PROMISSe

PROMISSe
Name	PROMISSe
Type	Clinical assessment system
Developed by	National Institutes of Health Clinical Center, Boston University, Northwestern University
Introduced	2010s
Purpose	Patient-reported outcome measurement

PROMISSe

PROMISSe is a patient-reported outcome measurement system developed to standardize assessment of symptoms and function across clinical care and research. It integrates item banks, computerized adaptive testing, and fixed short forms to measure domains such as pain, fatigue, physical function, and mental health, enabling comparison across studies, institutions, and populations. PROMISSe has been used in settings ranging from academic medical centers to regulatory submissions and large cohort studies.

Overview

PROMISSe provides calibrated item banks and scoring metrics aligned with population norms to quantify health-related quality of life. It employs computerized adaptive testing and fixed short forms drawn from item response theory to produce reliable, precise scores for domains including pain, fatigue, depression, anxiety, Physical activity, and Social support. The system is intended for use in clinical trials, observational studies, registries, and routine care at institutions such as Mayo Clinic, Cleveland Clinic, Johns Hopkins Hospital, and Mount Sinai Health System.

History and Development

Development began under the auspices of the National Institutes of Health initiative to improve patient-reported outcome measurement during the 2000s. Key contributors included investigators from Northwestern University, Duke University, Boston University, and the University of Washington, collaborating with psychometricians and clinicians. PROMISSe built on earlier work from legacy instruments such as the SF-36, Brief Pain Inventory, and Beck Depression Inventory by applying modern psychometric approaches developed in fields represented at conferences like the Psychometric Society meetings. Milestones include large-scale item calibration studies, integration into electronic health record pilots at centers like Partners HealthCare and regulatory qualification discussions with the U.S. Food and Drug Administration.

Methodology and Design

PROMISSe uses item response theory and computerized adaptive testing derived from frameworks discussed in Lord’s theory and implemented with models used in Item Response Theory research. Item banks were created through qualitative item development, cognitive interviewing, and quantitative calibration across large samples drawn from population panels including panels used by Knowledge Networks and community cohorts like Framingham Heart Study and National Health and Nutrition Examination Survey. Scoring is reported on T-score metrics standardized to reference populations similar to methods used in SF-12 scoring. The system supports cross-walking to legacy measures such as EQ-5D and PROMIS-mapped scales developed for comparative effectiveness research, and interfaces with standards like Health Level Seven International for electronic health record integration.

Clinical Applications and Use Cases

PROMISSe has been applied across specialties including Oncology, Rheumatology, Orthopaedics, Cardiology, Neurology, Psychiatry, and Pediatrics. Use cases include symptom monitoring in oncology centers such as MD Anderson Cancer Center and survivorship programs at Dana-Farber Cancer Institute, postoperative recovery tracking at Hospital for Special Surgery, and population health studies conducted by institutions like Kaiser Permanente. It has supported outcomes measurement in randomized trials sponsored by organizations such as National Cancer Institute and comparative effectiveness studies commissioned by Agency for Healthcare Research and Quality.

Validation and Performance

Psychometric validation studies have evaluated PROMISSe domains against legacy instruments like the SF-36, EQ-5D, Brief Pain Inventory, and Patient Health Questionnaire-9. Studies published by teams at University of California, San Francisco, Vanderbilt University, and University of Michigan reported high convergent validity, responsiveness, and test-retest reliability for many domains. Performance metrics include minimal clinically important differences established in specialty cohorts, and calibration across demographic strata in samples reminiscent of those used by Behavioral Risk Factor Surveillance System data. Cross-cultural adaptation and translation efforts involved guidelines from World Health Organization procedures and investigators affiliated with multilingual research networks.

Implementation and Adoption

Adoption pathways have included integration into electronic health records at Epic Systems Corporation-based sites, clinical registries like American College of Surgeons National Surgical Quality Improvement Program, and patient portals enabled by organizations such as MyChart. Academic consortia, professional societies including American Society of Clinical Oncology and American Academy of Neurology, and health services researchers have promoted use in practice guidelines and core outcome sets. Implementation challenges reported by Department of Veterans Affairs and large health systems include workflow integration, clinician training, and IT interoperability, with solutions drawn from projects led at Rutgers University and University of Pennsylvania.

Ethical, Regulatory, and Privacy Considerations

Use of PROMISSe in clinical care and research implicates privacy frameworks overseen by agencies like the U.S. Department of Health and Human Services and regulations such as the Health Insurance Portability and Accountability Act of 1996. Ethical oversight by institutional review boards at institutions such as Harvard Medical School and Stanford University School of Medicine governs research deployments, while regulatory engagement with the U.S. Food and Drug Administration has addressed qualification of patient-reported outcome measures for labeling claims. Equity and accessibility efforts reference guidance from Office of Minority Health and standards advocated by National Academies of Sciences, Engineering, and Medicine to ensure representativeness and to mitigate bias across populations.

Category:Patient-reported outcome measures