Data Quality Assessment for Comparative Effectiveness Research in Distributed Data Networks
Bibliographic Data
| ID | 9104456 |
|---|---|
| Authors | Jeffrey S Brown (0000-0002-9340-7189, Harvard University, corresponding author), Michael Kahn (0000-0002-8673-0682), Michael G Kahn (0000-0003-4786-6875, University of Colorado Denver), Sengwee Toh (0000-0002-5160-0810, Harvard University, corresponding author) |
| Year | 2013 |
| Volume | 51 |
| Issue | Supplement 8Suppl 3 |
| Pages | S22-S29 |
| Publication date | 2013-08-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Medical Care (JOURNAL) |
| Journal identifiers | ISSN: 0025-7079 • E-ISSN: 1537-1948 |
| Publisher | Ovid Technologies (Wolters Kluwer Health) (PUBLISHER) |
| DOI | 10.1097/mlr.0b013e31829b1e2c |
| PMID | 23793049 |
| PMCID | PMC4306391 |
| OpenAlex | W2065268028 |
| Language | EN |
| Citations received | 5 |
| References cited | 6 |
BACKGROUND: Electronic health information routinely collected during health care delivery and reimbursement can help address the need for evidence about the real-world effectiveness, safety, and quality of medical care. Often, distributed networks that combine information from multiple sources are needed to generate this real-world evidence. OBJECTIVE: We provide a set of field-tested best practices and a set of recommendations for data quality checking for comparative effectiveness research (CER) in distributed data networks. METHODS: Explore the requirements for data quality checking and describe data quality approaches undertaken by several existing multi-site networks. RESULTS: There are no established standards regarding how to evaluate the quality of electronic health data for CER within distributed networks. Data checks of increasing complexity are often used, ranging from consistency with syntactic rules to evaluation of semantics and consistency within and across sites. Temporal trends within and across sites are widely used, as are checks of each data refresh or update. Rates of specific events and exposures by age group, sex, and month are also common. DISCUSSION: Secondary use of electronic health data for CER holds promise but is complex, especially in distributed data networks that incorporate periodic data refreshes. The viability of a learning health system is dependent on a robust understanding of the quality, validity, and optimal secondary uses of routinely collected electronic health data within distributed health data networks. Robust data quality checking can strengthen confidence in findings based on distributed data network
Consistency (knowledge bases) · Data collection · Data consistency · Data integrity · Data mining · Data quality · Data science · Data set · Database · Metric (unit) · Minimum Data Set · Quality (philosophy) · Set (abstract data type) · Artificial Intelligence · Computer Science · Data Quality and Management · Electronic Health Records Systems · Health Policy Implementation Science · Medicine
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| Citations per year | 0.45 |
|---|---|
| Citation span | 2015 - 2025 (11) |
| Highly cited | No |