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

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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 info@naacos.com.

Speaker Emails

Jeffrey FarberEmail: jeffrey.farber@mssm.edu

Jill KalmanEmail: jill.kalman@mountsinai.org

Upcoming

• Our next webinar series will be for new 2014 ACOs:“Critical Startup Lessons for New ACOs”

• 5 webinar series beginning January 28• Slides and recording of today’s webinar will be posted on

our website, www.NAACOS.com/webinar, by tomorrow.• 2014 Spring Conference: April 23-25 in Baltimore

• Registration opening soon• Check back to our website for future webinar series and

conference information

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