Transcript PRESENTATION TITLE IS ARIAL BOLD, CAPS, 30 PT

Integrating Quality
Measures into EHRs:
Available Tools and
Lessons Learned
November 17, 2010
Meaningful Use Summary
• 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.
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Data Driven Healthcare Organization
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.
Q
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T
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Data Driven Healthcare Organization
GOALS
Data Acquisition
Data Integration
Data Analysis
Establish
Standards of Care
Screen and Identify
Patients
Provide DataDriven Support
to Clinicians
REPORTING
INTERVENTION
Clinical, Operational
or Research Goals
ANALYSIS
Data is Used to Assess Needs, Drive Interventions and Report
Performance in a Closed-loop Continuous Improvement Model
Measure Quality,
Outcomes and
Costs
Report Results
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Getting Started
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?
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Getting Started
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
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Components of a Data Driven Healthcare Organization
Data Driven Use Models
Financial
Intelligence
Clinical
Intelligence
• Quality/Outcomes Management
• High-Risk/Cost Case
Management
• Adverse Event Detection
• Infection Control
• Compliance Reporting
• Drug Utilization Reviews
Operational
Intelligence
• Capacity Planning and
Management
• Patient Flow Management (ED
and Inpatient)
• Resource Utilization
• Patient Scheduling/Access
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Patient Cost Analysis
Service Line Analysis
Procedure Cost Analysis
Referral Analysis
Revenue Cycle Analysis
and Coding Optimization
• Market Analysis
Predictive
Prospective
Retrospective
Research and
Surveillance
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Health Services Research
Comparative Effectiveness
Drug Outcomes Research
Drug Safety Surveillance
Disease Surveillance
Patient Recruitment
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Components of a Data Driven Healthcare Organization
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?
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Components of a Data Driven Healthcare Organization
Defining Data Management
Data Architecture
“Blueprint” for the data warehouse or data mall
Technical Architecture
Data Governance
Server configuration and
administration, security
management, database
administration
Data ownership and
stewardship roles,
responsibilities and workflow
Metadata
Data Acquisition
Definitions, algorithms, impact
analysis and data lineage reporting
Integration of data from
disparate source systems
Analysis and Reporting
Dashboards, drill-down analytics and operational reporting
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Components of a Data Driven Healthcare Organization
Journey to Data Maturity
DISTINCTIVE
ADVANCED
FOUNDATIONAL
BASIC
Full Realization
Data stewardship
integrated with operational
committee structure
Partial Integration
Stewardship implemented at dept level
Enterprise strategy defined
Foundational projects planned
Building Blocks to Success
Stewardship capability defined
Consistent reusable data between applications
Starting Gate
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Components of a Data Driven Healthcare Organization
Governance Challenge
Strategy
Project
Sponsors
Leadership
Prioritization
Vision
Integrated
Governance
Model
Policy
Groups
Accountability
Information
Services
Oversight
Project
Committees
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CPOE Benefits
Example of Immediate Indicators for CPOE
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
What percent of physicians place orders using CPOE?
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.
What percent of orders are done via Communication Type?
Number of physician pharmacy call-backs to clarify medication orders?
What percent of delinquent charts per month?
Monthly ALOS per DRG
Number of cancelled/voided diagnostic orders (laboratory & radiology per week/month)
Turn Around Time (TAT) from diagnostic order to result documentation (Both Stat/Now
and Routine Categories)
"Overall" scores for AMI,CAP, CHF, and Pregnancy JCAHO core measures
Number per 100 admissions of Adverse Drug Events (ADEs)
Turn around time from order to administration of medication
Number of non-formulary medication orders per week/month (TNF)
ICU LOS
TAT for Blood Admin from Order to First Administration
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CPOE Benefits
Example of Long Term Indicators for CPOE
1.
2.
3.
4.
5.
6.
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13.
14.
Total cost per discharge (for DRGs of interest)
Use of peer-reviewed Order Sets (pathways and protocols) Physician and Nursing
Number of unexpected admission/ readmission to higher level of care per
week/month
Number of Ventilator Days /ICU stay
Number of Dietary Order Clarifications
Orders within a Care Plan
% of patients receiving Electronic Prescriptions ED
% of ED Physicians using Electronic Prescriptions
Reason for Radiology Exam Completed (Compare only the pre to post conversion
statistics)
Appropriate Reason for Exam (When field is changed to reflect another choice)
Reason for Consult Given (Free Text Field Use — Y or N)
Total cost per discharge (for DRGs of interest)
Use of peer-reviewed Order Sets (pathways and protocols) Physician and Nursing
Number of unexpected admission/readmission to higher level of care per
week/month
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CPOE Benefits
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
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CPOE Benefits
Score Card Example
Physician:
Specialty:
UTILIZATION OF FACILITIES
QUARTERLY REPORTING PERIOD:
INTERNAL MEDICINE
1st Quarter 2010
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
TOTAL
YTD
# 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
ALL SECTION
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
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 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
0
0
0
0
0
0
0
0
0
0
0
0
0
# 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
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
NONCOMPLIANT MEDICATION ORDERS
MEDICAL RECORDS
# of Suspension Days
PROCEDURES
PSYCHIATRY SECTION
0
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CPOE Benefits
Benefits Example
Reduction in Inappropriate Use of High Cost Drugs
Potential 7-Year Incremental Profitability Improvement
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.
$6.8-$8.6 Million
Impact Areas
Benefits Model Results
1) Outpatient Oncology eligibility improvement
2) Reduced inpatient use of Albumin
3) Substituion of Boniva, Levofloxin and Vancomycin by 30%
4) Reduced denials and short-payments
Reduced Utilization of High-Cost Drugs
Incremental 7 Year Profitability (000's)
IIncr. Profitability
Description
Technology Enablers
1) Workflow Engine/Business Rules
2) Contract Management
3) CPOE/Clinical Decision Support
$5,000
$4,000
$3,000
$2,000
$1,000
$0
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
Model Design
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
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Lessons Learned
Impact on the Organization
Data Continuum
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
• 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
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Lessons Learned
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)?
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Lessons Learned
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
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Case Study
Debbie Newman, MBA, CPHIMS
Director of Process Improvement
Licking 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 Office
Electronic 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 Bell
[email protected]
Debbie Newman
[email protected]
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THANK YOU