Obtaining & Reporting Quality: At-Risk Measures
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Agenda
1.Introductions2.Housekeeping3.Presentation 4.Q & A5.Follow-up
• NAACOS’ Spring 2014 Conference • April 23-25 in Baltimore• Registration to open next week – check
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Housekeeping
1. Panelists will present for approximately 40 minutes2. Q&As will take the remainder of the 1 hour
• Submit anonymous written questions using the Q/A tab (not chat) on dashboard
3. Webinar is being recorded• Slides and recording will be available at
www.NAACOS.com/webinars.
Today’s Presenters
Jill Kalman, Mount Sinai Medical Center
Jill Kalman is medical director of the PACT progam, the transitional program to reduce readmissions at Mount Sinai Medical Center and the director of the cardiomyopathy program. Her primary focus in clinical investigation in heart failure focuses on novel medical therapies, technologies and device therapy in all stages of heart failure. Dr. Kalman received her medical degree from the Mount Sinai School of Medicine.
Today’s Presenters
Jeffrey Farber, Mount Sinai Medical Center
Jeffrey Farber is chief medical officer of Mount Sinai Care, the ACO of Mount Sinai Medical Center and is associate professor of geriatrics and palliative medicine as well as hospital medicine. Dr. Farber is the chair of their utilization management committee, and led his hospital’s team effort during the Recovery Audit Contractor (RAC) demonstration project. Dr. Farber received his M.D. at the Albert Einstein College of Medicine.
Obtaining and Reporting Quality At-Risk MeasuresJeffrey Farber, M.D., MBA
Jill Kalman, M.D.Mount Sinai Health System
New York, New York
January 8, 2014
Mount Sinai Medical Center
Founded in 1852
1,171-bed tertiary-care teaching and research Hospital
183 Hospital based practices
3,500 Physicians, residents, and fellows
2000 Nurses
58,000 Discharges
95,000 ED visits
One million ambulatory visits in hospital clinics and Family Practice Associates
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Mount Sinai’s Integrated Approach to Accountable Care and Population Management
Medicare Shared Savings Program
NY State Health Home and PCMH Initiatives
Centers for Medicare and Medicaid Innovations (CMMI)
Grant(Geriatrics ED)
Community-based Care Transitions Program
(C-PACT)
Patient
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Mount Sinai’s Shared Savings Program
Medicare Shared Savings ACO effective July 2012 – Mount Sinai Care LLC
• 21,000 Medicare FFS Beneficiaries• 11 Practices• 130 Primary Care Physicians• 3 EMRs• 1 Health Information Exchange• Providers use Multiple RHIOs
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Practice LocationsCoffey Geriatrics
FPA Primary Care
Internal Medicine Associates
Mount Sinai Visiting Docs
Chelsea Village
North Shore Medical Group
Mount Sinai Medical Associates
Cosmatos/Melis
Steinway Medical Group
Mount Sinai Multispecialty
Riverside Medical Group
Legend: Practice Names
Manhasset Medical Group
Brooklyn Heights Medical Group
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Creating a “Win/Win” Environment for Physicians
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Progress Notes
Discharge Summaries
Screenings DiagnosesCare Plans
Rx Lists Referrals
Procedures
Panel Management
Disease Registries
Risk Stratification
Quality Reporting
Physicians as Providers
and
Consumers of Data
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Implementing Panel and Disease Management Tools
• Work Group meets bi-weekly• Membership:
– Co-leads: Director of Care Coordination Medical Director of Primary Care– 7 MDs – 1 NP, CDE– 1 Care Coordinator– 1 Epic Resource– 1 Administrator of Ambulatory Care
• Start with quality measures for which performance is low (Fall Risk and Depression Screenings)
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Develop and Leverage Panel Management Toolsand Disease Registries
• Best Practice Alerts for regular screenings and prevention • Disease registries to customize screenings and interventions
by condition• Work lists generated and managed by care coordinators; next
day and next week reports• Proactive vs. Reactive – less work required by MD during visit
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Panel Management
• Create triggers and alerts to proactively order tests and procedures…not waiting until the patient arrives in the office– Breast Cx– Colorectal Screenings– HbA1c and Lipid testing– Depression and Fall Risk Screening
• Reports generated for care coordinators to identify admitted or ED patients; next day and next week appts with care gaps– Identify patients by Program (ACO, C-PACT, Health Home, GEDI-WISE)– Identify patients by Risk
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Demographics
PCP MRN Patient DOB Age Gender Program Flag Firm 1 Risk Level MyChart Appt Provider Appt Date/Time
ED & IP History# MSMC ED Visits Past 12 Mos # MSMC Admissions Past 12 Mos
Preventive Care/Screening
BMI (16) 2
ACO BP Screening (21) 3
ACO: BP>140/90 (HTN) (28) 4
ACO: Mammogram (20) 5
Last Cervical Cancer Screening Date6
ACO: Colorectal Screening (19)7
ACO: Flu (14)8
ACO: Pneum Vacc (15)9
ACO: Tobacco Screening (17)10
ACO: Depression Screening
(18)11ACO: Falls
Screening (13)12
Disease
ACO: A1C >8(22)13
Last Microalbumin
Result14Date of Last
Microalbumin15ACO: DM-
LDL>100 (23)16Date of Last Foot Exam 17
Date of Last Ophthalmology
Screening 18
ACO: BP>140/90 (DM) (24)19
ACO: DM & IVD- Aspririn or
Antiplatelet Use (26)20
ACO: DM-Tobacco Use
(25)21
ACO: IVD-LDLC In Control (29)22
ACO: IVD Antithromboti
c Use (30)23
ACO: HF-
Bblocker/LVSD (31)24
ACO: CAD-
Statin Rx (32)25
ACO: CAD w/DM or CAD w/
LVEF>40 (33)26Asthma Meds27
Disease Registries Asthma Registry CAD Registry CHF Registry COPD Registry Diabetes Registry
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Utilizing Quality Data to Engage Physicians
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Population Management Strategy
• Leverage and Develop IT Infrastructure to Share Data• Provide Financial Incentives for Participation in Achieving
Population Management Goals• Invest in Care Coordination Resources so Physicians can Focus
on Care and not Care Coordination• Invest in IT resources to consolidate and normalize data
regardless of source• Broaden scope of impact by utilizing panel management and
disease registry tools• Feedback Loop to Physicians on Quality and Risk based on
Documentation and/or Administrative Data
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ACO Performance Indicators
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Patient Satisfaction
Utilization
Outcomes
Performance in each category drives the amount of shared savings received
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Impact of Quality on the Medicare Shared Savings Calculation – Macro Level
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Expected Cost
Actual Cost $5,000
$6,000
$7,000
$8,000
$9,000
$10,000
$11,000
$12,000
$300 * 20,000 =$6,000,000
Medicare Mount Sinai Care
40%
50%
$3,000,000
Quality Score
Step 1:Calculate Expected – Actual Cost*
Step 2:Calculate % of shared savings allocated to Mount Sinai Care
% of the maximum allowed (50%) is based on quality score
$3,000,000
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Impact of Quality on the Distribution of Shared Savings – Micro Level
Shared Savings
PhysiciansCare
Coordination Programs
ACO Overhead
Data is distributed to MDs based on the following variables:
# of Beneficiaries
Quality Score Utilization Acuity
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Transparent Reporting of Quality to Physicians
• Scheduled quarterly meetings with Primary Care Practices – Medical Directors– Administrative Directors– Nursing Leadership– Providers– Care Coordinators
• Reports are at the physician level - currently de-identified• Comparison to the ACO as a whole• Each metric is accompanied by EMR screen shot identifying
acceptable means of documentation• Discussion is essential to identify workflow and/or system
issues
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ACO Quality Measure - #15
Pneumococcal Vaccination (NQF # 43)
Rates by Provider
23
ACO Quality Measure - #16Adult Weight Screening (BMI) & Follow-up (NQF #421)
Room for Improvement – Low Hanging Fruit
Rates are artificially low…height not always captured
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ACO Quality Measure - #19Colorectal Cancer Screening (NQF #34)
Variability within a Practice
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Lessons Learned
• Variation in documentation even within practices• EMRs are catching up with Population Management Initiatives
– workarounds may be required• Physician issues with the EMR can overtake discussions on
quality – recognize that this is the vehicle the MDs are using to satisfy these requirements and include EMR team in meetings
• Multiple quality initiatives are happening concurrently and require physician involvement – leverage care coordinators as much as possible
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Next Steps
• Incorporate CMS benchmarks• Trend reports to show performance over time• Incorporate utilization data into reports• Roll-out additional panel management initiatives and measure
impact in future reports: – Fall Risk Screening– Depression Screening– HbA1c and Lipid Tests
• Expand meetings to include specialists commonly involved in care of patients (Endocrinologists, Cardiologists, Oncologists)
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Utilizing Data for Risk Stratification
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Transformational Change and Integration of Resources
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The Readmission Imperative: Identifying Patients at Risk• Predicting and identifying which patients are at greatest risk
of readmission is challenging to health systems. There is need to target high risk patients for care transition interventions.
• Current risk/predictive models can be challenging and utilize data that may not be readily available in real time in all hospitals.
• Hospitalization history alone to target patients for transitional care has historic significance at Mount Sinai and is easily available.
• We have validated this approach with more formal risk models based on factors that characterize patients through demographics and co-morbidities.
Problems Addressed
• Admission history was traditionally used to identify patients at high-risk of readmission, so interventions could be targeted
• Without integrated EHR, identification was very labor and paper intensive
• It did not identify those with a high-risk for readmission, but do not have a history of admissions
• Preventive Admission Care Team (PACT) needed an automated process to assist in the workflow
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Predictive Modeling• Using logistic regression, we developed a risk prediction model
for readmission within 30-days.• The model, which used patient demographics and relevant co-
morbidities was developed in a cohort of hospitalized Medicare FFS beneficiaries with a high proportion of cardiovascular disease.
• The higher the risk score, the higher the risk of readmission• Scores of 0-2 had a 7% risk of readmission, whereas scores of 3 or
4 and above 5 had 30-day readmission rates of 19% and 29% respectively.
• We applied this risk scoring model to patients enrolled in the PACT program, who had been identified solely by hospitalization history. The goal was to determine if the PACT patients would have been identified as high risk based on the regression model
Scoring Model
Scoring Model
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Distribution patients enrolled in PACT by 30-day readmission score
All PACT patients
41; 10%
60; 15%
306; 75%
Low Risk Medium RiskHigh Risk
Low Risk Medium Risk High RiskScore 0-1 2-3 4+# Patients 41 60 306% of Total 10% 15% 75%
0 2 4 6 8 10 12 14 16 1905
101520253035404550
Num
ber o
f Pati
ents
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Effect of PACT
Risk LevelRisk Score
Range:
In PACT Never in PACT Partial PACT Previous PACT All
Total 16.9% (441/2613) 18.5% (1559/8418) 10.2% (32/313) 25.5% (188/736) 18.4% (2220/12080)----------------------------- Missing 50.0% (1/2) 75.0% (3/4) 0.0% (0/1) 75.0% (3/4) 63.6% (7/11)1 High 10-18 39.1% (91/233) 38.0% (124/326) 14.8% (8/54) 31.0% (45/145) 35.4% (268/758)2 Moderate 4-9 18.8% (221/1176) 29.1% (758/2609) 7.8% (12/154) 24.9% (94/378) 25.1% (1085/4317)3 Low 0-3 10.6% (128/1202) 12.3% (674/5479) 11.5% (12/104) 22.0% (46/209) 12.3% (860/6994)
DRAFT 9/7/13 (Oct. 2012 – July 2013)
July dataset limited to Medicare FFS patients.4. New Risk Levels Based on Risk Score and PACT Effect: 0-3 Low, 4-9 - Moderate, 10-18 High
Table 4A. Effect of PACT on 30-day Readmission Rates Per New Risk Level (CCTP Method October 2012-2013)
Design
• The model was validated in actual clinical practice
• The PACT model was fully implemented in Epic
• Medicare data was used so the model could incorporate any prior admissions in New York, not just those occurring at Mount Sinai
• Social workers now document the psychosocial assessment and scoring using Epic automation
• The flag symbol is now displayed on various screens for clinicians across the continuum of care
The PACT Model of Transitional CareUse of data for patient targeting and programmatic advancements
Embedding Risk Flags into the EMR Banner
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FLAG TYPE: CRITERIA:
VERY HIGH Risk Score ≥ 5 ORReadmission history* at MSHORReadmission history* to other hospital
HIGHRisk Score of 3 or 4
* Readmission history = 1 admission within 30 days of the present or 2 admissions within the prior 6 months.
Making Risk Visible
Inpatient Header
Ambulatory Header
Overall Process of the PACT Program
Daily PACT Patient Identification Process
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EMR generated daily list of
hospitalized patients sent to
PACT at 5AM
PACT Supervisors sort patients by insurance and
prioritize based on readmission risk
PACT Social Workers assess patients in order of priority
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PACT and the Community
• What services and influences outside the hospital impact the ability to change readmission rates?
Lessons Learned
• Industry-standard assessments identify high-risk patients based on diagnosis and comorbiditieso When data specific to past medical encounter history
and key demographic data were added, the identification process was greatly enhanced
• Effective use of the of the patient’s problem list was very important
• If MSMC had started looking for data earlier (as soon as the Epic go-live), we could have been more accurate with population and future needs predictions
Lessons Learned
• Point of care integration was desired
• Integration required external calculations, reporting, modifier setting, and flaggingo Flagging for more than just reporting
• This integration has proven useful to many other of our Care Coordination Efforts including our Accountable Care Organization
• Project would have benefited from starting with an IT design group focusing on process; this would have assisted in helping locate patients
Future Directions: Analytics
Population Analytics
Psychosocial and Functional Status Drivers of Readmissions
Improved Health Outcomes
Sustainable, Scalable and Replicable
Questions?
Submit anonymous written questions using the Q/A tab (not chat) on dashboard
If you did not have a chance to ask a question today or have new questions, please send to [email protected].
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