Application of Risk Adjustment
(PROBE)
Provider profiling
Risk management
Outcomes of health care measurement
Budgeting (Pricing)
Efficiency measurement
Characteristics of a good model
VARIABLE CRISP CARD
Valid
Adequately documented
Rigorous
Inputs to parameter values appropriate
Arbitrage free
Behaviour reasonable
Length/expense of run not too long/high
Easy to understand
Communicable re workings & output
Reflects risk profile of contracts modelled
Independent verification of outputs
Sensible joint behaviour of variables (dynamics)
Parameters allow for sign features
Clear results
A range of implementation methods
Refineable
Developable
Assumptions Required
RIM PINT CREW
Risk discount rate
Investment returns
Mortality / Morbidity rates
Profitability requirements
Inflation
New business volume & mix (demographic, financial)
Tax rates
Commission
Reserving basis
Expenses
Withdrawals
Product Design & Pricing - Go to
CRUDS CA
C - Customer acceptability (clear in benefit, amount and variability of prems)
R - Regulator requirements
U - Underwriting methodology
D - Distributors needs
S - Systems and other internal constraints
C - Company culture in style & price —> consistency with other products
A - Adequate profitability/ return on capital
GLM: Suitability of explanatory variable
GLM: Disadvantages of One-way analysis
GLM: Assumptions of classical linear models
GLM: Drawbacks of the normal model for multiple linear regression
GLM: GLM addresses some of the linear modelling shortcomings/ Benefits of GLM
RL SCAAH
GLM: Characteristics of a Link Function
GLM: Properties of the exponential family
GLM: The Tweedie distribution
Direct modelling of pure premium or incurred loss data for PMI business is problematic
A typical pure premium distribution will consist of a large smile (i.e. a point mass) at zero (where policies have no claim)
And then a wide range of amounts (where policies have had claims)
Tweedie dist. can handle this well
GLM: Pitfalls of using GLM vs One-Way
GLM: Methods for testing the appropriateness of model
Deviance residuals - measures the distance between the observation response & the fitted values
Standard Pearson - the difference between the observation response & the predicted value, adjusted for the standard deviation of the predicted value & the leverage of the observed value.
Residual plots - plots of residual against fitted values, which should be symmetrical about the x - axis & fairly constant across the width of fitted values & average residual of zero
Cook’s distance - used to test the influence of a data point in models results, if cook’s distance >=1 should be investigated —> excluded or capped
What determines the cover limit
(SCR T)
Size of scheme - no. of lives covered
Compulsory or Voluntary membership
Required take up rate/ proportion taking up cover if voluntary
Total & average sum insured
Data sources
PROMOTAR
P - population data (provided by government)
R - reinsurer’s data
O - own data of company
M - Market data (including insured lives data and published returns)
O - overseas data
T - trade magazines
A - actuarial consultant’s data
R - rate table software
Data Challenges
Aims of Managed Care
Risks of managed care
Examples of managed care techniques/ strategies
TRRRP HCF
T - Treatment protocols —>governs the treatment & medicines that a member gets access to for certain conditions
R - Reimbursement methods
R - Risk sharing i.e co-payment
R - Referrals (GP to Specialist) —> will only cover the full specialist fee if referred by GP to reduce unnecessary specialist visits
P - Provider networks
H - Hospital pre-authorisation
C - Cases and disease management
F - Formulary medicines (generic medicine)
Characteristics of provider networks
Negotiated prices
Insurer has better control on procedures taken I.e. treatment protocols
Able to monitor data
Able to check efficiency of each provider
Minimise fraud
Able to incentivise provider for not being wasteful/ for being under budget
Policyholders may not have access to provider networks, leading to a decrease in sales
Value add services
Considerations when deciding on the risk factors:
Data easily obtainable
Objectives of factors chosen
Verifiable data
Not politically sensitive
Cognisant of particular features of the country
Parsimonious (captures as much information as possible in a few as possible features)
Balance between demand-side and supply side factors
Relevant & up-to-date
Inexpensive to collect relevant data
Case mix
Is a special case is risk adjustment
Used to compare treatment costs
Case mix reflects the severity of each case on a risk-adjustment
Risk prediction
Used to predict future costs
With reference to differences observed in the past