analytics in healthcare bhuvaneashwar 11th_march
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ANALYTICS FOR HEALTHCARE PROVIDERSAPPLICATIONS, TRENDS AND FUTURE OF ANALYTICS IN HEALTHCAREBHUVANEASHWAR SUBRAMANIANHEWLETT PACKARD ENTERPRISE
HEALTHCARE ANALYTICS THEN & NOW1854 CHOLERA ENDEMIC, LONDON 2014 EBOLA EPIDEMIC
- Rudimentary cluster mapping- Manual and inaccurate analysis- Retrospective
- (Biomosaic tool), CDC Emergency Response Center
- Sophisticated predictive modeling- data from mobile phones,
historical epidemiological data- Multiple data sources- High computing power
A BRIEF STORY ON HOW HPE DEPLOYED ANALYTICS TO IMPROVE PATIENT ENGAGEMENT AT LUCILE PACKARD CHILDREN’S HOSPITAL
DRIVER 1:HEALTHCARE TODAY HAS BECOME DATA CENTRIC
2000
BIOLOGICAL DATABASES IN 10 YEARS
80 Mb PATIENT DATA GENERATED PER YEAR
600 Bn
SEQUENCED NUCLEOTIDES PER WEEK ON AN ILLUMINA HISEQ
DRIVER 2: MEDICAL ERRORS LEADING TO INCREASED CASUALITIES AND COST OF CARE
1:300
1:10,000
CHANCE OF MEDICAL CASUALITY
CHANCE OF AIR TRAVEL CASUALITY
1.4 Mn
PEOPLE SUFFERING FROM HOSPITAL INFECTIONS WORLDWIDE
1.3 Mn
DEATHS CAUSED BY INFECTIONSTHROUGH UNSTERILIZED INSTRUMENTS
DRIVER 3:HEALTHCARE EVOLUTION TOWARDS EVIDENCE BASED MEDICINE AND ACCOUNTABLE CARE DELIVERY
Ecos
yste
m in
tegr
atio
n
Today’shealthcare
Collaborativehealthcare
Evidence BasedPersonalized Healthcare
Low
High
Integratedhealthcare
Different providers willbe at different stages
•Stand-alone•Best of breed•Fragmented systems
• Integrated EMR, EHR, PMS, CPOE•Real-time alerts •HIE/improved access to data
•Tight linkage between physicians & hospitals•Care collaboration•Regional, state and national RHIOS, NHIN•Patient access to data
•Personalized/evidence-based clinical decision support•Patient engagement
Quality of Care and OutcomesLow High
THE FOUR V’S TOGETHER DEFINE THE IMPORTANCE OF ANALYTICS FOR HEALTHCARE
VOLUME VARIETY
VALUE VELOCITY
• 500 petabytes to 25,000 Petabytes by 2020• Key sources :MRI,CT & PET Scans
• 1hr to sequence whole genome of humans•50% reduction in time for genome sequencing for rare diseases
Behavioural data,
Environmental dataMedical record data
Vital sign dataNutritional data
Pharmaocological data
• <50% hospital labour compensation ratio• $300 Bn cost savings for hospitals in US.
ANALYTICS APPLICATIONS ACROSS THE CARE DELIVERY SPECTRUM
CLINICAL ANALYTICS
BUSINESS ANALYTICS
PATIENT COMPLIANCE
CLINICAL HEALTH OUTCOMES ANALYTICS1
RESEARCH &DEVELOPMENT ANALYTICS
2
DISEASE MANAGEMENT
TREATMENT EFFECTIVENESS
SITE SELECTION
TARGETED THERAPEUTICS
PATIENT COHORT IDENTIFICATION
5
OPERATIONAL ANALYTICS
FACILITY UTILIZATION
STAFFUTILIZATION
PROCESS QUALITY CONTROL COMPLIANCE REPORTING
3 MARKETING ANALYTICS
CUSTOMER SEGMENTATIONSOCIAL
NETWORK ANALYSIS
PRICING OPTIMIZATIONCUSTOMER LIFETIME VALUE
4 FINANCE AND FRAUD
BILLING QUALITY
FRAUD DETECTION
RISK MANAGEMENT
THE DATA FOR APPLYING ANALYTICS ACROSS THE HEALTHCARE SPECTRUM COMES FROM SEVERAL SOURCES
• Video conferences• Downloads• Call notes• SMS• Web chat• Blogs• Social networks• Mobile apps• Sensors• Survey response• Emails
• Revenue management• Claims• EMRs• ICD 9-10• Meaningful use • Lab/radiology notes• P4P reporting• Quality reporting • Clinical quality
measures• Transcription • Population health mgmt
Billions ofdaily interactions
Millions of daily
transactions
Enterprise information that comes from line of business
systems that provide structured database information that is
used to run the business
Global information that comes from internal and external unstructured sources that is used to gain insight on the business drivers
&
SCENARIOS WHERE HEALTHCARE ANALYTICS CAN BRING COST SAVINGS
Identifying cost effective ways oftreating patient through
comparative analyses
Analyzing disease patternsMonitoring disease outbreaks
Aid in vaccine development and Population safety measures
1
2
Analyse patient data from E.H.Rand several unstructured sources,
Financial data, genomic data determine risk of disease recurrence., hospitalization
3
Conducting genomic analysis cost
Effectively and integrating Genomic information into patient
Diagnosis and treatment
4
HEALTHCARE
PROCESSES
Clinical Operations
Public Health
Evidence Based Medicine
Genomic Analysis
APPLYING ANALYTICS IN HEALTHCARE SETTINGS DELIVERS A DATA-DRIVEN ACTIONABLE APPROACH TO TREATING DISEASES
A USE CASE ON DEVELOPING A TREATMENT APPROACH TO DIABETES
Obtaining generic population level data
on diabetes
Localizing context to diabetes patients visiting a treatment
center
Identify diabetic patients
with a high chance of
hospitalization
Organize hospital
resources to effectively
deliver care management
and avoid hospitalization• National
Prevalence for Diabetes is 8.3%
• Hypertension is a major co-morbidity for diabetes
• 35,000 individuals suffer from diabetes in our region
1000 diabetes patients visit our center every year
Total cost of treating patients
per year is $7,000Cost increased by 15% over last year
Assign patient level risk scores on
hospital sample to develop an
evidence based prediction model to determine potential
admits next year
Prioritize patients by risk score and
allocate care management resources to
address at risk patients & take steps to prevent hospitalization
USE CASE : AN APPROACH FOR PREDICTING HOSPITAL ADMISSION RISK FOR DIABETIC PATIENTS
• Local patient data• Regional and
national data sets• Device data• Patient
engagement data• Genomic,
Environmental data• Activity based
costing data
Data WarehouseWorkload
1
Workload 2
Workload 3
User Defined Classification& Association Rules
Regression Decision TreeClustering
Pattern DiscoveryTechniques and Tools
Visualization Output
Plasmaglucose
BMI Readmit risk
<127.5 <26.5 No risk
<157.5 >26.5 High API enabled transfer of clinical workflowE.H.R
Clinical Apps
Ordering, Supply Refills
Improved Diagnosis
Care ManagementAltered treatment
programs
Clinical and Operational Outcomes
SQL QueryingHIVE
R Studio
EVOLUTION OF ANALYTICS IN A HEALTHCARE PROVIDER SETTING AND CAPABILITY PRIORITIES IN ANALYTICS
STAGE 1 Rookie
•Monitordashboards• Receive patient data reports• Visualize patient data
STAGE 2 Dabbler
Analyze past patientbehavior• Perform ad hocdata analysis• Develop 360-degreeview of patients
0102030405060708090
100• Build models
Incorporate machine learning
techniques• Identify
patient risks and opportunities
• Real time prescriptive analytics
• Provide point-of-care
decision support
STAGE 3 Pros
STAGE 4Gurus
EMERGING TRENDS IN HEALTHCARE ANALYTICS ADOPTION
INTEGRATING CLINICAL, FINANCIAL AND QUALITY DATA TO DELIVER VALUE
BASED CARE
1 IMPROVING QUALITY OF REMOTE CARE DELIVERY
THROUGH ANALYTICS2
IMPROVING PATIENT ENGAGEMENT AND STAFF
RESPONSE3 DEVELOPING PERSONALIZED
TREATMENTS AND THERAPEUTICS
4
Kaiser Permanente – sepsis risk Max Hospitals- Deep Venous Thrombosis
Narayana Health Telemedicine e-Health CenterRemote Care Analytics Dashboard
Lucile Packard Children’s HospitalOperating Room Scheduling Dashboard
Moffitt Cancer Center Gene Expression BasedRadiosensitivity Index for Cancer
PROMINENT CHALLENGES IN DEPLOYING ANALYTICS IN HEALTHCARE
HEALTHCARE ANALYTICS
16
A HOST OF TOOLS FOR HEALTHCARE PROVIDERS TO MAKE SENSE OF DATA AT ALL TIMES
HealthcareAnalytics
ToolkitHealthcare Data ProgrammingData Mining
File Distribution, Processing and configuration
Infrastructure
Databases
BI Tools & Visualization
FUTURE DIRECTIONS FOR ANALYTICS IN HEALTHCARE
PRESCRIPTIVE ANALYTICS WOULD BECOME INCREASINGLY PROMINENT IN
HOSPITAL OPERATIONSProvide “in-cotext”, real time interpretation of
scenarios designed through predictive analytics: Adjusting resource allocation
STARTUPS ENGAGING WITH HEALTHCARE ORGANIZATIONS TO DESIGN CUSTOM
PREDICTIVE ANALYTICS SOLUTIONS FOR DISEASE MANAGEMENT
2
Oncora Medical is working with hospitals to improve real time treatments for radiation oncology
3
INTEGRATION OF HETEROGENOUS DATA SOURCES
TO DELIVER EVIDENCE BASED MEDICINE
1
Integration of EMR, genomic data, wearable data, epidemiological data with social and behavioural
data
APPENDIX
HOW CAN HOSPITALS ADOPT AN ANALYTICS APPROACHConcept
Statement
Proposal
Methodology
Deployment
Determine the need by mapping the situation to
the 4 V’s
What is the problem being
addressed?Why take an
analytics approach ?
Variable selectionPlatform and Tools
Analytical techniquesAssociation, Results
Expected
Evaluation Validation Testing
First cut at establishing the
need for a project involving analytics
Expand on the concept note to highlight the key
questions and justify the costs
involved in analytics
implementations
Break down the broad questions into actionable objectives and apply the right kind of analytical
tools
Break down the broad questions into actionable objectives and map the kind of tools and platforms
to use
EMERGING TRENDS IN ADOPTION OF HEALTHCARE ANALYTICS
INTEGRATING CLINICAL, FINANCIAL AND QUALITY DATA TO DELIVER
VALUE BASED CARE
IMPROVING QUALITY OF REMOTE CARE DELIVERY THROUGH ANALYTICS
DASHBOARDS
EMPLOYING ANALYTICS TO IMPROVE PATIENT ENGAGEMENT
1 2Greater than 64% of Hospital
Executives believe that implementing analytics would improve health
outcomes and support value based careBest Practice:
• Kaiser Permanente, integrated clinical, E.H.R
data and operational kpis to predict potential sepsis risk in patients and advance treatment• Max Hospitals, India deployed analytics to
detect patients with risk for acquired Deep Venous Thrombosis
Shortage of doctors,particularly in developing countries is causing
hospitals to depend more on analytics dashboards to determine availability of
paramedical staff and evaluate treatmentsBest Practice :
Narayana Health partnered with Hewlett Packard to develop the eHealth Center, which usedAnalytics dashboards to determine disease spread in Region and define treatment options based on historicalData.3
Analytics is being used to address chronically ill patients by processing
data streamed from patient wearables to determine emergency response and
patient alerts/communications
Fact : Critical patients account for 78 percent of all healthcare spending,81 percent of in-patient stays
EMPLOYING ANALYTICS TO DEVELOP PERSONALIZED TREATMENTS AND
THERAPEUTICS4Genomic , pharmacological and
conventional diagnostic data are being integrated to develop personalized
therapeutics and treatment options for cancerBest Practice :
HPE developed an operating room scheduling dashboard, that captured data on intensive care patients from their electronic records and sensors attached to vital sign monitors at Lucile Packard Hospital and helped reduce casualities.
Best Practice : Moffit Cancer Center developed a gene expression based radio sensitivity Index that accurately predicts the outcomes of radiation therapy for various cancers across patient strata.
THE FOUR V’S FOR HEALTHCARE TOGETHER DEFINE THE IMPORTANCE OF ANALYTICS IN HEALTHCAREVolume
• The volume of healthcare data is expected to grow 50 fold from 500 petabytes to 25,000 petabytes by 2020
• Primary contributors to the data volume would include high resolution MRI scans, CT Scans and PET scans
• Data volumes are expected to increase primarily due to government mandates to store patient data for the longest periods possible
• High resolution healthcare scans are also expected to increase the data volumeVariety• The variety of data includes text, images, videos
• The primary sources for data are expected to be patient records, patient wearables, high throughput sequencing data from genomics experiments
Velocity• Speed at which data is generated from a patient interaction or the rate at which
biomedical data is generated
• Shift from static data like X-Rays, EMRs to real time data from wearable monitoring and genome sequencers
Value• Operational efficiencies, to reduce costs, waste, and fraud through more efficient
methods for data integration, management, analysis, and service delivery.• Business process enhancements, to find new ways of delivering care while
efficiently allocating services to enable sustainable management of the population health
KEY CHALLENGES TO APPLYING ANALYTICS IN HEALTHCARE• Intepreting structured
data and unstructured data consistently,as most of the data generated is managed for size through Electronic health records and genomic data platforms.
• Currently high blood pressure can be expressed in 127 terms. Integrating data from genomic expression studies with healthcare records and standardize medical ontologies is a critical challenge
• Analytics solutions specific to healthcare will be necessary to improve specificity and veracity of healthcare outcomes
• Security threats challenge the ability to facilitate information exchange and use open source software to analyse proprietary patient data.
Effective interpretation of healthcare
and life sciences data
Standardizing clinical
ontologies
Lack of comprehensive healthcare
specific analytics
solutions .
Threats of data
breachesand siloed
departmental data
BLOCKCHAIN IN HEALTHCARE TO IMPROVE EFFECTIVE INTEGRATION OF HEALTHCARE INFORMATION
Source: Deloitte