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© 2013 Health Catalystwww.healthcatalyst.com
© 2013 Health Catalystwww.healthcatalyst.com
The Age of AnalyticsHealthcare 2.0:
Dale Sanders, Aug 2013
© 2013 Health Catalystwww.healthcatalyst.com
Overview
2
Key Principl
es
• Every Industry: How Data Management Evolves
• Preserving Agility: Analytic Data Binding
• The Triple Aim: Closed Loop Analytics
• The Roadmap: Healthcare Analytic Adoption Model Ev
aluating Option
s
• Strategy & Vendor Options
• A Checklist: Population Health Management
Organi
zationa
l Issues • ACO Data
Acquisition Timeline
• Data Governance
Time Allowing: Health Catalyst Screen Shots
© 2013 Health Catalystwww.healthcatalyst.com3
Evolution of Data ManagementEvery industry follows the same pattern
Data Collection1
2 Data Sharing
3 Data Analysis
Billing, Radiology and Lab systems; EMRs, etc.
Local Area Networks, Health Information Exchanges
Enterprise Data Warehouses (EDW)
© 2013 Health Catalystwww.healthcatalyst.com
Who or what are we
monitoring?
What are our goals?
What are we measuring?
How will we achieve them?
Data and Technology
Organization and Culture
Regularly return to these fundamentalsThe Four Questions of Analytics
© 2013 Health Catalystwww.healthcatalyst.com
55
Atomic data must be “bound” to business rules about that data and to vocabularies related to that data in order to create information
Vocabulary binding in healthcare is pretty obvious● Unique patient and provider identifiers● Standard facility, department, and revenue center codes● Standard definitions for gender, race, ethnicity● ICD, CPT, SNOMED, LOINC, RxNorm, RADLEX, etc.
Examples of binding data to business rules● Length of stay● Patient relationship attribution to a provider● Revenue (or expense) allocation and projections to a department● Revenue (or expense) allocation and projections to a physician● Data definitions of general disease states and patient registries● Patient exclusion criteria from disease/population management● Patient admission/discharge/transfer rules
Binding Data to Create Information
© 2013 Health Catalystwww.healthcatalyst.com
Data Binding
Vocabulary
“systolic &diastolicblood pressure”
Rules
“normal”
Pieces ofmeaningless
data
11560
Bindsdata to
Software Programming
© 2013 Health Catalystwww.healthcatalyst.com7
Why Is This Concept Important?
Two tests for tight, early binding
Knowing when to bind data, and howtightly, to vocabularies and rules is
THE KEY to analytic success and agility
Is the rule or vocabulary widely accepted as true and accurate in the organization or industry?
Comprehensive Agreement
Is the rule or vocabulary stable and rarely change?
PersistentAgreement
Acknowledgements to Mark Beyer of Gartner
© 2013 Health Catalystwww.healthcatalyst.com
ACADEMIC
STATE
SOURCEDATA CONTENT
SOURCE SYSTEMANALYTICS
CUSTOMIZED DATA MARTS
DATAANALYSIS
OTHERS
HR
FINANCIAL
CLINICAL
SUPPLIES
INTE
RN
AL
EX
TER
NA
L
ACADEMIC
STATE
OTHERS
HR
FINANCIAL
CLINICAL
SUPPLIES
RESEASRCH REGISTRIES
QlikView
Microsoft Access/ODBC
Web applications
Excel
SAS, SPSS
Et al
OPERATIONAL EVENTS
CLINICAL EVENTS
COMPLIANCE AND PAYER MEASURES
DISEASE REGISTRIES
MATERIALS MANAGEMENT
3
Data Rules and Vocabulary Binding Points
High Comprehension & Persistence of vocabulary & business rules? => Early binding
Low Comprehension and Persistence of vocabulary or business rules? => Late binding
Six Points to Bind Data in an Analytic System
421 5 6
© 2013 Health Catalystwww.healthcatalyst.com9
Words of CautionHealthcare suffers from a low degree of Comprehensive and Persistent agreement on many topics that impact analytics
The vast majority of vendors and home grown data warehouses bind to rules and vocabulary too early and too tightly, in comprehensive enterprise data models
Analytic agility and adaptability suffers greatly• “We’ve been building our EDW for two years.”• “I asked for that report last month.”
Closed Loop Analytics: The Triple Aim Google: “Intermountain antibiotic assistant”
© 2013 Health Catalystwww.healthcatalyst.com
Center of the Universe Shifts
11
EMR
Analytics& EDW
Analytics& EDW
EMR
CopernicusPtolemy
© 2013 Health Catalystwww.healthcatalyst.com
Healthcare Analytic Adoption ModelLevel 8 Cost per Unit of Health Reimbursement &
Prescriptive AnalyticsContracting for & managing health. Customizing patient care based on population outcomes.
Level 7 Cost per Capita Reimbursement & Predictive Analytics
Diagnosis-based financial reimbursement, managing risk proactively, measuring true outcomes
Level 6 Cost per Case Reimbursement& The Triple Aim
Procedure-based financial risk and applying “closed loop” analytics at the point of care
Level 5 Clinical Effectiveness & Population Management Measuring & managing evidence based care
Level 4 Automated External Reporting Efficient, consistent production; agility, and governance
Level 3 Automated Internal Reporting Efficient, consistent production; widespread access to KPIs
Level 2 Standardized Vocabulary & Patient Registries Relating and organizing the core data
Level 1 Integrated, Enterprise Data Warehouse Foundation of data and technology
Level 0 Fragmented Point Solutions Inefficient, inconsistent versions of the truth
Level 8Cost per Unit of Health Reimbursement & Prescriptive Analytics: Providers Analytic motive expands to wellness management and mass customization of care. Physicians, hospitals, employers, payers and members/patients collaborate to share risk and reward (e.g., financial reward to patients for healthy behavior). Analytics expands to include NLP of text, prescriptive analytics, and interventional decision support. Prescriptive analytics are available at the point of care to improve patient specific outcomes based upon population outcomes. Data content expands to include genomic and familial information. The EDW is updated within a few minutes of changes in the source systems.
Level 7Cost per Capita Reimbursement & Predictive Analytics: Analytic motive expands to address diagnosis-based, fixed-fee per capita reimbursement models. Focus expands from management of cases to collaboration with clinician and payer partners to manage episodes of care, using predictive modeling, forecasting, and risk stratification to support outreach, triage, escalation and referrals. Patients are flagged in registries who are unable or will not participate in care protocols. Data content expands to include external pharmacy data and protocol-specific patient reported outcomes. On average, the EDW is updated within one hour or less of source system changes.
Level 6Cost per Case Reimbursement & The Triple Aim: The “accountable care organization” shares in the financial risk and reward that is tied to clinical outcomes. At least 50% of acute care cases are managed under bundled payments. Analytics are available at the point of care to support the Triple Aim of maximizing the quality of individual patient care, population management, and the economics of care. Data content expands to include bedside devices and detailed activity based costing. Data governance plays a major role in the accuracy of metrics supporting quality-based compensation plans for clinicians and executives. On average, the EDW is updated within one day of source system changes. The EDW reports organizationally to a C-level executive who is accountable for balancing cost of care and quality of care.
Level 5
Clinical Effectiveness & Population Management: Analytic motive is focused on measuring clinical effectiveness that maximizes quality and minimizes waste and variability. Data governance expands to support care management teams that are focused on improving the health of patient populations. Permanent multidisciplinary teams are in-place that continuously monitor opportunities to improve quality, and reduce risk and cost, across acute care processes, chronic diseases, patient safety scenarios, and internal workflows. Precision of registries is improved by including data from lab, pharmacy, and clinical observations in the definition of the patient cohorts. EDW content is organized into evidence-based, standardized data marts that combine clinical and cost data associated with patient registries. Data content expands to include insurance claims. On average, the EDW is updated within one week of source system changes.
Level 4Automated External Reporting: Analytic motive is focused on consistent, efficient production of reports required for regulatory and accreditation requirements (e.g. CMS, Joint Commission, tumor registry, communicable diseases); payer incentives (e.g. MU, PQRS, VBP, readmission reduction); and specialty society databases (e.g. STS,NRMI, Vermont-Oxford). Adherence to industry-standard vocabularies is required. Clinical text data content is available for simple key word searches. Centralized data governance exists for review and approval of externally released data.
Level 3Automated Internal Reporting: Analytic motive is focused on consistent, efficient production of reports supporting basic management and operation of the healthcare organization. Key performance indicators are easily accessible from the executive level to the front-line manager. Corporate and business unit data analysts meet regularly to collaborate and steer the EDW. Data governance expands to raise the data literacy of the organization and develop a data acquisition strategy for Levels 4 and above.
Level 2Standardized Vocabulary & Patient Registries: Master vocabulary and reference data identified and standardized across disparate source system content in the data warehouse. Naming, definition, and data types are consistent with local standards. Patient registries are defined solely on ICD billing data. Data governance forms around the definition and evolution of patient registries and master data management.
Level 1Integrated, Enterprise Data Warehouse: At a minimum, the following data are co-located in a single data warehouse, locally or hosted: HIMSS EMR Stage 3 data, Revenue Cycle, Financial, Costing, Supply Chain, and Patient Experience. Searchable metadata repository is available across the enterprise. Data content includes insurance claims, if possible. Data warehouse is updated within one month of changes in the source system. Data governance is forming around the data quality of source systems. The EDW reports organizationally to the CIO.
Level 0Fragmented Point Solutions: Vendor-based and internally developed applications are used to address specific analytic needs as they arise. The fragmented Point Solutions are neither co-located in a data warehouse nor otherwise architecturally integrated with one another. Overlapping data content leads to multiple versions of analytic truth. Reports are labor intensive and inconsistent. Data governance is non-existent.
©
Healthcare Analytic Adoption Model
© 2013 Health Catalystwww.healthcatalyst.com14
The ACO Data Acquisition ChecklistBilling data1
Lab data2
Imaging data3
Inpatient EMR data4
Outpatient EMR data5
Claims Data6
HIE Data7
Detailed cost accounting8
Bedside monitoring data9
External pharmacy data10
Familial data11
Home monitoring data12
Patient reported outcomes data13
Long term care facility data14
Genomic data15
Real-time 7x24 biometric monitoring for all patients in the ACO16
NO
W1-
2 Y
EA
RS
2-4
YE
AR
S
© 2013 Health Catalystwww.healthcatalyst.com
© 2013 Health Catalystwww.healthcatalyst.com
Evaluating Options
15
16
Strategy and Analytic OptionsStrategy Option
Pros & Cons Example Vendors
Buy & Build from an Analytics Platform Vendor
Highest degree of analytic flexibility and adaptability Requires a data driven culture with high aspirations that views analytics as a clear
business differentiator Best suited for a culture with a higher degree of data literacy and data
management skills Slow initial time-to-value plagues some vendors Inconsistent ROI track record, but when ROI occurs, it’s big
Caradigm Intelligence Platform Health Catalyst Healthcare Data Works IBM Healthcare Data Model Oracle Healthcare Data Model Recombinant (Deloitte)
Buy from an Analytics Service Provider
Best suited for cultures that want to avoid the details of analytics and data management, but aspire to improve basic internal and external reporting
Inter-organizational benchmarking and comparative analytics is a natural part of the business model and service
Limited analytic flexibility and adaptability Substantive ROI is not well-documented nor widely acknowledged
Explorys Humedica Lumeris Premier Alliance Truven Analytics Suite
Buy “Best of Breed” Point Solutions
Leverages expertise and very specific analytics applications in business and clinical areas that are not always available in other options
Does not facilitate data integration; i.e., does not provide a single analytic perspective on patient care and costs
Costly and complicated to maintain
AltaSoft Crimson Suite EPSI MedeAnalytics Medventive Midas Omincell
Buy from your EMR Vendor
Offers the possibility of “closed loop analytics” driving analytics back to the point of care, in the EMR and clinical workflow
No proven track record with analytics to date from the EMR vendors Tend to be very focused on analytics that are specific to the EMR vendor’s data Less flexible and adaptable to new sources of data and analytic use cases,
especially complex ones
Allscripts Sunrise Cerner PowerInsight Epic Clarity & Cogito McKesson Horizon Meditech Data Repository Siemens Decision Support
Population Health Management A Checklist for Requirements and Functionality
1. Evidence based definitions of patients to include in the PHM registries
2. Clinician-patient attribution algorithms
3. Discrete, evidence based methods for flagging patients in the registries that are difficult to manage in the protocol, or should be excluded from the registry, altogether
4. Evidence based triage and clinical protocols for single disease states
5. Evidence based triage and clinical protocols for comorbid patients
6. Metrics and monitoring of clinical effectiveness and total cost of care (to the system and the patient)
7. Risk stratified work queues for outreach that feed care management teams and processes
8. Access to test results and medication compliance data outside the core healthcare delivery organization
9. Patient engagement and communication system about their care, including coordination of benefits
10. Patient education material and a distribution system, tailored to their status and protocol
11. Patient reported outcomes measurement system, tailored to their status and protocol
12. Inter-physician/clinician communication system about overlapping patients
© 2013 Health Catalystwww.healthcatalyst.com18
Data GovernanceGovern to the least extent required for the common good
Base your committee charter on…
Encouraging more, not less, data access
Increasing data contentin the EDW
Campaigning fordata literacy Resolving analytic priorities
Enhancing data quality Establishing standards for Master Reference Data
© 2013 Health Catalystwww.healthcatalyst.com19
In Summary• Analytics is the R in the ROI of IT investments
• This is a new chance for healthcare to do the right thing, the first time, with IT strategy and investments
• Unlike the bumpy road of EMR adoption
• C-level executives need to be highly literate in data management
• You can’t delegate all the details. The consequences to the business are too significant.
• All industries now move at the speed of software and analytics• Test for Comprehensive and Persistent understanding
• Follow the curriculum of the Analytic Adoption Model• Be deliberate and stick to the fundamentals
© 2013 Health Catalystwww.healthcatalyst.com20
Thank You!• Questions?
• Contact information• dale.sanders@healthcatalyst.com• dale.sanders@hsa.ky• @drsanders• www.linkedin.com/in/dalersanders
© 2013 Health Catalystwww.healthcatalyst.com
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Catalyst Screen Shots
21
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Key Process Analysis
Identifying Variability
Opportunities For Waste Reduction and Quality Improvement
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Hospital and Clinical Operations
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Executive Dashboard
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Population Explorer
On The Path To
Population Health Management
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Population Health Management
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© 2013 Health Catalystwww.healthcatalyst.com43
The Age of AnalyticsTaking the next step…• Download the Late-Binding Data
Warehouse White Paper
www.healthcatalyst.com/LateBindingWhitePaper
• Download the Analytics Adoption Model White Paperwww.healthcatalyst.com/analytics-adoption-model
• Contact us to learn more about our solutions and communication tools
www.healthcatalyst.com/company/contact-us
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