Demystifying casemix funding and hospital-acquired complication penalties: the coding continuum

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Mr Jake Valentine

Jake has an interest in enhancing the utility of hospital discharge coding data for infectious disease surveillance in patients with cancer. Jake works closely with relevant stakeholders to ensure that Australia's capitation Activity Based Funding model is sustainable and equitable for all Australian hospitals managing healthcare-associated infections in hospitalised cancer patients.

In Australia, Weighted Inlier Equivalent Separation (WIES) funding works in a capitation Activity Based Funding (ABF) system, meaning that hospitals are funded on the basis of their patient case-mix. In Victoria, hospitals are grouped into local hospital networks (LHN) based on their geographical proximity to each other. Rather than be funded at an individual hospital-level, Victorian healthcare facilities are funded as LHNs. This means that one LHN will receive the same level of funding in WIES as compared to a neighbouring LHN which may receive more or less funding in WIES depending on the complexity of their patient case-mix. The principles underpinning ABF ensure there is equitable access to Commonwealth funding across all LHNs and Australian jurisdictions; is technical efficiency and sustainability in reducing long-term health expenditures; and most importantly, is patient, not provider focussed.  

 The Commonwealth Government reimburses states and territories approximately 45% of healthcare expenditure, with the remaining paid for by the respective jurisdiction. The amount paid for by the Commonwealth Government is called the National Efficient Price (NEP) (or WIES base rate in Victoria). The NEP typically increases each financial year to reflect the rising cost of healthcare. For example, the WIES base rate in 2016/17 was $4,640, meaning the Federal Government will pay a fixed rate of $4,640 for each episode-of-care, or separation. However, this can change depending on the type of Australian Refined Diagnosis Related Group (AR-DRG) allocated to this episode-of-care. Each AR-DRG carries its own inlier weight. If the inlier weight > 1.0, the Commonwealth will pay more than the NEP. Conversely, if the AR-DRG < 1.0, the LHN will receive less than the NEP in that given financial year. Using the same principle, if the inlier weight = 1.0, the LHN will receive $4,640 for that episode-of-care. To complicate things further, the amount of WIES funding varies depending on the patient’s LOS. Let’s discuss this together by referring to my WIES funding algorithm (Figure 1):

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Figure 1. WIES funding algorithm. AR-DRG, Australian Refined Diagnosis Related Group; FY, financial year; HAI, healthcare-associated infection; ICD-10-AM, International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification; ICU, intensive care unit; IHPA, Independent Hospital Pricing Authority; LOS, length of stay; NEP, National Efficient Price; NHCDC, National Hospital Cost Data Collection; WIES, Weighted Inlier Equivalent Separation

The technical specifications of WIES funding are complex, but are central in understanding the importance of robust and reproducible administrative coding data for health services provision. The State Government of Victoria’s Department of Health and Human Services’ current funding model for acute inpatient services is called WIES (also known as the National Weighted Activity Units (NWAU) in other states and territories). There are four key steps in determining the level of WIES funding provided from Commonwealth and State Governments (Figure 1):

1.      Step 1) Determine the AR-DRG

2.      Step 2) WIES weight plus adjustment

3.      Step 3) Determine inlier equivalent

4.      Step 4) Calculate WIES payment

Step 1) Inpatient diagnoses will be recorded in a medical chart. This information is then codified into ICD-10-AM codes, and will typically fall under one of three categories – (i) primary diagnoses, (ii) secondary diagnoses, and (iii) complications (e.g. healthcare-associated infections (HAI)). These codes, along with the patient’s age, gender, LOS (in days), procedures and other clinical characteristics are then further coded into a single AR-DRG code. 

Step 2) Length of stay (LOS) data for each AR-DRG is submitted to the Independent Hospital Pricing Authority (IHPA) from every public and private hospital in Australia. The IHPA then standardise or ‘average out’ the LOS for each AR-DRG in the National Hospital Cost Data Collection (NHCDC), giving us a low and high inlier boundary. For example, AR-DRG A01Z (2016/17 FY) has a LOS inlier boundary of 10 – 93 days (average: 27.9 days). This means most (but not all) patients designated code A01Z have a LOS between 10 – 93 days. In this instance, the inlier weight for that given AR-DRG is multiplied by the NEP to determine the level of WIES funding. Simple enough, right? But what happens if the patient’s LOS doesn’t fall in the inlier boundary?

Step 3) If the patient’s LOS falls below the low inlier boundary for that given AR-DRG, then a low outlier per diem weight is applied. Similarly, if it falls above the upper boundary, then a high outlier per diem weight is applied. This information is freely available in the NHCDC.

Step 4) Now for the business end of WIES funding: calculating payments. Depending on the patient’s LOS, one of four scenarios typically occur. The best illustration of this is provided in Figure 1. In addition, some patients are more expensive than others irrespective of their AR-DRG, for example, if a patient is admitted to the ICU and/or requires mechanical ventilation. In this instance, an additional co-payment in WIES is provided to LHNs to reflect the heightened cost in managing these patients.  

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Figure 2. How Activity Based Funding is back-paid to acute public hospitals depending on patient length of stay (in days). Schematic adapted from the Independent Hospital Pricing Authority National Pricing Model Technical Specifications 2012-2013.

For healthcare providers, there is a period during inpatient stay where LHNs could receive more funding than what they pay in healthcare costs as per the WIES algorithm. Arbitrarily depicted in Figure 2, the red line is WIES payment and the green line is how much the hospital is spending in healthcare costs. Where the red line is situated above the green reflects the cost accrued is less than the funding paid by governments (i.e. a profitable period for hospitals!). When we enter the inlier boundary period for that AR-DRG, WIES funding is fixed, despite the fact that the hospital will be spending more and more each day in healthcare costs. Where we go above and beyond the high inlier boundary, WIES funding will increase on a per diem basis, but may not reflect the amount the hospital is spending in managing these complex patients.

Let’s bring this full circle for infections in cancer. The Federal Government will now withhold a percentage of WIES payments to LHNs when ICD-10-AM codes denoting certain HAIs are reported to the IHPA. This is called the Hospital-Acquired Complication policy. Historically, ICD-10-AM codes miss true HAI diagnoses in the medical chart, or in some instances, code an infection even when one was never present! In cancer patients, their immune system is suppressed, placing them at heightened risk of infection as compared to a non-cancer patient case-mix. This means more infections are occurring in cancer patients, increasing the probability of miscoding patients as having a HAI. Returning to Step 1 of the WIES funding algorithm, this is bad news, because cancer hospitals may be unfairly penalised in WIES funding if these codes are reported to the IHPA (despite these HAIs never occurring in the first place). The consequence – a negative funding adjustment is applied to this episode-of-care where a HAI code is reported, meaning the LHN will receive a percentage (and not the full amount) of funding in WIES, leaving the LHN to find alternative funding to fund the difference. Added to this is the fact that cancer patients with HAIs have an increased LOS, meaning these patients are likely to extend beyond the high inlier LOS boundary where WIES funding received may not reflect hospital expenditure in managing these HAIs.

The objective of the HAC policy is to provide a funding disincentive to encourage healthcare facilities to prevent the occurrence of HACs, thus improving patient safety and the quality of inpatient care. The issue … HAC penalties are contingent on the quality of administrative coding data, with some LHNs performing better than others in coding true cases of HAIs. Coding data have a broader worldwide reach, enabling system-wide health services research and monitoring of performance markers in cancer patients. Administrative HAC coding, as it stands, is not optimised for cancer patients. Efforts are needed to ensure that ICD-10-AM codes denoting HAIs, particularly in high-risk cancer patients, are robust and reproducible enough to accurately measure quality improvement programmes.  

More resources :

 1.      Valentine JC, Haeusler GM, Worth LJ, Thursky KA. Sepsis incidence and mortality are underestimated in Australian intensive care unit administrative data. Med J Aust 2019; 210(4): 188-.e1.

2.      Calderwood et al., Centers for medicare and medicaid services hospital-acquired conditions policy for central line-associated bloodstream infection (CLABSI) and cather-associated urinary tract infection (CAUTI) shows minimal impact on hospital reimbursement. Infect Control Hosp Epidemiol 2018; 39(8):897-901

3.      Lee et al., Effect of Nonpayment for Preventable Infections in U.S. Hospitals. N Engl J Med 2012; 367(15):1428-1437

4.      Rhee et al., Impact of the 2012 medicaid health care-acquired conditions policy on catheter-associated urinary tract infection and vascular catheter-associated infection billing. Open Forum Infect Dis 2018; 5(9):1-4

5.      Pricing and funding for safety and quality. Sydney, New South Wales, Australia: Independent Hospital Pricing Authority, 2018-19:1-52.

6.      Kawai AT, Calderwood MS, Jin R, et al. Impact of the Centers for Medicare and Medicaid Services Hospital-Acquired Conditions Policy on Billing Rates for 2 Targeted Healthcare-Associated Infections. Infect Control Hosp Epidemiol 2015; 36(8): 871-7.

7.      Risk adjustment model for Hospital Acquired Complications - Technical Specifications. Sydney, New South Wales, Australia: Independent Hospital Pricing Authority, 2017:64.

8.      Shepheard J, Lapiz E, Read C, Jackson TJ. Reconciling hospital-acquired complications and CHADx+ in Victorian coded hospital data. Health Inf Manag 2018: 1-11.