Submitted By: Kevin Jung / University of California San Francisco Breast Care Center | |
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Data Element Information | |
Use Case Description(s) | |
Use Case Description | Symptom burden and reduced quality of life (QOL) are considerable hurdles in oncology. Patient-Reported Outcomes Measurement Information System (PROMIS) can be utilized to establish a mean symptom burden, examine potential drivers, and characterize severe symptom burden in breast cancer patient subgroups with the goal of characterizing stage IV patient QOL and triaging patients to individualized supportive care services. |
Estimate the breadth of applicability of the use case(s) for this data element | Level 2 – elements pertain to most or all patients, providers or requestors. 240 stakeholders |
Link to use case project page | https://www.fda.gov/science-research/advancing-regulatory-science/source-data-capture-electronic-health-records-ehrs-using-standardized-clinical-research-data |
Healthcare Aims |
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Maturity of Use and Technical Specifications for Data Element | |
Applicable Standard(s) | ICD-10 : https://www.cms.gov/Medicare/Coding/ICD10 SNOMED CT: https://www.snomed.org/ |
Additional Specifications | FHIR R4 Implementation Guide |
Current Use | This data element has been used at scale between multiple different production environments to support the majority of anticipated stakeholders |
Supporting Artifacts |
Level 2 – at scale, or in more widespread production use (routinely collected already) on several different EHR/HIT systems. 1. Food and Drug Administration. Electronic Source Data in Clinical Investigations. fda.gov. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/electronic-source-data-clinical-investigations. Published September 2013. Accessed May 1, 2019. 2. 111th Congress of the United States of America. H.R.3590: Patient Protection and Affordable Care Act. congress.gov. https://www.congress.gov/bill/111th-congress/house-bill/3590. Published March 23, 2010. Accessed May 1, 2019. 3. Food and Drug Administration. Centers of Excellence in Regulatory Science and Innovation (CERSIs). fda.gov. https://www.fda.gov/science-research/advancing-regulatory-science/centers-excellence-regulatory-science-and-innovation-cersis. Published June 9, 2018. Accessed May 1, 2019. 4. CFR - Code of Federal Regulations Title 21 Part 11. fda.gov. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?CFRPart=11. Accessed May 1, 2019. 5. Food and Drug Administration. Computerized Systems Used in Clinical Investigations: Guidance for Industry. fda.gov. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/computerized-systems-used-clinical-investigations. Published May 2017. Accessed May 2019. 6. Food and Drug Administration. Use of Electronic Health Record Data in Clinical Investigations. fda.gov. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/use-electronic-health-record-data-clinical-investigations-guidance-industry. Published July 2018. Accessed May 1, 2019. 7. US Department of Health and Human Services, Asst. Secretary of Planning and Evaluation. Patient-Centered Outcomes Research Trust Fund. aspe.hhs.gov. https://aspe.hhs.gov/patient-centered-outcomes-research-trust-fund. Published November 23, 2015. Accessed May 1, 2019. 8. Wrenn JO, Stein DM, Bakken S, Stetson PD. Quantifying clinical narrative redundancy in an electronic health record. J Am Med Inform Assoc . 2010;17(1):49-53. doi:10.1197/jamia.M3390. 9. Siegler EL, Adelman R. Copy and Paste: A Remediable Hazard of Electronic Health Records. Am J Med. 2009;122(6):495-496. doi:10.1016/j.amjmed.2009.02.010. 10. Kalra D, Schmidt A, Potts H, Dupont D, Sundgren M, De Moor G. Case Report from the EHR4CR Project—A European Survey on Electronic Health Records Systems for Clinical Research. Health Connections. 2011;1(2):108-113. 11. Retrieve Form for Data Capture - IHE Wiki. wiki.ihe.net. https://wiki.ihe.net/index.php/Retrieve_Form_for_Data_Capture. Published July 20, 2017. Accessed May 13, 2019. |
Extent of exchange | 5 or more. This data element has been tested at scale between multiple different production environments to support the majority of anticipated stakeholders. |
Supporting Artifacts |
Level 2 – exchanged between 4 or more different EHR/HIT systems. More routinely exchanged between multiple different systems can justify adding to next draft version. 1. Food and Drug Administration. Electronic Source Data in Clinical Investigations. fda.gov. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/electronic-source-data-clinical-investigations. Published September 2013. Accessed May 1, 2019. 2. 111th Congress of the United States of America. H.R.3590: Patient Protection and Affordable Care Act. congress.gov. https://www.congress.gov/bill/111th-congress/house-bill/3590. Published March 23, 2010. Accessed May 1, 2019. 3. Food and Drug Administration. Centers of Excellence in Regulatory Science and Innovation (CERSIs). fda.gov. https://www.fda.gov/science-research/advancing-regulatory-science/centers-excellence-regulatory-science-and-innovation-cersis. Published June 9, 2018. Accessed May 1, 2019. 4. CFR - Code of Federal Regulations Title 21 Part 11. fda.gov. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?CFRPart=11. Accessed May 1, 2019. 5. Food and Drug Administration. Computerized Systems Used in Clinical Investigations: Guidance for Industry. fda.gov. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/computerized-systems-used-clinical-investigations. Published May 2017. Accessed May 2019. 6. Food and Drug Administration. Use of Electronic Health Record Data in Clinical Investigations. fda.gov. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/use-electronic-health-record-data-clinical-investigations-guidance-industry. Published July 2018. Accessed May 1, 2019. 7. US Department of Health and Human Services, Asst. Secretary of Planning and Evaluation. Patient-Centered Outcomes Research Trust Fund. aspe.hhs.gov. https://aspe.hhs.gov/patient-centered-outcomes-research-trust-fund. Published November 23, 2015. Accessed May 1, 2019. 8. Wrenn JO, Stein DM, Bakken S, Stetson PD. Quantifying clinical narrative redundancy in an electronic health record. J Am Med Inform Assoc . 2010;17(1):49-53. doi:10.1197/jamia.M3390. 9. Siegler EL, Adelman R. Copy and Paste: A Remediable Hazard of Electronic Health Records. Am J Med. 2009;122(6):495-496. doi:10.1016/j.amjmed.2009.02.010. 10. Kalra D, Schmidt A, Potts H, Dupont D, Sundgren M, De Moor G. Case Report from the EHR4CR Project—A European Survey on Electronic Health Records Systems for Clinical Research. Health Connections. 2011;1(2):108-113. 11. Retrieve Form for Data Capture - IHE Wiki. wiki.ihe.net. https://wiki.ihe.net/index.php/Retrieve_Form_for_Data_Capture. Published July 20, 2017. Accessed May 13, 2019. |
Potential Challenges | |
Restrictions on Standardization (e.g. proprietary code) | None |
Restrictions on Use (e.g. licensing, user fees) | None |
Privacy and Security Concerns | A patient’s PROMIS information is protected by HIPAA. |
Estimate of Overall Burden | None |
Assessments of a health-related matter of interest, importance, or worry to a patient, patient’s family, or patient’s healthcare provider that could identify a need, problem, or condition.
Data Element |
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Promise Preference (PROPr) Utility Score
Description
PROPr is a score for the PROMIS measurement system. PROPr combines scores from 7 PROMIS domains into a single preference-based score (also called a health utility score). This score captures the preferences of the general adult US population. |
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