Integrating Quality Measures into EHRs:

Available Tools and Lessons Learned

November 17, 2010

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• Quality reporting requirements in the Final Rule

– Hospitals have 15 mandatory quality measures• 2 ED throughput measures• 7 stroke measures• 6 VTE measures

– Eligible professionals have 3 required core measures and 3 additional measures selected from a menu of 38• Core measures include: hypertension measurement, tobacco use and

intervention, and adult weight management• Alternative core measures include weight management in children, flu

shots for patients over 50 and childhood immunizations

• The final rule reinforced the requirement that all data must be in structured format, all requirements must be met using certified EHR technology, and that the rules apply to all patients, not just Medicare and Medicaid patient.

Meaningful Use Summary

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Why This Journey to be a Data Driven Organization?

• It is not the computer itself that has value, – but the access to information, – the ability to track and avoid errors, – the speed and reliability in finding what we need, in

making decisions, and in implementing care without error or delay.

• Computers let us achieve a level of quality care that we could never achieve without them.

QUALITY

Data Driven Healthcare Organization

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Data is Used to Assess Needs, Drive Interventions and Report Performance in a Closed-loop Continuous Improvement Model

Clinical, Operational or Research Goals

Data Acquisition Data Integration Data Analysis

Establish Standards of Care

Screen and Identify Patients

Provide Data-Driven Support

to Clinicians

Measure Quality, Outcomes and

CostsReport Results

GO

ALS

AN

ALY

SIS

INTE

RVE

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REP

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Data Driven Healthcare Organization

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What is the Current State of Your Data?

• What do we currently measure?

• How do we measure it?

• Why do we measure it?

• Does a data dictionary exist for all data elements?

• What is the current state of our data and it’s integrity?

• Does existing workflow support data collection requirements and outcome objectives?

Getting Started

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Laying the Foundation

• Data Use Models — Information needs must be focused on solving identified problems across the organization

• Data Governance — Establish data owners/stewards and processes for making data related decisions

• Data Quality — Determine how data quality will be measured and how quality issues will be remediated. Data quality criteria needs to be set for each data field of interest. Is the data valid? eg. Indicate preliminary and final on lab tests. Does the data make sense?

• Data Standards — Organizational decisions on data definitions, medical vocabularies and naming conventions must be made early and consistently enforced

Getting Started

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Data Driven Use Models

Clinical Intelligence

Operational Intelligence

Financial Intelligence

Research and Surveillance

RetrospectiveProspectivePredictive

• Quality/Outcomes Management• High-Risk/Cost Case

Management• Adverse Event Detection• Infection Control• Compliance Reporting• Drug Utilization Reviews

• Capacity Planning and Management

• Patient Flow Management (ED and Inpatient)

• Resource Utilization• Patient Scheduling/Access

• Patient Cost Analysis• Service Line Analysis• Procedure Cost Analysis• Referral Analysis• Revenue Cycle Analysis

and Coding Optimization• Market Analysis

• Health Services Research• Comparative Effectiveness• Drug Outcomes Research• Drug Safety Surveillance• Disease Surveillance• Patient Recruitment

Components of a Data Driven Healthcare Organization

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Are You Set Up to Manage Data?

• Do medical staff bylaws and policies support outcome objectives?

• What tools are available to collect aggregate data?

• Does the reappointment process support review of data at the individual physician level (how do I compare to my peers?)

• Do we have IT tools to support the amount of data to be collected?(SQL db, Business Objects)

• Do we have the IT tools to distribute data in a meaningful format?(physician report cards)

• Does a committee structure exist to guide us through this journey?

Components of a Data Driven Healthcare Organization

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Defining Data Management

Data Architecture

Data Governance

Data Acquisition

Analysis and Reporting

Metadata

Technical Architecture

Server configuration and administration, security management, database

administration

“Blueprint” for the data warehouse or data mall

Integration of data from disparate source systems

Definitions, algorithms, impact analysis and data lineage reporting

Data ownership and stewardship roles,

responsibilities and workflow

Dashboards, drill-down analytics and operational reporting

Components of a Data Driven Healthcare Organization

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Journey to Data Maturity

Full RealizationData stewardship

integrated with operational committee structure

Partial IntegrationStewardship implemented at dept level

Enterprise strategy definedFoundational projects planned

Building Blocks to SuccessStewardship capability defined

Consistent reusable data between applications

Starting Gate

DISTINCTIVE

ADVANCED

FOUNDATIONAL

BASIC

Components of a Data Driven Healthcare Organization

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Governance Challenge

Leadership

Project Committees

Project Sponsors

Policy Groups

InformationServices

Vision

Strategy

Accountability Oversight

IntegratedGovernance

Model

Prioritization

Components of a Data Driven Healthcare Organization

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Example of Immediate Indicators for CPOE CPOE Benefits

1. What percent of physicians place orders using CPOE?2. What is the Target?Measuring 30-60-90 days from implementation with a goal of 70% for Community Hospitals

80% for more academic centers and high deployment of hospitalists.3. What percent of orders are done via Communication Type?4. Number of physician pharmacy call-backs to clarify medication orders?5. What percent of delinquent charts per month? 6. Monthly ALOS per DRG 7. Number of cancelled/voided diagnostic orders (laboratory & radiology per week/month) 8. Turn Around Time (TAT) from diagnostic order to result documentation (Both Stat/Now

and Routine Categories) 9. "Overall" scores for AMI,CAP, CHF, and Pregnancy JCAHO core measures10. Number per 100 admissions of Adverse Drug Events (ADEs) 11. Turn around time from order to administration of medication12. Number of non-formulary medication orders per week/month (TNF) 13. ICU LOS 14. TAT for Blood Admin from Order to First Administration

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Example of Long Term Indicators for CPOE CPOE Benefits

1. Total cost per discharge (for DRGs of interest)2. Use of peer-reviewed Order Sets (pathways and protocols) Physician and Nursing3. Number of unexpected admission/ readmission to higher level of care per

week/month 4. Number of Ventilator Days /ICU stay5. Number of Dietary Order Clarifications6. Orders within a Care Plan7. % of patients receiving Electronic Prescriptions ED8. % of ED Physicians using Electronic Prescriptions9. Reason for Radiology Exam Completed (Compare only the pre to post conversion

statistics)10. Appropriate Reason for Exam (When field is changed to reflect another choice)11. Reason for Consult Given (Free Text Field Use — Y or N)12. Total cost per discharge (for DRGs of interest)13. Use of peer-reviewed Order Sets (pathways and protocols) Physician and Nursing14. Number of unexpected admission/readmission to higher level of care per

week/month

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Indentify Baseline Data

• Determine what current data elements can be collected and utilized as baseline data

• Baseline data must be used to determine benefits and return on investment

CPOE Benefits

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Physician: QUARTERLY REPORTING PERIOD:Specialty: INTERNAL MEDICINE 1st Quarter 2010UTILIZATION OF FACILITIES

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TOTAL YTDALL SECTION

# of Admissions 0 0 0 0 0 0 0 0 0 0 0 0 0

Consultation 0 0 0 0 0 0 0 0 0 0 0 0 0

ALOS 0 0 0 0 0 0 0 0 0 0 0 0 N/A

AMA’s 0 0 0 0 0 0 0 0 0 0 0 0 0

Blood Usage – (# Units Met Criteria vs. Total Units) 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 N/A

Blood Usage – % Meets Criteria 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% N/A

Communication Errors/ Complaints on Provider 0 0 0 0 0 0 0 0 0 0 0 0 0

Deaths 0 0 0 0 0 0 0 0 0 0 0 0 0

# Deaths Met Criteria for Autopsy 0 0 0 0 0 0 0 0 0 0 0 0 0

# Autopsy Offered to Family 0 0 0 0 0 0 0 0 0 0 0 0 0

Transfers to STACH 0 0 0 0 0 0 0 0 0 0 0 0 0

NONCOMPLIANT MEDICATION ORDERS                          

Inappropriate Abbreviations 0 0 0 0 0 0 0 0 0 0 0 0 0

Incomplete Orders 0 0 0 0 0 0 0 0 0 0 0 0 0

Range Orders 0 0 0 0 0 0 0 0 0 0 0 0 0

PRN Without Indications 0 0 0 0 0 0 0 0 0 0 0 0 0

Leading/Trailing Zeros 0 0 0 0 0 0 0 0 0 0 0 0 0

MEDICAL RECORDS                          

Medical H&P - (total met H&P criteria vs. total H&Ps) 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 N/A

Medical H&P - (total # in chart within 24-hr vs. total # H&Ps) 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 N/A

Medical H&P Compliance Rate (%) 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% N/A

BHU H&P - (total # in chart within 24-hr vs. total # H&Ps) 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 N/A

BHU H&P Compliance Rate 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% N/A

VO/TO Signed w/in 48 Hours (total # signed vs. total orders) 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 0/0 N/A

VO/TO Signed w/in 48 Hours (%) 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% N/A

# of Suspension Days 0 0 0 0 0 0 0 0 0 0 0 0 0

PROCEDURES                          

# of Procedures in OR 0 0 0 0 0 0 0 0 0 0 0 0 0

Complications from Procedure(s) 0 0 0 0 0 0 0 0 0 0 0 0 0

Post Operative Deaths 0 0 0 0 0 0 0 0 0 0 0 0 0

# Moderate Sedation Cases 0 0 0 0 0 0 0 0 0 0 0 0 0

PSYCHIATRY SECTION                          

Readmission W/in 5-days 0 0 0 0 0 0 0 0 0 0 0 0 0

Seclusion/ Restraint 0 0 0 0 0 0 0 0 0 0 0 0 0

Seclusion Only 0 0 0 0 0 0 0 0 0 0 0 0 0

Self-Inflicted Injury/Suicide Attempt Requiring Treatment 0 0 0 0 0 0 0 0 0 0 0 0 0

Score Card ExampleCPOE Benefits

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Benefits Example

Description Potential 7-Year Incremental Profitability Improvement

Impact Areas Benefits Model Results

Technology Enablers

Model Design

Reduction in Inappropriate Use of High Cost Drugs

$6.8-$8.6 Million

There is a direct relationship between the efficiency of the Revenue Cycle and High-cost drug denials, particularly Chemotherapy drugs. With the workflow engine, contract management and decision support rules, caregivers can make informed choices regarding the clinical indications, financial implications and the alternatives to high cost drugs.

1) Outpatient Oncology eligibility improvement2) Reduced inpatient use of Albumin3) Substituion of Boniva, Levofloxin and Vancomycin by 30%4) Reduced denials and short-payments

1) Workflow Engine/Business Rules2) Contract Management3) CPOE/Clinical Decision Support

Baseline- Forecasted future units X Avg Net Rev/Unit and Cost/Unit- Forecasted Denial $ based on current Denial Rates X future Rev Forecast = Future baseline Utilization, total Net Rev & total cost

Future State- Identified 5 factors impacting model - 1) Med Nec 2) Auth 3) Dosing 4) Substitution 5) Clinical indication and set % goals- Apply goals to future units, gross rev and cost factors per drug = Change in Gross Rev and Cost - Apply payer mix and avg net reimb. rate to Gross Rev=Net - Net Change in Reimb. Rev + Net Cost reduction = Change in profitability opportunity- Adoption rate X Opportunity = Realized Profitability

Year 1 Year 2 Year 3 Year 4 Year 5 Year 6 Year 7

High End - Reduced Use of High Cost Drugs $0 $0 $0 $1,936 $2,061 $2,195 $2,372

Low End - Reduced Use of High Cost Drugs $0 $0 $0 $1,566 $1,649 $1,734 $1,858

$0 $1,000 $2,000 $3,000 $4,000 $5,000

IIncr

. Pro

fitab

ility

Reduced Utilization of High-Cost Drugs Incremental 7 Year Profitability (000's)

CPOE Benefits

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Impact on the Organization

• Develop a solution that sits above enterprise revenue cycle and clinical applications to make data accessible and actionable

• Identifies and proactively manages patients with high risk conditions

• Manages core measure patients concurrently (while outcomes can be improved) not just retrospectively

Data Continuum

Lessons Learned

Identify Readmissions

Detection of Existing Condition and

Assessment of Risk of Condition Developing

Monitoring of Patient at Risk of Condition

Developing

Intervention for Managing Condition or for Managing Risk

Evaluate Potential for Readmission

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Ask Your Organization

1.Key Questions• What is the current state of your

data?• Are you set up to manage data

(governance)?• What is the most efficient data

management solution (architecture)?

• What is your capability to derive intelligence from data to effect process improvement (informatics)?

• What is your budget and timeframe (scope)?

Lessons Learned

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Everything Must be Done With the Final Destination in Mind

• Start slow and grow, avoid taking on too many improvements at once

• Avoid unstructured text as much as possible

• Establish a culture whereby quality is top of mind– More than a single committee– Each committee meeting quality is woven into the discussion

• Communication strategy for entire organization so they gain understanding

• Organizational change methodology– Leverage for each time change the documentation or ordering

pathways and associated processes

Lessons Learned

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Case Study

Debbie Newman, MBA, CPHIMSDebbie Newman, MBA, CPHIMSDirector of Process ImprovementDirector of Process ImprovementLicking Memorial Health SystemsLicking Memorial Health Systems

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Licking Memorial Health Systems – Licking County, Ohio

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CMS Requirements & Effects–CMS Core measure requirements

• Hospital based activity

–CMS and State - Anticipated core measure requirements• Expansion into cross-organization activity• Expansion into claims based measures

–2014 CMS and State – What’s coming?• ED throughput (admit decision to admit, door to

admit)• Global immunization measures

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CMS Requirements & Effects

What’s the big deal?

Hospital requirements =physician requirements

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LMHS Lessons Learned

•Early adoption can have it’s complexities– Example – Maternity laceration prevalence– Example – Community Report Cards

•Software not created to mine data– Is a purchased EMR truly set up to evaluate quality?– Are reports available to account for CMS complexities?

• Infrastructure not in place to affect data– How do we evaluate outliers?– Who is involved in evaluations?– How do we communicate to physicians / clinicians?

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Electronic Medical Record Observations

•Access – Total access for all providers?

•Real time – Scanning delays – true access when needed?

•Printing – Where and when appropriate?

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Electronic Medical Record Observations

•Downtime – What is the process for downtime?

•Duplication – Where are areas for simplification?

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Hospital or Physician OfficeElectronic Medical Record Success Stories

•Recall notification• Who received a particular implant?

•Best practice • How are we managing our Core Measure patients?

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Software Limitations & Lessons Learned•Basic software may be too versatile

•Not set up for monitoring quality

•Garbage in – garbage out

•Need standard responses to measure quality

•Need central Quality Committee

•Early adoption has its downfalls

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Quality Report Cards

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Question and Answers

Have a question?

Beverly BellBBell20@csc.com

Debbie NewmanDnewman@LMHealth.org

THANK YOU