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Objective 3 Flashcards

1
Q

Factors When Building a Clinical Algorithm

A
  1. Diagnoses
  2. Source of the diagnosis (claims, lab values, medical charts, etc)
  3. If source is claims, what claims should be considered?
  4. If claim contains more than one diagnosis, how many diagnoses will be considered for identification?
  5. Over what time span will a diagnosis have to appear to be incorporated in the algorithm?
  6. What procedures may help determine the level of severity of diagnosis?
  7. What prescription drugs may be used to identify conditions?
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2
Q

Challenges When Constructing a Condition-Based Model

A
  1. Large number and different types of codes for procedures and drugs
  2. Level of severity at which to recognize the condition
  3. Impact of co-morbidities (whether to maintain separate conditions and then combine or to create combinations of conditions)
  4. Degree of certainty with which the diagnosis has been identified
  5. Extent of ”coverage” of the data (e.g. self-reported data may not be complete)
  6. Type of benefit design underlying the data
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3
Q

Sources of Algorithms

A
  1. Centers for Medicare and Medicaid Services (CMS)
  2. Grouper Models
  3. Literature
  4. NCQA (National Committee for Quality Assurance) / HEDIS (Healthcare Effectiveness Data and Information Set)
  5. Population Health Alliance
  6. Quality Reporting and Improvement Organizations
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4
Q

Uses of HEDIS Data

A
  1. Select the best health plan for their needs (by employers, consultants and consumers)
  2. May be used in Pay For Performance programs
  3. Health plans seeking accreditation
  4. Plans participating in Medicare must submit data on HEDIS-developed measures of quality
  5. Many state governments require plans participating in Medicaid to report HEDIS data
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5
Q

Major Publishers of Quality Measures (used for sources of algorithms)

A
  1. National Quality Forum (NQF)
  2. Agency for Healthcare Research and Quality (AHRQ)
  3. Joint Commission
  4. Centers for Medicare and Medicaid Services (CMS)
  5. Hospital Quality Alliance (HQA)
  6. Measures Applications Partnership (MAP)
  7. American Medical Association Physician Consortium for Performance Improvement (PCPI)
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6
Q

Reasons Why Grouper Models May be Preferable

A
  1. Considerable amount of work involved in building algorithm from scratch
  2. Models must be maintained to accommodate new codes (new drug codes are released monthly)
  3. Risk adjustment, providers and plans may require a commercially available, consistent model to be used, and it must be available for review and validation
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7
Q

Principles of Grouper Model Design

A
  1. Diagnostic categories - clinically meaningful
  2. Diagnostic categories - predict medical expenses
  3. Diagnostic categories - have adequate sample size to permit stable estimates
  4. Hierarchies used to characterize illness level within each disease process
  5. Discretionary diagnostic/testing categories excluded
  6. Diagnostic classification - encourage specific coding
  7. Diagnostic classification - don’t reward coding proliferation
  8. Providers not penalized for recording additional diagnoses
  9. Classification system - internally consistent
  10. Diagnostic system - assign all codes (ICD-9/10)
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8
Q

Types of DRGs

A
  1. Health Care Finance Administration-DRG (HCFA-DRG) - used for Medicare Prospective Payment System for hospitals (this is now renamed CMS)
  2. Refined-DRG (R-DRG) - Introduces presence or absence of complications and co-morbidities (CC)
  3. Severity DRG (S-DRG) - Refined DRGs to more adequately adjust for patient severity
  4. All Patient-DRG (AP-DRG) and All Patient Refined-DRG (APR-DRG) - Modification of HCFA-DRG that provides support for transplants, high-risk obstetric care, nutritional disorders and pediatrics
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9
Q

Factors Enabled by DxCG Intelligence model

A
  1. Identification of high cost cases - for care/disease management
  2. Comparative profiling against costs and outcomes while adjusting for differences in health
  3. Establishment of payment schemes
  4. Reimbursement, negotiation of payments and incentives
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10
Q

Instances When Predicting Risk Separately by Categories is Helpful (under MARA model)

A
  1. Scores are used to modify payments to health plans based on plan’s members
  2. Allocating capitation amounts to providers
  3. Granularly measuring risk in provider profiling or payment applications
  4. Calculating historical trends for different components
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11
Q

Core Principles in Developing Medicare Episode Groups (MEG)

A
  1. Episode of care - considers all care for one medical condition for one patient
  2. Different levels of severity - accounted for by episode grouper
  3. Diagnosis may evolve - grouper should accommodate evolving diagnosis
  4. Episode classification system - clinically meaningful to providers
  5. Episode of care system - comprehensive, parsimonious and transparent
  6. Describe episode by condition patient is diagnosed with
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12
Q

Types of Drug Based Risk Adjustment Models

A

Medicaid Rx

  • Assigns member to one of more than 45 medical condition categories, based on prescription drug use and demographics
  • Predicts overall medical costs (not just drug costs)

Pharmacy Risk Groups (PRGs)

  • Assigns member to one or more of 107 Pharmacy Risk Groups, based on prescription drug use, drug interaction and demographics
  • Provides prospective and retrospective risk score

RxGroups (DxCG)

  • Two classification systems - medical diagnosis classification and pharmacy classification
  • Pharmacy model predicts total medical cost for each patient based on RxGroup and age/sex
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13
Q

Common Features of Medicare Prospective Payment Systems

A
  1. System of Averages
  2. Relative Weights
  3. Conversion Factor
  4. Access and Quality
  5. Outliers
  6. Increased Complexity
  7. Updates
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14
Q

Challenges with Patient Classification Systems Based on Coding Systems

A
  1. Need for New DRGs
  2. ICD Coding
  3. Upcoding
  4. New Coding Systems
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15
Q

Two major types of DxCG models

A
  1. Concurrent: Used to reproduce actual historical costs

2. Prospective: Predicts what costs will be for a group in the future, based on inherent conditions

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16
Q

Goals of Risk Adjustment in Medicaid (in context of Arizona)

A

Move program forward to align payment with relative health risk of members of each plan

Be accurate and unbiased

  • Accurate = relatively high correlation between projected cost and actual cost
  • Unbiased = methodology should not over-compensate for some risk factors at expense of others

Be as simple as possible while accomplishing these goals
Minimize the administrative burden of developing and implementing the methodology

Be budget neutral to program in total

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17
Q

Medicaid Groups That Will NOT Have Claims Based Risk Adjustment

A
  1. Reconciled risk groups - actual claims are used to determine reimbursement
  2. Delivery supplemental rates - supplementary case rates paid for maternity deliveries
  3. Option 1 and 2 transplant members - case rates paid for transplants. These people were previously eligible for Medicaid but have subsequently lost eligibility due to excess income
  4. SOBRA Family Planning - supplemental payments for woman eligible for family planning services but not other Medicaid benefits
18
Q

Medicaid Costs That Were Excluded from Weights in Calibrated Model (reimbursed in other ways)

A
  1. Prior Period Coverage (PPC)
  2. Costs above reinsurance thresholds for which health plans are not at risk
  3. Behavioral Health covered by Arizona Dept of Health Services
  4. Children’s Rehab Services
  5. Maternity costs covered by Delivery Supplement
19
Q

Categories for Risk Adjustment in Medicare Advantage (Existing enrollees)

A
  1. Community - Non-Dual, Aged
  2. Community - Non-Dual, Disabled
  3. Community - Dual - Full Benefits, Aged
  4. Community - Dual - Full Benefits
  5. Community - Disabled Dual - Partial Benefits
  6. Community - Aged Dual - Partial Benefits, Disabled
  7. Institutional
20
Q

Categories for Risk Adjustment in Medicare Advantage (New enrollees)

A
  1. Non-Medicaid, Not Originally Disabled
  2. Medicaid, Not Originally Disabled
  3. Non-Medicaid, Originally Disabled
  4. Medicaid, Originally Disabled
21
Q

Items to Be Projected by MAO Actuary from Base Year to Contract Year

A
  1. Enrollment level (in member months)
  2. Revenue
  3. Average population risk score
  4. Claims
  5. Non-benefit expense
  6. Profit
22
Q

Categories for Risk Adjustment for Medicare Part D

A
  1. Aged, Low Income (LI)
  2. Aged, Non Low Income (NLI)
  3. Disabled, Low Income (LI)
  4. Disabled, Non Low Income (NLI)
23
Q

ACA Attempts to Overcome Anti-Selection and Instability

A
  1. Subsidies for applicants with limited income
  2. Mandates - employer mandate and individual mandate
  3. Risk adjustment - transfer revenue from plans with low-risk populations to plans with high-risk
24
Q

Allowable Rating Factors Under ACA

A
  1. Age (3:1 max ratio)
  2. Location
  3. Family Size
  4. Tobacco use (1.5:1 max ratio)
25
Q

Reasons ACA Used Concurrent Model for Risk Adjustment in Exchanges

A
  1. First year of ACA didn’t have historical data to use for a prospective calculation (Note - Massachusetts used demographic only factors for the first years to overcome this)
  2. Prospective models are less accurate than concurrent models (based on SOA study)
  3. Churn rates of members through exchanges means that many plans wouldn’t have claims data on members [not an original consideration but realized after the fact]
26
Q

Issues in Medicare HCCs (related to risk adjustment)

A
  1. Only use 70 (seventy) HCCs for risk scoring (though CMS-HCC maps 189 HCCs)
  2. Observation of conditions over period of two years (recommends using 2 years of diagnosis data)
  3. Number of conditions (simple observation and inclusion of number of conditions - predictive of higher risk)
  4. Considerable variation within HCCs of patient severity and experience (ok on average, inaccurate on individual level)
  5. Certain racial groups and income levels likely to be higher consumers of healthcare
27
Q

Issues with Massachusetts Risk Adjustment

A
  1. Applies to gross premium, not cost of insurance or pure premium
  2. Bias against zero-condition members (counterintuitive result - loss ratios begin high, then decline with age)
  3. Bias against limited network and other lower cost plans (limited networks tend to be lower cost, allowing them to charge lower premiums)
  4. Risk adjustment operates on state level (rather than regional) (wide variations exist in networks, costs and utilization in different areas of the state)
28
Q

Potential Sources of Bias in Risk Adjustment Transfers

A
  1. Partial Year Enrollment
  2. High-Cost Cases (risk scores don’t track costs well at extremes of risk-cost distribution)
  3. Market-Share
  4. Lack of Historical Data
  5. Only a Fraction of Members Trigger Conditions
  6. Prospective vs. Concurrent Models
  7. 1 Prospective model - Medicare Advantage, Part D, Massachusetts
  8. 2 CMS rejected prospective model
  9. 2.1 Predictive accuracy better with concurrent model (according to SOA study)
  10. 2.2 Data wouldn’t be consistently available due to churn of members
29
Q

Key Learnings for ACOs

A
  1. Need high quality data analytics
  2. ACO emphasis on EMR/EHR
  3. Differences in EMR/EHRs
  4. Importance of economics
  5. 1 Three year contracts for Medicare Shared Savings Program (MSSP) (to continue in business, ACO needs to generate savings in this period)
  6. 2 Focus on patients with greatest opportunity for cost reduction
  7. Importance of planning and understanding the opportunity
  8. 1 Need good focus (What patients? What conditions? What gaps? What are desired outcomes?)
  9. 2 Construct plan
30
Q

Requirements for ACO to Be Allowed to Share Savings with CMS

A
  1. Meet certain quality standards
  2. 1 31 measures, including Patient/Caregiver Experience, Care Coordination/Patient Safety, Preventive Health, and At-Risk Population
  3. Surpass savings hurdle rate
  4. 1 Range from 2% for largest ACOs (60,000 or more Medicare members) to 4% for smaller ACOs (5,000 members)
31
Q

Ways for Provider Group-Based ACO to Generate Savings

A
  1. Care coordination
  2. Access to integrated records and consistent management
  3. Develop network of efficient providers
  4. Focus on quality results in fewer unnecessary services
32
Q

Basic CMS Beneficiary Assignment Process

A

Determine ACO cohorts (group of participating partners) using taxpayer identification number (TIN): Provider must agree to participate and comply with program regulations

ACO submits and certifies Participant List (of finalized participating providers). CMS confirms eligibility

Patients assessed against list of participating professionals to determine if the
ACO has plurality of primary care services to particular patient

33
Q

Uses of ACO’s Certified Participant List

A
  1. Recalculate ACO’s historical benchmark
  2. Determine ACO’s quality sample
  3. Determine performance year expenditures (shared savings/losses)
  4. Produce quarterly and annual feedback reports
34
Q

Criteria for Beneficiary to Be Assigned to Participating ACO

A
  1. Record of Medicare enrollment
  2. At least one month of Part A and Part B enrollment (no months of Part A only or Part B only)
  3. No months of Medicare group private enrollment (MA) (Only FFS plans)
  4. Assigned to only one Medicare shared savings initiative
  5. Live in US state, territory or possession
  6. Have a primary care service with physician at the ACO
  7. Receive largest share of primary care services from the participating ACO
35
Q

Medicare Eligibility Enrollment Types for Separate Expenditure Calculations for ACO Beneficiaries

A
  1. ESRD (end stage renal disease)
  2. Disabled
  3. Aged/dual-eligible Medicare and Medicaid
  4. Aged/non dual-eligible (eligible for Medicare but not Medicaid)
36
Q

ACO Risk Adjustment - Blending Risk Scores of New and Existing Members to Create Single Risk Ratio

A

Continuously-assigned

  • CMS-HCC claims-based risk adjustment (When Avg. Risk Ratio Less Than 1)
  • Demographic risk adjustment (When Avg. Risk Ratio Greater Than 1)

Newly-assigned - CMS-HCC risk adjustment (Always)

Create a single risk ratio, weighted by relative membership of new and continuous members

37
Q

ACA Risk Adjustment: How was CMS-HCC model adapted for HHS-HHC?

A
  1. Prediction year: The CMS-HCC model is prospective rather than concurrent
  2. Population: The CMS-HCCs were developed from the aged and disabled, not the private individual and small group
  3. Type of spending: The CMS-HCCs predict medical spending excluding drug as compared to medical and drug
38
Q

ACA Risk Adjustment: Criteria for Including HCCs in the Model

A
  1. Represent clinically significant, well defined, and costly medical conditions that are likely to be diagnosed, coded, and treated if they are present
  2. Are not especially subject to discretionary diagnostic coding or diagnostic discovery
  3. Do not primarily represent poor quality or avoidable complications of medical care
  4. Identify chronic, predictable, or other conditions that are subject to insurer risk selection, risk segmentation, or provider network selection
39
Q

Describe the ACA risk adjustment model

A
  1. Uses demographics and diagnoses to determine a risk score
  2. Claims data: from employer-sponsored insurance to calibrate the model
  3. Model Type: Concurrent Model
  4. Adapted the HHS-HCC model from the CMS-HCC 5. Separate Adult, Child and Infant Models
  5. 1 Adult and child models have similar specifications
  6. 2 There are four Age 0 birth maturity categories and a single Age 1 Maturity category
  7. 3 There are 5 infant disease severity categories.
  8. 4 Infants also have 2 additive terms for sex (male age 0 and male age 1)
  9. Expenditures for which plans are liable. Excludes enrollee cost sharing.
  10. 1 The plan liability risk score does not equal a total expenditure risk score times AV
  11. Induced Demand Due to Cost Sharing Reductions: a multiplicative adjustment
  12. Disease Interactions Indicator
  13. Predicted Plan Liability Expenditures
  14. 1 Adults and children: the sum of age/sex, HCC, disease interaction coefficients
  15. 2 Infants: the sum of maturity/disease-severity category and additive sex coefficients
  16. Model calculates an individual’s plan liability risk score (PLRS)
40
Q

Benefits and Concerns about Adding Prescription Drug Utilization to the HHS-HCC Risk Adjustment Model

A
  1. Benefits
  2. 1 Imputing Missing Diagnoses
  3. 2 Severity Indicator for a Specific Diagnosis
  4. 3 More Timely, Standardized Data
  5. 4 Mitigates Financial Disincentive to Prescribe Expensive Medications
  6. Concerns
  7. 1 Gaming, Perverse Incentives, and Discretionary Prescribing
  8. 2 Sensitivity of Risk Adjustment to Variations in Prescription Drug Utilization
  9. 5 Multiple Indications of Most Drugs
  10. 3 Added Administrative Burden, Complexity, and Costs
  11. 4 Availability of Outpatient Drug Data Only
41
Q

Criteria for Evaluating Risk Adjustment Models Incorporating Prescription Drug Utilization

A
  1. Clinical/Face Validity - Valid relationship between risk markers (drugs, diagnoses) and health care costs
  2. Empirical/Predictive Accuracy - Drugs should increase the model’s accuracy to predict total expenditures
  3. Sensitivity to Variations in Prescription Drug Utilization - Not overly sensitive to variations in discretionary drug utilization
  4. Incentives for Prescription Drug Utilization - Minimize incentive for inappropriate use of drug or over-prescribing
  5. Incentives for Diagnosis Reporting - Accurate and complete diagnosis reporting should not be discouraged
42
Q

Criteria for Selecting Drug-Diagnosis Pairs for Hybrid HHS-HCC Risk Adjustment Model

A
  1. Drugs with pattern of non-discretionary prescribing
  2. Avoid drugs with incentives for over-prescribing
  3. Avoid drugs with variations in prescribing across providers, areas, etc
  4. Carefully consider high-cost drugs (risk adjustment may already account for this) 5. Avoid drugs indicated for multiple diagnoses
  5. Carefully consider drugs in areas with rapid rate of technological changes
  6. Avoid drugs indicated for diagnoses not included in HHS-HCC model

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