Objective 1 Flashcards
Disease Management Characteristics suitable for clinical intervention Analysis downsides
DM largely an outsourced function over the telephone ”Big five” chronic diseases Characteristics suitable for clinical intervention - Average cost sufficiently high to warrant intervention - Disease remains the rest of the patoent’s life - - Manageable with pharmaceutical therapy and lifestyle changes - Patient can take responsibility of their own condition Analysis downsides - no single agreed upon lost of conditions - Prevalence overestimated among dual eligible - Health plan population likely younger and fewer chronic
Care Management Programs and Interventions Examples
- Pre-Authorization 2. Concurrent Review 3. Case Management: coordinates care of a patient with serious illness 4. Demand Management: Passive informational intervention 5. Disease Management 6. Specialty Case Management: Care manager responsible for coordinating patient’s care, Financial responsibility along with service responsibility 1. Population Health Management: Emphasis on wellness, prevention 2. Care coordination and other intervention model enhancements: chronic care model, medical home, Embedded care manager, Transition models, External care manager, Palliative care, care coordination demonstrations 3. The Patient Centered Medical Home and Pay for Performance
ACO model overlooks some key learnings of DM programs Structure and organization of ACO’s
ACO model overlooks some key learnings of DM programs - Need data analytics as close to real time as possible - Economics - Plan the number of interaction Structure and organization of ACO’s - network of doctors and hospitals responsible for caring for patients - 2 models of ACO gain sharing: one-sided and two-sided - to be allowed to share savings with CMS, must report on quality metrics and surpass savings hurdle rate - ACOs are not limited to Medicare
Care Management Programs and Interventions Card 3 of 3
- Non-traditional providers and care settings: pharmacists as healthcare providers 1.1 Drug utilization review (DUR) programs manage price and utilization 1.2 Medication therapy management (MTM) 1.3 Pharmacist-delivered care management programs 1.3.1 Advantages:TherearemorepharmaciststhanPCPs,Somepharmaciesareopen24/7, Patients interact with their pharmacist more often 1.3.2 Medicationpossessionratio(MPR) Nmbrofdayssupplyinthepatientspossession 1.3.3 Portionofdayscovered(PDC) 2. Non-traditional providers and care settings: Clinics Nmbrofdaysduringtheperiodthepatientcouldhavehadthedrug Nmbrdaysofcoverage 2.1 Retail (convenient care) clinics (CCCs) 2.2 Employer worksite clinics 2.3 Urgent care clinics 2.4 Federally qualified health centers (FQHCs) 3. Gaps in care and quality improvement programs 4. Telemedicine, telehealth, and automated monitoring 5. Bundled payment initiatives
Describe the Risk Management Economic Model High level? Components? Key concepts?
- Examine link between DM program risk, cost and savings 2. Quantify savings at different points in the risk distribution Key concepts: - risk varies within population as well as over time - patients can move between risk categories - small % of poulation accounts for large % of expense 3. Components 3.1 Risk stratification 3.2 Prevalence of different chronic diseases 3.3 Chronic disease cost 3.4 Payer risk 3.5 Targeting and risk 3.6 Estimated event cost 3.7 Contact rate 3.8 Engagement rate 3.9 Member re-stratification rates
Designing a DM Program - Metrics that should be explicitly recognized
- Types of interventions to be used 1. Number and risk-intensity of members to be targeted 4. Methodology for contacting and enrolling members 6. Timing and numbers of members to be contacted 3. Number of staff, their cost, and other program costs 5. Rules for integrating program with rest of care system 7. Predicted behavior absent intervention and predicted effectiveness at modifying that behavior
Describe DM ROI calculation
In other applications, return expressed on a Net basis. In Disease Management express return in gross terms Program cost (denominator) - Direct costs (salaries of internal staff; vendor fees) - Indirect costs of internal support - Management costs - Overhead and other allocated costs - Set-up costs: one-time expenses 3. Savings due to the Program (numerator): Savings result from decreased resource utilization
What is Opportunity Analysis?
Demonstrates improvements that could result from applying evidence-based CM programs. Apply programs to members likely to achieve the Triple Aim (advancing health of populations, improving individual patients experiences, and reducing per capita health care costs) Several basic components are required: - Knowledge of member benefit design. - Information on any evidence-based care management programs - Eligibility and claims data for the prior 2 or 3 years. While an Opportunity Analysis is retrospective, the results are applied prospectively to identify members meeting the profile of opportunity population.
Designing a Program (a.k.a. The Opportunity Analysis Process)
Analytics - Group conditions by the type of intervention that is typically applied to their management - Also consider comorbidities - Requires further drill down analysis within the different categories - A further segmentation may be made by cost and frequency Searching the Evidence Base Step 1: Search for Relevant Publications Step 2: Assess the Quality of Evidence Step 3: Determining Generalizability Step 4: Developing a Suggested Program Weighing the Economics. - Expected cost per risk-ranked patient - The cost of alternative treatment from which we derive potential savings. - The expected accuracy of our identification algorithm. - Apply a timing factor - Not all patients offered a program will engage, due to: Difficulty reaching the patient, cognitive impairment, Unwillingness to cede hope of recovery.
Member Stratification For Opportunity Analysis vs. Care Management
- Care Management program planning follows one of several typical models: Run a predictive model stratifying members by their risk score. 1.1.1 Disadv: prevalence of members who are minimally intervenable 1.1.2 Disadv: operational problems if population is very diverse in term of conditions and needs A condition-specific model: Prevalence of co-morbidities means ultimately need to address all conditions of the high risk population. A rules-based approach: Often relies on clinicians for identifying candidates; however, some clinicians are not good at identifying candidates. 2. Opportunity analysis addresses a number of shortcomings with these models. 2.1 It maintains stratification of model 1, but assigns lower priority to less intervenable patients 2.2 Favors programs that target members with common risk profiles.
The Value of Opportunity Analysis (a.k.a. Reasons for taking this alternative, more structured approach)
- Clinicians are not particularly good at identifying high-risk patients. 3. Resources are devoted to patients where they can have the most beneficial effect. 6. Provides a specific plan with targets for comparing actual outcomes 4. Understanding which sub-populations are intervenable and the value of that intervention 2. The economics of program planning cannot be ignored 5. Plan for the numbers of patients, the number of care-givers and the estimated financial return.
What is a Propensity Score?
Summarizes multiple characteristics into a single value, allowing matches on the score rather than directly on characteristics. The score should not be the only criterion on which the members are matched Disadv: it controls only for observable and not unobservable variables (e.g. willingness to change behavior).
Compare propensity scores and risk adjustment
- In Risk Adjustment, a score is based on a members age, sex, and diagnosis. 2. The risk score takes into account more detail than the propensity score. 3. Risk adjustment has the advantage that it uses the entire population. 4. Propensity matching can result in many members being discarded when there is incomplete overlap between populations.
Describe the (1) Actuarially-Adjusted Historical Control Methodology, (2) Exposure Categories, (3) DM Savings Calculation
Actuarially-Adjusted Historical Control Methodology - Objective criteria define inclusion in reference or intervention population - Trend is derived from ”Index” population - Methodology is ”open group”, comparable population selected each period Actuarial Method Exposure categories - Member months between start and end dates of study - Managed vs. Measured Populations: Populations need not be same - Chronic and Non-Chronic (”Index”) Members - Excluded Members (reasons) - Class not receptive to disease management - Candidate for program administered by another vendor - Claims subject to sharp discontinuity An application of the Actuarial Method: calculating DM savings - Record number of Total, Chronic and Index members - Cost PMPM for each group summarized - Calculate index trend - Baseline PMPM * Trend less Actual PMPM = Reduced cost PMPM - Reduced cost PMPM * Member months in Measurement Period = Savings
General Approach To Propensity Score Matching
- Run logistic regression to create a propensity score 1.1 Assign a value of 1 to participants (the treatment group) and 0 to nonparticipants 1.2 ln [p/(1-p)] = a + B * X + e. p is the probability of the member being in the treatment group, p/(1-p) is the odds ratio 1.3 Solve for p, propensity score. p = exppaBXq 1exppaBX q 2. Match each participant based on propensity score 2.1 A target member may be matched to one or many comparison members 2.2 Important considerations for matching include: 2.2.1 Withorwithoutreplacementdecision 2.2.2 Whatdeterminesclosenessofamatch 2.2.3 Whatconstitutesasatisfactorypercentageofmatchedmembers 2.3 Methods include: Nearest Neighbor Matching, Caliper Matching, Mahalanobis Metric Matching, Stratification Matching 2.4 There is a trade-off between the number of matches and closeness of the score. 3. Testing The Results 3.1 Testing for bias is difficult because propensity score only adjusts for observable variables. 3.2 Models should use the minimum number of variables necessary 3.3 Validate that the matched sample adjusts for observed differences.
