Reducing length of stay (LOS): an open-market approach to hospital discharge planning

Post-acute care transitions with an open-market approach to hospital discharge planning: a look at length of stay (LOS)

Russ Graney

CEO & Founder, Aidin

Executive summary

Post-acute care (PAC) is a key driver of Medicare spending and variation, and stakeholders are highly motivated to improve the quality of care while reducing costs. As policymakers move towards value-based models to manage financial risks after a patient is discharged, hospital executives  face a relatively  new challenge-integrating acute patient care with PAC.

Targeting PAC transitions to ensure that patients are discharged to clinicallyappropriate providers in a coordinated and systematic manner is an opportunity to reduce cost. However, the  communication between acute and postacute settings, which has  been infrequent or lacking in the past, is challenging when sharing information is burdened by low interoperability of technologies among providers. Additionally, case managers are increasingly burdened with identifying the most clinically appropriate PAC provider who meets logistical, patient, and payer benchmarks. Increasingly, organizations will seek innovative approaches that improve acute and PAC-provider engagement and subsequently impact cost, quality and patient satisfaction expectations.

This whitepaper will explore the application of a PAC transition management tool that utilizes an open-market model to identify the most appropriate and available PAC providers for patients, who can then make informed choices based on hospital/health systemdefined operational and clinical performance benchmarks. Using a key quality-improvement measure, hospital length of stay (LOS), the analysis demonstrates the impact that the transition management tool had on PAC discharges in five academic medical centers in the United States.

The shift to value based PAC delivery

Rising Medicare costs have redirected stakeholder efforts to the management of PAC services.

In 2013 a report released by the National Academy of Medicine found that 73% of variation in  total Medicare spending was due to PAC services, with Medicare PAC payments expanding faster than most other categories of spending.

Since then, PAC has emerged as a key driver of Medicare spending and variation, causing both policymakers and providers to direct their attention to improving the management of patients requiring PAC.

The response has been a shift to valuebased care models, which focus on integrating the overall continuum of care to guide patients to clinically appropriate PAC settings that meet established performance and quality metrics, improve the PAC experience, and  reduce overall cost .

From a policy-driven perspective, Medicare’s bundled-payment and shared savings programs are designed to incentivize the integration of provider services such that PAC providers are essentially an extension of a hospital’s care delivery model. For providers, however, participation in PAC bundled payment approaches  is easier said than  done. While there is increased awareness among healthcare organizations about the  need  to develop and use evidence-based care pathways and tools for selecting and managing post-acute placements, meaningful improvements in PAC will require a commitment  to the  coordination of care between acute and PAC providers.

Yet historically, there has been little coordination across provider settings when patients are discharged to PAC.

Consequently, integrating care among acute and PAC providers demands rethinking the care continuum. This means adopting new approaches for moving patients from one provider to another more efficiently, without compromising the quality of care. Although formal guidance for post-hospitalization planning is limited, stakeholders, such as the American Hospital Association , site several core competencies that are critical for integrating care across providers. Among them include the establishment of PAC partnerships that can balance clinical, operational, and financial needs; the collection of and access to data and quality metrics that will enable evidencebased decision-making; enhanced capability for information and electronic health record (EHR) sharing that will mitigate readmissions and prioritize protocol changes; and streamlining the hospital PAC discharge process to expedite timely discharges while ensuring placements are both clinically appropriate and patientcentered.

A key barrier to acquiring these competencies is communication access to timely, relevant data and frequent information sharing across care settings are critical for delivering coordinated care among multiple providers. Whether it’s the ability of providers to communicate information that often comes from disparate sources and is stored in different systems, or a mechanism for easily identifying and evaluating appropriate PAC providers, the exchange of information drives hospital discharge planning. Yet healthcare organizations face logistical and costly challenges when attempting to share information in order to make clinical and operational decisions. Moving forward, hospitals and other entities, such as ACOs, will be looking for innovative solutions to guide posthospitalization planning, with a goal to efficiently select the first PAC setting in a manner that will reduce variation in care and overall cost.

Targeting the hospital discharge setting: PAC transitions shape financial and quality outcomes.

Because patients are  transitioning from  one site of care to another, hospital discharge planning is a critical juncture in the continuum of care (Figure 1). Hospital discharge delays have direct and indirect effects on both patient outcomes and incurred costs.

The direct consequence of a discharge delay, extended length of stay (LOS), is not only associated with clinical risks to the patient, but has been identified as one of the major drivers of resource consumption and significant hospital cost increases.

Figure 1: PAC transit ions are central for shaping financial and quality outcomes
Figure 2: Discharge disposition of inpatient stays and length of acute hospital stays by discharge disposition, 2013

Discharge delays are also disproportionately associated with PAC dispositions (Figure 2). The average length of an acute hospital stay for patients discharged to PAC is nearly two times longer than stays with a routine discharge (7.0 vs 3.6 days).

Although both clinical and non-clinical factors cause discharge delays, it’s estimated that only about 20% of delays to PAC are for clinical reasons. A lack of discharge site coordination, however, is commonly associated with extended LOS for patients discharged to PAC. The underlying factors associated with the discharge setting that have been cited include difficulties in finding an appropriate PAC facility for placement, inhospital operational delays, and payer-related issues.

One recent study of patients in an academic tertiary care hospital with long LOS (over 30 days) found  that discharge site coordination was the most frequent cause of delay, affecting 56% of  patients and accounting for 80% of total non-medical postponement  days. Finding a safe PAC discharge site was cited as the primary barrier for timely discharge.

Another retrospective cohort study of adult patients with traumatic  brain  injury admitted to four level 1 U.S. trauma centers between 2015 and 2018 found that the most common causes of discharge delay were insurance processing /authorization and the lack of an accepting bed by a PAC provider. Notably, patients discharged to a skilled nursing facility (SNF) or intermediate care facility were 10 times more likely to experience a delay compared to those discharged to home.

These findings underscore the need to target hospital discharge planning for improvements moving forward, with a particular focus on quickly identifying the most clinically appropriate PAC provider who accepts the patient’s insurance and can admit the  patient by the anticipated discharge date. All with an underlying commitment to patient choice.

Case managers, however, face logistical burdens when reaching out to and managing responses from multiple providers for multiple patients. As the literature suggests, discharge delays are often attributed to the need for repeated contact with potential providers to determine their eligibility to provide care and an inability to efficiently track those communications in a timely manner. Further, acute care organizations often lack resources for accessing the data they need to drive evidence-base decision-making.

As hospitals and health systems consider new rules of engagement to better coordinate services with PAC providers, innovative tools that can ensure efficient, secure, and flexible communication  between providers to facilitate timely, clinically appropriate PAC transitions will be highly regarded assets for building valuebased care delivery models.

The impact of an open-market approach to PAC transition management on LOS at 5 academic medical centers in the United States

Aidin is a PAC transition management tool that employs new methodologies to enable efficient, evidencebased hospital discharge planning.

Using an open-market approach driven by a cloud-based encrypted platform and real-time data collection capabilities, this different approach allows case managers to efficiently discharge patients to the highestquality provider of their choice.

The approach is centered around three key functionalities: harnessing an open-market referral system for PAC providers; implementing an efficient discharge workflow structure; and providing access to data and quality metrics for evidence-based decision-making (Figure 3). Importantly, because it uses a secure communication platform for information-sharing that doesn’t depend on shared technical systems, software, or platforms, it enables the type of communication  between  acute hospital settings and PAC providers that is essential for promoting coordinated patient care.

An open-market approach connects the PAC provider marketplace with acute care case managers.

By tapping into an open marketplace, this management  tool adopts an innovative approach to make it easier for case managers to connect with myriad potential post-acute providers to provide patients a view of all their available  options. Providers can  also claim their profile in a webbased secure platform to participate and receive referral requests.

However, if they prefer, they can communicate with case managers on platforms they typically use, such as fax or email, without ever engaging with the platform. Further, the provider profiles they create in Aidin serve an important function for case managers-the ability to send provider requests only to the most clinically-appropriate providers for a particular patient.

Providing structure and tracking discharge workflow tasks.

The management tool  dashboard  centralizes the discharge workflow, tracking each step in the process for case managers (Figure 3). When case managers submit referrals to providers, each step is tracked based on the anticipated discharge date. The system manages individual workflows, keeping them on track with built-i n reminders that allow case managers to easily manage multiple patient discharges at once.

Providers respond to referral submissions based on eligibility parameters, such as their clinical capabilities and bed availability. When the  response deadline arrives, the case manager creates a provider list for the patient, which will only include providers who are clinically appropriate, have capacity to admit them, and for whom insurance issues have been addressed. Once the patient and/or their family choses a provider, the case manager is able to move forward with the discharge.

Continual collection and storage of performance metrics data to enable informed decision-making.

This innovative tool collects data that helps improve PAC transitions in three important ways (Figure 3). First, it tracks the workflow so it is easy to monitor how closely each step in the process adheres to internal deadlines set by the management tool.

Open market approach

This data helps organizations identify and address issues that  may cause delays. Second, it collects and stores data on PAC providers. Each PAC provider’s profile has information, such as their clinical capabilities and location, that allows case managers to easily generate clinicallyappropriate provider lists that also contain quality performance metrics data, for patients and their families to make their decision. Profile and performance data, collected and stored by Aidin, are reflected as “Provider Badges” and “Referral Badges,” which are awarded to each provider. When patients are given a list of providers, they are able to see all available post-acute providers in their community, and choose the highest-quality provider who meets their needs.

Finally, the data collected by Aidin allows administrators and executives to easily monitor the overall performance of PAC transitions in their hospital. Users can create discharge workflow and PAC provider reports for evaluating metrics at any time, and long-range data allows administrators to analyze the financial impact of implementing the tool over time.

Thus, this innovative PAC management approach uses methodologies that help ensure discharges are expedited more efficiently because many barriers that typically slow down the discharge process have been addressed before the patient is given a choice of provider, and its automated tracking system mitigates delays due to miscommunication or heavy workloads.

Additionally, the data-capturing function allows both acute and  post-acute  providers to continually assess performance metrics as they strive for seamless PAC transitions for patients.

Figure 3: Aidin PAC Transition Management Tool

The case management methodology was implemented by 5 U.S.based medical centers seeking to improve their PAC transition process.

Aidin was adopted by five U.S.-based medical centers: Medical Center A, a CMS 5-star hospital in Arlington, VA; Medical Center B, a 5hospital system in downtown Houston, TX; Medical Center C, a 4-hospital system in downtown Los Angeles, CA; Medical Center D, an 800-bed academic medical center in Nashville, TN; and Medical Center E, a 5hospital system in Columbus, OH. All five medical centers were using an electronic referral management system in their discharge workflows before making the transition, yet continued to struggle in meeting their PAC transition performance goals (Table 1). Baseline LOS measurements were determined by evaluating the medical discharge records of patients discharged to PAC for 6 months prior to implementing the new system into their discharge process (Table 1).

The impact  of  implementing  the management tool was evaluated by collecting LOS data ranging from 812 months after it was adopted by case managers. The riskadjusted LOS at the end of the measurement period was then compared to the baseline LOS measurement.

Table 1: Baseline information and LOS (days)

Baseline LOS

Four of the five Medical Centers reported a baseline LOS that was higher than the national average LOS for PAC discharges among U.S. Hospitals (Figure 4).i“l The mean baseline LOS across medical centers was 11.9 days, or  70% higher than the average LOS for post-acute care discharge of 7 days.16 1 Only one medical center, Medical Center A, reported a LOS below the national average, with a LOS of 6.4 days. However, this value was still well-above 3.6 days, which is the national average for routine discharge (Figure 4).

Figure 4. Baseline length of stay (LOS), U.S. Medical Centers

The implementation of a new PAC transition approach led to overall LOS reductions for all Medical Centers.

Across medical centers, adopting the new PAC transition methodology led to a mean risk-adjusted LOS reduction of 0.86 days, representing a 7.2% reduction in LOS after implementing the tool (Figure 5). The reduction in risk-adjusted LOS ranged from 0.36 days to 2.06 days. Notably, the three medical centers with the highest baseline LOS experienced the greatest reductions. Medical Center C, Medical Center E, and Medical Center B experienced reductions in risk-adjusted LOS of 2.06, 0.66, and 0.64 days, respectively. This represented LOS reductions of 8.9%, 6.7%, and 5.3% from their respective baseline values.

Figure 5. Risk-adjusted LOS reductions after implementing the PAC transition management tool

Mean risk-adjusted LOS reductions by discharge disposition

When risk-adjusted LOS reductions by discharge site were analyzed, patients discharged to a SNF or a long-term care hospital (LTCH) had the greatest reductions in discharge delays (Figure6). The mean riskadjusted LOS reduction for patients discharged to SNF was 1.17 days and to LTCH was 2.16 days, which represented mean reductions of 10.4 % and 8.6% from baseline values, respectively.

Across medical centers, mean LOS reductions for the 5 discharge sites ranged from 0.36 days to 2.16 days, with mean reductions greater than 0.5 days reported for all dispositions except for hospice. For patients discharged to an inpatient rehabilitation facility, home health, or hospice, the mean reductions in risk-adjusted LOS were 0.70, 0.71, and 0.36 days, respectively.

Figure 6. Mean risk-adjusted LOS reductions by discharge site

LOS reductions in patients discharged to SNF, HH, and LTCH

Risk-adjusted LOS reductions for discharges to SNF and HH were examined in more detail, as these two PAC dispositions account for 90% of PAC discharges (Figure 2). For patients discharged to SNF, LOS reductions ranged from 0.4 days to 2.6 days among medical centers, with all five reporting reductions of >5% from baseline (Figure 7). Two facilities saw the riskadjusted LOS for SNF patients decrease greater than 10% from baseline. Medical Center B and Medical Center D reduced the LOS for patients discharged to an SNF by 2.6 days and 1.2 days, respectively, representing reductions of 21% and 12.5% from their respective baseline values.

For patients discharged to HH, LOS reductions ranged from 0.3 days to 1.3 days. Again, all five medical centers reduced the LOS for HH discharges by >5% from baseline, with Medical Center C reporting reductions of 10.6%, and Medical Center D and Medical Center E each reporting LOS reductions of 9.2%. The risk-adjusted LOS reduction for patients discharged to HH at Medical Center C was 1.3 days, and Medical Center D and Medical Center E saw LOS reductions of 0.6 and 0.73 days, respectively.

Of the three facilities reporting LOS for patients discharged to LTCH, all three saw reductions of over 1 day. Medical Center C, Medical Center D, and Medical Center B reduced the LOS for LTCH discharges by 3.17, 1.7, and 1.6 days respectively.

Figure 7. Risk-adjusted LOS reductions for HH and SNF discharges


After implementing Aidin, a management tool that uses innovative methodologies to enable a new approach to PAC transition management, all five medical centers saw LOS reductions, with an average reduction of 7.2% across facilities. Overall riskadjusted LOS reductions ranged from 0.36 days to 2.06 days, and the facility with the highest LOS at baseline, Medical Center C, experienced the greatest reduction, reporting an overall risk-adjusted LOS reduction of 2.06 days.

There was evidence that LOS reductions had an impact on downstream measurements as well. Medical Center C projected that the LOS reductions that occurred after adoption of the tool would lead to an additional 600 inpatient beds per year and would reduce annual costs by $1.7M. And Medical Center D, which saw a reduction in overall LOS by 0.6 days after implementing the PAC transition management tool, saw a tangible improvement in PAC placements. Using this innovative open market process, PAC referrals that required follow-up by Medical Center D staff were reduced from 30% to 11%, with 89% of patients placed in the first contact to providers. Notably, 92% of patients who were given a choice of 2 or more providers chose the highest quality provider, a striking comparison to the MedPAC reported average of 20%.

When LOS reductions were evaluated by PAC disposition, Aidin had an impact on the two dispositions that account for the majority of PAC discharges, HH and SNF. Across medical centers, the mean reduct ion in riskadjusted LOS for HH and SNF placements were 0.7 days and 1.2 days, respectively. This represents a mean reduction in LOS from baseline of 8.5% for patients discharged to HH and 10.4% for those discharged to a SNF. Also of note were the mean reductions in risk-adjusted LOS for LTCH discharges. Although LTCH discharges only represent around 2% of total PAC discharges, the average LOS for these patients approaches two weeks (13.5 days) (Figure 2). The mean reduction in LOS for LTCH patients was 2.16 days, with all three centers reporting on LTCH incurring reductions of over 1 day (range, 1.6-3.17 days).

This analysis demonstrates that Aidin was able to help reduce LOS for patients who are discharged to key PAC settings that account for Medicare spending. Further, there is preliminary evidence that LOS reductions had a positive impact on downstream outcomes, such as cost and provider choice. These findings support a role for innovative case management tools that will improve discharge setting workflows and help ensure that patients are treated in the most cost-effective, clinically appropriate PAC setting.


  1. Kennedy G, et al. Med Care Res Rev. 2020;77(4):312-323.
  2. American Hospital Association. TrendWatch Report: The Role of Post-Acute Care in New Care Delivery Models. December 2015. American Hospital Association, Washington, DC. Accessed October 29, 2020.
  3. Mechan ic R. N Engl J Med. 2014;370(8):692-694.
  4. Abrams A and Phillips G. AHA Trustee Services. Partnering with post-acute providers for better care. American Hospital Association. URL. Accessed October 29, 2020.
  5. Hwabejire JO, et al. JAMA Surg. 2013;148(10):956-961.
  6. Tian W. (AHRQ). An All-Payer View of Hospital Discharge to Post Acute Care, 2013. HCUP Statistical Brief #205. May 2016. Agency for Healthcare Research and Quality, Rockville, MD. Accessed October 29, 2020.
  7. Sorensen M, et al. Patient Saf Surg. 2020;14(2).
  8. Zhao EJ, et al. Postgrad Med J. 2018;94(1116):546 550