Mohsen Shabahang, MD, PhD

The purpose of this study was to examine the factors associated with discharge lag time and how lag time relates to the discharge process.

Delays in patient discharge have major implications for patient bed allocation and hospital throughput. Discharge lag time is a metric that can be used to monitor discharge efficiency.

This was a retrospective review of all patients undergoing colorectal surgery who were discharged from a tertiary care facility between January 1, 2007, and May 21, 2014.&ensp;The effects of various factors were examined using χ

 tests for categorical data and 2-sample 

 tests for continuous data.&ensp;Any factor that had a 

 value <.20 on univariate analysis was incorporated into the multivariate ordinal logistic model.

During the study period, 1707 patients were discharged. Factors that were found to correlate with a longer discharge time using univariate analysis (

 <.05) were gender, primary diagnosis of cancer, type of surgical procedure, time the order was written, use of a discharge navigator, and discharge destination. Day of the week did not meet statistical significance on univariate analysis, but was included in the multi&shy;variate analysis. Upon multivariate analysis, the only factors that correlated with a longer lag time were gender (

 = .012), day of the week that the discharge occurred (

Discharge orders placed earlier in the morning cause an increase in lag time and a backlog of patient discharges. It is important for hospitals to have adequate personnel to handle these early discharges.

The American Journal of Accountable Care. 2018;6(1):20-24

With healthcare spending continuing to increase while reimbursements are becoming more regulated and fixed, many healthcare systems have begun looking at ways to reduce costs.

 Efficient hospital discharge is one component of a strategy to decrease healthcare spending.

Delays in discharge not only increase the cost of the hospitalization for the patient and the facility, but they also decrease hospital revenue by occupying beds that could go to other patients.

 Discharge delays also affect patient satisfaction, because other patients remain in the emergency department or postanesthesia care unit for extended times, and can hinder patient care by preventing prompt transfers to tertiary care facilities. Reasons for delays in discharge and increased length of stay for seemingly unnecessary reasons have been well studied. The problem is multifactorial and usually due to poor coordination of care and lack of communication,

 which are issues that could be remedied with personnel dedicated to the discharge process.

The use of discharge navigators, coordinators, and/or planners has been increasing in an effort to help streamline the transition to posthospital care.

 Discharge navigators have been shown to be effective at increasing patient satisfaction with the discharge process and are more likely to have discharge documentation, follow-up, and prescriptions prescribed and filled earlier, while alleviating some of the burden carried by the patient’s medical team.

 These discharge navigators act as a major component of the continuum of care between the inpatient and outpatient settings.

Discharge lag time, the time from discharge order entry to actual patient exit from the hospital, is one metric used to quantify discharge efficiency. The ability to better understand this variable will allow hospitals to plan for bed allocation. The goal of this study was to examine discharge lag times for patients undergoing colorectal operations and examine factors that increase or decrease these times.

This study was a retrospective review of all patients 18 years or older undergoing major colorectal surgery who were discharged from Geisinger Medical Center (GMC) in Danville, Pennsylvania, between January 1, 2007, and May 21, 2014. This study was reviewed and approved by the GMC institutional review board.

The primary outcome of the study was discharge lag time, which was defined as the interval between when the discharge order was written and the patient’s departure from the hospital. Discharge lag time was categorized in 3 groups: less than 2 hours, 2 to 4 hours, and more than 4 hours.

The discharge navigator is a physician extender who meets with the surgical team early in the morning to get the plan for patients about their possible discharge and what needs to be done in order to get the patient out of the hospital. These individuals work closely with the surgical team, patient, and care manager to facilitate each patient’s discharge. They place the order for discharge, schedule follow-up appointments, handle wound and drain care education, and provide prescriptions for medications upon discharge. They are only employed Monday through Friday; therefore, all discharges occurring during the weekend are the responsibility of the physician.

Patient, perioperative, and peridischarge variables were used to test associations among the variables and discharge lag times. Patient characteristics such as age, gender, body mass index (BMI), Charlson Comorbidity Index (CCI) score, and distance from hospital (using great-circle distance calculation from the patient’s home zip code to the hospital) were captured. Surgical characteristics such as primary diagnosis, procedure, approach, and duration of surgery also were collected. The discharge characteristics collected were day of discharge (eg, day of the week), time the order was written, presence of a discharge navigator, number of days since surgery (postoperative day), and discharge destination. Data were retrospectively collected from the electronic health record and the institution’s comprehensive enterprise-level data warehouse.

The characteristics of the study population were described using mean ± standard deviation for continuous data, median (interquartile range) for nonparametric data, and frequency (percentage) for categorical data. Ordinal logistic regression was used to test the association of each variable with discharge lag time, and data were represented as odds ratios (ORs) and 95% CIs. Any variable with a 

value <.20 on univariate analysis was incorporated into a multivariate ordinal logistic model. All significance testing was 2-sided, with α set at 0.05. Analyses were performed using SAS 9.4 (SAS Institute; Cary, North Carolina).

A total of 1707 patients met inclusion criteria; a breakdown of the demographics of the population can be seen in 

. Of these, 746 patients had a discharge lag time of less than 2 hours, 612 had a discharge lag time between 2 and 4 hours, and 349 patients had a discharge lag time longer than 4 hours.

Patient, perioperative, and peridischarge variables used to test associations among the variables and discharge lag times are shown in 

. The mean patient age was 61.4 years, which was not associated with discharge lag time (OR, 1.004; 95% CI, 0.998-1.009). The mean BMI was 29.6, which was not associated with delays in discharge (OR, 1.006; 95% CI, 0.994-1.018). Female patients (51.9%) had significantly longer lag times (OR, 1.224; 95% CI, 1.025-1.461) than male patients. The median CCI score was 2 and was not associated with discharge lag time (OR, 0.999; 95% CI, 0.961-1.040). The median distance patients lived from the hospital was 21.5 miles and was not associated with discharge efficiency (OR, 1.003; 95% CI, 0.996-1.010).

Patients with colon or rectal cancer (39.2%) had significantly longer lag times (OR, 1.294; 95% CI, 1.079-1.551) compared with patients who were undergoing surgery for a noncancer-related reason. Patients who had undergone colon resection (67.1%) were found to have shorter lag times compared with those who had undergone rectal resection (OR, 0.826; 95% CI, 0.658-0.997). Surgical duration and approach were not associated with discharge efficiency.

The median postoperative day discharge was 5 days and was not associated with lag time (OR, 0.997; 95% CI, 0.985-1.009). The majority of patients were discharged to their home (76.2%), followed by a skilled nursing facility (SNF) (19.2%) and other (eg, prison, long-term acute care hospital, nursing home, death) (4.6%), which showed a difference in lag time among the 3 groups in that patients discharged to their home had a shorter discharge lag time.

The majority of discharge orders were written before 11 am (51.9%), which was found to be associated with a significant increase in lag time (OR, 9.789; 95% CI, 7.313-13.103) compared with

orders placed after 2 pm. Orders written by a discharge navigator (24%) also increased lag times (OR, 1.798; 95% CI, 1.462-2.212) compared with discharge orders written by a physician. The day of the week (including weekends) was not associated with a delay in discharge.

Next, a multivariate analysis was performed; results are shown in 

. Female patients were again found to have longer discharge lag times (OR, 1.272; 95% CI, 1.054-1.535) than males. Primary diagnosis of cancer and type of resection were no longer associated with lag time; however, day of the week was significantly associated with lag time (

= .03). Discharge orders written before 11 am had significantly longer lag times compared with orders written after 2 pm&#8203; (OR, 9.845; 95% CI, 7.283-13.309). Patients discharged to their home had shorter lag times than those who were discharged to SNFs (OR, 0.738; 95% CI, 0.580-0.939).

Similar to the findings of prior research regarding the discharge process, the results of this study also indicate that delays in discharge are multifactorial. Women had longer discharge lag times, as did patients discharged to SNFs. The hour at which the discharge order was written had a significant effect on discharge efficiency, with earlier orders associated with longer lag times.

Findings of a study by Bozorghadad et al had demonstrated that the use of discharge navigators allowed for earlier discharge from the hospital.

 However, that study did not specifically look at lag time. Interestingly, our study revealed that discharge navigators increase lag time. One can theorize that the effect of the discharge navigator and the time of the order entry are interrelated. Increased efficiency of the discharge navigator in order entry may be creating a backlog of patients and thereby creating a backlog of work for nursing to get these patients discharged.

The most important finding of this study is that the lag time was highest when discharge orders were written before 11 am. This is most likely due to the fact that the highest number of discharges were taking place in that time period. This phenomenon can be remedied by adding personnel during these hours. With this additional support, hospitals can achieve the goals of early discharge and shorter lag time.

Discharge destination was found to be statistically different in terms of lag time

on both univariate and multivariate analysis. Discharge to home had the shortest lag time, whereas being discharged to a SNF increased this time. This could be due to poor communication and coordination with the receiving facility and/or issues with coordination of transportation services. In addition, bed availability at the receiving institution can affect lag time. Transportation is a factor that, in many cases, is beyond the hospital’s control. However, better control of transportation in the future could improve discharge efficiency.

Hospital discharge efficiency is multifactorial and often influenced by factors beyond the hospital’s control. Elements of the discharge process, such as discharge navigators, which improve certain aspects of the discharge process (ie, time of day that orders are written; earlier-in-the-day discharge), can disrupt other aspects (discharge lag time). To achieve a more efficient process, it is necessary for hospitals to have an appropriate number of staff to deal with these earlier discharges to allow beds to be free for incoming patients.

Department of General Surgery, Geisinger Medical Center (CW, SB, LS, C-YS, JD, MH, JW, JB, MS), Danville, PA.

 The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Concept and design (CW, SB, LS, C-YS, JW, JB, MS); acquisition of data (CW, SB, LS, MS); analysis and interpretation of data (CW, LS, JD, JB, MS); drafting of the manuscript (CW, SB, C-YS, JD, MH, JW, JB); critical revision of the manuscript for important intellectual content (CW, SB, LS, C-YS, MH, JW, MS); statistical analysis (JD, JB); provision of study materials or patients (CW, SB, LS, CY-S, JD); obtaining funding (CW, MH, JB, MS); administrative, technical, or logistic support (MH, MS); and supervision (JW, MS).

Charles Walker, MD, Geisinger Medical Center, 100 N Academy Ave, MC 21-69, Danville, PA 17822. Email: cwwalker@geisinger.edu.

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Articles

Examination of factors associated with discharge lag time and how this metric plays an important role in managing hospital throughput.

Predictive Factors of Discharge Navigation Lag Time

 The aim of this study was to determine the effectiveness of a discharge navigation program on the time of patient discharge at a tertiary care center.

Delayed patient discharges present challenges to the efficient throughput of patients. A standardized discharge navigation program is one potential method to improve time of patient discharge.

 This was a retrospective review of adult patients who underwent an elective colectomy and were discharged home between January 2007 and May 2014. A program was set up utilizing navigators to facilitate and standardize the discharge process. Three groups were compared: Phase I (prior to implementation), Phase II (1 navigator focusing on same-day discharge), and Phase III (2 navigators focusing on same-day and future discharges). The main outcome measures were patient discharge time, the time discharge orders were written, readmission, and length of stay.

 After the discharge navigation program was implemented, more discharge orders were written before 11 AM (65.3% post implementation vs 50% pre-implementation; 

<.01) and more patients left the hospital prior to 2 PM (60.2% vs 52.5%; 

 = .02). Median discharge time also decreased post implementation by 30 minutes (

 = .01). Readmission rates (15.2% vs 15.7%; 

 = .82) and median length of stay (4 vs 4 days; 

 = .21) remained equivalent between both groups.

 The implementation of a standardized discharge navigation program in patients undergoing elective colorectal surgery led to a significantly earlier time of discharge without adversely affecting the readmission rate.In the era of increasing cost constraints, inefficiencies in patient discharge present a major challenge to patient throughput in many health systems. Inefficient discharge results in over-occupancy, which can prevent the admission and treatment of new patients.

 A study conducted by Amato-Vealey et al determined that delayed discharge leads to a 4-stage domino effect of patient gridlock, beginning with the hospital reaching maximum capacity and ultimately being unable to admit new patients.

 The first stage begins with delayed discharge from medical and surgical floors, causing the inpatient consensus to reach maximum capacity. This prevents patients from being transferred out of the intensive care unit (ICU) in the second stage. The hospital is then operating at full census in the third stage, and the emergency department (ED), postanesthesia care unit, and ICU cannot discharge or move patients to other care areas. This backup finally results in delayed or cancelled surgical procedures, preventing the hospital from admitting new patients in the final stage.

Patient gridlock, a direct result of delayed discharge, inhibits efficient hospital throughput and causes diminished quality of care.

Another study, conducted by Khanna et al, confirms the importance of discharging patients earlier in the day in order to improve hospital occupancy rates.

 They concluded that on days when the “peak” of discharge rates lagged behind the admissions peak by more than 5 hours, rates of overcrowding increased significantly. However, hospital occupancy rates decreased when the majority of discharges occurred earlier in the day, specifically within 5 hours of the peak admission time. For all the reasons stated, it is imperative to tackle the issue of delays in discharge.

Healthcare analysts have looked to other industries for potential solutions. A study conducted by Zygourakis et al determined that the principles used in hotel customer service could be utilized to make the necessary adjustments to improve overall patient satisfaction.

The researchers used a method that provided more information pre- and postoperatively, and during the discharge process, in order to facilitate communication among patients, caregivers, and nursing staff. Using a model from the hotel industry, patients were provided with “preoperative expectation letters,” effectively reducing preoperative anxiety and demonstrating that the application of these general principles to improve customer service and satisfaction can efficiently be applied to the patient discharge process. Richman et al explored another potential solution through their research on a 3-step process used to identify, frame, and receive feedback regarding the barriers and means to improve post-ED and inpatient discharge care coordination.

They found that the lack of both a consistent and timely notification system, as well as an integrated electronic health record (EHR), played significant roles in hindering timely patient discharge.

Many attempts have been made to solve the issue of delay in discharge. However, few solutions have shown the desired outcome. A novel standardized discharge navigation program was developed for colorectal surgery patients at a rural tertiary care center. The program was established in order to create a standardized discharge protocol that could eventually be used throughout the entire hospital. The aim of this study was to determine the effect of this navigation program on the efficiency of the discharge process.

This was a retrospective cohort study conducted in the Department of General Surgery at a tertiary care center. Using a retrospective database, 1005 patients older than 18 years who underwent elective inpatient colorectal surgical procedures between January 2007 and May 2014 were identified. Additional inclusion criteria further limited the cohort to patients discharged during the workweek (discharge navigators were unavailable on the weekends) and whose disposition was to their homes. This study was reviewed and approved by the institutional review board.

The discharge navigation program was conceived and implemented in September 2011. A physician extender was hired with the sole responsibility of facilitating the discharge of general surgical patients. The discharge process was as follows:

          1. Navigator met with house staff after morning rounds to identify patients ready for discharge;

          5. Confirmation of follow-up appointments.

Due to the high volume of patients, the physician extender was only able to concentrate on the patients who were slated for discharge that day. In order to enhance the program, a second discharge navigator was added in May 2013. This allowed one navigator to focus on the discharges for the day of and the second navigator to engage the patients early in the admission process in order to make the appropriate preparations for discharge.

Patient Cohorts Patients were divided into 3 cohorts based on the implementation of the discharge navigation program. Phase I, from January 2007 to September 2011, included 636 patients before the program was put into place. Phase II, from September 2011 to May 2013, included 220 patients discharged after the program was put into effect with only 1 discharge navigator. Phase III, from May 2013 to May 2014, included 149 patients discharged with 2 navigators fully operational (

Patient demographics included gender, age, body mass index, duration of surgery, and comorbidities. Other surgical characteristics measured included the primary diagnosis and type of surgical procedure performed. Discharge characteristics, such as the day of the week patients were discharged from the hospital, were also collected. Patients were compared within and between cohorts, and data was collected retrospectively from the institution’s EHR and from paper charts prior to the induction of the EHR and Epic Systems.

Primary Outcome Measure: Time of Patient Discharge 

Time of patient discharge was defined as the actual hour of day the patient left the hospital; this information was obtained from the EHR. The 3 phase cohorts were compared for patients that were discharged before 2 PM and for patients discharged after 2 PM— that time being the benchmark set by our institution.

Secondary outcome measures were the time orders were written, 30- day readmission rates, 30-day return to ED rates, and length of stay (LOS); this information was obtained from the EHR. Secondary outcomes were measured and compared throughout the 3 phases of the implementation of the discharge navigation program.

The characteristics of the study population were described using means ± standard deviation for continuous data, median (interquartile range [IQR]) for nonparametric data, and frequencies (%) for categorical data. Characteristics were compared between groups using 

2 or Fisher’s exact test for categorical data, 2 sample t tests for continuous data, and the Wilcoxon rank-sum test for nonparametric data. All significance testing was 2-sided with an alpha set at the level of .05. Analyses were performed using SAS version 9.4 (SAS Institute, Cary, North Carolina).

The demographic data related to the cohorts from Phases I through III were comparable. The only exception was with age (

), as more operations were being done on younger patients in Phase II and III compared with Phase I. Primary diagnosis (

 = .01) were found to be significantly different between the cohorts (Table 3). In Phase II and III, more patients were diagnosed with colon cancer (odds ratio [OR], 1.6; 95% CI, 1.1- 2.3), diverticular disease (OR, 1.5; 95% CI, 1.02-2.2), and inflammatory bowel disease (OR, 2.4; 95% CI, 1.5-3.8) when referenced to other disease. In Phase I, more patients were diagnosed with rectal cancer (OR, 1.9; 95% CI 1.1-3.3) when referenced to inflammatory bowel disease. In Phases II and III, less patients underwent left colectomy (OR, 0.3; 95% CI, 0.1-0.6), right colectomy (OR, 0.3; 95% CI, 0.2-0.7), and sigmoid/lower anterior resection of the colon (OR, 0.3; 95% CI, 0.1-0.6) when referenced to transverse procedures. Finally, the distribution of discharged patients over the days of the week was similar.

Primary Outcome: Time of Patient Discharge 

In Phase I, 52.5% of patients left the hospital by 2 PM. This increased to 60.2% in Phases II and III. These results were found to be statistically significant (

). The overall median discharge time was also calculated, and it improved by 30 minutes between phases. While the median time of patient discharge was 1:45 PM in Phase I, patients were discharged by 1:15 PM in Phases II and III. This was also determined to be statistically significant (

Secondary Outcomes: Time of Written Orders, Readmission and Return to ED Rates, and LOS 

In Phase I, 50% of discharge orders were written before 11 AM, 30% were written between 11 AM and 2 PM, and 20% were written after 2 PM. By Phases II and III, the number of orders written before 11 AM increased to 65%, which was found to be statistically significant (

<.0001). Additionally, only 26% of orders were written between 11 AM and 2 PM, and the final 9% were written after 2 PM (

). No significant changes were determined in LOS and readmission and return to ED (within 30 days) rates throughout the 3 phases (

Delayed discharge has continued to affect the efficient operations of multiple departments within hospitals.

 The discharge navigation program was conceived at this tertiary care center because of consistent delays in patient discharge: the program was meant to improve the hospital’s ability to admit and treat new patients by facilitating the timely discharge of patients already in the hospital. This program focused on discharging patients earlier in the day; it did not focus on LOS or readmission rates. The program began with 1 navigator coordinating same-day discharges, but an increased need resulted in acquiring a second navigator. The 2 navigators worked together to identify patients ready for discharge on a given day, while simultaneously beginning the coordination of patient discharge soon after their admission to the hospital.

Throughout this study, we demonstrate that the implementation of the program resulted in a significant increase in the percentage of discharge orders written before 11 AM and more patients being discharged and leaving the hospital by 2 PM. This program proved to be successful because the discharge navigators facilitated the entire patient discharge process, from writing orders earlier in the day to coordinating patients’ postoperative care, appointments, and medications in a standardized and reproducible fashion. Other studies have also confirmed that utilizing extra personnel (either nurses or specific discharge coordinators) to standardize the discharge planning process and education resulted in decreased discharge delay.

 Additionally, the principles of this program were similar to those in the software system developed by Maloney et al, in which hospitals could facilitate communication between disciplines and establish an efficient bed management process to improve the patient discharge process.

 Like the discharge navigation program in this study, the software included succinct, easily accessible patient information that would ultimately trigger the patient’s discharge once he or she met all of the necessary criteria. This program resulted in fewer cancelled procedures, a decrease in the patients’ median LOS, and an increase in inpatient admission and discharge rates.

The results of our study also parallel previous research conducted by Khanna et al, which concluded that discharging patients a minimum of 1 hour earlier had significant effects on reducing the average and improving the maximum occupancy levels.

 Both studies demonstrated that earlier patient discharge facilitates efficient patient throughput within individual departments, as well as throughout the entire hospital. Additionally, a study conducted by White et al addressed the issue of delayed discharge by developing a standardized protocol for 11 specific pediatric diagnoses.

After determining that various physicians had different discharge criteria, the development of this protocol facilitated enhanced communication between nurses and residents, which, in turn, improved discharge and readmission rates and decreased LOS. Additional studies also confirm that standardizing the discharge process can increase bed capacity and hospital throughput.

Alternative approaches have also been taken in other studies in an attempt to improve and ultimately eliminate delayed patient discharge. Multiple factors contributing to untimely discharge have been evaluated, including a lack of immediate availability of a transportation escort, a lack of nurses available to facilitate discharge, delayed writing of discharge orders, and an overall lack of communication and organization between various personnel and departments.

The results of these previous studies support the conclusions of this research in confirming that the greatest obstacle to timely patient discharge is the lack of a standardized protocol specifically facilitating interdepartmental communication during the discharge process. The discharge navigation program conceived at this tertiary care center served to do exactly that and succeeded, as shown through the earlier writing of discharge orders and the discharge of patients earlier in the day. Although our program was successful in improving the median patient discharge time, it contradicted previous research conducted by Khanna et al, which concluded that earlier discharge (ie, discharging most patients within 5 hours of the majority of admissions) improved patient flow, occupancy levels, and reduced access block and patient LOS.

We determined that readmission and return to ED—both within 30 days&mdash;remained unaffected by changes in discharge time, but that LOS increased with the progression of the study. However, this may have been due to the increased complexity of procedures performed in the later phases of the study.

There were some potential limitations to this study, including its being retrospective in nature. Additionally, a more equivalent distribution of patients throughout the phases, along with a larger sample size, may have been beneficial. Despite these limitations, the results of this study demonstrate that the implementation of the discharge navigation program begins to address and solve the issue of delayed patient discharge. The program contributed significantly to the writing of discharge orders earlier in the day and earlier patient discharge from the hospital.

The findings of this study demonstrate that a standardized approach to discharge can improve hospital throughput and potentially lead to a reduction in errors and improved overall patient satisfaction. In the future, we plan to continue researching the effectiveness of the discharge navigation program. The next step includes determining the relationship between the time discharge orders are written and its effect on discharge lag time. The lag time is defined as the time the patient physically leaves the hospital minus the time the discharge order was written. We also plan to look at the effect of the program on venous thromboembolism prophylaxis and errors in medication reconciliation.

 Department of General Surgery, Geisinger Medical Center (SB, JD, LS, C-YS, JB, MH, AP, MS), Washington, DC.

Concept and design (SB, JB, MH, AP, C-YS, LS, MS); acquisition of data (SB, C-YS, LS); analysis and interpretation of data (SB, JB, JD, AP, C-YS, LS); drafting of the manuscript (SB, JB, JD, MH, AP, MS); critical revision of the manuscript for important intellectual content (SB, AP, JB, C-YS, LS); statistical analysis (SB, JD); administrative, technical, or logistic support (SB, MH, AP, MS); and supervision (AP).

 Sayeh Bozorghadad, BS, 1330 New Hampshire Ave NW, #714, Washington, DC 20036. E-mail: sayehb20@gmail.com.

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This study demonstrates that the implementation of a discharge navigation program led to earlier writing of discharge orders and patient release from the hospital.

Mohsen Shabahang, MD, PhD

Articles

Predictive Factors of Discharge Navigation Lag Time

A Hospital Discharge Navigation Program: The Positive Impact of Facilitating the Discharge Navigation Process