Case Study "Using Real Time Clinical Data To Support Patient Risk Stratification in The Clinical...

Using Real Time Clinical Data To

Support Patient Risk Stratification

in The Clinical Care Setting

May 20, 2015

IHT2 Health IT Summit in Boston

Speaker

Devore Culver

Executive Director & CEO,

HealthInfoNet

Presentation Outline

Background

Analytic and Reporting Service

Case Study: Population Management – ED

Utilization

Adoption and Feedback

Q&A

Background

• HealthInfoNet operates the Maine health information

exchange HIE, and is an independent, nonprofit organization.

• Since 2009, 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.

What’s in the HIE 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)

• 35 of 37 hospitals (all

hospitals under contract)

• 38 FQHC sites

• 400+ ambulatory sites

including physician practices

behavioral health and long

term care facilities

HIE Connections

www.hinfonet.org

Most recent HIE Usage Stats http://www.hinfonet.org/products-services/product-use-statistics

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http://www.hinfonet.org/products-services/product-use-statistics







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 HIE clinical data to predict utilization, disease and mortality.

• Offered in partnership with HBI Solutions (www.hbisolutions.com)

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.

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.

Reporting and Analytics Modules

• Population Risk: Identify populations and individuals most at risk

for future high costs, inpatient admissions, and emergency room

visits.

• 30-Day Readmission / Return Risk: Identify inpatient encounters

most at risk for 30-day readmissions or 30 day ED revisits.

• Variation Management: Understand resource variation by disease

and cost category (length of stay, laboratory, radiology, etc...) to

reduce unnecessary practice variation.

• 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.

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, AMI, and mortality

o Risk of 30 day readmission, risk of 30 day ED return

• Variation management application

• Performance benchmarking application

• Market share and patient origin application

• Natural language processing integration

Available in the Future

• Claims data integration – Medicaid population

• New risk models – Hypertension, CHF, Coronary Artery Disease, COPD

Market Share

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This chart shows the hospital market share performance for Orthopedic Surgery patients residing in Penobscot County

Hospital 1

Hospital 2

Each bubble represents a

hospital. The larger the bubble

the greater the market share.

Market Share Trending

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Hospital 1

Hospital 2

This trends show market share performance over time for the top 2 hospitals for Orthopedic Surgery patients residing in

Penobscot County

Hospital KPI Performance

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This dashboard shows performance for key measures compared to an all-hospital target. Performance against target is

assessed over time. This organization has performed better than target over the last 6 months for readmission

management

Hospital 1

Case Study:

Population

Management: ED

Utilization

Copyright 2015, HBI Solutions, Inc.

Case Study:

Population Management: ED Utilization


On the Population Utilization Risk landing page, the user views and understands the latest

risk profile for their patients, including the number of patients at each risk level. This helps

the user understand the best allocation of care management resources to at risk patients.


User views and gains insight on the distribution

of future ED visit risk; decides to focus on the

highest risk patients – those patients with a risk

score (probability) greater than 40 - 40% or

more likely to visit and ED in the future 12

months.

Case Study:



To view individual patients at high risk for a

future ED visit, user selects appropriate criteria

in patient list filters.

Case Study:



Selecting a patient from the patient list, user can see the risk and visit history of the

patient. In this instance, the patient’s ED risk (red line) has risen significantly over

the last 3 months.

Case Study:



Selecting a patient from the patient list, user we can see the list of chronic diseases,

and medications.

Case Study:



Selecting a patient from the patient list, users can view interventions for specific

patient risks including polypharmacy, chronic diseases, and emergency and

inpatient utilization.

Case Study:



Results: this ACO has been successful in reducing the ED visits per 1000

members per month by 14%.

Case Study:


ED Visits / 1000 / Month

14% drop

“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.”

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

William Wood, MD, St. Joseph Healthcare

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

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)

Discussion/Questions Devore Culver

Executive Director & CEO, HealthInfoNet

[email protected]

www.hinfonet.org

mailto:[email protected]

http://www.hinfonet.org

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