HPE  Big  Data  PlatformHealthcare  AnalyticsAlan  Stein,  MD  PhDHealthcare  Practice  Lead,  SW  Big  DataApril  2016

FutureSources

• Video• Biometrics• Geotracking• SMS• Web  chat• Physiologic  monitoring• Social  networks• Mobile  apps• Sensors• Survey  response• Biochemical  Assays

• Revenue  management• Claims• EMRs• ICD  9-­10• Genetic  Sequences• Lab  values• Medication  records  • Clinician/caretaker  notes  

• Radiology  reports• Pathology  readings• Clinical  quality  measures

• Population  health  data

CurrentSources

Traditional  HLS  data  can  be  structured or  unstructured,  and  limited,  or  voluminous in  

nature

Nontraditional  healthcare  data  will  challenge  current  methods  of  data  capture  and  analytics

Current  and  Future  Healthcare  Data

We  want  to  turn  data into  information

Millions  of  daily  

transactions

Multitude  of  Haven  Big  Data  Use-­cases  Improving patient care, quality outcomes, and speed to market while reducing overall costs

• Cost,  utilization,  performance,  &  quality  variable  analytics

• Claim  and  member  data  analytics• Fraud  detection  &  prevention• Medical  and  pharmaceutical  diagnostics• Compliance  testing• Clinical  data  analysis• Patient  record  analysis• Internal  risk  assessment  • Logistics  optimization• Supply  chain  optimization• Equipment  monitoring

• Customer  behavior  analysis• Web  application  optimization• Operations  analytics• Marketing  campaign  optimization• Brand  management  • Social  media  analytics• Pricing  optimization• Revenue  assurance• Security  analytics• Defect  tracking• Risk  management• Sentiment  analysis

• Clickstream  analysis• Influencer  analysis• IT  infrastructure  analysis• Legal  discovery• Enterprise  search• Warranty  management• Social  CRM  /  network  analysis• Churn  mitigation• Brand  monitoring• Cross  and  Up  sell• Loyalty  &  promotion  analysis

Drug  development

Scientific  research

Evidence  based  medicine

Healthcare  outcomes  analysis

Patient  analytics

Clinical  data  analysis

Use  Cases

Sample  HC  Analytics  questions

• What  are  the  top  5  reasons  for  an  ER  “frequent  flyer”  visit?    How  does  this  vary  for  patients  that  live  alone  vs  those  that  live  with  family?

• What  percentage  of  primary  Type  1  diabetes  care  pediatric  patients  were  on  an  insulin  pump  in  the  last  calendar  year?

• What  is  the  incidence  of  Pressure  Sores  /  Bed-­Hour?

• In  the  last  30  days,  how  many  patient  events    Involving  Rapid  Response  or  Crash  Team  occurred?    What  hospital  units  were  involved?

• How  does  a  missed  cardiology  follow-­up  appointment  affect  the  likelihood  of  a  hospitalisation  within  the  next  30  days?

Real  world  use-­cases

–Clinical  concept  based  surveillance  for  VTE  events  by  QI  team  including  suspect  cohort  generation  and  computer  facilitated  chart  abstraction–Clinical   feature  extraction  from  patient  narratives  including  genetic  testing  to  identify  phenotype/genotype  relationships–Diagnosis   related  group  precoding/postcoding confirmation  based  on  a  combination  of  quantitative  and  qualitative  criteria–Post-­acute  discharge  referral  analysis  (CHF  and  arthroplasty)  to  evaluate  cost,  outcomes,  and  patterns,  while  segmenting  by  health  status  and  disease  acuity– Identification  of  uncoded metabolic  conditions  by  laboratory  values  and  computer  facilitated  chart  review

5

Extracting  value  from  data  through  a  Platform

Self  service  analytics

New  approach:  Self  Service  AnalyticsAccelerates  access  to  comprehensive  insights

Business  Users Intuitive  Data  Interaction

Seamless  integration  of  structured  and  unstructured  data

…90%  data  is  untapped  for  KPIs  and  analytics

Transaction  RecordsQualitative  Human  DataQuantitative  Machine  Data

Financial  &  Operational  Transactions

Admission  notes  Discharge  summaries  Progress  notes  Imaging  study  results  Consultant  reports

Medication  recordsLaboratory  resultsPhysiologic  testingBiometric  sensorsRFID  tags

David  Yachnin

UnstructuredData

Connector

Connector

HCA

Reporting  tools

Statistical  analysis  tools

PatternMatchersMappers Ontologies

TransactionalDatabase

HPE  Healthcare  Rapid  Deploy  Solution

ETL  tools

IDOLUnstructured  

Index  

VerticaAnalytic  Database

IDOLEnrichment

HAFV

Haven  App  

Framework

DashboardUI

Schema

Hadoop

SQL  for  Hadoop

HPE  Vertica  Technology

Achieve  best  data  query  performance  with  unique  Vertica  column  store

Linear  scaling  by  adding  more  resources  on  the  fly

Store  more  data,  provide  more  views,  use  less  

hardware

Query  and  load  24x7  with  zero  administration

Columnar storage and execution Clustering Compression Continuous

performance

Automated  Performance  Tuning

Database DesignTime-­series,  geospatial,  click-­stream

and  an  SDK  for  more

Advanced Analytics

HPE IDOL Technology

– Scales  Massively– Single integration  and  access  layer  for  all  data  types  (structured  &  unstructured)

– Open  Platform  with  REST  APIs/  Standard  Based– Manages data in-place– Conceptual and  machine   learning capabilities– Automatically categorizes  &  tags  content– Granular  security model  to  support  HIPAA

IDOL is an information processing and indexing layer that:

Cerner Corporation

– Challenge– Improve  efficiency  and  quality  of  patient  care  with  better  productivity  of  clinician  users

– Solution– Cerner  Millennium  health  care  platform– HP  HAVEn  engines:    HP  Vertica  Analytics  Platform,  Hadoop

– Expected  result• 6,000%  faster  analysis  of  timers  helps  Cerner  gain  insight  into  how  physicians  and  other  use  Millennium  and  make  suggestions    about  using  it  more  efficiently  so  users  become  more  efficient  physicians

• Rapid  analysis  of  2  million  alerts  daily  enables  Cerner  to  know  what  will  happen,  then  head  off  problems  before  they  happen

HPE Vertica helps to optimize health information solutions

Human genome sequencing and medical research centers in New York Diagnose disease and develop more effective treatments for patients– Challenge

• Scalable  platform  to  handle  16  TB  of  data  output  per  day

• Solution  must  be  cost-­efficient  and  support  cutting-­edge  research

– Solution– HP  Vertica    Analytics    Platform

– Expected  result

• Ability  to  house  output  from  deployment  of  Illimina  HiSeq  X  Ten  genome  sequencing  appliances

• Functionality  to  support  the  enormous  amounts  of  data  sequencers  generate

• Query  speed  to  correlate  output  for  time-­sensitive  reporting• Cost-­savings  with  software  that  runs  on  off-­the-­shelf  hardware

Big Data vs. “Big BI”

Big  BI:1. Same  analyses  as  before,  just  more  data

2. Batch  or  warehouse-­type  processing3. Informative,  but  not  really  actionable

Big  Data:1. Joining  data  sets  never  before  joined,  asking  questions  never  before  asked2. Real-­time  or  near-­real-­time,  leading   to  predictive/persuasive3. Action  oriented

HPE  HCAS  genomic  related  functionso automatic  abstraction  of  clinical  concepts  from  the  patient  summaries

o correlation  between  clinical  features  and  genetic  variations

o cluster  visualizations  to  determine  variant  cohorts  for  select  phenotypes

o cluster  visualizations  to  determine  phenotype  cohorts  for  select  variants

Related  genomic  queries:o how  many  variants  of  type  X  are  in  the  population?

oHow  many  variants  are  heterozygous?

oWhat  is  allele  distribution  of  all  variants?

oWhat  variant  differences  do  we  find  when  we  compare  cohort  A  against  cohort  B?  

o In  how  many  variants  is  a  particular  mutation  seen?

Genomic  Medicine

DRG  AssignmentsDRG  coding  details• Was  the  patient  receiving  antibiotics  prior  to  this  admission?  [  ]  Yes  [  ]  No

• Presenting  symptoms  upon  admission:  ___________________________

• Were  positive  blood  cultures  present?  [  ]  Yes  [  ]  No• If  YES,  list  the  organism• If  YES,  was  there  physician  documentation  that  the  blood  culture  was  contaminated?  [  ]  Yes  [  ]  No

• Did  the  attending  physician  document  urosepsis  as  the  final  principal  diagnosis?  [  ]  Yes  [  ]  No• If  YES,  the  correct  ICD-­9-­CM  code  for  urosepsis  is  599.0.

DRG  coding  detailsSEPTICEMIA  /  SEPSIS  /  SIRS  INDICATIONS

– Clinical  indication  of  septicemia/sepsis/SIRS:  • Acute  mental  status  changes• Positive  blood  culture• Fever  >100.4ºF  (38ºC)  PR  or  Hypothermia  <  97ºF  (36ºC)  PR• Heart  rate  >100  beats/minute• Respiratory  rate  >  24  breaths/minute  or  pCO2  <  32  mmHg• WBC  >  12,000/cu.mm  or  <  4,000/cu.mm  or  >  10%  bands• Physician  documentation  of  decreased  urinary  output/oliguria• Arterial  pH  less  than  7.35  (metabolic  acidosis)• Elevated  blood  lactate  levels

DetailsIf  septicemia/sepsis  is  substantiated:• A.  What  did  the  physician  document  in  the  medical  record  as  the  condition  responsible  for  the  septicemia/sepsis  diagnosis?• Pyelonephritis• Pneumonia• Cellulitis• Meningitis• Cholangitis• Peritonitis• Other  (specify):  

• B.  Does  the  physician  documentation  indicate  that  the  septicemia/sepsis  is  due  to  an  internal  device,  implant,  or  catheter?  [  ]  Yes  [  ]  No

Analytics  queries

• Quick  navigation  of  cohort  based  on  structured  codings• Septicemia  (038.x)• Sepsis  (038.x  and  995.9x)  • SIRS  (995.9x)  • Severe  Sepsis  (995.92)  • Septic  Shock  (038.x,  995.92,  785.52)

• Semantic  search  for  chart  abstraction• Blood  culture  results

Analytics  queries

• Urosepsis documentation• Documentation  indicates  Urosepsis• Documentation  indicates  Urosepsis but  record  does  not  have  ICD-­9-­CM  code  for  urosepsis (599.0)

Analytics  queries

• Semantic  search  for  clinical  indicators• Acute  mental  status  changes• Positive  blood  culture• Fever  >100.4ºF  (38ºC)  PR  or  Hypothermia  <  97ºF  (36ºC)  PR• Heart  rate  >100  beats/minute• Respiratory  rate  >  24  breaths/minute  or  pCO2  <  32  mmHg• WBC  >  12,000/cu.mm  or  <  4,000/cu.mm  or  >  10%  bands• Physician  documentation  of  decreased  urinary  output/oliguria• Arterial  pH  less  than  7.35  (metabolic  acidosis)• Elevated  blood  lactate  levels

Analytics  queries

• Semantic  search  for  clinical  indicators• Arterial  pH  <  7.30• Hypotension  (SBP  <  90  mmHg/or  SBP  decrease  >  40  mmHg)• Arterial  hypoxemia  (ratio  of  PaO2  over  FIO2  <  300  torr)• Acute  oliguria  (urine  output  <  30  mL/hour  for  more  than  2  hours)• Creatinine  >  2.0,  or  increase  >  0.5  mg/dl• Coagulation  abnormalities  (INR  >  1.5  or  PTT  >  60  secs)• Ileus  (absent  bowel  sounds)• Thrombocytopenia  (platelet  count  <  100,000  pL-­1)• Hyperbilirubinemia  (plasma  total  bilirubin  >  4  mg/dl  or  70  mmol/L)• Decreased  mental  status• Decreased  peripheral  pulses

Analytics  queries

• Semantic  search  for  clinical  indicators• Is  the  septicemia/sepsis  diagnosis  clearly  substantiated  (through  physician  documentation  of  clinical  indications,  positive  blood  

culture,  etc.)?  [  ]  Yes  [  ]  No

Analytics  queries

• Semantic  search  for  clinical  indicators  the  condition  responsible  for  the  septicemia/sepsis  diagnosis:• Pyelonephritis• Pneumonia• Cellulitis• Meningitis• Cholangitis• Peritonitis• Other  (specify):  

• Does  the  physician  documentation  indicate  that  the  septicemia/sepsis  is  due  to  an  internal  device,  implant,  or  catheter?  [  ]  Yes  [  ]  No

Episode  Dimension  Building

23

Episode  Trigger

Episode  Duration

Claims  Included  in  Spend

Non-­Risk  Adjusted  Episode  Spend

Identify  PAPEpisode  Level  

Exclusions

Identify  PAP  who  pass  Quality  Metrics

Risk  Adjustment

Calculate  Risk/Gain  Share  amounts

Episode  Algorithm

Vendor  Extracts  in  Vertica

Visualize  Episode  Report

Stepwise  Episode  Model

START

Lucile  Packard  Children’s  Hospital  (Stanford)– History  of  Partnership

• Development  partnership:  HP  Healthcare  Analytics  for  structured/unstructured  data• POC:  Multi-­patient  Semantic  Search• Pilot:  Facilitate  USNWR  survey• Quality  and  clinical  effectiveness  access  to  ~115K  patients,  ~390K  encounters,  ~3M  documents

– Healthcare  Analytics  powered  by  HP  haven

• Cohort  Identification:  Cross  patient  search,  intuitive  UI• Chart  Abstraction:  Rapid  review  of  individual  patient  records• Deeper  Analysis:  Hypothesis  generation

Lucile  Packard  Children’s  Hospital  Stanford– Current  State

• EMR  conversion  (Cerner  to  Epic)  in  May,  2014• 750k  encounters,  155k  patients,  ~1M  notes• Preparing  for  weekly  batch  updates

– Challenge:  Venous  Thromboembolism  (VTE)

• Hospital  Acquired  Condition  (HAC),  incidence  about  4/1000  in  pediatrics• Difficult  to  identify  for  reporting,  much  less  for  mitigation  and  prevention• Current  process  is  inefficient,  and  lacks  sensitivity

Venous  Thromboembolism:  Traditional  Workflow

Venous  Thromboembolism– Traditional  Workflow

• Report  identifies  3-­5  patients  per  month,  perhaps  1  true  positive• EHR-­based  chart  abstraction  takes  hours-­days• <  5  VTE  patients  identified  in  2015

– HP  Healthcare  Analytics

• Semantic  search  identifies  15-­30  potential  events  per  month• Computer-­assisted  chart  abstraction  takes  minutes

– Additional  Quality/Clinical  Effectiveness  Use  Cases

• Generalization  to  other  Hospital  Acquired  Conditions  (e.g.  obstetric  adverse  events)

• Deeper  analysis  of  identified  events,  risk  factors,  development  of  care  protocols

Venous  Thromboembolism  Analysis

Lucile  Packard  Children’s  Hospital  Stanford– Additional  Uses  for  HPE  Healthcare  Analytics

• Self  service  analytics  tool  for  faculty  physicians  for  clinical  care• Support  the  concept  of  a  Learning  Healthcare  System• Insight  into  past  experience  (i.e.  practice-­based  evidence)• Allow   for  increasingly  data  driven  care  decisions

• Complements  Epic  SlicerDicer (cohort  identification  for  structured  Epic  data)• API  development  for  Epic  integration

• Analytics  tool  for  Research/Discovery• Cohort  Discovery• Hypothesis  generation

The  Big  Data  Journey

Business  Intelligence

Analytics

Business  Apps

Discover  Insights

OperationalizeInsights

Data  warehousing  for  monitoring

Data  warehousing  for  monitoring

Statistical  modeling   to  extract  insights

Statistical  modeling   to  extract  insights

Specialized  apps  to  automate  business  processes

Specialized  apps  to  automate  business  processes

Discover  insights  to  identify  new  business  

opportunities

Discover  insights  to  identify  new  business  

opportunities

Operationalize  insights  to  transform  the  business

Operationalize  insights  to  transform  the  business

Data Warehouses Analytics Packages Business Systems Data Lakes Hybrid Data Management

Enabling Technologies

Usage

Ad Hoc

Operational

Predictive

Prescriptive

Reporting

DiscoveryEnvironments