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Splunking HL7 Healthcare Data for Business Value

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Splunk
Splunk

Healthcare data is time-oriented and diverse. HL7 (Health Level Seven International) is a set of interoperability standards, formats and definitions for exchanging data between software applications used by healthcare providers. In this session, learn how to leverage HL7 data for business value. Through a presentation and demo’s, we will discuss a variety of HL7 use cases from exploring HL7 data within Splunk, addressing missing orders investigations, queuing up integrations, and others. Also, you can learn about the health of the system that is providing these services by using Splunk ITSI.

Technology
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1 Shirley Golen Healthcare Industry Solutions Expert #SplunkLive IMPROVING HEALTHCARE OPERATIONS USING PROCESS DATA MINING Jon Yost Sales Engineer
Safe Harbor Statement During the course of this presentation, we may make forward looking statements regarding future events or the expected performance of the company. We caution you that such statements reflect our current expectations and estimates based on factors currently known to us and that actual events or results could differ materially. For important factors that may cause actual results to differ from those contained in our forward-looking statements, please review our filings with the SEC. The forward-looking statements made in this presentation are being made as of the time and date of its live presentation. If reviewed after its live presentation, this presentation may not contain current or accurate information. We do not assume any obligation to update any forward looking statements we may make. In addition, any information about our roadmap outlines our general product direction and is subject to change at any time without notice. It is for informational purposes only and shall not be incorporated into any contract or other commitment. Splunk undertakes no obligation either to develop the features or functionality described orto includeany suchfeatureor functionalityina futurerelease. 2
3 80% of healthcare data is unstructured and the other 20% are locked in silos, paper trails and legacy IT systems Increasing pressure on IT-related budgets, CIOs, and IT departments for solutions that deliver value including investing in big data and analytics Industry and congressional testimony have serious concerns about the current levels of interoperability across the HC ecosystem to achieve Value-Based Care Ongoing and increasing need to demonstrate regulatory compliance and privacy monitoring Alarming increase in security breaches; 100M healthcare records compromised in first half of 2015 Splunk has seen strong traction in the ecosystem for IT ops, regulatory compliance, identifying security threats, and medical device visibility 3 Market Drivers
4 Telemedicine, sensors Patient engagement via digital touch points Security and privacy Real-time healthcare system IT Analyze streams of data in real time Improve patient experience Prevent security breaches and protect patient privacy Improved analysis of patient activity/event data Healthcare IT – Rapidly Changing Landscape 4 SPLUNK DELIVERS VALUE Drivers Value
Virtual Physical Cloud 5 Healthcare Data Is Time Oriented and Diverse EHR Systems Web Services Developers App Support Telecoms Networking Desktops Servers Security Devices Storage Messaging Patient Surveys Clickstream HIE Patient Networks Healthcare Apps IT Systems and Med Devices Patient-Generated Data Medical Devices CDR Mobile PHI Access Audit Logs HL7 Messaging Sensors Departmental and Homegrown Applications
COLLECT DATA FROM ANYWHERE SEARCH AND ANALYZE EVERYTHING GAIN REAL-TIME OPERATIONAL INTELLIGENCE The Power of Splunk
One Platform, Multiple Use Cases in Healthcare Platform for Machine Data IT, Application & Device Operations Security, Compliance, Privacy Fraud Business Analytics Care Coordination, Internet of Things 7
8 Delivering Value Across the Organization Easy access to logs. Quickly identify and fix bugs. Monitor adoption and patient engagement Improve patient Experience Most often used features and functions Increase user engagement by refining product features Visibility into compliance and security incidents Improve compliance and security posture End-to-end visibility in real- time. Ensure systems are in a healthy state Steve Application Developer Raj System Architect Sarah Security Architect Anna Product Manager Bob Line of Business Executive Adoption patterns of clinical applications and devices Adopt practices to comply with regulations Charles Provider IT Business
9 Splunk in Healthcare
10 MSH|^~&|EPIC|RMH||RMH|20150324025727| REGBATCH|ADT^A03|725264|T|2.3| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA PID|1||3100068291^^^EPI^MR||REGISTRATION^ ADAM^K||19450923|M||Caucasian|1 PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||10 485^BLAIR^SCOTT^C^^^^^STARPROV^^ MSH|^~&|EPIC|RMH||RMH|20150324025727| REGBATCH|ADT^A03|725264|T|2.3| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA PID|1||3100068291^^^EPI^MR||REGISTRATION^ ADAM^K||19450923|M||Caucasian|1 PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||10 485^BLAIR^SCOTT^C^^^^^STARPROV^^ MSH|^~&|EPIC|RMH||RMH|20150324025727| REGBATCH|ADT^A03|725264|T|2.3| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA PID|1||3100068291^^^EPI^MR||REGISTRATION^ ADAM^K||19450923|M||Caucasian|1 PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||10 485^BLAIR^SCOTT^C^^^^^STARPROV^^ $ $ $$
11 :-) Brand Sentiment Higher NPS 360O Customer View Loyal Customers Product Recommendation More Sales Propensity to Churn Greater Retention Real-time Demand/ Supply Forecast More Efficient Predictive Maintenance Less Downtime Fraud Detection Lower Risk Network Optimization Lower Cost Insider Threats Greater Security Risk Mitigation, Real-time Retain Market Value Asset Tracking Increase Productivity Personalized Care Loyal Customers Sources of data Time series based data Online Services Web Services Servers Security GPS Location Storage Desktops Networks Packaged Applications Custom ApplicationsMessaging Telecoms Online Shopping Cart Web Clickstreams Databases Energy Meters Call Detail Records Smartphones and Devices RFID On- Premises Private Cloud Public Cloud A whole class of new use cases (new questions) More complete picture of the business process The desire for business operations “as it happens” Exhaust from Apps and Devices Volume | Velocity | Variety | Variability
12 Virtual Physical Cloud Healthcare Data is Time Oriented and Diverse EHR Systems Web Services Developers App Support Telecoms Networking Desktops Servers Security Devices Storage Messaging Claims Clickstream HIE Patient Portals Healthcare Apps IT Systems and Med Devices Patient-Facing Data Medical Devices CDR Medical Records PHI Access Audit Logs Billing Departmental and Homegrown Applications HL7 Messaging
13 Legacy systems limit high-velocity data collection Analytics constrained by inflexible tools Data silos prevent insights across IT Lack of platform capabilities restricts customization Getting Insights Is Not Easy 13
14 1 Correlating Data Provides Critical Insights to Business Rx ID Pt Comment Time Waiting for RN Rad ID Hospital’s ID Rx ID Patient ID Lab ID Patient ID Patient ID Sources Order Processing Survey Triage Care IVR Middleware Error
15 Splunk’s Value to Healthcare IT • Easily ingest diverse, heterogeneous healthcare data without ETL tools, special connectors, or need to adhere to strict backend “datastore” schemas. • Collect data from even restricted systems (Splunk Stream). • Easily enrich and dynamically “normalize” data at search time. • Augment Healthcare Analytics with previously untapped sources of data. • Enhanced IT Service Intelligence across your healthcare enterprise.
16 HL7 Standard The Health Level Seven (HL7) standard is a framework and standards for exchange, integration, sharing and retrieval of electronic health information. HL7 is the global authority on standards for interoperability of health information technology with members in over 55 countries. HL7’s vision is to create the best and most widely used standards in healthcare.
17 HL7 Messaging – Versions 1 MSH|^~&|EHR|A_HOSP||A_HOSP|20150324025727|REGBATCH|ADT^A03|725264|T|2.3||||||||| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA^^^^^|20150323235900| PID|1||3100068291^^^EPI^MR||REGISTRATION^ADAM^K||19450923|M||Caucasian|123 FARM PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||10485^BLAIR^SCOTT^C^^^^^STARPROV^^^^STARPROV|10485^BLAIR^ ….. Version 2.x Version 3.x (XML) <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" />
18 HL7 Messaging – Versions 1 HL7 FHIR (JSON) { "resourceType": "Patient", "id": "pat1", "text": { "status": "generated", "div": "<div>n n <p>Patient Donald DUCK @ Acme Healthcare, Inc. MR = 654321</p>n n </div>" }, "identifier": [ { "use": "usual", "type": { "coding": [ { "system": "http://hl7.org/fhir/v2/0203", "code": "MR" } ] ….......
19 Anatomy HL7 v2 Message 1 MSH|^~&|EHR|A_HOSP||A_HOSP|20150324025727|REGBATCH|ADT^A03|725264|T|2.3||||||||| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA^^^^^|20150323235900| PID|1||3100068291^^^EPI^MR||REGISTRATION^ADAM^K||19450923|M||Caucasian|123 FARM PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||10485^BLAIR^SCOTT^C^^^^^STARPROV^^^^STARPROV|10485^BLAIR^ PV2||||||||||||||||||||||N||||||||||||||||||||||||||| ZPV||||||||||||||||||||| ZMP|1|||||N||||^^^^|||^^^^|||||^^^^||||^^^^||Not eligible for Medicare||||^^^^|||||^^^^|0 CR CR CR CR CR CR CR Patient Name Date of Birth T_50005_1T_50005_1{CONNID 0} {IPVERSION 4} {CLIENTIP 172.17.154.197} {CLIENTPORT 43724}
20 Anatomy HL7 v2 Message 2 MSH|^~&|EHR|A_HOSP||A_HOSP|20150324025727|REGBATCH|ADT^A03|725264|T|2.3||||||||| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA^^^^^|20150323235900| PID|1||3100068291^^^EPI^MR||REGISTRATION^ADAM^K||19450923|M||Caucasian|123 FARM PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||10485^BLAIR^SCOTT^C^^^^^STARPROV^^^^STARPROV|10485^BLAIR^ PV2||||||||||||||||||||||N||||||||||||||||||||||||||| ZPV||||||||||||||||||||| ZMP|1|||||N||||^^^^|||^^^^|||||^^^^||||^^^^||Not eligible for Medicare||||^^^^|||||^^^^|0 CR CR CR CR CR CR CR 184 Unique Messages 144 Unique Segments
21 HL7 Message Structure https://msdn.microsoft.com/en-us/library/ee409289.aspx
22 Interface Engine Log – HL7 Source - Ideal 2 MSH|^~&|EHR|A_HOSP||A_HOSP|20150324025727|REGBATCH|ADT^A03|725264|T|2.3||||||||| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA^^^^^|20150323235900| PID|1||3100068291^^^EPI^MR||REGISTRATION^ADAM^K||19450923|M||Caucasian|123 FARM PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||10485^BLAIR^SCOTT^C^^^^^STARPROV^^^^STARPROV|10485^BLAIR^ PV2||||||||||||||||||||||N||||||||||||||||||||||||||| ZPV||||||||||||||||||||| ZMP|1|||||N||||^^^^|||^^^^|||||^^^^||||^^^^||Not eligible for Medicare||||^^^^|||||^^^^|0 MSH|^~&|EPIC|RMH||RMH|20150324025727|REGBATCH|ADT^A03|725266|T|2.3||||||||| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA^^^^^|20150323235900| PID|1||3100068293^^^EPI^MR||REGISTRATION^JASPER||19880305|M||Caucasian|321 HEATHMOOR DR PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||11178^DEEP^DONALD^P^^^^^STARPROV^^^^ CR CR CR CR CR CR CR CR CR CR CR CR CR
23 Interface Engine Log – HL7 Source - Actual MSH|^~&|EHR|A_HOSP||A_HOSP|20150324025727|REGBATCH|ADT^A03|725264|T|2.3||||||||| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA^^^^^|20150323235900| PID|1||3100068291^^^EPI^MR||REGISTRATION^ADAM^K||19450923|M||Caucasian|123 FARM PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||10485^BLAIR^SCOTT^C^^^^^STARPROV^^^^STARPROV|10485^BLAIR^SCOTT^CPV2|||||||||| |||||N||||||||||||||||||||||||||| ZPV||||||||||||||||||||| ZMP|1|||||N||||^^^^|||^^^^|||||^^^^||||^^^^||Not eligible for Medicare||||^^^^|||||^^^^|0|||^^^^||||||||||||||||||||||||||||| T_50005_1T_50005_1{CONNID 0} {IPVERSION 4} {CLIENTIP 172.17.154.197} {CLIENTPORT43724}MSH|^~&|EPIC|RMH ||RMH|20150324025727|REGBATCH|ADT^A03|725266|T|2.3||||||||| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA^^^^^|20150323235900| PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||11178^DEEP^DONALD^P^^^^^STARPROV^^^^STARPROV|11178^DEEP^
24 Splunk – Plan of Attack MSH|^~&|EHR|A_HOSP||A_HOSP|20150324025727|REGBATCH|ADT^A03|725264|T|2.3||||||||| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA^^^^^|20150323235900| PID|1||3100068291^^^EPI^MR||REGISTRATION^ADAM^K||19450923|M||Caucasian|123 FARM PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||10485^BLAIR^SCOTT^C^^^^^STARPROV^^^^STARPROV|10485^BLAIR^ PV2||||||||||||||||||||||N||||||||||||||||||||||||||| ZPV||||||||||||||||||||| ZMP|1|||||N||||^^^^|||^^^^|||||^^^^||||^^^^||Not eligible for Medicare||||^^^^|||||^^^^|0 T_50005_1T_50005_1{CONNID 0} {IPVERSION 4} {CLIENTIP 172.17.154.197} {CLIENTPORT 43724} DATETIME_CONFIG = /etc/apps/Splunk_TA_cloverleaf-HL7/datetime.xml #multiple formats TIME_PREFIX = MSH|^~&|(.*?|){4} MAX_TIMESTAMP_LOOKAHEAD = 15 LINE_BREAKER = (?<!|)(xe2)?MSH| #Break at MSH ONLY if no preceeding pipe props.conf
25 Splunk – Plan of Attack MSH|^~&|EHR|A_HOSP||A_HOSP|20150324025727|REGBATCH|ADT^A03|725264|T|2.3||||||||| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA^^^^^|20150323235900| PID|1||3100068291^^^EPI^MR||REGISTRATION^ADAM^K||19450923|M||Caucasian|123 FARM PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||10485^BLAIR^SCOTT^C^^^^^STARPROV^^^^STARPROV|10485^BLAIR^ PV2||||||||||||||||||||||N||||||||||||||||||||||||||| ZPV||||||||||||||||||||| ZMP|1|||||N||||^^^^|||^^^^|||||^^^^||||^^^^||Not eligible for Medicare||||^^^^|||||^^^^|0 REPORT-HL7_Segments_and_Fields = HL7_Segment_Template, ABS_Fields, ACC_Fields, …. [HL7_Segment_Template] REGEX = ^([A-Z]{2}[A-Z0-9]{1})|([^n]+)n FORMAT = $1::$2 MV_ADD = true transforms.conf [ABS_Fields] DELIMS = "|” FIELDS = ABS_1,ABS_2,ABS_3,ABS_4,ABS_5,… SOURCE_KEY = ABS
26 Macro -> props.conf and transforms.conf
27 Interface Engine 27 Billing Pharmacy Radiology Healthcare Information System / Electronic Medical Record (EMR) HL7
28 Interface Engine 28 Billing Pharmacy Radiology Healthcare Information System / Electronic Medical Record (EMR) • Parse • Reformat/Transform • Convert Data Type • Enrich • Route (content-based) Interface Engine HL7 HL7
29 How to Splunk HL7? Billing Pharmacy Radiology Healthcare Information System / Electronic Medical Record (EMR) HL7 HL7 Universal Forwarder
30 How to Splunk HL7? 30 Billing Pharmacy Radiology Healthcare Information System / Electronic Medical Record (EMR) HL7 HL7 TCP
31 How to Splunk HL7? Billing Pharmacy Radiology Healthcare Information System / Electronic Medical Record (EMR) HL7 HL7 Splunk Stream* *Persistent connections for MLLP supported in future releases
32 Data Integration: Ingest and Model Any Data 3 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" } ] } Patient identifier name telecom gender birthDate deceased address maritalStatus …. active
33 Tagging for “Normalization” 3 Patient identifier name telecom gender birthDate deceased address maritalStatus …. active
Event Tagging 3  Classify and group common events  Capture and share knowledge  Based on search  Use in combination with fields and tags to define event topography
35  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 3 Tag=GLYCEMIC, ASTHMA tag::DX=diabetes type 2 Tag=diabetes* 1 2 3
Aliases 3  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
1) Regular Expression 2) Natural Language Processing using SDK and REST API 3 Feature Extraction from Texts
38 Demo
39 What can Machine Learning Do? • Optimizing access to treatments such as chemotherapy • Increase operating rooms efficiency • In-patient bed capacity • Decrease wait times • Etc..
Anomaly Detection 4
41 Splunk Value Summary • HL7 can be VERY Complex/Complicated • Splunk can slice through that sh*t it like a Ninja! • Serious freaking value!
42 Available Now HL7 Add-On for Splunk https://splunkbase.splunk.com/app/3283/
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Splunking HL7 Healthcare Data for Business Value

  • 1. 1 Shirley Golen Healthcare Industry Solutions Expert #SplunkLive IMPROVING HEALTHCARE OPERATIONS USING PROCESS DATA MINING Jon Yost Sales Engineer
  • 2. Safe Harbor Statement During the course of this presentation, we may make forward looking statements regarding future events or the expected performance of the company. We caution you that such statements reflect our current expectations and estimates based on factors currently known to us and that actual events or results could differ materially. For important factors that may cause actual results to differ from those contained in our forward-looking statements, please review our filings with the SEC. The forward-looking statements made in this presentation are being made as of the time and date of its live presentation. If reviewed after its live presentation, this presentation may not contain current or accurate information. We do not assume any obligation to update any forward looking statements we may make. In addition, any information about our roadmap outlines our general product direction and is subject to change at any time without notice. It is for informational purposes only and shall not be incorporated into any contract or other commitment. Splunk undertakes no obligation either to develop the features or functionality described orto includeany suchfeatureor functionalityina futurerelease. 2
  • 3. 3 80% of healthcare data is unstructured and the other 20% are locked in silos, paper trails and legacy IT systems Increasing pressure on IT-related budgets, CIOs, and IT departments for solutions that deliver value including investing in big data and analytics Industry and congressional testimony have serious concerns about the current levels of interoperability across the HC ecosystem to achieve Value-Based Care Ongoing and increasing need to demonstrate regulatory compliance and privacy monitoring Alarming increase in security breaches; 100M healthcare records compromised in first half of 2015 Splunk has seen strong traction in the ecosystem for IT ops, regulatory compliance, identifying security threats, and medical device visibility 3 Market Drivers
  • 4. 4 Telemedicine, sensors Patient engagement via digital touch points Security and privacy Real-time healthcare system IT Analyze streams of data in real time Improve patient experience Prevent security breaches and protect patient privacy Improved analysis of patient activity/event data Healthcare IT – Rapidly Changing Landscape 4 SPLUNK DELIVERS VALUE Drivers Value
  • 5. Virtual Physical Cloud 5 Healthcare Data Is Time Oriented and Diverse EHR Systems Web Services Developers App Support Telecoms Networking Desktops Servers Security Devices Storage Messaging Patient Surveys Clickstream HIE Patient Networks Healthcare Apps IT Systems and Med Devices Patient-Generated Data Medical Devices CDR Mobile PHI Access Audit Logs HL7 Messaging Sensors Departmental and Homegrown Applications
  • 6. COLLECT DATA FROM ANYWHERE SEARCH AND ANALYZE EVERYTHING GAIN REAL-TIME OPERATIONAL INTELLIGENCE The Power of Splunk
  • 7. One Platform, Multiple Use Cases in Healthcare Platform for Machine Data IT, Application & Device Operations Security, Compliance, Privacy Fraud Business Analytics Care Coordination, Internet of Things 7
  • 8. 8 Delivering Value Across the Organization Easy access to logs. Quickly identify and fix bugs. Monitor adoption and patient engagement Improve patient Experience Most often used features and functions Increase user engagement by refining product features Visibility into compliance and security incidents Improve compliance and security posture End-to-end visibility in real- time. Ensure systems are in a healthy state Steve Application Developer Raj System Architect Sarah Security Architect Anna Product Manager Bob Line of Business Executive Adoption patterns of clinical applications and devices Adopt practices to comply with regulations Charles Provider IT Business
  • 10. 10 MSH|^~&|EPIC|RMH||RMH|20150324025727| REGBATCH|ADT^A03|725264|T|2.3| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA PID|1||3100068291^^^EPI^MR||REGISTRATION^ ADAM^K||19450923|M||Caucasian|1 PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||10 485^BLAIR^SCOTT^C^^^^^STARPROV^^ MSH|^~&|EPIC|RMH||RMH|20150324025727| REGBATCH|ADT^A03|725264|T|2.3| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA PID|1||3100068291^^^EPI^MR||REGISTRATION^ ADAM^K||19450923|M||Caucasian|1 PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||10 485^BLAIR^SCOTT^C^^^^^STARPROV^^ MSH|^~&|EPIC|RMH||RMH|20150324025727| REGBATCH|ADT^A03|725264|T|2.3| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA PID|1||3100068291^^^EPI^MR||REGISTRATION^ ADAM^K||19450923|M||Caucasian|1 PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||10 485^BLAIR^SCOTT^C^^^^^STARPROV^^ $ $ $$
  • 11. 11 :-) Brand Sentiment Higher NPS 360O Customer View Loyal Customers Product Recommendation More Sales Propensity to Churn Greater Retention Real-time Demand/ Supply Forecast More Efficient Predictive Maintenance Less Downtime Fraud Detection Lower Risk Network Optimization Lower Cost Insider Threats Greater Security Risk Mitigation, Real-time Retain Market Value Asset Tracking Increase Productivity Personalized Care Loyal Customers Sources of data Time series based data Online Services Web Services Servers Security GPS Location Storage Desktops Networks Packaged Applications Custom ApplicationsMessaging Telecoms Online Shopping Cart Web Clickstreams Databases Energy Meters Call Detail Records Smartphones and Devices RFID On- Premises Private Cloud Public Cloud A whole class of new use cases (new questions) More complete picture of the business process The desire for business operations “as it happens” Exhaust from Apps and Devices Volume | Velocity | Variety | Variability
  • 12. 12 Virtual Physical Cloud Healthcare Data is Time Oriented and Diverse EHR Systems Web Services Developers App Support Telecoms Networking Desktops Servers Security Devices Storage Messaging Claims Clickstream HIE Patient Portals Healthcare Apps IT Systems and Med Devices Patient-Facing Data Medical Devices CDR Medical Records PHI Access Audit Logs Billing Departmental and Homegrown Applications HL7 Messaging
  • 13. 13 Legacy systems limit high-velocity data collection Analytics constrained by inflexible tools Data silos prevent insights across IT Lack of platform capabilities restricts customization Getting Insights Is Not Easy 13
  • 14. 14 1 Correlating Data Provides Critical Insights to Business Rx ID Pt Comment Time Waiting for RN Rad ID Hospital’s ID Rx ID Patient ID Lab ID Patient ID Patient ID Sources Order Processing Survey Triage Care IVR Middleware Error
  • 15. 15 Splunk’s Value to Healthcare IT • Easily ingest diverse, heterogeneous healthcare data without ETL tools, special connectors, or need to adhere to strict backend “datastore” schemas. • Collect data from even restricted systems (Splunk Stream). • Easily enrich and dynamically “normalize” data at search time. • Augment Healthcare Analytics with previously untapped sources of data. • Enhanced IT Service Intelligence across your healthcare enterprise.
  • 16. 16 HL7 Standard The Health Level Seven (HL7) standard is a framework and standards for exchange, integration, sharing and retrieval of electronic health information. HL7 is the global authority on standards for interoperability of health information technology with members in over 55 countries. HL7’s vision is to create the best and most widely used standards in healthcare.
  • 17. 17 HL7 Messaging – Versions 1 MSH|^~&|EHR|A_HOSP||A_HOSP|20150324025727|REGBATCH|ADT^A03|725264|T|2.3||||||||| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA^^^^^|20150323235900| PID|1||3100068291^^^EPI^MR||REGISTRATION^ADAM^K||19450923|M||Caucasian|123 FARM PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||10485^BLAIR^SCOTT^C^^^^^STARPROV^^^^STARPROV|10485^BLAIR^ ….. Version 2.x Version 3.x (XML) <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" />
  • 18. 18 HL7 Messaging – Versions 1 HL7 FHIR (JSON) { "resourceType": "Patient", "id": "pat1", "text": { "status": "generated", "div": "<div>n n <p>Patient Donald DUCK @ Acme Healthcare, Inc. MR = 654321</p>n n </div>" }, "identifier": [ { "use": "usual", "type": { "coding": [ { "system": "http://hl7.org/fhir/v2/0203", "code": "MR" } ] ….......
  • 19. 19 Anatomy HL7 v2 Message 1 MSH|^~&|EHR|A_HOSP||A_HOSP|20150324025727|REGBATCH|ADT^A03|725264|T|2.3||||||||| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA^^^^^|20150323235900| PID|1||3100068291^^^EPI^MR||REGISTRATION^ADAM^K||19450923|M||Caucasian|123 FARM PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||10485^BLAIR^SCOTT^C^^^^^STARPROV^^^^STARPROV|10485^BLAIR^ PV2||||||||||||||||||||||N||||||||||||||||||||||||||| ZPV||||||||||||||||||||| ZMP|1|||||N||||^^^^|||^^^^|||||^^^^||||^^^^||Not eligible for Medicare||||^^^^|||||^^^^|0 CR CR CR CR CR CR CR Patient Name Date of Birth T_50005_1T_50005_1{CONNID 0} {IPVERSION 4} {CLIENTIP 172.17.154.197} {CLIENTPORT 43724}
  • 20. 20 Anatomy HL7 v2 Message 2 MSH|^~&|EHR|A_HOSP||A_HOSP|20150324025727|REGBATCH|ADT^A03|725264|T|2.3||||||||| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA^^^^^|20150323235900| PID|1||3100068291^^^EPI^MR||REGISTRATION^ADAM^K||19450923|M||Caucasian|123 FARM PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||10485^BLAIR^SCOTT^C^^^^^STARPROV^^^^STARPROV|10485^BLAIR^ PV2||||||||||||||||||||||N||||||||||||||||||||||||||| ZPV||||||||||||||||||||| ZMP|1|||||N||||^^^^|||^^^^|||||^^^^||||^^^^||Not eligible for Medicare||||^^^^|||||^^^^|0 CR CR CR CR CR CR CR 184 Unique Messages 144 Unique Segments
  • 22. 22 Interface Engine Log – HL7 Source - Ideal 2 MSH|^~&|EHR|A_HOSP||A_HOSP|20150324025727|REGBATCH|ADT^A03|725264|T|2.3||||||||| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA^^^^^|20150323235900| PID|1||3100068291^^^EPI^MR||REGISTRATION^ADAM^K||19450923|M||Caucasian|123 FARM PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||10485^BLAIR^SCOTT^C^^^^^STARPROV^^^^STARPROV|10485^BLAIR^ PV2||||||||||||||||||||||N||||||||||||||||||||||||||| ZPV||||||||||||||||||||| ZMP|1|||||N||||^^^^|||^^^^|||||^^^^||||^^^^||Not eligible for Medicare||||^^^^|||||^^^^|0 MSH|^~&|EPIC|RMH||RMH|20150324025727|REGBATCH|ADT^A03|725266|T|2.3||||||||| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA^^^^^|20150323235900| PID|1||3100068293^^^EPI^MR||REGISTRATION^JASPER||19880305|M||Caucasian|321 HEATHMOOR DR PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||11178^DEEP^DONALD^P^^^^^STARPROV^^^^ CR CR CR CR CR CR CR CR CR CR CR CR CR
  • 23. 23 Interface Engine Log – HL7 Source - Actual MSH|^~&|EHR|A_HOSP||A_HOSP|20150324025727|REGBATCH|ADT^A03|725264|T|2.3||||||||| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA^^^^^|20150323235900| PID|1||3100068291^^^EPI^MR||REGISTRATION^ADAM^K||19450923|M||Caucasian|123 FARM PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||10485^BLAIR^SCOTT^C^^^^^STARPROV^^^^STARPROV|10485^BLAIR^SCOTT^CPV2|||||||||| |||||N||||||||||||||||||||||||||| ZPV||||||||||||||||||||| ZMP|1|||||N||||^^^^|||^^^^|||||^^^^||||^^^^||Not eligible for Medicare||||^^^^|||||^^^^|0|||^^^^||||||||||||||||||||||||||||| T_50005_1T_50005_1{CONNID 0} {IPVERSION 4} {CLIENTIP 172.17.154.197} {CLIENTPORT43724}MSH|^~&|EPIC|RMH ||RMH|20150324025727|REGBATCH|ADT^A03|725266|T|2.3||||||||| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA^^^^^|20150323235900| PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||11178^DEEP^DONALD^P^^^^^STARPROV^^^^STARPROV|11178^DEEP^
  • 24. 24 Splunk – Plan of Attack MSH|^~&|EHR|A_HOSP||A_HOSP|20150324025727|REGBATCH|ADT^A03|725264|T|2.3||||||||| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA^^^^^|20150323235900| PID|1||3100068291^^^EPI^MR||REGISTRATION^ADAM^K||19450923|M||Caucasian|123 FARM PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||10485^BLAIR^SCOTT^C^^^^^STARPROV^^^^STARPROV|10485^BLAIR^ PV2||||||||||||||||||||||N||||||||||||||||||||||||||| ZPV||||||||||||||||||||| ZMP|1|||||N||||^^^^|||^^^^|||||^^^^||||^^^^||Not eligible for Medicare||||^^^^|||||^^^^|0 T_50005_1T_50005_1{CONNID 0} {IPVERSION 4} {CLIENTIP 172.17.154.197} {CLIENTPORT 43724} DATETIME_CONFIG = /etc/apps/Splunk_TA_cloverleaf-HL7/datetime.xml #multiple formats TIME_PREFIX = MSH|^~&|(.*?|){4} MAX_TIMESTAMP_LOOKAHEAD = 15 LINE_BREAKER = (?<!|)(xe2)?MSH| #Break at MSH ONLY if no preceeding pipe props.conf
  • 25. 25 Splunk – Plan of Attack MSH|^~&|EHR|A_HOSP||A_HOSP|20150324025727|REGBATCH|ADT^A03|725264|T|2.3||||||||| EVN|A03|20150324025727||ADT_EVENT|Model User^PRELUDE^BATCH^JOB^^^^^OHSA^^^^^|20150323235900| PID|1||3100068291^^^EPI^MR||REGISTRATION^ADAM^K||19450923|M||Caucasian|123 FARM PD1|||||||||||||||||| PV1|1|R|NUCL^^^RMH1^^^^^^^DEPID|EL|||10485^BLAIR^SCOTT^C^^^^^STARPROV^^^^STARPROV|10485^BLAIR^ PV2||||||||||||||||||||||N||||||||||||||||||||||||||| ZPV||||||||||||||||||||| ZMP|1|||||N||||^^^^|||^^^^|||||^^^^||||^^^^||Not eligible for Medicare||||^^^^|||||^^^^|0 REPORT-HL7_Segments_and_Fields = HL7_Segment_Template, ABS_Fields, ACC_Fields, …. [HL7_Segment_Template] REGEX = ^([A-Z]{2}[A-Z0-9]{1})|([^n]+)n FORMAT = $1::$2 MV_ADD = true transforms.conf [ABS_Fields] DELIMS = "|” FIELDS = ABS_1,ABS_2,ABS_3,ABS_4,ABS_5,… SOURCE_KEY = ABS
  • 26. 26 Macro -> props.conf and transforms.conf
  • 27. 27 Interface Engine 27 Billing Pharmacy Radiology Healthcare Information System / Electronic Medical Record (EMR) HL7
  • 28. 28 Interface Engine 28 Billing Pharmacy Radiology Healthcare Information System / Electronic Medical Record (EMR) • Parse • Reformat/Transform • Convert Data Type • Enrich • Route (content-based) Interface Engine HL7 HL7
  • 29. 29 How to Splunk HL7? Billing Pharmacy Radiology Healthcare Information System / Electronic Medical Record (EMR) HL7 HL7 Universal Forwarder
  • 30. 30 How to Splunk HL7? 30 Billing Pharmacy Radiology Healthcare Information System / Electronic Medical Record (EMR) HL7 HL7 TCP
  • 31. 31 How to Splunk HL7? Billing Pharmacy Radiology Healthcare Information System / Electronic Medical Record (EMR) HL7 HL7 Splunk Stream* *Persistent connections for MLLP supported in future releases
  • 32. 32 Data Integration: Ingest and Model Any Data 3 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" } ] } Patient identifier name telecom gender birthDate deceased address maritalStatus …. active
  • 34. Event Tagging 3  Classify and group common events  Capture and share knowledge  Based on search  Use in combination with fields and tags to define event topography
  • 35. 35  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 3 Tag=GLYCEMIC, ASTHMA tag::DX=diabetes type 2 Tag=diabetes* 1 2 3
  • 36. Aliases 3  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
  • 37. 1) Regular Expression 2) Natural Language Processing using SDK and REST API 3 Feature Extraction from Texts
  • 39. 39 What can Machine Learning Do? • Optimizing access to treatments such as chemotherapy • Increase operating rooms efficiency • In-patient bed capacity • Decrease wait times • Etc..
  • 41. 41 Splunk Value Summary • HL7 can be VERY Complex/Complicated • Splunk can slice through that sh*t it like a Ninja! • Serious freaking value!
  • 42. 42 Available Now HL7 Add-On for Splunk https://splunkbase.splunk.com/app/3283/

Editor's Notes

  1. Good morning everyone and welcome to SplunkLive! I couldn’t be more excited to be here with all of you today.
  2. Standard safe harbor statement: There may be some forward looking statements that we may make in this discussion doesn’t indicate any obligation to develop any specific features
  3. Majority of healthcare data
  4. Machine data insights deliver value across a range of CIO’s strategic priorities for IT and the business. CIOs need to increasingly balance the need for innovation, helping drive business growth with ongoing maintenance, improving security posture among other things. With better visibility from machine data, CIO effectively address broad range of needs – helping them create a meaningful competitive advantage for the business.
  5. And this is where Splunk could really help. We take data in from anywhere and we correlate it whether it’s semi-structured or unstructured data. And that enables a HC organization to cut through these data silo’s in order to search and analyze everything in real-time.
  6. IT ops: Aetna improving the self-service member/customer requests Security: for customers such as the Vancouver Clinic and Middlesex Hospital Fraud: Surescripts Business Analytics: Myriad Genetics Care Coodination: iRhythm-medical device supplu chain and actual use and pt behavior coming from their medical device
  7. HC Industry Value Chain stakeholders of “Payers, Marketplace Insurance & Fiscal Agents, Providers, Purchasers, Producers, Consolidators,” and the following is how we impact the stakeholders with applicability across the entire vertical of the HC industry…
  8. Over the past decade, Healthcare Informatics has recognized healthcare leadership teams who have gone above and beyond in their use of information technology solutions with the Innovator Awards.  But those innovators could not have achieved such success without dedicated vendor partners.  To that end, Healthcare Informatics now brings the Leading Edge Awards, honoring vendors whose combination of expertise and innovation are shaping the future of healthcare systems.  Splunk was selected for the 2015 Leading Edge Award for Interoperability from Healthcare Informatics! The reason we received the award is because Splunk enables interoperability. We collect and visualize any type of unstructured or semi-structured data in real time. As a result, healthcare organizations can drive more business value because Splunk can be applied in so many ways. Whether it’s securing PHI and your infrastructure, compliance reporting or monitoring the use of clinical devices, you can address many of your initiatives with a single platform – Splunk!
  9. A defining characteristic of modern health care is the rapidly accelerating increase in information that is available to assist with the delivery of care and system management. Time oriented data, 2. High diversity, 3. Some data is functional others are event logs generated by machines. Data came from activities which are part of sequential process Data is timestamped Activities are interdependent discrete events Machine data is generated by many different sources within the healthcare IT infrastructure. These sources include healthcare specific data sources such as electronic health record (EHR) systems, HL7 messaging, and connected medical devices. The data sources include core IT systems that support different applications such as desktops, servers, storage and network devices. Finally, they include all the patient facing applications and systems – portals, billing systems, claim management systems. Machine data generated by this infrastructure shares the core characteristics of big data – lot of data (high volume), created rapidly (high velocity), from different sources (variety), and data that changes over time (variability). Getting timely and relevant insight into this data can be a source of huge value for the healthcare ecosystem.
  10. Health Level Seven (HL7) International is one of several American National Standards Institute (ANSI) -accredited Standards Developing Organizations (SDOs) operating in the healthcare arena. HL7 provides standards for interoperability that improve care delivery, optimize workflow, reduce ambiguity and enhance knowledge transfer among all of our stakeholders, including healthcare providers, government agencies, the vendor community, fellow SDOs and patients. Provides healthcare systems a standard for clinical and administration purposes, particularly between systems/apps responsible for patient care, medical devices, pharmacy, billing, imaging, etc.
  11. Health Level Seven (HL7) International is one of several American National Standards Institute (ANSI) -accredited Standards Developing Organizations (SDOs) operating in the healthcare arena. HL7 provides standards for interoperability that improve care delivery, optimize workflow, reduce ambiguity and enhance knowledge transfer among all of our stakeholders, including healthcare providers, government agencies, the vendor community, fellow SDOs and patients. Provides healthcare systems a standard for clinical and administration purposes, particularly between systems/apps responsible for patient care, medical devices, pharmacy, billing, imaging, etc.
  12. Each message starts with an MSH Composed of multiple segments, each ending with a single carriage return. Each segment begins with three letters , contains specific information: EVN event, PID patient demographics, PV1 Patient Visit, Each field has information Sometime’s there coded, so Splunk is IDEAL to enrich! Types of messages, ADT (admission, discharge, transfers), Orders, Reports, etc.