Distributed Data Analytics & Querying: EHR Value to Health Centers & Public Health

Transcription

1 Distributed Data Analytics & Querying: EHR Value to Health Centers & Public Health Annual Community Health Institute May 9-11, 2012 Resort & Conference Center of Hyannis Hyannis, MA Moderator: Rick Shoup, PhD Chief Technology Officer, Massachusetts ehealth Institute Jeffrey Brown, PhD Assistant Professor, Department of Population Medicine, Harvard Medical School & the Harvard Pilgrim Health Care Institute Richard Elmore Coordinator, Query Health, Office of the National Coordinator for Health Information, US Department of Health & Human Services Michael Klompas, MD, MPH Department of Population Medicine, Harvard Medical School & the Harvard Pilgrim Health Care Institute Joshua Vogel, MPH Epidemiologist/Data Analyst, Massachusetts Department of Public Health

2 PHIConnect CDC Center of Excellence in Public Health Informatics Electronic Support for Public Health Public Health Surveillance Platform Community Health Institute May 9, 2012 Michael Klompas MD, MPH, FRCPC CDC Center of Excellence in Public Health Informatics Harvard Medical School and Harvard Pilgrim Health Care Institute Boston, MA

3 PHIConnect CDC Center of Excellence in Public Health Informatics Outline Background on ESP Case identification strategies Reportable diseases Syndromic surveillance Chronic diseases

4 PHIConnect CDC Center of Excellence in Public Health Informatics No health department, State or local, can effectively prevent or control disease without knowledge of when, where, and under what conditions cases are occurring Introductory statement printed each week in Public Health Reports,

5 PHIConnect CDC Center of Excellence in Public Health Informatics

6 PHIConnect CDC Center of Excellence in Public Health Informatics Electronic Support for Public Health (ESP) Software and architecture to extract, analyze, and transmit electronic health information from providers to public health. Surveys codified electronic health record data for patients with conditions of public health interest Generates secure electronic reports for the state health department Designed to be compatible with any EHR system JAMIA 2009;16:18-24 MMWR 2008;57: Advances Disease Surveillance 2007;3:3

7 PHIConnect CDC Center of Excellence in Public Health Informatics Current ESP installations Northern Berkshires, MA Health Info Exchange 14 sites 50,000 patients Cambridge Health Alliance 20 sites 400,000 patients Atrius Health 27 Sites 700,000 pts MetroHealth Cleveland, OH 250,000 patients Google Maps

8 PHIConnect CDC Center of Excellence in Public Health Informatics esphealth.org Source code and documentation available free of charge from esphealth.org

9 PHIConnect CDC Center of Excellence in Public Health Informatics ESP: Automated disease detection and reporting for public health Practice EMR s ESP Server Health Department diagnoses D P H lab results meds vital signs demographics HL7 electronic case reports or aggregate summaries JAMIA 2009;16:18-24

10 PHIConnect CDC Center of Excellence in Public Health Informatics Decoupled architecture EMR ESP ESP decoupled from host electronic medical record Implications Allows system to be agnostic to the source EMR (local codes translated to common nomenclature) Offloads computing burden from clinical systems (and keeps ESP invisible to clinicians) Can still remain within host practice s firewall Universal Unobtrusive Secure

11 CASE IDENTIFICATION PHIConnect CDC Center of Excellence in Public Health Informatics

12 PHIConnect CDC Center of Excellence in Public Health Informatics ICD9 s ESP chlamydia type 1 diabetes active tuberculosis

13 PHIConnect CDC Center of Excellence in Public Health Informatics Case Identification Limitations of ICD9 s Condition Sensitivity Positive Predictive Value Acute hepatitis C 63% 22% Tuberculosis 100% 17% Postherpetic neuralgia 59% 84% Gestational diabetes 91% 53%

14 PHIConnect CDC Center of Excellence in Public Health Informatics Blind to purely clinical diagnoses e.g. culture negative TB, early Lyme, PID Multiple reports for same episode e.g. hepatitis C Case Identification Limitations of lab tests Poor discriminator between active & resolved, acute & chronic disease e.g. acute vs chronic hep B, current vs remote Lyme

15 PHIConnect CDC Center of Excellence in Public Health Informatics Solution Integrate multiple streams of data from the EMR to increase sensitivity and specificity Lab orders Lab results (present and past) ICD9 diagnoses (present and past) Medication prescriptions

16 PHIConnect CDC Center of Excellence in Public Health Informatics Case Identification Logic Active Tuberculosis Any of the following: Prescription for pyrazinamide OR Order for (AFB smear or AFB culture) followed by ICD9 code for TB within 60 days OR Order for 2 or more anti-tuberculous medications followed by an ICD9 code for TB within 60 days Performance (Atrius Health, ) 20 patients identified, all with clinician-suspected TB 14 cases confirmed (2 culture-negative disease, 1 previously unreported to health department) No known missed cases Public Health Reports 2010:125:843

17 PHIConnect CDC Center of Excellence in Public Health Informatics Both of the following: Case Identification Logic: Acute Hepatitis B ICD9 for jaundice OR liver function tests > 5x normal IgM to core antigen All four of the following: OR ICD9 for jaundice OR liver function tests > 5x normal Hep B surface antigen or e antigen present No prior positive Hep B specific lab tests No present or prior ICD9 code for chronic hepatitis B Sensitivity: 99% PPV: 97% PLoS ONE 2008:3:e2626

18 PHIConnect CDC Center of Excellence in Public Health Informatics Sorting through positive Hep B Results - ESP versus ELR 8 acute 593 chronic cases 601 distinct patients 2648 positive test results for hepatitis B

19 INFECTIOUS DISEASE CASE REPORTING PHIConnect CDC Center of Excellence in Public Health Informatics

20 PHIConnect CDC Center of Excellence in Public Health Informatics ESP Case Reporting Atrius Health and MetroHealth, June 2006-July 2011 Condition Total Cases Chlamydia 10,406 Gonorrhea 2,056 Pelvic inflammatory disease 122 Acute hepatitis A 21 Acute hepatitis B 56 Acute hepatitis C 74 Tuberculosis 168 Syphilis 313

21 PHIConnect CDC Center of Excellence in Public Health Informatics Manual versus electronic reporting Atrius Health (variable time periods) Manual Reports * ESP Change Chlamydia % Gonorrhea % Pelvic Inflammatory Disease 0 25 Acute Hepatitis B % Acute Hepatitis C % Tuberculosis % MMWR 2008;57: PLoS ONE 2008;e2626 Public Health Reports 2010;125:843

22 SYNDROMIC SURVEILLANCE PHIConnect CDC Center of Excellence in Public Health Informatics

23 % Visits with ILI PHIConnect CDC Center of Excellence in Public Health Informatics Syndromic Surveillance Influenza-Like Illness, Atrius Health, /10/2009 2/10/2010 6/10/ /10/2010 2/10/2011 6/10/ /10/2011 2/10/2012 Week ending date

24 PHIConnect CDC Center of Excellence in Public Health Informatics CHRONIC DISEASE SURVEILLANCE

25 PHIConnect CDC Center of Excellence in Public Health Informatics Criteria for Frank Diabetes Hemoglobin A1C 6.5 Fasting glucose 126 Random glucose 200 on two or more occasions Prescription for INSULIN outside of pregnancy ICD9 code 250.x (DM) on two or more occasions Prescription for any of the following: GLYBURIDE, GLICLAZIDE, GLIPIZIDE, GLIMEPIRIDE PIOGLITAZONE, ROSIGLITAZONE REPAGLINIDE, NATEGLINIDE, MEGLITINIDE SITAGLIPTIN EXENATIDE, PRAMLINTIDE

26 PHIConnect CDC Center of Excellence in Public Health Informatics Summarizing and sharing data: The RiskScape Web-based interface that automatically displays and analyze chronic disease surveillance data derived from electronic health records Helps users easily visualize and manipulate surveillance data in order to rapidly identify outliers and trends Designed to be commensurate with the unique strengths of the data Electronic and analysis ready Timely (updated daily) Clinically rich includes clinician-measured demographics, vitals (height, weight, blood pressure), lab tests, medications, vaccines, and outcomes

27 Select an Outcome PHIConnect CDC Center of Excellence in Public Health Informatics

28 Add Filters (optional) PHIConnect CDC Center of Excellence in Public Health Informatics

29 PHIConnect CDC Center of Excellence in Public Health Informatics Automatically Map Prevalence by Zip

30 PHIConnect CDC Center of Excellence in Public Health Informatics Automatically stratify by age, sex, race, BMI, BP, etc. Prevalence of type 2 diabetes remains correlated with age in young people Type 2 diabetes more prevalent in males versus females in people under age 40

31 PHIConnect CDC Center of Excellence in Public Health Informatics Hone in on Areas of Particular Interest

32 PHIConnect CDC Center of Excellence in Public Health Informatics Hone in on Areas of Particular Interest Prevalence of type 2 diabetes amongst people under age 40 is 1.0% in this zipcode versus 0.26% in the full state

33 PHIConnect CDC Center of Excellence in Public Health Informatics Stratify & compare disease prevalence in this zip to the full state Obese people in this zip (blue) have higher prevalence of diabetes compared to obese people in the rest of MA (orange) Hypertensive people in this zip (blue) have higher prevalence of diabetes compared to hypertensives in the rest of MA (orange)

34 PHIConnect CDC Center of Excellence in Public Health Informatics Evaluate whether patients are meeting clinical targets 57% of people with type 2 diabetes have high blood pressure 51% of people with type 2 diabetes have a hemoglobin A1C above 6.5

35 PHIConnect CDC Center of Excellence in Public Health Informatics Obesity (BMI >30) in Eastern Massachusetts

36 PHIConnect CDC Center of Excellence in Public Health Informatics Hypertension in Eastern Massachusetts

37 PHIConnect CDC Center of Excellence in Public Health Informatics Summary Electronic health record systems can facilitate realtime, automated public health surveillance Automated mapping and stratification can identify geographic areas and subpopulations at increased risk of disease Potential populations for targeted health interventions Future Steps: Increase the number of outcomes and clinical parameters available for display in RiskScape Add in automated cluster detection using SatScan Create a distributed network of ESP sites

38 PHIConnect CDC Center of Excellence in Public Health Informatics Harvard Dept of Population Medicine Richard Platt Ross Lazarus Emma Eggleston Julie Lankiewicz Michael Murphy Massachusetts Dept of Public Health Patricia Daly Paul Oppedisano Gillian Haney Josh Vogel Ohio Department of Health Lilith Tatham MetroHealth, OH David Kaelber Guptha Baskaran Atrius Health Ben Kruskal Mike Lee Cambridge Health Alliance Michelle Weiss Brian Herrick ESP Team Northern Berkshires ehealth Collaborative Don LeBreux Commonwealth Informatics Contact:

39 PHIConnect CDC Center of Excellence in Public Health Informatics MDPHnet Distributed Data Approach Jeffrey Brown, PhD May 9,

40 Overview Background: Approach to Distributed Querying Distributed Networks Implementing MDPHNet MDPHNet Demonstration 40

41 Approach to Distributed Querying 41

42 Distributed Querying Guiding Principles Data partners maintain control of their data Data partners ongoing involvement is needed to interpret findings Little or no exchange of person-level data is needed Secondary use can t interfere with primary use Few data elements are needed to answer most questions Sharing computer programs rather than protocols reduces effort and improves consistency 42

43 Distributed Data/ Distributed Analysis Data partners keep and analyze their own data Standardize the data using a common data model Distribute code to partners for local execution Provide results, not data, to requestor 43

44 PopMedNet Architecture Overview Network Portal Data Partner Researchers Internet Presentation Layer Public Admin DataMart Administrator DataMart Application Presentation Layer Web Services DataMart Administrator Access Control Network Network Administrators Security Manager Rights Manager Roles Manager Content Manager Search Manager Security Manager Business Objects Web Services Request Manager Results Manager Business Objects Organization Project User DataMart Security Manager Connection Manager Workflow Manager Audit Manager Document Manager Archive Manager IRB Manager Search Manager Model Manager Internet Data Source Manager Data Manager Data Source Database Request Manager Schema Models Data Source Results Manager Meta Data Data Partner Host Data Source Common Data Model Data Access Portal Database EMR Database Archive Data Vault Repository Database

45 Technical Protocols and Overview - 1 Network Portal Multi-tiered.NET MS SQL Server application Access via HTTPS Communications via Web Services Role / rights based access Hosting and support Hosted in a Tier III SAS 70 FISMA certified data center Redundant infrastructure 99.9% Application uptime SLA SAN based data storage & backup Remote data replication Secure VPN administration via RSA token ID 45

46 Technical Protocols and Overview - 2 DataMart Client application Small footprint Easy to install on desktop or server Developed in C# Connects to any ODBC compliant data source Software updates downloaded from network portal 46

47 PopMedNet Design Features Any data model from any source Flexible and secure distributed querying Execution of custom analytic code Menu-driven queries Role-based access control Data partner autonomy Query execution options range from fully automated to manual Auditing Software-enabled governance 47

48 PopMedNet Implementation Features Secure, private multi-center research network Open source application Data partners maintain control of their data Flexible governance, access control, permissions, auditing Secure FISMA-compliant platform Mature documentation and set-up procedures Scalable: easy to add new data, new partners Interoperable with other PopMedNet networks 48

49 Security Features FISMA compliant tier III data center 3rd-party secure audit completed Enhanced system procedures Securely store credentials as Salted Hashes No maximum password length, require expiration, enforce history Use cryptographically secure random values for session IDs (.Net Type 4 GUID) Cookies marked as SECURE, SESSION & HTTPONLY and the cookie domain Transmission Require/force Secure Socket layer (SSL) for all communications Enable strongest cipher suites and Transport Layer Security (TLS) versions Web Service and Portal Authorization Ensure all submissions are performed via POST method Do not publish WSDL Limit the number and size of file submissions Passed multiple independent security audits and penetration tests 49

50 Existing Networks 50

51 PopMedNet Networks SPAN: Scalable PArtnering Network for CER (AHRQ) ADHD, Obesity PEAL: Population-Based Effectiveness in Asthma and Lung Diseases Network (AHRQ) Mini-Sentinel (FDA) Safety of marketed medical products HMO Research Network MDPHNet (ONC): MA Department of Public Health ONC QueryHealth Pilots (planned) 51

52 PopMedNet and National Standards PMN is a key component of the ONC s QueryHealth Initiative ONC national standard for distributed querying QueryHealth Initiative uses PMN as the distributed querying platform for policy and governance Standards & Interoperability (S&I) Framework: 52

53 QueryHealth Query Lifecycle 53

54 PopMedNet Website 54

55 Implementing MDPHNet 55

56 Implementing MDPHnet 1. Implement the common data model (ESP) 2. Store files is relational DB (need ODBC connection) 3. Establish settings on the Network secure portal Notifications Permissions 4. Install the PopMedNet DataMart Client on local system 5. Establish settings on the DM Client software (logins, data connection) 6. Respond to queries via the DM Client 56

57 MDPHnet Features 1. Menu Driven Querying of ESP Data Model 2. Project Based Network Management 3. Granular Access Control 4. Scheduled Reports 57

58 1. Menu Driven Query Capability Customizable reporting template (menu-driven) Menu-driven query interface can identify cohorts of interest based on inclusion and exclusion criteria and Boolean operators Time window Demographics Diagnoses EXAMPLE: Identify and characterize a cohort of patients with a diagnosis of asthma in 2010 Query interface generates standardized executable code for distribution to sites 58

59 2. Project Based Network Management Organizational entity Projects will be developed Combined with users, organizations, and query types, projects give more precise query authorization control and role-based access control Assigns roles based on projects, thus allowing a user to have multiple roles under a single login credential. EXAMPLE Establish projects for ILI and diabetes, assigning specific users, query types, and queries to each project. Users create and distribute queries based on their role within a project, and query response determined based on those factors 59

60 3. Granular Access Control Fine-grained control of query authorization that allows data partners to give permission to execute queries to individual users for specific query types and projects EXAMPLE Data partner gives specific MDPH staff permission for automated execution of queries for a specified project (e.g., ILI), but would require manual review of the same query coming from a different user, or the same user for a different project 60

61 4. Scheduled Reports Capability to Store a query Schedule routine distribution of the query Data partners to specify how they will be processed EXAMPLE As part of the ILI project, MDPH develops a query for distribution every Monday morning. Data partners determine how to process the query (manually or automatically) based on the project, user, and query. 61

62 MDPHnet Demonstration 62

63 PopMedNet Login PopMedNet Demonstration Portal 63

64 MDPHnet Login 64

65 MDPHnet Home Page 65

66 Request Summary Page 66

67 Request Selector Dialog Box 67

68 Request Detail Page 68

69 ICD-9 Code Selector 69

70 Request DataMart Routing 70

71 DataMart Client Application 71

72 DataMart Client Request Detail 72

73 DataMart Client Request Detail 73

74 Response Detail/Results Page 74

75 Response Detail/Results Page 75

76 Response Detail/Results Page 76

77 Thank You 77

78 Distributed Population Queries National Strategies Rich Elmore, Coordinator Query Health May 2012

79 Vision Enable a learning health system to understand population measures of health, performance, disease and quality, while respecting patient privacy, to improve patient and population health and reduce costs.

80 Distributed queries unambiguously define a population from a larger set Questions about disease outbreaks, prevention activities, health research, quality measures, etc.

81 Distributed Query Networks Voluntary, No Central Planning Community of participants that voluntarily agree to interact with each other. There will be many networks; requestors and responders may participate in multiple networks. Query Requestors Participating Responders

82 Query Health Pilots Pilot Focus RI Queries RI Policy Layer Data Sources Kickoff NYC & NYS Depts. of Public Health FDA Mini- Sentinel CDC Mass. Dept. of Public Health CQM Diabetes (NYC) Hypertension (NYS) Use of clinical data sources for FDA questions Front-end to Bio-Sense 2 i2b2 PMN RHIOs (Intersystems & Axolotl) EHRs (ecw to start) May 2012 PMN PMN i2b2/beth Israel June 2012 TBD TBD Bio-Sense 2 June 2012 Diabetes PMN PMN MDPHNet July 2012 Quality Measures hquery PMN EHR Vendor July 2012

83 New York City / New York State Pilot Information Requestors Data Sources NYC PCIP Sends Query to Data Sources Distributes Query Results to Information Requestor Axolotl RHIO Intersystems RHIO NYS DOH Sends Query to Data Sources Distributes Query Results to Information Requestor ecw EHR

84 Query Health Standards and Reference Implementation Stack Reference Implementation Stack Develop modular, testable portfolio of Query Health standards and specifications that can adopted by the industry, and support key HITECH and govt. priorities Vocabulary & Code Sets SNOMED-CT LOINC ICD-10 RxNorm Content Structure Clinical Element Data Dictionary Queries & Responses The Question New HQMF The Results New QRDA 2 & 3 Privacy & Security Query Envelope Privacy Policy Enablement Foundation: Distributed Query Solutions i2b2 PopMedNet hquery 84

85 The Query New HQMF Health Quality Measure Format HQMF newly modified to support the needs for dynamic population queries: More executable Simplified Advantages for query Avoids yet another standard Secure (vs procedural approach) Works across diverse platforms Benefits Speed and Cost

86 The Query Envelope Query agnostic Content agnostic Metadata facilitates privacy guidance from HIT Policy Committee RESTful interface specification

87 The Data Clinical Element Data Dictionary Demographic Patient Contact Information Payer Information Healthcare Provider Allergies & Adverse Reactions Encounter Surgery Diagnosis Medication Procedure Immunization Advance Directive Vital Signs Physical Exam Family History Social History Order Result Medical Equipment Care Setting Enrollment Facility ONC S&I Framework deliverable Standards independent dictionary 87

88 The Results New QRDA Quality Reporting Document Architecture Category I Patient Level Category II Patient Populations Category III Population Measures

89 Operational Guidance U.S. Health and Human Services Office of the National Coordinator for Health IT Standards & Interoperability Framework Query Health Technical Approach Operational Guidelines March 26, 2012

90 Query Health How it works together 90

91 The path to critical mass Today, distributed queries are generally limited to organizations with large IT & research budgets Missing: Primary Care, FQHCs, CAHs, HIEs, etc In other words, most places where clinical care is delivered and recorded MDPHNet is strategically important 91