201801 Clinical Research

Track Name

Clinical Research

Submitting WG/Project/Implementer Group

Biopharma FHIR project group

Justification

This track will continue to further explore the benefits of FHIR for clinical research of new biopharmaceutical experimental treatments, and to increase visibility of FHIR within the biopharmaceutical community. This track advances the use of FHIR resources as eSource data used to pre-populate clinical research case report forms for both regulated and non-regulated clinical research. This builds on previous explorations in Connectathon 13-16. Plans for this connection include participation from at least 5 Pharmaceutical companies, Members of TransCelerate Biopharma, Inc. and technology implementers will simulate using FHIR to populate and manage clinical study databases. A set of 2-3 detailed use case scenarios will be proposed. This work will inform development of profiles and IGs to support clinical research using FHIR.

Clinical Research studies currently require the redundant entry of clinical data that already typically reside in Meaningful Use conformant EHR systems. EHR data represents original records in electronic format that can be used as eSource and directly imported into clinical research EDC databases so as to improve the quality and consistency of data between EHR and EDC systems and eliminate the need for redundant data entry and to improve the ability to use real world data in research. Establishing interoperability between EHR and EDC systems to streamline and modernize clinical investigations should improve data accuracy, patient safety, and clinical research efficiency. Given the extreme cost and extended time required for randomized clinical trials, it would be substantially better to utilize EHR source data to directly populate clinical trial databases wherever feasible and to use FHIR to gain increased visibility into patient encounters and patient-originated data. As stated in its May 2016 FDA draft guidance titled “Use of Electronic Health Record Data in Clinical Investigations” FDA “encourages sponsors and clinical investigators to work with the entities that control the EHRs, such as health care organizations, to use EHRs and EDC systems that are interoperable. . . Establishing interoperability between EHR and EDC systems to streamline and modernize clinical investigations should improve data accuracy, patient safety, and clinical research efficiency.”

Proposed Track Lead

See Connectathon_Track_Lead_Responsibilities Responsibilities Geoff Low (glow@mdsol.com); Trisha Simpson (trisha.simpson@ucb.com)

Expected participants

TransCelerate Biopharma, Inc., UCB, Pfizer, GSK, Merck, Lilly, Medidata, Oracle Health Sciences, Clinical Ink, others

Roles

Please include information here regarding how much advance preparation will be required if creating a client and/or server.

FHIR Client

Support the sending from an EHR or patient device of clinical research study data: create, read, search and update to a clinical study database system.

Clinical Trial Designer

Identifies data relevant to research studies that may be collected from EHRs or SMART-on-FHIR devices. Sets up patient matching criteria and identifiers for ResearchStudy and ResearchSubject for a synthetic test study. Creates study database, mappings/interfaces, EDC case report forms with variable mappings to FHIR that will receive EHR patient data for clinical trial subjects. Generate updates from EDC to apply to the EHR. Data Collector[edit] Queries API to identify patients by Study and Subject identifiers to pull EHR data for demographics, medications and lab data that maps directly to variables on eCRF. Provides data generated by patients to add to study databases or EHRs.

Subject Matter Expert

SME creates and tests queries to identify patients who match eligibility criteria for a study or review data.

Scenarios

EMR to EDC

Advance the use of FHIR resources as eSource data used to pre-populate clinical research case report forms for both regulated and non-regulated clinical research. Run test scripts to verify reliability and accuracy of transfer.

Action:

Identify a Patient in an EHR who is enrolled in a ResearchStudy, extract relevant EHR data that can be mapped to a clinical research Electronic Data Capture (EDC) database, import into EDC Study Database to auto-populate eCRFs. Identify changed data in the EHR that needs to be updated in the EDC database.

Precondition:

Values for ResearchStudy and ResearchSubject for a named study exist in EHR. Patient records include demographics, MedicationStatement, Lab observation data, possibly problems, diagnosis. At least one patient has at least 2 sets of lab observations for at least 3 lab tests. Additional information, such as LOINC codes for the set of lab tests to be used and mappings to CDISC for these will be specified in advance.

Success Criteria:

Test script verifies that the App is able to import EHR data for at least one subject in each of 3 different EHRs (preferably including 1 Epic system, 1 Cerner system and 1 other system) and auto-populate eCRFs in an EDC database.

Bonus point:

identify and extract relevant unstructured data that may be related to a pre-specified disease conditions. Apply changes to the eSource (using a flag that identifies changes) with audit trail.

Real World Evidence Scenario

Receive and apply Real World Evidence updates to the study database as new or changed data is recorded in the site EHR or received from patients for participating study subjects. Collect specific patient data to support outcomes research.

In the Real World, Patients seek care from many healthcare institutions (Sites) as needed, with few or any pre-scheduled visits. Clinical Research needs a scalable and automated way to know about and extract EHR data from Sites where Patients participating in a Study receive care. Key Point – no one knows when or at what Sites the Patents receive care beforehand. Direct transfers from Site EHR systems to Study databases are ideal. Alternately, patient may extract data from an EHR via SMART apps and send the data to a Study database themselves.

Action: Enter new data in EHR for a current ResearchSubject after new patient encounter is recorded in EHR. Or create an App which allows the recording of data by patient and remote site; integrate the captured data with the site EHR if possible and extract this data directly from remote EHR if possible. Output data to the sponsor in an agreed dataset format.

Precondition:

Patient is enrolled as a ResearchSubject for a ResearchStudy with available clinical data. Data that might be suitable for this scenario (for a sample HCRU study) may include duration of visit, procedures any diagnoses or treatments and questionnaires

Success Criteria:

App can allow data entry by patient or collect data directly from and EHR and automatically integrate data back into the investigator EHR (or produce an integration preferred data file that could be imported into a separate research study database). Also can generate a near real time updated file for transfer back to the sponsor of this study specific data captured from remote sites. Automation of data collection is ideal -- Upload of data directly from a remote site’s EHR is likely preferable than the more pragmatic manual data entry into the app

Bonus point 1:

Use CDS Hooks to trigger update after new patient encounter is recorded. Import patient-reported data from a SMART-on-FHIR app.

Bonus point 2:

Create a "Broker Agent" service that will register study participant services, receive updates from site and forward to sponsor study database.

Lab Data Import

Some clinical trial Sites contain in-house laboratory capability allowing them to quickly assess patient health from analysis of lab samples (e.g., blood draws). Many such sites capture lab analysis results in their local EHR systems. When lab results are of interest to clinical research Study Teams, both the Site and the Study Team benefit from direct exchange of lab results from the Site EHR to the Study database as this reduces data latency and data exchange effort.  To achieve direct lab data exchange at scale the Site must (1) implement a FHIR server and expose the requisite FHIR resources, the Study Team must (2) be able to convert the retrieved FHIR resources into data structures usable by Sponsor data systems, and (3) high volume lab-result data exchange between Site FHIR server and Study Team data system must be possible.

Action: 1. Actions. a. Extract FHIR resources from a FHIR server for three patients for a limited number (less than 10) of lab results. Goal is to prove out the use of FHIR resources to express patient lab results for clinical research as well as the underlying data model. b. Convert extracted resource data into CDISC-LDM/XML data structures. Goal is to prove out the transformation of FHIR resources into a common Pharmaceutical/Sponsor data structure. c. Use the FHIR R4 bulk data mechanism to extract all relevant FHIR resource records for the three patients in (1a) and convert them CDISC-LDM/XML as in (1b). Goal is to prove out use of the bulk data transfer mechanism in the context of lab results.

Precondition:

a. At least 3 patients are enrolled as a ResearchSubject for a ResearchStudy with available lab data. b. Source FHIR server that implements the following resources -- Location, Organization, Practitioner, Observation, Encounter, Specimen, Research Subject, Study, Patient, Procedure c. Source FHIR server with test data for at least 3 patients for the FHIR resources listed in (2a) for at least 10 complete lab results per patient – total of at least 30 lab results. d. Data model mapping the FHIR resources in (2a) to CDISC-LDM (to be supplied by the CDISC E2C project). e. Java code to transform FHIR resources into CDISC-LDM/XML (to be supplied).

Success Criteria:

a. Data from the listed FHIR resources can be extracted from the test FHIR server. b. Extracted data can be converted into the appropriate format (e.g., CDISC-LDM/XML) to allow import into a clinical study database.. c. Data transfer mechanism demonstrated for at least 30 patient lab results as CDISC-LDM/XML output

Bonus point:

Use new R4 Bulk Data Access ndJSON format.