An Introduction to HealthInfoNet s HIE Reporting & Analytics. 6th Annual APS Healthcare Maine Conference May 14, 2015

Transcription

1 An Introduction to HealthInfoNet s HIE Reporting & Analytics 6th Annual APS Healthcare Maine Conference May 14, 2015

2 Presentation Outline HealthInfoNet Background Current Status of health information exchange Serving Mental and Behavioral Communities HealthInfoNet Analytic and Reporting Services

3 How does it work? HealthInfoNet s system combines information from separate health care sites to create a single electronic patient health record. Patient health information is automatically uploaded from a provider s electronic medical record system. The information is standardized and aggregated across care sites. HealthInfoNet automates reporting of certain illnesses and conditions like Lyme disease or food poisoning, to public health experts at the Maine CDC.

4 What is in the system? Patient Identifier and Demographics Encounter History Laboratory and Microbiology Results Vital signs Radiology Reports Adverse Reactions/Allergies Medication History Diagnosis/Conditions/Problems (primary and secondary) Immunizations Dictated/Transcribed Documents Continuity of Care Documents (CCD)

5 HIE Connections 35 of 37 hospitals (all hospitals under contract) 38 FQHC sites 400+ ambulatory sites including physician practices behavioral health and long term care facilities

6 HIE Penetration Of Maine Health Care Delivery Market By Segment Statewide HIN Enrollment HIE Status Hospitals Primary Providers Onboarding Goal 2015 Beginning of 2015 All 37 bidirectional 32 bidirectional Specialists 8 practices No defined target BH Orgs FQHC 20 No defined target LTC HHA 935 FTEs 490 FTEs Percent of Total (Estimates) 86% 83% 22% 10% 63% 9%

7 HIE Population Statistics As of May 1, ,506,781 lives in the HealthInfoNet database (this includes 97% of Maine s resident population) 198,173 Non-Maine residents have clinical data in the exchange 17,319 individuals have opted out (1.14%) 2,709 Maine clinicians and support staff are active users of the exchange 55% of active users accessed the exchange in April, 2015

8 Most recent HIE Usage Stats 8

9 Serving Mental & Behavioral Health Care Coordination 2011 change in Maine State law enabling licensed Maine mental health providers/organizations to exchange clinical data with HIN Maine s Opt In consent management process Initial pilot efforts with connecting mental/behavioral health providers to the statewide exchange States Innovation Models (SIM Grant) and bidirectional connection of mental/behavioral health providers 9

10 Reporting & Analytics Next generation of HIE Available to HIE bi-directional (sharing data) clients. Helps providers drive quality and cost improvements, manage risk and population health, and inform operational decision making. Uses real-time clinical data from the HIE to make a series of predictions. Offered in partnership with HBI Solutions (

11 Benefits: Improved Quality Better target care for patients with chronic disease to prevent complications and hospitalizations. Identify your patients most at risk for future utilization and help them avoid unnecessary ER and hospital visits, tests and procedures. Use real time data to identify quality measure gaps to put performance improvement plans in place quicker.

12 Benefits: Lower Costs Determine if market share targets for key service lines are met. Better identify services lines that are not hitting key performance measures. Prevent unnecessary visits for high cost and repeat services. Lower out of pocket costs for patients Avoid penalties for readmissions and repeat tests and procedures. Identify and reduce higher than expected hospital lengths of stay.

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

14 Analytic Platform: Solution Road Map Available Today Population health application o Utilization monitoring and trending o Disease prevalence o Risk of emergency visit, risk of inpatient admission, cost risk o Risk of diabetes, stroke, and AMI o Risk of 30 day readmission, risk of 30 day ED return Variation management application Performance benchmarking application Market share and patient origin application Available in the Future Natural language processing data integration Claims data analysis Medicaid population New risk models - mortality, CHF, Coronary Artery Disease, COPD

15 Live Demonstration 15

16 Feedback from Users The greatest barrier to managing patients at high risk for readmission is identifying them quickly. It s easy to capture the patients that we know need a lot of help. My goal was to reach those patients that are doing OK but might be getting into trouble. Nurse care managers are a limited resource and we have to use our time wisely. Using HealthInfoNet s analytics tool, I can focus my time on the patients most at risk. Jessica Taylor, RN, St. Joseph Healthcare In today s health care market, everyone is working hard to reduce costs. Historically making cost predictions based on risk meant turning to outdated claims data. HealthInfoNet s analytics tool couples 837 claims data with real-time clinical data. This allows us to negotiate with payers, using data more current that what they re using. William Wood, MD, St. Joseph Healthcare

17 Analytic Platform: Current Adoption General Acute Care Hospitals Budgeting and volume forecasting Throughput management - high risk ED patients / over utilizers 30-day readmission management ACO Pioneer CMS, State Employees, Commercial Population management risk stratification and proactive care management Medical Group with Insurance Product Population management risk stratification and proactive care management

18 Early Assessment of Impact Subjective Findings Analytic findings are believable Outperforms existing manual risk assessment tools Risk trending over set time frames very powerful Near real time data access fills huge patient management needs Clinical and encounter data can generate reliable predictive analytics Empirical Findings (now in process) Impact on resource consumption (ED Visits, Inpatient Admissions, Readmissions Clinical Performance (decline in population risk)

19 Discussion/Questions Devore Culver Executive Director & CEO, HealthInfoNet