Improving Care Coordination and Readmissions Using Real Time Predictive Analytics from an HIE New Jersey / Delaware Valley HIMSS Conference Atlantic City, NJ October 29,

Speakers Dev Culver, Executive Director, HealthInfoNet Eric Widen, CEO, HBI Solutions 2

Agenda Background – HealthInfoNet – HBI Solutions Case study: St. Joseph’s Healthcare Summary and Q&A 3

Background: HealthInfoNet Quick Facts  Founded 2004  Independent, nonprofit organization  Operates the Maine HIE  Provides a single, state-wide electronic patient health record  Real time data from provider electronic health records  Data is standardized and aggregated  Provides reporting and alerts  Disease reporting to CDC  Real time clinical rules and alerts  Predictive risk scores

 HL7 messages  ADT  Laboratory orders and results  Outpatient prescriptions  Clinical notes and documents  Coding Background: HealthInfoNet Key Data  35 of 37 hospitals (all to connect in 2014)  38 FQHC sites  400+ ambulatory sites Connections  >1.4 million patients  >600,000 annual encounters  >3500 users Key Statistics

 Healthcare analytics company founded in 2011  Based in Silicon Valley  Leader in real time patient risk applications  Applications are used by providers, payers, ACOs and HIEs HBI team collective experience Mission: Improve patient and member health using data science to predict health risks and reduce practice variation Unique talent and experience:  Stanford researchers, data scientists  Frontline physicians  Performance improvement practitioners  Healthcare IT executives 6 Background: HBI Solutions

Background: HIE Analytic Offering Modules Population Health 30-Day Readmission / Return Risk Variation Management Hospital Performance Volume and Market Share Identify populations and individuals most at risk for future high costs, inpatient admissions, and emergency room visits. Identify inpatient encounters most at risk for 30-day readmissions or 30 day ED revisits. Understand resource variation by disease and cost category (length of stay, laboratory, radiology, etc...) to reduce unnec­essary practice variation. Compare actual to target performance for key performance indicators (KPI) using case mix and severity adjusted targets, including statewide norms. Track and trend volumes and market share by service area, disease, payer and patient demo­graphics.

Patient History Patient Risk of Event or Outcome Risk Model Development 1000s of Patient Features Age Gender Geography Income Education Race Diagnoses Procedures Chronic conditions Visit and admission history Outpatient medications Vital signs Lab orders and results Radiology orders Social characteristics Behavioral characteristics Multivariate Statistical Modeling – Decision Tree Analysis Machine Learning Available Risk Models Population Risk Models (predicts future 12 months) Predicted future cost Risk of inpatient admission Risk of emergency dept (ED) visit Risk of diabetes Risk of stroke Risk of AMI Risk of hypertension Risk of mortality Event Based Risk Models (predicts future 30 days) Risk of 30 day readmission Risk of 30 day ED re-visit Background: HIE Analytic Offering Predictive Risk Models

Background: HIE Analytic Offering Predictive Risk Use Cases 9

Background: HIE Analytic Offering Adoption Health Systems Fee for Service Community Hospitals ACOs Medical Group with Insurance Product State Medicaid Program Federally Qualified Health Centers The following types of healthcare organizations are using the HIE analytic applications for predictive risk management, population health management, budget forecasting, market share intelligence, and throughput management.

CASE STUDY: ST. JOSEPH HEALTHCARE 11

Background: Saint Joseph Healthcare Healthcare system in Bangor ME – 112 bed acute care community hospital – Primary care and specialty physician practices – 20,000 covered lives – Partner with FQHC Participates in several ACOs – Medicare shared savings – Medicaid – Commercial: CIGNA, Anthem, Harvard Pilgrim Using real time predictive risk scores daily to manage patients 12

Workflow: Ambulatory Patient Risk Management St. Joe’s Ambulatory Patient Population Management 20,000 Patients Assigned to St. Joe’s PCPs Low Risk Medium Risk High Risk Ambulatory based care managers assess real time population risk scores, including patient risks for costs, admission, ED visit, disease, and mortality. The practice sets thresholds for each risk category to flag “high” risk patients. Care managers proactively reach out to high risk patients to provide education and manage care gaps. 13

Workflow: Ambulatory Patient Risk Management Population Health Dashboard / Patient List – Understand patients at risk for ED visits, IP admissions, disease and cost

Workflow: Ambulatory to Acute Patient Risk Management St. Joe’s Ambulatory Patient Population 20,000 Patients Assigned to St. Joe’s PCPs Low Risk Medium Risk High Risk St. Joe’s Acute Inpatient and Emergency Patient Risk Management 6,000 Annual Inpatient Discharges 20,000 Annual Emergency Visits % Visit St. Joe’s Hospital Low Risk Medium Risk High Risk Upon admission, hospital based care managers assess real time risk scores for 30 day return to the hospital, and develop appropriate discharge plans Low Risk Medium Risk High Risk Community At Large Population 15

Workflow: Ambulatory to Acute Patient Risk Management Inpatient Encounter List – Understand patients at risk for 30 day readmissions 16

Workflow: Acute to Ambulatory Patient Risk Management St. Joe’s Ambulatory Patient Population 20,000 ACO Patients Assigned to St. Joe’s PCPs Low Risk Medium Risk High Risk St. Joe’s Acute Inpatient and Emergency Event Management 6,000 Annual Inpatient Discharges 20,000 Annual Emergency Visits Low Risk Medium Risk High Risk Post discharge, patients assigned to St. Joe’s PCPs are handed off to the ambulatory care manager for follow up. Patient’s risk drives the post-discharge care plan. Low Risk Medium Risk High Risk Community At Large Population 17 Patients discharge back to home

Reduced readmission rate overall and below target Results: St. Joe’s Readmission Rate Trend 18 Actual Performance Target (State Adjusted Norm) Performance

Saint Joseph Summary Findings Real time risk scores using clinical data from EHR Time savings and productivity improvement Algorithms have identified at-risk patients that would have been missed Algorithms provide better prediction at the higher risk levels HIE provides longitudinal patient record across Maine Risk scores are helpful - a robust care team and processes, however, are required to impact patient outcomes We use analytics across the care continuum 19