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Life is better healthy. ACO – Data Aggregation to Support Population Health Dena M. Ragusa, MS ACO Manager of Informatics, DSRIP Project Manager

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Life is better healthy.

ACO – Data Aggregation to Support Population Health

Dena M. Ragusa, MSACO Manager of Informatics,

DSRIP Project Manager

Agenda

2November 21, 2014

• Introduction 

• Data Aggregation

• ACO Patient Flagging 

• Analytics and Regulatory Reporting

• Lessons Learned

November 21, 2014 3

INTRODUCTION

Better Health

Better Care

Lower Costs321

What is an Accountable Care Organization (ACO)?

Definition:  A group of payers, physicians, hospitals and other providers that collaborate to provide efficient, high quality and coordinated care for a select group of patients.

Triple Aim

Barnabas Health Accountable Care Structures

November 21, 2014 5

~ 30,000 Beneficiaries ~ 800 Employed and community providers

6 BHS acute hospitals + external partner hospital 

Medicare Shared Savings Program (MSSP)

Horizon BCBSNJ Medicare Advantage

Our Partnership with HEALTHEC (HEC)

November 21, 2014 6

( )

Partnership with the New Jersey Health Information Technology Center

(NJ-HITEC)

November 21, 2014 7

• Accountable Care Organization (ACO)• Meaningful Use (MU)• Physician Quality Reporting Initiative (PQRS)• ICD-10• Patient Centered Medical Home (PCMH)• Comprehensive Primary Care Initiative (CPCi)• Education

November 21, 2014 8

DATA AGGREGATION

November 21, 2014 9

Claims Data Pharmacy Claims

Beneficiary Data

Data Warehouse

Data Aggregation Plan

AssessmentsEHR Lab Results

Case & DiseaseManagement

Care Plans 

Patient Dashboard

Audit  Reporting Patient Rosters

Cost & UtilizationReporting

Population Management

November 21, 2014 10

Data Integration – Multiple Systems, Organizations, and Sources

Varying levels of integration makes data aggregation 

difficult.

Data Integration Challenges

November 21, 2014 11

• CMS member rosters provide limited patient demographic information

• Establishing unique patient IDs and matching algorithm 

• System query and filtering limitations

• Data not entered into structured fields, some EHRs do not have structured fields to store all information

• EHRs having multiple fields to store the same information, identify provider workflows

• Vendor participation in data extraction projects

BHS Inpatient System

BHS Inpatient System

BHS Outpatient System 

BHS Outpatient System 

BHS Outpatient System 2

BHS Outpatient System 2

Partner Hospital System

Partner Hospital System

External Vendor Database

External Vendor Database

Member Rosters and Patient Identifiers

November 21, 2014 12

Data Warehouse

Payors

Member Rosters

Claims Data

Member Rosters

• Address, phone number, email• Enterprise Master Patient Index (EMPI)• Medical Record Numbers (MRNs)

c

Electronic Health Record (EHR) - Clinical Data

Summary:Clinical data extracts from Cerner and NextGen EHR systems including patient details,  visit information, medications,  vitals, allergies & contradictions,  lab & radiology results, and immunizations. 

Benefits:• Fill in claim data gaps and claim lag• Robust analytics and quality measurement • Reduce the amount of manual extraction for annual 

reporting

November 21, 2014 13

BHS Cerner and CentraState NextGen Data

November 21, 2014 14

ACO Patient

Cerner Ambulatory EHR

Nextgen Ambulatory EHR

Clinical FilesPCP / Specialist

AnalyticsCare

Management

• Patient Level ACO Dashboards• Organizational ACO Dashboard• Annual Reporting

Data Warehouse

EHR Data Populates Evidence-Based Gaps in Care

November 21, 2014 15

Lab Data

Summary:Develop Observation Result Unsolicited (ORU) interfaces to collect lab results processed by Barnabas facilities.

• Focused on lab results to support ACO metrics

Benefits:• Fill in claim data gaps and claim lag• Lab data for analytics and disease management• Collect data for affiliate provider’s patients where EHR 

data are unavailable• Results are collected in real timeNovember 21, 2014 16

Lab Results from BHSLaboratory Information Systems (LIS)

ACO Patient

Barnabas Lab

Barnabas Health

InterfaceEngine

ORU ORU

AnalyticsCare

Management

• Disease Management• Care Management• Reporting

Data Warehouse

ACO Patient Registration Events

Summary:Implement Admission, Discharge, and Transfer (ADT) interfaces between HEC and Barnabas Health Hospitals to capture registration events for ACO patients.

• Registration events include Inpatient, Outpatient, and ED Visits

Benefits:• Provider alerts when ACO patients have a registration event • Provides real time information • Analytics 

November 21, 2014 18

BHS Registration ADT Events in Real Time

ACO Patient

Barnabas InpatientFacility

Barnabas Health

InterfaceEngine

ADT ADT

AnalyticsCare

Management

• Provider alerts• Follow up visit within 30 days• Care coordination• Reporting

Data Warehouse

Provider Alerts and Patient Registration Events

November 21, 2014 20

Today’s Activity:

• 25 patients got admitted• 2 patients got transferred• 12 patients got discharged• 8 patients received their lab reports

November 21, 2014 21

ACO PATIENT FLAGGING

Identify ACO Patient in Inpatient Clinical Systems

November 21, 2014 22

Summary:Flag ACO patients in Barnabas inpatient facilities and update flag status in real time as patients present.

Benefits:• Most timely notification process, captures new 

patients • Supports ACO workflows in acute care facilities• Data extract and reporting needs

Real Time ACO Patient Notifications in BHSInpatient Systems

November 21, 2014 23

ACO Patient

Data Warehouse

Barnabas InpatientFacility

Barnabas Health

InterfaceEngine

ADT

Barnabas InpatientCerner

ADT

Clinical Staff

• Transition of Care• ED workflows• Care Coordination

Clinical Staff

ACO FLAG ACO FLAG

ACO Patient Flag in Outpatient EHR Systems

November 21, 2014 24

Summary: Identify ACO patients in Barnabas outpatient EHR systems.

Benefits:• Facilitates ACO workflows in outpatient setting• Supports clinical data extraction and reporting 

needs 

November 21, 2014 25

Outpatient Cerner – ACO Flag

ACO Patient

Barnabas OutpatientCerner

ACO Providers

Member Rosters

Payors

Individualized Care Plans

*Identify Problems, Barriers, Goals and Interventions

*Identify Risk Levels through Stratification

*Evidence based Gaps in Care

*Subject, Objective, Assessment, and Plan (SOAP) Notes

*Claim Details

Care Plans and Claims Data

November 21, 2014 26

Summary of ACO Data Flow

November 21, 2014 27

November 21, 2014 28

DATA ANALYTICS AND REGULATORYREPORTING

Claim Based Analytics

51

48

46.547

47.548

48.549

49.550

50.551

51.5

My %Non Emergent Rate

Speciality Group% Non Emergent

Provider > Non Emergent ER Visits Provider > High Cost Members

Member Name

Sex Primary Diagnosis Group

Total $

XXXX M Cancer of colon 55,400XXXX F Chronic kidney disease 31,201XXXX M Syncope 29,358XXXX M Chronic kidney disease 26,396XXXX F Diabetes mellitus with 

complications26,091

t• Monthly Patient Panel

• Admits/1000• ER Visits/1000

• Imaging Costs and Utilization• Financial Efficiency Ranking

• Bed Days/1000

Claims Based - Population Stratification

Tier Category# of 

Patients% of Total 

PatientsTotal Cost

% of Total Cost

Average Cost Per Patient

Average Risk Score

1 Complex Case Management 800 3% 30,000,000 25% 40,000 3.64

2 Disease Management (CHF,CAD,COPD,DM,ASTHMA) 9000

39% 55,000,000 30% 6771 2.7

3 Wellness/Prevention 6000 28% 15,000,000 10.% 2485 4.3

Population Stratification

• Verisk Critical Risk score (DxCG) > 5.00 • Amount Paid >$100k • >3 admits• >6 ER visits within 12 months

Tier 1Complex Case Management

Quality Reporting

November 21, 2014 31

MSSP • Annual quality reporting to CMS• 22 Metrics• Subset of population ‐ 6000+ Patients in sample

Horizon• Quarterly quality reporting• 5 Metrics• All patients

NJ-HITEC and Quality Reporting

November 21, 2014 32

Patient Sample received for audit

Claims and clinical data (EHR and Lab) are matched against 

audit sample

Data populates metrics in NJ‐HITEC’s Quality 

Data CAPTURE Tool and remaining gaps 

are identified

Pre‐populated data can be viewed and/or edited 

where applicable

Chart abstractors  receive automated 

work lists to complete chart abstraction

Once abstraction is complete, XML data file is 

generated and submitted 

electronically

November 21, 2014 33

LESSONS LEARNED

Lessons Learned and Future Goals

November 21, 2014 34

Extracting unique data files from multiple systems has many challenges, consider utilizing CCDs

EHR Optimization is key as data must be stored in a structured format

Grand amounts of data can be valuable, but a plan to educate and engage providers is imperative

Technology should not be a solution, but an enabler

November 21, 2014 35

THANK YOU

Dena M. Ragusa, MSACO Manager of Informatics,

DSRIP Project [email protected]