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Obtaining & Reporting Quality: At-Risk Measures Welcome The webinar will start at 1:00 pm ET. It is interactive, so please make sure that you have connected via phone with your audio pin. Call-in information is shown on your dashboard, right side of screen . Introductions Housekeeping
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Obtaining & Reporting Quality: At-Risk MeasuresWelcomeThe webinar will start at 1:00 pm ET. It is interactive, so please make sure that you have connected via phone with your audio pin. Call-in information is shown on your dashboard, right side of screen.
Introductions • Housekeeping • Presentation • Q & A • Follow-up • NAACOS’ Spring 2014 Conference • April 23-25 in Baltimore • Registration to open next week – check www.naacos.com/conference for updates Agenda
Panelists will present for approximately 40 minutes • Q&As will take the remainder of the 1 hour • Submit anonymous written questions using the Q/A tab (not chat) on dashboard • Webinar is being recorded • Slides and recording will be available at www.NAACOS.com/webinars. Housekeeping
Jill Kalman, Mount Sinai Medical Center Jill Kalmanis 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. Today’s Presenters
Obtaining and Reporting Quality At-Risk MeasuresJeffrey Farber, M.D., MBAJill Kalman, M.D.Mount Sinai Health SystemNew 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
Mount Sinai’s Integrated Approach to Accountable Care and Population Management
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
Practice Locations Legend: Practice Names Coffey 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 Brooklyn Heights Medical Group Manhasset Medical Group
Creating a “Win/Win” Environment for Physicians Physicians as Providers and Consumers of Data Panel Management Disease Registries Risk Stratification Quality Reporting
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)
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
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
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
ACO Performance Indicators Performance in each category drives the amount of shared savings received
Impact of Quality on the Medicare Shared Savings Calculation – Macro Level Step 1: Calculate Expected – Actual Cost* Step 2: Calculate % of shared savings allocated to Mount Sinai Care $300 * 20,000 =$6,000,000 Quality Score $3,000,000 % of the maximum allowed (50%) is based on quality score
Impact of Quality on the Distribution of Shared Savings – Micro Level Data is distributed to MDs based on the following variables: Quality Score # of Beneficiaries Utilization Acuity
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
Rates by Provider ACO Quality Measure - #15Pneumococcal Vaccination (NQF #43)
Room for Improvement – Low Hanging Fruit ACO Quality Measure - #16Adult Weight Screening (BMI) & Follow-up (NQF #421) Rates are artificially low…height not always captured
Variability within a Practice ACO Quality Measure - #19Colorectal Cancer Screening (NQF #34)
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
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)
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
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
Distribution patients enrolled in PACT by 30-day readmission score All PACT patients
Effect of PACT 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 advancementsEmbedding Risk Flags into the EMR Banner * 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
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 comorbidities • 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 flagging • 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
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. Questions?
Jeffrey Farber Email: jeffrey.farber@mssm.edu Jill Kalman Email: jill.kalman@mountsinai.org Speaker Emails
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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