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Proactive discharge planning for more efficient patient flow: reduce “avoidable” days through actionable data

  • Pallabi Sanyal-Dey, MD, FHM

    Director Client Services, iQueue for Inpatient Flow, Associate Clinical Professor, UCSF School of Medicine

As the physicians, nurses, staff, and patient caregivers involved know all too well, inpatient discharge planning is critical to minimizing length of stay (LOS) and supporting the patient care journey. The process, however, is complex. Staffing shortages, fluctuating inpatient capacity, and economic pressures have complicated it even further.

Hospital personnel do their best in these circumstances to be mindful of LOS, which inevitably means beginning the discharge planning process as soon as the patient is admitted, if not before. These discharges are all unique and involve different complications and needs. However, staff are often forced to follow the same process for each discharge because they lack access to the actionable data that would help them make the best decisions for each patient and prioritize discharges. 

The manual and chaotic work of daily bed management and discharge planning, coupled with the lack of predictability, frustrates staff and providers, disrupts efficient patient flow, and negatively impacts the patient’s experience and quality of care.  One particular downstream impact is the unnecessary increase of LOS in the form of avoidable days, or the number of days a patient remains as an inpatient even though he or she is medically ready for discharge. 

Avoidable days can cost a hospital thousands of dollars each month, and have become an acute challenge for many health organizations recently. 

According to a financial overview by Healthcare Dive, regarding Q3 2022, large nonprofits like “”Providence, Intermountain, Sutter, Mass General Brigham and Advocate Aurora are among the systems that reported higher lengths of stay and lower discharges this summer compared to 2021, while “for-profit operators HCA Healthcare and UHS also reported year-over-year increases in lengths of stay…from 4.94 days to 4.99 days, and from 4.8 days to 4.9 days, respectively.” 

These LOS increases contribute to poor financial outcomes and lowered performance ratings by agencies, as well as health risks to vulnerable patients who need to move on to the next phase of their care journeys. They are mitigated, however, by better discharge planning that accounts for likely barriers before they arise, rather than allowing them to contribute to avoidable days. 

Ineffective discharge planning adds avoidable days to LOS

Avoidable days, or the number of days a patient remains as an inpatient even though he/she/they are medically ready for discharge, can cost a hospital thousands of dollars each month. These generally occur because of some kind of avoidable delay, including “any barrier to facilitating effective, efficient, timely, and safe care.” Such barriers can be as simple as not securing necessary durable medical equipment (DME) for a patient post-discharge. They can also be much more complex, like delays in securing a room at a post-acute facility (i.e. a rehab or skilled nursing facility) for the patient to enter once he/she is no longer an inpatient. The last barrier is especially critical today A May 2022 survey by the American Health Care Association and National Center for Assisted Living found that 58% of the 14,000 nursing and assisted living facilities it covered were limiting admissions due to staff shortages. Further, some patients across the US are waiting months for admission to the facility they need.

Providers and staff can remain ahead of  these discharge barriers if they are able to identify them as soon as they arise, with comprehensive data analytic tools that support such proactive discharge planning. 

The cost of avoidable days to the hospital

Advisory Board analyzed CMS FFS (fee for service) claims data from 2017 and 2018 and found the average number of avoidable days per patient was 1.2 days. The average length of stay (ALOS) for patients was around 4.2 days for the same period, making the percentage of hospital avoidable days 25%. This calculates to 10.8 million avoidable inpatient days, which is equal to 29,590 full hospital beds for one entire year.  

In other words, for a 500-bed acute care hospital, assuming their avoidable days are 25% annually and their average room cost is $2,873/day, the total annual savings for reducing avoidable days by just 5% can be approximately $6.5M

Decreasing the number avoidable days: the first steps to driving more efficient patient flow

Early, effective, and proactive discharge planning is key to decreasing avoidable days. This involves integrating a myriad of processes, tasks, and people to ensure a patient is discharged efficiently, safely, and on time. The following processes, coupled with data analytics, can be implemented to assist organizations in streamlining the discharge process for individual patients and driving efficient patient flow overall.

  • Establish the goal and set the expectations: Ensuring the patient care team is aware of the discharge goal, in this case the estimated date of discharge (EDD), organically aligns that team. Everyone who is caring for the patient, including the patient themselves and their family and other caregivers, will be aware of the care timeline and can help ensure all necessary assessments, tests, procedures, and discharge needs are identified and addressed early in the stay. 
  • Implement multidisciplinary rounds: Utilizing this patient-centered model of care enables all care team members to contribute to the care plan and help identify any potential barriers early and throughout the patient’s journey. Many healthcare systems are well into this journey; what remains is a need for more active, high value problem solving during these rounds to make it worth everyone’s time.
  • Identify potential discharge barriers before they become delays: Noting any tests, procedures, and/or assessments that must be completed early during the patient’s admission (i.e. imaging procedure, PT/OT assessment); any education that may need to be provided to the patient and/or family (i.e. medication management, nutrition); and/or any needs the patient may have post-discharge (i.e. DME, transportation home, or admission to an SNF) will ensure the care team can address these potential barriers before they delay the patient’s discharge.  

These steps are best supported by a predictive data analytics tool like iQueue for Inpatient Beds that will keep the entire inpatient team informed by the same single source of truth, and flag operational issues that will likely need to be addressed. This type of actionable data also enables staff and providers to prioritize patients that they can favorably impact versus putting all resources towards a very complex patient situation where the patient may not get discharged, regardless of efforts. Learn how hospitals are identifying and preventing avoidable delays by applying machine learning and natural language processing technology.

A version of this blog was originally published on Becker’s Hospital Review

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