BACKGROUND

Hospital discharge delays can negatively affect patient flow and hospital charges. Our primary aim was to increase the percentage of acute care cardiology patients discharged within 2 hours of meeting standardized medically ready (MedR) discharge criteria. Secondary aims were to reduce length of stay (LOS) and lower hospital charges.

METHODS

A multidisciplinary team used quality improvement methods to implement and study MedR discharge criteria in our hospital electronic health record. The criteria were ordered on admission and modified on daily rounds. Bedside nurses documented the time when all MedR discharge criteria were met. A statistical process control chart measured interventions over time. Discharge before noon and 30-day readmissions were also tracked. Average LOS was examined, comparing the first 6 months of the intervention period to the last 6 months. Inpatient charges were reviewed for patients with >2 hours MedR discharge delay.

RESULTS

The mean percentage of patients discharged within 2 hours of meeting MedR discharge criteria increased from 20% to 78% over 22 months, with more patients discharged before noon (19%–32%). Median LOS decreased from 11 days (interquartile range: 6–21) to 10 days (interquartile range: 5–19) (P = .047), whereas 30-day readmission remained stable at 16.3%. A total of 265 delayed MedR discharges beyond 2 hours occurred. The sum of inpatient charges from care provided after meeting MedR criteria was $332 038 (average $1253 per delayed discharge).

CONCLUSIONS

Discharge timeliness in pediatric acute care cardiology patients can be improved by standardizing medical discharge criteria, which may shorten LOS and decrease medical charges.

Hospital discharge delays have many negative consequences. A patient being discharged later than medically necessary can lead to bed-blocking (occupying a hospital bed unnecessarily), patient redirection to inappropriate care areas, patient and/or family dissatisfaction, and increased health care costs.13  The National Academy of Medicine and Institute for Healthcare Improvement have issued statements about improving health care system quality, emphasizing care that is safe, timely, effective, patient-centered, equitable, and efficient.46  However, many hospitals continue to have delays that impact patient flow and timeliness of discharges. One pediatric study reported 22.8% of hospital discharges were delayed, and, on average, 10% of the total hospital stay was considered unnecessary.7 

Understanding causes of delay is critical to improving discharge efficiency. In a previous study, researchers adapted a tool to identify variables that lead to discharge delays for pediatric patients.8,9  Previous efforts to streamline the discharge process have used discharge checklists and proactive discharge planning.10,11  Although critical, these strategies do not reliably target physician variability, a common discharge delay reason.5,7  Providers are vulnerable to excessively conservative management and interprovider variability in clinical decision-making.7 

Previous local improvement work integrating standardized medical discharge criteria, termed medically ready (MedR) criteria, in general pediatric inpatients on the hospital medicine (HM) service led to improved discharge timeliness and reduced length of stay (LOS) without increasing readmissions.12  A key driver of these results was decreasing provider variation by defining discharge criteria for common diagnoses integrated into the electronic health record (EHR). Multiple interventions led to an increase in patients discharged within 2 hours of reaching MedR criteria from 42% to 80%. Subsequent success occurred when the process was spread to medically complex HM patients with longer LOS and greater hospital charges.13  Although promising, the impact of a similar process on subspecialty patients has not previously been studied.

Through quality improvement (QI) methods,14  we assessed whether application of cardiology-specific standardized MedR discharge criteria on a dedicated acute care cardiology (ACC) unit was feasible and sustainable. Our specific, measurable, attainable, relevant, time-bound (SMART)15  aim was to increase the percentage of patients in ACC discharged from the hospital within 2 hours of reaching MedR goals from 20% to 80% over 18 months. Our secondary aims were to decrease LOS and hospital charges.

The improvement study took place within the Heart Institute at Cincinnati Children’s Hospital, a pediatric and adult congenital cardiology division in a tertiary care hospital. Over the study period, the average daily ACC unit census was 15.1 patients (17-bed unit), with 98% of patients cared for by 1 of 2 cardiology service lines (congenital cardiology or heart failure or transplant). Teams included pediatric residents, advanced practice providers, and cardiology fellows supervised by an attending physician.

A multidisciplinary study team included pediatric cardiology and HM attending physicians, nurse managers, advanced practice providers, bedside nurses, parents of ACC patients, and a QI analyst. The team mapped the discharge process and identified key drivers (Fig 1). The SMART aim was based on previous QI work on our HM service.12,13  Interventions were aimed at identified drivers, with emphasis on learning from process failures.

FIGURE 1

Inpatient cardiology medical readiness key driver diagram. The project key driver diagram includes the global and SMART Aims.

FIGURE 1

Inpatient cardiology medical readiness key driver diagram. The project key driver diagram includes the global and SMART Aims.

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MedR Process Implementation

From January 2014 to October 2015, we tested and implemented MedR discharge criteria on all cardiology patients discharged from the ACC unit. MedR is defined as the moment when a patient is ready to be discharged from the hospital solely on the basis of medical or clinical criteria specific to their medical condition.12,16  These MedR discharge criteria do not include necessary logistic goals of discharge (ie, transportation or medication availability). Although logistic criteria are necessary for safe discharges, they are not drivers of the patient clinical recovery rate. As a result, patients are afforded a 2-hour time frame once MedR criteria are met to allow patients, families, and staff to finalize logistic discharge needs. In the ideal state, the discharging team aims to coordinate logistic needs proactively before the patient meets MedR discharge criteria.

Nurses documented in real time when patients met each of their MedR discharge criteria. At study inception in January 2014, MedR documentation was recorded on paper; by May 2014, MedR criteria were integrated into the EHR and charted in real time. For patients remaining in the hospital >2 hours past their MedR time, the bedside nurse documented the reason(s) for delay, which served as the focus of subsequent interventions. If the patient’s MedR time occurred between 9:00 pm and 7:00 am, the MedR measurement began at 7:00 am (consequently, the earliest time for MedR failure was 9:00 am). Patients could leave overnight if desired.

Creation of MedR Discharge Criteria

Reducing unintended variation in MedR discharge criteria was a primary focus in this study. MedR discharge criteria were created at the study outset through stakeholder consensus and organized into 6 diagnosis subsets: congenital heart disease (CHD) medical, CHD postoperative, heart failure or transplant medical, transplant postoperative (transplant during current hospitalization), electrophysiology medical, and catheterization and electrophysiology postprocedure (Supplemental Figure 4). Given dependence on stakeholder expertise without benefit of direct evidence, testing of MedR criteria was integral to achieving consensus. Key to agreement was the inclusion of the patient and/or parent perspective, leading to, in May 2014, the creation of EHR admission order sets with these modified MedR discharge criteria embedded.

Shared Ownership of the Process

Shared process ownership was cultivated among key stakeholders (physicians, advanced practice nurse prescribers, bedside nurses, patients, and families) by using targeted education highlighting the benefits of efficient discharges. Starting in June of 2014, a Plan-Do-Study-Act (PDSA) ramp targeted bedside, family-centered rounds by building a rounding tool, which outlined progress toward MedR to facilitate detailed daily discussion. After a series of intervention tests, the end result was the following: rounds begin with the bedside nurse describing important events from the previous day, followed by the traditional medical provider data summary and plan-of-care discussion, and then the team’s attention returns to the nurse to review the MedR discharge criteria and progress toward goals. Necessary modifications to the criteria over the course of a hospitalization because of changes in clinical course are modified directly in the EHR identical to the familiar processes of medication or nursing orders.

Discharge Barriers Anticipated and Mitigated

Discharge failures were captured by the bedside nurse so the team could target interventions to mitigate delays. Top delays related to physicians, parents, pharmacy, and nursing. Physician delays included unclear discharge goals, suboptimal discharge anticipation, and waiting on consultant evaluation. Parental delays included lack of physical presence to complete discharge education and/or available transportation. Pharmacy delays mirrored those in previous studies12,13 : lack of preferred pharmacy documentation, delayed prescription writing, and caregivers failing to retrieve medications. Nursing delays included lack of timely discharge education with an overreliance on day shift and poor handoff communication of discharge goals.

To mitigate these barriers, the team focused on proactively planning for discharge throughout each patient’s stay. A discharge checklist (Supplemental Figure 5), in use before the start of the study, was modified to track specific education and logistic needs. Over the course of 2014, the application of the discharge checklist was modified accordingly: visible in patient rooms for shared family and care team access, standardized components applicable to all patients in ACC, customized items specific to patient subtypes, and daily checklist review at the close of each patient rounds with an outline of tasks to be completed each day.

Timely Feedback of Failures

An automated report facilitated active learning from discharge failures. Patients with process failures, including lack of the EHR time stamp or failure to place MedR discharge orders, were addressed directly with providers to provide or elicit feedback. Care was taken not to place blame on any one person but rather to facilitate system-level learning. To promote transparency, unit and system performance were posted on the unit, sessions were held with providers to review process changes, and family feedback was elicited. Lastly, starting in August of 2014, a daily automated e-mail report of discharge failures >2 hours past MedR was sent to study leadership to provide feedback and identify future interventions.

PDSA cycles were used to implement and learn from the planned interventions.14  Before the PDSA cycles, baseline data were collected from January to December 2013. During the intervention period, January 2014 to October 2015, data were prospectively collected. Patients with undocumented or unknown MedR time were excluded.

The measure included in the primary outcome was the percentage of patients discharged >2 hours after meeting MedR discharge criteria, and secondary measures were (1) percentage of patients discharged before noon; (2) hospital charges registered >2 hours after meeting MedR criteria; (3) hospital LOS; (4) MedR physician and nursing process measures; and (5) 30-day all-cause readmission rate as a balancing measure (Table 1).

TABLE 1

Outcome, Process, and Balancing Measure Definitions

Type of measureMeasureDefinition
Primary outcome Patients discharged within 2 h of meeting MedR discharge criteria (%) Percent of patients discharged within 2 h of meeting MedR discharge criteria. Calculated as the number of patients discharged within 2 h of the criteria time stamp divided by the total number of discharged patients who have MedR orders in the EHR. 
Secondary outcome Patients discharged before noon (%) Percentage of patients discharged before 12:00 pm Eastern Time each day. 
 Delayed discharge charges (dollars) Total charges (in dollars) billed after a patient met MedR criteria plus the 2 h grace period. Includes room charges, medications, supplies, support services, and nonessential therapies. Charges reported as mean and median dollars per patient and total charges for all patients together. 
 LOS (days) Average number of days inpatient for the included STC benchmark procedures. 
Process measures Physician process measures A measure to assess provider compliance with the placement of MedR orders in the EHR. Calculated as a percentage: number of patients with MedR discharge criteria orders divided by the total number of patients. 
 Nursing process measures A measure to assess nursing compliance with the process measure of placing a timestamp when MedR is achieved. Calculated as a percentage: the number of patients with MedR discharge criteria timestamps divided by the total number of patients with defined criteria. 
Balancing measures Readmission rates Percentage of patients who are discharged and return within 30 d of discharge. 
Type of measureMeasureDefinition
Primary outcome Patients discharged within 2 h of meeting MedR discharge criteria (%) Percent of patients discharged within 2 h of meeting MedR discharge criteria. Calculated as the number of patients discharged within 2 h of the criteria time stamp divided by the total number of discharged patients who have MedR orders in the EHR. 
Secondary outcome Patients discharged before noon (%) Percentage of patients discharged before 12:00 pm Eastern Time each day. 
 Delayed discharge charges (dollars) Total charges (in dollars) billed after a patient met MedR criteria plus the 2 h grace period. Includes room charges, medications, supplies, support services, and nonessential therapies. Charges reported as mean and median dollars per patient and total charges for all patients together. 
 LOS (days) Average number of days inpatient for the included STC benchmark procedures. 
Process measures Physician process measures A measure to assess provider compliance with the placement of MedR orders in the EHR. Calculated as a percentage: number of patients with MedR discharge criteria orders divided by the total number of patients. 
 Nursing process measures A measure to assess nursing compliance with the process measure of placing a timestamp when MedR is achieved. Calculated as a percentage: the number of patients with MedR discharge criteria timestamps divided by the total number of patients with defined criteria. 
Balancing measures Readmission rates Percentage of patients who are discharged and return within 30 d of discharge. 
TABLE 2

Median Hospital LOS

STS Benchmark SurgeryMedian Hospital LOS in Days (Interquartile Range; n)
Phase IPhase II
Coarctation of the aorta 6 (4–11; 5) 5 (4–12; 5) 
Atrioventricular canal 9.5 (7.8–44; 4) 6 (4.8–8.5; 8) 
Tetralogy of fallot 10.5 (7.8–12.3; 8) 10 (4.5–17; 11) 
Ventricular septal defect 4 (4–4; 6) 5 (4–17; 15) 
Norwood 35 (34.5–35.5; 2) 32.5 (27–36.8; 8) 
Bidirectional Glenn 16 (6.5–32.5; 11) 9 (7–31; 9) 
Fontan 12 (8.8–19; 4) 10 (9.8–15; 16) 
Arterial switch operation 17 (13.8–20.8; 4) 14.5 (12.8–18.3; 8) 
Median of all operations 11 (44) 10 (80) 
STS Benchmark SurgeryMedian Hospital LOS in Days (Interquartile Range; n)
Phase IPhase II
Coarctation of the aorta 6 (4–11; 5) 5 (4–12; 5) 
Atrioventricular canal 9.5 (7.8–44; 4) 6 (4.8–8.5; 8) 
Tetralogy of fallot 10.5 (7.8–12.3; 8) 10 (4.5–17; 11) 
Ventricular septal defect 4 (4–4; 6) 5 (4–17; 15) 
Norwood 35 (34.5–35.5; 2) 32.5 (27–36.8; 8) 
Bidirectional Glenn 16 (6.5–32.5; 11) 9 (7–31; 9) 
Fontan 12 (8.8–19; 4) 10 (9.8–15; 16) 
Arterial switch operation 17 (13.8–20.8; 4) 14.5 (12.8–18.3; 8) 
Median of all operations 11 (44) 10 (80) 

Discharge before noon is an established metric used to assess discharge clustering in the late afternoon.1719  Documenting discharge time relative to noon ensured the validity of the MedR time; there is risk that timestamp completion of MedR criteria might be electively delayed closer to the actual discharge time, leading to more patients inaccurately documented as successes. This delay could falsely increase the primary outcome measure without improving discharge efficiency. As a result, MedR time and before-noon measures were used in parallel to summarize progress.

Hospital LOS was primarily calculated from a subset of postoperative patients with CHD. To provide the most meaningful comparison, we selected patients who underwent 1 of 8 benchmark procedures, as defined by the Society of Thoracic Surgeons (STS).20  For each procedure type, we compared median number of days hospitalized during the first 6 months of the study period (January 2014–June 2014) to the median during the last 6 months (May 2015–October 2015). We also examined LOS for all patients in ACC, including medical admissions. Given the absence of appropriate case-mix adjustment when analyzing these full ACC populations, greater emphasis was placed on the surgical population. During the study period, there were no concurrent QI efforts to address hospital LOS.

To determine financial impact, we retrospectively acquired 14 months of hospital charge data (September 2014–October 2015) for patients discharged >2 hours after achieving MedR criteria. The data included all charges incurred after a patient was deemed MedR for discharge, including medications, medical supplies, support staff services, nonessential therapies, and prorated room and bed rates. Charge data were reviewed in an itemized fashion to ensure that no charges were included that would suggest a patient was not MedR for discharge. For example, if a patient remained hospitalized for 8 hours after reaching MedR status because of transportation, and during this time, they received medications consistent with their soon-to-be outpatient schedule, these post-MedR medication charges were captured as charge data. For patients with observation status, charges for extra hours of stay were included. If the patient met MedR criteria but remained in the hospital until the next calendar day, the additional day(s) of room charges were also included.

Using control charts,21,22  we determined the effect of interventions over time for the primary and secondary outcomes. Standard control chart rules for common cause and special cause variation were used.23  When examining the impact of MedR on LOS, we compared these unpaired 6-month intervals using a Student’s t test (P value <.05). To understand financial implications associated with delayed discharges, charge data were divided into categories: room charges, medications, and supply and miscellaneous charges.

This institutional QI work to improve discharge timeliness was not related to human subjects research.

From January 2014 to October 2015, the mean percentage of patients in ACC discharged within 2 hours of meeting MedR criteria increased from 20% to 78% (Fig 2). Concurrently, the mean percentage of patients discharged before noon increased from 19% to 32% (Fig 3). By the end of the study period, the percentage of patients who had EHR-documented MedR discharge orders (provider process measure) was 87%. The percentage of patients who had an EHR timestamp placed after meeting discharge criteria (nurse process measure) was 89%.

FIGURE 2

The percent of patients discharged within 2 hours of meeting their medical readiness for discharge criteria. The study period is from January 2014 to October 2015. The data collection process was integrated into the EHR in September 2014. PDSA ramps (1–8) are annotated to denote timing and described thematically in the banner above the figure. D/C, discharge discontinue; EMR, electronic medical record; MD, medical doctor; RN, registered nurse.

FIGURE 2

The percent of patients discharged within 2 hours of meeting their medical readiness for discharge criteria. The study period is from January 2014 to October 2015. The data collection process was integrated into the EHR in September 2014. PDSA ramps (1–8) are annotated to denote timing and described thematically in the banner above the figure. D/C, discharge discontinue; EMR, electronic medical record; MD, medical doctor; RN, registered nurse.

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FIGURE 3

The percent of patients discharged before noon. Historical baseline data are provided from January 2013 until the start of the study in January 2014, revealing a rate of 19%. The data are then represented until the end of the study in October 2015, with a rate of discharge before noon of 32%.

FIGURE 3

The percent of patients discharged before noon. Historical baseline data are provided from January 2013 until the start of the study in January 2014, revealing a rate of 19%. The data are then represented until the end of the study in October 2015, with a rate of discharge before noon of 32%.

Close modal

For the 8 STS benchmark postoperative surgeries examined, median hospital LOS decreased from 11 days (interquartile range: 6–21) to 10 days (interquartile range: 5–19), comparing the first 6 months of the intervention phase to the last 6 months (P = .047). Substantial reduction was noted for atrioventricular canal repair, 9.5 days to 6 days, and bidirectional Glenn procedures, 16 days to 9 days (Table 2). When we examined all ACC hospitalizations, including nonsurgical hospitalizations, there was an average LOS reduction of 11.5%, from 9.43 to 8.37 days. The all-cause 30-day readmission rate remained unchanged at 16.3% throughout the intervention period.

From September 2014 to October 2015, charge data were obtained for 265 delayed discharges. The sum of charges for care provided after meeting MedR criteria was $332 038. Mean additional charge per delayed discharge was $1253, and median charge was $128. The majority of charges were room charges totaling $263 857, whereas medications were $24 080 and miscellaneous patient supplies and ancillary services totaled $44 101.

Longitudinal data were collected to document the reasons for failed timely discharges. In total, there were 155 delayed discharges with nursing documentation of a failure reason (Supplemental Figure 6). The most common reason for delayed discharge was transportation, which constituted 19.4% of patients (30 discharges). Home health care delay (16.1% or 25 patients) and patient or parent delay (12.3% or 19 patients) were other common delays.

Using the Model for Improvement,14  we increased the percentage of pediatric patients in ACC discharged within 2 hours of MedR for discharge from 20% to nearly 80%. This improvement was secondary to standardization of the discharge process. As the process measures indicate, there was broad buy-in from key stakeholders, driven by the reliability of shared unit performance, such as automated daily failure reports, to provide timely process feedback.

The resulting reasons for timely discharge failure highlight our success in limiting process variability. By study close, only 3% of patients had “physician delay” as a delayed discharge reason, whereas previous literature suggests that this would be the most common reason for a delay (as high as 42%).7  Rather, we found that 32% of patients did not discharge in a timely manner because of transportation or patient or parent delay factors. These results suggest that further improvement in the discharge process should target family involvement, including accurate discharge forecasting to support preparation and institutional efforts to optimize transportation options.

We demonstrated that the percentage of patients discharged before noon increased from 19% to 32%, which confirmed that patients were objectively discharged earlier relative to baseline observations. Although it was employed as a secondary measure, we found the before-noon variable useful to ensure success was not the result of manipulation of the MedR timestamp. Logistically, earlier discharges lead to greater bed availability, allowing for new admissions, ICU transfers, and planned surgeries.24 

Reduced hospital LOS reflects further evidence of our discharge process improvement. We recognize that hospital LOS is a multifaceted measure and that reductions may relate to other improvements in clinical care. However, importantly, there were no other concurrent LOS QI initiatives during the study period, and yet LOS was reduced by 1 day. This positive trend underscores the overall systematic improvement as well as the impact of an efficient discharge process. Importantly, the all-cause readmission rate did not increase.

Aside from the impact delayed discharges can have on bed space, there are also financial implications. Our data are unique in quantifying hospital charges associated with delayed discharge. We found the mean charge per patient for delayed discharge was $1253. The difference between the observed mean and median reveals that a small proportion of patients accrued large charges per delayed discharge. Whereas most patients are delayed only marginally within 4 hours of their MedR time, some patients remain an additional day (or more) in the hospital. Indeed, 80% of the total charges ($263 857 of $332 038) were hospital room charges. These patient charge analyses do not take into account the potential indirect financial impact of a delayed discharge on other patients’ charges; having an inadequate number of available beds could delay a surgery or a critical-care-to-ACC patient transfer.

Although with this QI study we improved discharge efficiency in a complex subspecialty cardiology population, the results are consistent with previous efforts across general pediatric populations.12,13  These similarities highlight the fundamental benefits of standardizing the hospital discharge approach, the use of shared mental models about patient clinical progress, and the opportunity to measure patient flow efficiency in a patient-factor–driven outcome metric.

The data collection method changed from manual to automated during the study period. As a result, there is potential that the baseline period may not be comprehensively captured in our data. However, given the proactive efforts used to optimize data recording at the time, including daily consultation with nursing staff, it is unlikely that we overestimated the baseline discharge timeliness with this approach. Additionally, the baseline rate of before-noon discharge improved in parallel across the intervention phase, and, given that these data were automated throughout the entire study period, it suggests that the measured improvement was genuine.

Only one-third of delayed discharges had delay reasons documented by the bedside nurse, which may have impacted our ability to attribute proportional cause. However, because there was no disincentive to report delay reasons, we do not believe that the delay reasons were systematically misclassified. In regard to the hospital charge data, a minority of patients constitute a large proportion of the hospital charges, which may overestimate the typical cost of a delayed discharge. Also of note is that this is charge data and not direct cost to the family.

Finally, this work was uniquely adapted to the ACC unit at a single institution with a track record of MedR work on HM units. As a result, the nature of stakeholder buy-in may have been impacted by the local improvement climate. However, we believe that the central construct of our measured improvement is based on the principle of limiting unnecessary variation, which is broadly applicable to other units and other patient types, both general and complex.

Discharge timeliness in pediatric cardiology patients was sustainably improved by standardizing medical discharge criteria. This improved timeliness resulted in a reduction of LOS without an increase in readmissions. These discharge delays are associated with increased hospital charges.

Dr Madsen conceptualized and designed the study, drafted the initial manuscript, collected and analyzed data, and reviewed and revised the manuscript; Dr Porter, Ms Cable, Dr Hanke, Ms Hoerst, and Dr Brower participated in study design, analyzed data, and critically reviewed the manuscript for important intellectual content; Dr Neogi collected data, conducted analyses, and critically reviewed the manuscript for important intellectual content; Drs White and Statile participated in study design, analyzed data, and reviewed and revised the manuscript; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

FUNDING: Dr White is on faculty at the Institute for Healthcare Improvement. No funding was secured for this study.

     
  • ACC

    acute care cardiology

  •  
  • CHD

    congenital heart disease

  •  
  • EHR

    electronic health record

  •  
  • HM

    hospital medicine

  •  
  • LOS

    length of stay

  •  
  • MedR

    medically ready

  •  
  • PDSA

    Plan-Do-Study-Act

  •  
  • QI

    quality improvement

  •  
  • SMART

    specific, measurable, attainable, relevant, time-bound

  •  
  • STS

    Society of Thoracic Surgeons

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Competing Interests

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

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