SplunkLive! Houston Improving Healthcare Operations
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Improving Healthcare Operations Using Process Data Mining It’s estimated that 80% of healthcare data is unstructured, which makes it challenging to do any sort of analytics to drive improvements in population health, patient care and operational efficiency. Machine learning techniques can be utilized to predict future events from similar past events, anticipate resource capacity issues and proactively identify bottlenecks and patient outcome risks. This session will provide an overview of how process data mining can be applied to healthcare and provide real-world examples of process data mining in action.
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SplunkLive! Houston Improving Healthcare Operations
- 1. Copyright © 2015 Splunk Inc. Improving Healthcare Operations Justin Boucher Sr. Sales Engineer Federal Healthcare
- 2. 2 Agenda Problem Background and Motivation Phase 1: Operational Informatics Phase 2: Portfolio Rationalization Phase 3: Healthcare Informatics Product Demonstration
- 3. 3 3 Process Phases Operational Informatics 1 Portfolio Rationalization 2 3 Healthcare Informatics and Machine Learning
- 4. 4 :-) 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
- 5. 5 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
- 6. 6 Domains of Data Diversity in Health Data 6 Subjects Persons, Sensors, Actuators, Mobile Devices Information Users Clinical, Family, Patient System and Locations Home, Hospital, ER, Nursing Homes Ownership and Management
- 7. 7 Virtual Physical Cloud Healthcare Data is Time Oriented and Diverse 7 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
- 8. 8 HL7 Messaging – Two Main Versions 8 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 <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" />
- 9. 9 Interface Engine 9 Billing Pharmacy Radiology Healthcare Information System / Electronic Medical Record (EMR) HL7
- 10. 10 Interface Engine 10 Billing Pharmacy Radiology Healthcare Information System / Electronic Medical Record (EMR) • Parse • Reformat/Transform • Convert Data Type • Enrich • Route (content-based) Interface Engine HL7 HL7
- 11. 11 Anatomy HL7 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}
- 13. 13 13 Process Phases Operational Intelligence 1 Portfolio Rationalization 2 3 Healthcare Informatics and Machine Learning
- 14. A large Military Health system 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
- 15. 15 15 Process Phases Operational Intelligence 1 Portfolio Rationalization 2 3 Healthcare Informatics and Machine Learning
- 16. 16 What can Machine Learning Do? • Optimizing access to treatments such as chemotherapy • Increase operating rooms efficiency • In-patient bed capacity • Decrease wait times • Etc..
- 17. End to End Process KPI Dashboards 1 • Waiting Times/Delays at Highly Utilized Flow Steps (ex: ER Wait Time, Outpatient wait time, Number of Patients Waiting ) • Patient Arrival and Departure Patterns by Time • Service Time (ex: Time for a Brain MRI/patient) • Bed, OR, Staff Capacity Utilization by Services • Device Availability, Cycle Time, and Throughput • Current discharge to bed readiness time
- 19. 19 1 Patient Flow Between Process Steps
- 20. 20 http://convergingdata.com Emergency Department “at a glance”
- 21. 21 http://convergingdata.com Emergency Senior Assessment Drilldown Detect Potential Cross Departmental Hand-Off Issues
- 22. 22 Automatic Notifications: Actionable Alerting
- 23. Real-time Dashboards Ensure Transparency
- 24. Establish a Culture of Continuous Improvement: Use data to change practice if warranted
- 25. Compare KPIs over time
- 26. Visualize flow and frequency
- 27. Visualize Relations Ex: relations among procedures and assets used
- 28. 28 Required Capabilities 2 Schema-less approach/ late binding to schema Dynamic “normalization” of data Agile analytics and reporting Scalable search and analytics Seamless operational integration
- 29. 30 Data Integration: Ingest any text data 3 MSH|^~&|EPIC|MGH||MGH|20150324190937|OHEDSCRIBE|ADT^A08|725 467|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
- 31. 32 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
- 32. 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
- 33. 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
- 34. 1) Regular Expression 2) Natural Language Processing using SDK and REST API 3 Feature Extraction from Texts
- 36. Demo
- 38. Thank You
Editor's Notes
- Do we know what a drug or diagnosis code means and does it mean the same in different EHRs? Similarly, do we know what an EHR event in an EHR event log means and does it mean the same in different systems. This last will be important for comparing process models, as EHRs are so user- customizable. “Check Meds” in one EHR might be called “Medications” in another. What exactly does “Check Meds” mean? Where, exactly, does it fit in a hierarchy of tasks, such as “checking” other things besides medications or involvement of medications in other activities besides “checking”? Is asking a patient about medications (or retrieving the medication list from online) an example of “Check Meds”? Is there a difference in the ordering and frequency of activities between patients that were treated by either a high- or low-volume surgeon? (control-flow perspective) Is there a difference in resource involvement between patients that were treated by either a high- or low-volume surgeon? (organisational perspective) Is there a difference in time-related performance between patients that were treated by either a high- or low-volume surgeon? (performance perspective) Is there a difference in the ordering and frequency of activities between patients that had surgical continuity and patients that had surgical discontinuity? (control-flow perspective) 2. Is there a difference in resource involvement between patients that had surgical continuity and patients that had surgical discontinuity? (organisational perspective) 3. Is there a difference in time-related performance between patients that had surgical continuity and patients that had surgical discontinuity? (performance perspective) Is there a difference in the ordering and frequency of activities between patients that had a throughput time of 80 and 40 minutes or less in respectively the pre-operative and final postoperative examination and patients with a longer throughput time? Is there a difference in organizational resource involvement between that had a throughput time of 80 and 40 minutes or less in respectively the pre-operative and final postoperative examination and patients with a longer throughput time? Is there a difference in time-related performance between patients that had a throughput time of 80 and 40 minutes or less in respectively the pre-operative and final postoperative examination and patients with a longer throughput time? It is apparent that the business processes in the medical domain are dynamic, ad-hoc, unstructured and multi-disciplinary in nature. he goal of clustering is to obtain homogeneous group of patients.
- To frame our discussion, let’s use this example of a patient calling ACME Hospital’s nurse triage line asking about a medication that you were prescribed after being discharged for knee surgery, you call the 1-800#, and then tweet about your experience. All these events are captured - as they occur - in the machine data. Each of the underlying systems has the potential to generate millions of machine data events daily. Here we see small excerpts from just some of them. When we look more closely at the data we see that it contains valuable information (immediatly we see the time you waited on the phone for the triage RN, your patient ID, radiology ID from your MRI, lab ID when they had blood on standby for your surgery) – right down to what was tweeted. What’s important, is first of all, the ability to actually see across all these data sources, but then also to correlate related events and provide meaningful insight. If you can correlate and visualize the data, you can build a picture of activity, behavior and experience. And what if you can do all of this in real-time? You can respond more quickly to events that matter. This example ties into your scenario but you can also extrapolate this example to a wide range of use cases – security and fraud, transaction monitoring and analysis, web analytics, IT operations and so on.
- Subjects, locations, users, different data governance rules and standards that may conflict with each other
- 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.
- 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.
- 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.
- Alerts are triggered when certain conditions are met by the results of the search upon which it is based. Alerts can be based on both historical and real-time searches. When an alert is triggered, it performs an alert action. This action can be the sending of the alert information to a designated set of email addresses, or the posting of the alert information to an RSS feed. Alerts can also be set up to run a custom script when they are triggered. You can base these alerts on a wide range of threshold and trend-based scenarios, including empty shopping carts, brute force firewall attacks, and server system errors.
- By looking at historical demand patterns, and operational constraints, sophisticated forecasting algorithms can predict the daily volume and mix of patient volume and orchestrate appointment slots such that there are no “gaps” between treatments. This radically improves chair utilization, lowers patient waiting times and reduces the overall cost of operations. Doing this without sophisticated data science is hard — for example, just arranging the order in which 70 patients can be slotted for their treatments in a 35-chair infusion center is a number exceeding 10^100, as this analysis shows. Trying to solve this problem with pen, paper or Excel is a pointless exercise.
- Study after study shows that the OR utilization at most large hospitals is at best 50-60 percent. In most hospitals, operating rooms are allocated to surgeons using “blocks” — for simplicity, the blocks are often either half-day or full-day blocks. Even the most prolific and productive surgeons often don’t fully utilize the blocks they are given, and the process for reallocating blocks on a monthly basis or even for last minute block swaps is cumbersome and manual. Using data science and machine learning, hospitals can monitor utilization, identify pockets for improvement, automatically reallocate underutilized blocks, and improve overall operating room utilization. A 3-5 point improvement in block utilization is worth $2 million per year for a surgical suite with just four operating rooms.
- Imagine looking at each overnight patient, finding the 1,000 prior patients over the last two years who entered the hospital with a similar diagnostic or procedure code and reviewing their “flight path” through the hospital (i.e., # days spent in each of the units prior to discharge); then, an aggregate probabilistic assessment of the likely occupancy of each unit could be developed. Not only would it provide a better answer for today, it would also help anticipate the evolving unit capacity situation over the next 5-7 days, thereby leading to smarter operational decisions on transfers, elective surgery rescheduling, etc. Vmware – House of Demos app. VM forest, esx server. Status of VMs when you click on particular one. One of the most useful types of visualizations is a “Sankey diagram”, which is used to describe flows through systems. These can be customer flows through marketing or sales funnels, traffic flows through the actual network, energy flows through a physical system, capital flows through a financial system, etc. It’s a very streamlined form of visualization that cuts out everything unrelated to “flow”. Technically, this is a graph visualization: the nodes are smushed to these bars along the side, and edges are represented by these fat bars connecting nodes. The width of a node is proportional to the volume of flow in and out of the node, and the width of an edge is proportional to the flow from the start node to the end node.
- Customer journey: convert, repeat Mobile Patent Suits Dashed links are resolved suits; green links are licensing. “Thomson Reuters published a rather abysmal infographic showing the "bowl of spaghetti" that is current flurry of patent-related suits in the mobile communications industry. So, inspired by a comment by John Firebaugh, I remade the visualization to better convey the network. That company in the center? Yeah, it's the world's largest, so little wonder it has the most incoming suits.” mbostock’s block #1153292 August 18, 2011 http://bl.ocks.org/mbostock/1153292
- One reason for agility is handling of data in scale using parallel data processing techniques. And lastly, we enable operational integration- two ways 1) speed of computations, 2) second is system integration through REST API support.
- Splunk products are being used for data volumes ranging from gigabytes to hundreds of terabytes per day. Splunk software and cloud services reliably collects and indexes machine data, from a single source to tens of thousands of sources. All in real time. Once data is in Splunk Enterprise, you can search, analyze, report on and share insights form your data. The Splunk Enterprise platform is optimized for real-time, low-latency and interactivity, making it easy to explore, analyze and visualize your data. This is described as Operational Intelligence. The insights gained from machine data support a number of use cases and can drive value across your organization. [In North America] Splunk Cloud is available in North America and offers Splunk Enterprise as a cloud-based service – essentially empowering you with Operational Intelligence without any operational effort.