| Title |
Algorithm linking patients and general practices in Denmark using the Danish National Health Service Register
|
|---|---|
| Published in |
Clinical Epidemiology, August 2016
|
| DOI | 10.2147/clep.s108307 |
| Pubmed ID | |
| Authors |
Maiken Ina Siegismund Kjaersgaard, Peter Vedsted, Erik Thorlund Parner, Bodil Hammer Bech, Mogens Vestergaard, Kaare Rud Flarup, Morten Fenger-Grøn |
| Abstract |
The patient list system in Denmark assigns virtually all residents to a general practice. Nevertheless, historical information on this link between patient and general practice is not readily available for research purposes. To develop, implement, and evaluate the performance of an algorithm linking individual patients to their general practice by using information from the Danish National Health Service Register and the Danish Civil Registration System. The National Health Service Register contains information on all services provided by general practitioners from 1990 and onward. On the basis of these data and information on migration history and death obtained from the Civil Registration System, we developed an algorithm that allocated patients to a general practice on a monthly basis. We evaluated the performance of the algorithm between 2002 and 2007. During this time period, we had access to information on the link between patients and general practices. Agreement was assessed by the proportion of months for which the algorithm allocated patients to the correct general practice. We also assessed the proportion of all patients in the patient list system for which the algorithm was able to suggest an allocation. The overall agreement between algorithm and patient lists was 98.6%. We found slightly higher agreement for women (98.8%) than for men (98.4%) and lower agreement in the age group 18-34 years (97.1%) compared to all other age groups (≥98.6%). The algorithm had assigned 83% of all patients in the patient list system after 1 year of follow-up, 91% after 2 years of follow-up, and peaked at 94% during the fourth year. We developed an algorithm that enables valid and nearly complete linkage between patients and general practices. The algorithm performs better in subgroups of patients with high health care needs. The algorithm constitutes a valuable tool for primary health care research. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 33% |
| Scientists | 1 | 33% |
| Practitioners (doctors, other healthcare professionals) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 17 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 35% |
| Librarian | 2 | 12% |
| Student > Bachelor | 2 | 12% |
| Student > Master | 2 | 12% |
| Student > Ph. D. Student | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 3 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 8 | 47% |
| Nursing and Health Professions | 2 | 12% |
| Agricultural and Biological Sciences | 1 | 6% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 6% |
| Social Sciences | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 3 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 August 2016.
All research outputs
#15,982,793
of 25,373,627 outputs
Outputs from Clinical Epidemiology
#466
of 793 outputs
Outputs of similar age
#229,264
of 381,036 outputs
Outputs of similar age from Clinical Epidemiology
#3
of 12 outputs
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