The Myth of LOS Revisited: Part 2

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Original story posted on: November 11, 2020

It’s time to move on from los to cost per case.

There is an argument to be made that length of stay (LOS) is no longer a relevant metric, as the healthcare industry slowly shifts to shared savings programs, direct contracting entities, next-generation Accountable Care Organizations (ACOs), or fixed payment rates – whether DRGs or negotiated contract rates.

The bottom line is that if patient care cost is less than the payment rate, it produces a profit margin. If patient care cost is greater than the payment rate, the hospital ends up in the red. In either case, length of stay has nothing to do with reimbursement. 

If costs are the primary concern for all profit-seeking organizations, then initiatives must now focus on lowering variable costs per case, which are primarily the product of physician practice behaviors and inefficient service delivery, which generate avoidable days.

Physician Practice Profiles

Variations in physician practice behaviors are well-documented. There is a body of research demonstrating that Medicare spending per beneficiary (MSPB) varies widely across geographic areas.

The conventional wisdom from leaders in this research area, the Dartmouth Institute for Health Policy & Clinical Practice, is that little of this variation is accounted for by variation in income, prices, demographics, or health status, but instead, most of the variation represents differences in “practice styles.”(Skinner and Fisher, 2010). Research also suggests that physicians differ substantially in their recommended treatment plans, based on their political affiliation.

There is research suggesting that a physician's own malpractice history also affects his or her subsequent quality of care, spending, and outcomes – and there are studies showing how differences in medical training programs impact physicians later in their careers, and how physician practice patterns respond to the local economic environments in which they practice.

These variations exist, and are often costly to the patient and the hospital, but they represent opportunities to bring information directly to the physician and let them determine what is best for themselves, their patients, and their practices. In my experience, most physicians want to reduce costs – without being hassled, and while improving quality of care – so their outcome data reflects their commitment to quality and drives effective throughput, so they don’t waste time.

Each hospital is replete with data on physician-specific spending, which serves as the basis to analyze resource utilization. Generating the information is a two-step process. Rather than just using claims data, which does not take multiple diagnoses into consideration, and may therefore skew results, a risk-adjusting system allows comparisons of co-morbidities and complications by weighing the multiple procedures and diagnoses that are documented by physicians and coded by coding professionals. 

Within every 3M™ Coding and Reimbursement System is the 3M™ APR DRG Classification System. Based on the coding of each case, and consideration of patient attributes such as gender, age, risk factors, conditions, and complications, the APR-DRG software assigns a severity of illness (SOI) and risk of mortality (ROM) level to each patient, ranging from 1 to 4. The SOI classification system captures the extent of physiologic decompensation or loss of function in an organ system. The four SOI classes correlate to minor, moderate, major, or extreme severity. The assumption is that patients in higher SOI levels will consume more resources than patients grouped within lower levels.

The second step to this process is creating a “bucket list” of every resource used in the care of a specific APR-DRG patient population. Each service, treatment, and supply has an assigned revenue code, based on the chargemaster, which can be grouped into categories (all blood bank products, all imaging services, all respiratory therapy services, etc.). Indeed, before undertaking this process, the chargemaster must be “vetted” for consistency and accuracy before a savvy IT professional integrates one database into the other, through the 3M interface, to create a simple Excel report that can be reviewed by the utilization review committee (URC).

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While the data is presented in dollar terms, these reports are not meant to be financial reports or cost reports. The dollars simply serve as surrogate measures to profile resource utilization and practice decisions made by individual doctors. 

The final report is helpful in many ways. First, it compares apples to apples by analyzing the same fields (codes) for each patient. Second, it provides insights into how physicians practice and use resources in the care of their patients. 

Using the sample report as an example, it’s noted that with the exception of Dr. Cole, Frank, Grand, Jacson, Kyle, and Leslie, physicians place their modestly ill (SOI-2) pneumonia patients in the ICU, a costly practice decision, while their colleagues admit them to a medical bed. Thirdly, it provides insight into documentation acumen. Since the benefit of using the APR-DRG system is to level the severity field among similar patient populations, no physician can use the excuse that “my patients are sicker” to explain wide variation in resource utilization. It’s possible, therefore, that some physicians, like Dr. Harold, are indeed caring for “sicker” patients, but her documentation lacks specificity in capturing the intensity of the medical management her patients require, resulting in improper coding assignments. All of the data regarding Dr. Harold’s patients is being reviewed by outside agencies and payer organizations, which see patterns that simply may not accurately reflect the patients under her care. In this case, a conversation between the physician advisor and Dr. Harold, using this data as a resource, can be of great value.  

Numerous studies conducted over the years have concluded that when physicians are shown costs of care for their patients, they order less expensive medications and decrease the volume of lab tests, resulting in lowering costs per case. Knowing the costs of hospital resources appears to make physicians more thoughtful about what might be best for the patient. In Dr. Harold’s case, knowing how documentation skews her resource utilization outcomes may be all the motivation she needs to reach out to a clinical documentation improvement (CDI) specialist to get on the right track.

Through-Put and Avoidable Days

“Achieving hospital-wide patient flow, and ultimately improving outcomes and the experience of care for patients, requires an appreciation of the hospital as an interconnected, interdependent system of care. It also requires strong leadership,” according to a recent white paper, “Achieving Hospital-Wide Patient Flow,” by the Boston-based Institute for Healthcare Improvement. The siloed nature of most hospitals, with their traditional, Tayloresque organizational structure, creates multiple challenges in the quest for efficient throughput and lower LOS. There are many variables that directly and indirectly affect efforts to overcome these challenges, which can be predominantly categorized into issues arising from avoidable days in the hospital due to process inefficiencies and physician behaviors. It is estimated that 25 percent of hospital days, 1.2 days per patient, could be avoided if delivery-of-care processes and physicians’ practice decisions were more efficiently managed to avoid gaps and delays. 

The only way to avoid those gaps and delays is to understand the root causes. Convene a diverse group of stakeholders and give them some Post-it notes. Ask them to identify every possible reason they can think of explaining why a patient remains in the hospital longer than expected. Have the group arrange the Post-it notes on a board into categories: for example, group all potentially avoidable delays/days due to patients and families, avoidable delays/day due to physicians and staff, avoidable delays/days due to hospital systems, and avoidable days due to community/payer services.

DELAYS/AVOIDABLE DAYS DUE TO SYSTEM

DELAYS/AVOIDABLE DAYS DUE TO MEDICAL STAFF

DELAYS/AVOIDABLE DAYS DUE TO FAMILY AND PAYER

S1:   Imaging test scheduling delay

M1:  Documentation does not support inpatient admission

P1:  Nosocomial or Iatrogenic occurrence 

S2:   Imaging test report delay

M2:  Tests could have been done outpatient

P2:  Patient/family decision to refuse

S3:   Thalium scan not available on weekend

M3:  Delay in ordering treatment

P3:  Family availability

S4:   Critical care bed not available

M4:  Physician ordered diagnostics prior to DC

P4:  Patient/family uncooperative  (counseling referral)

S5:   Unable to find post-acute SNF that will accept uninsured patient.

M5:  Consult delay in responding

P5:  Patient refusing treatment

S6:   Meal delivered to NPO patient

M6:  Lack of communication between physicians

P6:  Patient refuses to return to transferring facility

S7:   Equipment not available

M7:  Delay in writing orders

P7:  Lab tech sent away by family/patient

S8:   Therapy not available on weekend

M8:  On-call physician won’t discharge

P8:  Patient’s spouse unable to care for patient at home

S9:   Surgery/procedure not done on weekend

M9:  MD refuses to move patient to lower level of care

P9:   PT/OT/ST slow to respond to consult

S10:  No transport to PAF

M10: Physician post-acute preference n/a

P10:  PT note suggests PT before d/c

S11: Facility refuses to accept transfer over weekend/holiday

M11: Physician would not transfer from ED direct to community facility

P11:  Radiology report suggests additional testing

S12:  SNF bed not available

M12: Testing not related to reason for admission

P12:  No one in family available to take patient home in morning

S13: Hospital rehab unit refused patient admission 

M13: Resident waiting for attending before discharging patient

P13:   Patient/family report that home has no electricity

S14:  Awaiting paperwork (Medicaid app., PAS, etc.)

M14: ED physician refuses observation recovery

P14:  Payer refuses auth for IRF; OK for SNF

S15:  Registration did not verify payer

M15: No documentation to support why patient was kept as observation after routine outpatient procedure

P15:  Family refuses to take patient home; no funds for post-acute placement

S16:  Cath lab closed on weekends and after 4 p.m.

M16: Hospitalist made routine rounds before discharge rounds

P16: Payer refuses physician for post-acute home care; physician will not discharge

S17: Documentation does not support hospital level of care  

M17:  Infectious disease consultant not available on Monday or Tuesday

P17:  Payer delay in providing post-acute authorization

S18: Patient admitted inpatient prior to scheduled, elective procedure

M18: Zynx order set not followed  (MI, CHF, COPD, pneumonia, chest pain, and CJR)

P18:  Payer refuses authorization for PT at SNF; OK for homecare

S19:  No SW covering ED for direct transfer

M19: Hospitalist refuses to D/C until consultants sign off

P19: Delay in finding translator

S20: No available bed in CDU; patient placed in inpatient bed

M20:  CABG team not available on weekend

P20: Patient’s functional status delays discharge to acute rehab facility

Once a list is compiled using every possible source of delay, print copies and distribute them to every UR specialist and case manager, and the nursing staff. Ask them to assign a sheet to each patient under their care, and ask them to informally capture any occurrence that is on the list. No explanations are needed, and do not worry about duplicates.

Upon each patient’s discharge, submit the completed sheet to a designated support team member who will compile and total the number of episodes over a 2-4 week period. At the end of that period, you will see a pattern emerge; some items will have frequent check marks, while others will have occasional check marks. Remove the items with occasional check marks and narrow down the list to the “precious few” - anywhere between 30 and 50 is not uncommon. This is when the capture process should be electronically embedded into the organization’s UR software, if feasible.

Then, meet with the CFO to explain what you are trying to do, and ask him or her for a metric to consistently apply to quantify each delay. The metric may be average cost per patient day, average revenue per patient day, or another; it really doesn’t matter, as long as the CFO backs it up as a good metric to monitor changes over time. Remember, this is not a financial accounting report, simply a representation of throughput obstacles and their potential costs to the organization.

On the sample report included, note that the revenue cycle director included a column to show the value of the UR team’s activities by quantifying the volume of incidents they were able to overcome due to their interventions.

Neither of these reports are absolute in their findings, but over the years that I’ve used them, they have been quite reliable in identifying opportunities to improve progression of care, reduce costs, and improve the patient experience. As an added value, the process of identifying and quantifying avoidable days may also demonstrate a correlation between the volume of avoidable days and the number of payer denials.

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Whatever the goals, these tools do not require major investment, but they do require a commitment among department heads and the C-suite to significantly improve progression of care to improve quality and reduce costs.  

Stefani Daniels, MSNA, ACM, CMAC

Stefani Daniels, RN, MSNA, CMAC, ACM, is the founder and managing partner of Phoenix Medical Management, Inc. She is a former hospital executive, chief nursing officer, and senior consultant with Johnson & Johnson. Stefani held faculty appointments at University of Pennsylvania, Nova Southeastern University, in the Graduate Division of the School of Business and Entrepreneurship and Columbia University in New York.

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