title: Application of frequent itemsets mining to analyze patterns of one-stop visits in Taiwan.
abstract: BACKGROUND
  The free choice of health care facilities without limitations on frequency of visits within the National Health Insurance in Taiwan gives rise to not only a high number of annual ambulatory visits per capita but also a unique "one-stop shopping"phenomenon, which refers to a patient' visits to several specialties of the same healthcare facility in one day. The visits to multiple physicians would increase the potential risk of polypharmacy. The aim of this study was to analyze the frequency and patterns of one-stop visits in Taiwan.
  
  
  RESULTS
  The claims datasets of 1 million nationally representative people within Taiwan's National Health Insurance in 2005 were used to calculate the number of patients with one-stop visits. The frequent itemsets mining was applied to compute the combination patterns of specialties in the one-stop visits. Among the total 13,682,469 ambulatory care visits in 2005, one-stop visits occurred 144,132 times and involved 296,822 visits (2.2% of all visits) by 66,294 (6.6%) persons. People tended to have this behavior with age and the percentage reached 27.5% (5,662 in 20,579) in the age group ≥80 years. In general, women were more likely to have one-stop visits than men (7.2% vs. 6.0%). Internal medicine plus ophthalmology was the most frequent combination with a visited frequency of 3,552 times (2.5%), followed by cardiology plus neurology with 3,183 times (2.2%). The most frequent three-specialty combination, cardiology plus neurology and gastroenterology, occurred only 111 times.
  
  
  CONCLUSIONS
  Without the novel computational technique, it would be hardly possible to analyze the extremely diverse combination patterns of specialties in one-stop visits. The results of the study could provide useful information either for the hospital manager to set up integrated services or for the policymaker to rebuild the health care system.
authors: Institute of Hospital and Health Care Administration, School of Medicine, National Yang-Ming University, Taipei, Taiwan.
authors: Chun-Yi
authors: CY
authors: Tu
authors: Tzeng-Ji
authors: TJ
authors: Chen
authors: Li-Fang
authors: LF
authors: Chou
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000553:Ambulatory Care / Q000706:statistics & numerical data*; D002648:Child; D002675:Child, Preschool; D057225:Data Mining / Q000379:methods*; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008511:Medicine / Q000706:statistics & numerical data; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D013624:Taiwan; D055815:Young Adult