The document discusses advancements in healthcare analytics, emphasizing the integration of clinical and administrative systems to improve clinical efficiency in real time. It outlines the role of clinical decision support systems and initiatives like the NIH's BD2K in utilizing big data for better patient outcomes. Additionally, it highlights the challenges of distributed queries in healthcare and the ongoing efforts to establish standards and governance for effective data sharing.

1. Midwest Hospital
Cloud Forum
September 9, 2013
Closing the Loop in Healthcare Analytics - Correlating Clinical
and Administrative Systems with Research Efforts to Deliver
Clinical Efficiency in Real Time
John Sharp, MSSA, PMP, FHIMSS
Cleveland Clinic

2. Learning Healthcare
System
• Adaptation to the pace of change
Stronger synchrony of efforts
Culture of shared responsibility
New clinical research paradigm
Clinical decision support systems
Universal electronic health records
Tools for database linkage, mining, and use
Notion of clinical data as a public good
Incentives aligned for practice-based evidence
Public engagement
Trusted scientific broker
Leadership

3. Grand Challenges in Clinical
Decision Support
• Improve the human-computer interface
• Disseminate best practices in CDS design, development,
and implementation
• Summarize patient-level information
• Prioritize and filter recommendations to the user
• Create an architecture for sharing executable CDS
modules and services
• Combine recommendations for patients with co-
morbidities
• Prioritize CDS content development and implementation
• Create internet-accessible clinical decision support
repositories
• Use free text information to drive clinical decision support
• Mine large clinical databases to create new

5. Big Data To
Knowledge – BD2K
• NIH initiative
• http://commonfund.nih.gov/bd2k/
• aims to facilitate broad use of biomedical big data,
develop and disseminate analysis methods and
software, enhance training for disciplines relevant
for large-scale data analysis, and establish
centers of excellence for biomedical big data.

6. IBM Watson
• Clinical Decision support
• Artificial intelligence
• add a natural language and semantic layer on top
of a search engine
• evaluates each potential answer based on
evidence it can gather through refined secondary
search
• http://www.ibm.com/developerworks/library/os-ind-
watson/

7. Query Health
• http://wiki.siframework.org/Query+Health

8. Distributed
Queries
• Distributed Query Challenges
• Absence of standards
• Integrating each data source is a heavy
lift
• cross-organizational governance
• Yet, path-breaking work is underway
• ISDS Distribute
• Primary Care Information Project
• FDA’s Mini-sentinel
• HMO Research Network
• MDPHNet
• i2b2 / SHRINE networks
• DARTNet
• OMOP
• CDC’s BioSense 2.0
• Questions that return population
measures (aggregate results) related to
disease outbreaks, post-market
surveillance, prevention, quality
performance, etc.
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9. Distributed Query Example
Mini-Sentinel & PopMedNet
• PopMedNet is proven across
several distributed query
networks, including Mini-
Sentinel
• Uniquely supports the policy
guidance from HIT Policy
Committee
• Targeting full implementation
of the Query Health proposed
standards
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10. A Vision for Broader Use of Electronic Health Information
in Evidence Development
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