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| − | Track Name[edit]
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| − | Clinical Research Track
| + | [[Category:201801_FHIR_Connectathon_Track_Proposals|Jan 2018 Proposals]] |
| − | Submitting WG/Project/Implementer Group[edit]
| + | __NOTOC__ |
| | + | =Track Name= |
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| − | Biopharma FHIR project group; TransCelerate Biopharma eSource Work Stream
| + | ==Submitting WG/Project/Implementer Group== |
| − | Justification[edit]
| + | <!-- Who is asking for this track? --> |
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| − | 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. It will also explore use of PlanDefinition in the context of research protocols. This builds on previous explorations in Connectathon 13-16.
| + | ==Justification== |
| − | Plans for this connection include participation from at least 5 Pharmaceutical companies, Members of TransCelerate Biopharma, Inc. consortium, who will be bringing programmers from technology implementers to 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.
| + | <!--Why is this an important track to include in the connectathon - include implementer need, impact on ballot, FMM readiness of the resources, etc. --> |
| − | 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. 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. The May 2016 FDA draft guidance titled “Use of Electronic Health Record Data in Clinical Investigations” encourages the sponsors of clinical research to use EHR data as noted in the statement below.
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| − | 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. EHRs may be interoperable with EDC systems in a variety of ways depending on supportive technologies and standards. Interoperable technology may involve automated electronic transmission of relevant EHR data to the EDC system. For example, data elements originating in an EHR (e.g., demographics, vital signs, past medical history, past surgical history, social history, medications, adverse reactions) may automatically populate the eCRFs within an EDC system.
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| − | 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. The May 2016 FDA draft guidance titled “Use of Electronic Health Record Data in Clinical Investigations” encourages the sponsors of clinical research to use EHR data as noted in the statement below:
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| − | "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. EHRs may be interoperable with EDC systems in a variety of ways depending on supportive technologies and standards. Interoperable technology may involve automated electronic transmission of relevant EHR data to the EDC system. For example, data elements originating in an EHR (e.g., demographics, vital signs, past medical history, past surgical history, social history, medications, adverse reactions) may automatically populate the eCRFs within an EDC system."
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| − | Proposed Track Lead[edit]
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| − | See Connectathon_Track_Lead_Responsibilities Wayne Kubick (wkubick@hl7.org), Sam Hume (swhume@gmail.com); Geoff Low (glow@mdsol.com); Trisha Simpson (trisha.simpson@ucb.com)
| + | ==Proposed Track Lead== |
| − | Expected participants[edit]
| + | <!-- Name, email and Skype id of individual who will coordinate the track at the connectathon --> |
| | + | See [[Connectathon_Track_Lead_Responsibilities]] |
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| − | TransCelerate Biopharma, Inc., UCB, Pfizer, GSK, Merck, Lilly, Medidata, Oracle Health Sciences, Clinical Ink, others
| + | ==Expected participants== |
| − | Roles[edit]
| + | <!-- List of the individuals and/or organizations that have indicated a desire to attend the connectathon and implement this track --> |
| | | | |
| | + | ==Roles== |
| | Please include information here regarding how much advance preparation will be required if creating a client and/or server. | | Please include information here regarding how much advance preparation will be required if creating a client and/or server. |
| − | FHIR Client[edit]
| + | <!-- Roles are sets of functionality (generally defined by a Conformance resource) that a single system can take on --> |
| − | 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.
| + | ===Role 1 Name=== |
| − | FHIR Server[edit]
| + | <!-- Provide a description of the capabilities this role will have within the connectathon --> |
| − | Clinical Trial Designer[edit]
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| − | 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.
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| − | Data Collector[edit]
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| − | 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.
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| − | Subject Matter Expert[edit]
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| − | SME: Creates and tests queries to identify patients who match eligibility criteria for a study or review data.
| + | ==Scenarios== |
| − | Scenarios[edit] | + | <!-- What will be the actions performed by participants? --> |
| − | Scenario 1 Extract relevant EHR data for ResearchSubject and import into Study Database[edit]
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| − | Scenario 1 Lead: Michelle Crouthamel , TransCelerate
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| − | Objective: 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.
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| − | Background: Clinical Research studies currently require the redundant entry of clinical data that already typically resides in Meaningful Use conformant EHR systems. EHR data represents original records in electronic format that can be used as eSource to eliminate the need for redundant data entry in clinical research EDC databases.
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| − | 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.
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| − | Precondition: 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.
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| − | The following Eligibility Criteria are listed in the sample Protocol #205718
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| − | 1.1 Age
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| − | 1.2 Gender
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| − | 1.3 Diagnosis of Rhematoid Arthritis from past 12 months
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| − | 1.4 Medications from past 12 months
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| − | 1.5 HAQDI (Health assessment questionnaire disability index) score from past 12 months (or another patient reported outcome score such as SF-36)
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| − | 1.6 lab test: At least one of the following lab tests with results: anti-CCP, erythrocyte sedimentation rate (ESR, or sed rate), C-reactive protein (CRP), Electrolytes from past 12 months
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| − | Success Criteria: 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.
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| − | Bonus point : Document the mapping of relevant CCDS content to CDISC standard data elements.
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| − | Bonus point 2: identify and extract relevant unstructured data that may be relate to rheumatoid arthritis conditions.
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| − | Scenario 2 Receive and apply Real World Evidence updates to the study database as new or changed data is recorded in the EHR or received from patients[edit] | + | ===Scenario Step 1 Name=== |
| − | Scenario 2 Lead: TBD
| + | :Action: <!--Who does what? (Use the role names listed above when referring to the participants --> |
| − | Objective: An App that could allow data to be collected from a patient in a Real World Evidence (RWE) Standard of Care (SoC) study, recording visits/encounters to, and reasons for visits/encounters to healthcare institutions and transfer this data back to a central investigator site in near real time so that follow up can be made while the study continues.
| + | :Precondition: <!-- What setup is required prior to executing this step? --> |
| − | Background: This track is intended to advance the use of FHIR resources and eSource data to collect specific patient data in as low-interventional manner as possible in order to support real world outcomes research. Healthcare Resource Utilization (HCRU) data are a critical component of RWE data packages used by companies to demonstrate to Healthcare Payers that a medicine can have a positive impact on their healthcare system.
| + | :Success Criteria: <!-- How will the participants know if the test was successful? --> |
| − | Collecting this data can be challenging since a patient in a SoC setting may seek care from any number of healthcare institutions within their country. In order to track this data, it would be helpful if the patient could record in an App any such visits and even select reasons for visiting, to distinguish between a visit to a friend in a hospital and a visit to a hospital for their own welfare/care. Developing an App that would record such information and trigger a transfer of the data from the EHR to an investigator site (or an EDC system) in near real time so that the investigator can themselves interview the patient to get more details during a future scheduled clinical trial visit. Most RWE studies have few, or more likely no scheduled visits for patients. Alternatively, a patient may extract from or provide relevant information to an EHR using a SMART app that records healthcare institution visits with reasons for the visit and other useful data (such as questionnaires). Ideally data could be extracted directly from a target institution’s EHR and be transferred to a clinical study database.
| + | :Bonus point: <!-- Any additional complexity to make the scenario more challenging --> |
| − | 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.
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| − | 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.
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| − | Bonus point : Use CDS Hooks to trigger update after new patient encounter is recorded. | |
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| − | Scenario 3 Assess use of R4 Bulk Data Access to transfer lab data for multiple patients. [edit]
| + | <!-- Provide a description of each task --> |
| − | Scenario 3 Lead: Don Jennings, TransCelerate
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| − | Objective: Access and transfer lab data for multiple patients enrolled in a clinical study.
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| − | Background: This track is intended to advance the use of FHIR resources and eSource data to access bulk data for multiple patients at a time for loading into a clinical research database.
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| − | Currently bulk data transfers are managed through specific interfaces between sponsor companies or CROs and Central Labs engaged to analyze specimens for research subjects in a particular clinical trial. Accessing lab data that is generated directly at the site, as in the case of samples taken in conjunction with serious adverse events, is typically processed by hand entry of data from printed reports. = Ideally data could be extracted directly from a target institution’s EHR and be transferred to a clinical study database.
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| − | Action: Access and pull existing lab data for a current ResearchSubject recorded in EHR.
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| − | Precondition: Patient is a candidate subject or already 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.
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| − | 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.
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| − | 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.
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| − | TestScript(s)[edit] | + | ==TestScript(s)== |
| − | tbd
| + | <!-- Optional (for initial proposal): Provide links to the TestScript instance(s) that define the behavior to be tested. |
| | + | These should be committed to SVN under trunk/connectathons/[connectathon] |
| | + | --> |
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| − | Copyright © Health Level Seven International ® ALL RIGHTS RESERVED. The reproduction of this material in any form is strictly forbidden without the written permission of the publisher.
| + | ==Security and Privacy Considerations== |
| − | Category: 201801 FHIR Connectathon Track Proposals
| + | <!-- Optional (for initial proposal): Address the topic of Privacy and Security. |
| | + | * What Authentication/Authorization will be used (e.g. SMART on FHIR (OAuth), HEART (UMA/OAuth), IHE IUA (OAuth), generic OAuth, generic SAML, mutual-Auth-TLS), or explicitly indicate that it is out of scope and left to implementations. |
| | + | * What Privacy Consent management will be used? When the Consent Resource is used, define how. |
| | + | * What Audit Logging will be used? When the AuditEvent Resource is used, define expectations of what events will be logged and what each AuditEvent will contain. |
| | + | * How will Provenance be used? Provenance use should be mandated when data is imported from other systems, so as to track that source of that data. Provenance should be used when data is authored by unusual sources, such as the Patient themselves or devices. |
| | + | * How will security-labels be used? Security-labels are meta tags used to classify the data into various confidentiality, sensitivity, and integrity classifications. These security-labels are then available for use and available for access control decisions. |
| | + | I am happy to help: JohnMoehrke@gmail.com -- security co-chair |
| | + | --> |
