De-identified data quality assessment approaches by data vendors who license data to healthcare and life sciences researchers.
| Autor: | Erwin Johnson C; Policy Evidence Research (Global Market Access), Merck & Co., Inc., Kenilworth, New Jersey, USA., Colquhoun D; Customer Research, Frost & Sullivan, Toronto, Ontario, Canada., Ruppar DA; Health & Life Sciences, Frost & Sullivan, San Antonio, Texas, USA., Vetter S; Customer Research, Frost & Sullivan, San Antonio, Texas, USA. |
|---|---|
| Jazyk: | angličtina |
| Zdroj: | JAMIA open [JAMIA Open] 2022 Nov 02; Vol. 5 (4), pp. ooac093. Date of Electronic Publication: 2022 Nov 02 (Print Publication: 2022). |
| DOI: | 10.1093/jamiaopen/ooac093 |
| Abstrakt: | Objective: To gain insights into how data vendor companies (DVs), an important source of de-identified/anonymized licensed patient-related data (D/ALD) used in clinical informatics research in life sciences and the pharmaceutical industry, characterize, conduct, and communicate data quality assessments to researcher purchasers of D/ALD. Materials and Methods: A qualitative study with interviews of DVs executives and decision-makers in data quality assessments ( n = 12) and content analysis of interviews transcripts. Results: Data quality, from the perspective of DVs, is characterized by how it is defined, validated, and processed. DVs identify data quality as the main contributor to successful collaborations with life sciences/pharmaceutical research partners. Data quality feedback from clients provides the basis for DVs reviews and inspections of quality processes. DVs value customer interactions, view collaboration, shared common goals, mutual expertise, and communication related to data quality as success factors. Conclusion: Data quality evaluation practices are important. However, no uniform DVs industry standards for data quality assessment were identified. DVs describe their orientation to data quality evaluation as a direct result of not only the complex nature of data sources, but also of techniques, processes, and approaches used to construct data sets. Because real-world data (RWD), eg, patient data from electronic medical records, is used for real-world evidence (RWE) generation, the use of D/ALD will expand and require refinement. The focus on (and rigor in) data quality assessment (particularly in research necessary to make regulatory decisions) will require more structure, standards, and collaboration between DVs, life sciences/pharmaceutical, informaticists, and RWD/RWE policy-making stakeholders. (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.) |
| Databáze: | MEDLINE |
| Externí odkaz: |
|