Activate Your Data and Healthcare Ecosystem

Luc ChamberlandWW Business Development Executive

June 18, 2015

Solutions targeting holistic care management reduce costs and deliver better quality outcomes

Care Outside the Hospital

Care for High cost/ High need Population

Care Inside Hospitals

Wellness Disease Mgmt.

Co

sts

Late Stage/Co-Morbidity Mgmt.

WellnessDiagnosis and

Early InterventionDisease Maintenance

Costs increase along the continuum of care

20% of people receiving care

consume 80% of

expenditures

The Healthcare Industry is dealing with data overload The average person projected to generate over 1 million gigabytes of health-related data

60%Volume, Variety, Velocity, Veracity

30%Volume

10%Variety

Clinical

Genomics

Exogenous

1100 TerabytesGenerated per lifetime

6 TBPer lifetime

0.4 TBPer lifetime

Source: "J.M. McGinnis et al., “The Case for More Active Policy Attention to Health Promotion,” Health

Affairs 21, no. 2 (2002):78–93

Determinants of health

3

Healthcare has Mountains of Unstructured Content

• How are you measuring and

reducing preventative

readmissions?

• How are you providing

clinicians with targeted

diagnostic assistance?

• Which patients are

following discharge

instructions?

• How are you using data to

predict intervention

program candidates?

• Would revealing insights

trapped in unstructured

information facilitate more

informed decision making?

� Physician notes and discharge summaries

� Patient history, symptoms and non-symptoms

� Pathology reports

� Tweets, text messages and online forums

� Satisfaction surveys

� Claims and case management data

� Forms based data and comments

� Emails and correspondence

� Trusted reference journals including portals

� Paper based records and documents

Over 80% of stored health information is unstructured*

Does unlocking the unstructured data help accelerate your transformation?

Biggest blind spot still remains unstructured data

6

6

Clinical Decision SupportEvidence-based Decisions

Clinical AnalyticsClinical Protocols

Private and Public InsurersPatient Education

Disease ManagementFraud PreventionRisk Management

Public HealthPandemic readiness

Vaccine inventory & distributionSanitation & public safety

Medical DevicesConsumer Relationships

Wellness and Care ServicesClinical Trials

EmployersBenefit Plan Design

Health & Wellness Programs

Transaction ServicesClaims Processing

Banks: Health Savings Accounts and Payments

Hospitals & PhysiciansElectronic Medical RecordsHealth information exchange

Patient ID & eHealth

Pharmaciese-prescribingNew services

GovernmentHealthcare PolicyMedical Research

Regulatory Compliance

Medical Research CentersClinical Research

Cohort StudiesClinical Trials

Patient EducationHealthy Lifestyles and DietLiving with Chronic Disease

Health ClubsHealth & Wellness Programs

Life SciencesClinical Development

Clinical TrialsMedication Compliance

Retail ClinicsConsumer Services

Patients /Consumers

Ecosystem Collaboration

Every organization is on its own analytics journey

Foundational

• What happened?

• When and where?

• How much?

Advanced, Predictive

• What will happen?

• What will be the impact?

• Dashboards

• Clinical data repositories

• Departmental data marts

• Enterprise data warehouse

BI Reporting

• Enterprise analytics

• Unstructured content analytics

• Outcomes analytics

• Evidence-based medicine

Population Analytics

• Streaming analytics

• Similarity analytics

• Personalized healthcare

• Consumer engagement

• Cognitive Computing

Care Optimization

Prescriptive

• What are potential scenarios?

• What is the best course?

• How can we pre-empt and mitigate the crisis?

IBM integrated portfolio for Smarter Care

Care identification

Coordination

Care planning Care delivery Outcome evaluation

Analytics and Cognitive Computing

Foundation

Data warehouse and data models

“Single view” customer EMPI (MDM)

Portals, mobile and collaboration

Remote monitoring and medical device connectivity

Paper and Fax capture, conversion and extraction

Population analytics Diagnostic support Care pathways Operational reporting

Cognitive computing

BI, reports and dashboards

Comprehensive global consulting, technology, infrastructure and managed services

IBM Care ManagementGenerate

individualized care plans

Analyzed Unstructured Data

Patient 360 View

Comprehensive Care Plan

Other Data Sources

EnterpriseServices

Unstructured data

Claims

EMR / EHR

Analysts

Multi-disciplinary Care Team

Provide Insight at point of care

Doctor’s notes

Case worker’s notes

Social Workers

Medical Professionals

Mental Health Professionals

Care Workers

Ingest and Unify Data

Unify and synchronize fragmented clinical, social and behavioral health information to create Mary’s personalized care plan

Support bidirectional integration with EMRs and other source systems following health care standards for data exchange

Leverage graphical mapping tooling. connectors (nodes), IHE, HL7, and Continua schemas and development pattern for easy integration

IBM Care Management

Standards Driven Integration Support

Care Workers AnalystsSocial Workers

Mental Health Professionals

Multi-disciplinary Care Team

Medical Professionals

Use intuitive and flexible outcome planning interface to compose comprehensive care plans for Mary

Visualize biopsychosocial profile of the client in 360 degree page

Collaborate across diverse stakeholders efficiently coordinating care, locating and referring care providers and optimizing resources

Patient Centered, Team Based Care

360 Degree View - Visualization of biopsychosocial profile

Receive referrals for leveraging configurable workflow and automatically create an outcome plan

Electronic Medical Records adoption reaches $22.3B adoption by 2015

HOWEVEREMR records do not support the aspirations or the workflow of integrated care,

but are a complementary enabler for integrated care solutions

Sources: EMR Adoption statistics Accenture 20141 Rudin, Bates 2014, 2 Bates 2010, 3 O’Malley et al 2010, 4 Graetz 2009, 5 Rudin 2014, 6 PWC 7 Cipriano et al

Pro

active

De

live

ry

Improved outcomes

Integrated Care

+ Multiple provider+ Patient engagement

+ Personalized care plans+ Workflow & Collaboration

Electronic Medical Records (EMR)

Provider-centricBilling oriented

Mature reporting

EMRs

Single EMR

Structured

Provider-centric

Predefined terms

System of record

Uniform care

Care Pathways

Multi-EMR integration

Dynamic, ad-hoc

Patient-centric

Non-standard terms

Support for future goals

Personalized plans

Group decision making

Integrated Care

Catalan Institute of Health, Catalonia, Spain, collaborates across clinicians and social care teams to cut costs and improve outcomes

Business problem:

Rising chronic disease in an aging population are consuming more

healthcare resources

Collaboration

Unified view of care plan across stakeholders increases effectiveness and informs adjustments

Coordination

Resources responsible for referral management and in home care delivery can collaboratively and quickly support incoming requests

Knowledge

Sharing of best practices and holistic view of the patient enables individualized care plans that engage clinical and social providers

Solution:

Targeted program for elderly aimed at improving adherence in

care programs, enhancing patient quality of life and satisfaction

with the healthcare system, and controlling costs

Clinicians and social workers

coordinate care planning and delivery

with a comprehensive view of the

individual

Outcome

• Cúram Social Program Management• IBM® Cognos® Business Intelligence V10• IBM DB2® Advanced Enterprise Server Edition• IBM InfoSphere® Warehouse Enterprise Edition• IBM WebSphere® Application Server• IBM SPSS® Modeler• IBM Global Business Services® – Application

Innovation Services• IBM Alliance Partner Otsuka Pharmaceutical

Co. Ltd.

South Florida Behavioral Health Network provides individual-centric treatment through coordinated care management and analytics

30 – 50% decreaseanticipated in the probability of re-arrest when integrated behavioral health treatment starts within 90 days

Solution components

Big Data & Analytics

Business challenge: People with mental illness who rely on publicly funded medical care are among the most vulnerable, often ending up incarcerated instead of receiving needed treatment. Even within this mental healthcare provider network, without a systematic view, treatment and follow-up care could still be disjointed, leading to preventable crises and incarceration.

The smarter solution: The network is combining coordinated care management and healthcare analytics to help deliver more consistent, harmonized patient care. Analytics personalize follow-up referrals by matching the individual’s unique needs to provider characteristics such as specialty, treatment options, location and languages spoken. Automated alerts and provider accountability help prevent individuals from falling through the cracks and ending up in crisis.

[W]e look proactively for creative solutions to coordinate care for our patients…

[such as this] innovative approach to improving efficiencies within our…system.

Automated alertssupport provider accountability and crisis prevention

Provides insightinto treatment-and-cost effectiveness and near-real time visibility of provider activity

#ibmiod

Applying Natural Language Processing

• Accurately identify and extract facts from text including negation

“55%” = LVEF“Patient does not show signs” = Negative Symptom

• Accurately interpret and assign values to ambiguous statements

“around 55%” = LVEF

“Shows slightly elevated levels” = if condition A = 10%, if condition B = 20%

• Infer meaning from non-contextual content

“Cut back from two packs to one per day” = Smoker

• Find inconsistencies between data sets

• Cleanse, enhance and normalize raw data

“Myocardia infarction” and “heart attack” = equal same thing

Correct misspellings and abbreviations through NLP

Enhance or augment by assigning correct RxNorm, SNOMED, ICD or other codes / terminology. “Broken femur” (diagnosis) -> 821.00 (ICD9)

• Preserve and structure facts and concepts from contextual content.

– Augment structured data in clinical systems (EMRs)

15

A 42-year old white male

presents for a physical. He

recently had a right

hemicolectomy invasive

grade 2 (of 4) adenocarcinoma

in the ilocecal valve was found

and excised. At the same time

he had an appendectomy.

The appendix showed no

diagnostic abnormality.

Patient Age: 42Gender: MaleRace: White

Procedure hemicolectomydiagnosis: invasive

adenocarcinomaanatomical site: ileocecal valvegrade: 2 (of 4)

Procedure appendectomydiagnosis: normalanatomical site: appendix

Care Management delivers out of the box value for content analytics

Problems

– Result of a series of interim annotations that identify diseases, symptoms, and disorders

– Normalize to standard terms and standard coding systems including SNOMED CT, ICD9, ICD10, HCC, CCS

– Capture timeframes of the problem

– determine if past or current problem

– Determine confidence (Positive, Negative, Rule Out)

Procedures

– Identify compound procedures

– Normalize to standard terms and standard coding systems including SNOMED CT, CCS, CPT

– Capture timeframes of the procedure

Medications

– Series of interim annotations that identify drugs, administrations, measurements

– Normalize to standard terms RxNorm

Cancer Diagnosis

– Attributes: Name, Date, Modality, Grade (Scale, Value), Behavior, Site, Measurement

•16

Allergies Drug allergies, generic allergies e.g. food

Demographic and Social

• Patient Age

• Living Arrangement

• Employment status

• Smoking status

• Alcohol use

Compliance & Noncompliance• Patient's history of medication

compliance with directions such as "take all doses, even if you feel better earlier“

• Noncompliance - Patient's history of medication noncompliance with directions.

Labs resultsType of lab test performed, unit of measure,

result value

Ejection Fraction – in support of CHF use cases

100+ dictionaries, 800+ parsing rules

Care Management Analytics Use Cases

Regulatory Measures• Quality measure gaps

– Meaningful use gaps

– PQRS

– HEDIS

• Risk-adjusted scoring

17

Clinical and Research• EMR enrichment

• Post-discharge follow-up

• Screen research subjects

• Identify risk factors

• Detect adverse events

Payers Providers Life Sciences

UNC HealthcareImprove reporting and post-discharge communication of adverse events

10%+Quality improvement of PQRS measures

Proactive care communicationEnsure relevant, accurate and timely communication across transitions of care by automatically generating reminders and alerts to inform the care team

Reduce readmission by extracting predictors of risk from clinical notes

Business problem: Some of the data required to calculate PQRS measures are locked in clinical notes. Also, the need to reduce hospital readmissions is a major challenge and expensive for most healthcare providers in terms of financial penalties and unreimbursed care. Improve patient health with better follow-up of post-discharge instructions and further tests and treatment plans after the leaving hospital.

Discharge instructions consist of many pages of free-text notes and can be difficult for patients and care managers to decipher creating the potential to miss valuable information such as medications, diagnosis and follow on appointments.

Solution: Care Management leverages unstructured data from discharge instructions, in the form of reminders and alerts, to better enable post-discharge healthcare providers and empower those responsible for patient centered care.

Hospital staff can now use the solution to analyze unstructured text for key discharge terminology using natural language processing to determine the context of the content, extracts any relevant data from discharge summaries, doctors’ notes, UKG reports and other unstructured discharge related content, and converts it into structured data. This structured data is then used to generate alerts and reports for patients’ primary care doctors and other caregivers. Clearer data and better communication between health professionals helps ensure that patients keep their follow-up appointments and complete their post-discharge treatment. Not only can patients stay healthier, but the hospital can also save millions of dollars on costly hospital readmissions.

https://www.youtube.com/watch?v=LQTXQsAnq7s

Risk-Adjust CMS Payments by Finding Comorbidities to Influence HCC Scores

19

Payer leverages IBM Care Management, helping them achieve quality measures and save more than $2.5M annually

Business Challenge– Three million members across 21 states.

– Payer’s mission: improve the health of the community through health insurance solutions for the under-insured and uninsured.

– Challenge: quality of the information provided by its existing HEDIS (Healthcare Effectiveness Data and Information Set) reporting system. There were significant gaps, which resulted in limited insight around the effectiveness of the care plan.

– The key contributor to this issue was data trapped in clinical and physician notes that were unstructured. Payer did have a concession plan that involved a third party manually reviewing each member’s chart, but this was not only time consuming and costly, it was not delivering effective results.

IBM Solution– Parse unstructured clinical notes to improve the quality of medical records

– Reduce annual labor costs by $2.5M USD by eliminating the need for manual analysis of charts by a third party company

– Improve distribution and response time to enterprise-wide HEDIS rate calculations

20

Care Management analytics and Epic integration

• Care providers are adopting electronic medical records but traditional doctors’ notes still play an important role in tracking & managing patients

• Q1 2014: Integration testing with the Epic EMR 2014 release & IBM Advanced Care Insights for Natural Language Processing (NLP) has been successfully completed, solidifying leadership of both companies in their respective markets

What’s new?

• Traditionally a manual process, IBM’s software can analyze doctors’ notes & transform them into a format that can be readily uploaded into the patient record, including automatically adding industry standard diagnosis & treatment codes

• Allows doctors to accurately capture information from unstructured text in real-time, to improve patient outcomes & simplify administrative processes

What value does this provide?

• Empowers 297 Health Systems that have adopted Epic to capture actionable insight from IBM’s NLP capabilities – the same technology utilized in the revolutionary Watson cognitive system

What does this mean?

Clinician Use Case of Epic-NLP IntegrationStep 1: Clinician Enters New Encounter Note in Text Field

Clinician Use Case of Epic-NLP Integration Step 2: View additional medical problems that are recognized via NLP

Clinician Use Case of Epic-NLP Integration Result: appropriate condition codes are generated

Case Study: Readmission predictors at Seton

The Data We Thought Would Be Useful … Wasn’t

• 113 candidate predictors from structured and unstructured data sources

• Structured data was less reliable then unstructured data – increased the reliance on unstructured data

New Insights Uncovered by Combining Content and Predictive Analytics

• LVEF and Smoking are significant indicators of CHF but not readmissions

• Assisted Living and Drug and Alcohol Abuse emerged as key predictors (only found in unstructured data)

• Many predictors are found in “History” notations and observations

Predictor Analysis % EncountersStructured Data

% Encounters Unstructured

Data

Ejection Fraction (LVEF) 2% 74%

Smoking Indicator 35%(65% Accurate)

81%(95% Accurate)

Living Arrangements <1% 73%(100% Accurate)

Drug and Alcohol Abuse

16% 81%

Assisted Living 0% 13%

25

1. Jugular Venous Distention Indicator

2. Paid by Medicaid Indicator

3. Immunity Disorder Disease Indicator

4. Cardiac Rehab Admit Diagnosis with CHF Indicator

5. Lack of Emotion Support Indicator

6. Self COPD Moderate Limit Health History Indicator

7. With Genitourinary System and Endocrine Disorders

8. Heart Failure History

9. High BNP Indicator

10. Low Hemoglobin Indicator

11. Low Sodium Level Indicator

12. Assisted Living (from ICA Extract)

13. High Cholesterol History

14. Presence of Blood Diseases in Diagnosis History

15. High Blood Pressure Health History

16. Self Alcohol / Drug Use Indicator (Cerner + ICA)

17. Heart Attack History

18. Heart Disease History

Top 18 Indicators

Model Accuracy, precision and recall

Relevent Not relevent

Recall Fraction of relevantInstances retrieved

PrecisionFraction of retrievedInstances that are relevant

Retrieved by model

False negatives

False positives

Similarity analytics supports data-driven decisions based on comparisons to a meaningful cohort

Physicians have limited time and resources to focus on complex care dilemmas, yet many

patients have multiple conditions

Clinical trials and health research typically focus on single diseases

Treatment guidelines are usually developed with “standardized” reference data

Care delivery tends to be ad hoc in nature; care guidelines are not followed 40 percent of

the time

Why not augment care-delivery guidelines with population-specific insights—including those derived from unstructured data—to enhance decision making?

83 percent of Medicaid patients have at least

one chronic condition (almost 25 percent have at

least five comorbidities)2

83%

1

Medicare patients with 5 or more chronic conditions accounted for 76 percent of all Medicare expenditures3

76%

5

1 RAND Health, Projection of Chronic Illness Prevalence and Cost Inflation, October 2000.2 Health Affairs, The rise in spending among Medicare beneficiaries: the role of chronic disease prevalence and changes in treatment intensity, K.E. Thorpe and D.H. Howard,

August 22, 2006.

28

For this patient …

• Analyze longitudinal data to develop profile across 30,000+ possible points of comparison

• Determine the individual risk factors for this patient based on the desired outcome

• Create an outcomes based personalized

How Similarity Analytics Work, Part 1

29

Based on this personalized profile …

• Find the most similar patients (or dynamic cohort) from entire population

• Analyze the attributes and outcomes for this cohort (across 30,000+ dimensions)

• Predict the probability of the desired outcome for patient in question

• Suggest a personalized care plan based on the unique needs of this patient

Desired

Outcomes

Historical Observation Window Prediction Window

This Patient’s Longitudinal Data Predicted Outcome For This Patient

Dynamic Cohort Longitudinal Data with Outcomes

How Similarity Analytics Work, Part 2

30

Treatment EfficacyIdentifies the outcomes of drug treatments prescribed to groups of similar patients

31

Thank you

Luc Chamberlandlchamber@ca.ibm.com