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On Slinkys, Fleas, and Health Care and Nursing Reform
Thomas CoxUrsine
On Slinkys, Fleas, and Health Care and Nursing Reform
Thomas CoxUrsine
On Slinkys, Fleas, and Health Care and Nursing Reform
Thomas CoxUrsine
On Slinkys, Fleas, and Health Care and Nursing Reform
Thomas CoxUrsine
On Slinkys, Fleas, and Health Care and Nursing Reform
Thomas CoxUrsine
On Slinkys, Fleas, and Health Care and Nursing Reform
Thomas CoxUrsine
On Slinkys, Fleas, and Health Care and Nursing Reform
Thomas CoxUrsine
On Slinkys, Fleas, and Health Care and Nursing Reform
Thomas CoxUrsine
On Slinkys, Fleas, and Health Care and Nursing Reform
Thomas CoxUrsine
On Slinkys, Fleas, and Health Care and Nursing Reform
Thomas CoxUrsine
On Slinkys, Fleas, and Health Care and Nursing Reform
Thomas CoxUrsine
On Slinkys, Fleas, and Health Care and Nursing Reform
Thomas CoxUrsine
On Slinkys, Fleas, and Health Care and Nursing Reform
Thomas CoxUrsine
Whole, Part, or Hole?
The Importance Of The Slinky
Slinky looks a lot like the normal curve – If Slinkys could they would move in the shape of normal curves
Insurance risk transfers to providers are eerily similar to a Slinkys patterns as we shift hands
Sometimes it is mostly in the left hand, others the right
Insurer loss ratios wobble: Low to High
Slinky’s rhythmic pattern is manifest in loss ratios
Slinkys gone wild: Katrina, AIG, Health care finance
The Importance Of Slinky
Retained insurance risks are like a tight, stable Slinky
Insurance risk transfers are like a stretched out Slinky
If hands are too far apart, Slinky collapses
If risk portfolios are too small, insurers collapse
Failure to support our health care system from known risks is like failing to hold your Slinky with two hands
Risk transfers impair the health care system and public health
The Importance Of SlinkyThe side to side motion of the Slinky is akin to shifting revenues and costs of insurers and health providers
When Slinky is out of balance we see increasing disparities in health care, access problems, declining public health indices, human suffering
Slinkys stretched beyond their structural integrity limits are like flawed finance mechanisms – on the edge
If we appreciate Slinky’s pattern, we can appreciate the pattern of health care (finance) reform and its likely impact on nursing
Risk Theory & Aesthetics
Risk theory (RT) does not assume nor produce certainty. It quantifies uncertainty, the fleeting, ephemeral nature of human experience
RT should be the method of choice for understanding nursing phenomena from a Rogerian perspective
RT, like choreology*, is the aesthetic and scientific study of the performance art of nurses and nursing
Dance is no less aesthetic because we document it and the documentation is its own aesthetic...
Joan and Rudolf Benesh 1956
Adopt your chosen perspectives
Quantitative work – its quantitativeQualitative – Ongoing discourse on health care financeCritical social theory – Deconstruct dominant paradigmsConservative – Call for a return to traditional valuesLiberation – Reveals hidden truthsEmpowerment – Provider/Consumer enlightenmentHolism – Details threats to holism in finance mechanismsHermeneutics – Re-interpretation of the word “Is” as in “What is insurance?”
Looking for research $ – Fertile ground – nobody is doing it
Want to promote Rogerian perspectives – Acausality and the serendipity of pattern and others may actually “get it”
What Can We Nurses Learn From AIG?
AIG's Financial Products division wrote hundreds of billions of dollars in credit default swaps
AIG had insufficient surplus and inadequate reserves
AIG was a Slinky stretched to the point of collapse
Stockholders and taxpayers had to “bear” the burden of Slinky’s collapse
Whole, Part, or Hole?
The Slinky, AIG and Nursing
Health facility Marketing/Finance divisions act like AIG’s Financial Products division
Sell services then tell nursing to “make it work”
Revenues are inadequate and facilities lack resources
Cost cutting measures exacerbate reduce inadequacy
Nursing and nurses struggle with scarce resources, cost cutting imperatives, lack of redundancy, end up providing inadequate care
Clients bear the burdens: delayed/denied care, long waiting times, and unresponsive providers
How Do Risk Transfers Arise
Managed care and capitation are gold standards and dominant paradigms in health care reform
But risk transfers are pervasive: Medicare and Medicaid managed care, Prospective Payment Systems for Hospitals, Physicians, Long Term Care and Home Health, Occurrence payments, Pay for performance, Prometheus…
Reform that does not address the Slinkyesque impact of inefficient risk management is meaningless
A national health insurer is a first, incomplete, step without risk management reform
Why Health Providers Are Insurers
Uncertain costs assumed for fixed payments “Insurance”
“Insurer” has unpleasant implications in meetings between providers and clients – so advocates avoid “insurance”
Euphemism: Providers manage “financial risks” caused by their own inefficiency not “insurance risks”
Financial risks exist at the start and persist during contracts
Providers do not know how to become “efficient” and efficiency always involves tradeoffs – who benefits from efficiency and who is hurt…
Whole, Part, or Hole?
Katrina & Health Care Reform
We have not learned the lessons:
Providers/Insurers should have had resources adequate to serve clients for weeks, not hours or days
You get paid on XX/01 to provide care through XX/31
Pre-paid, population focused, payment models can not pay providers enough for these risks or fund their true costs
Hospitals and NHs woefully unprepared for a certain event
Neither providers nor transferors have been held to account nor are they likely to be…
“Who could have anticipated…” Syndrome
What We Need to Understand About Health Care (Finance) Reform
Risk management through real insurance is key
How insurers manage risk – the Slinky Effect
How insurer size effects efficiency: loss ratio variation and maximum sustainable benefits
Nurses are affected by comprehensible forces
It is easy to understand insurance – insurance was in use long before modern actuarial risk theory
Deconstructing Myths
Everyone keeps $10,000; $100,000 in reserve$10,000 $3.07 Trillion idled$100,000 $30.7 Trillion idled
De-regulate insurance and cross State linesInsurer misconduct is common, de-regulation would increase misconduct with little recourse for victims
Tax credits for purchasing individual insuranceIndividual underwriting costs exceed tax breaks
Extend litigation protectionsLitigation protections reward past/future misconduct
The Patterns Are ExquisiteAll It Takes Is Appreciation
The normal curve is without an aesthetic equal
Regularity, symmetry, ease of definition, perspicacity, and near universal applicability
To the mathematically trained it is the equivalent of the best opera, ballet, art, music, cuisine
The dynamic motion of a Slinky is like the random aspect of insurer loss ratios, a cyclical pattern of ups and downs, the intensity and rhythm of which vary with the size, shape, and extension of the Slinky
Exemplar Insurer - Slide 1
A Slinky in balanceAssumptionsWrites 1,000,000 policies per yearCharges $4,000 per person
Expected Loss ratio = $0.75Expense ratio = $0.15Profit margin = $0.05Risk premium = $0.05
Insurers select portfolios from normally distributed portfolio collections with Standard Error = $0.05Insurer has probability 0.9987 of remaining solventProviders and Insurers are EFFICIENT!!!!
Exemplar Insurer - Slide 2
Implications
ProbabilitiesEarns profits > $0.05 (LR<$0.80) 0.8413
Breaks even (No operating loss) 0.9772
Remains solvent (LR<$0.90) 0.9987
FinancialRevenues $4,000,000,000Maximum sustainable benefits $3,000Surplus required for solvency $202,400,000
Whole, Part, or Hole?
Slinky ImbalanceProbabilities of Higher Loss Ratios By
Insurer Size
Loss Ratio 307,000,000 10,000,000 1,000,000 100,000 10,0000.75 0.5000 0.5000 0.5000 0.5000 0.50000.80 0.0000 0.0008 0.1587 0.3759 0.46020.85 0.0000 0.0000 0.0228 0.2635 0.42070.90 0.0000 0.0000 0.0013 0.1714 0.38210.95 0.0000 0.0000 0.0000 0.1029 0.34461.00 0.0000 0.0000 0.0000 0.0569 0.3085
Loss ratios higher than $0.85 are operating losses for insurers.
Insurer must draw against surplus to pay current costs when LR > $0.85
Our Exemplar can sustain a loss of $0.90 but will become insolvent above that level and will not be able to write new policies close to that level.
Comparing Insurers By SizeInsurer Size 307,000,000 10,000,000 1,000,000 100,000 10,000Standard Error 0.0029 0.0158 0.0500 0.1581 0.5000Prob Profits GE 5% 1.0000 0.9992 0.8413 0.6241 0.5398
1.0000 1.0000 0.9772 0.7365 0.57930.7587 0.7976 0.9006 1.2261 1.9132
$0 $0 $202,400,000 $150,440,000 $56,228,000Surplus for 1,000,000 Policies $0 $0 $202,400,000 $1,504,400,000 $5,622,800,000Maximum Sustainable Benefits 0.7971 0.7842 0.7500 0.6419 0.3000Efficiency 99.64% 98.03% 93.75% 80.24% 37.50%
Prob No Oper LossMin LR w Prob geq 0.9987Surplus Req 99.87% Solv
Slinky says:Standard errors of portfolio collections by insurer size
30,700 insurers (10,000) policies) 0.5000
3,070 insurers (100,000 policies) 0.1581
307 insurers (1,000,000 policies) 0.0500
National health insurer (307,000,000 policies) 0.0029
Comparing Insurers By SizeInsurer Size 307,000,000 10,000,000 1,000,000 100,000 10,000Standard Error 0.0029 0.0158 0.0500 0.1581 0.5000Prob Profits GE 5% 1.0000 0.9992 0.8413 0.6241 0.5398
1.0000 1.0000 0.9772 0.7365 0.57930.7587 0.7976 0.9006 1.2261 1.9132
$0 $0 $202,400,000 $150,440,000 $56,228,000Surplus for 1,000,000 Policies $0 $0 $202,400,000 $1,504,400,000 $5,622,800,000Maximum Sustainable Benefits 0.7971 0.7842 0.7500 0.6419 0.3000Efficiency 99.64% 98.03% 93.75% 80.24% 37.50%
Prob No Oper LossMin LR w Prob geq 0.9987Surplus Req 99.87% Solv
Slinky says:Probability of profitability by insurer size
30,700 insurers (10,000) policies) 0.5398
3,070 insurers (100,000 policies) 0.6241
307 insurers (1,000,000 policies) 0.8413
National health insurer (307,000,000 policies) 1.0000
Comparing Insurers By SizeInsurer Size 307,000,000 10,000,000 1,000,000 100,000 10,000Standard Error 0.0029 0.0158 0.0500 0.1581 0.5000Prob Profits GE 5% 1.0000 0.9992 0.8413 0.6241 0.5398
1.0000 1.0000 0.9772 0.7365 0.57930.7587 0.7976 0.9006 1.2261 1.9132
$0 $0 $202,400,000 $150,440,000 $56,228,000Surplus for 1,000,000 Policies $0 $0 $202,400,000 $1,504,400,000 $5,622,800,000Maximum Sustainable Benefits 0.7971 0.7842 0.7500 0.6419 0.3000Efficiency 99.64% 98.03% 93.75% 80.24% 37.50%
Prob No Oper LossMin LR w Prob geq 0.9987Surplus Req 99.87% Solv
Slinky says:Probability of loss ratios > 0.85 (Net operating loss)
30,700 insurers (10,000) policies) = 0.4207
3,070 insurers (100,000 policies) = 0.2635
307 insurers (1,000,000 policies) = 0.0228
National health insurer (307,000,000 policies) = 0.0000
Comparing Insurers By SizeInsurer Size 307,000,000 10,000,000 1,000,000 100,000 10,000Standard Error 0.0029 0.0158 0.0500 0.1581 0.5000Prob Profits GE 5% 1.0000 0.9992 0.8413 0.6241 0.5398
1.0000 1.0000 0.9772 0.7365 0.57930.7587 0.7976 0.9006 1.2261 1.9132
$0 $0 $202,400,000 $150,440,000 $56,228,000Surplus for 1,000,000 Policies $0 $0 $202,400,000 $1,504,400,000 $5,622,800,000Maximum Sustainable Benefits 0.7971 0.7842 0.7500 0.6419 0.3000Efficiency 99.64% 98.03% 93.75% 80.24% 37.50%
Prob No Oper LossMin LR w Prob geq 0.9987Surplus Req 99.87% Solv
Slinky says:Surplus requirements by insurer size
30,700 insurers (10,000 policies) $1,726 Billion
3,070 insurers (100,000 policies) 462 Billion
307 insurers (1,000,000 policies) 62 Billion
National health insurer needs no surplus $0.00
Maximum Sustainable Benefits
All insurers seek profits of $0.05 or higher with probability 0.8413 (The Exemplar Insurer’s level)
The maximum sustainable benefit is calculated by subtracting the Se for each insurer from the maximum possible payout of $0.80
Maximum Sustainable Benefit
Insurers seek profits of $0.05 or higher with probability 0.8413 (The Exemplar Insurer’s level)
Calculate the maximum sustainable benefit by subtracting Se from the maximum payout of $0.80 consistent with profit objective.
$0.80 - 1 * Se
Exemplar: = $0.80 - $0.05
= $0.75
***** Comparing Insurers By Size *****Insurer Size 307,000,000 10,000,000 1,000,000 100,000 10,000Standard Error 0.0029 0.0158 0.0500 0.1581 0.5Prob Profits GE 5% 1.0000 0.9992 0.8413 0.6241 0.54
1.0000 1.0000 0.9772 0.7365 0.580.7587 0.7976 0.9006 1.2261 1.91
$0 $0 $202,400,000 $150,440,000 $56,228,000Surplus for 1,000,000 Policies $0 $0 $202,400,000 $1,504,400,000 $5,622,800,000Maximum Sustainable Benefits 0.7971 0.7842 0.7500 0.6419 0.3000Efficiency 99.64% 98.03% 93.75% 80.24% 37.50%
Prob No Oper LossMin LR w Prob geq 0.9987Surplus Req 99.87% Solv
Slinky says:Maximum sustainable benefits per person
30,700 insurers (10,000 policies) $0.3000
3,070 insurers (100,000 policies) $0.6419
307 insurers (1,000,000 policies) $0.7500
National health insurer $0.7971
Comparing Insurers By SizeInsurer Size 307,000,000 10,000,000 1,000,000 100,000 10,000Standard Error 0.0029 0.0158 0.0500 0.1581 0.5Prob Profits GE 5% 1.0000 0.9992 0.8413 0.6241 0.54
1.0000 1.0000 0.9772 0.7365 0.580.7587 0.7976 0.9006 1.2261 1.91
$0 $0 $202,400,000 $150,440,000 $56,228,000Surplus for 1,000,000 Policies $0 $0 $202,400,000 $1,504,400,000 $5,622,800,000Maximum Sustainable Benefits 0.7971 0.7842 0.7500 0.6419 0.3000Efficiency 99.64% 98.03% 93.75% 80.24% 37.50%
Prob No Oper LossMin LR w Prob geq 0.9987Surplus Req 99.87% Solv
Slinky says:Maximum sustainable benefits in $ per person
30,700 insurers (10,000 policies) $1,200
3,070 insurers (100,000 policies) $2,568
307 insurers (1,000,000 policies) $3,000
National health insurer $3,189
Comparing Insurers By SizeInsurer Size 307,000,000 10,000,000 1,000,000 100,000 10,000Standard Error 0.0029 0.0158 0.0500 0.1581 0.5000Prob Profits GE 5% 1.0000 0.9992 0.8413 0.6241 0.5398
1.0000 1.0000 0.9772 0.7365 0.57930.7587 0.7976 0.9006 1.2261 1.9132
$0 $0 $202,400,000 $150,440,000 $56,228,000Surplus for 1,000,000 Policies $0 $0 $202,400,000 $1,504,400,000 $5,622,800,000Maximum Sustainable Benefits 0.7971 0.7842 0.7500 0.6419 0.3000Efficiency 99.64% 98.03% 93.75% 80.24% 37.50%
Prob No Oper LossMin LR w Prob geq 0.9987Surplus Req 99.87% Solv
Slinky says:Benefit distribution efficiency by insurer size
30,700 insurers (10,000 policies) 37.50%
3,070 insurers (100,000 policies) 80.24%
307 insurers (1,000,000 policies) 93.75%
National health insurer 99.64%
Whole, Part, or Hole?
Health Services & Disparities Research
We see the impact of risk dis-aggregation on insurer efficiency
The smaller the insurer, the lower the Maximum Sustainable Benefit
Why are health services researchers failing to identify and quantify a service capacity loss this large?
Why did decades of research fail to connect cigarettes to smoking and lung cancer and heart disease?
Ethics: Old & New
IssueIssue Pre-Managed CarePre-Managed Care Managed CareManaged CarePatient autonomy Patients make decisions Insurer/Providers make
decisions
Informed consent Payment mechanisms not an issue Clients misinformed about conflicted roles, who pays & who makes decisions
Paternalism Common but conflicts between providers and payers mediate
Pervasive – no mediation, provider/insurer knows best
Beneficence No interventional limits How little can I do for each client?
Non-maleficence Unnecessary Dx/Tx Denied necessary Dx/Tx
Conflicts of interest Provider profit seeking Provider cost cutting
Distributional ethics According to need, time of arrival, extant resources…
Less than or at unique benefit plan mandates
How Do Providers Deal With Risk?Exactly the opposite of how they should
Cut costs:Cheaper remote suppliers – 100s of miles awayCentral storerooms/Limited supply access
Eliminate redundant staff – Send nurses homeJust enough equipment for routine needs
Strain Capacity:Keep beds filled, appointments bookedConstant turnoverStaff/Resources fully committed - No slackRely on “just in time” resource replacementDependent on supply chain integrity
How Should Providers Deal With Risk?
Maintain much larger supply storesDistributed supplies and increased access
Maintain redundant staffMore equipment than needed for routine needs
Create more capacity:Keep beds in reserve, spare appointmentsReduce patient flow – let staff regroupDevelop/Maintain slack resources“Well ahead of time” resource replacementDecrease dependence on supply chainReduce demands on staff
Maintain ability to respond to unusually high demand
Old Fee For Service Paradigm
Providers and clients are in “voluntary” relationships
Providers well being independent of client choices
Acute and chronic clients increase revenues
Providers free to open/close facilities at will
Dissatisfied clients can go elsewhere
Providers not obliged to stay in natural disasters
Providers not obliged to maintain redundant resources
Ethical conflicts – Who we agree to serve…
Pre-payment Paradigm
Providers and clients are in “involuntary” relationships
Provider’s well being depends on client choices
Acute and chronic clients increase costs
Providers not free to open/close facilities
Clients stuck in potentially unresponsive networks
Providers - obliged to stay in natural disasters
Providers should maintain redundant resources, not rely on supply chain integrity
Ethical conflicts – Who we deny, undisclosed roles
Non-Treatment RisksThe Limits of Cost Cutting
Can providers save money by reducing expensive Dx tests?
Must cut many tests - What are the implications?
Clients not getting expensive diagnostic tests with P = 0.01 of positive results have probability = 0.01 of non-diagnosis
Providers not performing 100 tests in a year have probability of at least one missed diagnosis = 0.63
If providers do this for 5 years, P[Diagnostic Failure] = 0.90 If providers do this for 35 years, P[Diagnostic Failure] = 1.00
How Nursing Is & Will Be Affected
The implementation models are clear:
Non-Health Care Sector: Outsourcing – Marginalized unskilled laborers, then skilled laborers, then low level white collar workers, then middle management, then skilled professionals
Health Care Sector: Marginalize high cost sectors (Hospitals and specialists), then primary care, then NPs, RN, LPNs, CNAs, PCAs
Cost cutting efforts appear to be benefiting nurses, but we will be marginalized by lower cost providers
Not seeing the train does not diminish the impact
Health Care In The Future
Impossible to sustain/expand labor intensive care
Number of nurses needed based on old paradigms – like the need for finance MBAs in Spring, 2008
Workplaces need radical change to meet boomer generation demand: high tech, robotics, low touch, highly automated, fewer professionals, more paras
Academicians see growth as good – but working nurses will face higher patient loads and stiffer job competition in the future
Health care industry knows what it is doing – do we?
The MCO/Insurer Shell GameMCO/Insurer negotiates contracts with gov’t, labor, employers
MCO/I negotiates contracts with providers
Providers cannot share – Not aware of each other’s behavior
MCO/I uses hidden information about other provider’s behavior to negotiate more favorable terms
MCO/I squeezes providers who fear no contracts/patients
Difference between claims costs in the MCO/I’s premiums and their provider contract costs is pure profit
Wild West – MCO/Is are not looking for 5% returns
What services do MCO/Is actually provide to justify profits?
Pattern Appreciation
Using 24 observations (Jan-Feb 1801) by Giuseppe Piazzi, 24 year old Carl Gauss calculates where Ceres, the smallest dwarf planet in the Solar System, will be in December, 1801...
The world's leading astronomers had no idea where to look
Gauss and Piazzi both appreciated Ceres’ pattern… In different ways…
Patterns exist/persist whether seen or not
An incidental case of pattern appreciation...
Pattern Appreciation 1
The ephemeral nature of pattern is never more obvious than in an insurance company's executive dining room, when the Claims VP just learned a $1,000,000 claim has been submitted.
The “pattern” of seasonal good tidings and good will to all is disrupted as each executive begins their appreciation of new, unfolding, universes...
It is no different when risk assuming health care providers contend with “high cost” clients
Pattern Appreciation 2
Back to Slinky
Insurance executives’ patterns change with the daily ups and downs of the sales and claims departments
New information constantly alters anticipated results
Insurer tapestries change with single eventsKatrina led to the most egregious cases of shortchanging
policyholders in the history of insurance
9/11: Rogerian (re)insurers: 2 planes, hit 2 buildings, at different times – A single event
We should only pay one “per occurrence” benefit
Why Small Insurers Are Less EfficientInsurance is based on the Central Limit Theorem a rhythm much like the dynamic pattern of a Slinky
CLT defines relationship between sample (portfolio) size and measurement error in estimates of population parameters: Population Loss Ratio (PLR)
Large insurers Actual Loss Ratios (sample estimates) closer to PLR than smaller insurers’ Actual Loss Ratios
Small insurers have higher probabilities of extreme loss ratios and unfavorable operating results than larger insurers
Small insurers must decrease benefits to compensate for the wide swings they may experience
Why Health Providers Are Inefficient Insurers
Insurer risk declines with portfolio size
Entities transferring risk are large: MCOs, Medicare, Medicaid, “profit sharing” and “risk sharing” insurers
Transferred portfolios are smaller than transferring entity’s portfolio, often 2 –10 % or less
We use the Central Limit Theorem to appreciate the increased loss ratio variation from risk dis-aggregation – simple relationship using the square root of relative portfolio size
Flawed thinking: Risk assuming providers have same experience as risk transferors
Unpleasant Risk Assuming Provider Truths
Bear far greater risk than acknowledged
No difference between “Financial” & “Insurance” risk
Do not accept roles nor do they act as responsible insurers
Pattern of well being illusory – Ignored duties and responsibilities, overly dependent on payers, poor fiscal/clinical accountability
Inappropriate transition from “FFS” to “Insurer” paradigm
Ignoring population health reduces current costs – No choice because revenues are inadequate
Profiteering easy – Nobody minding the store
Risk Appreciating Nurses
Arrives earlyAssesses environment and clientsHits the supply room, stocks up on needed itemsAssures availability of supplies for othersReady for shift reportProvides caring service throughout the shiftUnhampered by forgotten or unavailable resourcesPaces self, anticipates lost time, high risk needsEnds shift:
Clients got what they neededNurse not unduly stressedEnvironment more sound for their presenceCogently reports to incoming staff
Non-Risk Appreciating Nurse
Arrives just on time or lateHits the supply room as needed, multiple times a day Unprepared for shift reportIgnores looming shortages – Someone else will do itHampered by unanticipated and unavailable resourcesFails to anticipate lost time, high risk client’s needsEnds shift:
Clients, at best, get some of what they neededNurse stressed outEnvironment more fragile and unstableResources depletedNot prepared to report to incoming nurse
Merchant of Venice
My ventures are not in one bottom trusted,Nor to one place; nor is my whole estate
Upon the fortune of this present year:Therefore, my merchandise makes me not sad.
Shakespeare
Risk Diversification Strategies
Spread portfolio out among different payorsProviders dependent on 2 – 3 large MCOs, Medicare, Medicaid
Vary the risk in portfoliosProviders are having more risks transferred and risks are increasingly correlated
Vary portfolios by industry, or by geographyProviders concentrated by geography and industry - locked in to contracts based on facility location
Portfolio diversification reduces risk, reduces the rates of return, and serves as a hedge against a single large payor having too much power
Conclusions
Risk assuming health care providers (RAHCPs) furnish less service than non-risk assuming health care providers
RAHCPs become less, not more efficient
RAHCPs are as fiscally sound as AIG in 2007 – Not adequately prepared for the obligations assumed