Reducing 30-Day Readmissions with Smooth Transitions of Care · 2013: Observed-to-expected (O/E),...
Transcript of Reducing 30-Day Readmissions with Smooth Transitions of Care · 2013: Observed-to-expected (O/E),...
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“Reducing 30-Day Readmissions with Smooth Transitions of Care”
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Housekeeping
• Moderator – Anthony Guerra, editor-in-chief, healthsystemCIO.com
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Agenda — Approximately 45 Minutes
• 20-30 minutes: Dwayne McNeil, Assistant VP, IS, Carolinas HealthCare System
• 5 minutes: A Word From Our Sponsor: Deborah Bulger, VP, Product Marketing, McKesson
• 10-15 minutes: Q&A w/Dwayne McNeil
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“Reducing 30-Day Readmissions with Smooth Transitions of Care”
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Reducing 30-Day Readmissions
PCPCS
5
Carolinas HealthCare System Introduction
Readmissions Background
Applying Analytics to Readmissions
Results
Next Steps – Current Focus
Q&A
Today’s Topics:
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Carolinas HealthCare System Introduction
PCPCS
6
39 hospitals and 900+ care locations in North Carolina, South Carolina and
Georgia
More than 7,400 licensed beds
11 million patient encounters per year
2,500+ system-employed physicians, 15,000+ nurses and 60,000 teammates
$1.5 billion in community benefit in 2013
More than $8 billion in annual revenue
More than 50 disease-specific certifications from The Joint Commission –
one of the highest totals in the country among comparable systems
The region’s only Level I trauma center
One of five academic medical centers in North Carolina
One of the largest HIT and EMR systems in the country
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CHS Information Services
PCPCS
7
Overall Information Services responsibility for Acute and Ambulatory Care, Continuing Care, Corporate Operations and our physician practice Medical Group
2 Main Data Center Locations: 801 S. McDowell Data Center and CMC-NorthEast campus, plus disaster recovery location on the CMC Campus (LCH Data Center)
Information Services By-the-Numbers (yearly totals):
• 490,000 customer support calls through the 24/7 Support Center
• 60,000 IS customer requests (OSRs), and 9,800 non-IS related requests
• 2,500 training sessions for staff and physicians (35,000+ attendees)
• 3,700 system, file and print servers, with 3,500+ terabytes of storage
• 42,000 email accounts and 67,000+ devices (PC’s, Laptops, Tablets, Printers and Mobile Phones)
• 12.7 million sq. ft. of wireless network coverage across our care locations
• 800+ applications supported
• 700+ IS teammates
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Readmission Imperative
PCPCS
8
The Right Thing to Do. Better Care for Our Patients.
Medicare Payment Advisory Commission. 2007. Report to the
Congress: Promoting Greater Efficiency in Medicare.
18 percent of Medicare
patients discharged from the
hospital have a readmission
within 30 days of discharge,
accounting for $15 billion in
spending.
• Pay for Performance programs
• Value Based Purchasing
• Insurance contracting
• Mitigate/eliminate CMS financial
readmission penalties
• Benchmarked & public quality
metrics
• One of most significant drivers of
higher cost (payer perspective)
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Readmission Imperative
9
CMS Hospital Readmission Reduction Program
Objectives: Improve Quality and Reduce Cost
Initial conditions (2013):
• Acute Myocardial Infarction
• Heart Failure
• Pneumonia
2015 expansion:
• COPD
• Elective Hip and Knee
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Readmissions as a CHS Goal
10
2009: Readmission baseline established
2010: Number of 30-day all-cause readmissions for AMI, HF and PN; Medicare only
2011: Rate of 30 day all cause readmissions following an index admission for AMI, HF and
PN; patients age ≥ 18 years; all payers
2012: Rate of 30-day all cause readmissions following an index admission for AMI, HF and
PN; patients age ≥ 18 years; all payers (definition modified to match CMS)
2013: Observed-to-expected (O/E), defined as the number of unplanned readmissions within
30 days of index admission, divided by the expected number of readmissions; patients age ≥
18 years; all payers
2014: Same as 2013 with minor definition change to match CMS
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Reducing 30-Day Readmissions
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Recent Areas for Readmission Action:
• Awareness & Education
• Identification of high risk patients (analytics)
• Standardize interventions for high risk patients
• Communication of high risk patients to providers
• Evaluation of each readmission cause
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Patient-Centered, Point of Care
Clinical Decision Support
Learning Collaborative
AnalyticsClinical Practice
PCPCS
12
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Analytically-driven, Personalized Care Value
13
Point-of-Care“Touch points”
UniqueIndividuals
Cost of Healthcare
Value
Population Health
Patient Experience
of Care: Quality and
Satisfaction
Applications
Rules
Interventions
Analytics
PatientProfile
Transactions
Social MediaEHR
Vendors
Pharmacy
Consumer Data
Claims
Lab
Information Infrastructure
Clinical Expertise
Learning Collaborative
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Project Vision
14
“We will analyze health and consumer data for
insights into individuals’ clinical risks and through the CHS Learning Collaborative…
…enable the best intervention and treatment decisions at the point-of-care…
…that optimize quality and cost-effective health services.”
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Patient Risk Assessment
15
Case Manager VariationCare manager ability to find and assess
risk factors varies
Done at AdmissionCare managers only have capacity to
assess patient risk at admission
Automation Decreases
VariationPatient risk is automatically calculated for
the care managers
Updated HourlyA patient’s condition and likelihood for
readmission can change throughout a
hospital stay; our tool captures these
changes hourly as clinical data change
Historical Future State
Limited CapabilityCare managers assign risk based on a
a few simple criteria that group patients
into two buckets: low risk and high risk
Risk Assessed from Predictive
Model Patient risk for readmission is predicted,
automatically, from over 40 key variables
pulled from Cerner
Done After EMR and
Patient ReviewCare managers need to review the
patient’s chart and examine the patient
prior to assessing risk
Done Prior to Seeing PatientAllows care managers to work more
effectively by prioritizing their workflow and
more efficiently through automating the
risk assessment.
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Care Interventions
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Difficult to HardwireCare managers required to
recognize a certain patient type
and remember what interventions
are to be assigned to the patient
Difficult to Measure
InterventionsCurrent care management tools
do not allow for evaluation of
intervention efficacy; limits our
ability to leverage our System
Recommendations
Assigned AutomaticallyPatients automatically assigned
interventions based on their
personal characteristics
Measure Efficacy of
InterventionsCapture of interventions and
data around outcomes will allow
us to measure the efficacy of
interventions and determine
patients who optimally benefit
Historical Future State
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Analytics: Risk Model Accuracy
17
• CHS risk model accuracy
of 79%
• Our model is better than
most other predictive
models in published
literature
• Based on 2 years of
readmission modeling by
our Dickson Advanced
Analytics (DA2) teamFalse Positive Rate
Tru
e Po
siti
ve R
ate
0%0%
100%
100%
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Analytics: Segmentation Model
18
• Purpose of Segmentation Model
• Example Segments
Population Segments Low Risk Medium Risk High RiskVery High
RiskTotal
Insured Healthy Adult 14.37% 10.91% 6.04% 4.18% 35.50%
Medicaid Pediatric 4.13% 2.54% 1.18% 0.36% 8.21%
Medicare Independent 5.13% 6.56% 6.10% 5.12% 22.91%
Medicare w/ Frequent Visits
and 9X90.78% 2.65% 5.61% 5.19% 14.24%
Middle Age w/ Frequent
Visits and Comorbidity0.55% 2.31% 6.03% 10.25% 19.14%
Total 25.00% 25.00% 25.00% 25.00% 100.00%
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Analytics: Risk Model Design
19
• Risk model predicts a patient’s individual risk for a 30-day,
unplanned readmission
• Our Canopy EMR and Enterprise Data Warehouse are
primary data sources
– Pulling over 40 predictive fields hourly to risk score
patients
– Using over 15 operational fields hourly to support
decision making
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Validation of our Readmission Risk Model
0% 10% 20%
Low
Medium
High
Very High
Pre
dic
ted R
isk
Actual Readmission Rate
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Readmission Analytics Current Status
Solution fully-deployed during Q3 2013 at our Metro hospitals
65,000+ Patients have had their risk for readmission automatically
calculated on an hourly basis
97,000+ Interventions have been assigned by our Case Managers
to patients based on their risk for readmission and clinical segment
208 Case Managers actively use the tool
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Readmission Analytics Key Points
Our analytics model is accurately identifying patients
with low/high risk of readmission
Case management teams are able to focus their
resources on the right patients and we are improving
our interventions to “best fit” patients
Impacting readmission requires interventions,
collaboration and a team approach
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Our Readmission Trends
23
0.60
0.70
0.80
0.90
1.00
2010 2011 2012 2013 Baseline
Readm
issio
ns O
/E
SA Average
Carolinas HealthCare System
SA Top Quartile
SA Top Decile
Carolinas HealthCare System Hospital-Wide ReadmissionsSystem and SA Benchmark Performance
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Highlight: Readmissions and Heart Failure
24
CHS developed the Heart Successprogram in 2013
Key components: • Risk assessment• Transition clinic• Co-management of the
patient with PCP• Telehealth interaction• Remote patient monitoring
when needed
Results:
• Reduction in all-cause CHFreadmissions by 3.5% to 13.89
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Readmissions and Heart Failure
25
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Other Resources We Use to Tackle
Readmissions Customer Relationship Management: eVariant
• Discharge call management
• Transitional Care Management team
• Interfaced with our analytics warehouse
Interactive Patient Education: GetWell
• Patient education readmission intervention options
• Interfaced with our EMR for tracking & analytics
Fully leverage our clinical call center: TeleHealth Solutions
• 24x7 access
• Integrated with acute, specialty, primary care and
continuing care (post acute)
Online patient portal (web & mobile): MyCarolinas
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Next Steps
27
Continue to refine our interventions & personalized
solutions, and spread across our enterprise
Enhance our view of patients through care transitions
transitions across all care locations
Improve our population health care management
solutions – broaden the team of support using
clinical decision support and automated workflow
Leverage virtual visit and online patient engagement initiatives
Innovate with new strategies and technologies
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“Reducing 30-Day Readmissions with Smooth Transitions of Care”
Deborah Bulger, VP, Product Marketing, McKesson
Connected Care & Analytics
29
Readmissions is a $25B issuePart of a larger $700B cost reduction opportunity
Source: NEHI health policy institute
Connected Care & Analytics
30
Readmission Management Continuum
Post acute
Acute
Risk
identification
1
Connected Care & Analytics
31
Readmission Management Continuum
Post acute
Acute
Risk
identification
Care
transition
1
2
Connected Care & Analytics
32
Readmission Management Continuum
Post acute
Acute
Risk
identification
Care
transition
Post
discharge
1
2
3
Connected Care & Analytics
33
Readmission Management Continuum
Post acute
Acute
Risk
identification
Care
transition
Post
discharge
Measurement
1
2
3
4
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Q&A
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Thank You!
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• Thanks to our sponsor: McKesson!
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