Improving Patient Outcomes through Big Data Real Time Surveillance

Paul KudrleLead Developer – Wolters Kluwers Clinical Solutions

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• Introduction• Background• Big data solution• Challenges• Future

Agenda

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• Master’s Degree is Computer Science from Cal-Poly Pomona

• NASA

• Systems Engineer – deep space missions

• Consultant (big data on the small scale)

• Nano-tech – atom probe microscope

• Bio-tech - DNA analysis

• Lead Engineer and Team Lead for Pharmacy One Source – Wolters Kluwer

My background

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Pharmacy One Source

Nine SAAS healthcare applications

 

• 1 in 3 hospitals in the U.S. utilize one or more of our applications

• Active community of > 44,000 of pharmacy professionals and hospital clinicians

• Purchased by Wolters Kluwers in 2010-11 and now part of Wolters Kluwers Clinicial Services

Sentri7 application

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Surveillance

Intervention

Documentation

Reporting

What do we mean by…

Surveillance: Actively monitoring patients to identify opportunities for intervention

Intervention: changing patient care to improve outcomes

Outcomes:

Quality & Safety

Efficacy

Efficiency

Cost effectiveness

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SaaS Model

• We receive data from hospitals via HL7 standard message format and support 500+ hospitals

• End users access through browser based client apps and mobile devices

• Clinicians – Infection Prevention

• Pharmacists

• We manage our own hardware and data centers

• Move to cloud potentially in the future

• HIPPA

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Electronic Medical Records (EMR)

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Visit

Order Tests

Surgeries

Lab

results

EMR

Observations

Patient

Clinician

Lab technician

Medications

Diagnoses

Vitals

Request for data from EMR

• Clinician/Pharmacists wants to find patients…

• Too many drugs or wrong combination of drugs

• Infection not responding to drug for a specific patient

• IT – create report

• Overworked and understaffed

• Batch systems

• EMR

• Long delivery times

• Batch systems

• Speed is of the essence for results

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Enhancing the EMR

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EMRClinician

Sentri7•Activating data•Intelligent Decisions

Sentri7 surveillance rule builder

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- Pull based user centric rule evaluation- End user required to hit a web page to find results.

- If they don’t log in then results are not changed

- SQL based query executions- SQL stored procedures

- Expensive big box machines- Lots of processors

- Database locking

- Data storage/persistence layer bottlenecks

- Difficult to scale individual sites to meet their demands- A lot of smaller sites impacted by one larger hospital

Legacy Rule Engine

Perception of responsiveness

1. Speed is everything—this is what information system users value most.

2. Anticipate needs and deliver in real time—deliver information when needed.

-“Ten Commandments for Effective Clinical Decision Support” American Medical Informatics Association, 2003

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Impact of Sepsis on Patient Health

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Angus DC et al. Crit Care Med 2001;29(7):1303-1310.

Patient Lives

• > 200,000 deaths per year in the US

• Mortality from severe sepsis is between 28-50% (with standard care)

Hospital Costs

• $16.7 billion in total annual U.S. costs• Average cost = $22,100 - $29,900• Average LOS = 19.6 - 23.3 days

At very high risk for severe sepsis with shock

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Group A

-Pulse > 90

-Resp < 20

-PaCO2 < 32 mmHg

-Temp not in the range 96.8 F to 100.4 F

Group B

-WBC Count not in the range 4 thou/uL to 12 thou/uL

-Band > 10 %

Group C-Lactic Acid >= 3.5 mmol/L-Serum Creatinine > 2 mg/dL-Estimated Creatinine Clearance < 50 ml/min-Platelet count < 80 thou/uL-Active Drug Order where Drug Name is not in the list ("WARFARIN SODIUM", "ENOXAPARIN SODIUM", "HEPARIN SODIUM, PORCINE") AND has one of the following:

-PT (INR) > 1.5-aPTT > 60 sec

-BILIRUBIN TOTAL > 2 mg/dL AND ALT (SGPT) >114 U/L-VENOUS O2 SATURATION (VO2HB) < 70%-pH < 7.30-PaO2 < 80 mmHg-pH < 7.35 AND PaCO2 < 50 mmHg

Group D-SYSTOLIC BP < 90-SYSTOLIC BP has decreased 28%

Show all patients that have at least one match from each of the following groups in the last 24 hours.

Meet the scalability challenge to support real-time results

• Every day we process over 8 million messages supporting over 1,000,000 active patients

• Run 35,000 rules in real-time

• Drug orders

• Vitals data

• Lab data

• Textual observations

• Surgery data

• Combinations of any of these

• Our architecture needs to be able to scale beyond 15 million HL7 messages a day

• Enable our rules to process the messages and generate results back to the client in less than 10-secs, regardless of complexity

Real-time results vs big data challenges

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Focus on ambitious goals aligned with additional business needs

Be cost-effective:

• Increase production capacity but save Capex

• Drive down our cost per hospital

• Create a bridge to the cloud to get us out of the hardware business

• We could not overhaul all of our code base to a completely new paradigm

• Need something that could sit on top of existing systems

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• Hadoop / MapReduce is a framework for processing huge datasets on certain kinds of distributable problems using a large number of computers (nodes)

• Pros

• Designed to process massive amounts of data

• Allows for distributed processing of map and reduce operations

• Mature

• Low cost

• Growing and growing business and development community

• Cons

• Not real-time

• Batch processing

Hadoop

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• Could not find a single bullet that could solve all our needs and solutions

• A combination of technologies was needed

• Poll/Pull based solutions can be difficult to scale especially over shared resources

• SQL Server based solutions are not ideal as we want to remove load off the main servers and have them be repositories and move toward read only replicas for client applications

Solution(s)

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GigaSpaces XAP – Elastic Application Platform

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DATA PROCESSING EVENTS & MESSAGING WEB APPLICATIONSUPPORT

MANAGEMENT & MONITORING

HIGH AVAILABILITY ELASTICITY CLOUD READINESS

GigaSpaces – a Commercial Java Spaces Implementation

• GigaSpaces extends Java Spaces adding processing units concept: deployable code units & execution.

• Grid = data + code

• Data in the grid

• Rules in the grid

• Elastic grid, redundancy, real-time self-healing, load balancing

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GigaSpaces

• No centralized point of failure

• Fast in-memory data grid for collocating data and processing

• Self-healing grid

• No emergency calls on Saturday evening while 4 hours away from a computer

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Drools

• Embedded rule engine based in Java

• Business rules for insurance, finance, etc

• Expert System

• Allows for “Hot” deployable code – add/remove code in real-time

• Utilizes Rete Algorithm

• Shared processing path between similar rules

• Promotes speed at the expense of memory

• Lots of companies build their own rule engine

• All suffer performance issues that Drools and Rete algorithm has solved

• Drools developers are domain experts in rule systems and continually evolving the platform

• Typically they are brute force

• Temporal Reasoning

• Time based rules

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GigaSpaces + Drools

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Performance

Collocate rules with the data allowing extreme low latency rules execution

Rules Management

Dynamically load/unload rules leveraging GigaSpaces & Drools APIs

Ultra Scalable

Parallel rules execution across the different data-grid partitions

Elasticity

Scale- up/down, in/out system and leverage extra resources on-demand within private cloud or public cloud.

High-Availability

Continuous availability running rules 24X7 without any downtime

Current Architecture

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Massively Parallel Expert System

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Clinica

l

Decisi

on

Supp

ort

Surveillanc

e Rules

GigaSpaces Grid + Drools

Surveillance Results

Results support our goals

• On a single feed our processing jumped from 30 mps to >130 mps

• Hospital cannot send the data fast enough

• 95% of our clients have moved onto the platform in 6 months

• Average response time for client surveillance rules to display in the user interface reduced by 98%

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Results (average time to run rules)

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Hospital AHospital BHospital CHospital DHospital E

Tim

e in m

illiseconds

Real-time impact

• We receive an HL7 message from a Hospital we are able to run that specific data change against 300+ rules for a hospital

• 300 rules processing in under 30 milliseconds

• Some rules never worked under old system (timed out)

• Pharmacists and Clinicians know the exact time a patient qualified for a rule.

• We can tell them why!

• All rules are constantly running for all patients

• Average 1-2K patients per hospital

• Growing to 9-10K patients for larger sites

• Expect growth

• Some patients will qualify over time without receiving any updates (lab not ordered)

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New product horizons

• Notifications

• Patient qualifies for a rule -> pager, SMS, email notification sent

• Pharmacist notified of a patient on too many drugs or the wrong combination

• Sepsis benefits

• Speed matters

• <1 hour response time needed

• Deliver decision support to the point of care

• Integrate solutions to our other Wolters Kluwers Clinicial Services solutions

• Up to Date supplies content

• Health Language supplies industry standardization

• Better benefits of integration

• Everyone wants in

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