ASSOCIATION OF SELF-REPORTED HOSPITAL DISCHARGE HANDOFFS WITH 30-DAY READMISSIONS

Abstract

Background

Poor provider communication across health care settings may lead to adverse outcomes. We sought to determine the frequency with which inpatient providers report communicating directly with outpatient providers and whether direct communication was associated with 30-day readmissions.

Methods

We included the initial hospitalization for consecutive patients discharged from the Medicine service at a large, academic medical center from September, 2010 to December, 2011. Self-reported communication was captured from a mandatory electronic discharge worksheet field. Thirty day readmissions, length of stay (LOS), and demographics were obtained from administrative databases. We used multivariable logistic regression models to examine, first, the association between direct communication and patient age, sex, LOS, race, payer, expected 30-day readmission rate based on diagnosis and illness severity, and physician type and, second, the association between 30-day readmission and direct communication, adjusting for patient and physician-level factors.

Results

Of 13,954 hospitalizations, 6,635 met inclusion criteria. Successful direct communication occurred in 2,438 (36.7%). The most frequently reported reason for lack of direct communication was the provider’s perception that the discharge summary was adequate. Predictors of direct communication, adjusting for all other variables, included patients cared for by hospitalists without house-staff (OR = 1.81; 95% CI 1.59-2.08), high expected 30-day readmission rate (OR = 1.18, 1.10-1.28 per 10%), and insurance by Medicare (OR = 1.35; 95% CI 1.16-1.56) and private insurance companies (OR = 1.35; 95% CI 1.18-1.56) compared to Medicaid. Direct communication with the outpatient provider was not associated with readmissions (OR = 1.08, 0.92-1.26) in adjusted analysis.

Conclusions

Self-reported direct communication between inpatient and outpatient providers occurred at a low rate, but was not associated with readmissions. This suggests that enhancing inter-provider communication at hospital discharge may not, in isolation, prevent readmissions.

INTRODUCTION

Communication between inpatient and outpatient providers is often inadequate, and poor communication may be exacerbated by the growing Hospitalist model of inpatient care.^1-4 Several studies have demonstrated that adverse events and medical errors that occur after discharge can result from the discontinuity of care between the inpatient and outpatient settings.^5-9

There are two main ways that inpatient and outpatient physicians communicate about their mutual patients—through discharge summaries and by directly talking with one another. Written discharge summaries are often unavailable at a patient’s first follow-up appointment,^1-4 and its absence has been shown to limit a primary provider’s ability to provide care in nearly a quarter of post-discharge follow-up appointments.⁵ Reports in the literature are conflicting as to the importance of the discharge summary for reducing 30-day readmissions. Some studies report an increase in 30-day readmission rates in patients whose physicians do not have a discharge summary available at their follow-up visits,^3,11 while others have not found a difference in 30-day readmission rates.^2,10,12

Though direct communication may be an important means to improve care transitions and reduce readmissions,¹³ this method has been infrequently studied. In a survey of family providers, the majority felt it was important to hear from the inpatient provider at admission (73%) and at discharge (78%).⁴ However, outpatient providers have reported low rates of direct communication with inpatient physicians.^{4, 9} There has been little prior research on documented attempts by inpatient providers to directly communicate with outpatient providers, and whether such attempts are associated with 30-day readmission rates.

METHODS

Study Design and Oversight

We conducted a single-center prospective study of self-reported communication patterns by discharging providers on inpatient medical services from September, 2010 to December, 2011 at Johns Hopkins Hospital, a 1000-bed urban, academic center. This project was approved by the Johns Hopkins University School of Medicine Institutional Review Board.

We included first admissions for all patients discharged from the medical services. We excluded patients whose outpatient provider was the inpatient attending, those who had planned or routine admissions, those without outpatient providers, those who died in the hospital, and those discharged to other healthcare facilities.

Institutional Operational Background

At Johns Hopkins Hospital, providers are required to complete an electronic discharge worksheet in the inpatient electronic medical record. This document is printed at the time of discharge and given to the patient. The discharge worksheet includes the inpatient physician’s name and contact information, medications at discharge, follow-up appointments, and discharge instructions. This document is automatically faxed to referring providers who are electronically “linked” to the patient in an auto-fax system which resides outside of the electronic medical record. Due to Health Insurance Portability and Accountability Act (HIPAA) regulations, linkages are purged between hospitalizations and referring providers must be re-linked to their patients in this system de novo during each hospitalization. Auditing indicates that this linkage occurs inconsistently and, when done, is often incorrect. Like the discharge worksheet, the discharge summary is sent via the auto-fax system to providers who are linked to the hospitalization, but are often not completed at the time of the follow-up visit. Providers are allowed 30 days to complete discharge summaries per institutional bylaws.

Prior to implementing this project, we had received consistent feedback from outpatient providers that inpatient providers at our institution failed to communicate with them in an effective fashion when their patients were hospitalized. Referring providers complained that they often were unaware that one of their patients had been hospitalized and often did not receive any paperwork related to the hospitalization prior to the post-discharge follow-up visit. This was hypothesized to contribute to unnecessary rehospitalizations, but we lacked adequate data on communication patterns and whether these patterns were indeed associated with readmissions.

Assessing Direct Communication with Outpatient Primary Care Providers

Beginning in September, 2010, the discharge worksheet was modified so that all providers were required to complete the following field on communication with the outpatient provider: “communication about this hospitalization with provider(s) primarily responsible for outpatient management-can be a PCP or a specialist.” Physicians were given the following options from a drop-down menu: ‘successful communication’, ‘attempted but unsuccessful communication’, ‘unsure if communication was done’, and ‘communication was not attempted’. If the physician did not attempt communication, he or she was required to select one of seven reasons: ‘discharge worksheet or summary will be adequate’, ‘outpatient provider is within the same hospital system’, ‘no outpatient provider’, ‘patient or family member plans to update outpatient provider’, ‘admission was planned or routine’, ‘outpatient provider is the inpatient attending and ‘it is not needed’. Providers were also able to use a free text section to leave additional comments.

We categorized direct communication as successful if ‘successful communication’ was selected. We considered the communication unsuccessful for all other categories. As a separate analysis, we also examined ‘attempted but unsuccessful communication’ as a separate category, comparing it with successful communication. Finally, as a sensitivity analysis, we included patients who lacked a primary provider and those patients whose primary provider was the inpatient attending of record. In this sensitivity analysis, we labeled communication as “successful” for those whose primary provider was the inpatient attending and “unsuccessful” for patients who lacked an outpatient provider.

30-day Readmissions

Any re-hospitalization to Johns Hopkins Hospital within 30 days of discharge from the first visit was classified as a readmission. We did not seek to exclude readmissions that were planned.

Readmission Risk-Adjustment Methodology

Though it is important to account for patient-related and diagnosis-related predictors of readmission in our modeling, there are currently no widely used and accepted risk stratification tools for use in a general medical population. The Centers for Medicare & Medicaid Services (CMS) risk adjustment methodology used for public reporting is disease specific (for acute myocardial infarction, heart failure, and pneumonia), as are other tools that have been developed.^14-16 Other models have been developed for application in specific populations¹⁷ and a recent systematic review has highlighted limited discriminative ability of existing models.¹⁸ The group from Yale that developed the disease-specific measures for CMS has since been tasked to develop an all-cause readmission measure for public reporting purposes, but this measure has not yet been finalized and is intended for use in the Medicare population. Whether it will apply to younger populations and uninsured patients (as included in our study) is unknown.

Based on these limitations of existing risk-stratification tools, we elected to use a methodology that is based on the approach used by the Maryland Health Services Cost Review Commission’s Admission Readmission Revenue (ARR). This approach utilizes paired all-payer-refined diagnosis related group (APRDRG) as a categorical variable in conjunction with severity-of-illness (SOI) scores, with adjustments for age, race, and insurance status. We used University HealthSystem Consortium (UHC) raw data from calendar year 2010 to calculate predicted 30-day readmission rates by APRDRG and SOI. UHC is a cooperative of 116 US academic medical centers and more than 250 affiliated hospitals accounting for a majority of the nation’s non-profit academic medical centers. Our information was obtained from the Clinical Database/Resource Manager (CDB/RM) Report Builder, an on-line query tool based on patient level discharge abstract data, including patient demographic and hospital encounter characteristics.

Specifically, for each APRDRG-SOI combination, we divided the total number of readmitted patients in the UHC dataset by the total number of discharged patients with that APRDRG-SOI combination. For example, in the UHC 2010 data, there were 4,775 patients discharged with a primary APRDRG of 194 (heart failure) and a severity score of 1, of whom 677 (14.2%) were readmitted to the same hospital within 30 days. For the same APRDRG of 194, a level 4 severity score was associated with a 25.5% 30-day readmission rate. Each patient in our dataset with an APRDRG of 194 and a level 1 severity score was therefore assigned an expected 30-day readmission rate of 14.2%, and each with APRDRG of 194 and a level 4 severity was assigned a 25.5% expected 30-day readmission rate. A similar approach was used for all APRDRG-defined diagnoses and severities to establish an expected 30-day readmission rate for each patient.

Statistical Analysis

We report descriptive data as proportions or means as appropriate. We used logistic regression with 95% confidence intervals (CIs) to identify factors that were associated with successful communication (versus unsuccessful) in unadjusted analyses. We chose variables based on possible or plausible associations with either communication patterns or readmissions. Patient age,^19-21 sex, payer (Medicare, Medicaid, private, other),^20-22 race (white versus non-white)²⁰ and length of stay (LOS)^22,23 have been evaluated in prior risk models on hospital readmission, and were included. Hospitalist attending without house-staff (versus non-hospitalist or hospitalist with house-staff), and expected 30-day readmission rate (continuous, as defined above) were chosen because we thought they would be associated with handoff rates and might impact 30-day readmissions.

We incorporated the above variables in a multivariate logistic regression model to determine which factors were independently associated with successful communication. Finally, we performed multivariate logistic regression analysis with 30-day readmissions as the dependent variable using the above variables in addition to communication status (successful versus unsuccessful). All hypothesis tests were 2-tailed with the alpha set at 0.05.

RESULTS

Direct communication with Outpatient Providers

In the study period, there were 13,954 hospitalizations. Of those 9,719 were for initial visits. After additional exclusions, we were left with 6,635 hospitalizations for analysis, Figure 1. Successful communication was reported in 2,438 (36.7%) cases, attempted but unsuccessful communication was reported in 585 cases (8.8%), and no attempts were reported in 3,612 (54.4%) of cases. The most common reason for not attempting direct communication was the provider’s assertion that the discharge summary was adequate (Table 1). There was a modest but significant trend toward higher rates of direct communication over time. Treating time as a continuous variable, the OR for successful handoff on the last day of the study period was 1.22 (1.03-1.44) relative to the first day of the study period. This corresponds to a fitted direct communication rate during the first month of the study of 34.7% and during the last month of the study of 39.1%. Visual inspection of the data did not suggest that there was an inflection point or specific time point at which a dramatic change in handoff rates occurred.

Table 1.

Description of Handoff Patterns

	N (%) (total N = 6635)
Successful	2438 (36.7)
Unsuccessful	4197 (63.3)
1. Discharge summary is adequate	1459 (22.0)
2. Unsure if communication done	1319 (19.9)
3. Attempted but unsuccessful	585 (8.8)
4. Provider within health care system	578 (8.7)
5. Patient or family plan to update provider personally	165 (2.5)
6. Other reasons	91 (1.4)

Variables Associated with Successful Communication

In univariate analyses, variables associated with successful communication included: patient cared for by hospitalist attending without house-staff (OR = 1.85; 95% CI 1.61-2.11), higher expected 30-day readmission rate (OR = 1.21, CI 1.14-1.31 per 10%), longer LOS (OR 1.02, 1.01-1.03 per day), female sex (OR = 1.11,CI 1.01-1.23) and patients insured by Medicare (OR = 1.30, CI 1.14-1.48) and private companies (OR = 1.29, CI 1.13-1.48) as compared to Medicaid. Age and race were not associated with successful communication overall (Table 2). Compared to patients with successful communication, attempted but unsuccessful communication was more common in non-whites (OR = 1.27, CI 1.06-1.53), and in patients insured by Medicaid when compared to Medicare (OR = 1.38, CI 1.11-1.72) or private companies (OR = 1.80, CI 1.42-2.29).

Table 2.

Univariate and Multivariate Analyses of Factors Associated with Successful Direct Communication between Inpatient and Outpatient Physicians

Predictors	Successful handoff (N = 2438)	Unsuccessful handoff (N = 4197)	Univariate analysis		Multivariate analysis
			Odds Ratio for successful handoff (95% CI)	P value	Odds Ratio for successful handoff (95% CI)	P value
Mean age	55.5	55.2	1.01 (0.98-1.04) per decade	0.54	1.00 (0.96-1.03) per decade	0.81
Female N (%)	1289 (52.9)	2107 (50.2)	1.11 (1.01-1.23)	0.04	1.09 (0.99-1.21)	0.08
Length of Stay	4.07	3.71	1.02 (1.01-1.03) per day	0.001	1.01 (1.00-1.02) per day	0.06
White Race¶ N (%)	1041 (42.7)	1791 (42.7)	1.00 (0.90-1.11)	0.98	0.96 (0.87-1.07)	0.52
Hospitalist N (%) (without housestaff)	501 (20.1)	516 (12.3)	1.85 (1.61-2.11)	<0.001	1.81 (1.59-2.08)	<0.001
Payor N (%)
-Medicaid	590 (24.2)	1225 (29.2)	1.0 (referent)		1.0 (referent)
-Medicare	958 (39.3)	1531(36.5)	1.30 (1.14-1.48)	<0.001	1.35 (1.16-1.56)	<0.001
-Private	804(33.0)	1291 (30.8)	1.29 (1.13-1.48)	<0.001	1.35 (1.18-1.56)	<0.001
-Other	86 (3.5)	150 (3.6)	1.19 (0.89-1.58)	0.23	1.29 (0.97-1.73)	0.08
Expected 30-day readmit rate (%) *	17.0	16.0	1.21 (1.14-1.31) per 10%	<0.001	1.18 (1.10-1.28) per 10%	<0.001

^¶Almost all non-white patients were “Black”

^*Expected 30-day readmission rate was defined as the observed 30-day same-hospital readmission rate for all patients with a given APRDRG and severity of illness score for patients in the University HealthSystem Consortium database (calculated based on calendar year 2010 data).

In multivariate analyses, patients cared for by hospitalist attending without house-staff (OR = 1.81, C 1.59-2.08) and patients insured by Medicare (OR = 1.35, CI 1.16-1.56) and private companies (OR = 1.35, CI 1.18-1.56) when compared to Medicaid remained positive predictors of successful communication, (Table 2).

Communication and Readmissions

Fourteen percent of patients were readmitted to our hospital within 30 days. Successful communication was not significantly associated with readmissions in univariate (OR = 1.14, CI 0.98-1.33) or multivariate (OR = 1.08, CI 0.92-1.26) analyses, (Table 3). LOS and expected 30-day readmission rate were predictors of 30-day readmissions in multivariate analyses. In the sensitivity analysis in which we included patients who lacked a primary provider and those patients whose primary provider was the inpatient attending of record, labeling communication as “successful” for those whose primary provider was the inpatient attending, and “unsuccessful” for patients who lacked an outpatient provider, the adjusted odds ratio was virtually unchanged (1.07, 95% CI 0.91-1.24).

Table 3.

Multivariate Analysis of Predictors of 30-day Readmissions

Predictors	Odds Ratio (95% CI)	P value
Successful direct communication	1.08 (0.92-1.26)	0.33
Age	0.97 (0.92-1.03) per decade	0.33
Female	1.05 (0.90-1.22)	0.52
Length-of-stay	1.03 (1.01-1.05) per day	<0.001
White Race	1.03 (0.87- 1.21)	0.74
Hospitalist without housestaff	0.94 (0.76-1.15)	0.53
Payor
-Medicaid	1.0 (referent)
-Medicare	0.92 (0.74-1.14)	0.45
-Private	0.98 (0.80-1.20)	0.84
-Other	0.99 (0.63-1.51)	0.97
Expected 30-day readmission rate (%)*	1.67 (1.50-1.86) per 10%	<0.001

^*Expected 30-day readmission rate was defined as the observed 30-day same-hospital readmission rate for all patients with a given APRDRG and severity of illness score for patients in the University HealthSystem Consortium database (calculated based on calendar year 2010 data).

DISCUSSION

In our study the reported rate of direct communication from inpatient to outpatient providers was low, and there was no association between successful communication and 30-day same hospital readmissions. Combined with results from research showing a lack of association between discharge communication as defined by the availability of discharge summaries and 30-day readmissions has been reported, the results suggest that inter-provider communication alone may not be sufficient to reduce readmissions.¹⁰ However, it is likely that direct communication can improve the handoff process, independent of its impact on readmissions. The majority of outpatient providers report that they would like direct verbal communication at discharge,⁴ and in another survey of primary care providers of hospitalized seniors, 30% of the PCPs reported being unaware of the hospitalization, highlighting the need for improved communication.⁹

The higher rate of communication by hospitalists working independently compared to house-staff services may be due to competing responsibilities in the setting of new house-staff work hour regulations. Additionally, on the house-staff teams, there may be greater confusion over who is responsible for the communication. Additionally, hospitalists at our institution are made aware of their own reported handoff rates, and this type of provider-specific feedback may have played a role in fostering a greater attention to the handoff process amongst hospitalists.

Interestingly, the expected 30-day readmission rate (defined by diagnosis and illness severity) was a significant predictor of direct communication, which could be explained by the providers prioritizing communication efforts for sicker patients. This is notable given that Allaudeen et al reported that providers on the general medicine service of an urban medical center were not able to accurately predict those at higher risk of readmission.²⁴

There was a higher rate of communication in patients insured by Medicare and private companies compared to Medicaid; this observation deserves further evaluation. A major contributor to this disparity was that providers were more often unsuccessful in attempting to reach providers for patients with Medicaid. However, we do not know the basis for these failed attempts or the extent to which providers may have made repeated attempts to contact some referring providers but not others. Possible explanations may include perceived lack of engagement of the primary provider or difficulty in reaching primary providers who practice in resource-challenged clinics that may not, for example, have adequate staff support for efficient phone triage. Regardless, it is known that underinsurance can have an important adverse impact on health outcomes.^25-28

The results of our study should be taken in the context of several limitations. First, our measure of communication was obtained through self-report by the inpatient provider, which may be overestimated due to social desirability bias. Second, we could not account for communication undertaken by other providers outside the primary team (such as consultants). Third, we were unable to examine the quality of the communication, which may have varied in important ways. Fourth, our study was performed at a single academic medical center and might not be generalizable to other clinical settings. Fifth, we reported on 30-day same hospital readmission and were unable to examine readmissions to surrounding hospitals. Sixth, we evaluated the effects of communication on readmission rates but did not examine other outcomes that could be associated with communication, such as patient or referring provider satisfaction. Seventh, although there was a modest increase in direct communication rates over our study period, our study design did not allow for assessment of a true pre-intervention communication rate to determine whether the electronic prompt we placed in the discharge worksheet actually changed behavior. Finally, as highlighted above, there is not yet an established, validated, and widely accepted tool for risk-adjusting readmission rates, and there may be important variables that we did not include in our risk-adjustment methodology.

In sum, our findings suggest that direct communication between inpatient and outpatient providers is undertaken in only a minority of cases. Although we did not see a direct relationship between direct communication and readmissions, it remains possible that direct communication between providers may improve other aspects of quality of care.