Using Predictive Analytics
to Reduce COPD
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Information about PinnacleHealth
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Today’s Environment
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PinnacleHealth Case Study
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Questions?
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Non-profit, community teaching health system
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Harrisburg Hospital founded in 1873
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720 beds in three hospitals
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43,000 discharges
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114,000 ED visits
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Participating in CMS ACO and Bundled Payment initiatives
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Overall Readmission rate 11.9%
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Readmission rate for patients with COPD 18%, Heart Failure
25%
PinnacleHealth Vision
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Statistics now at the core of modern medicine
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Predictive analytics is now a business IMPERATIVE
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Hidden in the vast amounts of generated data are discoveries that
could lead to better outcomes and costs
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Pressing need to turn data into information, information into
knowledge, and knowledge into action
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Like many others, Pinnacle is atrophic in this space and needed to
exercise the muscle; needs to become a core competency
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Key question is how to use this technology to improve health care
delivery AND OUTCOMES versus just score keeping
Typical approach to “Predictive Analytics” at Pinnacle
Triggers
•Medical spend threshold, Inpatient and/or ER visit counts and Specific Diagnoses to identify patients
Problem
• Identifies Patients Too Late in the Care Process to Make a Real Impact
Symptom of the Problem
What does Pinnacle need?
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Appropriate Information to Identify the Patient at the Right Time and at
the Point of Care
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Identify appropriate patients for interventions.
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Evaluate Risk – Patients with most identifiable gaps in guidelines or
forecasted acute care and assessment of cost impact.
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Patient specific actionable information:
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Clinical History
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Risk Profile
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Gap Report
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Disease Profiling – Understanding the history
Pinnacle’s Opportunity?
The Future: (Closed Loop Awareness Systems) – Altering the health system’s behavior in response to patient patterns in ways that will improve patient outcomes and makes the organization more successful at pursuing its goals.
• PinnacleHealth can identify and fulfill new clinical needs for a patient often before the patient knows themselves. To do so, new data points have to be collected to identify and simulate patient patterns.
• If managed properly, medical analytics can be developed to facilitate shared decision making.
Pinnacle’s “Prescriptive” Solution
• “Build a model that is as simple as possible, yet not simpler….”
– Provide Risk Identification and impact analysis for all patients not just catastrophic.
• Helps with “Regression to the Mean” issues as you have treatments across the Care Continuum.
– Forecast Days between Exacerbations and Acute Length of Stay. • Understand the scalability of disease states.
• Individualized Action - plans per Patient.
• Identify and study best opportunity for achieving reductions in total costs for chronic illness care.
• Best opportunity to impact cost by intervening with evidence based guidelines such as Home Health and Paramedicine Interventions.
COPD Project Assumptions and Hypothesis
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Acknowledgements:
– There is no unique methodology to predict when a patient with COPD will be admitted/readmitted.
– One needs a battery of tools constructed as observables such as a registry of patients with COPD, a long treatment history, and the clinical data that is relevant to the patient dynamics as a system.
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Model and Intervention Testing:
– Hypothesis H1: Patients who are admitted with a diagnosis of COPD can be
diagnosed as “at risk” in real – time before the occurrence of a readmission for COPD.
– Hypothesis H2: Interventions made subsequent to the “at – risk” prediction
Integration of Analytics at
Point of Care COPD
Established Process:
• COPD Analytics Methodology – presented and agreed upon by Pulmonary expert 1/27/14 • Medical Practice team developed bundled care pathway protocols in relation to predictive
rule sets – complete: standard order set provided.
• Research group evaluation of data requirements – 2/17/14 through 3/30/14. The
pulmonology team established clinical indicators and algorithms as potential predictors of COPD exacerbation and readmission.
• Modeling Methodology and Validation – 3/01/14 to current. The modeling team translated data into machine tools that recognized COPD exacerbation patterns.
• Model Demonstration – 4/17/14, a “Live” demonstration of actual patient data was
Integration of Analytics at Point of Care COPD –
continued…
Future Considerations - continued:
• Predictive Analytics Workflow Development – A clinical team will create a
workflow for incorporating decision support tools in existing protocols, electronic medical records (EMR) and team rounding.
• Create a registry to track predictions, outcomes and long-term trends – Design a predictive analytic technology infrastructure for scalability.
• Modeling team will work with Care Management to identify further predictive rule sets around non-clinical variables – Mass customize models across the health
system to specific care team needs.
Model Criteria for COPD
Model Criteria for Readmission Risk 2 Age <69 Risk 1 or 3 Ages 69-77 Risk 1 or 2 Ages >77 Cardiac Comorbidity Score <= 80% General Health Score 4-80% Albu. and Hemo. Normal Albu. or Hemo. Outlier Cardiac Comorbidity Score > 80% General Health Score > 80% Modeling allows PH to assign risk values to every aspect of the patient episode.
Model Criteria for Readmission Admission History Risk Cat. 2 Admission History Risk Cat 1 Age < 69 Ages 69-77 General Health Score 4-21% General Health Score 21-53% Hemo. Outlier Hemo. Normal Admission History Risk Cat. 3 Age < 77
General Health Score >53%
Anti-Pysch. or
Model Criteria for Readmission Admission History Risk Cat. 2 Admission History Risk Cat 1 Age < 69 Ages 69-77 General Health Score 4-21% General Health Score 21-53% Hemo. Outlier Hemo. Normal Admission History Risk Cat. 3 Age < 77
General Health Score >53%
Anti-Pysch. or
Depress. No Anti- Pysch. Or Depress.
COPD Project Summary
• Clinical Team developed COPD Risk Protocol: – CAT Score Completed
– Smoking Cessation Consult Completed
– Respiratory Completed Inhaler/Respiratory Medication Education – PAM Survey Completed (future)
– COPD Action Plan Completed by Patient and Reviewed on Rounds – Patient Physically had all Medications Prior to Discharge
– Medication Teach Back Completed
– PCP Appointment Scheduled within 7 Days of Discharge
– Pulmonary Appointment Scheduled within 4 Weeks of Discharge – PFT Scheduled for Post Discharge
Model Criteria for Readmission Admission History Risk Cat. 2 Admission History Risk Cat 1 Age < 69 Ages 69-77 General Health Score 4-21% General Health Score 21-53% Hemo. Outlier Hemo. Normal Admission History Risk Cat. 3 Age < 77
General Health Score >53%
Anti-Pysch. or
Depress. No Anti- Pysch. Or Depress.
COPD Modeling Effectiveness
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Difference between statistical significance and operational
significance
– Operational effectiveness means that whenever Pinnacle builds a model, data is partitioned into three subsets: training, testing, and validation.
– The model is built on the data in the training subset, and then evaluated on the testing subset.
– Now that you’ve gone through this process several times, however, you have a
problem—because you’ve been using the testing subset to help guide how you define the settings and parameters of your model for training, it’s no longer fully independent. – This is why you need a third subset of validation data. Ideally, the data you use to test
Model Criteria for Readmission Admission History Risk Cat. 2 Admission History Risk Cat 1 Age < 69 Ages 69-77 General Health Score 4-21% General Health Score 21-53% Hemo. Outlier Hemo. Normal Admission History Risk Cat. 3 Age < 77
General Health Score >53%
Anti-Pysch. or
Depress. No Anti- Pysch. Or Depress.
Testing Data - COPD Project Summary
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Pinnacle’s Testing Data shows that 40% of COPD patients tend to be
readmitted.
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Our Prescriptive Analytical tool can accurately identify 75% of those
patients.
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Also, we can predict which of those patients will be admitted, when they
will be admitted, and how long they will stay.
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Next step:
Model Criteria for Readmission Admission History Risk Cat. 2 Admission History Risk Cat 1 Age < 69 Ages 69-77 General Health Score 4-21% General Health Score 21-53% Hemo. Outlier Hemo. Normal Admission History Risk Cat. 3 Age < 77
General Health Score >53%
Anti-Pysch. or
Depress. No Anti- Pysch. Or Depress.
Model Criteria for Readmission Admission History Risk Cat. 2 Admission History Risk Cat 1 Age < 69 Ages 69-77 General Health Score 4-21% General Health Score 21-53% Hemo. Outlier Hemo. Normal Admission History Risk Cat. 3 Age < 77
General Health Score >53%
Anti-Pysch. or
Depress. No Anti- Pysch. Or Depress.
Key Factors for Success
Physician led from the start, kept them engaged, and they followed
through.
Multidisciplinary team including systems engineer, data scientist,
performance and quality improvement, care coordination, primary care,
and pulmonary physicians.
Medical director committed resources; other divisions (teaching, medical
practice) followed suit.
