1 / 52

Shaun J. Grannis, MD, MS, FACMI, FAAFP

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.

edana
Télécharger la présentation

Shaun J. Grannis, MD, MS, FACMI, FAAFP

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Data Aggregation, Liquidity, andthe 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

  2. What We’ll Cover The Context The Problem Potential Solutions

  3. 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”

  4. 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

  5. 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, Hillestad 2. Social Transformation of American Medicine, Starr

  6. 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

  7. 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

  8. Variation in Medicare Reimbursement Rates

  9. Healthcare Labor Productivity Kocher R, SahniNR. Rethinking Health Care Labor.N Engl J Med 2011; 365:1370-1372. October 13, 2011

  10. Results delivery • Secure document transfer • Shared EMR • Credentialing • Eligibility checking Hospitals Hospital Payers • Results delivery • Secure document transfer • Shared EMR • CPOE • Credentialing • Eligibility checking Health Information Exchange Physicians Labs • Results delivery Labs Data Repository Network Applications • Surveillance • Reportable conditions • Results delivery • De-identified, longitudinalclinical data Public Health Outpatient RX • Secure document transfer • Quality Reporting Payer Physician Office Public Health • De-identified, longitudinalclinical data Researchers Ambulatory Centers INPC Data Management and Services Data Management Data Access & Use

  11. Clinical Abstract 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.

  12. Notifiable Condition Detection

  13. System Overview: Notifiable Condition Detector E-mail Summary Abnormal flag, Organism name in Dwyer II, Value above threshold Realtime Compare to Dwyer I Daily Batch To Public Health Reportable Conditions Databases Inbound Message Potentially Reportable Reportable Condition To Infection Control Record Count as denominator Print Reports

  14. ELR Completeness 4,785 total reportable cases INPC– 4,625 (97%) Health Dept – 905 (19%) Hospitals – 1,142 (24%)

  15. Timeliness ELR identified cases 7.9 days earlier than did spontaneous reporting.

  16. Prepopulated Reporting Forms

  17. Clinical Messaging/Public Health Communication

  18. Sample Pre-populated Reporting Form

  19. Reporting Form

  20. Patient Demographics Clinical Data Provider Demographics

  21. Understanding Reporting Workflow

  22. Pre-populated form Information flow

  23. Outcome measures Time-to-treatment Timeliness of reporting to public health Completeness of reporting data Level of communication among PH and clinical providers

  24. Syndromic Surveillance

  25. Over 110 Indiana Emergency Departments contribute 2.1 million visits to the system each year.

  26. Network Connection HL7 ADT message Hospital Interface Engine (Routing) Hospital Firewall (Encryption) Hospital ED Registration Imported into Clinical Repository Message Listener Message Processor Clinical Repository Firewall (Decryption) Information Flow: Clinical

  27. Network Connection HL7 ADT message Hospital Interface Engine (Routing) Hospital Firewall (Encryption) Hospital ED Registration Batched, delivered to ISDH every 3 hours Message Listener Message Processor Firewall (Decryption) Information Flow: PH Surveillance Public Health

  28. Neuro Event

  29. GI Event

  30. Natural Disaster

  31. H1N1 Surveillance

  32. Flu ICD9 Flu CC H1N1,Oct 2009 H1N1, April 2009 Pneumonia ICD9 Pneumonia CC ILI ICD9 ILI CC All Flu Tests Positive Flu Tests Positive Rate

  33. All Health Care is Not Local: An Evaluation of the Distribution of Emergency Department Care Delivered in Indiana

  34. 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.

  35. Leveraging Analytics to Enable Accountable Care • Patients receive healthcare from multiple providers and across organizations • More than 40% of ED visits are for patients having data at multiple institutions 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.

  36. 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.

  37. 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. http://www.innovations.ahrq.gov/content.aspx?id=3988

  38. 84% PPV for predicting which patients who will use ED > 16 times in two years.

  39. Supporting ACO Services • Care management support • HIE Information matched to CMS defined ACO population • Patient care summaries extracted • Delivered to ACO care management systems via CDA documents • Readmission risk stratification (LACE model) • Adaptation underway

  40. Integrating socio-behavioral determinants of health using geospatial information Patient Address Change 1 ADT Processor 2 Update person_address table with new address information person_address table 6 • In real-time, Address Update Detector detects and writes address changes to the post_processing table 3 post_processing table Geo-Coding Application Address Update Detector Call Polis Center web service which returns geo-coded addresses 4 5 Geo-Coding app reads the post_processing table Polis Web Service 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.

  41. Improving Efficiency of Data Integration

  42. A Cautionary Note: the Era of “Big Medical Data”, Analytics, and Data Quality

  43. 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.

  44. “Using Information Entropy to Monitor Chief Complaint Characteristics and Quality”

  45. Data Quality Missing data rate for a sample of clinical transactions received by the INPC in 2008. Supplemental data is often necessary to enhance practice based population health processes

More Related