session 6 predictive analytic models: a must in the journey to...
TRANSCRIPT
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Session 6
Predictive Analytic Models: A Must in the Journey to Reducing Readmissions
Karen Tomes, RN, MA, PHNVice President, Care Management and CoordinationAllina Health
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Learning Objectives
• Recognize the key elements of a care management redesign that help lower potentially preventable readmissions.
• Determine how predictive analytics can help risk-stratify patients to determine an optimal care transition plan.
• Recognize the role of education to engage members of the care team, patients, and families to lower readmissions.
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We believe that patients deserve to receive the optimum level of follow-up care and
support after discharge from the hospital—which in turn reduces hospital readmissions
and costs.
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Poll Question # 1
Is your organization facing penalties related to high readmission rates?
a) Yesb) Noc) Unsure or not applicable
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20%in 30 days
33%in 90 days
$41.3 billion in additional
hospital costs
Much of this is avoidable
Approximately
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Allina Health: Region’s Largest Health Care Organization
• 13 Hospitals• 82 Clinic sites• 3 Ambulatory care centers• Pharmacy, hospice, home care,
medical equipment• 26,000 employees • 5,000 physicians• 2.8 million+ clinic visits• 110,000+ inpatient hospital
admissions• 1,658 staffed beds• 3.4B in revenue• 32% Twin Cities market share
Allina Health is dedicated to the prevention and treatment of illness and enhancing the greater health of individuals, families, and communities throughout Minnesota and western Wisconsin.
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Background
Potentially preventable readmissions (PPRs) are common and costly.
Hospitals need to manage PPRs to improve quality and avoid penalties.
Many factors along the care continuum influence PPRs.
Managing care transitions at the time of discharge is key to reducing PPRs.
PPRs are readmissions analytically determined to be related by 3M software outputs.
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The Problem and Opportunity
Increasing focus on readmissions (state and national)
Lack of readily accessible and relevant data
Significant variation in case finding
Complex discharge needs
40% of readmissions occur within 7 days of discharge.
60% of Allina Health’s patients meet this criteria.
Resulting in poor care transition management at discharge for those who need it most.
Distributed across various locations and across multiple tools.
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Patients Are Most Vulnerable in the First Week After Discharge
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Num
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f Pat
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Apr. 2015-Mar. 2016 Discharges
Medical SurgicalMedical Cumulative Surgical Cumulative
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The Turning Point
1. Allina Health recognized that in order to reduce readmissions it needed to implement systemwide change.
2. Effective change would require a multi-pronged approach: • Care management process—how and at what points in the
care process. • Analytics—predictive models and measurement.• Education and awareness.
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Results
10.3% 27% $3.7overall reduction in PPRs
• Reduction in all risk categories
• 4.3% reduction for high-risk patients
• 21.3% reduction percent for moderate/high-risk patients
reduction for patients with clinic follow-up within 5 days
reduction in variable direct costs
million
Results period: January – December 2015
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Allina Health Achieved a Sustained Risk-Adjusted Reduction in PPRs
0.60
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0.90
1.00
1.10
1.206-
2012
7-20
128-
2012
9-20
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-201
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2015
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Act
ual-t
o-Ex
pect
ed P
PR R
atio
Month-Year
Actual/Expected PPR Ratio Lower Control Limit Mean Upper Control Limit
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Allina Health Has Also Seen an Increase in the Care Transition Patient Experience Measure
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Jan2015
Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan2016
Feb Mar Apr May Jun Jul Aug Sep Oct Nov YearEndGoal
Perc
entil
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Percentile
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The Case for Redesigning Care Management
At Allina Health:• 5% of patients are at a high
risk for a readmission.
• 17% of patients are at a moderate-high risk for a readmission.
• 60% of patients have complex discharge needs based on a combination of clinical, social, and post-acute needs.
Patient Census Analytics Dashboard
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How We Achieved Our Results
Care Process Management Process RedesignAnalytics and Predictive Models
Education and Awareness
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Care Management Process Redesign
Created complex discharge planning (CDP) teams: Partner with care management team to
determine patient needs during hospital stay and at discharge.
Develop and execute coordinated care management discharge plans.
Conduct care management transition conferences.
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Care Management Process Redesign
Care Management Transition Conferences (TC)• Facilitated meeting to outline care management plan
post discharge: Patient, patient’s family, RN care coordinator, and physician.
• Post-acute partner attends when indicated. After TC, care management coordinates care plan and
provides ongoing updates. From April 2012 to March 2016, Allina Health has completed
11,969 TCs.
• Continuous improvement of TC process: Feedback from involved clinicians to identify issues and
barriers.
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Analytics and Predictive Models
• Model utilizes EHR data to identify and stratify patient populations into risk categories.
• Predicts a patient’s risk of readmission within 30 days. Risk level (high, moderate-high,
moderate, low) based on: • Prior utilization of Emergency
Department and inpatient services• Medical history• Demographic information• Current clinical data
Developed a predictive model
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The Predictive Model Stratifies Patients Accurately When Analyzing Historical DataAllina Health, Potentially Preventable Readmission Rate by Risk Level, Apr. 2015-Mar. 2016 Discharges
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eadm
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30-Day PPR Rate 7-Day PPR Rate Readmission Volumes
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Analytics and Predictive Models
• Enables point-of-care identification of patients who might benefit from a TC. Risk levels from predictive model appear in
application 24-48 hours after admission.
• Enables Allina Health to track effectiveness of programs and interventions on readmissions. Based on key clinical variables, they are
able to compare outcomes of patients who received TC against those who did not.
Incorporated readmission metrics into organization-wide scorecard.
• Plan to incorporate risk model into EHR.
Implemented an analytics platform
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Measures of Caring ScorecardReadmissions metric added to increase systemwide visibility
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Example of Evaluating Effectiveness Based on Risk StratificationNumber needed to treat with a follow-up appointment to avoid readmissions decreases when evaluated at the low readmission risk level (2015 discharges)
14 14 17
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0102030405060708090
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High Risk Moderate-HighRisk
Moderate Low
Patie
nts
Seen
Number Needed to Treat with Follow-up Appt Within 5 Days to Prevent a 30-Day Readmission for
Surgical Hospital Discharges
Means, for example,
that 12 high-risk medical patients or 14 surgical
patients need to
typically be seen five days from
discharge to prevent 1
readmission.
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High Risk Moderate-HighRisk
Moderate Risk Low Risk
Number Needed to Treat with Follow-up Appt Within 5 Days to Prevent a 30-Day
Readmission for Medical Hospital Discharges
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Poll Question # 2
Does your organization currently use predictive analytics to drive outcomes improvement initiatives?
a) Yesb) Noc) Unsure or not applicable
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Success required active engagement of patients and care management teams.• Used TC to educate and support patients,
family, and outpatient caregivers. • Supported with standardized and easy-to-
understand materials.
• Internal education and marketing plan to engage care teams.• Newsletter, intranet site provide program material
and updates.
Education and Awareness
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Summary of How We Achieved Our Results
Using care management process redesign, predictive analytics, and education, we
were able to significantly reduce PPRs and save costs.
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Lessons Learned
• Predictive analytics and a readmission risk model are effective in identifying and managing patients at all readmission risk levels.
• Interventions need to be multi-pronged to impact all risk levels.• Example: follow-up appointments for all patients
• A standardized, interdisciplinary care management transition conference is very effective in engaging patients and families at high risk of a readmission.
• Changing the culture requires a lot of heavy lifting.
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Future Plans
1. Expand readmissions program and hardwire the process into the care management model to increase reliability and effectiveness.
2. Continuously refine predictive analytics to improve capabilities. Add readmission risk scores to electronic medical record so caregivers have the information front and center.
3. Integrate other sources of data into patient census analytics application.• Claims data
• Patient and family data (bi-directional)
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Analytic Insights
AQuestions &
Answers
28
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What You Learned…
Write down the key things you’ve learned related to each of the learning objectives
after attending this session
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Thank You