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Improving Healthcare with Healthcare Intelligence

2012 OCHIN Learning ForumSaturday, 15 November 2012

Dick Gibson MD PhDChief Healthcare Intelligence Officer

Providence Health & Services – Renton WA

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Agenda

• External and Internal Environment.• Three Data Platforms:

• EMR Reporting.• Caradigm (formerly 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• 65,000 employees• 2,500 employed physicians• 285 clinics• 400,000 member health plan• $11Billion 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.• Caradigm Amalga.• Enterprise Data Warehouse (EDW).

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EpicClarity

ReportingDatabase

CaradigmAmalga

EnterpriseData

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 near realtime.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 Intelligencewith three overlapping platforms

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EMFI

Community Lead

AK WA/MT OR/CA

(EnterpriseMaster File

Infrastructure)

Single EpicClarity Reporting

Database

EpicHyperspaceTransaction

System

ThreeIdenticalinstances

1111

Increased use of benchmarkingmeans more data need to be collected

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Thanks to Jeff Westcott MDat 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 ListsTo Select

Report Parameters

1515

Crystal ReportsPrintout

1616

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 StoreTables & SQL ViewsOptimized by Use

DataElements

SECURITY

AmalgaClient

Raw data feeds

GET STORE SHOW

ParsersMessage Filer

Caradigm 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.• 121 near realtime interfaces.• Outbound alerts connected to paging systems.• Data presented in simple Excel-like row & column format.

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Users can sort, filter, exclude columns

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Users can select filters within a column

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From any row, one can jump to complete

patient-level details

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Location history – Useful to infection prevention practitioners

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Hovering over a cell brings up all the

details

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Modified Early Warning System (MEWS)

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Sepsis Scoring

Scoring based on:• Vital signs.• 20 Lab analytes.

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Input formfor Global

TriggerTool

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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• C. difficile tracking.• Antimicrobial Stewardship.• Critical Care Acute Physiology Score.

Providence’s Use of Amalga

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Readmission Manager

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Prediction Model for Readmission Manager(1 of 2)

Factors positively correlated with 30-day readmission:• Stayed less than one day in the hospital.• Hour of visit = midnight.• Admission is unscheduled.• Number of visits in last six months: 6-20.• Diagnoses included: disorders of fluid, electrolytes, acid-base,

acute renal failure.

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Prediction Model for Readmission Manager(2 of 2)

Factors negatively correlated with 30-day readmission:• Only one chronic condition.• Stayed 3-12 days in the hospital.• No chronic conditions.• No prior admissions.• Average gap in visits in the past year: 61-365 days.• Marital status = single.

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Enterprise Data Warehouse (EDW)

• Retrospective and analytical.• Updated nightly.• Useful for reports and ad hoc analyses that cross multiple

clinical, financial, and administrative systems.• Users may find it easier to use than reporting against the

transaction system.• Presents users with a single naming convention and allows use

of multiple tools.

Enterprise Data Warehouse

CostingEpic Mat Mgt MD Cred Pt SatisStagingLayer

SourceSystems CostingEpic Mat Mgt MD Cred Pt Satis

NightlyCopy

• Bring over data from sources (transaction systems), table for table, without changing the data.

• Staging layer adopts the data model of the source system.• Able to trace data back to the original source system entry.

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Enterprise Data Warehouse

CostingEpic Mat Mgt MD Cred Pt Satis

EpicSourceMart

CostSourceMart

SupplyChain

SourceMart

ProviderSourceMart

ClientSatis

SourceMart

StagingLayer

SourceMarts

• Source Marts allow users to report and query directly from copy of the source system.

• Best for when all needed data come from one source system.

• Relieves transaction system from the CPU slowdown of reporting.

• New data are available each day for operational needs.

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Enterprise Data Warehouse

CostingEpic Mat Mgt MD Cred Pt SatisStagingLayer

DataBus

Patient ID

Stock Keeping Unit (SKU)

Provider ID

Encounter ID

• Need to conform dimensions that need to be linked in multiple source systems -- for example:

• Patient ID in one system is Variable Integer.

• Patient ID in second system is Variable 12 Characters.

• Simplified data bus eliminates detailed, time-consuming data model.

• Allows flexibility in bringing on new system with its own different data model. 34

Enterprise Data Warehouse

CostingEpic Mat Mgt MD Cred Pt SatisStagingLayer

DataBus

Patient ID

Stock Keeping Unit (SKU)

Provider ID

Encounter ID

Vag DeliverySubject Mart

Total HipSubject

Mart

ProviderComp

Subj Mart

NursingProductivitySubject Mart

SubjectMarts

• Subject Marts can be configured quickly to meet specific analytical needs.

• Data can be copied if needed for performance reasons.• Single source of truth for the organization.

Enterprise Data Warehouse

CostingEpic Mat Mgt MD Cred Pt Satis

EpicSourceMart

CostSourceMart

SupplyChain

SourceMart

ProviderSourceMart

ClientSatis

SourceMart

StagingLayer

DataBus

Patient ID

Stock Keeping Unit (SKU)

Provider ID

Encounter ID

Vag DeliverySubject Mart

Total HipSubject

Mart

ProviderComp

Subj Mart

NursingProductivitySubject Mart

SubjectMarts

SourceMarts

CostingEpic Mat Mgt MD Cred Pt Satis

EpicTeam

FinanceTeam

SupplyChainTeam

MedicalStaff

Office

QualityDept

Data Quality

• Feedback to operational teams is crucial for improving overall value of the data and hence organizational success.

• Data Stewards are appointed from each major source area.• Operations, not Healthcare Intelligence, determines the

tolerance for• Null fields.• Duplicates.• Discharge Dates before Admission Dates.

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Enterprise Data Warehouse

DataBus

Patient ID

Stock Keeping Unit (SKU)

Provider ID

Encounter ID

• Master Data Management for major assets of the health system:• Patient Identification.• Provider Identification.• Facility and Department Identification.

• Data Stewards from each major source system are responsible for that Master Data Table for entire company.

• Master Data become the single source of truth.

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Organization-wide Reference Data

• May be data and tables internal to the organization:• Surgical packs.• Drug formularies.• Job classifications.

• May be data and tables external and industry-wide:• Diagnosis codes (ICD-10).• Procedure codes (ICD-10 and CPT-4).• Medication names and ingredients.• Standard addresses, zip codes, and census tracts.

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Standard Naming Convention

• Using CamelCase naming of tables and fields.• Ending with Data Type indicator.

PatientFirstNameTXT. (indicating Text)OperationStartTimeDTS. (indicating DateTime Stamp)

• Intuitive to any user who understanding clinical or business operations.

• They can pull data together on-the-fly to answer questions.• Acts as a Semantic Layer in front of all the multiple source

systems.

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Benefits of the Naming Convention

Before After

90% time extracting data10% time interpreting data

10% time extracting data90% time interpreting data

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Metadata

• “Data about data.”• Descriptions of the tables and the columns within the

tables.• Shows the Original Source Name and the Standardized

Name for the table and the columns.• Conditions under which the data were collected.• When to use one column versus another.• A list of acceptable values or a sample thereof.

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Registries

• Can be considered another kind of data mart: a collection of data about patients of special interest.

• The EDW will likely have most of the transaction databases needed to fill the registry.

• There is usually a need to collect human-abstracted data and type it in to the database.

• Need a way to provide customizable data entry applications without requiring Information Services staff.

• How to securely collect data from the patient via the Internet?

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Microsoft Presentation Tools

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Adequate Tools for each User Role

• Highly formatted, scheduled, widely distributed Reports:• Microsoft Report Builder.• SAP Business Objects Crystal Reports.

• Ad hoc reporting (querying):• Microsoft PowerPivot (extension to Excel).• SAP Business Objects Web Intelligence (WebI).

• Data Visualization• Microsoft Power View.• Tableau.

• Dashboards• Microsoft PerformancePoint.• SAP Business Objects Xcelsius.

• Data Mining and Statistical tools: SPSS, SAS, R.

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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 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 usebest practice

Order Sets

Patientoutcome is

captured usingDocumentation

Templates

Patient status iscaptured usingDocumentation

Templates

HC Intelligenceanalyzes data for

most effectivetreatment

Documentationand Order Sets

are changed basedon new information

Basis of a continuously learning system

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60%

62%

64%

66%

68%

70%

72%

74%

76%

78%

80%

82%

84%

86%

88%

90%

92%

94%

96%

98%

0

50

100

150

200

250

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?

Improving Healthcare with Healthcare Intelligence

2012 OCHIN Learning ForumSaturday, 15 November 2012

Dick Gibson MD PhDChief Healthcare Intelligence Officer

Providence Health & Services – Renton WA