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Improving Health with

Healthcare Intelligence

iHT2 Health IT Summit Seattle

Thursday, 23 August 2012

Dick Gibson MD PhD

Chief Healthcare Intelligence Officer

Providence Health & Services – Renton WA

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Agenda

• External and Internal Environment.

• Three Data Platforms:

• EMR Reporting.

• Microsoft Amalga.

• Enterprise Data Warehouse.

• Continuously Learning Organization.

• Big Data.

• Conclusions.

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What we believe about the future

• More care done for lower Per Member Per Month.

• Mental Health Care & Post Acute Care will grow significantly.

• Less reliance on physicians & more on alternative providers.

• More care delivered at home, at work, & on mobile devices.

• More self-care with Internet information sources.

• More scrutiny of our care by regulatory & consumer bodies.

• More telehealth, teleradiology, telepharmacy, etc.

• Genomic and proteomic data will revolutionize healthcare but not for a few years.

• More reimbursement by Health Savings Accounts (more retail a la carte buying) and by global premium.

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Our overall motivation

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What does this mean for our healthcare?

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If it is not indicated, we don’t do it.

If it is indicated, we do it reliably.

If we do it, we do it flawlessly.

We study our results and we continuously improve.

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• 32 hospitals • 7,000 beds • 64,000 employees • 2,300 employed physicians • 285 clinics • 400,000 member health plan • $10Billion Net Revenue

Including Swedish Health Services

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Two kinds of information systems

• Transaction Systems: Epic Hyperspace for healthcare.

• Captures all characterizations of the patient’s status.

• Both Pre-intervention & Post-intervention.

• Captures all our interventions: diagnostic & therapeutic.

• Point-of-care Clinical Decision Support guides providers.

• Reporting Systems: Retrospectively examine outcomes.

• Epic Clarity Reporting Database.

• Amalga.

• Enterprise Data Warehouse.

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Epic Clarity

Reporting Database

Microsoft Amalga

Enterprise Data

Warehouse

Routine scheduled operations reports.

Everything within Epic.

Can be integrated with transaction system.

Updated nightly.

Meaningful Use reports.

Used by dept manager.

Current care of active inpts.

Data updated immediately.

Alert leads to immediate intervention.

Data from multiple clinical systems.

Used by clinician, RN manager, or MD manager.

Review care over time.

All patients, all settings.

Data updated nightly.

Retrospective analysis guides decision–making.

All clinical & financial data.

Used by manager or analyst.

Health Care Intelligence

with three overlapping platforms

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EMFI

Community Lead

AK WA/MT OR/CA

(Enterprise Master File

Infrastructure)

Single Epic Clarity Reporting

Database

Epic Hyperspace Transaction

System

Three Identical instances

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Increased use of benchmarking

means more data need to be collected

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Thanks to Jeff Westcott MD at Swedish

And a lot of the data must come

from doctors at the point of care

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For Epic Clarity Reporting: SAP Business Objects

Crystal Reports

• Operational reports built by IT, read by manager.

• Precise, pixel perfect formatting.

• High volume publishing.

• Predictable questions.

Web Intelligence

• Query and analysis, sort, filter, drill down.

• Business user or analyst interacts with the data.

• Basic formatting only.

• Unpredictable questions.

Gradual trend from reports to analytics

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Crystal Reports

Drop Down Lists To Select

Report Parameters

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Crystal Reports Printout

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Web Intelligence

Pull the Data Fields here that you want

to see on the screen.

Pull the Data Fields here to determine what records to include

in the output.

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Data Acquisition & Distribution Engine (DADE)

Message Receiver

Lifetime Raw

Message Archive

Message Queue

Data Store Tables & SQL Views Optimized by Use

Data Elements

S

E

C

U

R

I

T

Y

Amalga Client

Raw data feeds

GET STORE SHOW

Parsers Message

Filer

Microsoft Amalga is a new entry in data management

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Amalga collects data from multiple disparate transaction systems into one alerting engine

• 117 servers.

• 87 Terabytes of provisioned storage.

• 150 realtime interfaces.

• Outbound alerts connected to paging system.

• Data presented in simple Excel-like row & column format.

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Modified Early Warning System (MEWS)

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Users can sort, filter, exclude columns

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Currently Active

• Modified Early Warning System (MEWS).

• Sepsis Scoring.

• Catheter Associated Urinary Tract Infection.

• Central Line Associated Blood Stream Infection.

In Process

• Readmission Manager

• Infection Control.

• Antimicrobial Stewardship.

• Pressure Ulcer Reduction.

• Falls Prevention.

Providence’s Use of Amalga

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Enterprise Data Warehouse Stack

Lab

EHR

Costing

Gen Ledger

Regist

Time & Att

Sources Data Management Layers

Mat Mgt

MD Cred

Pt Satis

Staging

Master Data & Conformed Dimensions

Finance

Data Marts

Surgery

Quality

Office

Bundles

Claims

Integration Data Services

Revenue

Patient MD Meds

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Source to Staging & Data Quality

• Bring over data, table for table, without changing the data.

• Relieve transaction system from the CPU slowdown of reporting.

• Examine Staging data quality and give feedback to Operations.

• Looking for null fields, Discharge dates before Admission dates, surgical stays without surgeon, etc.

Sources Staging

DATA QUALITY

Lab

EHR

Costing

Gen Ledger

Regist

Time & Att

Mat Mgt

MD Cred

Pt Satis Claims

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Master Data Management & Data Stewards

• A few key tables of the most important assets.

• Patients.

• Providers.

• Orderables: implants, medications, surgical supplies.

• Departments.

• Single source of truth for entire organization.

• These become the “Conformed Dimensions” of the facts.

• A Data Steward is appointed to manage the master table for a given data type for entire organization.

Patient MD Meds

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Data Integration Layer

• Patient, provider, & med names are replaced by keys from Master Tables.

• Addresses are validated & cleansed by outside Reference Tables.

• A patient’s multiple identities from multiple EMRs are associated with a unique key in the Patient Master Table and that key is used in this layer.

• Normalized tables are created for each of the major entities: Patients, Encounters, Orderables, etc.

Max data

break down

Patient MD Meds

Staging Data Integration

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Data Services Layer & Data Marts

• A large, denormalized Surgery Table is created by joining the Integration Layer Patient, Encounter, Procedure, Cost tables.

• Services is a permanent data store.

• Data Marts are built expressly for rapid visualization, query, and interactive analytics.

• Data Marts can be built and torn down rapidly to meet specific needs.

Data regrouped

for reporting

Finance

Surgery

Quality

Office

Bundles

Revenue

Data Services

Layer

Data Marts

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Semantic & Presentation Layers

• Semantic Layer uses column names that are familiar to business users.

• Standard Reporting & Dashboards for routine monitoring by untrained users.

• Special training for ad hoc query and interactive analytics.

• Statistical packages used for data mining.

Finance

Surgery

Quality

Office

Bundles

Revenue

Data Marts

Dashboard

Query & Analysis

Data Mining SE

MA

NTI

C L

AYE

R

Standard Reporting

Presentation Layer

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Benefits of the Semantic Layer

90% time extracting data 10% time interpreting data

10% time extracting data 90% time interpreting data

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Microsoft Presentation Tools

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What can Healthcare Intelligence do?

• Analyze emergency department patient throughput.

• Provide insight to revenue cycle performance in each work queue.

• Assess PMG physician clinical and productivity performance.

• Calculate cost of an individual encounter or average cost of a service line in preparation for bundled or global payment.

• Predict nurse staffing need for a shift in two weeks.

• Highlight sources and cost of physician variation in normal vaginal delivery and newborn care.

• Link physician office waiting time with client satisfaction.

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Three stages of healthcare intelligence

Prescriptive-we can suggest best diagnostic & treatment approach for patients with multiple chronic conditions.

Predictive-we know who is likely to be severely ill next year.

Descriptive-we know what we did and what works.

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Nov 2011

An example of prescriptive analytics

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What did their own patients tell them?

• Overall 98 patients with lupus, 10 of them developed thrombosis (blood clots).

• 15x: Relative risk of thrombosis with lupus and persistent proteinuria (protein in urine) vs lupus without proteinuria.

• 12x: Relative risk of thrombosis with lupus and pancreatitis (inflammation of pancreas) versus lupus without pancreatitis.

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Importance of this NEJM report

• First report of using EMR patient data search to aid immediate care of a patient.

• More EMRs lead to more data.

• Idea can scale with large combined data sets.

• Potentially better than anecdotal or expert opinion.

• Challenge will be system speed and relevance of findings.

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Epic and Amalga/EDW promote a continuous improvement cycle

Clinicians use best practice

Order Sets

Patient outcome is

captured using Documentation

Templates

Patient status is captured using Documentation

Templates

HC Intelligence analyzes data for

most effective treatment

Documentation and Order Sets

are changed based on new information

Basis of a continuously learning system

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Number of Primary Care Physicians at a given Treatment Quality score

2005

2010

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What is Big Data?

• Data that are hard to process by routine computing methods.

• Gartner calls it “Extreme Computing.”

• Any one of three characteristics can make data “Big.” Often it is more than one characteristic.

• Volume.

• Velocity.

• Variety.

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What are sources of Big Data in healthcare?

• Physician freetext dictation.

• EHR access logs.

• Medical images.

• Ubiquitous vital signs and fluid sampling from microchips embedded in garments worn at home and office.

• Detailed patient histories of all their habits, symptoms, families, food, activities, moods, purchases, thoughts.

• Electronic medical record entries nationwide.

• Freetext textbook and journal articles.

• Genomics, proteomics, human microbiomics.

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Big Data will revolutionize healthcare.

• Will require massive scale computing.

• But it will take 5-10 years.

• It’s not Either/Or – it’s Both/And.

• Meanwhile we need to master regular data.

• Indications for diagnostic & therapeutic intervention.

• High reliability healthcare.

• Patient throughput.

• Client satisfaction.

• Full value when Big Data combined with clinical, financial, and operational database across millions of patients.

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Conclusions

• Do the Right Thing, Do the Right Thing Right (David Eddy).

• Different data platforms serve different needs.

• We will need to use our EMRs to collect specific physician data.

• It’s the people and effort behind the technology that count.

• The EMR is the collector of data and it is also the Action Arm where knowledge is put back into practice.

• We need to continue to master regular data while we get ready for the revolution of Big Data.

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Questions?

iHT2 Health IT Summit Seattle

Thursday, 23 August 2012

Dick Gibson MD PhD

Chief Healthcare Intelligence Officer

Providence Health & Services – Renton WA