Hospice_Benefit by wpr1947

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									The Medical Hospice Benefit: The Effectiveness of
     Price Incentives in Health Care Policy

    Written By Vivian Hamilton, McGill University
        RAND Journal of Economics 24(1993)
               Presented By Jing Zhou
                      1. Introduction

• 1. This paper mainly examines the effectiveness of per-
  diem reimbursement within the context of the Medicare
  Hospice Benefit program.
• 2. Specifically, this paper gives answers to the following
  three questions:
• 1. Does the rate of reimbursement influence the individual
  hospice’s certification decision?
• 2. Does the rate of reimbursement influence the access to
  hospice care?
• 3. How to explain the the variations in certification rates
  across different regions in US?
    2. A Description of Hospices and the Medicare
                   Hospice Benefit

• 1. Hospices care for terminally ill persons who have exhausted their
  efforts to cure the disease that afflicts them
• 2. The Medicare Hospice Benefit lays out the type of care hospices are
  expected to provide(HCFA, 1988)
• 3. A hospice that becomes under the Medicare Hospice Benefit
  (certified) must provide the expected care to its patients.
• 4. Then the Health Care Financing Administration(HCFA) reimburses
  hospices for their services on a per-diem basis.
• 5. So the reimbursement rate plays a significant role in determining
  whether or not a hospice seeks medicare certification
                     3. The Model(1)

• 1. The basic model in this paper is a model of the hospice’s
  certification decision process.

• 2. Since 95 percent of hospices are nonprofit organizations,
  the primary goal of a hospice is not likely to be profit
  maximization. Instead, utility maximization is its goal.

• 3. So the hospice chooses to be certified if doing so leads
  to an increase in utility.
                     3. The Model(2)

• 1. In this model, We should consider the factors that will
  affect a hospice’s utility before and after it is certified
• 2. The number of patients is an important index of a
  hospice’s utility. Why?
• 3. Providing services on a large scale is assumed to lead to
  the enhancement of the prestige of the institution and the
  likelihood of its ability to continue operating within the
  community.
• 4. High quantity may also attract donations.
                      3. The Model(3)

• 1. The other important factor influencing a hospice’s utility
  is its costs and revenues from operation. Why?
• 2. Excess revenues can be used to satisfy other possible
  utility-increasing objectives, such as improving facilities or
  increasing salaries.
• 3. So the model of the hospice’s certification decision
  process should take into consideration the quantity and the
  revenues and costs changes before and after a hospice is
  certified.
                     3. The Model(4)

• 1. The basic model in Hamilton’s paper is:

            I *  Z   (log P  log P ) 
             i     i           ic      in i
• 2. I i*is a continuous, unobservable index. When it is greater
                                 I




  than 0, this shows the hospice is certified.Zi is a vector of
  explanatory variables that affect the hospice’s expected
  costs and revenue before and after the certification
• 3. log Pic and log Pinare the expected value of the logarithm of
  the number of patients a hospice will serve if it is certified
  and noncertified.
                                    3. The Model(5)

•   1.We also have two patient equations for each hospice:
•        log Pic  X i         If Certified        (1)
                        1 i1
•       log Pin  X i          If Noncertified      (2)
                        2 i2
•   Where X i is a vector of market-specific variables that characterizes the
    demand for the hospice’s services.
•   2. Taking expectation of equations (1) and (2), we have:
       E(log Pic X i )  X i 
                              1
       E (log Pin X i )  X i 
                                2
•   3.Taking these two Conditions into our original model, we have
         I * Z    ( X   X  ) 
•         i    i         i 1   i 2    i                   (3)
•   4. Estimation of Equation (1) through (3) will determine which factors are
    significant in explaining a hospice’s certification decision
•
                           3. The Model(6)

• 1. The explanatory variables in the vector include four kinds of
  variable:
• 1. Reimbursement rate: Hospice Rate and Home Health Rate
•   2. Case Mix variables: % Female, % Noncancer and Length of Stay
•   3. Organizational structure variables: Hospital Dummy, Home Health
    Dummy, % Volunteers, Voluntary Nonprofit and Years of Operation
•   4. Labor Cost Variables: Hospital Cost Index and Home Health Cost Index


• 2. The explanatory variables used to estimate the patient equations
  include region-specific demographic variables, County-specific health
  care market variables and some organizational structure variables.
                       4. Estimation

• 1. This system of Equations is a switching regression
  model with endogeneous switching(Maddala, 1983)
• 2. Using OLS to get the parameters in (1) and (2), then
  using the derived parameters to run a probit estimation of
  equation(3) will yield inconsistent estimates
• 3. Using the maximum likelihood method to estimate this
  system of equations is a more appropriate approach.
• 4. Most data are from National Hospice
  Organization(NHO)’s annual hospice census(1987). The
  county-level data are from the Department of Health and
  Human Services’ 1989 Area Resource File(ARF)
                  5. Estimation Results

• Results from the certification-decision equation (3)
• 1. Holding all other factors constant, a $1.00 increase in
  the hospice reimbursement rate increases the probability of
  certification by 1.7%. This shows hospices do indeed
  respond to price incentives.
• 2. A percentage increase in the number of patients a
  hospice can expect has a positive impact on its probability
  of certification.
• 3. Some other variables, such as a higher share of female
  patients and years of operation also have positive influence
  on a hospice’s probability of certification
                  Estimation Results(2)

• Results from the patients equation(1) and (2)
• 1.A higher percentage of specialists has a positive
  influence on certified hospice size.
• 2. The total number of hospices in the county has a
  negative impact on the number of patients a certified
  hospice serves.
• 3. These market variables influence’s size differently when
  it is certified as opposed to noncertified. Generally,
  hospice can expect a significant increase in size with
  certification.
                  Estimation Results(3)

• 1.Using a probit equation, this paper also shows the
  variation in the Medicare reimbursement rates across
  regions play a significant role in explaining the observed
  differences in certification across geographic divisions and
  between urban and rural areas.
• 2. The underlying reason for this variation is that the wage
  indices used to adjust the Hospice Benefit rates don’t
  correctly account for variations in cost across regions.
• 3. Even using two other alternative wage indicies, the
  Hospital Cost Index and the Home Health Cost Index, we
  can barely alter the observed differential.
                            6. Conclusion

• 1. The hospices are indeed responsive to change in reimbursement rate.

• 2. The fixed-price reimbursement mechanism can be an effective
  instrument in encouraging the provision of more high-quality hospices
  care for Medicare enrollees.

• 3. However, this mechanism increased access comes at a price: the
  higher reimbursement rate may reduce the cost-effectiveness to the
  Medicare program of hospice care relative to conventional care.

• 4. In addition, reimbursement rare must be set carefully in order to
  narrow the variations in certification rates across different regions.
                    Comments
• 1. This paper uses cross-sectional data analysis method.
  This mainly is due to the limits of data in 1993. In today,
  maybe we can also use time series analysis to do the test
  work.
• 2. This paper does not consider the influence of private
  insurance policies because at that time, such insurance did
  not pay much for hospice care. However, now many
  private insurers also have added coverage for hospice care.
  So the model here may needed modification.

								
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