS‐DRG assignment, whereas 980 patients (13.8%) had a change in MS‐DRG assignment. Patients with a change in MS‐DRG assignment were significantly different from those without a change in MS‐DRG assignment on all patient‐level characteristics and all but 1 hospital characteristic (Table 1). The variable with the largest absolute difference between those with and without a change in MS‐DRG was the actual position of the HAC; 86.7% of those with an MS‐DRG change had their HAC in the second position, whereas those without a change had only 11.5% in the second position.

After controlling for patient and hospital characteristics, an HAC in the second position in the list of ICD‐9 codes was associated with the greatest likelihood of a change in MS‐DRG assignment (P<0.001) (Table 2). Each additional ICD‐9 code decreased the odds of an MS‐DRG change (P=0.004), demonstrating that having more secondary diagnosis codes was associated with a lesser likelihood of an MS‐DRG change. After including the individual HACs in the regression model, the second position remained associated with the likelihood of a change in MS‐DRG assignment (results not shown). The predictive accuracy of our model did not improve, however, with the addition of type of HAC. The area under the ROC curve was 0.94 in both models, indicating high predictive power.

The proportion of cases with a change in MS‐DRG by severity of illness is reported in Table 3. The largest proportion of cases with a change in MS‐DRG was in the minor severity of illness category (41.3%), whereas only 2.6% of cases with an extreme severity of illness had a change in MS‐DRG. Figure 1 shows ROC curves stratified by severity of illness. Figure 1A illustrates the ROC curves for the 121 (1.7%) patients with minor severity of illness. The area under the ROC curve for the model including HAC position only was 0.74, indicating moderate predictive power. The inclusion of HAC type increased the predictive power to 0.91, and inclusion of sociodemographic characteristics further increased the predictive power to 0.95. Figure 1BD illustrates the ROC curves for moderate, major, and extreme severities of illness. For more severe illnesses, the predictive accuracy of the models with only HAC position were similar to the full models, demonstrating that HAC position alone had a high predictive power for change in MS‐DRG assignment.

Figure 1

In a sensitivity analysis that evaluated the robustness of our results to the specification of disease burden, inclusion of the number of comorbid conditions did not improve the predictive accuracy of the model. Although inclusion of individual comorbid conditions rather than number of diagnosis codes attenuated the odds ratio (OR) for HAC position (OR: 40.5 in the original model vs OR: 32.9 in the model with individual comorbid conditions), the improvement of the predictive accuracy of the model was small (area under the ROC curve=0.936 in the original model vs 0.943 in the model with individual conditions, P<0.001) (results not shown). In a sensitivity analysis using a hierarchical logistic regression model that included hospital random effects, hospital‐level variation in coding practices did not attenuate the relationship between HAC position and MS‐DRG change (results not shown).

DISCUSSION

This study investigated the association of a change in MS‐DRG assignment and position of the ICD‐9 diagnosis codes for HACs in a sample of patients discharged from US academic medical centers. We found that only 14% of the MS‐DRGs for patients with an HAC would have experienced a change in DRG assignment. Our results are consistent with those of Teufack et al.,[14] who estimated the economic impact of CMS' HAC policy for neurosurgery services at a single hospital to be 0.007% of overall net revenues. Nevertheless, the majority of hospitals have increased their efforts to prevent HACs that are included in CMS' policy.[15] At the same time, most hospitals have not increased their budgets for preventing HACs, and instead have reallocated resources from nontargeted HACs to those included in CMS' policy.

The low proportion of records that are impacted by the policy may be partially explained by the fact that CMS' policy only has an impact on reimbursement for MS‐DRGs with multiple levels. For example, heart failure has 3 levels of reimbursement in the MS‐DRG system (Table 4). Prior to CMS' policy, a heart failure patient with an air embolism as an HAC would have been classified in the most severe MS‐DRG (291), whereas after implementation the patient would be classified in the least severe MS‐DRG, if no other complication or comorbidity (CC) or a major complication or comorbidity (MCC) were present. Chest pain has only 1 level, and reimbursement for a patient with an HAC and classified in the chest pain MS‐DRG would not be impacted by CMS' policy. Most hospitalized patients are complicated, and the proportion of patients who are complicated will continue to increase over time as less complex care shifts to the ambulatory setting. The relative effectiveness of CMS' policy is likely to diminish with the continued shift of care to the ambulatory setting.

Patient discharges with a diagnosis code for as HAC in the second position were substantially more likely to have a change in MS‐DRG assignment compared to cases with an HAC listed lower in the final list of diagnosis codes. Perhaps it is not surprising that MS‐DRG assignment is most likely to change when the HAC is in the second position, because an ICD‐9 diagnosis code in this position is more likely to be a major complication or comorbidity. For HACs listed in a lower position of the list of ICD‐9 diagnosis codes, it is likely that the patient had another major complication or comorbidity listed in the second position that would have maintained classification in the same MS‐DRG. Our results suggest that physicians and hospitals caring for patients with lower complexity of illness will sustain a higher financial burden as a result of an HAC under CMS' policy compared to providers whose patients sustain the exact same HAC but have underlying medical care of greater complexity.

These results raise further concerns about the ability of CMS' payment policy to improve quality. One criticism of CMS' policy is that all HACs are not universally preventable. If they are not preventable, payment reductions promulgated via the policy would be punitive rather than incentivizing. In their study of central catheter‐associated bloodstream infections and catheter‐associated urinary tract infections, for example, Lee et al. found no change in infection rates after implementation of CMS' policy.[16] As such, some have suggested HACs should not be used to determine reimbursement, and CMS should abandon its current nonpayment policy.[4, 17] Our findings echo this criticism given that the financial penalty for an HAC depends on whether a patient is more or less complex.

Because coding emanates from physician documentation, a uniform documentation process must exist to ensure nonvariable coding practices.[1, 2, 7, 9] This is not the case, however, and some hospitals comanage documentation to refine or maximize the number of ICD‐9 diagnosis and procedure codes. Furthermore, there are certain differences in the documentation practices of individual physicians. If physician documentation and coding variation leads to fewer ICD‐9 codes during an encounter, the chance that an HAC will influence MS‐DRG change increases.

Another source of variation in coding practices found in this study was code sequencing. Although guidelines for appropriate ICD‐9 diagnosis coding currently exist, individual subjectivity remains. The most essential step in the coding process is identifying the principal diagnosis by extrapolating from physician documentation and clinical data. For example, when a patient is admitted for chest pain, and after some evaluation it is determined that the patient experienced a myocardial infarction, then myocardial infarction becomes the principal diagnosis. Based on that principal diagnosis, coders must select the relevant secondary diagnoses. The process involves a series of steps that must be followed exactly in order to ensure accurate coding.[12] There are no guidelines by which coding personnel must follow to sequence secondary diagnoses, with the exception of listed MCCs and CCs prior to other secondary diagnoses. Ultimately, the order by which these codes are assigned may result in unfavorable variation in MS‐DRG assignment.[1, 2, 4, 7, 8, 9, 17]

There are a number of limitations to this study. First, our cohort included only UHC‐affiliated academic medical centers, which may not represent all acute‐care hospitals and their coding practices. Although our data are for discharges prior to implementation of the policy, we were able to analyze the anticipated impact of the policy prior to any direct or indirect changes in coding that may have occurred in response to CMS' policy. Additionally, the number of diagnosis codes accepted by CMS was expanded from 9 to 25 in 2011. Future analyses that include MS‐DRG classifications with the expanded number of diagnosis codes should be conducted to validate our findings and determine whether any changes have occurred over time. It is not known whether low illness severity scores signify patient or hospital characteristics. If they represent patient characteristics, then CMS' policy will disproportionately affect hospitals taking care of less severely ill patients. Alternatively, if hospital coding practice explains more of the variation in the number of ICD‐9 codes (and thus severity of illness), then the system of adjudicating reimbursement via HACs to incentivize quality of care will be flawed, as there is no standard position for HACs on a more lengthy diagnosis list. Finally, we did not evaluate the change in DRG weight with the reassignment of MS‐DRG if the HAC had been included in the calculation. Future work should evaluate whether there is a differential impact of the policy by change in MS‐DRG weight.

CONCLUSION

Under CMS' current policy, hospitals and physicians caring for patients with lower severity of illness and have an HAC will be penalized by CMS disproportionately more than those caring for more complex, sicker patients with the identical HAC. If, in fact, HACs are indicators of a hospital's quality of care, then the CMS policy will likely do little to foster improved quality unless there is a reduction in coding practice variation and modifications to ensure that the policy impacts reimbursement, independent of severity of illness.

One financial incentive to improve quality of care is the Centers for Medicare and Medicaid Services' (CMS) policy to not pay additionally for certain adverse events that are classified as hospital‐acquired conditions (HACs).[1, 2, 3] HACs are specific conditions that occur during the hospital stay and presumably could have been prevented.[4, 5, 6] Under the CMS policy, if an HAC occurs during a patient's stay, that condition is not included in the Medicare Severity Diagnosis‐Related Group (MS‐DRG) assignment.

The MS‐DRG assigned to a patient discharge determines reimbursement. Each MS‐DRG is assigned a weight, which is used to adjust for the fact that the treatment of different conditions consume different resources and have difference costs. Groups of patients who are expected to require above‐average resources have a higher weight than those who require fewer resources, and higher‐weighted MS‐DRG assignment results in a higher payment. In some cases, the inclusion of the diagnosis code of an HAC in the determination of the MS‐DRG results in a higher complexity level and higher DRG weight. The policy is designed to shift the incremental costs associated with treating the HAC to the hospital. As of October 2009, there were 10 HACs included in the CMS nonpayment program (see Supporting Table 1 in the online version of this article). CMS expanded the list of HACs to include 13 conditions in 2013.

Withholding additional reimbursement for an HAC has been controversial. One area of debate is that the assignment of an HAC may be imprecise, in part due to the variation in how physicians document in the medical record.[1, 2, 6, 7, 8, 9] Coding is derived from documentation in physician notes and is the primary mechanism for assigning International Classification of Diseases, 9th Revision, Clinical Modification (ICD‐9) diagnosis codes to the patient's encounter. The coding process begins with health information technicians (ie, medical record coders) reviewing all medical record documentation to assign diagnosis and procedure codes using the ICD‐9 codes.[10] Primary and secondary diagnoses are determined by certain definitions in the hospital setting. Secondary diagnoses can be further separated into complications or comorbidities in the MS‐DRG system, which can affect reimbursement. The MS‐DRG is then determined using these diagnosis and procedure codes. Physician documentation is the principal source of data for hospital billing, because health information technicians (ie, medical record coders) must assign a code based on what is documented in the chart. If key medical detail is missing or language is ambiguous, then coding can be inaccurate, which may lead to inappropriate compensation.[11]

Accurate and complete ICD‐9 diagnosis and procedure coding is essential for correct MS‐DRG assignment and reimbursement.[12] Physicians may influence coding prioritization by either over‐emphasizing a patient diagnosis or by downplaying the significance of new findings. In addition, unless the physician uses specific, accurate, and accepted terminology, the diagnosis may not even appear in the list of diagnosis codes. Medical records with nonstandard abbreviations may result in coder‐omission of key diagnoses. Finally, when clinicians use qualified diagnoses such as rule‐out or probable, the final diagnosis coded may not be accurate.[10]

Although the CMS policy creates a financial incentive for hospitals to improve quality, the extent to which the policy actually impacts reimbursement across multiple HACs has not been quantified. Additionally, if HACsas a policy initiativereflect actual quality of care, then the position of the ICD‐9 code should not affect MS‐DRG assignment. In this study we evaluated the extent to which MS‐DRG assignment would have been influenced by the presence of an HAC and tested the association of the position of an HAC in the list of ICD‐9 diagnosis codes with changes in MS‐DRG assignment.

METHODS

RESULTS

Of the 954,946 discharges from UHC academic medical centers, 7027 patients (0.7%) had an HAC. Of the patients with an HAC, 6047 did not change MS‐DRG assignment, whereas 980 patients (13.8%) had a change in MS‐DRG assignment. Patients with a change in MS‐DRG assignment were significantly different from those without a change in MS‐DRG assignment on all patient‐level characteristics and all but 1 hospital characteristic (Table 1). The variable with the largest absolute difference between those with and without a change in MS‐DRG was the actual position of the HAC; 86.7% of those with an MS‐DRG change had their HAC in the second position, whereas those without a change had only 11.5% in the second position.

After controlling for patient and hospital characteristics, an HAC in the second position in the list of ICD‐9 codes was associated with the greatest likelihood of a change in MS‐DRG assignment (P<0.001) (Table 2). Each additional ICD‐9 code decreased the odds of an MS‐DRG change (P=0.004), demonstrating that having more secondary diagnosis codes was associated with a lesser likelihood of an MS‐DRG change. After including the individual HACs in the regression model, the second position remained associated with the likelihood of a change in MS‐DRG assignment (results not shown). The predictive accuracy of our model did not improve, however, with the addition of type of HAC. The area under the ROC curve was 0.94 in both models, indicating high predictive power.

The proportion of cases with a change in MS‐DRG by severity of illness is reported in Table 3. The largest proportion of cases with a change in MS‐DRG was in the minor severity of illness category (41.3%), whereas only 2.6% of cases with an extreme severity of illness had a change in MS‐DRG. Figure 1 shows ROC curves stratified by severity of illness. Figure 1A illustrates the ROC curves for the 121 (1.7%) patients with minor severity of illness. The area under the ROC curve for the model including HAC position only was 0.74, indicating moderate predictive power. The inclusion of HAC type increased the predictive power to 0.91, and inclusion of sociodemographic characteristics further increased the predictive power to 0.95. Figure 1BD illustrates the ROC curves for moderate, major, and extreme severities of illness. For more severe illnesses, the predictive accuracy of the models with only HAC position were similar to the full models, demonstrating that HAC position alone had a high predictive power for change in MS‐DRG assignment.

Figure 1

In a sensitivity analysis that evaluated the robustness of our results to the specification of disease burden, inclusion of the number of comorbid conditions did not improve the predictive accuracy of the model. Although inclusion of individual comorbid conditions rather than number of diagnosis codes attenuated the odds ratio (OR) for HAC position (OR: 40.5 in the original model vs OR: 32.9 in the model with individual comorbid conditions), the improvement of the predictive accuracy of the model was small (area under the ROC curve=0.936 in the original model vs 0.943 in the model with individual conditions, P<0.001) (results not shown). In a sensitivity analysis using a hierarchical logistic regression model that included hospital random effects, hospital‐level variation in coding practices did not attenuate the relationship between HAC position and MS‐DRG change (results not shown).

DISCUSSION

This study investigated the association of a change in MS‐DRG assignment and position of the ICD‐9 diagnosis codes for HACs in a sample of patients discharged from US academic medical centers. We found that only 14% of the MS‐DRGs for patients with an HAC would have experienced a change in DRG assignment. Our results are consistent with those of Teufack et al.,[14] who estimated the economic impact of CMS' HAC policy for neurosurgery services at a single hospital to be 0.007% of overall net revenues. Nevertheless, the majority of hospitals have increased their efforts to prevent HACs that are included in CMS' policy.[15] At the same time, most hospitals have not increased their budgets for preventing HACs, and instead have reallocated resources from nontargeted HACs to those included in CMS' policy.

The low proportion of records that are impacted by the policy may be partially explained by the fact that CMS' policy only has an impact on reimbursement for MS‐DRGs with multiple levels. For example, heart failure has 3 levels of reimbursement in the MS‐DRG system (Table 4). Prior to CMS' policy, a heart failure patient with an air embolism as an HAC would have been classified in the most severe MS‐DRG (291), whereas after implementation the patient would be classified in the least severe MS‐DRG, if no other complication or comorbidity (CC) or a major complication or comorbidity (MCC) were present. Chest pain has only 1 level, and reimbursement for a patient with an HAC and classified in the chest pain MS‐DRG would not be impacted by CMS' policy. Most hospitalized patients are complicated, and the proportion of patients who are complicated will continue to increase over time as less complex care shifts to the ambulatory setting. The relative effectiveness of CMS' policy is likely to diminish with the continued shift of care to the ambulatory setting.

Patient discharges with a diagnosis code for as HAC in the second position were substantially more likely to have a change in MS‐DRG assignment compared to cases with an HAC listed lower in the final list of diagnosis codes. Perhaps it is not surprising that MS‐DRG assignment is most likely to change when the HAC is in the second position, because an ICD‐9 diagnosis code in this position is more likely to be a major complication or comorbidity. For HACs listed in a lower position of the list of ICD‐9 diagnosis codes, it is likely that the patient had another major complication or comorbidity listed in the second position that would have maintained classification in the same MS‐DRG. Our results suggest that physicians and hospitals caring for patients with lower complexity of illness will sustain a higher financial burden as a result of an HAC under CMS' policy compared to providers whose patients sustain the exact same HAC but have underlying medical care of greater complexity.

These results raise further concerns about the ability of CMS' payment policy to improve quality. One criticism of CMS' policy is that all HACs are not universally preventable. If they are not preventable, payment reductions promulgated via the policy would be punitive rather than incentivizing. In their study of central catheter‐associated bloodstream infections and catheter‐associated urinary tract infections, for example, Lee et al. found no change in infection rates after implementation of CMS' policy.[16] As such, some have suggested HACs should not be used to determine reimbursement, and CMS should abandon its current nonpayment policy.[4, 17] Our findings echo this criticism given that the financial penalty for an HAC depends on whether a patient is more or less complex.

Because coding emanates from physician documentation, a uniform documentation process must exist to ensure nonvariable coding practices.[1, 2, 7, 9] This is not the case, however, and some hospitals comanage documentation to refine or maximize the number of ICD‐9 diagnosis and procedure codes. Furthermore, there are certain differences in the documentation practices of individual physicians. If physician documentation and coding variation leads to fewer ICD‐9 codes during an encounter, the chance that an HAC will influence MS‐DRG change increases.

Another source of variation in coding practices found in this study was code sequencing. Although guidelines for appropriate ICD‐9 diagnosis coding currently exist, individual subjectivity remains. The most essential step in the coding process is identifying the principal diagnosis by extrapolating from physician documentation and clinical data. For example, when a patient is admitted for chest pain, and after some evaluation it is determined that the patient experienced a myocardial infarction, then myocardial infarction becomes the principal diagnosis. Based on that principal diagnosis, coders must select the relevant secondary diagnoses. The process involves a series of steps that must be followed exactly in order to ensure accurate coding.[12] There are no guidelines by which coding personnel must follow to sequence secondary diagnoses, with the exception of listed MCCs and CCs prior to other secondary diagnoses. Ultimately, the order by which these codes are assigned may result in unfavorable variation in MS‐DRG assignment.[1, 2, 4, 7, 8, 9, 17]

There are a number of limitations to this study. First, our cohort included only UHC‐affiliated academic medical centers, which may not represent all acute‐care hospitals and their coding practices. Although our data are for discharges prior to implementation of the policy, we were able to analyze the anticipated impact of the policy prior to any direct or indirect changes in coding that may have occurred in response to CMS' policy. Additionally, the number of diagnosis codes accepted by CMS was expanded from 9 to 25 in 2011. Future analyses that include MS‐DRG classifications with the expanded number of diagnosis codes should be conducted to validate our findings and determine whether any changes have occurred over time. It is not known whether low illness severity scores signify patient or hospital characteristics. If they represent patient characteristics, then CMS' policy will disproportionately affect hospitals taking care of less severely ill patients. Alternatively, if hospital coding practice explains more of the variation in the number of ICD‐9 codes (and thus severity of illness), then the system of adjudicating reimbursement via HACs to incentivize quality of care will be flawed, as there is no standard position for HACs on a more lengthy diagnosis list. Finally, we did not evaluate the change in DRG weight with the reassignment of MS‐DRG if the HAC had been included in the calculation. Future work should evaluate whether there is a differential impact of the policy by change in MS‐DRG weight.

CONCLUSION

Under CMS' current policy, hospitals and physicians caring for patients with lower severity of illness and have an HAC will be penalized by CMS disproportionately more than those caring for more complex, sicker patients with the identical HAC. If, in fact, HACs are indicators of a hospital's quality of care, then the CMS policy will likely do little to foster improved quality unless there is a reduction in coding practice variation and modifications to ensure that the policy impacts reimbursement, independent of severity of illness.

One financial incentive to improve quality of care is the Centers for Medicare and Medicaid Services' (CMS) policy to not pay additionally for certain adverse events that are classified as hospital‐acquired conditions (HACs).[1, 2, 3] HACs are specific conditions that occur during the hospital stay and presumably could have been prevented.[4, 5, 6] Under the CMS policy, if an HAC occurs during a patient's stay, that condition is not included in the Medicare Severity Diagnosis‐Related Group (MS‐DRG) assignment.

The MS‐DRG assigned to a patient discharge determines reimbursement. Each MS‐DRG is assigned a weight, which is used to adjust for the fact that the treatment of different conditions consume different resources and have difference costs. Groups of patients who are expected to require above‐average resources have a higher weight than those who require fewer resources, and higher‐weighted MS‐DRG assignment results in a higher payment. In some cases, the inclusion of the diagnosis code of an HAC in the determination of the MS‐DRG results in a higher complexity level and higher DRG weight. The policy is designed to shift the incremental costs associated with treating the HAC to the hospital. As of October 2009, there were 10 HACs included in the CMS nonpayment program (see Supporting Table 1 in the online version of this article). CMS expanded the list of HACs to include 13 conditions in 2013.

Withholding additional reimbursement for an HAC has been controversial. One area of debate is that the assignment of an HAC may be imprecise, in part due to the variation in how physicians document in the medical record.[1, 2, 6, 7, 8, 9] Coding is derived from documentation in physician notes and is the primary mechanism for assigning International Classification of Diseases, 9th Revision, Clinical Modification (ICD‐9) diagnosis codes to the patient's encounter. The coding process begins with health information technicians (ie, medical record coders) reviewing all medical record documentation to assign diagnosis and procedure codes using the ICD‐9 codes.[10] Primary and secondary diagnoses are determined by certain definitions in the hospital setting. Secondary diagnoses can be further separated into complications or comorbidities in the MS‐DRG system, which can affect reimbursement. The MS‐DRG is then determined using these diagnosis and procedure codes. Physician documentation is the principal source of data for hospital billing, because health information technicians (ie, medical record coders) must assign a code based on what is documented in the chart. If key medical detail is missing or language is ambiguous, then coding can be inaccurate, which may lead to inappropriate compensation.[11]

Accurate and complete ICD‐9 diagnosis and procedure coding is essential for correct MS‐DRG assignment and reimbursement.[12] Physicians may influence coding prioritization by either over‐emphasizing a patient diagnosis or by downplaying the significance of new findings. In addition, unless the physician uses specific, accurate, and accepted terminology, the diagnosis may not even appear in the list of diagnosis codes. Medical records with nonstandard abbreviations may result in coder‐omission of key diagnoses. Finally, when clinicians use qualified diagnoses such as rule‐out or probable, the final diagnosis coded may not be accurate.[10]

Although the CMS policy creates a financial incentive for hospitals to improve quality, the extent to which the policy actually impacts reimbursement across multiple HACs has not been quantified. Additionally, if HACsas a policy initiativereflect actual quality of care, then the position of the ICD‐9 code should not affect MS‐DRG assignment. In this study we evaluated the extent to which MS‐DRG assignment would have been influenced by the presence of an HAC and tested the association of the position of an HAC in the list of ICD‐9 diagnosis codes with changes in MS‐DRG assignment.

METHODS

RESULTS

Of the 954,946 discharges from UHC academic medical centers, 7027 patients (0.7%) had an HAC. Of the patients with an HAC, 6047 did not change MS‐DRG assignment, whereas 980 patients (13.8%) had a change in MS‐DRG assignment. Patients with a change in MS‐DRG assignment were significantly different from those without a change in MS‐DRG assignment on all patient‐level characteristics and all but 1 hospital characteristic (Table 1). The variable with the largest absolute difference between those with and without a change in MS‐DRG was the actual position of the HAC; 86.7% of those with an MS‐DRG change had their HAC in the second position, whereas those without a change had only 11.5% in the second position.

After controlling for patient and hospital characteristics, an HAC in the second position in the list of ICD‐9 codes was associated with the greatest likelihood of a change in MS‐DRG assignment (P<0.001) (Table 2). Each additional ICD‐9 code decreased the odds of an MS‐DRG change (P=0.004), demonstrating that having more secondary diagnosis codes was associated with a lesser likelihood of an MS‐DRG change. After including the individual HACs in the regression model, the second position remained associated with the likelihood of a change in MS‐DRG assignment (results not shown). The predictive accuracy of our model did not improve, however, with the addition of type of HAC. The area under the ROC curve was 0.94 in both models, indicating high predictive power.

The proportion of cases with a change in MS‐DRG by severity of illness is reported in Table 3. The largest proportion of cases with a change in MS‐DRG was in the minor severity of illness category (41.3%), whereas only 2.6% of cases with an extreme severity of illness had a change in MS‐DRG. Figure 1 shows ROC curves stratified by severity of illness. Figure 1A illustrates the ROC curves for the 121 (1.7%) patients with minor severity of illness. The area under the ROC curve for the model including HAC position only was 0.74, indicating moderate predictive power. The inclusion of HAC type increased the predictive power to 0.91, and inclusion of sociodemographic characteristics further increased the predictive power to 0.95. Figure 1BD illustrates the ROC curves for moderate, major, and extreme severities of illness. For more severe illnesses, the predictive accuracy of the models with only HAC position were similar to the full models, demonstrating that HAC position alone had a high predictive power for change in MS‐DRG assignment.

Figure 1

In a sensitivity analysis that evaluated the robustness of our results to the specification of disease burden, inclusion of the number of comorbid conditions did not improve the predictive accuracy of the model. Although inclusion of individual comorbid conditions rather than number of diagnosis codes attenuated the odds ratio (OR) for HAC position (OR: 40.5 in the original model vs OR: 32.9 in the model with individual comorbid conditions), the improvement of the predictive accuracy of the model was small (area under the ROC curve=0.936 in the original model vs 0.943 in the model with individual conditions, P<0.001) (results not shown). In a sensitivity analysis using a hierarchical logistic regression model that included hospital random effects, hospital‐level variation in coding practices did not attenuate the relationship between HAC position and MS‐DRG change (results not shown).

DISCUSSION

This study investigated the association of a change in MS‐DRG assignment and position of the ICD‐9 diagnosis codes for HACs in a sample of patients discharged from US academic medical centers. We found that only 14% of the MS‐DRGs for patients with an HAC would have experienced a change in DRG assignment. Our results are consistent with those of Teufack et al.,[14] who estimated the economic impact of CMS' HAC policy for neurosurgery services at a single hospital to be 0.007% of overall net revenues. Nevertheless, the majority of hospitals have increased their efforts to prevent HACs that are included in CMS' policy.[15] At the same time, most hospitals have not increased their budgets for preventing HACs, and instead have reallocated resources from nontargeted HACs to those included in CMS' policy.

The low proportion of records that are impacted by the policy may be partially explained by the fact that CMS' policy only has an impact on reimbursement for MS‐DRGs with multiple levels. For example, heart failure has 3 levels of reimbursement in the MS‐DRG system (Table 4). Prior to CMS' policy, a heart failure patient with an air embolism as an HAC would have been classified in the most severe MS‐DRG (291), whereas after implementation the patient would be classified in the least severe MS‐DRG, if no other complication or comorbidity (CC) or a major complication or comorbidity (MCC) were present. Chest pain has only 1 level, and reimbursement for a patient with an HAC and classified in the chest pain MS‐DRG would not be impacted by CMS' policy. Most hospitalized patients are complicated, and the proportion of patients who are complicated will continue to increase over time as less complex care shifts to the ambulatory setting. The relative effectiveness of CMS' policy is likely to diminish with the continued shift of care to the ambulatory setting.

Patient discharges with a diagnosis code for as HAC in the second position were substantially more likely to have a change in MS‐DRG assignment compared to cases with an HAC listed lower in the final list of diagnosis codes. Perhaps it is not surprising that MS‐DRG assignment is most likely to change when the HAC is in the second position, because an ICD‐9 diagnosis code in this position is more likely to be a major complication or comorbidity. For HACs listed in a lower position of the list of ICD‐9 diagnosis codes, it is likely that the patient had another major complication or comorbidity listed in the second position that would have maintained classification in the same MS‐DRG. Our results suggest that physicians and hospitals caring for patients with lower complexity of illness will sustain a higher financial burden as a result of an HAC under CMS' policy compared to providers whose patients sustain the exact same HAC but have underlying medical care of greater complexity.

These results raise further concerns about the ability of CMS' payment policy to improve quality. One criticism of CMS' policy is that all HACs are not universally preventable. If they are not preventable, payment reductions promulgated via the policy would be punitive rather than incentivizing. In their study of central catheter‐associated bloodstream infections and catheter‐associated urinary tract infections, for example, Lee et al. found no change in infection rates after implementation of CMS' policy.[16] As such, some have suggested HACs should not be used to determine reimbursement, and CMS should abandon its current nonpayment policy.[4, 17] Our findings echo this criticism given that the financial penalty for an HAC depends on whether a patient is more or less complex.

Because coding emanates from physician documentation, a uniform documentation process must exist to ensure nonvariable coding practices.[1, 2, 7, 9] This is not the case, however, and some hospitals comanage documentation to refine or maximize the number of ICD‐9 diagnosis and procedure codes. Furthermore, there are certain differences in the documentation practices of individual physicians. If physician documentation and coding variation leads to fewer ICD‐9 codes during an encounter, the chance that an HAC will influence MS‐DRG change increases.

Another source of variation in coding practices found in this study was code sequencing. Although guidelines for appropriate ICD‐9 diagnosis coding currently exist, individual subjectivity remains. The most essential step in the coding process is identifying the principal diagnosis by extrapolating from physician documentation and clinical data. For example, when a patient is admitted for chest pain, and after some evaluation it is determined that the patient experienced a myocardial infarction, then myocardial infarction becomes the principal diagnosis. Based on that principal diagnosis, coders must select the relevant secondary diagnoses. The process involves a series of steps that must be followed exactly in order to ensure accurate coding.[12] There are no guidelines by which coding personnel must follow to sequence secondary diagnoses, with the exception of listed MCCs and CCs prior to other secondary diagnoses. Ultimately, the order by which these codes are assigned may result in unfavorable variation in MS‐DRG assignment.[1, 2, 4, 7, 8, 9, 17]

There are a number of limitations to this study. First, our cohort included only UHC‐affiliated academic medical centers, which may not represent all acute‐care hospitals and their coding practices. Although our data are for discharges prior to implementation of the policy, we were able to analyze the anticipated impact of the policy prior to any direct or indirect changes in coding that may have occurred in response to CMS' policy. Additionally, the number of diagnosis codes accepted by CMS was expanded from 9 to 25 in 2011. Future analyses that include MS‐DRG classifications with the expanded number of diagnosis codes should be conducted to validate our findings and determine whether any changes have occurred over time. It is not known whether low illness severity scores signify patient or hospital characteristics. If they represent patient characteristics, then CMS' policy will disproportionately affect hospitals taking care of less severely ill patients. Alternatively, if hospital coding practice explains more of the variation in the number of ICD‐9 codes (and thus severity of illness), then the system of adjudicating reimbursement via HACs to incentivize quality of care will be flawed, as there is no standard position for HACs on a more lengthy diagnosis list. Finally, we did not evaluate the change in DRG weight with the reassignment of MS‐DRG if the HAC had been included in the calculation. Future work should evaluate whether there is a differential impact of the policy by change in MS‐DRG weight.

CONCLUSION

Under CMS' current policy, hospitals and physicians caring for patients with lower severity of illness and have an HAC will be penalized by CMS disproportionately more than those caring for more complex, sicker patients with the identical HAC. If, in fact, HACs are indicators of a hospital's quality of care, then the CMS policy will likely do little to foster improved quality unless there is a reduction in coding practice variation and modifications to ensure that the policy impacts reimbursement, independent of severity of illness.

Poorly coordinated care between hospital and outpatient settings contributes to medical errors, poor outcomes, and high costs.[1, 2, 3] Recent policy has sought to motivate better care coordination after hospital discharge. Financial penalties for excessive hospital readmissionsa perceived marker of poorly coordinated carehave motivated hospitals to adopt transitional care programs to improve postdischarge care coordination.[4] However, the success of hospital‐initiated transitional care strategies in reducing hospital readmissions has been limited.[5] This may be due to the fact that many factors driving hospital readmissions, such as chronic medical illness, patient education, and availability of outpatient care, are outside of a hospital's control.[5, 6] Even among the most comprehensive hospital‐based transitional care intervention strategies, there is little evidence of active engagement of primary care providers or collaboration between hospitals and primary care practices in the transitional care planning process.[5] Better engagement of primary care into transitional care strategies may improve postdischarge care coordination.[7, 8]

The potential benefits of collaboration are particularly salient in healthcare safety nets.[9] The US health safety net is a patchwork of providers, funding, and programs unified by a shared missiondelivering care to patients regardless of ability to payrather than a coordinated system with shared governance.[9] Safety‐net hospitals are at risk for higher‐than‐average readmissions penalties.[10, 11] Medicaid expansion under the Affordable Care Act will likely increase demand for services in these settings, which could worsen fragmentation of care as a result of strained capacity.[12] Collaboration between hospitals and primary care clinics in the safety net could help overcome fragmentation, improve efficiencies in care, and reduce costs and readmissions.[12, 13, 14, 15]

Despite the potential benefits, we found no studies on how to enable collaboration between hospitals and primary care. We sought to understand systems‐level factors limiting and facilitating collaboration between hospitals and primary care practices around coordinating inpatient‐to‐outpatient care transitions by conducting a qualitative study, focusing on the perspective of primary care leaders in the safety net.

STUDY DATA AND METHODS

We conducted semistructured telephone interviews with primary care leaders in health safety nets across California from August 2012 through October 2012, prior to the implementation of the federal hospital readmissions penalties program. Primary care leaders were defined as clinicians or nonclinicians holding leadership positions, including chief executive officers, clinic medical directors, and local experts in care coordination or quality improvement. We defined safety‐net clinics as federally qualified health centers (FQHCs) and/or FQHC Look‐Alikes (clinics that meet eligibility requirements and receive the same benefits as FQHCs, except for Public Health Service Section 330 grants), community health centers, and public hospital‐affiliated clinics operating under a traditional fee‐for‐service model and serving a high proportion of Medicaid and uninsured patients.[9, 16] We defined public hospitals as government‐owned hospitals that provide care for individuals with limited access elsewhere.[17]

RESULTS

Of 52 individuals contacted from 39 different organizations, 23 did not respond, 4 declined to participate, and 25 were scheduled for an interview. We interviewed 22 primary care leaders across 11 California counties (Table 1) and identified themes around factors influencing collaboration with hospitals (Table 2). Most respondents had prior positive experiences collaborating with hospitals on small, focused projects. However, they asserted the need for better hospitalclinic collaboration, and thought collaboration was critical to achieving high‐quality care transitions. We did not observe any differences in perspectives expressed by clinician versus nonclinician leaders. Nonparticipants were more likely than participants to be from northern rural or central counties, FQHCs, and smaller clinic settings.

DISCUSSION

We found that safety‐net primary care leaders identified several barriers to collaboration with hospitals: (1) lack of financial incentives for collaboration, (2) competing priorities, (3) mismatched expectations about the role and capacity of primary care, and (4) poor communication infrastructure. Interpersonal networking and use of EHRs helped overcome these obstacles to a limited extent.

Prior studies demonstrate that early follow‐up, timely communication, and continuity with primary care after hospital discharge are associated with improved postdischarge outcomes.[8, 28, 29, 30] Despite evidence that collaboration between primary care and hospitals may help optimize postdischarge outcomes, our study is the first to describe primary care leaders' perspectives on potential targets for improving collaboration between hospitals and primary care to improve care transitions.

Our results highlight the need to modify payment models to align financial incentives across settings for collaboration. Otherwise, it may be difficult for hospitals to engage primary care in collaborative efforts to improve care transitions. Recent pilot payment models aim to motivate improved postdischarge care coordination. The Centers for Medicare and Medicaid Services implemented two new Current Procedural Terminology Transitional Care Management codes to enable reimbursement of outpatient physicians for management of patients transitioning from the hospital to the community. This model does not require communication between accepting (outpatient) and discharging (hospital) physicians or other hospital staff.[31] Another pilot program pays primary care clinics $6 per beneficiary per month if they become level 3 patient‐centered medical homes, which have stringent requirements for communication and coordination with hospitals for postdischarge care.[32] Capitated payment models, such as expansion of Medicaid managed care, and shared‐savings models, such accountable care organizations, aim to promote shared responsibility between hospitals and primary care by creating financial incentives to prevent hospitalizations through effective use of outpatient resources. The effectiveness of these strategies to improve care transitions is not yet established.

Many tout the adoption of EHRs as a means to improve communication and collaboration across settings.[33] However, policies narrowly focused on EHR adoption fail to address broader issues regarding lack of EHR interoperability and inconsistently applied privacy regulations under HIPAA, which were substantial barriers to information sharing. Stage 2 meaningful use criteria will address some interoperability issues by implementing standards for exchange of laboratory data and summary care records for care transitions.[34] Additional regulatory policies should promote uniform application of privacy regulations to enable more fluid sharing of electronic data across various healthcare settings. Locally and regionally negotiated data sharing agreements, as well as arrangements such as regional health information exchanges, could temporize these issues until broader policies are enacted.

EHRs did not obviate the need for meaningful interpersonal communication between providers. Hospital‐based quality improvement teams could create networking opportunities to foster relationship‐building and communication across settings. Leadership should consider scheduling protected time to facilitate attendance. Colocation of outpatient staff, such as nurse coordinators and office managers, in the hospital may also improve relationship building and care coordination.[35] Such measures would bridge the perceived divide between inpatient and outpatient care, and create avenues to find mutually beneficial solutions to improving postdischarge care transitions.[36]

Our results should be interpreted in light of several limitations. This study focused on primary care practices in the California safety net; given variations in safety nets across different contexts, the transferability of our findings may be limited. Second, rural perspectives were relatively under‐represented in our study sample; there may be additional unidentified issues specific to rural areas or specific to other nonparticipants that may have not been captured in this study. For this hypothesis‐generating study, we focused on the perspectives of primary care leaders. Triangulating perspectives of other stakeholders, including hospital leadership, mental health, social services, and payer organizations, will offer a more comprehensive analysis of barriers and enablers to hospitalprimary care collaboration. We were unable to collect data on the payer mix of each facility, which may influence the perceived financial barriers to collaboration among facilities. However, we anticipate that the broader theme of lack of financial incentives for collaboration will resonate across many settings, as collaboration between inpatient and outpatient providers in general has been largely unfunded by payers.[37, 38, 39] Further, most primary care providers (PCPs) in and outside of safety‐net settings operate on slim margins that cannot support additional time by PCPs or staff to coordinate care transitions.[39, 40] Because our study was completed prior to the implementation of several new payment models motivating postdischarge care coordination, we were unable to assess their effect on clinics' collaboration with hospitals.

In conclusion, efforts to improve collaboration between clinical settings around postdischarge care transitions will require targeted policy and quality improvement efforts in 3 specific areas. Policy makers and administrators with the power to negotiate payment schemes and regulatory policies should first align financial incentives across settings to support postdischarge transitions and care coordination, and second, improve EHR interoperability and uniform application of HIPAA regulations. Third, clinic and hospital leaders, and front‐line providers should enhance opportunities for interpersonal networking between providers in hospital and primary care settings. With the expansion of insurance coverage and increased demand for primary care in the safety net and other settings, policies to promote care coordination should consider the perspective of both hospital and clinic incentives and mechanisms for coordinating care across settings.

Poorly coordinated care between hospital and outpatient settings contributes to medical errors, poor outcomes, and high costs.[1, 2, 3] Recent policy has sought to motivate better care coordination after hospital discharge. Financial penalties for excessive hospital readmissionsa perceived marker of poorly coordinated carehave motivated hospitals to adopt transitional care programs to improve postdischarge care coordination.[4] However, the success of hospital‐initiated transitional care strategies in reducing hospital readmissions has been limited.[5] This may be due to the fact that many factors driving hospital readmissions, such as chronic medical illness, patient education, and availability of outpatient care, are outside of a hospital's control.[5, 6] Even among the most comprehensive hospital‐based transitional care intervention strategies, there is little evidence of active engagement of primary care providers or collaboration between hospitals and primary care practices in the transitional care planning process.[5] Better engagement of primary care into transitional care strategies may improve postdischarge care coordination.[7, 8]

The potential benefits of collaboration are particularly salient in healthcare safety nets.[9] The US health safety net is a patchwork of providers, funding, and programs unified by a shared missiondelivering care to patients regardless of ability to payrather than a coordinated system with shared governance.[9] Safety‐net hospitals are at risk for higher‐than‐average readmissions penalties.[10, 11] Medicaid expansion under the Affordable Care Act will likely increase demand for services in these settings, which could worsen fragmentation of care as a result of strained capacity.[12] Collaboration between hospitals and primary care clinics in the safety net could help overcome fragmentation, improve efficiencies in care, and reduce costs and readmissions.[12, 13, 14, 15]

Despite the potential benefits, we found no studies on how to enable collaboration between hospitals and primary care. We sought to understand systems‐level factors limiting and facilitating collaboration between hospitals and primary care practices around coordinating inpatient‐to‐outpatient care transitions by conducting a qualitative study, focusing on the perspective of primary care leaders in the safety net.

STUDY DATA AND METHODS

We conducted semistructured telephone interviews with primary care leaders in health safety nets across California from August 2012 through October 2012, prior to the implementation of the federal hospital readmissions penalties program. Primary care leaders were defined as clinicians or nonclinicians holding leadership positions, including chief executive officers, clinic medical directors, and local experts in care coordination or quality improvement. We defined safety‐net clinics as federally qualified health centers (FQHCs) and/or FQHC Look‐Alikes (clinics that meet eligibility requirements and receive the same benefits as FQHCs, except for Public Health Service Section 330 grants), community health centers, and public hospital‐affiliated clinics operating under a traditional fee‐for‐service model and serving a high proportion of Medicaid and uninsured patients.[9, 16] We defined public hospitals as government‐owned hospitals that provide care for individuals with limited access elsewhere.[17]

RESULTS

Of 52 individuals contacted from 39 different organizations, 23 did not respond, 4 declined to participate, and 25 were scheduled for an interview. We interviewed 22 primary care leaders across 11 California counties (Table 1) and identified themes around factors influencing collaboration with hospitals (Table 2). Most respondents had prior positive experiences collaborating with hospitals on small, focused projects. However, they asserted the need for better hospitalclinic collaboration, and thought collaboration was critical to achieving high‐quality care transitions. We did not observe any differences in perspectives expressed by clinician versus nonclinician leaders. Nonparticipants were more likely than participants to be from northern rural or central counties, FQHCs, and smaller clinic settings.

DISCUSSION

We found that safety‐net primary care leaders identified several barriers to collaboration with hospitals: (1) lack of financial incentives for collaboration, (2) competing priorities, (3) mismatched expectations about the role and capacity of primary care, and (4) poor communication infrastructure. Interpersonal networking and use of EHRs helped overcome these obstacles to a limited extent.

Prior studies demonstrate that early follow‐up, timely communication, and continuity with primary care after hospital discharge are associated with improved postdischarge outcomes.[8, 28, 29, 30] Despite evidence that collaboration between primary care and hospitals may help optimize postdischarge outcomes, our study is the first to describe primary care leaders' perspectives on potential targets for improving collaboration between hospitals and primary care to improve care transitions.

Our results highlight the need to modify payment models to align financial incentives across settings for collaboration. Otherwise, it may be difficult for hospitals to engage primary care in collaborative efforts to improve care transitions. Recent pilot payment models aim to motivate improved postdischarge care coordination. The Centers for Medicare and Medicaid Services implemented two new Current Procedural Terminology Transitional Care Management codes to enable reimbursement of outpatient physicians for management of patients transitioning from the hospital to the community. This model does not require communication between accepting (outpatient) and discharging (hospital) physicians or other hospital staff.[31] Another pilot program pays primary care clinics $6 per beneficiary per month if they become level 3 patient‐centered medical homes, which have stringent requirements for communication and coordination with hospitals for postdischarge care.[32] Capitated payment models, such as expansion of Medicaid managed care, and shared‐savings models, such accountable care organizations, aim to promote shared responsibility between hospitals and primary care by creating financial incentives to prevent hospitalizations through effective use of outpatient resources. The effectiveness of these strategies to improve care transitions is not yet established.

Many tout the adoption of EHRs as a means to improve communication and collaboration across settings.[33] However, policies narrowly focused on EHR adoption fail to address broader issues regarding lack of EHR interoperability and inconsistently applied privacy regulations under HIPAA, which were substantial barriers to information sharing. Stage 2 meaningful use criteria will address some interoperability issues by implementing standards for exchange of laboratory data and summary care records for care transitions.[34] Additional regulatory policies should promote uniform application of privacy regulations to enable more fluid sharing of electronic data across various healthcare settings. Locally and regionally negotiated data sharing agreements, as well as arrangements such as regional health information exchanges, could temporize these issues until broader policies are enacted.

EHRs did not obviate the need for meaningful interpersonal communication between providers. Hospital‐based quality improvement teams could create networking opportunities to foster relationship‐building and communication across settings. Leadership should consider scheduling protected time to facilitate attendance. Colocation of outpatient staff, such as nurse coordinators and office managers, in the hospital may also improve relationship building and care coordination.[35] Such measures would bridge the perceived divide between inpatient and outpatient care, and create avenues to find mutually beneficial solutions to improving postdischarge care transitions.[36]

Our results should be interpreted in light of several limitations. This study focused on primary care practices in the California safety net; given variations in safety nets across different contexts, the transferability of our findings may be limited. Second, rural perspectives were relatively under‐represented in our study sample; there may be additional unidentified issues specific to rural areas or specific to other nonparticipants that may have not been captured in this study. For this hypothesis‐generating study, we focused on the perspectives of primary care leaders. Triangulating perspectives of other stakeholders, including hospital leadership, mental health, social services, and payer organizations, will offer a more comprehensive analysis of barriers and enablers to hospitalprimary care collaboration. We were unable to collect data on the payer mix of each facility, which may influence the perceived financial barriers to collaboration among facilities. However, we anticipate that the broader theme of lack of financial incentives for collaboration will resonate across many settings, as collaboration between inpatient and outpatient providers in general has been largely unfunded by payers.[37, 38, 39] Further, most primary care providers (PCPs) in and outside of safety‐net settings operate on slim margins that cannot support additional time by PCPs or staff to coordinate care transitions.[39, 40] Because our study was completed prior to the implementation of several new payment models motivating postdischarge care coordination, we were unable to assess their effect on clinics' collaboration with hospitals.

In conclusion, efforts to improve collaboration between clinical settings around postdischarge care transitions will require targeted policy and quality improvement efforts in 3 specific areas. Policy makers and administrators with the power to negotiate payment schemes and regulatory policies should first align financial incentives across settings to support postdischarge transitions and care coordination, and second, improve EHR interoperability and uniform application of HIPAA regulations. Third, clinic and hospital leaders, and front‐line providers should enhance opportunities for interpersonal networking between providers in hospital and primary care settings. With the expansion of insurance coverage and increased demand for primary care in the safety net and other settings, policies to promote care coordination should consider the perspective of both hospital and clinic incentives and mechanisms for coordinating care across settings.

Poorly coordinated care between hospital and outpatient settings contributes to medical errors, poor outcomes, and high costs.[1, 2, 3] Recent policy has sought to motivate better care coordination after hospital discharge. Financial penalties for excessive hospital readmissionsa perceived marker of poorly coordinated carehave motivated hospitals to adopt transitional care programs to improve postdischarge care coordination.[4] However, the success of hospital‐initiated transitional care strategies in reducing hospital readmissions has been limited.[5] This may be due to the fact that many factors driving hospital readmissions, such as chronic medical illness, patient education, and availability of outpatient care, are outside of a hospital's control.[5, 6] Even among the most comprehensive hospital‐based transitional care intervention strategies, there is little evidence of active engagement of primary care providers or collaboration between hospitals and primary care practices in the transitional care planning process.[5] Better engagement of primary care into transitional care strategies may improve postdischarge care coordination.[7, 8]

The potential benefits of collaboration are particularly salient in healthcare safety nets.[9] The US health safety net is a patchwork of providers, funding, and programs unified by a shared missiondelivering care to patients regardless of ability to payrather than a coordinated system with shared governance.[9] Safety‐net hospitals are at risk for higher‐than‐average readmissions penalties.[10, 11] Medicaid expansion under the Affordable Care Act will likely increase demand for services in these settings, which could worsen fragmentation of care as a result of strained capacity.[12] Collaboration between hospitals and primary care clinics in the safety net could help overcome fragmentation, improve efficiencies in care, and reduce costs and readmissions.[12, 13, 14, 15]

Despite the potential benefits, we found no studies on how to enable collaboration between hospitals and primary care. We sought to understand systems‐level factors limiting and facilitating collaboration between hospitals and primary care practices around coordinating inpatient‐to‐outpatient care transitions by conducting a qualitative study, focusing on the perspective of primary care leaders in the safety net.

STUDY DATA AND METHODS

We conducted semistructured telephone interviews with primary care leaders in health safety nets across California from August 2012 through October 2012, prior to the implementation of the federal hospital readmissions penalties program. Primary care leaders were defined as clinicians or nonclinicians holding leadership positions, including chief executive officers, clinic medical directors, and local experts in care coordination or quality improvement. We defined safety‐net clinics as federally qualified health centers (FQHCs) and/or FQHC Look‐Alikes (clinics that meet eligibility requirements and receive the same benefits as FQHCs, except for Public Health Service Section 330 grants), community health centers, and public hospital‐affiliated clinics operating under a traditional fee‐for‐service model and serving a high proportion of Medicaid and uninsured patients.[9, 16] We defined public hospitals as government‐owned hospitals that provide care for individuals with limited access elsewhere.[17]

RESULTS

Of 52 individuals contacted from 39 different organizations, 23 did not respond, 4 declined to participate, and 25 were scheduled for an interview. We interviewed 22 primary care leaders across 11 California counties (Table 1) and identified themes around factors influencing collaboration with hospitals (Table 2). Most respondents had prior positive experiences collaborating with hospitals on small, focused projects. However, they asserted the need for better hospitalclinic collaboration, and thought collaboration was critical to achieving high‐quality care transitions. We did not observe any differences in perspectives expressed by clinician versus nonclinician leaders. Nonparticipants were more likely than participants to be from northern rural or central counties, FQHCs, and smaller clinic settings.

DISCUSSION

We found that safety‐net primary care leaders identified several barriers to collaboration with hospitals: (1) lack of financial incentives for collaboration, (2) competing priorities, (3) mismatched expectations about the role and capacity of primary care, and (4) poor communication infrastructure. Interpersonal networking and use of EHRs helped overcome these obstacles to a limited extent.

Prior studies demonstrate that early follow‐up, timely communication, and continuity with primary care after hospital discharge are associated with improved postdischarge outcomes.[8, 28, 29, 30] Despite evidence that collaboration between primary care and hospitals may help optimize postdischarge outcomes, our study is the first to describe primary care leaders' perspectives on potential targets for improving collaboration between hospitals and primary care to improve care transitions.

Our results highlight the need to modify payment models to align financial incentives across settings for collaboration. Otherwise, it may be difficult for hospitals to engage primary care in collaborative efforts to improve care transitions. Recent pilot payment models aim to motivate improved postdischarge care coordination. The Centers for Medicare and Medicaid Services implemented two new Current Procedural Terminology Transitional Care Management codes to enable reimbursement of outpatient physicians for management of patients transitioning from the hospital to the community. This model does not require communication between accepting (outpatient) and discharging (hospital) physicians or other hospital staff.[31] Another pilot program pays primary care clinics $6 per beneficiary per month if they become level 3 patient‐centered medical homes, which have stringent requirements for communication and coordination with hospitals for postdischarge care.[32] Capitated payment models, such as expansion of Medicaid managed care, and shared‐savings models, such accountable care organizations, aim to promote shared responsibility between hospitals and primary care by creating financial incentives to prevent hospitalizations through effective use of outpatient resources. The effectiveness of these strategies to improve care transitions is not yet established.

Many tout the adoption of EHRs as a means to improve communication and collaboration across settings.[33] However, policies narrowly focused on EHR adoption fail to address broader issues regarding lack of EHR interoperability and inconsistently applied privacy regulations under HIPAA, which were substantial barriers to information sharing. Stage 2 meaningful use criteria will address some interoperability issues by implementing standards for exchange of laboratory data and summary care records for care transitions.[34] Additional regulatory policies should promote uniform application of privacy regulations to enable more fluid sharing of electronic data across various healthcare settings. Locally and regionally negotiated data sharing agreements, as well as arrangements such as regional health information exchanges, could temporize these issues until broader policies are enacted.

EHRs did not obviate the need for meaningful interpersonal communication between providers. Hospital‐based quality improvement teams could create networking opportunities to foster relationship‐building and communication across settings. Leadership should consider scheduling protected time to facilitate attendance. Colocation of outpatient staff, such as nurse coordinators and office managers, in the hospital may also improve relationship building and care coordination.[35] Such measures would bridge the perceived divide between inpatient and outpatient care, and create avenues to find mutually beneficial solutions to improving postdischarge care transitions.[36]

Our results should be interpreted in light of several limitations. This study focused on primary care practices in the California safety net; given variations in safety nets across different contexts, the transferability of our findings may be limited. Second, rural perspectives were relatively under‐represented in our study sample; there may be additional unidentified issues specific to rural areas or specific to other nonparticipants that may have not been captured in this study. For this hypothesis‐generating study, we focused on the perspectives of primary care leaders. Triangulating perspectives of other stakeholders, including hospital leadership, mental health, social services, and payer organizations, will offer a more comprehensive analysis of barriers and enablers to hospitalprimary care collaboration. We were unable to collect data on the payer mix of each facility, which may influence the perceived financial barriers to collaboration among facilities. However, we anticipate that the broader theme of lack of financial incentives for collaboration will resonate across many settings, as collaboration between inpatient and outpatient providers in general has been largely unfunded by payers.[37, 38, 39] Further, most primary care providers (PCPs) in and outside of safety‐net settings operate on slim margins that cannot support additional time by PCPs or staff to coordinate care transitions.[39, 40] Because our study was completed prior to the implementation of several new payment models motivating postdischarge care coordination, we were unable to assess their effect on clinics' collaboration with hospitals.

In conclusion, efforts to improve collaboration between clinical settings around postdischarge care transitions will require targeted policy and quality improvement efforts in 3 specific areas. Policy makers and administrators with the power to negotiate payment schemes and regulatory policies should first align financial incentives across settings to support postdischarge transitions and care coordination, and second, improve EHR interoperability and uniform application of HIPAA regulations. Third, clinic and hospital leaders, and front‐line providers should enhance opportunities for interpersonal networking between providers in hospital and primary care settings. With the expansion of insurance coverage and increased demand for primary care in the safety net and other settings, policies to promote care coordination should consider the perspective of both hospital and clinic incentives and mechanisms for coordinating care across settings.

Clinical estimation of volume status in hospitalized medical patients is an important part of bedside examination, guiding management decisions for many common medical conditions such as heart failure, hyponatremia, and gastrointestinal bleeding. Despite the importance of bedside volume status assessment in clinical care, there are many barriers to its accurate estimation. Specific to the jugular venous pressure (JVP), estimation of its height relies on the transmission of venous pulsations to the overlying skin[1] and has been reported to not be visible in up to 80% of the time in critically ill patients.[2] Additional difficulty in its estimation may be encountered if the central venous pressure is either too high, too low, or obscured by a short or obese neck.[3] Furthermore, in medical patients with respiratory dysfunction, large variations of central venous pressures pose an additional challenge for the bedside examination.[1] Other clinical parameters, such as lung auscultation for crackles and identification of peripheral edema, are likewise equally problematic,[4] and despite training, housestaff may recognize fewer than 50% of respiratory findings at the bedside.[5]

The overall burden of volume status assessment requirements placed on housestaff is unknown. We hypothesize that housestaff are frequently asked to make volume status assessments on admitted medical patients. If this is true, we argue for the need for educating them on the use of additional bedside tools that can assist in volume status determination. An example of such a tool is the use of bedside ultrasound. The objective of this brief report was to conduct a survey to determine the frequency of clinical volume status assessments needed on medical inpatients and secondarily discuss the potential use of bedside ultrasound for volume status determination.

METHOD

RESULTS

The 13 randomly selected study dates included 10 weekdays and 3 weekend days. Of the 39 eligible housestaff who were on call during those study dates, 31 (79%) unique individuals consented to and completed the survey. The baseline characteristics of the study participants are reported in Table 1.

A total of 455 on‐call hours were logged, with a total of 197 pages received during the study period. Median shift duration was 12 hours (IQR=1224 hours, range=724 hours) with a median of 5 pages received per shift (IQR=310). Of the 197 total pages received, 41 of these (21%) were felt by the participants to warrant a volume status assessment.

Of the 14 volume status assessment parameters considered, housestaff used a mean of 73 parameters per assessment. The most frequently used parameters in volume status assessment were the patient's history (90%), respiratory examination (76%), JVP (73%), blood pressure (71%), and heart rate (71%) (Figure 1). In 35 of these 41 assessments (85%), housestaff indicated examining the patient for JVP, respiratory examination, edema, heart sound, or abdominal jugular reflux. Of those who examined the patient, an average of 31 physical examination findings were sought. Of the 6 patients who were not examined, housestaff reported being very certain of the patients' volume status using nonphysical examination parameters.

Figure 1

In 24 cases (59%) the intravenous was changed (ie, type of intravenous fluid used, rate change, starting or stopping of fluids). In 9 cases (22%) a diuretic was given, and in 15 cases (37%) a chest radiograph was ordered.

DISCUSSION

In this brief report, we identified that over 20% of pages over a call shift regarding admitted medical patients required volume status assessments by medical housestaff. Despite moderate self‐reported competence in the ability to assess volume status, barriers to volume status determination, such as conflicting physical examination findings and suboptimal patient examinations, were present in up to 20% of the assessments.

Other studies have similarly shown trainees with difficulty regarding clinical examinations for volume status. In these studies, difficulty with findings ranged between 16% to well over 50%.[1, 2, 3, 5] To our knowledge, this is the first report on the estimated burden of volume status assessments borne by medical housestaff. Together, our results on the burden of volume status assessments and the uncertainty regarding volume status assessments argue for the need for either better education of examination skills, or alternatively, additional tools for volume status assessments.

Although future studies evaluating the effects of improving education on examination skills and accuracy would be helpful, it has been previously reported that even attending physicians' examination skills were poor.[3] Suboptimal educator's skills, coupled with less‐than‐ideal patient characteristics in some settings, such as obesity and anatomical variations, suggest that education of bedside examination skills alone is unlikely to optimally assist clinicians with volume status assessments. Therefore, we believe our results argue for the need for additional tools for determining volume status in patients.

Bedside ultrasound is a promising tool that may be of use in this setting. It can assist in volume status assessments in a number of ways. First, for example, the height of the JVP can be located on ultrasound, using a linear transducer, as the site of where the vein tapers, using either a longitudinal or transverse view.[10] This measurement can be readily obtained even in obese patients.[10] Second, pulmonary findings, such as pleural effusions and the appearance of bilateral B lines would be suggestive of volume overload.[11, 12] The presence of unilateral B lines and consolidation/hepatization, on the other hand, would be suggestive of an infective or atelectatic process.[11, 12, 13] Last, a small inferior vena cava (IVC) diameter (<2 cm) or collapsibility of >50%, although more controversial, may be able to help identify patients who may benefit from intravascular fluid loading.[13, 14] Response of IVC diameter to passive leg raise may also be assessed.[13] Feasibility wise, many of these bedside skills require minimal training, even for novices. As little as 3 to 4 hours of training may suffice.[12, 15]

Although the use of bedside ultrasound holds promise, a number of important questions should be addressed. First, can trainees be taught to use ultrasound accurately and reliably? If so, can ultrasound be incorporated into clinical care or would the time required to perform these additional examinations be prohibitive? Second, how will its use impact on volume status estimation accuracy and clinical outcomes? Third, what may be some unintended consequences of introducing this tool into the existing educational curriculum? Future studies addressing these questions are needed to better assist educators in optimizing an educational curriculum that would best benefit learners and patients.

Some limitations in our study include the fact that first, this is a single‐centered study. However, as previously stated, our results regarding difficulty with clinical examination findings are in keeping with findings from other centers.[1, 2, 3, 5] Second, our results are based on what housestaff felt necessitated volume status assessments, rather than what calls truly needed volume status assessments. In addition, the number of pages received was by self‐report. However, housestaff are more likely to under‐report by forgetting to log their pages, rather than to over‐report. Thus, our results are likely a conservative estimate of the burden of volume status assessments faced by medical housestaff. Third, some parameters were not included in our survey. For example, ordering of B‐type natriuretic peptide required a cardiology consultation at our center, and thus this investigation is not readily available to us. Daily weights, urea to creatinine ratio, and fractional excretion of sodium were not included based on feedback from our pilot survey suggesting that these parameters were not commonly used or available for admitted patients. Thus, overall confidence in volume status assessments may differ should these parameters be routinely employed. Fourth, our participants were predominantly junior learners. Therefore, our results may not generalize to centers where patients are managed primarily by more senior learners. Last, our results pertain only to patients admitted to internal medicine. For patients in the intensive care unit or coronary care unit, the burden of volume status assessments is likely even higher.

These limitations notwithstanding, our results do raise a potential concern regarding the current practice by which patients' volume statuses are assessed. We urge educators to consider incorporating bedside ultrasound training for volume status into the internal medicine curriculum and to address the need for future studies on its utility for volume status assessments.

Clinical estimation of volume status in hospitalized medical patients is an important part of bedside examination, guiding management decisions for many common medical conditions such as heart failure, hyponatremia, and gastrointestinal bleeding. Despite the importance of bedside volume status assessment in clinical care, there are many barriers to its accurate estimation. Specific to the jugular venous pressure (JVP), estimation of its height relies on the transmission of venous pulsations to the overlying skin[1] and has been reported to not be visible in up to 80% of the time in critically ill patients.[2] Additional difficulty in its estimation may be encountered if the central venous pressure is either too high, too low, or obscured by a short or obese neck.[3] Furthermore, in medical patients with respiratory dysfunction, large variations of central venous pressures pose an additional challenge for the bedside examination.[1] Other clinical parameters, such as lung auscultation for crackles and identification of peripheral edema, are likewise equally problematic,[4] and despite training, housestaff may recognize fewer than 50% of respiratory findings at the bedside.[5]

The overall burden of volume status assessment requirements placed on housestaff is unknown. We hypothesize that housestaff are frequently asked to make volume status assessments on admitted medical patients. If this is true, we argue for the need for educating them on the use of additional bedside tools that can assist in volume status determination. An example of such a tool is the use of bedside ultrasound. The objective of this brief report was to conduct a survey to determine the frequency of clinical volume status assessments needed on medical inpatients and secondarily discuss the potential use of bedside ultrasound for volume status determination.

METHOD

RESULTS

The 13 randomly selected study dates included 10 weekdays and 3 weekend days. Of the 39 eligible housestaff who were on call during those study dates, 31 (79%) unique individuals consented to and completed the survey. The baseline characteristics of the study participants are reported in Table 1.

A total of 455 on‐call hours were logged, with a total of 197 pages received during the study period. Median shift duration was 12 hours (IQR=1224 hours, range=724 hours) with a median of 5 pages received per shift (IQR=310). Of the 197 total pages received, 41 of these (21%) were felt by the participants to warrant a volume status assessment.

Of the 14 volume status assessment parameters considered, housestaff used a mean of 73 parameters per assessment. The most frequently used parameters in volume status assessment were the patient's history (90%), respiratory examination (76%), JVP (73%), blood pressure (71%), and heart rate (71%) (Figure 1). In 35 of these 41 assessments (85%), housestaff indicated examining the patient for JVP, respiratory examination, edema, heart sound, or abdominal jugular reflux. Of those who examined the patient, an average of 31 physical examination findings were sought. Of the 6 patients who were not examined, housestaff reported being very certain of the patients' volume status using nonphysical examination parameters.

Figure 1

In 24 cases (59%) the intravenous was changed (ie, type of intravenous fluid used, rate change, starting or stopping of fluids). In 9 cases (22%) a diuretic was given, and in 15 cases (37%) a chest radiograph was ordered.

DISCUSSION

In this brief report, we identified that over 20% of pages over a call shift regarding admitted medical patients required volume status assessments by medical housestaff. Despite moderate self‐reported competence in the ability to assess volume status, barriers to volume status determination, such as conflicting physical examination findings and suboptimal patient examinations, were present in up to 20% of the assessments.

Other studies have similarly shown trainees with difficulty regarding clinical examinations for volume status. In these studies, difficulty with findings ranged between 16% to well over 50%.[1, 2, 3, 5] To our knowledge, this is the first report on the estimated burden of volume status assessments borne by medical housestaff. Together, our results on the burden of volume status assessments and the uncertainty regarding volume status assessments argue for the need for either better education of examination skills, or alternatively, additional tools for volume status assessments.

Although future studies evaluating the effects of improving education on examination skills and accuracy would be helpful, it has been previously reported that even attending physicians' examination skills were poor.[3] Suboptimal educator's skills, coupled with less‐than‐ideal patient characteristics in some settings, such as obesity and anatomical variations, suggest that education of bedside examination skills alone is unlikely to optimally assist clinicians with volume status assessments. Therefore, we believe our results argue for the need for additional tools for determining volume status in patients.

Bedside ultrasound is a promising tool that may be of use in this setting. It can assist in volume status assessments in a number of ways. First, for example, the height of the JVP can be located on ultrasound, using a linear transducer, as the site of where the vein tapers, using either a longitudinal or transverse view.[10] This measurement can be readily obtained even in obese patients.[10] Second, pulmonary findings, such as pleural effusions and the appearance of bilateral B lines would be suggestive of volume overload.[11, 12] The presence of unilateral B lines and consolidation/hepatization, on the other hand, would be suggestive of an infective or atelectatic process.[11, 12, 13] Last, a small inferior vena cava (IVC) diameter (<2 cm) or collapsibility of >50%, although more controversial, may be able to help identify patients who may benefit from intravascular fluid loading.[13, 14] Response of IVC diameter to passive leg raise may also be assessed.[13] Feasibility wise, many of these bedside skills require minimal training, even for novices. As little as 3 to 4 hours of training may suffice.[12, 15]

Although the use of bedside ultrasound holds promise, a number of important questions should be addressed. First, can trainees be taught to use ultrasound accurately and reliably? If so, can ultrasound be incorporated into clinical care or would the time required to perform these additional examinations be prohibitive? Second, how will its use impact on volume status estimation accuracy and clinical outcomes? Third, what may be some unintended consequences of introducing this tool into the existing educational curriculum? Future studies addressing these questions are needed to better assist educators in optimizing an educational curriculum that would best benefit learners and patients.

Some limitations in our study include the fact that first, this is a single‐centered study. However, as previously stated, our results regarding difficulty with clinical examination findings are in keeping with findings from other centers.[1, 2, 3, 5] Second, our results are based on what housestaff felt necessitated volume status assessments, rather than what calls truly needed volume status assessments. In addition, the number of pages received was by self‐report. However, housestaff are more likely to under‐report by forgetting to log their pages, rather than to over‐report. Thus, our results are likely a conservative estimate of the burden of volume status assessments faced by medical housestaff. Third, some parameters were not included in our survey. For example, ordering of B‐type natriuretic peptide required a cardiology consultation at our center, and thus this investigation is not readily available to us. Daily weights, urea to creatinine ratio, and fractional excretion of sodium were not included based on feedback from our pilot survey suggesting that these parameters were not commonly used or available for admitted patients. Thus, overall confidence in volume status assessments may differ should these parameters be routinely employed. Fourth, our participants were predominantly junior learners. Therefore, our results may not generalize to centers where patients are managed primarily by more senior learners. Last, our results pertain only to patients admitted to internal medicine. For patients in the intensive care unit or coronary care unit, the burden of volume status assessments is likely even higher.

These limitations notwithstanding, our results do raise a potential concern regarding the current practice by which patients' volume statuses are assessed. We urge educators to consider incorporating bedside ultrasound training for volume status into the internal medicine curriculum and to address the need for future studies on its utility for volume status assessments.

Clinical estimation of volume status in hospitalized medical patients is an important part of bedside examination, guiding management decisions for many common medical conditions such as heart failure, hyponatremia, and gastrointestinal bleeding. Despite the importance of bedside volume status assessment in clinical care, there are many barriers to its accurate estimation. Specific to the jugular venous pressure (JVP), estimation of its height relies on the transmission of venous pulsations to the overlying skin[1] and has been reported to not be visible in up to 80% of the time in critically ill patients.[2] Additional difficulty in its estimation may be encountered if the central venous pressure is either too high, too low, or obscured by a short or obese neck.[3] Furthermore, in medical patients with respiratory dysfunction, large variations of central venous pressures pose an additional challenge for the bedside examination.[1] Other clinical parameters, such as lung auscultation for crackles and identification of peripheral edema, are likewise equally problematic,[4] and despite training, housestaff may recognize fewer than 50% of respiratory findings at the bedside.[5]

The overall burden of volume status assessment requirements placed on housestaff is unknown. We hypothesize that housestaff are frequently asked to make volume status assessments on admitted medical patients. If this is true, we argue for the need for educating them on the use of additional bedside tools that can assist in volume status determination. An example of such a tool is the use of bedside ultrasound. The objective of this brief report was to conduct a survey to determine the frequency of clinical volume status assessments needed on medical inpatients and secondarily discuss the potential use of bedside ultrasound for volume status determination.

METHOD

RESULTS

The 13 randomly selected study dates included 10 weekdays and 3 weekend days. Of the 39 eligible housestaff who were on call during those study dates, 31 (79%) unique individuals consented to and completed the survey. The baseline characteristics of the study participants are reported in Table 1.

A total of 455 on‐call hours were logged, with a total of 197 pages received during the study period. Median shift duration was 12 hours (IQR=1224 hours, range=724 hours) with a median of 5 pages received per shift (IQR=310). Of the 197 total pages received, 41 of these (21%) were felt by the participants to warrant a volume status assessment.

Of the 14 volume status assessment parameters considered, housestaff used a mean of 73 parameters per assessment. The most frequently used parameters in volume status assessment were the patient's history (90%), respiratory examination (76%), JVP (73%), blood pressure (71%), and heart rate (71%) (Figure 1). In 35 of these 41 assessments (85%), housestaff indicated examining the patient for JVP, respiratory examination, edema, heart sound, or abdominal jugular reflux. Of those who examined the patient, an average of 31 physical examination findings were sought. Of the 6 patients who were not examined, housestaff reported being very certain of the patients' volume status using nonphysical examination parameters.

Figure 1

In 24 cases (59%) the intravenous was changed (ie, type of intravenous fluid used, rate change, starting or stopping of fluids). In 9 cases (22%) a diuretic was given, and in 15 cases (37%) a chest radiograph was ordered.

DISCUSSION

In this brief report, we identified that over 20% of pages over a call shift regarding admitted medical patients required volume status assessments by medical housestaff. Despite moderate self‐reported competence in the ability to assess volume status, barriers to volume status determination, such as conflicting physical examination findings and suboptimal patient examinations, were present in up to 20% of the assessments.

Other studies have similarly shown trainees with difficulty regarding clinical examinations for volume status. In these studies, difficulty with findings ranged between 16% to well over 50%.[1, 2, 3, 5] To our knowledge, this is the first report on the estimated burden of volume status assessments borne by medical housestaff. Together, our results on the burden of volume status assessments and the uncertainty regarding volume status assessments argue for the need for either better education of examination skills, or alternatively, additional tools for volume status assessments.

Although future studies evaluating the effects of improving education on examination skills and accuracy would be helpful, it has been previously reported that even attending physicians' examination skills were poor.[3] Suboptimal educator's skills, coupled with less‐than‐ideal patient characteristics in some settings, such as obesity and anatomical variations, suggest that education of bedside examination skills alone is unlikely to optimally assist clinicians with volume status assessments. Therefore, we believe our results argue for the need for additional tools for determining volume status in patients.

Bedside ultrasound is a promising tool that may be of use in this setting. It can assist in volume status assessments in a number of ways. First, for example, the height of the JVP can be located on ultrasound, using a linear transducer, as the site of where the vein tapers, using either a longitudinal or transverse view.[10] This measurement can be readily obtained even in obese patients.[10] Second, pulmonary findings, such as pleural effusions and the appearance of bilateral B lines would be suggestive of volume overload.[11, 12] The presence of unilateral B lines and consolidation/hepatization, on the other hand, would be suggestive of an infective or atelectatic process.[11, 12, 13] Last, a small inferior vena cava (IVC) diameter (<2 cm) or collapsibility of >50%, although more controversial, may be able to help identify patients who may benefit from intravascular fluid loading.[13, 14] Response of IVC diameter to passive leg raise may also be assessed.[13] Feasibility wise, many of these bedside skills require minimal training, even for novices. As little as 3 to 4 hours of training may suffice.[12, 15]

Although the use of bedside ultrasound holds promise, a number of important questions should be addressed. First, can trainees be taught to use ultrasound accurately and reliably? If so, can ultrasound be incorporated into clinical care or would the time required to perform these additional examinations be prohibitive? Second, how will its use impact on volume status estimation accuracy and clinical outcomes? Third, what may be some unintended consequences of introducing this tool into the existing educational curriculum? Future studies addressing these questions are needed to better assist educators in optimizing an educational curriculum that would best benefit learners and patients.

Some limitations in our study include the fact that first, this is a single‐centered study. However, as previously stated, our results regarding difficulty with clinical examination findings are in keeping with findings from other centers.[1, 2, 3, 5] Second, our results are based on what housestaff felt necessitated volume status assessments, rather than what calls truly needed volume status assessments. In addition, the number of pages received was by self‐report. However, housestaff are more likely to under‐report by forgetting to log their pages, rather than to over‐report. Thus, our results are likely a conservative estimate of the burden of volume status assessments faced by medical housestaff. Third, some parameters were not included in our survey. For example, ordering of B‐type natriuretic peptide required a cardiology consultation at our center, and thus this investigation is not readily available to us. Daily weights, urea to creatinine ratio, and fractional excretion of sodium were not included based on feedback from our pilot survey suggesting that these parameters were not commonly used or available for admitted patients. Thus, overall confidence in volume status assessments may differ should these parameters be routinely employed. Fourth, our participants were predominantly junior learners. Therefore, our results may not generalize to centers where patients are managed primarily by more senior learners. Last, our results pertain only to patients admitted to internal medicine. For patients in the intensive care unit or coronary care unit, the burden of volume status assessments is likely even higher.

These limitations notwithstanding, our results do raise a potential concern regarding the current practice by which patients' volume statuses are assessed. We urge educators to consider incorporating bedside ultrasound training for volume status into the internal medicine curriculum and to address the need for future studies on its utility for volume status assessments.