Diabetes and Asthma Case Identification, Validation, and Representativeness When Using Electronic Health Data to Construct Registries for Comparative Effectiveness and Epidemiologic Research
Bibliographic Data
| ID | 9104433 |
|---|---|
| Authors | Jay R Desai, Jay Desai (0000-0002-3010-8501, H ea lt hP ar tn er s, corresponding author), Pingsheng Wu (0000-0003-4947-3063, Vanderbilt University), Greg A Nichols (Kaiser Permanente), Tracy A Lieu (0000-0002-3516-4617, Boston Children's Hospital), Patrick J O’Connor (0000-0002-9249-4938, H ea lt hP ar tn er s, corresponding author) |
| Year | 2012 |
| Volume | 50 |
| Pages | S30-S35 |
| Publication date | 2012-07-01 |
| Peer Reviewed | Yes |
| 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.0b013e318259c011 |
| PMID | 22692256 |
| OpenAlex | W1964662096 |
| Language | EN |
| Citations received | 3 |
| References cited | 25 |
BACKGROUND: Advances in health information technology and widespread use of electronic health data offer new opportunities for development of large scale multisite disease-specific patient registries. Such registries use existing data, can be constructed at relatively low cost, include large numbers of patients, and once created can be used to address many issues with a short time between posing a question and obtaining an answer. Potential applications include comparative effectiveness research, public health surveillance, mapping and improving quality of clinical care, and others. OBJECTIVE AND DISCUSSION: This paper describes selected conceptual and practical challenges related to development of multisite diabetes and asthma registries, including development of case definitions, validation of case identification methods, variation in electronic health data sources; representativeness of registry populations, including the impact of attrition. Specific challenges are illustrated with data from actual registries
Alternative medicine · Asthma · Attrition · Comparative effectiveness research · Construct (python library) · Data quality · Data science · Disease · Disease registry · Identification (biology) · MEDLINE · Public health · Representativeness heuristic · Scale (ratio) · Asthma and respiratory diseases · Computer Science · Electronic Health Records Systems · Machine Learning in Healthcare · Medicine · Nursing · Psychology
| Unique citing works | 3 |
|---|---|
| Citations per year | 0,21 |
| Citation span | 2012 - 2022 (11) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 3 |