Distributed Networking
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1 Distributed Networking Millions of people. Strong collaborations. Privacy first. Jeffrey Brown, Lesley Curtis, Richard Platt Harvard Pilgrim Health Care Institute and Harvard Medical School Duke Medical School March 15, 2013
2 The goal Facilitate multi-site research collaborations between investigators and data stewards by creating secure networking capabilities and analysis tools 2
3 Not the goal We will not create a new stand-alone network with its own research agenda or content experts Investigators will not have access to data without data stewards active engagement 3
4 4 Reminder: Mini-Sentinel s foundation Strong collaborations between investigators and data partners Creation of a community of trust with shared goals, backed by clear governance policies Data partners participation as collaborators Data partners voluntary participation on a case-by-case basis
5 February 10, Volume 364:
6 Use case: Assess disease burden/outcomes An NIDDK program officer wants to characterize the use and outcomes of insulin pumps for diabetes The Collaboratory networking center uses pre-existing ( canned ) programs to query electronic data from millions of people to assess: Frequency of use Characteristics of the users (age, sex, prior treatment history) Frequency of selected outcomes before and after initiation of use 6
7 Use case: Pragmatic clinical trial design Investigators planning a multi-center pragmatic trial of stroke prevention regimens want to assess the feasibility of embedding a clinical trial in care settings The Collaboratory networking center queries electronic health data to : Assess baseline hospitalization rate with a stroke diagnosis Identify organizations with enough potential study participants Identify potential study participants all identifiable information stays with the host organization 7
8 Use case: Pragmatic clinical trial follow up Investigators conducting a multi-center pragmatic trial of stroke prevention regimens want to simplify follow up The Collaboratory networking center supports clinical organizations periodic scans of their electronic data covering study participants to identify Dispensing of prescription medications, including dates, names, and amounts dispensed All inpatient and ambulatory medical encounters, with dates and diagnoses and procedures 8
9 Use case: Reuse of research data A clinically rich research dataset of patients with incident hypertension contains longitudinal records of all blood pressure measurements, BMI, medical utilization, diagnoses, treatments, and laboratory test results The data steward uses the Collaboratory s networking capability to allow an investigator at another organization to submit analytic programs The output does not contain direct identifiers 9
10 Use case: Single study private network A multi-center pragmatic trial team wants to create a pooled final analysis data file The Collaboratory networking center establishes a private distributed network To distribute programs that create separate analysis files at each site To securely transfer the analysis files to the analyst 10
11 Benefits Assessing disease burden New capability, speed, low cost, privacy protection Trial design / follow-up New capability, speed, low cost, privacy protection Reuse of data HIPAA compliance Avoids need to create limited or de-identified datasets In some cases, full datasets are more useful Data sharing Avoids need for some data use or business associate agreements Preserves clinical organizations sharing restrictions Private network Secure access, auditable procedures 11
12 NIH Distributed Networking Coordinating Center Health Plan 1 Health Plan 2 CTSA 1 CTSA 2 Registry Research Dataset 1 Research Dataset 2 Leverages existing networks data and analysis tools Can use many data types, e.g., EHR, claims, registries Can use many data models, e.g., Mini-Sentinel, i2b2, OMOP Can use existing querying tools, e.g., Mini-Sentinel modular programs Every use requires the agreement of the data steward 12
13 What is a distributed research network? NIH Distributed Network Coordinating Center 1 Secure Network Portal Data Steward 1 Review & Run Query 2 Enroll 3 Demographics Utilization Pharmacy Etc Review & Run Query 3 Data Steward N Enroll Demographics Utilization Pharmacy Etc Review & Return Results 4 Review & Return Results User creates and submits query (a computer program) 2- Data stewards retrieve query 3- Data stewards review and run query against their local data 4- Data stewards review results 5- Data stewards return results via secure network 6 Results are aggregated 13
14 14 Mini-Sentinel s Common Data Model Enrollment Demographic Dispensing Encounter Lab Result Vital Signs Person ID Person ID Person ID Person ID Person ID Person ID Enrollment start & end dates Drug coverage Medical coverage Death Birth date Sex Race Etc. Cause of Death Dispensing date National drug code (NDC) Days supply Amount dispensed Diagnosis Dates of service Provider seen Type of encounter Facility Etc. Procedure Dates of order, collection & result Test type, immediacy & location Procedure code & type Test result & unit Abnormal result indicator Etc. Date & time of measurement Height Weight Diastolic & systolic BP Tobacco use & type BP type & position Person ID Person ID Person ID Person ID Date of death Cause of death Date Dates of service Source Confidence Etc. Diagnosis code & code type Source Confidence Etc. Principle diagnosis flag Encounter type & provider Diagnosis code & type Procedure code & type Encounter type & provider Etc. Etc.
15 15 Mini-Sentinel s distributed dataset data checks ~400 data checks per refresh 100+ tables per data partner per refresh
16 16 Ready to use tools for common data model
17 Current Networks Data Steward Funder AHRQ FDA ONC SPAN PEAL Mini-Sentinel MDPHnet HMORNnet HMO Research Network (# sites in each network) (11) (4) (13) (7) Vanderbilt Aetna Humana Optum (United Healthcare) WellPoint (HealthCore) Massachusetts League of Community Health Centers AtriusHealth Beth Israel Deaconess Medical Center (Query Health Pilot) 18
18 Distributed Data / Distributed Analysis Data stewards keep and analyze their own data Standardize the data using a common data model Distribute code to stewards for local execution Provide results, not data, to requestor All activities audited and secure 19
19 System Architecture Deployment Overview Firewall Network Security (IDS/IPS, VPN/RSA) Web Servers / Reverse Proxies/Load Balancers Firewall Firewall Internet System Administrator (Two Factor AuthN) FISMA Compliant Data Center Data Administrators & Reviewers (Two Factor AuthN) HTTPS, TLS Data Steward Organization Internet HTTPS, TLS User and DataMart Provisioning And Administration DataMart Management (Metadata, Authorization) User Account Management (Groups/Roles/User Accounts) PMN Portal Workflow Job Scheduling Request/ Response Mgr User Interface HTTPS, Mutual TLS Optional Site to Site VPN DMZ Data Mart Client Optional REST Internal Data Source (Common Data Model) ETL DataWarehouse / Repositories DMZ Non DMZ (Internal Components) Audit Investigator Observer Enhanced Investigator PMN Software Supports multiple deployment models Agnostic to data center infrastructure and complements existing network infrastructure VM based deployments enabling ease of disaster recovery and planning Seamless overlay of VPN Connections (Remote Access, Site to Site, Two Factor User Authentication) Supports consolidation of remote sites into the data center for central management (Data Steward Components can be hosted in a central data center similar to the PMN Portal) Secure End to End connection (Encrypted Transport using X.509 certificates) Supports industry standard RBAC configuration for users Supports Data Source provisioning based on RBAC and additional data source specific metadata Queries distributed using a PULL model instead of PUSH model 20
20 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 steward autonomy Query execution options range from fully automated to manual Auditing Software-enabled governance 21
21 Implementation Features Secure, private multi-center research network Open source application Data stewards maintain control of their data Flexible governance, access control, permissions, auditing Mature documentation and set-up procedures Scalable: easy to add new data, new partners Interoperable with other networks using same networking platform (PopMedNet) 22
22 Security Features FISMA compliant tier III data center 3rd-party secure audit completed Passed multiple independent security audits and penetration tests 23
23 National Standards The networking platform (PopMedNet) 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: 24
24 Governance (proposed) Data stewards retain control of their data All activities are opt-in Data stewards can choose to be full partners in the design and implementation of research Data steward costs must be reimbursed Includes amortizing cost of maintaining data in query-able form TBD: A board of representatives to engage NIH leadership 25
25 Operations Each data steward designates a single contact for new queries Each data steward uses its own process for deciding whether to participate in any activity 26
26 Fine print Current resources will support ~20 sites Using existing data resources is fast; developing new ones is slow Most current resources have extensive claims data, and limited EHR data Using existing analysis tools is fast; developing new ones is slow Ability to query multiple sites requires Each site s data to be in the same format Consistent definitions of variables 27
27 Timeline General querying capability begins July 2013 for organizations participating in existing networks 28
28 Thank you! 29
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