Under the national leadership of AARP, 42 states and territories have introduced the Caregiver Advise Record and Enable (CARE) Act, 32 have passed it, and the following 30 have enacted it into law: Arkansas, California, Colorado, Connecticut, District of Columbia, Illinois, Indiana, Louisiana, Maine, Maryland, Michigan, Minnesota, Mississippi, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, Oklahoma, Oregon, Pennsylvania, Puerto Rico, Rhode Island, Utah, Virginia, Virgin Islands, West Virginia, Washington and Wyoming (as of June 6, 2016). The CARE Act requires hospitals to: (1) record the name of the family caregiver in the medical record, (2) inform the family caregiver when the patient is to be discharged, and (3) provide the family caregiver with education and instruction on the medical tasks that he or she will need to perform for the patient upon return home.[1, 2]

The family caregiver is to be identified by the patient. Because the patient is the source of the information, Health Insurance Portability and Accountability Act concerns are minimal. A family caregiver need not be related to the patient by blood or marriage; a friend, neighbor, partner, or paid caregiver could be identified by the patient as serving in this role. If the patient does not identify a family caregiver (due to the absence of such an individual, concern for potential burden on a loved one, desire for confidentiality, transient or progressive cognitive impairment, or other reasons), this also needs to be documented, though the additional provisions of the CARE Act would not be applicable. As some states have made additions or individual modifications to the CARE Act, the reader is encouraged to learn more about state‐specific differences that might impact implementation.[2, 3]

The impetus for the CARE Act emerges from challenges faced by both family caregivers and healthcare professionals. The 3 care elements included in the Act appear to have considerable face validity for what would constitute good transitional care. To further explore why this is necessary, we need to begin by asking why are these 3 care elements not routine, and why did an advocacy organization resort to a legislative solution to formally recognize and include family caregivers in discharge preparation?

Family caregivers, when able and willing, often play an instrumental role in the care of their loved ones, particularly during the vulnerable time of transitions out of the hospital.[4, 5] They are often the first line of defense for detecting lapses in quality or safety as care is transitioned from the hospital. Family caregivers frequently take on a primary or secondary role in operationalizing and executing the discharge plan. Nearly half of family caregivers perform skilled medical or nursing tasks for their loved ones (eg, wound care, injections, complex medication management, operating specialized medical equipment) often with insufficient assistance or training from healthcare professionals.[6]

Lack of sufficient time might be a major reason why the 3 care elements identified in the CARE Act are not routinely addressed by the discharging team, which may include hospitalists, nurses, pharmacists, social workers, and other clinicians. However, there may be other reasons as well, including a lack of knowledge, confidence, or tools for how to best prepare the patient and family caregiver. This is compounded by the absence of routine feedback loops for gauging the effectiveness of discharge preparation beyond a patient's readmission to the same facility. If hospital‐based clinicians were asked to rank order their daily tasks from greatest sense of professional gratification to lowest, discharge preparation would likely appear toward the bottom of the list.[7, 8]

Meanwhile, hospitalists and hospital clinical leaders are struggling to keep pace with a confluence of new demands that include value‐based purchasing initiatives and population health efforts, to name but a few. Although current Centers for Medicare and Medicaid Services' (CMS) Hospital Conditions of Participation for Discharge Planning do not require recognition or preparation of family caregivers, CMS' newly proposed revisions emphasize better preparation of family caregivers to be active partners upon hospital discharge.[9] Thus, although it might be reflexive to view the CARE Act in isolation as yet 1 more initiative requiring new effort and resources to address, widening the lens may confirm that the contributions of family caregivers are integral and aligned across nearly all efforts aimed at promoting greater value, and in this light could be viewed as complementary rather than competitive.

Innovation or new resources may be needed to implement the CARE Act. In the absence of a step‐by‐step user's guide, hospitals may wish to take advantage of valuable publicly available resources that encourage more effective collaboration between family caregivers and healthcare professionals (Table 1).

Operationalizing the CARE Act may initially appear simple but in practice will not be easy. The first care element focuses on identifying the family caregiver. Next, Step in Care offers a practical guide for how to identify the family caregiver in a busy hospital environment (Table 1). The guide advises health professionals on how to identify the person most likely to assume responsibility for care after discharge by asking a series of questions: Who assists you at home? Who do you call in case of emergency? Who helps with medications or doctor appointments? The guide cautions health professionals not to assume that individuals encountered at the patient's bedside are necessarily the family caregivers. They may be covering for the family caregiver, who has other duties (eg, job, child care).

The second CARE Act element entails informing the family caregiver when the patient will be discharged. At present there is no standardization of this practice. Many hospitals conduct interdisciplinary rounds, during which a discharge date is frequently estimated. A designated member of the inpatient team (eg, primary nurse, social worker, care manager) might be tasked with notifying the family caregiver of this estimated date (either in person, by telephone, or using other approved mode[s] of communication). Ideally, this notification should be conveyed as soon as the inpatient care team can foresee a discharge date, as it would be preferable to give the family caregiver an estimate that turns out to be a day or 2 off and needs to be revised than to inform the family caregiver at the last minute. The white board in the patient's room may serve as a reminder to both the patient and family caregiver as well as to other members of the inpatient care team.

The third CARE Act element could be facilitated with the Family Caregiver Activation in Transitions (FCAT) tool, a self‐efficacy measure of transition specific tasks. The FCAT tool is designed to facilitate more productive interactions and guide the care team in understanding what common transition‐related areas family caregivers would like to feel more prepared for or confident with. The FCAT tool can be administered by a health professional or self‐administered by a family caregiver and takes approximately 2 minutes to complete[10] (Table 1).

Hospital leaders might consider creating an interdisciplinary team charged with facilitating the implementation of the CARE Act. Specifically, this team might develop guidelines and serve as a forum whereby clinicians might share particularly challenging cases. Similarly, for ongoing input and suggestions for how to further improve all aspects of hospital care, including the discharge experience, hospitals are encouraged to form and foster patient and family advisory councils (Table 1).

Finally, when it comes to improving the hospital discharge experience for family caregivers, there is no us and them. Despite our professional advantages, each of has had or will likely have an opportunity to overcome the many gaps in hospital discharge planning, not just as healthcare professionals but also in our roles as adult children, spouses, and parents. In this regard, we are all invested in improving the discharge experience.

Under the national leadership of AARP, 42 states and territories have introduced the Caregiver Advise Record and Enable (CARE) Act, 32 have passed it, and the following 30 have enacted it into law: Arkansas, California, Colorado, Connecticut, District of Columbia, Illinois, Indiana, Louisiana, Maine, Maryland, Michigan, Minnesota, Mississippi, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, Oklahoma, Oregon, Pennsylvania, Puerto Rico, Rhode Island, Utah, Virginia, Virgin Islands, West Virginia, Washington and Wyoming (as of June 6, 2016). The CARE Act requires hospitals to: (1) record the name of the family caregiver in the medical record, (2) inform the family caregiver when the patient is to be discharged, and (3) provide the family caregiver with education and instruction on the medical tasks that he or she will need to perform for the patient upon return home.[1, 2]

The family caregiver is to be identified by the patient. Because the patient is the source of the information, Health Insurance Portability and Accountability Act concerns are minimal. A family caregiver need not be related to the patient by blood or marriage; a friend, neighbor, partner, or paid caregiver could be identified by the patient as serving in this role. If the patient does not identify a family caregiver (due to the absence of such an individual, concern for potential burden on a loved one, desire for confidentiality, transient or progressive cognitive impairment, or other reasons), this also needs to be documented, though the additional provisions of the CARE Act would not be applicable. As some states have made additions or individual modifications to the CARE Act, the reader is encouraged to learn more about state‐specific differences that might impact implementation.[2, 3]

The impetus for the CARE Act emerges from challenges faced by both family caregivers and healthcare professionals. The 3 care elements included in the Act appear to have considerable face validity for what would constitute good transitional care. To further explore why this is necessary, we need to begin by asking why are these 3 care elements not routine, and why did an advocacy organization resort to a legislative solution to formally recognize and include family caregivers in discharge preparation?

Family caregivers, when able and willing, often play an instrumental role in the care of their loved ones, particularly during the vulnerable time of transitions out of the hospital.[4, 5] They are often the first line of defense for detecting lapses in quality or safety as care is transitioned from the hospital. Family caregivers frequently take on a primary or secondary role in operationalizing and executing the discharge plan. Nearly half of family caregivers perform skilled medical or nursing tasks for their loved ones (eg, wound care, injections, complex medication management, operating specialized medical equipment) often with insufficient assistance or training from healthcare professionals.[6]

Lack of sufficient time might be a major reason why the 3 care elements identified in the CARE Act are not routinely addressed by the discharging team, which may include hospitalists, nurses, pharmacists, social workers, and other clinicians. However, there may be other reasons as well, including a lack of knowledge, confidence, or tools for how to best prepare the patient and family caregiver. This is compounded by the absence of routine feedback loops for gauging the effectiveness of discharge preparation beyond a patient's readmission to the same facility. If hospital‐based clinicians were asked to rank order their daily tasks from greatest sense of professional gratification to lowest, discharge preparation would likely appear toward the bottom of the list.[7, 8]

Meanwhile, hospitalists and hospital clinical leaders are struggling to keep pace with a confluence of new demands that include value‐based purchasing initiatives and population health efforts, to name but a few. Although current Centers for Medicare and Medicaid Services' (CMS) Hospital Conditions of Participation for Discharge Planning do not require recognition or preparation of family caregivers, CMS' newly proposed revisions emphasize better preparation of family caregivers to be active partners upon hospital discharge.[9] Thus, although it might be reflexive to view the CARE Act in isolation as yet 1 more initiative requiring new effort and resources to address, widening the lens may confirm that the contributions of family caregivers are integral and aligned across nearly all efforts aimed at promoting greater value, and in this light could be viewed as complementary rather than competitive.

Innovation or new resources may be needed to implement the CARE Act. In the absence of a step‐by‐step user's guide, hospitals may wish to take advantage of valuable publicly available resources that encourage more effective collaboration between family caregivers and healthcare professionals (Table 1).

Operationalizing the CARE Act may initially appear simple but in practice will not be easy. The first care element focuses on identifying the family caregiver. Next, Step in Care offers a practical guide for how to identify the family caregiver in a busy hospital environment (Table 1). The guide advises health professionals on how to identify the person most likely to assume responsibility for care after discharge by asking a series of questions: Who assists you at home? Who do you call in case of emergency? Who helps with medications or doctor appointments? The guide cautions health professionals not to assume that individuals encountered at the patient's bedside are necessarily the family caregivers. They may be covering for the family caregiver, who has other duties (eg, job, child care).

The second CARE Act element entails informing the family caregiver when the patient will be discharged. At present there is no standardization of this practice. Many hospitals conduct interdisciplinary rounds, during which a discharge date is frequently estimated. A designated member of the inpatient team (eg, primary nurse, social worker, care manager) might be tasked with notifying the family caregiver of this estimated date (either in person, by telephone, or using other approved mode[s] of communication). Ideally, this notification should be conveyed as soon as the inpatient care team can foresee a discharge date, as it would be preferable to give the family caregiver an estimate that turns out to be a day or 2 off and needs to be revised than to inform the family caregiver at the last minute. The white board in the patient's room may serve as a reminder to both the patient and family caregiver as well as to other members of the inpatient care team.

The third CARE Act element could be facilitated with the Family Caregiver Activation in Transitions (FCAT) tool, a self‐efficacy measure of transition specific tasks. The FCAT tool is designed to facilitate more productive interactions and guide the care team in understanding what common transition‐related areas family caregivers would like to feel more prepared for or confident with. The FCAT tool can be administered by a health professional or self‐administered by a family caregiver and takes approximately 2 minutes to complete[10] (Table 1).

Hospital leaders might consider creating an interdisciplinary team charged with facilitating the implementation of the CARE Act. Specifically, this team might develop guidelines and serve as a forum whereby clinicians might share particularly challenging cases. Similarly, for ongoing input and suggestions for how to further improve all aspects of hospital care, including the discharge experience, hospitals are encouraged to form and foster patient and family advisory councils (Table 1).

Finally, when it comes to improving the hospital discharge experience for family caregivers, there is no us and them. Despite our professional advantages, each of has had or will likely have an opportunity to overcome the many gaps in hospital discharge planning, not just as healthcare professionals but also in our roles as adult children, spouses, and parents. In this regard, we are all invested in improving the discharge experience.

Under the national leadership of AARP, 42 states and territories have introduced the Caregiver Advise Record and Enable (CARE) Act, 32 have passed it, and the following 30 have enacted it into law: Arkansas, California, Colorado, Connecticut, District of Columbia, Illinois, Indiana, Louisiana, Maine, Maryland, Michigan, Minnesota, Mississippi, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, Oklahoma, Oregon, Pennsylvania, Puerto Rico, Rhode Island, Utah, Virginia, Virgin Islands, West Virginia, Washington and Wyoming (as of June 6, 2016). The CARE Act requires hospitals to: (1) record the name of the family caregiver in the medical record, (2) inform the family caregiver when the patient is to be discharged, and (3) provide the family caregiver with education and instruction on the medical tasks that he or she will need to perform for the patient upon return home.[1, 2]

The family caregiver is to be identified by the patient. Because the patient is the source of the information, Health Insurance Portability and Accountability Act concerns are minimal. A family caregiver need not be related to the patient by blood or marriage; a friend, neighbor, partner, or paid caregiver could be identified by the patient as serving in this role. If the patient does not identify a family caregiver (due to the absence of such an individual, concern for potential burden on a loved one, desire for confidentiality, transient or progressive cognitive impairment, or other reasons), this also needs to be documented, though the additional provisions of the CARE Act would not be applicable. As some states have made additions or individual modifications to the CARE Act, the reader is encouraged to learn more about state‐specific differences that might impact implementation.[2, 3]

The impetus for the CARE Act emerges from challenges faced by both family caregivers and healthcare professionals. The 3 care elements included in the Act appear to have considerable face validity for what would constitute good transitional care. To further explore why this is necessary, we need to begin by asking why are these 3 care elements not routine, and why did an advocacy organization resort to a legislative solution to formally recognize and include family caregivers in discharge preparation?

Family caregivers, when able and willing, often play an instrumental role in the care of their loved ones, particularly during the vulnerable time of transitions out of the hospital.[4, 5] They are often the first line of defense for detecting lapses in quality or safety as care is transitioned from the hospital. Family caregivers frequently take on a primary or secondary role in operationalizing and executing the discharge plan. Nearly half of family caregivers perform skilled medical or nursing tasks for their loved ones (eg, wound care, injections, complex medication management, operating specialized medical equipment) often with insufficient assistance or training from healthcare professionals.[6]

Lack of sufficient time might be a major reason why the 3 care elements identified in the CARE Act are not routinely addressed by the discharging team, which may include hospitalists, nurses, pharmacists, social workers, and other clinicians. However, there may be other reasons as well, including a lack of knowledge, confidence, or tools for how to best prepare the patient and family caregiver. This is compounded by the absence of routine feedback loops for gauging the effectiveness of discharge preparation beyond a patient's readmission to the same facility. If hospital‐based clinicians were asked to rank order their daily tasks from greatest sense of professional gratification to lowest, discharge preparation would likely appear toward the bottom of the list.[7, 8]

Meanwhile, hospitalists and hospital clinical leaders are struggling to keep pace with a confluence of new demands that include value‐based purchasing initiatives and population health efforts, to name but a few. Although current Centers for Medicare and Medicaid Services' (CMS) Hospital Conditions of Participation for Discharge Planning do not require recognition or preparation of family caregivers, CMS' newly proposed revisions emphasize better preparation of family caregivers to be active partners upon hospital discharge.[9] Thus, although it might be reflexive to view the CARE Act in isolation as yet 1 more initiative requiring new effort and resources to address, widening the lens may confirm that the contributions of family caregivers are integral and aligned across nearly all efforts aimed at promoting greater value, and in this light could be viewed as complementary rather than competitive.

Innovation or new resources may be needed to implement the CARE Act. In the absence of a step‐by‐step user's guide, hospitals may wish to take advantage of valuable publicly available resources that encourage more effective collaboration between family caregivers and healthcare professionals (Table 1).

Operationalizing the CARE Act may initially appear simple but in practice will not be easy. The first care element focuses on identifying the family caregiver. Next, Step in Care offers a practical guide for how to identify the family caregiver in a busy hospital environment (Table 1). The guide advises health professionals on how to identify the person most likely to assume responsibility for care after discharge by asking a series of questions: Who assists you at home? Who do you call in case of emergency? Who helps with medications or doctor appointments? The guide cautions health professionals not to assume that individuals encountered at the patient's bedside are necessarily the family caregivers. They may be covering for the family caregiver, who has other duties (eg, job, child care).

The second CARE Act element entails informing the family caregiver when the patient will be discharged. At present there is no standardization of this practice. Many hospitals conduct interdisciplinary rounds, during which a discharge date is frequently estimated. A designated member of the inpatient team (eg, primary nurse, social worker, care manager) might be tasked with notifying the family caregiver of this estimated date (either in person, by telephone, or using other approved mode[s] of communication). Ideally, this notification should be conveyed as soon as the inpatient care team can foresee a discharge date, as it would be preferable to give the family caregiver an estimate that turns out to be a day or 2 off and needs to be revised than to inform the family caregiver at the last minute. The white board in the patient's room may serve as a reminder to both the patient and family caregiver as well as to other members of the inpatient care team.

The third CARE Act element could be facilitated with the Family Caregiver Activation in Transitions (FCAT) tool, a self‐efficacy measure of transition specific tasks. The FCAT tool is designed to facilitate more productive interactions and guide the care team in understanding what common transition‐related areas family caregivers would like to feel more prepared for or confident with. The FCAT tool can be administered by a health professional or self‐administered by a family caregiver and takes approximately 2 minutes to complete[10] (Table 1).

Hospital leaders might consider creating an interdisciplinary team charged with facilitating the implementation of the CARE Act. Specifically, this team might develop guidelines and serve as a forum whereby clinicians might share particularly challenging cases. Similarly, for ongoing input and suggestions for how to further improve all aspects of hospital care, including the discharge experience, hospitals are encouraged to form and foster patient and family advisory councils (Table 1).

Finally, when it comes to improving the hospital discharge experience for family caregivers, there is no us and them. Despite our professional advantages, each of has had or will likely have an opportunity to overcome the many gaps in hospital discharge planning, not just as healthcare professionals but also in our roles as adult children, spouses, and parents. In this regard, we are all invested in improving the discharge experience.

Sitting while interacting with patients is standard in the outpatient setting and encouraged in the inpatient setting as a best practice.[1, 2] Michael W. Kahn defined etiquette‐based medicine as a set of easily taught behaviors that demonstrate respect for the patient; sitting at the bedside is included.[1] A prominent healthcare consulting group also recommends that physicians and nurses sit at the bedside, claiming that the patient will estimate you were in the room 3 times longer.[3] Previous studies suggest patients may perceive physicians who sit at the bedside as more compassionate and as spending more time with them, and may perceive the overall interaction as more positive when the physician sits.[4, 5, 6] Two small studies found that patients perceived the physician as having spent more time with them if he or she sat rather than stood.[5, 6] A study in the emergency department found no effect of posture on patient perception of physician communication skills, and a study of a single attending neurosurgeon found that patients reported a better understanding of their condition when the physician sat.[5, 6] The effect of physician posture on hospitalist physician‐patient communication has not been previously studied. Despite evidence that sitting in the inpatient setting may improve physician‐patient communication, studies suggest that physicians rarely sit at the bedside of inpatients.[7, 8]

We conducted a cluster‐randomized trial of the impact of hospitalist physician posture during morning rounds. We hypothesized that patients whose physician sat rather than stood would perceive that their physician spent more time with them and would rate the physician's communication skills more highly. We also hypothesized that sitting would not prolong the length of the patient‐physician encounter.

PATIENTS AND METHODS

We conducted a cluster‐randomized clinical trial with a crossover component randomizing physicians on the order of sit/stand within a consecutive 7‐day workweek. We enrolled patients being cared for by attending hospitalists on a resident‐uncovered general internal medicine service in an academic tertiary care hospital. We also enrolled the hospitalists and collected demographics and practice information. Wall‐mounted folding chairs (Figure 1) were installed in all rooms on two 28‐bed units for use by physicians. Eligible patients were newly admitted or transferred from the intensive care unit between June 2014 and June 2015, English speaking, and adults who consented to their own medical care. Physicians were randomly assigned to sit or stand during morning rounds for the first 3 days of their workweek. The last 4 days they provided care using the other posture. Blocks of 4 weeks were used to randomize the sit/stand order.

Figure 1

We measured the length of the physician‐patient interaction, asked both the physician and the patient to estimate the length of the interaction, and administered a written survey to the patient with questions about the physician's communication skills. Research assistants timed the interaction from outside the room and entered the room to consent patients and administer the survey after the physician departed. Survey questions were modeled on the physician communication questions from the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. We aggregated all answers other than the most positive answer because HCAHPS questions are analyzed according to a top box methodology. Adherence to the intervention was measured by asking the physician whether he or she actually sat or stood for each interaction. We administered a survey to physicians to collect demographics and feedback.

We estimated descriptive statistics for physician and patient participants using cross‐tabs and means. To estimate associations, we used logistic and linear regression that employed cluster‐adjusted t statistics and clustered patients within providers. This method optimizes estimation of standard errors (and corresponding confidence intervals and P values) when the number of clusters is small (16 physicians).[9] For our primary analysis, we analyzed as randomized using an intent‐to‐treat approach. In other words, those assigned to the standing group were analyzed in the standing group even if they actually sat (and vice versa). In a sensitivity analysis we used the same methods to analyze the data according actual provider posture as reported by the physician and not as randomized. We calculated the mean and range of the number of patients seen by physicians. We compared estimates of time spent between patients and providers and patients' satisfaction according to provider posture. We complied with the Consolidated Standards of Reporting Trials 2010 guidelines.[10] Our institutional review board approved this project. All participants provided written consent.

RESULTS

All 17 hospitalists attending on the service consented to participate; 1 did not see any patients involved in the study and was removed from the analysis. Sixty‐nine percent were female and 81% had been in practice for 3 years or less at the time of study enrollment; 94% reported standing when assigned to stand and 83% reported sitting when assigned to sit. We found 31% of physicians reported they routinely sat before participating in the study, and 81% said they would sit more after the study; this result approached statistical significance (exact McNemar P = 0.06). Of the 11 physicians who reported not routinely sitting before the study all, 7 cited not having a place to sit as a reason for not sitting. Other rationale provided included being too short to see the patient if seated, believing rounds would take more time if seated, and concerns about contact precautions. Comments in the postintervention survey regarding why providers planned to sit more centered around themes of having chairs available, thinking that sitting improves communication, and thinking that patients prefer providers to sit.

Two hundred eleven patients were assessed for eligibility. Fifty‐two were excluded (27 did not meet inclusion criteria and 25 declined to participate), leaving 159 participating patients. Seven patient‐physician pairs were inadvertently assigned the wrong intervention but were analyzed as randomized. There were no demographic differences between patient groups (Table 1). Physicians participating in the study saw an average of 13 study patients (range, 118) during the study. Mean time spent in the patient's room during rounds was 12:00 minutes for seated physicians and 12:10 for standing physicians (P = 0.84). Regardless of provider posture, patients overestimated the amount of time their physician spent in the room (mean difference 4:10 minutes, P = 0.01). Patients' estimates of the time the physician spent did not vary by posture (16:00 minutes for seated, 16:19 for standing, P = 0.86).

Patients whose physician sat on rounds were statistically significantly more likely to choose the answer always to the questions regarding their physician listening carefully to them (P = 0.02) and explaining things in a way that was easy to understand (P = 0.05, Table 2). There was no difference in the patients' response to questions about the physician interrupting the patient when talking or treating them with courtesy and respect. Nearly all patients chose just right when asked to rate the amount of time their physician had spent with them on rounds (Table 2). The results of our sensitivity analysis that classified physicians according to their actual posture yielded different results; none of the findings in that analysis including questions regarding the physician listening carefully or explaining things in a way that was easy to understand were statistically significant (see Supporting Information, Appendix 1, in the online version of this article).

DISCUSSION

In our study involving general medicine inpatients cared for by academic hospitalists, physicians did not spend more time in the room when seated, and were willing to adopt this practice. Patients perceived that seated compared to standing physicians listened more carefully and explained things in a way that was easy to understand when analyzed using an intent‐to‐treat approach. Patients did not perceive that seated physicians spent more time with them than standing physicians. To our knowledge, this is the first study showing the effects of hospitalist rounding posture on patient experience.

Our finding that patients rated seated physicians more highly on listening carefully and explaining things well indicates that training hospitalists to sit at the bedside may ultimately improve patient satisfaction. Our findings suggest seated interaction may improve satisfaction with communication without increasing time burden on physicians. However, given that these findings were not statistically significant when we analyzed our data according to actual behavior, larger studies should verify the impact of physician posture on patient experience.

Previous studies found that a minority of physicians sit in the inpatient setting, but did not study barriers to sitting while on rounds.[7, 8] A majority of physicians in our study sat when instructed to do so and when chairs were provided, and over 80% of physicians in our study said they planned to continue sitting while on rounds after the study was complete. A lack of chairs may be a major barrier to physicians adopting this facet of etiquette‐based medicine, and institutions wishing to promote this practice should consider providing chairs. Written comments from physician participants suggest physicians who are introduced to this practice enjoy sitting and think it improves physician‐patient communication. Further studies are needed to test our assumption that physicians continue sitting when chairs are provided.

Our work differs from previous studies. Johnson et al. studied interactions in the emergency room with a mean length of 8.6 minutes,[5] and Swayden et al. studied postoperative visits by a single neurosurgeon with a mean length of about 1 minute.[6] One explanation for the lack of a difference in time spent by posture might be that an average visit time of 12 minutes passes a threshold where patients make more accurate estimates of visit length or where factors other than posture more strongly influence perceptions of duration.

Limitations of our study include the relatively small sample size, single location, and limitation to English‐speaking patients able to consent themselves. Reasons for the limited sample size include that chairs were only installed in 2 units, and not all patients on the unit were under the care of participating physicians. Physician subjects were not blinded to their interactions being timed or to the fact that patients were surveyed about their communication skills. It is possible that factors that may have affected patients' responses such as severity of illness, number of consultants involved in their care, or prior experiences in the healthcare system were not equally distributed between our 2 groups. Additionally, our use of questions similar to those used in the HCAHPS instrument is not compliant with Centers for Medicare and Medicaid Services (CMS) policy. We caution others against using questions that might invalidate their hospital's participation in CMS payment programs.

Our study was limited to rounds involving 1 physician; our practice is that in a larger team the presenting member is encouraged to sit and others sit if there are additional chairs. Best practices on a teaching service are unclear and could be the subject of further study. The longer‐term sustainability of the practice of sitting on rounds is unclear. However, our physician subjects reported that they plan to continue to sit after the study, and we have shared the results with physicians in order to provide them with evidence supporting this practice. Not having a place to sit and thinking that sitting increases the amount of time spent on rounds were concerns provided in our preintervention survey, and we believe our study addresses these concerns.

Our study demonstrates the effects of a simple intervention on patient satisfaction without increasing burden on providers. Sitting at the bedside does not impact the amount of time spent with the patient, but may improve the patient's perception of the physician's communication skills and thus impact the patient experience. This simple intervention could improve patient satisfaction at little cost.

Sitting while interacting with patients is standard in the outpatient setting and encouraged in the inpatient setting as a best practice.[1, 2] Michael W. Kahn defined etiquette‐based medicine as a set of easily taught behaviors that demonstrate respect for the patient; sitting at the bedside is included.[1] A prominent healthcare consulting group also recommends that physicians and nurses sit at the bedside, claiming that the patient will estimate you were in the room 3 times longer.[3] Previous studies suggest patients may perceive physicians who sit at the bedside as more compassionate and as spending more time with them, and may perceive the overall interaction as more positive when the physician sits.[4, 5, 6] Two small studies found that patients perceived the physician as having spent more time with them if he or she sat rather than stood.[5, 6] A study in the emergency department found no effect of posture on patient perception of physician communication skills, and a study of a single attending neurosurgeon found that patients reported a better understanding of their condition when the physician sat.[5, 6] The effect of physician posture on hospitalist physician‐patient communication has not been previously studied. Despite evidence that sitting in the inpatient setting may improve physician‐patient communication, studies suggest that physicians rarely sit at the bedside of inpatients.[7, 8]

We conducted a cluster‐randomized trial of the impact of hospitalist physician posture during morning rounds. We hypothesized that patients whose physician sat rather than stood would perceive that their physician spent more time with them and would rate the physician's communication skills more highly. We also hypothesized that sitting would not prolong the length of the patient‐physician encounter.

PATIENTS AND METHODS

We conducted a cluster‐randomized clinical trial with a crossover component randomizing physicians on the order of sit/stand within a consecutive 7‐day workweek. We enrolled patients being cared for by attending hospitalists on a resident‐uncovered general internal medicine service in an academic tertiary care hospital. We also enrolled the hospitalists and collected demographics and practice information. Wall‐mounted folding chairs (Figure 1) were installed in all rooms on two 28‐bed units for use by physicians. Eligible patients were newly admitted or transferred from the intensive care unit between June 2014 and June 2015, English speaking, and adults who consented to their own medical care. Physicians were randomly assigned to sit or stand during morning rounds for the first 3 days of their workweek. The last 4 days they provided care using the other posture. Blocks of 4 weeks were used to randomize the sit/stand order.

Figure 1

We measured the length of the physician‐patient interaction, asked both the physician and the patient to estimate the length of the interaction, and administered a written survey to the patient with questions about the physician's communication skills. Research assistants timed the interaction from outside the room and entered the room to consent patients and administer the survey after the physician departed. Survey questions were modeled on the physician communication questions from the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. We aggregated all answers other than the most positive answer because HCAHPS questions are analyzed according to a top box methodology. Adherence to the intervention was measured by asking the physician whether he or she actually sat or stood for each interaction. We administered a survey to physicians to collect demographics and feedback.

We estimated descriptive statistics for physician and patient participants using cross‐tabs and means. To estimate associations, we used logistic and linear regression that employed cluster‐adjusted t statistics and clustered patients within providers. This method optimizes estimation of standard errors (and corresponding confidence intervals and P values) when the number of clusters is small (16 physicians).[9] For our primary analysis, we analyzed as randomized using an intent‐to‐treat approach. In other words, those assigned to the standing group were analyzed in the standing group even if they actually sat (and vice versa). In a sensitivity analysis we used the same methods to analyze the data according actual provider posture as reported by the physician and not as randomized. We calculated the mean and range of the number of patients seen by physicians. We compared estimates of time spent between patients and providers and patients' satisfaction according to provider posture. We complied with the Consolidated Standards of Reporting Trials 2010 guidelines.[10] Our institutional review board approved this project. All participants provided written consent.

RESULTS

All 17 hospitalists attending on the service consented to participate; 1 did not see any patients involved in the study and was removed from the analysis. Sixty‐nine percent were female and 81% had been in practice for 3 years or less at the time of study enrollment; 94% reported standing when assigned to stand and 83% reported sitting when assigned to sit. We found 31% of physicians reported they routinely sat before participating in the study, and 81% said they would sit more after the study; this result approached statistical significance (exact McNemar P = 0.06). Of the 11 physicians who reported not routinely sitting before the study all, 7 cited not having a place to sit as a reason for not sitting. Other rationale provided included being too short to see the patient if seated, believing rounds would take more time if seated, and concerns about contact precautions. Comments in the postintervention survey regarding why providers planned to sit more centered around themes of having chairs available, thinking that sitting improves communication, and thinking that patients prefer providers to sit.

Two hundred eleven patients were assessed for eligibility. Fifty‐two were excluded (27 did not meet inclusion criteria and 25 declined to participate), leaving 159 participating patients. Seven patient‐physician pairs were inadvertently assigned the wrong intervention but were analyzed as randomized. There were no demographic differences between patient groups (Table 1). Physicians participating in the study saw an average of 13 study patients (range, 118) during the study. Mean time spent in the patient's room during rounds was 12:00 minutes for seated physicians and 12:10 for standing physicians (P = 0.84). Regardless of provider posture, patients overestimated the amount of time their physician spent in the room (mean difference 4:10 minutes, P = 0.01). Patients' estimates of the time the physician spent did not vary by posture (16:00 minutes for seated, 16:19 for standing, P = 0.86).

Patients whose physician sat on rounds were statistically significantly more likely to choose the answer always to the questions regarding their physician listening carefully to them (P = 0.02) and explaining things in a way that was easy to understand (P = 0.05, Table 2). There was no difference in the patients' response to questions about the physician interrupting the patient when talking or treating them with courtesy and respect. Nearly all patients chose just right when asked to rate the amount of time their physician had spent with them on rounds (Table 2). The results of our sensitivity analysis that classified physicians according to their actual posture yielded different results; none of the findings in that analysis including questions regarding the physician listening carefully or explaining things in a way that was easy to understand were statistically significant (see Supporting Information, Appendix 1, in the online version of this article).

DISCUSSION

In our study involving general medicine inpatients cared for by academic hospitalists, physicians did not spend more time in the room when seated, and were willing to adopt this practice. Patients perceived that seated compared to standing physicians listened more carefully and explained things in a way that was easy to understand when analyzed using an intent‐to‐treat approach. Patients did not perceive that seated physicians spent more time with them than standing physicians. To our knowledge, this is the first study showing the effects of hospitalist rounding posture on patient experience.

Our finding that patients rated seated physicians more highly on listening carefully and explaining things well indicates that training hospitalists to sit at the bedside may ultimately improve patient satisfaction. Our findings suggest seated interaction may improve satisfaction with communication without increasing time burden on physicians. However, given that these findings were not statistically significant when we analyzed our data according to actual behavior, larger studies should verify the impact of physician posture on patient experience.

Previous studies found that a minority of physicians sit in the inpatient setting, but did not study barriers to sitting while on rounds.[7, 8] A majority of physicians in our study sat when instructed to do so and when chairs were provided, and over 80% of physicians in our study said they planned to continue sitting while on rounds after the study was complete. A lack of chairs may be a major barrier to physicians adopting this facet of etiquette‐based medicine, and institutions wishing to promote this practice should consider providing chairs. Written comments from physician participants suggest physicians who are introduced to this practice enjoy sitting and think it improves physician‐patient communication. Further studies are needed to test our assumption that physicians continue sitting when chairs are provided.

Our work differs from previous studies. Johnson et al. studied interactions in the emergency room with a mean length of 8.6 minutes,[5] and Swayden et al. studied postoperative visits by a single neurosurgeon with a mean length of about 1 minute.[6] One explanation for the lack of a difference in time spent by posture might be that an average visit time of 12 minutes passes a threshold where patients make more accurate estimates of visit length or where factors other than posture more strongly influence perceptions of duration.

Limitations of our study include the relatively small sample size, single location, and limitation to English‐speaking patients able to consent themselves. Reasons for the limited sample size include that chairs were only installed in 2 units, and not all patients on the unit were under the care of participating physicians. Physician subjects were not blinded to their interactions being timed or to the fact that patients were surveyed about their communication skills. It is possible that factors that may have affected patients' responses such as severity of illness, number of consultants involved in their care, or prior experiences in the healthcare system were not equally distributed between our 2 groups. Additionally, our use of questions similar to those used in the HCAHPS instrument is not compliant with Centers for Medicare and Medicaid Services (CMS) policy. We caution others against using questions that might invalidate their hospital's participation in CMS payment programs.

Our study was limited to rounds involving 1 physician; our practice is that in a larger team the presenting member is encouraged to sit and others sit if there are additional chairs. Best practices on a teaching service are unclear and could be the subject of further study. The longer‐term sustainability of the practice of sitting on rounds is unclear. However, our physician subjects reported that they plan to continue to sit after the study, and we have shared the results with physicians in order to provide them with evidence supporting this practice. Not having a place to sit and thinking that sitting increases the amount of time spent on rounds were concerns provided in our preintervention survey, and we believe our study addresses these concerns.

Our study demonstrates the effects of a simple intervention on patient satisfaction without increasing burden on providers. Sitting at the bedside does not impact the amount of time spent with the patient, but may improve the patient's perception of the physician's communication skills and thus impact the patient experience. This simple intervention could improve patient satisfaction at little cost.

Sitting while interacting with patients is standard in the outpatient setting and encouraged in the inpatient setting as a best practice.[1, 2] Michael W. Kahn defined etiquette‐based medicine as a set of easily taught behaviors that demonstrate respect for the patient; sitting at the bedside is included.[1] A prominent healthcare consulting group also recommends that physicians and nurses sit at the bedside, claiming that the patient will estimate you were in the room 3 times longer.[3] Previous studies suggest patients may perceive physicians who sit at the bedside as more compassionate and as spending more time with them, and may perceive the overall interaction as more positive when the physician sits.[4, 5, 6] Two small studies found that patients perceived the physician as having spent more time with them if he or she sat rather than stood.[5, 6] A study in the emergency department found no effect of posture on patient perception of physician communication skills, and a study of a single attending neurosurgeon found that patients reported a better understanding of their condition when the physician sat.[5, 6] The effect of physician posture on hospitalist physician‐patient communication has not been previously studied. Despite evidence that sitting in the inpatient setting may improve physician‐patient communication, studies suggest that physicians rarely sit at the bedside of inpatients.[7, 8]

We conducted a cluster‐randomized trial of the impact of hospitalist physician posture during morning rounds. We hypothesized that patients whose physician sat rather than stood would perceive that their physician spent more time with them and would rate the physician's communication skills more highly. We also hypothesized that sitting would not prolong the length of the patient‐physician encounter.

PATIENTS AND METHODS

We conducted a cluster‐randomized clinical trial with a crossover component randomizing physicians on the order of sit/stand within a consecutive 7‐day workweek. We enrolled patients being cared for by attending hospitalists on a resident‐uncovered general internal medicine service in an academic tertiary care hospital. We also enrolled the hospitalists and collected demographics and practice information. Wall‐mounted folding chairs (Figure 1) were installed in all rooms on two 28‐bed units for use by physicians. Eligible patients were newly admitted or transferred from the intensive care unit between June 2014 and June 2015, English speaking, and adults who consented to their own medical care. Physicians were randomly assigned to sit or stand during morning rounds for the first 3 days of their workweek. The last 4 days they provided care using the other posture. Blocks of 4 weeks were used to randomize the sit/stand order.

Figure 1

We measured the length of the physician‐patient interaction, asked both the physician and the patient to estimate the length of the interaction, and administered a written survey to the patient with questions about the physician's communication skills. Research assistants timed the interaction from outside the room and entered the room to consent patients and administer the survey after the physician departed. Survey questions were modeled on the physician communication questions from the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. We aggregated all answers other than the most positive answer because HCAHPS questions are analyzed according to a top box methodology. Adherence to the intervention was measured by asking the physician whether he or she actually sat or stood for each interaction. We administered a survey to physicians to collect demographics and feedback.

We estimated descriptive statistics for physician and patient participants using cross‐tabs and means. To estimate associations, we used logistic and linear regression that employed cluster‐adjusted t statistics and clustered patients within providers. This method optimizes estimation of standard errors (and corresponding confidence intervals and P values) when the number of clusters is small (16 physicians).[9] For our primary analysis, we analyzed as randomized using an intent‐to‐treat approach. In other words, those assigned to the standing group were analyzed in the standing group even if they actually sat (and vice versa). In a sensitivity analysis we used the same methods to analyze the data according actual provider posture as reported by the physician and not as randomized. We calculated the mean and range of the number of patients seen by physicians. We compared estimates of time spent between patients and providers and patients' satisfaction according to provider posture. We complied with the Consolidated Standards of Reporting Trials 2010 guidelines.[10] Our institutional review board approved this project. All participants provided written consent.

RESULTS

All 17 hospitalists attending on the service consented to participate; 1 did not see any patients involved in the study and was removed from the analysis. Sixty‐nine percent were female and 81% had been in practice for 3 years or less at the time of study enrollment; 94% reported standing when assigned to stand and 83% reported sitting when assigned to sit. We found 31% of physicians reported they routinely sat before participating in the study, and 81% said they would sit more after the study; this result approached statistical significance (exact McNemar P = 0.06). Of the 11 physicians who reported not routinely sitting before the study all, 7 cited not having a place to sit as a reason for not sitting. Other rationale provided included being too short to see the patient if seated, believing rounds would take more time if seated, and concerns about contact precautions. Comments in the postintervention survey regarding why providers planned to sit more centered around themes of having chairs available, thinking that sitting improves communication, and thinking that patients prefer providers to sit.

Two hundred eleven patients were assessed for eligibility. Fifty‐two were excluded (27 did not meet inclusion criteria and 25 declined to participate), leaving 159 participating patients. Seven patient‐physician pairs were inadvertently assigned the wrong intervention but were analyzed as randomized. There were no demographic differences between patient groups (Table 1). Physicians participating in the study saw an average of 13 study patients (range, 118) during the study. Mean time spent in the patient's room during rounds was 12:00 minutes for seated physicians and 12:10 for standing physicians (P = 0.84). Regardless of provider posture, patients overestimated the amount of time their physician spent in the room (mean difference 4:10 minutes, P = 0.01). Patients' estimates of the time the physician spent did not vary by posture (16:00 minutes for seated, 16:19 for standing, P = 0.86).

Patients whose physician sat on rounds were statistically significantly more likely to choose the answer always to the questions regarding their physician listening carefully to them (P = 0.02) and explaining things in a way that was easy to understand (P = 0.05, Table 2). There was no difference in the patients' response to questions about the physician interrupting the patient when talking or treating them with courtesy and respect. Nearly all patients chose just right when asked to rate the amount of time their physician had spent with them on rounds (Table 2). The results of our sensitivity analysis that classified physicians according to their actual posture yielded different results; none of the findings in that analysis including questions regarding the physician listening carefully or explaining things in a way that was easy to understand were statistically significant (see Supporting Information, Appendix 1, in the online version of this article).

DISCUSSION

In our study involving general medicine inpatients cared for by academic hospitalists, physicians did not spend more time in the room when seated, and were willing to adopt this practice. Patients perceived that seated compared to standing physicians listened more carefully and explained things in a way that was easy to understand when analyzed using an intent‐to‐treat approach. Patients did not perceive that seated physicians spent more time with them than standing physicians. To our knowledge, this is the first study showing the effects of hospitalist rounding posture on patient experience.

Our finding that patients rated seated physicians more highly on listening carefully and explaining things well indicates that training hospitalists to sit at the bedside may ultimately improve patient satisfaction. Our findings suggest seated interaction may improve satisfaction with communication without increasing time burden on physicians. However, given that these findings were not statistically significant when we analyzed our data according to actual behavior, larger studies should verify the impact of physician posture on patient experience.

Previous studies found that a minority of physicians sit in the inpatient setting, but did not study barriers to sitting while on rounds.[7, 8] A majority of physicians in our study sat when instructed to do so and when chairs were provided, and over 80% of physicians in our study said they planned to continue sitting while on rounds after the study was complete. A lack of chairs may be a major barrier to physicians adopting this facet of etiquette‐based medicine, and institutions wishing to promote this practice should consider providing chairs. Written comments from physician participants suggest physicians who are introduced to this practice enjoy sitting and think it improves physician‐patient communication. Further studies are needed to test our assumption that physicians continue sitting when chairs are provided.

Our work differs from previous studies. Johnson et al. studied interactions in the emergency room with a mean length of 8.6 minutes,[5] and Swayden et al. studied postoperative visits by a single neurosurgeon with a mean length of about 1 minute.[6] One explanation for the lack of a difference in time spent by posture might be that an average visit time of 12 minutes passes a threshold where patients make more accurate estimates of visit length or where factors other than posture more strongly influence perceptions of duration.

Limitations of our study include the relatively small sample size, single location, and limitation to English‐speaking patients able to consent themselves. Reasons for the limited sample size include that chairs were only installed in 2 units, and not all patients on the unit were under the care of participating physicians. Physician subjects were not blinded to their interactions being timed or to the fact that patients were surveyed about their communication skills. It is possible that factors that may have affected patients' responses such as severity of illness, number of consultants involved in their care, or prior experiences in the healthcare system were not equally distributed between our 2 groups. Additionally, our use of questions similar to those used in the HCAHPS instrument is not compliant with Centers for Medicare and Medicaid Services (CMS) policy. We caution others against using questions that might invalidate their hospital's participation in CMS payment programs.

Our study was limited to rounds involving 1 physician; our practice is that in a larger team the presenting member is encouraged to sit and others sit if there are additional chairs. Best practices on a teaching service are unclear and could be the subject of further study. The longer‐term sustainability of the practice of sitting on rounds is unclear. However, our physician subjects reported that they plan to continue to sit after the study, and we have shared the results with physicians in order to provide them with evidence supporting this practice. Not having a place to sit and thinking that sitting increases the amount of time spent on rounds were concerns provided in our preintervention survey, and we believe our study addresses these concerns.

Our study demonstrates the effects of a simple intervention on patient satisfaction without increasing burden on providers. Sitting at the bedside does not impact the amount of time spent with the patient, but may improve the patient's perception of the physician's communication skills and thus impact the patient experience. This simple intervention could improve patient satisfaction at little cost.

Hand hygiene (HH) is believed to be one of the single most important interventions to prevent healthcare‐associated infection, yet physicians are notorious for their poor compliance.[1, 2, 3] At our 800‐bed acute care academic hospital, we implemented a multifaceted HH program[4] in 2007, which was associated with improved HH compliance rates from 43% to 87%. Despite this improvement, HH compliance among physicians remained suboptimal, with rates below 60% in some patient areas. A targeted campaign focused on recruitment of physician champions, resulted in some improvement, but physician compliance has consistently remained below performance of nurses (70%75% for physicians vs 85%90% for nurses).

Our experience parallels the results seen in multinational surveys demonstrating consistently lower physician HH compliance.[5] Given the multiple improvement efforts directed at physicians and the apparent ceiling observed in HH performance, we wanted to confirm whether physicians are truly recalcitrant to cleaning their hands, or whether lower compliance among physicians reflected a differential in the Hawthorne effect inherent to direct observation methods. Specifically, we wondered if nurses tend to recognize auditors more readily than physicians and therefore show higher apparent HH compliance when auditors are present. We also wanted to verify whether the behavior of attending physicians influenced compliance of their physician trainees. To test these hypotheses, we trained 2 clinical observers to covertly measure HH compliance of nurses and physicians on 3 different clinical services.

METHODS

Between May 27, 2015 and July 31, 2015, 2 student observers joined clinical rotations on physician and nursing teams, respectively. Healthcare teams were unaware that the student observers were measuring HH compliance during their clinical rotation. Students rotated in the emergency department, general medical and surgical wards for no more than 1 week at a time to increase exposure to different providers and minimize risk of exposing the covert observation.

Prior to the study period, the students underwent training and validation with a hospital HH auditor at another clinical setting offsite to avoid any recognition of these students by healthcare providers as observers of HH at the main hospital. Training with the auditors occurred until interobserver agreement between all HH opportunities reached 100% agreement for 2 consecutive observation days.

During their rotations, students covertly recorded HH compliance based on moments of hand hygiene[4] and also noted location, presence, and compliance of the attending physician, team size during patient encounter, and isolation requirements. Both students measured HH compliance of nurses and physicians around them. Although students spent the majority of their time with their assigned physician or nurse teams, they did not limit their observations to these individuals only, but recorded compliance of any nurse or physician on the ward as long as they were within sight during an HH opportunity. To limit clustering of observations of the same healthcare worker, up to a maximum of 2 observations per healthcare worker per day was recorded.

We compared covertly measured HH compliance with data from overt observation by hospital auditors during the same time period. Differences in proportion of HH compliance were compared with hospital audits during the same period with a ² test. Difference between differences in overtly and covertly measured HH compliance for nurses and physicians was compared using Breslow day test.

The study was approved by the hospital's research ethics board. Although deception was used in this study,[2, 6] all data were collected for quality improvement purposes, and the aggregate results were disclosed to hospital staff following the study.

RESULTS

Covertly observed HH compliance was 50.0% (799/1597) compared with 83.7% (2769/3309) recorded by hospital auditors during the same time period (P < 0.0002) (Table 1). There was no significant difference in the compliance measured by each student (50.1%, 473/928 vs 48.7%, 326/669) (P = 0.3), and their results were combined for the rest of the analysis. Compliance before contact with the patient or patient environment was 43.1% (344/798), 74.3% (26/35) before clean/aseptic procedures, 34.8% (8/23) after potential body fluid exposure, and 56.8% (483/851) after contact with the patient or patient environment. Healthcare providers examining patients with isolation precautions were found to have a HH compliance of 74.8% (101/135) compared to 47.0% (385/820) when isolation precautions were not required (P < 0.0002).

Hospital auditor data showed that surgery and medicine had similarly high rates of compliance (91.0% and 85.0%, respectively), whereas the emergency department had a notably lower rate of 73.8%. Covert observation confirmed a lower rate in the emergency department (43.9%), but showed a higher compliance on general medicine than on surgery (58.9% vs 45.7%; P = 0.02). The difference in physician compliance between hospital auditors and covert observers was 19.0% (73.2%, 175/239 vs. 54.2%, 469/865); for nurses this difference was much higher at 40.7% (85.8%, 754/879 vs. 45.1%, 330/732) (P < 0.0001) (Table 1).

In terms of physician compliance, primary teams tended to have lower HH compliance of 50.4% (323/641) compared with consulting services at 57.0% (158/277) (P = 0.06). Team rounds of 3 members were associated with higher compliance compared with encounters involving <3 members (62.1%, 282/454 vs. 42.0%, 128/308) (P < 0.0002). Presence of attending physician did not affect trainee HH compliance (55.5%, 201/362 when attending present vs. 56.8%, 133/234 when attending absent; P = 0.79). However, trainee HH compliance improved markedly when attending staff cleaned their hands and decreased markedly when they did not (79.5%, 174/219 vs. 18.9%, 27/143; P < 0.0002).

DISCUSSION

We introduced covert HH observers at our hospital to determine whether differences in Hawthorne effect accounted for measured disparity between physician HH compliance, and to gain further insights into the barriers and enablers of physician HH compliance. We discovered that performance differences between physicians and nurses decreased when neither group was aware that HH was being measured, suggesting that healthcare professions are differentially affected by the Hawthorne effect. This difference may be explained by the continuity of nurses on the ward that makes them more aware of visitors like HH auditors,[7] compared with physicians who rotate periodically on the ward.

Although hospital auditors play a central role in HH education through in‐the‐moment feedback, use of these data to benchmark performance can lead to inappropriate inferences about HH compliance. Prior studies using automated HH surveillance have suggested that the magnitude of the Hawthorne effect varies based on baseline HH rates,[8] whereas our study suggests a differential Hawthorne effect between professions and clinical services. If we relied only on auditor data, we would have continued to believe that only physicians in our organization had poor HH compliance, and we would not be aware of the global nature of the HH problem.

Our results are similar to that of Pan et al., who used covert medical students to measure HH and found compliance of 44.1% compared with 94.1% by unit auditors.[2] Because their study involved an active feedback intervention, the differential in Hawthorne effect between professions could not be reliably assessed. However, they observed a progressive increase in nurse HH compliance using covert observation methods, suggesting improvement in HH performance independent of observer bias.[7]

Covert observation in our study also provided important insights regarding barriers and enablers of HH compliance. Self‐preservation behaviors were common among both nurses and physicians, as HH compliance was consistently higher after patient contact compared to before or when seeing patients who required additional precautions. This finding confirms that the perceived risk of transmission seems to be a powerful motivating factor for HH.[9] Larger groups of trainees were more likely to clean their hands, likely due to peer effects.[10] The strong impact of role modeling on HH was also noted as previously suggested in the literature,[3, 6] but our study captures the magnitude of this effect. Whether or not the attending physician cleaned their hands during rounds either positively or negatively influenced HH compliance of the rest of the physician team (80% when compliant vs 20% when noncompliant).

Our study has several important limitations. The differential Hawthorne effect seen at our center may not reflect other institutions that have numerous HH auditors or high staff turnover resulting in lower ability to recognize auditors. We cannot exclude the possibility of Hawthorne effect using covert methods that could have affected nurse and physician performance differently, but frequent rotation of the students helped maintain covertness of observations. Finally, due to the nature of the covert student observers, a longer observation time frame could not be sustained.

Our experience using covert HH auditors suggests that traditional HH audits not only overstate HH performance overall, but can lead to inaccurate inferences regarding HH performance due to relative differences in Hawthorne effect. The answer to the question regarding whether physicians clean their hands appears to be that they do just as often as nurses, but that all healthcare workers have tremendous room for improvement. We suggest that future improvement efforts will rely on more accurate HH monitoring systems and strong attending physician leadership to set an example for trainees.

Hand hygiene (HH) is believed to be one of the single most important interventions to prevent healthcare‐associated infection, yet physicians are notorious for their poor compliance.[1, 2, 3] At our 800‐bed acute care academic hospital, we implemented a multifaceted HH program[4] in 2007, which was associated with improved HH compliance rates from 43% to 87%. Despite this improvement, HH compliance among physicians remained suboptimal, with rates below 60% in some patient areas. A targeted campaign focused on recruitment of physician champions, resulted in some improvement, but physician compliance has consistently remained below performance of nurses (70%75% for physicians vs 85%90% for nurses).

Our experience parallels the results seen in multinational surveys demonstrating consistently lower physician HH compliance.[5] Given the multiple improvement efforts directed at physicians and the apparent ceiling observed in HH performance, we wanted to confirm whether physicians are truly recalcitrant to cleaning their hands, or whether lower compliance among physicians reflected a differential in the Hawthorne effect inherent to direct observation methods. Specifically, we wondered if nurses tend to recognize auditors more readily than physicians and therefore show higher apparent HH compliance when auditors are present. We also wanted to verify whether the behavior of attending physicians influenced compliance of their physician trainees. To test these hypotheses, we trained 2 clinical observers to covertly measure HH compliance of nurses and physicians on 3 different clinical services.

METHODS

Between May 27, 2015 and July 31, 2015, 2 student observers joined clinical rotations on physician and nursing teams, respectively. Healthcare teams were unaware that the student observers were measuring HH compliance during their clinical rotation. Students rotated in the emergency department, general medical and surgical wards for no more than 1 week at a time to increase exposure to different providers and minimize risk of exposing the covert observation.

Prior to the study period, the students underwent training and validation with a hospital HH auditor at another clinical setting offsite to avoid any recognition of these students by healthcare providers as observers of HH at the main hospital. Training with the auditors occurred until interobserver agreement between all HH opportunities reached 100% agreement for 2 consecutive observation days.

During their rotations, students covertly recorded HH compliance based on moments of hand hygiene[4] and also noted location, presence, and compliance of the attending physician, team size during patient encounter, and isolation requirements. Both students measured HH compliance of nurses and physicians around them. Although students spent the majority of their time with their assigned physician or nurse teams, they did not limit their observations to these individuals only, but recorded compliance of any nurse or physician on the ward as long as they were within sight during an HH opportunity. To limit clustering of observations of the same healthcare worker, up to a maximum of 2 observations per healthcare worker per day was recorded.

We compared covertly measured HH compliance with data from overt observation by hospital auditors during the same time period. Differences in proportion of HH compliance were compared with hospital audits during the same period with a ² test. Difference between differences in overtly and covertly measured HH compliance for nurses and physicians was compared using Breslow day test.

The study was approved by the hospital's research ethics board. Although deception was used in this study,[2, 6] all data were collected for quality improvement purposes, and the aggregate results were disclosed to hospital staff following the study.

RESULTS

Covertly observed HH compliance was 50.0% (799/1597) compared with 83.7% (2769/3309) recorded by hospital auditors during the same time period (P < 0.0002) (Table 1). There was no significant difference in the compliance measured by each student (50.1%, 473/928 vs 48.7%, 326/669) (P = 0.3), and their results were combined for the rest of the analysis. Compliance before contact with the patient or patient environment was 43.1% (344/798), 74.3% (26/35) before clean/aseptic procedures, 34.8% (8/23) after potential body fluid exposure, and 56.8% (483/851) after contact with the patient or patient environment. Healthcare providers examining patients with isolation precautions were found to have a HH compliance of 74.8% (101/135) compared to 47.0% (385/820) when isolation precautions were not required (P < 0.0002).

Hospital auditor data showed that surgery and medicine had similarly high rates of compliance (91.0% and 85.0%, respectively), whereas the emergency department had a notably lower rate of 73.8%. Covert observation confirmed a lower rate in the emergency department (43.9%), but showed a higher compliance on general medicine than on surgery (58.9% vs 45.7%; P = 0.02). The difference in physician compliance between hospital auditors and covert observers was 19.0% (73.2%, 175/239 vs. 54.2%, 469/865); for nurses this difference was much higher at 40.7% (85.8%, 754/879 vs. 45.1%, 330/732) (P < 0.0001) (Table 1).

In terms of physician compliance, primary teams tended to have lower HH compliance of 50.4% (323/641) compared with consulting services at 57.0% (158/277) (P = 0.06). Team rounds of 3 members were associated with higher compliance compared with encounters involving <3 members (62.1%, 282/454 vs. 42.0%, 128/308) (P < 0.0002). Presence of attending physician did not affect trainee HH compliance (55.5%, 201/362 when attending present vs. 56.8%, 133/234 when attending absent; P = 0.79). However, trainee HH compliance improved markedly when attending staff cleaned their hands and decreased markedly when they did not (79.5%, 174/219 vs. 18.9%, 27/143; P < 0.0002).

DISCUSSION

We introduced covert HH observers at our hospital to determine whether differences in Hawthorne effect accounted for measured disparity between physician HH compliance, and to gain further insights into the barriers and enablers of physician HH compliance. We discovered that performance differences between physicians and nurses decreased when neither group was aware that HH was being measured, suggesting that healthcare professions are differentially affected by the Hawthorne effect. This difference may be explained by the continuity of nurses on the ward that makes them more aware of visitors like HH auditors,[7] compared with physicians who rotate periodically on the ward.

Although hospital auditors play a central role in HH education through in‐the‐moment feedback, use of these data to benchmark performance can lead to inappropriate inferences about HH compliance. Prior studies using automated HH surveillance have suggested that the magnitude of the Hawthorne effect varies based on baseline HH rates,[8] whereas our study suggests a differential Hawthorne effect between professions and clinical services. If we relied only on auditor data, we would have continued to believe that only physicians in our organization had poor HH compliance, and we would not be aware of the global nature of the HH problem.

Our results are similar to that of Pan et al., who used covert medical students to measure HH and found compliance of 44.1% compared with 94.1% by unit auditors.[2] Because their study involved an active feedback intervention, the differential in Hawthorne effect between professions could not be reliably assessed. However, they observed a progressive increase in nurse HH compliance using covert observation methods, suggesting improvement in HH performance independent of observer bias.[7]

Covert observation in our study also provided important insights regarding barriers and enablers of HH compliance. Self‐preservation behaviors were common among both nurses and physicians, as HH compliance was consistently higher after patient contact compared to before or when seeing patients who required additional precautions. This finding confirms that the perceived risk of transmission seems to be a powerful motivating factor for HH.[9] Larger groups of trainees were more likely to clean their hands, likely due to peer effects.[10] The strong impact of role modeling on HH was also noted as previously suggested in the literature,[3, 6] but our study captures the magnitude of this effect. Whether or not the attending physician cleaned their hands during rounds either positively or negatively influenced HH compliance of the rest of the physician team (80% when compliant vs 20% when noncompliant).

Our study has several important limitations. The differential Hawthorne effect seen at our center may not reflect other institutions that have numerous HH auditors or high staff turnover resulting in lower ability to recognize auditors. We cannot exclude the possibility of Hawthorne effect using covert methods that could have affected nurse and physician performance differently, but frequent rotation of the students helped maintain covertness of observations. Finally, due to the nature of the covert student observers, a longer observation time frame could not be sustained.

Our experience using covert HH auditors suggests that traditional HH audits not only overstate HH performance overall, but can lead to inaccurate inferences regarding HH performance due to relative differences in Hawthorne effect. The answer to the question regarding whether physicians clean their hands appears to be that they do just as often as nurses, but that all healthcare workers have tremendous room for improvement. We suggest that future improvement efforts will rely on more accurate HH monitoring systems and strong attending physician leadership to set an example for trainees.

Hand hygiene (HH) is believed to be one of the single most important interventions to prevent healthcare‐associated infection, yet physicians are notorious for their poor compliance.[1, 2, 3] At our 800‐bed acute care academic hospital, we implemented a multifaceted HH program[4] in 2007, which was associated with improved HH compliance rates from 43% to 87%. Despite this improvement, HH compliance among physicians remained suboptimal, with rates below 60% in some patient areas. A targeted campaign focused on recruitment of physician champions, resulted in some improvement, but physician compliance has consistently remained below performance of nurses (70%75% for physicians vs 85%90% for nurses).

Our experience parallels the results seen in multinational surveys demonstrating consistently lower physician HH compliance.[5] Given the multiple improvement efforts directed at physicians and the apparent ceiling observed in HH performance, we wanted to confirm whether physicians are truly recalcitrant to cleaning their hands, or whether lower compliance among physicians reflected a differential in the Hawthorne effect inherent to direct observation methods. Specifically, we wondered if nurses tend to recognize auditors more readily than physicians and therefore show higher apparent HH compliance when auditors are present. We also wanted to verify whether the behavior of attending physicians influenced compliance of their physician trainees. To test these hypotheses, we trained 2 clinical observers to covertly measure HH compliance of nurses and physicians on 3 different clinical services.

METHODS

Between May 27, 2015 and July 31, 2015, 2 student observers joined clinical rotations on physician and nursing teams, respectively. Healthcare teams were unaware that the student observers were measuring HH compliance during their clinical rotation. Students rotated in the emergency department, general medical and surgical wards for no more than 1 week at a time to increase exposure to different providers and minimize risk of exposing the covert observation.

Prior to the study period, the students underwent training and validation with a hospital HH auditor at another clinical setting offsite to avoid any recognition of these students by healthcare providers as observers of HH at the main hospital. Training with the auditors occurred until interobserver agreement between all HH opportunities reached 100% agreement for 2 consecutive observation days.

During their rotations, students covertly recorded HH compliance based on moments of hand hygiene[4] and also noted location, presence, and compliance of the attending physician, team size during patient encounter, and isolation requirements. Both students measured HH compliance of nurses and physicians around them. Although students spent the majority of their time with their assigned physician or nurse teams, they did not limit their observations to these individuals only, but recorded compliance of any nurse or physician on the ward as long as they were within sight during an HH opportunity. To limit clustering of observations of the same healthcare worker, up to a maximum of 2 observations per healthcare worker per day was recorded.

We compared covertly measured HH compliance with data from overt observation by hospital auditors during the same time period. Differences in proportion of HH compliance were compared with hospital audits during the same period with a ² test. Difference between differences in overtly and covertly measured HH compliance for nurses and physicians was compared using Breslow day test.

The study was approved by the hospital's research ethics board. Although deception was used in this study,[2, 6] all data were collected for quality improvement purposes, and the aggregate results were disclosed to hospital staff following the study.

RESULTS

Covertly observed HH compliance was 50.0% (799/1597) compared with 83.7% (2769/3309) recorded by hospital auditors during the same time period (P < 0.0002) (Table 1). There was no significant difference in the compliance measured by each student (50.1%, 473/928 vs 48.7%, 326/669) (P = 0.3), and their results were combined for the rest of the analysis. Compliance before contact with the patient or patient environment was 43.1% (344/798), 74.3% (26/35) before clean/aseptic procedures, 34.8% (8/23) after potential body fluid exposure, and 56.8% (483/851) after contact with the patient or patient environment. Healthcare providers examining patients with isolation precautions were found to have a HH compliance of 74.8% (101/135) compared to 47.0% (385/820) when isolation precautions were not required (P < 0.0002).

Hospital auditor data showed that surgery and medicine had similarly high rates of compliance (91.0% and 85.0%, respectively), whereas the emergency department had a notably lower rate of 73.8%. Covert observation confirmed a lower rate in the emergency department (43.9%), but showed a higher compliance on general medicine than on surgery (58.9% vs 45.7%; P = 0.02). The difference in physician compliance between hospital auditors and covert observers was 19.0% (73.2%, 175/239 vs. 54.2%, 469/865); for nurses this difference was much higher at 40.7% (85.8%, 754/879 vs. 45.1%, 330/732) (P < 0.0001) (Table 1).

In terms of physician compliance, primary teams tended to have lower HH compliance of 50.4% (323/641) compared with consulting services at 57.0% (158/277) (P = 0.06). Team rounds of 3 members were associated with higher compliance compared with encounters involving <3 members (62.1%, 282/454 vs. 42.0%, 128/308) (P < 0.0002). Presence of attending physician did not affect trainee HH compliance (55.5%, 201/362 when attending present vs. 56.8%, 133/234 when attending absent; P = 0.79). However, trainee HH compliance improved markedly when attending staff cleaned their hands and decreased markedly when they did not (79.5%, 174/219 vs. 18.9%, 27/143; P < 0.0002).

DISCUSSION

We introduced covert HH observers at our hospital to determine whether differences in Hawthorne effect accounted for measured disparity between physician HH compliance, and to gain further insights into the barriers and enablers of physician HH compliance. We discovered that performance differences between physicians and nurses decreased when neither group was aware that HH was being measured, suggesting that healthcare professions are differentially affected by the Hawthorne effect. This difference may be explained by the continuity of nurses on the ward that makes them more aware of visitors like HH auditors,[7] compared with physicians who rotate periodically on the ward.

Although hospital auditors play a central role in HH education through in‐the‐moment feedback, use of these data to benchmark performance can lead to inappropriate inferences about HH compliance. Prior studies using automated HH surveillance have suggested that the magnitude of the Hawthorne effect varies based on baseline HH rates,[8] whereas our study suggests a differential Hawthorne effect between professions and clinical services. If we relied only on auditor data, we would have continued to believe that only physicians in our organization had poor HH compliance, and we would not be aware of the global nature of the HH problem.

Our results are similar to that of Pan et al., who used covert medical students to measure HH and found compliance of 44.1% compared with 94.1% by unit auditors.[2] Because their study involved an active feedback intervention, the differential in Hawthorne effect between professions could not be reliably assessed. However, they observed a progressive increase in nurse HH compliance using covert observation methods, suggesting improvement in HH performance independent of observer bias.[7]

Covert observation in our study also provided important insights regarding barriers and enablers of HH compliance. Self‐preservation behaviors were common among both nurses and physicians, as HH compliance was consistently higher after patient contact compared to before or when seeing patients who required additional precautions. This finding confirms that the perceived risk of transmission seems to be a powerful motivating factor for HH.[9] Larger groups of trainees were more likely to clean their hands, likely due to peer effects.[10] The strong impact of role modeling on HH was also noted as previously suggested in the literature,[3, 6] but our study captures the magnitude of this effect. Whether or not the attending physician cleaned their hands during rounds either positively or negatively influenced HH compliance of the rest of the physician team (80% when compliant vs 20% when noncompliant).

Our study has several important limitations. The differential Hawthorne effect seen at our center may not reflect other institutions that have numerous HH auditors or high staff turnover resulting in lower ability to recognize auditors. We cannot exclude the possibility of Hawthorne effect using covert methods that could have affected nurse and physician performance differently, but frequent rotation of the students helped maintain covertness of observations. Finally, due to the nature of the covert student observers, a longer observation time frame could not be sustained.

Our experience using covert HH auditors suggests that traditional HH audits not only overstate HH performance overall, but can lead to inaccurate inferences regarding HH performance due to relative differences in Hawthorne effect. The answer to the question regarding whether physicians clean their hands appears to be that they do just as often as nurses, but that all healthcare workers have tremendous room for improvement. We suggest that future improvement efforts will rely on more accurate HH monitoring systems and strong attending physician leadership to set an example for trainees.

Children with medical complexity (CMC) have complex and chronic health conditions that often involve multiple organ systems and severely affect cognitive and physical functioning. Although the prevalence of CMC is low (1% of all children), they account for nearly one‐fifth of all pediatric admissions and one‐half of all hospital days and charges in the United States.[1] Over the last decade, CMC have had a particularly large and increasing impact in tertiary‐care children's hospitals.[1, 2] The Institute of Medicine has identified CMC as a priority population for a revised healthcare system.[3]

Medical homes, hospitals, health plans, states, federal agencies, and others are striving to reduce excessive hospital use in CMC because of its high cost.[4, 5, 6] Containing length of stay (LOS)an increasingly used indicator of the time sensitiveness and efficiency of hospital careis a common aim across these initiatives. CMC have longer hospitalizations than children without medical complexity. Speculated reasons for this are that CMC tend to have (1) higher severity of acute illnesses (eg, pneumonia, cellulitis), (2) prolonged recovery time in the hospital, and (3) higher risk of adverse events in the hospital. Moreover, hospital clinicians caring for CMC often find it difficult to determine discharge readiness, given that many CMC do not return to a completely healthy baseline.[7]

Little is known about long LOS in CMC, including which CMC have the highest risk of experiencing such stays and which stays might have the greatest opportunity to be shortened. Patient characteristics associated with prolonged length of stay have been studied extensively for many pediatric conditions (eg, asthma).[8, 9, 10, 11, 12, 13, 14] However, most of these studies excluded CMC. Therefore, the objectives of this study were to examine (1) the prevalence of long LOS in CMC, (2) patient characteristics associated with long LOS, and (3) hospital‐to‐hospital variation in prevalence of long LOS hospitalizations.

METHODS

RESULTS

Study Population

There were 954,018 hospitalizations of 217,163 CMC at 44 children's hospitals included for analysis. Forty‐seven percent of hospitalizations were for females, 49.4% for non‐Hispanic whites, and 61.1% for children with government insurance. Fifteen percent (n = 142,082) had a long LOS of 10 days. The median (IQR) LOS of hospitalizations <10 days versus 10 days were 2 (IQR, 14) and 16 days (IQR, 1226), respectively. Long LOS hospitalizations accounted for 61.1% (3.7 million) hospital days and 61.8% ($13.7 billion) of total hospitalization costs for all CMC in the cohort (Table 1).

Table 1.Demographic, Clinical, and Hospitalization Characteristics of Hospitalized Children With Medical Complexity by Length of Stay*

Characteristic	Overall (n = 954,018)	Length of Stay
		<10 Days (n = 811,936)	10 Days (n = 142,082)
NOTE: Abbreviations: IQR, interquartile range. *All comparisons were significant at the P < 0.001 level.
Age at admission, y, %
<1	14.6	12.7	25.7
14	27.1	27.9	22.4
59	20.1	21.0	14.9
1018	33.6	34.0	31.7
18+	4.6	4.4	5.4
Gender, %
Female	47.0	46.9	47.5
Race/ethnicity, %
Non‐Hispanic white	49.4	49.4	49.4
Non‐Hispanic black	23.1	23.8	19.3
Hispanic	18.2	17.8	20.4
Asian	2.0	1.9	2.3
Other	7.4	7.1	8.6
Complex chronic condition, %
Any	79.5	77.3	91.8
Technology assistance	37.1	34.1	54.2
Gastrointestinal	30.0	27.2	45.9
Neuromuscular	28.2	27.7	30.9
Cardiovascular	16.8	14.5	29.9
Respiratory	14.1	11.5	29.4
Congenital/genetic defect	17.2	16.7	20.2
Metabolic	9.9	8.9	15.4
Renal	10.1	9.5	13.8
Hematology/emmmunodeficiency	11.7	12.0	10.0
Neonatal	3.8	3.1	7.7
Transplantation	4.5	4.2	6.7
Clinical risk group, %
Chronic condition in 2 systems	68.4	71.2	53.9
Catastrophic chronic condition	31.4	28.8	46.1
Distance from hospital to home residence in miles, median [IQR]	16.2 [7.440.4]	15.8 [7.338.7]	19.1 [8.552.6]
Transferred from outside hospital (%)	6.5	5.3	13.6
Admitted for surgery, %	23.4	20.7	38.7
Use of intensive care, %	19.6	14.9	46.5
Discharge disposition, %
Home	91.2	92.9	81.4
Home healthcare	4.5	3.5	9.9
Other	2.9	2.6	4.5
Postacute care facility	1.1	0.8	3.1
Died	0.4	0.3	1.1
Payor, %
Government	61.1	60.6	63.5
Private	33.2	33.6	30.9
Other	5.7	5.7	5.7
Hospital resource use
Median length of stay [IQR]	3 [16]	2 [14]	16 [1226]
Median hospital cost [IQR]	$8,144 [$4,122$18,447]	$6,689 [$3,685$12,395]	$49,207 [$29,444$95,738]
Total hospital cost, $, billions	$22.2	$8.5	$13.7

Demographics and Clinical Characteristics of Children With and Without Long LOS

Compared with hospitalized CMC with LOS <10 days, a higher percentage of hospitalizations with LOS 10 days were CMC age <1 year (25.7% vs 12.7%, P < 0.001) and Hispanic (20.4% vs 17.8%, P < 0.001). CMC hospitalizations with a long LOS had a higher percentage of any CCC (91.8% vs 77.3%, P < 0.001); the most common CCCs were gastrointestinal (45.9%), neuromuscular (30.9%), and cardiovascular (29.9%). Hospitalizations of CMC with a long LOS had a higher percentage of a catastrophic chronic condition (46.1% vs 28.8%, P < 0.001) and technology dependence (46.1% vs 28.8%, P < 0.001) (Table 1).

Hospitalization Characteristics of Children With and Without Long LOS

Compared with hospitalizations of CMC with LOS <10 days, hospitalizations of CMC with a long LOS more often involved transfer in from another hospital at admission (13.6% vs 5.3%, P < 0.001). CMC hospital stays with a long LOS more often involved surgery (38.7% vs 20.7%, P < 0.001) and use of intensive care (46.5% vs 14.9%; P < 0.001). A higher percentage of CMC with long LOS were discharged with home health services (9.9% vs 3.5%; P < 0.001) (Table 1).

The most common admitting diagnoses and CCCs for hospitalizations of CMC with long LOS are presented in Table 2. The two most prevalent APR‐DRGs in CMC hospitalizations lasting 10 days or longer were cystic fibrosis (10.7%) and respiratory system disease with ventilator support (5.5%). The two most common chronic condition characteristics represented among long CMC hospitalizations were gastrointestinal devices (eg, gastrostomy tube) (39.7%) and heart and great vessel malformations (eg, tetralogy of Fallot) (12.8%). The 5 most common CCC subcategories, as listed in Table 2, account for nearly 100% of the patients with long LOS hospitalizations.

Table 2.Most Common Reasons for Admission and Specific Complex Chronic Conditions for Hospitalized Children With Medical Complexity Who Had Length of Stay 10 Days

NOTE: *Reason for admission identified using All‐Patient Refined Diagnosis‐Related Groups. Complex chronic conditions identified using Feudtner and colleagues set of International Classification of Diseases, 9th Revision, Clinical Modification codes. Gastrointestinal devices include gastrostomy, gastrojejunostomy, colostomy. Respiratory devices include tracheostomy, noninvasive positive pressure, ventilator.
Most common reason for admission*
Cystic fibrosis	10.7%
Respiratory system diagnosis with ventilator support 96+ hours	5.5%
Malfunction, reaction, and complication of cardiac or vascular device or procedure	2.8%
Craniotomy except for trauma	2.6%
Major small and large bowel procedures	2.3%
Most common complex chronic condition
Gastrointestinal devices	39.7%
Heart and great vessel malformations	12.8%
Cystic fibrosis	12.5%
Dysrhythmias	11.0%
Respiratory devices	10.7%

Multivariable Analysis of Characteristics Associated With Long LOS

In multivariable analysis, the highest likelihood of long LOS was experienced by children who received care in the ICU (odds ratio [OR]: 3.5, 95% confidence interval [CI]: 3.43.5), who had a respiratory CCC (OR: 2.7, 95% CI: 2.62.7), and who were transferred from another acute care hospital at admission (OR: 2.1, 95% CI: 2.0, 2.1). The likelihood of long LOS was also higher in children <1 year of age (OR: 1.2, 95% CI: 1.21.3), and Hispanic children (OR: 1.1, 95% CI 1.0‐1.10) (Table 3). Similar multivariable findings were observed in sensitivity analysis using the 75th percentile of LOS (4 days) as the model outcome.

Table 3.Multivariable Analysis of the Likelihood of Long Length of Stay 10 Days

Characteristic	Odds Ratio (95% CI) of LOS 10 Days	P Value
NOTE: Abbreviations: CI, confidence interval; LOS, length of stay.
Use of intensive care	3.5 (3.4‐3.5)	<0.001
Transfer from another acute‐care hospital	2.1 (2.0‐2.1)	<0.001
Procedure/surgery	1.8 (1.8‐1.9)	<0.001
Complex chronic condition
Respiratory	2.7 (2.6‐2.7)	<0.001
Gastrointestinal	1.8 (1.8‐1.8)	<0.001
Metabolic	1.7 (1.7‐1.7)	<0.001
Cardiovascular	1.6 (1.5‐1.6)	<0.001
Neonatal	1.5 (1.5‐1.5)	<0.001
Renal	1.4 (1.4‐1.4)	<0.001
Transplant	1.4 (1.4‐1.4)	<0.001
Hematology and immunodeficiency	1.3 (1.3‐1.3)	<0.001
Technology assistance	1.1 (1.1, 1.1)	<0.001
Neuromuscular	0.9 (0.9‐0.9)	<0.001
Congenital or genetic defect	0.8 (0.8‐0.8)	<0.001
Age at admission, y
<1	1.2 (1.2‐1.3)	<0.001
14	0.5 (0.5‐0.5)	<0.001
59	0.6 (0.6‐0.6)	<0.001
1018	0.9 (0.9‐0.9)	<0.001
18+	Reference
Male	0.9 (0.9‐0.9)	<0.001
Race/ethnicity
Non‐Hispanic black	0.9 (0.9‐0.9)	<0.001
Hispanic	1.1 (1.0‐1.1)	<0.001
Asian	1.0 (1.0‐1.1)	0.3
Other	1.1 (1.1‐1.1)	<0.001
Non‐Hispanic white	Reference
Payor
Private	0.9 (0.8 0.9)	<0.001
Other	1.0 (1.0‐1.0)	0.4
Government	Reference
Season
Spring	1.0 (1.0 1.0)	<0.001
Summer	0.9 (0.9‐0.9)	<0.001
Fall	1.0 (0.9‐1.0)	<0.001
Winter	Reference

Variation in the Prevalence of Long LOS Across Children's Hospitals

After controlling for demographic, clinical, and hospital characteristics associated with long LOS, there was significant (P < 0.001) variation in the prevalence of long LOS for CMC across children's hospitals in the cohort (range, 10.3%21.8%) (Figure 1). Twelve (27%) hospitals had a significantly (P < 0.001) higher prevalence of long LOS for their hospitalized CMC, compared to the mean. Eighteen (41%) had a significantly (P < 0.001) lower prevalence of long LOS for their hospitalized CMC. There was also significant variation across hospitals with respect to cost, with 49.7% to 73.7% of all hospital costs of CMC attributed to long LOS hospitalizations. Finally, there was indirect correlation with the prevalence of LOS across hospitals and the hospitals' 30‐day readmission rate ( = 0.3; P = 0.04). As the prevalence of long LOS increased, the readmission rate decreased.

Figure 1

DISCUSSION

The main findings from this study suggest that a small percentage of CMC experiencing long LOS account for the majority of hospital bed days and cost of all hospitalized CMC in children's hospitals. The likelihood of long LOS varies significantly by CMC's age, race/ethnicity, and payor as well as by type and number of chronic conditions. Among CMC with long LOS, the use of gastrointestinal devices such as gastrostomy tubes, as well as congenital heart disease, were highly prevalent. In multivariable analysis, the characteristics most strongly associated with LOS 10 days were use of the ICU, respiratory complex chronic condition, and transfer from another medical facility at admission. After adjusting for these factors, there was significant variation in the prevalence of LOS 10 days for CMC across children's hospitals.

Although it is well known that CMC as a whole have a major impact on resource use in children's hospitals, this study reveals that 15% of hospitalizations of CMC account for 62% of all hospital costs of CMC. That is, a small fraction of hospitalizations of CMC is largely responsible for the significant financial impact of hospital resource use. To date, most clinical efforts and policies striving to reduce hospital use in CMC have focused on avoiding readmissions or index hospital admissions entirely, rather than improving the efficiency of hospital care after admission occurs.[23, 24, 25, 26] In the adult population, the impact of long LOS on hospital costs has been recognized, and several Medicare incentive programs have focused on in‐hospital timeliness and efficiency. As a result, LOS in Medicare beneficiaries has decreased dramatically over the past 2 decades.[27, 28, 29, 30] Optimizing the efficiency of hospital care for CMC may be an important goal to pursue, especially with precedent set in the adult literature.

Perhaps the substantial variation across hospitals in the prevalence of long LOS in CMC indicates opportunity to improve the efficiency of their inpatient care. This variation was not due to differences across hospitals' case mix of CMC. Further investigation is needed to determine how much of it is due to differences in quality of care. Clinical practice guidelines for hospital treatment of common illnesses usually exclude CMC. In our clinical experience across 9 children's hospitals, we have experienced varying approaches to setting discharge goals (ie, parameters on how healthy the child needs to be to ensure a successful hospital discharge) for CMC.[31] When the goals are absent or not clearly articulated, they can contribute to a prolonged hospitalization. Some families of CMC report significant issues when working with pediatric hospital staff to assess their child's discharge readiness.[7, 32, 33] In addition, there is significant variation across states and regions in access to and quality of post‐discharge health services (eg, home nursing, postacute care, durable medical equipment).[34, 35] In some areas, many CMC are not actively involved with their primary care physician.[5] These issues might also influence the ability of some children's hospitals to efficiently discharge CMC to a safe and supportive post‐discharge environment. Further examination of hospital outliersthose with the lowest and highest percentage of CMC hospitalizations with long LOSmay reveal opportunities to identify and spread best practices.

The demographic and clinical factors associated with long LOS in the present study, including age, ICU use, and transfer from another hospital, might help hospitals target which CMC have the greatest risk for experiencing long LOS. We found that infants age <1 year had longer LOS when compared with older children. Similar to our findings, younger‐aged children hospitalized with bronchiolitis have longer LOS.[36] Certainly, infants with medical complexity, in general, are a high‐acuity population with the potential for rapid clinical deterioration during an acute illness. Prolonged hospitalization for treatment and stabilization may be expected for many of them. Additional investigation is warranted to examine ICU use in CMC, and whether ICU admission or duration can be safely prevented or abbreviated. Opportunities to assess the quality of transfers into children's hospitals of CMC admitted to outside hospitals may be necessary. A study of pediatric burn patients reported that patients initially stabilized at a facility that was not a burn center and subsequently transferred to a burn center had a longer LOS than patients solely treated at a designated burn center.[37] Furthermore, events during transport itself may adversely impact the stability of an already fragile patient. Interventions to optimize the quality of care provided by transport teams have resulted in decreased LOS at the receiving hospital.[38]

This study's findings should be considered in the context of several limitations. Absent a gold‐standard definition of long LOS, we used the distribution of LOS across patients to inform our methods; LOS at the 90th percentile was selected as long. Although our sensitivity analysis using LOS at the 75th percentile produced similar findings, other cut points in LOS might be associated with different results. The study is not positioned to determine how much of the reported LOS was excessive, unnecessary, or preventable. The study findings may not generalize to types of hospitals not contained in PHIS (eg, nonchildren's hospitals and community hospitals). We did not focus on the impact of a new diagnosis (eg, new chronic illness) or acute in‐hospital event (eg, nosocomial infection) on prolonged LOS; future studies should investigate these clinical events with LOS.

PHIS does not contain information regarding characteristics that could influence LOS, including the children's social and familial attributes, transportation availability, home equipment needs, and local availability of postacute care facilities. Moreover, PHIS does not contain information about the hospital discharge procedures, process, or personnel across hospitals, which could influence LOS. Future studies on prolonged LOS should consider assessing this information. Because of the large sample size of hospitalizations included, the statistical power for the analyses was strong, rendering it possible that some findings that were statistically significant might have modest clinical significance (eg, relationship of Hispanic ethnicity with prolonged LOS). We could not determine why a positive correlation was not observed between hospitals' long LOS prevalence and their percentage of cost associated with long LOS; future studies should investigate the reasons for this finding.

Despite these limitations, the findings of the present study highlight the significance of long LOS in hospitalized CMC. These long hospitalizations account for a significant proportion of all hospital costs for this important population of children. The prevalence of long LOS for CMC varies considerably across children's hospitals, even after accounting for the case mix. Efforts to curtail hospital resource use and costs for CMC may benefit from focus on long LOS.

Children with medical complexity (CMC) have complex and chronic health conditions that often involve multiple organ systems and severely affect cognitive and physical functioning. Although the prevalence of CMC is low (1% of all children), they account for nearly one‐fifth of all pediatric admissions and one‐half of all hospital days and charges in the United States.[1] Over the last decade, CMC have had a particularly large and increasing impact in tertiary‐care children's hospitals.[1, 2] The Institute of Medicine has identified CMC as a priority population for a revised healthcare system.[3]

Medical homes, hospitals, health plans, states, federal agencies, and others are striving to reduce excessive hospital use in CMC because of its high cost.[4, 5, 6] Containing length of stay (LOS)an increasingly used indicator of the time sensitiveness and efficiency of hospital careis a common aim across these initiatives. CMC have longer hospitalizations than children without medical complexity. Speculated reasons for this are that CMC tend to have (1) higher severity of acute illnesses (eg, pneumonia, cellulitis), (2) prolonged recovery time in the hospital, and (3) higher risk of adverse events in the hospital. Moreover, hospital clinicians caring for CMC often find it difficult to determine discharge readiness, given that many CMC do not return to a completely healthy baseline.[7]

Little is known about long LOS in CMC, including which CMC have the highest risk of experiencing such stays and which stays might have the greatest opportunity to be shortened. Patient characteristics associated with prolonged length of stay have been studied extensively for many pediatric conditions (eg, asthma).[8, 9, 10, 11, 12, 13, 14] However, most of these studies excluded CMC. Therefore, the objectives of this study were to examine (1) the prevalence of long LOS in CMC, (2) patient characteristics associated with long LOS, and (3) hospital‐to‐hospital variation in prevalence of long LOS hospitalizations.

METHODS

RESULTS

Study Population

There were 954,018 hospitalizations of 217,163 CMC at 44 children's hospitals included for analysis. Forty‐seven percent of hospitalizations were for females, 49.4% for non‐Hispanic whites, and 61.1% for children with government insurance. Fifteen percent (n = 142,082) had a long LOS of 10 days. The median (IQR) LOS of hospitalizations <10 days versus 10 days were 2 (IQR, 14) and 16 days (IQR, 1226), respectively. Long LOS hospitalizations accounted for 61.1% (3.7 million) hospital days and 61.8% ($13.7 billion) of total hospitalization costs for all CMC in the cohort (Table 1).

Table 1.Demographic, Clinical, and Hospitalization Characteristics of Hospitalized Children With Medical Complexity by Length of Stay*

Characteristic	Overall (n = 954,018)	Length of Stay
		<10 Days (n = 811,936)	10 Days (n = 142,082)
NOTE: Abbreviations: IQR, interquartile range. *All comparisons were significant at the P < 0.001 level.
Age at admission, y, %
<1	14.6	12.7	25.7
14	27.1	27.9	22.4
59	20.1	21.0	14.9
1018	33.6	34.0	31.7
18+	4.6	4.4	5.4
Gender, %
Female	47.0	46.9	47.5
Race/ethnicity, %
Non‐Hispanic white	49.4	49.4	49.4
Non‐Hispanic black	23.1	23.8	19.3
Hispanic	18.2	17.8	20.4
Asian	2.0	1.9	2.3
Other	7.4	7.1	8.6
Complex chronic condition, %
Any	79.5	77.3	91.8
Technology assistance	37.1	34.1	54.2
Gastrointestinal	30.0	27.2	45.9
Neuromuscular	28.2	27.7	30.9
Cardiovascular	16.8	14.5	29.9
Respiratory	14.1	11.5	29.4
Congenital/genetic defect	17.2	16.7	20.2
Metabolic	9.9	8.9	15.4
Renal	10.1	9.5	13.8
Hematology/emmmunodeficiency	11.7	12.0	10.0
Neonatal	3.8	3.1	7.7
Transplantation	4.5	4.2	6.7
Clinical risk group, %
Chronic condition in 2 systems	68.4	71.2	53.9
Catastrophic chronic condition	31.4	28.8	46.1
Distance from hospital to home residence in miles, median [IQR]	16.2 [7.440.4]	15.8 [7.338.7]	19.1 [8.552.6]
Transferred from outside hospital (%)	6.5	5.3	13.6
Admitted for surgery, %	23.4	20.7	38.7
Use of intensive care, %	19.6	14.9	46.5
Discharge disposition, %
Home	91.2	92.9	81.4
Home healthcare	4.5	3.5	9.9
Other	2.9	2.6	4.5
Postacute care facility	1.1	0.8	3.1
Died	0.4	0.3	1.1
Payor, %
Government	61.1	60.6	63.5
Private	33.2	33.6	30.9
Other	5.7	5.7	5.7
Hospital resource use
Median length of stay [IQR]	3 [16]	2 [14]	16 [1226]
Median hospital cost [IQR]	$8,144 [$4,122$18,447]	$6,689 [$3,685$12,395]	$49,207 [$29,444$95,738]
Total hospital cost, $, billions	$22.2	$8.5	$13.7

Demographics and Clinical Characteristics of Children With and Without Long LOS

Compared with hospitalized CMC with LOS <10 days, a higher percentage of hospitalizations with LOS 10 days were CMC age <1 year (25.7% vs 12.7%, P < 0.001) and Hispanic (20.4% vs 17.8%, P < 0.001). CMC hospitalizations with a long LOS had a higher percentage of any CCC (91.8% vs 77.3%, P < 0.001); the most common CCCs were gastrointestinal (45.9%), neuromuscular (30.9%), and cardiovascular (29.9%). Hospitalizations of CMC with a long LOS had a higher percentage of a catastrophic chronic condition (46.1% vs 28.8%, P < 0.001) and technology dependence (46.1% vs 28.8%, P < 0.001) (Table 1).

Hospitalization Characteristics of Children With and Without Long LOS

Compared with hospitalizations of CMC with LOS <10 days, hospitalizations of CMC with a long LOS more often involved transfer in from another hospital at admission (13.6% vs 5.3%, P < 0.001). CMC hospital stays with a long LOS more often involved surgery (38.7% vs 20.7%, P < 0.001) and use of intensive care (46.5% vs 14.9%; P < 0.001). A higher percentage of CMC with long LOS were discharged with home health services (9.9% vs 3.5%; P < 0.001) (Table 1).

The most common admitting diagnoses and CCCs for hospitalizations of CMC with long LOS are presented in Table 2. The two most prevalent APR‐DRGs in CMC hospitalizations lasting 10 days or longer were cystic fibrosis (10.7%) and respiratory system disease with ventilator support (5.5%). The two most common chronic condition characteristics represented among long CMC hospitalizations were gastrointestinal devices (eg, gastrostomy tube) (39.7%) and heart and great vessel malformations (eg, tetralogy of Fallot) (12.8%). The 5 most common CCC subcategories, as listed in Table 2, account for nearly 100% of the patients with long LOS hospitalizations.

Table 2.Most Common Reasons for Admission and Specific Complex Chronic Conditions for Hospitalized Children With Medical Complexity Who Had Length of Stay 10 Days

NOTE: *Reason for admission identified using All‐Patient Refined Diagnosis‐Related Groups. Complex chronic conditions identified using Feudtner and colleagues set of International Classification of Diseases, 9th Revision, Clinical Modification codes. Gastrointestinal devices include gastrostomy, gastrojejunostomy, colostomy. Respiratory devices include tracheostomy, noninvasive positive pressure, ventilator.
Most common reason for admission*
Cystic fibrosis	10.7%
Respiratory system diagnosis with ventilator support 96+ hours	5.5%
Malfunction, reaction, and complication of cardiac or vascular device or procedure	2.8%
Craniotomy except for trauma	2.6%
Major small and large bowel procedures	2.3%
Most common complex chronic condition
Gastrointestinal devices	39.7%
Heart and great vessel malformations	12.8%
Cystic fibrosis	12.5%
Dysrhythmias	11.0%
Respiratory devices	10.7%

Multivariable Analysis of Characteristics Associated With Long LOS

In multivariable analysis, the highest likelihood of long LOS was experienced by children who received care in the ICU (odds ratio [OR]: 3.5, 95% confidence interval [CI]: 3.43.5), who had a respiratory CCC (OR: 2.7, 95% CI: 2.62.7), and who were transferred from another acute care hospital at admission (OR: 2.1, 95% CI: 2.0, 2.1). The likelihood of long LOS was also higher in children <1 year of age (OR: 1.2, 95% CI: 1.21.3), and Hispanic children (OR: 1.1, 95% CI 1.0‐1.10) (Table 3). Similar multivariable findings were observed in sensitivity analysis using the 75th percentile of LOS (4 days) as the model outcome.

Table 3.Multivariable Analysis of the Likelihood of Long Length of Stay 10 Days

Characteristic	Odds Ratio (95% CI) of LOS 10 Days	P Value
NOTE: Abbreviations: CI, confidence interval; LOS, length of stay.
Use of intensive care	3.5 (3.4‐3.5)	<0.001
Transfer from another acute‐care hospital	2.1 (2.0‐2.1)	<0.001
Procedure/surgery	1.8 (1.8‐1.9)	<0.001
Complex chronic condition
Respiratory	2.7 (2.6‐2.7)	<0.001
Gastrointestinal	1.8 (1.8‐1.8)	<0.001
Metabolic	1.7 (1.7‐1.7)	<0.001
Cardiovascular	1.6 (1.5‐1.6)	<0.001
Neonatal	1.5 (1.5‐1.5)	<0.001
Renal	1.4 (1.4‐1.4)	<0.001
Transplant	1.4 (1.4‐1.4)	<0.001
Hematology and immunodeficiency	1.3 (1.3‐1.3)	<0.001
Technology assistance	1.1 (1.1, 1.1)	<0.001
Neuromuscular	0.9 (0.9‐0.9)	<0.001
Congenital or genetic defect	0.8 (0.8‐0.8)	<0.001
Age at admission, y
<1	1.2 (1.2‐1.3)	<0.001
14	0.5 (0.5‐0.5)	<0.001
59	0.6 (0.6‐0.6)	<0.001
1018	0.9 (0.9‐0.9)	<0.001
18+	Reference
Male	0.9 (0.9‐0.9)	<0.001
Race/ethnicity
Non‐Hispanic black	0.9 (0.9‐0.9)	<0.001
Hispanic	1.1 (1.0‐1.1)	<0.001
Asian	1.0 (1.0‐1.1)	0.3
Other	1.1 (1.1‐1.1)	<0.001
Non‐Hispanic white	Reference
Payor
Private	0.9 (0.8 0.9)	<0.001
Other	1.0 (1.0‐1.0)	0.4
Government	Reference
Season
Spring	1.0 (1.0 1.0)	<0.001
Summer	0.9 (0.9‐0.9)	<0.001
Fall	1.0 (0.9‐1.0)	<0.001
Winter	Reference

Variation in the Prevalence of Long LOS Across Children's Hospitals

After controlling for demographic, clinical, and hospital characteristics associated with long LOS, there was significant (P < 0.001) variation in the prevalence of long LOS for CMC across children's hospitals in the cohort (range, 10.3%21.8%) (Figure 1). Twelve (27%) hospitals had a significantly (P < 0.001) higher prevalence of long LOS for their hospitalized CMC, compared to the mean. Eighteen (41%) had a significantly (P < 0.001) lower prevalence of long LOS for their hospitalized CMC. There was also significant variation across hospitals with respect to cost, with 49.7% to 73.7% of all hospital costs of CMC attributed to long LOS hospitalizations. Finally, there was indirect correlation with the prevalence of LOS across hospitals and the hospitals' 30‐day readmission rate ( = 0.3; P = 0.04). As the prevalence of long LOS increased, the readmission rate decreased.

Figure 1

DISCUSSION

The main findings from this study suggest that a small percentage of CMC experiencing long LOS account for the majority of hospital bed days and cost of all hospitalized CMC in children's hospitals. The likelihood of long LOS varies significantly by CMC's age, race/ethnicity, and payor as well as by type and number of chronic conditions. Among CMC with long LOS, the use of gastrointestinal devices such as gastrostomy tubes, as well as congenital heart disease, were highly prevalent. In multivariable analysis, the characteristics most strongly associated with LOS 10 days were use of the ICU, respiratory complex chronic condition, and transfer from another medical facility at admission. After adjusting for these factors, there was significant variation in the prevalence of LOS 10 days for CMC across children's hospitals.

Although it is well known that CMC as a whole have a major impact on resource use in children's hospitals, this study reveals that 15% of hospitalizations of CMC account for 62% of all hospital costs of CMC. That is, a small fraction of hospitalizations of CMC is largely responsible for the significant financial impact of hospital resource use. To date, most clinical efforts and policies striving to reduce hospital use in CMC have focused on avoiding readmissions or index hospital admissions entirely, rather than improving the efficiency of hospital care after admission occurs.[23, 24, 25, 26] In the adult population, the impact of long LOS on hospital costs has been recognized, and several Medicare incentive programs have focused on in‐hospital timeliness and efficiency. As a result, LOS in Medicare beneficiaries has decreased dramatically over the past 2 decades.[27, 28, 29, 30] Optimizing the efficiency of hospital care for CMC may be an important goal to pursue, especially with precedent set in the adult literature.

Perhaps the substantial variation across hospitals in the prevalence of long LOS in CMC indicates opportunity to improve the efficiency of their inpatient care. This variation was not due to differences across hospitals' case mix of CMC. Further investigation is needed to determine how much of it is due to differences in quality of care. Clinical practice guidelines for hospital treatment of common illnesses usually exclude CMC. In our clinical experience across 9 children's hospitals, we have experienced varying approaches to setting discharge goals (ie, parameters on how healthy the child needs to be to ensure a successful hospital discharge) for CMC.[31] When the goals are absent or not clearly articulated, they can contribute to a prolonged hospitalization. Some families of CMC report significant issues when working with pediatric hospital staff to assess their child's discharge readiness.[7, 32, 33] In addition, there is significant variation across states and regions in access to and quality of post‐discharge health services (eg, home nursing, postacute care, durable medical equipment).[34, 35] In some areas, many CMC are not actively involved with their primary care physician.[5] These issues might also influence the ability of some children's hospitals to efficiently discharge CMC to a safe and supportive post‐discharge environment. Further examination of hospital outliersthose with the lowest and highest percentage of CMC hospitalizations with long LOSmay reveal opportunities to identify and spread best practices.

The demographic and clinical factors associated with long LOS in the present study, including age, ICU use, and transfer from another hospital, might help hospitals target which CMC have the greatest risk for experiencing long LOS. We found that infants age <1 year had longer LOS when compared with older children. Similar to our findings, younger‐aged children hospitalized with bronchiolitis have longer LOS.[36] Certainly, infants with medical complexity, in general, are a high‐acuity population with the potential for rapid clinical deterioration during an acute illness. Prolonged hospitalization for treatment and stabilization may be expected for many of them. Additional investigation is warranted to examine ICU use in CMC, and whether ICU admission or duration can be safely prevented or abbreviated. Opportunities to assess the quality of transfers into children's hospitals of CMC admitted to outside hospitals may be necessary. A study of pediatric burn patients reported that patients initially stabilized at a facility that was not a burn center and subsequently transferred to a burn center had a longer LOS than patients solely treated at a designated burn center.[37] Furthermore, events during transport itself may adversely impact the stability of an already fragile patient. Interventions to optimize the quality of care provided by transport teams have resulted in decreased LOS at the receiving hospital.[38]

This study's findings should be considered in the context of several limitations. Absent a gold‐standard definition of long LOS, we used the distribution of LOS across patients to inform our methods; LOS at the 90th percentile was selected as long. Although our sensitivity analysis using LOS at the 75th percentile produced similar findings, other cut points in LOS might be associated with different results. The study is not positioned to determine how much of the reported LOS was excessive, unnecessary, or preventable. The study findings may not generalize to types of hospitals not contained in PHIS (eg, nonchildren's hospitals and community hospitals). We did not focus on the impact of a new diagnosis (eg, new chronic illness) or acute in‐hospital event (eg, nosocomial infection) on prolonged LOS; future studies should investigate these clinical events with LOS.

PHIS does not contain information regarding characteristics that could influence LOS, including the children's social and familial attributes, transportation availability, home equipment needs, and local availability of postacute care facilities. Moreover, PHIS does not contain information about the hospital discharge procedures, process, or personnel across hospitals, which could influence LOS. Future studies on prolonged LOS should consider assessing this information. Because of the large sample size of hospitalizations included, the statistical power for the analyses was strong, rendering it possible that some findings that were statistically significant might have modest clinical significance (eg, relationship of Hispanic ethnicity with prolonged LOS). We could not determine why a positive correlation was not observed between hospitals' long LOS prevalence and their percentage of cost associated with long LOS; future studies should investigate the reasons for this finding.

Despite these limitations, the findings of the present study highlight the significance of long LOS in hospitalized CMC. These long hospitalizations account for a significant proportion of all hospital costs for this important population of children. The prevalence of long LOS for CMC varies considerably across children's hospitals, even after accounting for the case mix. Efforts to curtail hospital resource use and costs for CMC may benefit from focus on long LOS.

Children with medical complexity (CMC) have complex and chronic health conditions that often involve multiple organ systems and severely affect cognitive and physical functioning. Although the prevalence of CMC is low (1% of all children), they account for nearly one‐fifth of all pediatric admissions and one‐half of all hospital days and charges in the United States.[1] Over the last decade, CMC have had a particularly large and increasing impact in tertiary‐care children's hospitals.[1, 2] The Institute of Medicine has identified CMC as a priority population for a revised healthcare system.[3]

Medical homes, hospitals, health plans, states, federal agencies, and others are striving to reduce excessive hospital use in CMC because of its high cost.[4, 5, 6] Containing length of stay (LOS)an increasingly used indicator of the time sensitiveness and efficiency of hospital careis a common aim across these initiatives. CMC have longer hospitalizations than children without medical complexity. Speculated reasons for this are that CMC tend to have (1) higher severity of acute illnesses (eg, pneumonia, cellulitis), (2) prolonged recovery time in the hospital, and (3) higher risk of adverse events in the hospital. Moreover, hospital clinicians caring for CMC often find it difficult to determine discharge readiness, given that many CMC do not return to a completely healthy baseline.[7]

Little is known about long LOS in CMC, including which CMC have the highest risk of experiencing such stays and which stays might have the greatest opportunity to be shortened. Patient characteristics associated with prolonged length of stay have been studied extensively for many pediatric conditions (eg, asthma).[8, 9, 10, 11, 12, 13, 14] However, most of these studies excluded CMC. Therefore, the objectives of this study were to examine (1) the prevalence of long LOS in CMC, (2) patient characteristics associated with long LOS, and (3) hospital‐to‐hospital variation in prevalence of long LOS hospitalizations.

METHODS

RESULTS

Study Population

There were 954,018 hospitalizations of 217,163 CMC at 44 children's hospitals included for analysis. Forty‐seven percent of hospitalizations were for females, 49.4% for non‐Hispanic whites, and 61.1% for children with government insurance. Fifteen percent (n = 142,082) had a long LOS of 10 days. The median (IQR) LOS of hospitalizations <10 days versus 10 days were 2 (IQR, 14) and 16 days (IQR, 1226), respectively. Long LOS hospitalizations accounted for 61.1% (3.7 million) hospital days and 61.8% ($13.7 billion) of total hospitalization costs for all CMC in the cohort (Table 1).

Table 1.Demographic, Clinical, and Hospitalization Characteristics of Hospitalized Children With Medical Complexity by Length of Stay*

Characteristic	Overall (n = 954,018)	Length of Stay
		<10 Days (n = 811,936)	10 Days (n = 142,082)
NOTE: Abbreviations: IQR, interquartile range. *All comparisons were significant at the P < 0.001 level.
Age at admission, y, %
<1	14.6	12.7	25.7
14	27.1	27.9	22.4
59	20.1	21.0	14.9
1018	33.6	34.0	31.7
18+	4.6	4.4	5.4
Gender, %
Female	47.0	46.9	47.5
Race/ethnicity, %
Non‐Hispanic white	49.4	49.4	49.4
Non‐Hispanic black	23.1	23.8	19.3
Hispanic	18.2	17.8	20.4
Asian	2.0	1.9	2.3
Other	7.4	7.1	8.6
Complex chronic condition, %
Any	79.5	77.3	91.8
Technology assistance	37.1	34.1	54.2
Gastrointestinal	30.0	27.2	45.9
Neuromuscular	28.2	27.7	30.9
Cardiovascular	16.8	14.5	29.9
Respiratory	14.1	11.5	29.4
Congenital/genetic defect	17.2	16.7	20.2
Metabolic	9.9	8.9	15.4
Renal	10.1	9.5	13.8
Hematology/emmmunodeficiency	11.7	12.0	10.0
Neonatal	3.8	3.1	7.7
Transplantation	4.5	4.2	6.7
Clinical risk group, %
Chronic condition in 2 systems	68.4	71.2	53.9
Catastrophic chronic condition	31.4	28.8	46.1
Distance from hospital to home residence in miles, median [IQR]	16.2 [7.440.4]	15.8 [7.338.7]	19.1 [8.552.6]
Transferred from outside hospital (%)	6.5	5.3	13.6
Admitted for surgery, %	23.4	20.7	38.7
Use of intensive care, %	19.6	14.9	46.5
Discharge disposition, %
Home	91.2	92.9	81.4
Home healthcare	4.5	3.5	9.9
Other	2.9	2.6	4.5
Postacute care facility	1.1	0.8	3.1
Died	0.4	0.3	1.1
Payor, %
Government	61.1	60.6	63.5
Private	33.2	33.6	30.9
Other	5.7	5.7	5.7
Hospital resource use
Median length of stay [IQR]	3 [16]	2 [14]	16 [1226]
Median hospital cost [IQR]	$8,144 [$4,122$18,447]	$6,689 [$3,685$12,395]	$49,207 [$29,444$95,738]
Total hospital cost, $, billions	$22.2	$8.5	$13.7

Demographics and Clinical Characteristics of Children With and Without Long LOS

Compared with hospitalized CMC with LOS <10 days, a higher percentage of hospitalizations with LOS 10 days were CMC age <1 year (25.7% vs 12.7%, P < 0.001) and Hispanic (20.4% vs 17.8%, P < 0.001). CMC hospitalizations with a long LOS had a higher percentage of any CCC (91.8% vs 77.3%, P < 0.001); the most common CCCs were gastrointestinal (45.9%), neuromuscular (30.9%), and cardiovascular (29.9%). Hospitalizations of CMC with a long LOS had a higher percentage of a catastrophic chronic condition (46.1% vs 28.8%, P < 0.001) and technology dependence (46.1% vs 28.8%, P < 0.001) (Table 1).

Hospitalization Characteristics of Children With and Without Long LOS

Compared with hospitalizations of CMC with LOS <10 days, hospitalizations of CMC with a long LOS more often involved transfer in from another hospital at admission (13.6% vs 5.3%, P < 0.001). CMC hospital stays with a long LOS more often involved surgery (38.7% vs 20.7%, P < 0.001) and use of intensive care (46.5% vs 14.9%; P < 0.001). A higher percentage of CMC with long LOS were discharged with home health services (9.9% vs 3.5%; P < 0.001) (Table 1).

The most common admitting diagnoses and CCCs for hospitalizations of CMC with long LOS are presented in Table 2. The two most prevalent APR‐DRGs in CMC hospitalizations lasting 10 days or longer were cystic fibrosis (10.7%) and respiratory system disease with ventilator support (5.5%). The two most common chronic condition characteristics represented among long CMC hospitalizations were gastrointestinal devices (eg, gastrostomy tube) (39.7%) and heart and great vessel malformations (eg, tetralogy of Fallot) (12.8%). The 5 most common CCC subcategories, as listed in Table 2, account for nearly 100% of the patients with long LOS hospitalizations.

Table 2.Most Common Reasons for Admission and Specific Complex Chronic Conditions for Hospitalized Children With Medical Complexity Who Had Length of Stay 10 Days

NOTE: *Reason for admission identified using All‐Patient Refined Diagnosis‐Related Groups. Complex chronic conditions identified using Feudtner and colleagues set of International Classification of Diseases, 9th Revision, Clinical Modification codes. Gastrointestinal devices include gastrostomy, gastrojejunostomy, colostomy. Respiratory devices include tracheostomy, noninvasive positive pressure, ventilator.
Most common reason for admission*
Cystic fibrosis	10.7%
Respiratory system diagnosis with ventilator support 96+ hours	5.5%
Malfunction, reaction, and complication of cardiac or vascular device or procedure	2.8%
Craniotomy except for trauma	2.6%
Major small and large bowel procedures	2.3%
Most common complex chronic condition
Gastrointestinal devices	39.7%
Heart and great vessel malformations	12.8%
Cystic fibrosis	12.5%
Dysrhythmias	11.0%
Respiratory devices	10.7%

Multivariable Analysis of Characteristics Associated With Long LOS

In multivariable analysis, the highest likelihood of long LOS was experienced by children who received care in the ICU (odds ratio [OR]: 3.5, 95% confidence interval [CI]: 3.43.5), who had a respiratory CCC (OR: 2.7, 95% CI: 2.62.7), and who were transferred from another acute care hospital at admission (OR: 2.1, 95% CI: 2.0, 2.1). The likelihood of long LOS was also higher in children <1 year of age (OR: 1.2, 95% CI: 1.21.3), and Hispanic children (OR: 1.1, 95% CI 1.0‐1.10) (Table 3). Similar multivariable findings were observed in sensitivity analysis using the 75th percentile of LOS (4 days) as the model outcome.

Table 3.Multivariable Analysis of the Likelihood of Long Length of Stay 10 Days

Characteristic	Odds Ratio (95% CI) of LOS 10 Days	P Value
NOTE: Abbreviations: CI, confidence interval; LOS, length of stay.
Use of intensive care	3.5 (3.4‐3.5)	<0.001
Transfer from another acute‐care hospital	2.1 (2.0‐2.1)	<0.001
Procedure/surgery	1.8 (1.8‐1.9)	<0.001
Complex chronic condition
Respiratory	2.7 (2.6‐2.7)	<0.001
Gastrointestinal	1.8 (1.8‐1.8)	<0.001
Metabolic	1.7 (1.7‐1.7)	<0.001
Cardiovascular	1.6 (1.5‐1.6)	<0.001
Neonatal	1.5 (1.5‐1.5)	<0.001
Renal	1.4 (1.4‐1.4)	<0.001
Transplant	1.4 (1.4‐1.4)	<0.001
Hematology and immunodeficiency	1.3 (1.3‐1.3)	<0.001
Technology assistance	1.1 (1.1, 1.1)	<0.001
Neuromuscular	0.9 (0.9‐0.9)	<0.001
Congenital or genetic defect	0.8 (0.8‐0.8)	<0.001
Age at admission, y
<1	1.2 (1.2‐1.3)	<0.001
14	0.5 (0.5‐0.5)	<0.001
59	0.6 (0.6‐0.6)	<0.001
1018	0.9 (0.9‐0.9)	<0.001
18+	Reference
Male	0.9 (0.9‐0.9)	<0.001
Race/ethnicity
Non‐Hispanic black	0.9 (0.9‐0.9)	<0.001
Hispanic	1.1 (1.0‐1.1)	<0.001
Asian	1.0 (1.0‐1.1)	0.3
Other	1.1 (1.1‐1.1)	<0.001
Non‐Hispanic white	Reference
Payor
Private	0.9 (0.8 0.9)	<0.001
Other	1.0 (1.0‐1.0)	0.4
Government	Reference
Season
Spring	1.0 (1.0 1.0)	<0.001
Summer	0.9 (0.9‐0.9)	<0.001
Fall	1.0 (0.9‐1.0)	<0.001
Winter	Reference

Variation in the Prevalence of Long LOS Across Children's Hospitals

After controlling for demographic, clinical, and hospital characteristics associated with long LOS, there was significant (P < 0.001) variation in the prevalence of long LOS for CMC across children's hospitals in the cohort (range, 10.3%21.8%) (Figure 1). Twelve (27%) hospitals had a significantly (P < 0.001) higher prevalence of long LOS for their hospitalized CMC, compared to the mean. Eighteen (41%) had a significantly (P < 0.001) lower prevalence of long LOS for their hospitalized CMC. There was also significant variation across hospitals with respect to cost, with 49.7% to 73.7% of all hospital costs of CMC attributed to long LOS hospitalizations. Finally, there was indirect correlation with the prevalence of LOS across hospitals and the hospitals' 30‐day readmission rate ( = 0.3; P = 0.04). As the prevalence of long LOS increased, the readmission rate decreased.

Figure 1

DISCUSSION

The main findings from this study suggest that a small percentage of CMC experiencing long LOS account for the majority of hospital bed days and cost of all hospitalized CMC in children's hospitals. The likelihood of long LOS varies significantly by CMC's age, race/ethnicity, and payor as well as by type and number of chronic conditions. Among CMC with long LOS, the use of gastrointestinal devices such as gastrostomy tubes, as well as congenital heart disease, were highly prevalent. In multivariable analysis, the characteristics most strongly associated with LOS 10 days were use of the ICU, respiratory complex chronic condition, and transfer from another medical facility at admission. After adjusting for these factors, there was significant variation in the prevalence of LOS 10 days for CMC across children's hospitals.

Although it is well known that CMC as a whole have a major impact on resource use in children's hospitals, this study reveals that 15% of hospitalizations of CMC account for 62% of all hospital costs of CMC. That is, a small fraction of hospitalizations of CMC is largely responsible for the significant financial impact of hospital resource use. To date, most clinical efforts and policies striving to reduce hospital use in CMC have focused on avoiding readmissions or index hospital admissions entirely, rather than improving the efficiency of hospital care after admission occurs.[23, 24, 25, 26] In the adult population, the impact of long LOS on hospital costs has been recognized, and several Medicare incentive programs have focused on in‐hospital timeliness and efficiency. As a result, LOS in Medicare beneficiaries has decreased dramatically over the past 2 decades.[27, 28, 29, 30] Optimizing the efficiency of hospital care for CMC may be an important goal to pursue, especially with precedent set in the adult literature.

Perhaps the substantial variation across hospitals in the prevalence of long LOS in CMC indicates opportunity to improve the efficiency of their inpatient care. This variation was not due to differences across hospitals' case mix of CMC. Further investigation is needed to determine how much of it is due to differences in quality of care. Clinical practice guidelines for hospital treatment of common illnesses usually exclude CMC. In our clinical experience across 9 children's hospitals, we have experienced varying approaches to setting discharge goals (ie, parameters on how healthy the child needs to be to ensure a successful hospital discharge) for CMC.[31] When the goals are absent or not clearly articulated, they can contribute to a prolonged hospitalization. Some families of CMC report significant issues when working with pediatric hospital staff to assess their child's discharge readiness.[7, 32, 33] In addition, there is significant variation across states and regions in access to and quality of post‐discharge health services (eg, home nursing, postacute care, durable medical equipment).[34, 35] In some areas, many CMC are not actively involved with their primary care physician.[5] These issues might also influence the ability of some children's hospitals to efficiently discharge CMC to a safe and supportive post‐discharge environment. Further examination of hospital outliersthose with the lowest and highest percentage of CMC hospitalizations with long LOSmay reveal opportunities to identify and spread best practices.

The demographic and clinical factors associated with long LOS in the present study, including age, ICU use, and transfer from another hospital, might help hospitals target which CMC have the greatest risk for experiencing long LOS. We found that infants age <1 year had longer LOS when compared with older children. Similar to our findings, younger‐aged children hospitalized with bronchiolitis have longer LOS.[36] Certainly, infants with medical complexity, in general, are a high‐acuity population with the potential for rapid clinical deterioration during an acute illness. Prolonged hospitalization for treatment and stabilization may be expected for many of them. Additional investigation is warranted to examine ICU use in CMC, and whether ICU admission or duration can be safely prevented or abbreviated. Opportunities to assess the quality of transfers into children's hospitals of CMC admitted to outside hospitals may be necessary. A study of pediatric burn patients reported that patients initially stabilized at a facility that was not a burn center and subsequently transferred to a burn center had a longer LOS than patients solely treated at a designated burn center.[37] Furthermore, events during transport itself may adversely impact the stability of an already fragile patient. Interventions to optimize the quality of care provided by transport teams have resulted in decreased LOS at the receiving hospital.[38]

This study's findings should be considered in the context of several limitations. Absent a gold‐standard definition of long LOS, we used the distribution of LOS across patients to inform our methods; LOS at the 90th percentile was selected as long. Although our sensitivity analysis using LOS at the 75th percentile produced similar findings, other cut points in LOS might be associated with different results. The study is not positioned to determine how much of the reported LOS was excessive, unnecessary, or preventable. The study findings may not generalize to types of hospitals not contained in PHIS (eg, nonchildren's hospitals and community hospitals). We did not focus on the impact of a new diagnosis (eg, new chronic illness) or acute in‐hospital event (eg, nosocomial infection) on prolonged LOS; future studies should investigate these clinical events with LOS.

PHIS does not contain information regarding characteristics that could influence LOS, including the children's social and familial attributes, transportation availability, home equipment needs, and local availability of postacute care facilities. Moreover, PHIS does not contain information about the hospital discharge procedures, process, or personnel across hospitals, which could influence LOS. Future studies on prolonged LOS should consider assessing this information. Because of the large sample size of hospitalizations included, the statistical power for the analyses was strong, rendering it possible that some findings that were statistically significant might have modest clinical significance (eg, relationship of Hispanic ethnicity with prolonged LOS). We could not determine why a positive correlation was not observed between hospitals' long LOS prevalence and their percentage of cost associated with long LOS; future studies should investigate the reasons for this finding.

Despite these limitations, the findings of the present study highlight the significance of long LOS in hospitalized CMC. These long hospitalizations account for a significant proportion of all hospital costs for this important population of children. The prevalence of long LOS for CMC varies considerably across children's hospitals, even after accounting for the case mix. Efforts to curtail hospital resource use and costs for CMC may benefit from focus on long LOS.