Using Case Costing Data and Case Mix for Funding and Benchmarking in Rehabilitation Hospitals

Using Case Costing Data and Case Mix for Funding and Benchmarking in Rehabilitation Hospitals

Grace Liu (York University, Canada)
DOI: 10.4018/978-1-4666-4321-5.ch005
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Abstract

The concern for Ontario hospitals in Canada is that the funding model has recently changed from global- to activity-based funding, which will affect hospitals’ operational budget and cost management. Hospitals will be reimbursed based on a pre-set payment price for each patient type or case mix treated. Specifically, the purpose of this chapter is to describe how case costing data and case mix information are collected and used for funding. A framework is proposed for health administrators, policy makers, and researchers to understand the input variables for determining resource utilization and long stay trim point. A clinical decision-making tool is demonstrated to assist hospital administrators to define admission criteria and predict length of stay and volumes with clinical teams. The chapter highlights the importance of data quality and use of comparative data and concludes with 10 key success factors for better funding and benchmarking for rehabilitation hospitals.
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Introduction

Case costing and case mix are widely used in health care systems internationally. In Ontario, Canada, case costing and case mix will be used as the funding model has recently changed from global funding to service based funding. Given this funding approach, it is crucial that hospitals manage their case costs and understand their various patient populations (or case mix). As case mix of patients will determine the funding allocated, hospital administrators need to admit appropriate case mix of patients, manage their costs and monitor the volume of services provided. The chapter will describe and analyze case costing data and case mix and to optimize funding and benchmarking in rehabilitation hospitals. A “Shrub” framework will be provided to understand the input variables for determining resource utilization and long stay trim point. A clinical decision-making tool is demonstrated for defining admission criteria and predict length of stay and volumes. It is important health administrators, policy makers and researchers considers data quality when analyzing case costing data and case mix for funding and benchmarking.

The objectives of the chapter are:

  • 1.

    To describe data collection systems:

    • a.

      Ontario Healthcare Reporting Standard Data.

    • b.

      Adult Inpatient Rehabilitation Minimum Data.

  • 2.

    To provide a “Shrub” framework to understand the factors that impact funding:

    • a.

      Describe “Input Variables for Determining Resource Utilization” and “Long Stay Trim Point Illustration for a particular Rehab Patient Group (RPG)”.

    • b.

      Clinical implications for rehabilitation hospitals.

  • 3.

    To demonstrate a clinical decision-making tool:

    • a.

      Defining admission criteria for rehabilitation hospitals.

    • b.

      Predicting length of stay and volumes with clinical teams.

  • 4.

    How to optimize funding and benchmarking:

    • a.

      Ensuring data accuracy and quality.

    • b.

      Using comparative data with peer groups.

    • c.

      Collecting data across the continuum of care.

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