SPECIAL REPORT | Alternative Reimbursement Models

Alternative Reimbursement Models

The model for total benefits paid (global fee) had a good fit, explaining approximately 54% of the variation. The important risk factors were age, level of care, the hospital group and the pre-existing conditions listed in the table below. Gender does not have a significant impact on the total benefits paid. Significant pre-existing conditions: total benefits paid  HIV  Bipolar mood disorder  Chronic renal disease  Diabetes mellitus type 1  Diabetes mellitus type 2  Epilepsy  Hyperlipidaemia  Hypertension  Hypothyroidism  Multiple sclerosis  Tuberculosis  Asthma  Bronchiectasis  Cardiac failure  Chronic obstructive pulmonary disorder  Parkinson's disease  Rheumatoid arthritis  Cancer

Board of Healthcare Funders Industry Technical Advisory Committee

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SPECIAL REPORT | Alternative Reimbursement Models

Published on Jun 7, 2021

Board Of Healthcare Funders

SPECIAL REPORT | Developing Alternative Reimbursement Models for Hospital Admissions related to COVID-19 This project is a collaborative effort between the Board of Healthcare Funders (BHF) and the Industry Technical Advisory Committee (ITAC). It commenced as a response to the ongoing impact of the COVID-19 pandemic in South Africa and in particular its management from a health financing perspective. The BHF was tasked with developing an alternative reimbursement model (ARM) for COVID-19 hospital admissions to assist member schemes with the management of the risk associated with COVID-19 claims – this report focuses exclusively on hospital costs. The BHF invited the industry to participate in a task group for this purpose.

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SPECIAL REPORT | Alternative Reimbursement Models

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9.3. ANNEXURE 3: Model Construction

9.2. ANNEXURE 2: Data Specification

Figure 17: Model result: Total benefit paid by hospital group and age

8. References

5.2. Risk-adjusted total benefits paid

5.3. Risk-adjusted average cost per day

Figure 9: COVID-19 admissions by age and gender after trimming data

Table 1: ITAC team members

1. Introduction