Rachel Lynn Richesson

Background: Patient registries are essential to rare disease research, but the implementation and maintenance of a registry requires substantial investments. Despite the large number of rare disease communities that currently have, or are planning for, a registry, there is little guidance on if and when a rare disease registry should be established. There is also little guidance on how to articulate registry objectives to address current and evolving scientific and community needs. Main Body: A practical framework is needed to assist rare disease registry stakeholders to understand and prioritize registry objectives and requirements for registry design, data collection, and funding. In this review, we synthesize existing patient registry classifications and prior reports of rare disease registry experience into a unified framework characterizing rare disease registries by their types of research questions and scientific needs. Conclusion: This simple framework can be used to help rare disease communities identify their research requirements, and support decision making around whether to initiate, and how invest in, registry infrastructure.

Background: Computational phenotyping from electronic health records (EHRs) is essential for clinical research, decision support, and quality/population health assessment, but the proliferation of algorithms for the same conditions makes it difficult to identify which algorithm is most appropriate for reuse. Objective: To develop a framework for assessing phenotyping algorithm fitness for purpose and reuse. Fitness for Purpose: Phenotyping algorithms are fit for purpose when they identify the intended population with performance characteristics appropriate for the intended application. Fitness for Reuse: Phenotyping algorithms are fit for reuse when the algorithm is implementable and generalizable—that is, it identifies the same intended population with similar performance characteristics when applied to a new setting. Conclusions: The PhenoFit framework provides a structured approach to evaluate and adapt phenotyping algorithms for new contexts increasing efficiency and consistency of identifying patient populations from EHRs.

Reference standards are vital for harmonizing heterogeneous clinical data for research, but little is known about their implementations or costs. We surveyed NIH Clinical and Translational Science Awards (CTSA) hubs to assess operational dimensions of institutional implementation, maintenance, and use of the Logical Observation Identifiers, Names, and Codes (LOINC) standard. Respondents (n = 19,30%) exhibited substantial variability in approaches to implementation. Differences in number and training of staff and frequency of mapping updates make it difficult to estimate costs and comparability of data across sites. CTSA and other multi-site research can benefit from operational definitions and recommended processes for LOINC implementation.

Objective: To characterize and demonstrate how to reduce the administrative burden experienced by patients when navigating medication affordability resources in the United States. Materials and Methods: Informed by administrative burden theory, we conducted an environmental scan of medication affordability resources for atrial fibrillation, and four common comorbidities (diabetes, heart failure, hypertension, and lipid disorder). We systematically searched for resources (eg, patient assistance programs, savings cards and nonprofit support) and extracted information about types, eligibility criteria, needed documentation, and application processes. Results: We identified 66 resources across 12 categories across the five conditions. The resources’ varied eligibility criteria, application processes, and requirements for providing sensitive financial documents could introduce multiple administrative costs for patients. Discussion: The volume and complexity of medication affordability resources and related application processes may create substantial administrative burden for patients that could prevent their use—especially when prescribed multiple medications. Conclusion: Medication affordability resource informatics tools that reduce administrative burden could advance equitable medication access.

PURPOSE: Given challenges with randomized trials, tumor registries, and insurance claims, electronic health record data are an appealing resource for studying prostate-specific antigen (PSA) screening for prostate cancer. Transparent, well-evaluated computable phenotypes that observe a stringent definition of screening ( v for-cause diagnosis- or symptom-directed testing) are critical for reproducibility and comparison with prospective cohorts. METHODS: A cohort of patients who underwent PSA testing in a primary care setting at a large, tertiary health care system was identified. Gold-standard labels for screening versus not screening were created via a combination of clinical note text review and exclusionary diagnosis codes. Ten computable phenotype definitions were created by urology content experts and then evaluated for sensitivity, specificity, and positive predictive value (PPV) and negative predictive value against gold-standard labels. RESULTS: Three hundred fifty-five patients with gold-standard labels were included in the final study cohort. Varying by how missing text data were classified (not applicable v screening), 149 (50.3%) and 208 (58.6%) patients underwent screening. No single phenotype optimized both sensitivity and PPV, although a composite definition that included either (1) absence of symptoms or (2) presence of an encounter for screening code achieved a very high PPV of 0.99 (95% CI, 0.96 to 1.00) with a reasonable sensitivity of 0.82 (95% CI, 0.75 to 0.88). CONCLUSION: We identify code-based PSA screening phenotypes with a range of performance characteristics. Prevalence of for-cause diagnosis- and symptom-directed testing are significant and may contaminate cohorts not taking related codes into account.