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
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.
Submitted By: Kevin Jung / University of California San Francisco Breast Care Center
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
  • Improving patient experience of care (quality and/or satisfaction)
  • Improving the health of populations
  • Reducing the cost of care
  • Improving provider experience of care
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
ONC Evaluation Details
Each submitted Data Element has been evaluated based on the following criteria. The overall Level classification is a composite of the maturity based on these individual criteria. This information can be used to identify areas that require additional work to raise the overall classification level and consideration for inclusion in future versions of USCDI
Criterion #1
Maturity - Current Standards		 Level 2 - Data element is represented by a terminology standard or SDO-balloted technical specification or implementation guide.
Criterion #2
Maturity - Current Use 		Level 2 - Data element is captured, stored, or accessed in multiple production EHRs or other HIT modules from more than one developer.
Criterion #3
Maturity - Current Exchange		 Level 2 - Data element is electronically exchanged between more than two production EHRs or other HIT modules of different developers using available interoperability standards.
Criterion #4
Use Case(s) - Breadth of Applicability		 Level 1 - Use cases apply to several care settings or specialties.
Evaluation Comment This data element represents an example of combined data elements for Data Class Health Status/Assessment at https://www.healthit.gov/isa/uscdi-data-class/health-statusassessments.

Comment

Submitted by milliewarner9 on

Promise Preference (PROPr) Utility Score

I agree with UCSF's submission of this data element as PROPr Utility Score serves as a comprehensive data point that summarizes patient preferences in terms of quality of life.

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