Data Aggregation, Liquidity, and the Learning Healthcare System: Perspectives from the Indiana Experience Shaun J. Grannis, MD, MS, FACMI, FAAFP Biomedical Informatics Research Scientist, The Regenstrief Institute Associate Professor of Family Medicine, Indiana University School of Medicine
Data Aggregation, Liquidity, and the Learning Healthcare System: Perspectives from the Indiana Experience. Shaun J. Grannis, MD, MS, FACMI, FAAFP Biomedical Informatics Research Scientist, The Regenstrief Institute Associate Professor of Family Medicine, Indiana University School of Medicine. - PowerPoint PPT Presentation
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Data Aggregation, Liquidity, andthe Learning Healthcare System:
Perspectives from the Indiana Experience
Shaun J. Grannis, MD, MS, FACMI, FAAFPBiomedical Informatics Research Scientist,
The Regenstrief InstituteAssociate Professor of Family Medicine,
Indiana University School of Medicine
What We’ll Cover
• The Context• The Problem• Potential Solutions
Regenstrief Institute
• Endowed by Sam Regenstrief - Inventor of the low cost front-loading dishwasher
• Supported the creation of the Institute to apply process improvement to medicine
• A medical informatics “skunkworks”
Regenstrief Informatics - What We Do
• Build medical information systems• Study systems and supporting technologies• Rationalize, organize and standardize health
care data• Pragmatists - needs driven, create solutions to
real-world problems• Describe what works and what doesn’t
High Costs and Inefficiencies• Very large and inefficient information enterprise
that still operates with substantial amounts of paper
• Costs are rising – $2.4 Trillion in 2009, ~$8,000/person – Growth outpacing inflation– Now $2.7 Trillion in annual spending (2012 est.)– May reach $4 Trillion by 2020 (!)
1. RAND Study, Hillestad2. Social Transformation of American Medicine,
Starr
Healthy Life Expectancy versus Expenditure per capita
Total Healthcare Expenditures per Capita $USPPP, 2006 or Latest
Source: OECD Health Database, June 2008 version; WHO World Health Data 2008; EU-15 average is the GDP weighted average
Infant Mortality versus Expenditure per capita
Total Healthcare Expenditures per Capita $USPPP, 2006 or Latest
Source: OECD Health Database, June 2008 version; WHO World Health Data 2008; EU-15 average is the GDP weighted average
Overhage JM, Dexter PR, Perkins SM, Cordell WH, McGoff J, McGrath R, McDonald CJ. A randomized, controlled trial of clinical information shared from another institution. Ann Emerg Med. 2002 Jan;39(1):14-23.
All Health Care is Not Local: An Evaluation of the Distribution of
Emergency Department Care Delivered in Indiana
All Health Care is Not Local
• Over 3 years, 2.8 million patients totaled 7.4 million visits for an average of 2.6 visits per patient.
• More than 40% of ED visits during the study period were for patients having data at multiple institutions.
• This population analysis suggests a pull model is necessary, and helps inform the ongoing dialog regarding the merits of peer-to-peer (push) and federated aggregate HIE (pull) NwHIN architectures.
Leveraging Analytics to Enable Accountable Care
A network diagram illustrating the connectedness among Indiana EDs that participate in PHESS. Circular nodes represent EDs; node size indicates the visit volume; node color indicates the centrality of the ED. The gray edges connecting nodes indicate where patient crossover occurs. EDs that share proportionally larger number of patients are clustered together. While general clusters of “medical trading areas” emerge, the myriad gray edges clearly illustrate how interconnected all EDs are to one another.
• Patients receive healthcare from multiple providers and across organizations
• More than 40% of ED visits are for patients having data at multiple institutions
Distribution of patients stratified by the total number of ED visits. Note that six patients visited the ED more than 300 times and a single patient accumulated 385 visits for the 3-year study period.
• Shifting nonurgent visits from ED to primary care: During the 6-month trial with 9 Central Indiana hospitals, the 320,000-member managed health plan reduced nonurgent ED visits among members served by these hospitals by 53 percent, while simultaneously increasing primary care office visits by 68 percent.
• Cost savings: The shift from ED to primary care visits that occurred during the pilot test saved the health plan an estimated $2 to $4 million over the 6-month period.
Update person_address table with new address information
person_address table
Address Update Detector
In real-time, Address Update Detector detects and writes address changes to the post_processing table
post_processing table
Geo-Coding Application
Geo-Coding app reads the post_processing table
5
Call Polis Center web service which returns geo-coded addresses
Polis Web Service
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4
6
1
2
Integrating socio-behavioral determinants of health using geospatial information
Comer KF, Grannis S, Dixon BE, Bodenhamer DJ, Wiehe SE. Incorporating geospatial capacity within clinical data systems to address social determinants of health. Public Health Rep. 2011 Sep-Oct;126 Suppl 3:54-61.
Improving Efficiency of Data Integration
A Cautionary Note: the Era of “Big Medical Data”, Analytics, and Data
Quality
Aggregate Data Example:Diabetes and Obesity Cohort“Coders should pay attention to the BMI because
it makes a difference in terms of reimbursement […]. A BMI of 40 or higher - diagnosis code V85.4 - is considered a complicating condition, meaning higher reimbursement when reporting this code along with the appropriate principal diagnosis.”
(http://medicalcodingpro.wordpress.com/page/2/)
Data often reflect financial incentives, not the true population distribution.
“Using Information Entropy to Monitor Chief Complaint Characteristics and Quality”
Data Quality• Supplemental data is often necessary to enhance
practice based population health processes
Missing data rate for a sample of clinical transactions received by the INPC in 2008.