When the Problem is the System: Compara5ve Health Systems Research
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1 When the Problem is the System: Compara5ve Health Systems Research William P. Moran, MD. MS Professor and Director, General Internal Medicine & Geriatrics, MUSC
2 Financial Declara5on I have a minor equity interest in Decision Dynamics, Inc. (DDI), Lexington SC, a company which produces care coordina5on somware
3 Health Care Reform Pressures Prevention: Shift from sick care to well care Incentives: Pay for value of care not volume of care Accountability of insurers and providers Inter-professional teams Coordinate and integrate care New delivery models o ACOs o PCMH
4 A A good GREAT hockey hockey player player plays plays where where the the puck is. puck is going to be.
5 Tools of System Change Health Systems Research and Analyses Policy & payment reform analyses Process engineering and informatics - Systems re-design Outcomes assessment: utilization, functional status
6 Wagner Chronic Care Model 6
7 What to do? Systems research must promote a loud signal to be heard amidst the clinical noise. Technology must be targeted to the most appropriate pa5ents: Risk stra5fica5on Decision support must be integrated into the data flow process Informa5on must be reliably delivered to the correct team member (frequently not the primary care provider) Clinically ac1onable signals should be sent to the correct team member Real 1me
8 Perspectives Macrosystem
9 9
10 Care Coordination and Integration
11 Current MACRO Initiatives CeHA: Carr et al. Evaluation: Feasibility Usability Efficiency Service Duplication Quality of Care
12
13 Current MACRO CER Initiatives PCORI (under revision): Moran et al. Objective 1 - Automating the notification process: Objective 2 - Assessing Medical Home-ness : Objective 3 - Assessing Transition Care Management Code usage
14 CER evaluation: Primary outcome: Acute care utilization Comparison to similar upstate control counties Adjusted using propensity scoring
15 Perspectives Microsystem
16 PATIENT Workflow in the real world
17 PATIENT Workflow in the real world
18 PATIENT Workflow in the real world
19 PATIENT
20 Have we improved care value? Have we made his life beier?
21 Current MICRO Initiatives UIM PCMH as a development model
22 Targeting the population: Data-Driven 0
23 Systems Methodology: Risk Stratification/Cluster Analysis
24 Risk Stratification/Cluster Analysis Ward s minimum variance (Jaccard s coefficient) Cluster A Cluster B Cluster E Cluster C Cluster D
25 Healthy Hypertension Hyperlipidemia & Hypertension Obesity & CV risk
26 Diabetes & CV risk Chronic lung disease, Depression & CV risk Renal Disease, Depression, & CV risk Complex, CV disease & Depressed
27 Diabetes & CV risk Chronic lung disease, Depression & CV risk Renal Disease, Depression, & CV risk Complex, CV disease & Depressed
28 Alert the right team member 4 Risk Strat Total Color Type Admit/ER Date/Time Programs Patient Name Select DOB Last Visit Description/CC Delete 1 MUSC ER 10/29/2013 Hypertension Pt 1 Patient 9/29/40 2/8/12 High BP Data Delete 2 MUSC Admit 10/29/2013 Diabetes, CKD, Hyperlipidemia Pt 2 Patient 9/29/50 2/18/12 SOB Data Delete 3 MUSC Admit 10/29/2013 Diabetes, Hyperlipidemia Patient High blood Pt 3 Data 9/29/60 2/20/12 sugar Delete 4 MUSC Admit 10/29/2013 Sickle Cell Pt 4 Patient 9/29/70 4/30/12 Data Sickle cell crisis Delete
29 Alert the right team member Patient Name Risk Strat Color Admit/ER Date/Time Cluster Programs Last Visit Next Visit PCP Description/CC Patient 2 Admit 10/29/2013 Complex Diabetes, CKD Hyperlipidemia 2/18/12 11/25/13 William P. Moran, MD SOB SF 12 Domains Last Assessment 2/18/12 Physical Functioning General health Pain Mental health Energy/Fatigue
30 Impact Assessment
31 Impact Assessment (cont.)
32 Current MICRO CER Initiative NIH R21 (under review): Mauldin, Moran, Egan, et al. STRATACARE: Risk-Stratification System for Complex Patients in Primary Care o Specific Aim 1: Among primary care patients throughout the state of SC, determine the patient-level and practice-level predictors of hospitalizations and ED visits i.e. risk-stratification. o Specific Aim 2: Among primary care patients throughout SC, determine the within-practice clusters of similar at risk patients i.e. clustering. o Specific Aim 3: Determine differences in clusters across primary care practice sites throughout the state of SC i.e. system differences.
33 Guiding our work Where is policy going? Where is systems research going?
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