Copyright © 2015 Splunk Inc.

Data Informed Healthcare Delivery

Process Improvement

2

Agenda

Problem Background and MotivationCapabilities: MethodologyCapabilities: Data Integration and Feature EngineeringCapabilities: Statistics, Machine Learning, and VisualizationOperational IntegrationProduct Demonstration

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1. Get Ready2. Travel by Car3. Conference

Starts4. Join Reception5. Have Dinner6. Go Home

1. Get Ready2. Travel by Car3. Conference

Starts4. Give a Talk5. Join Reception6. Have Dinner7. Go Home

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More Cases

1. Get Ready2. Travel by Car3. Conference Starts4. Join Reception5. Have Dinner6. Go Home7. Travel by Car

1. Get Ready2. Travel by Car3. Conference Starts4. Give a Talk5. Join Reception6. Have Dinner7. Go Home8. Travel by Car

1. Get Ready2. Travel by Air3. Conference Starts4. Give a Talk5. Join Reception6. Have Dinner7. Go Home8. Pay Parking9. Travel by Car

1. Get Ready2. Travel byTrain3. Conference Starts4. Join Reception5. Have Dinner6. Go Home7. Pay Parking8. Travel by Car

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Generalized Information Flow Model for Chronic Care

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3002133/

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Healthcare Delivery Workflow Examples #1

Healthcare Delivery Workflow Examples #2

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Health Events

1010

Process Mining

EventLog

Mining Techniques

MinedModel

1. Start2. Get Ready3. Travel by Train4. Beta Event Starts5. Visit Brewery6. Have Dinner7. Go Home8. Travel by Train

1. Start2. Get Ready3. Travel by Train4. Beta Event Starts5. Give a Talk6. Visit Brewery7. Have Dinner8. Go Home9. Travel by Train

1. Start2. Get Ready3. Travel by Car4. Beta Event Starts5. Give a Talk6. Visit Brewery7. Have Dinner8. Go Home9. Pay Parking10. Travel by Car

1. Start2. Get Ready3. Travel by Car4. Conference Starts5. Join Reception6. Have Dinner7. Go Home8. Pay Parking9. Travel by Car10. End

Start

Get Ready

Travel by CarTravel by Train

BETA PhD Day Starts

Visit Brewery

Have Dinner

Go Home

Travel by Train Pay for Parking

Travel by Car

End

Give a Talk

Start

Get Ready

Travel by Air

Travel by Car

Conference Starts

Give a Talk

Join Reception

Have Dinner

Go Home

Travel by Train

Travel by Car

Pay Parking

End

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What is Process Mining?

Source: http://tinyurl.com/qzqtas8

Analyze Observed Behavior from event data and metadata to discover patterns, monitor compliance, and optimize workflow.Performance Analysis Auditing/Security Detect Bottlenecks, Deviations in Flow

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Process Mining Use Cases

ACTION ORIENTED

Redesign Process

Adjust Parameters

Intervene (ad-hoc problem solving)

Support: Detect deviations and bottlenecks

Support: Predict, Recommend

GOAL ORIENTED

Improve KPIs related to Time

Improve KPIs related to Cost

Improve KPIs related to Quality

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Why Process Mining?Traditional As-Is analysis of business processes strongly based on the opinion of process expert. The basic idea is to assemble an appropriate team and to organize modeling sessions in which the knowledge of the team members is used to build an adequate As-Is process model.

Discover actual behavior of people, organization, and machines and relate to modeled behavior.

Correlate millions of ad-hoc events showing how reality is different from perceptions, opinions, and beliefs.

Provide clue for standardization and better prepare to handle ad-hoc events.

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Scheduling

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Waiting Time

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Surgical Checklist

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Care Coordination

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Virtual

Physical

Cloud

Healthcare Data is Time Oriented and Diverse

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EHR Systems

WebServices

Developers

App Support

Telecoms

Networking

Desktops

Servers

Security

Devices

StorageMessaging

Claims

Clickstream

HIE

PatientPortals

Healthcare Apps IT Systems and Med Devices Patient-Facing Data

MedicalDevices

CDR MedicalRecords

PHI Access Audit Logs

HL7 Messaging

Billing

Departmental and

HomegrownApplications

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Domains of Data Diversity in Health Data

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SubjectsPersons, Sensors, Actuators, Mobile

Devices

Information Users

Clinical, Family, Patient

System and Locations

Home, Hospital, ER, Nursing Homes

Ownership and Management

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Example of Events: Healthcare ServicesEntity ID Event ID Properties

Timestamp Activity Resource

12345678 4798669 02/06/2015 14:00 Primary Care Visit Pete

4798670 04/06/2015 11:00 Surgery Rose

4798671 04/06/2015 12:00 Primary Care Visit Pete

4798672 04/06/2015 10:00 Chemotherapy John

4798673 04/06/2015 15:00 Evaluation Pete

98765432 5798670 03/06/2015 14:00 Primary Care Visit Pete

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Example of Events: Resources (Devices/Beds)Entity ID Event ID Properties

Timestamp (creation)

Patient identifier Begin time End time

D1 4798669 02/06/2015 14:00

p1 14:00 15:00

4798670 04/06/2015 11:00

p2 15:15 16:30

4798671 04/06/2015 12:00

p3 16:45 17:00

4798672 04/06/2015 10:00

p4 17:15 18:00

4798673 04/06/2015 15:00

p5 18:15 19:00

D2 5798670 03/06/2015 14:00

p6 15:00 17:00

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Example of Events: MedicationsEntity ID Event ID Properties

Timestamp NDCNUM Days Supply

12345678 4798669 02/06/2015 14:00 378214605 30

4798670 04/06/2015 11:00 378024301 60

4798671 04/06/2015 12:00 378024301 90

4798672 04/06/2015 10:00 378024301 90

4798673 04/06/2015 15:00 228202996 90

98765432 5798670 03/06/2015 14:00 378024301 60

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Example of Events: Lab Entity ID Event ID Properties

Timestamp Key Value

12345678 4798669 02/06/2015 14:00 HbA1C 8%

4798670 04/06/2015 11:00 LDL 100 mg/dl

4798671 04/06/2015 12:00 HDL 50 mg/dl

4798672 04/06/2015 10:00 Systolic 110

4798673 04/06/2015 15:00 Diastolic 75

97865432 5798670 03/06/2015 14:00 HbA1C 9%

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US Army Dental Command gain operational visibility and improve dental service delivery with Splunk

• Integrate dental device logs, DICOM image metadata, and patient satisfaction surveys.

• Alerts in case of anomalies.

• Correlate wait time with patient satisfaction data and system performance degradations.

• Faster identification of system capacity bottlenecks such as excessive wait time.• Proactively find unused resources and reallocate the resources.• Saved millions by not buying new devices but optimize the current resource allocations

• Limited visibility into device bottlenecks and customer satisfaction factors.

• Limited data for capacity planning and workflow optimization

Key Challenges Key Splunk Functions

Business Value

28

Agenda

Problem Background and MotivationCapabilities: MethodologyCapabilities: Data Integration and Feature EngineeringCapabilities: Statistics, Machine Learning, and VisualizationOperational IntegrationProduct Demonstration

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Improvement Method

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Systems Engineering

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Call for efficiency, consistency, and safety

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Towards a Learning Healthcare System

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A Connected Healthcare System

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Discovery

Discover Diagnose Enhance

1Compliance

DetectMonitor Compare

2 3Enhancement

Forecast Predict

Recommend

Process Mining Methods

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Linking Data to Methods and Applications

Persist, Repeat

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Discovery

Compliance

Enhancement

Post Mortem

Pre Mortem

De-facto/Discovered Process Flow: Flow, Rules, Resources

Recommend next steps. Predict/Suggest Risks and likely future events

Create rules and KPI from discovered process flow

Monitor rules and KPI implemented in production

Create alerts on non-compliance

Data Method Application

37 37

Real World Business Questions/Formulate

HypothesesData Collection Data Preparation

Modeling/SimulationCommunication,

VisualizationReports, Findings

Evaluation

Data Science for Process Mining in Action

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Process Mining Platform

Real-Time Monitoring, Detection, and Predictions

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CaseManagement

Anomaly Detection, Linkage,

Correlations/ Patterns

AlertsPredictive Modeling/

Model Maintenance

Healthcare Events

Standard Reports/ Queries

Data Warehouse

Data Archival

Rules System

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Agenda

Problem Background and MotivationCapabilities: MethodologyCapabilities: Data Integration and Feature EngineeringCapabilities: Statistics, Machine Learning, and VisualizationOperational IntegrationProduct Demonstration

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Barriers for Business Value

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Ability to easily ingest diverse data

setsFlexibility to capture data

Restricted system access

Quickly getting value from data

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Required Capabilities

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Schema-less approach/ late

binding to schema

Dynamic “normalization”

of data

Agile analytics and reporting

Scalable search and analytics

Process Data Mining Core Engine

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Computational FrameworkIntegrate Untapped Data: Any Source, Type, Volume, Velocity

Healthcare Apps Data/HL7 Event Logs

Healthcare Apps Audit Logs

Medical Device (PACS)/RFID Metadata (logs)

Patient Generated Data

Hadoop Clusters Relational Database No SQL Data StoreSplunk Clusters

Explore Visualize Dashboard ShareAnalyze Monitor and alert

External Applications Integration

(SDK, REST API)

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Application Development Platform

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Data Integration: Ingest any text data

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MSH|^~\&|EPIC|MGH||MGH|20150324190937|OHEDSCRIBE|ADT^A08|725467|T|2.3|||||||||………PID|1||12345^^^EPI^MR||LUCUS^STEPHANEY||19751225|M|||^^^^^US^P|||||||6100215419|999-99-9999|||||||||||N||........

<recordTarget> <patientRole> <id extension="12345" root="PlaceholderOrganization" /> <addr use="HP”> <streetAddressLine>180 Fake Road</streetAddressLine> <city>Providence</city> <state>RI</state> <postalCode>02912</postalCode> <country>US</country> </addr> <telecom use="WP" value="tel:+1-401-867-7949" /> <patient> <name> <given>Stephaney</given> <family>Lucus</family> </name> <administrativeGenderCode code="F" codeSystem="2.16.840.1.113883.3.560.100.2" displayName="Male" />

{ "resourceType": "Patient", "identifier": [ { "system": "urn:oid:1.2.36.146.595.217.0.1", "value": "12345", "period": { "start": "2001-05-06" } } ], "name": [ { "use": "official", "family": [”Lucus"], "given": [”Stephaney”] }, ], "gender": { "coding": [ { "system": "http://hl7.org/fhir/v3/AdministrativeGender", "code": "M", "display": "Male" } ] }, "birthDate": "1974-12-25", "address": [ { "use": "home", "line": ["534 Erewhon St"], "city": "PleasantVille", "state": "Vic", "zip": "3999" } ]}

PatientidentifiernametelecomgenderbirthDatedeceasedaddressmaritalStatus….active

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Getting Data In

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Universal and Heavy Forwarders Modular Input

Stream, HTTP Event Collector RDBMS, Hadoop

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Tagging for “Normalization”

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PatientidentifiernametelecomgenderbirthDatedeceasedaddressmaritalStatus….active

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Search events with tag in any field

Search events with tag in a specific field

Search events with tag using wildcards

Adding Metadata Knowledge: Search with Tags

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Tag=GLYCEMIC, ASTHMA

tag::DX=diabetes type 2

Tag=diabetes*

1

2

3

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Aliases Normalize field labels to simplify search and correlation Apply multiple aliases to a single field

Example: Username | cs_username | User user Example: pid | patient | patient_id PATIENTID

Aliases appear alongside original fields

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Event Tagging Classify and group common events Capture and share knowledge Based on search Use in combination with fields and tags to define

event topography

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1) Regular Expression

2) Natural Language Processing using SDK and REST API

Feature Extraction from Texts

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AgendaProblem Background and MotivationCapabilities: MethodologyCapabilities: Data Integration and Feature EngineeringCapabilities: Statistics, Machine Learning, and VisualizationOperational IntegrationProduct Demonstration

Sparkline: Visualize frequency distributions Identify co-occurring spikes

Sankey Diagram: Visualize flow and frequency

Graphs: Visualize Network and Relations

Find Pathway of Similar Patients:Seeing Millions at a Time and Visualize Pathways

Parallel Coordinates: Visualize Multivariates

Tree-Map

We should stop acting as if our goal is to author extremely elegant theories, and instead embrace complexity and make use of the best

ally we have: the unreasonable effectiveness of data.

Clustering

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Association Rule

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Anomaly Detection

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AgendaProblem Background and MotivationCapabilities: MethodologyCapabilities: Data Integration and Feature EngineeringCapabilities: Statistics, Machine Learning, and VisualizationOperational IntegrationProduct Demonstration

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KPI Management

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Actionable Alerting

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Business Value of Process Mining

Save lives, reduce error, optimize time

1Reduce cost,

increase efficiency

2 3Improve patient

outcome, experience, and

engagement

68

Agenda

Problem Background and MotivationCapabilities: MethodologyCapabilities: Data Integration and Feature EngineeringCapabilities: Statistics, Machine Learning, and VisualizationOperational IntegrationProduct Demonstration

Thank You