# Patterns of Nonemergent Visits to Different Healthcare Facilities on the Same Day: A Nationwide Analysis in Taiwan

Authors: Meng-Hsuan Wu; Meng-Ju Wu; Li-Fang Chou; Tzeng-Ji Chen
Journal: The Scientific World Journal
 (2014)

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## Abstract

Doctor shopping is a common phenomenon in many countries. However, patterns of switching healthcare facilities on the same day were little known. The data were obtained from the longitudinal cohort datasets (LHID2010) of Taiwan’s National Health Insurance Research Database in 2010. Of 1,000,000 persons of the cohort with 13,276,928 nonemergent visits, 185,347 patients had visited different healthcare facilities within one day, with a total of 672,478 visits and 337,260 switches between facilities in 329,073 patient-days. While 63.0% (n=212,590) of all switches occurred between facilities of the same accreditation level, 14.1% (n=47,664) moved from lower to higher level, and 22.8% (n=77,006) moved in the opposite direction. In 33,689 switches, patients moved to the same specialty of another facility. In 48,324 switches, patients moved to another facility with the same diagnosis, and the most frequent diagnoses were diseases of the digestive system (11,148) and diseases of the respiratory system (10,393). In a densely populated country without strict referral regulation, a high percentage of Taiwanese people had the experience of visiting different healthcare facilities on the same day. The system of family physicians as personal doctors and gatekeepers to healthcare might ameliorate the harmful impact.


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## Body

## 1. Introduction

Doctor shopping is a common phenomenon in many countries of the world [1–4] and has been also observed in Taiwan [5, 6]. Unlike healthcare systems in most developed countries, the National Health Insurance (NHI) in Taiwan does not have a formal gatekeeper system with restrictive regulations in referral, leading to annual number of ambulatory care visits per inhabitant as high as 13.4 and the incubation of doctor shopping behavior [5]. The analyses of doctor shopping in Taiwan have been applied to acute illness (upper respiratory tract infection) [7], chronic diseases (hypertension and type 2 diabetes mellitus) [8], and cancer [6]. In general, doctor shopping is operationally measured as a patient’s visits to different physicians or healthcare facilities within a short period of time [5]. Two unique types of doctor shopping in Taiwan have been reported: (1) “one-stop visits” in which a patient pays visits to several specialties of the same healthcare facility within one day [5] and (2) “same-day visits” in which a patient pays visits to different healthcare facilities within one day. While the former phenomenon has been thoroughly analyzed with data mining technique [9], the latter deserves further detailed analysis.The aim of the current study was to calculate the nationwide prevalence of one patient’s visits to different healthcare facilities on the same day within Taiwan’s NHI in 2010. More importantly, patterns of switching specialties and healthcare facilities on the same day would be analyzed. The findings might provide evidence for discussion in health policymaking.


## 2. Materials and Methods

The conduct of the study had been approved by the Institutional Review Board of Taipei Veterans General Hospital, Taipei, Taiwan (2013-01-005E).


### 2.1. Data Sources

The data were obtained from the longitudinal cohort datasets of 1,000,000 beneficiaries (LHID2010) of the National Health Insurance Research Database (NHIRD), managed by the National Health Research Institutes in Miaoli, Taiwan. These 1,000,000 persons were randomly sampled from 23,251,700 persons who had been insured under the NHI in 2010 (http://nhird.nhri.org.tw/date_cohort.htm). The claims belonging to the cohort were extracted from the whole database to form a specific dataset for research use. According to the NHIRD, the cohort did not differ from the population in the distributions of age, sex, and income subject to premium. In the current study, only the datasets of ambulatory visits in 2010 were used. One record of an ambulatory visit contains the patient’s data (identification number, sex, birthday, visit date, and three diagnoses) and the provider’s data (identification number of the healthcare facility and visited specialty). The original identification number of every beneficiary and healthcare facility has been encrypted in the NHIRD to protect privacy. The uniqueness of each identification number remains after encryption. The master file of healthcare facilities (HOSB) was used to know the level of accreditation.


### 2.2. Study Design

In 2010, the 1,000,000-person cohort had 15,431,528 ambulatory visit records. We calculated only those visits with physician consultations of western medicine (WM), dentistry, and traditional Chinese medicine (TCM). The visits to emergency departments were excluded from analysis.The focus in the current study was on the patients who visited two or more healthcare facilities on the same day. The percentage of these patients in the cohort was computed and also stratified by age and sex. The number of visits involved in multiple visits to different healthcare facilities on the same day was additionally calculated.Furthermore, we analyzed the patterns of multiple visits to different healthcare facilities on the same day by comparing the accreditation level of healthcare facility, type of visited specialty, and primary diagnosis in each visit with those in the ensuing visit. The sequence numbers of visits embedded in each patient’s NHI chip card were used to clarify the direction of a patient’s flow from one facility to another, from one specialty to another, and from one diagnosis to another. Four accreditation levels of healthcare facilities exist in Taiwan: academic medical center, metropolitan hospital, local community hospital, and physician clinic. The specialization in Taiwan includes 43 major specialties and 22 subspecialties. To simplify the analysis, the diagnosis in coding of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), was grouped into the chapter of ICD-9-CM. Because a patient might move to and fro between healthcare facilities on the same day, we calculated only the first switch between any two facilities. Because a patient might visit more than one specialty in a facility, we compared only the last specialty of the outgoing facility with the first specialty of the incoming facility. The same method applied to the comparison between diagnoses.


### 2.3. Data Processing and Statistical Analysis

The Microsoft SQL Server 2012 was used for computing. The regular statistics were displayed. In calculating the percentages of patients in each age-sex group, the dominators were taken from the registry for 1,000,000 beneficiaries in 2010.


## 2.1. Data Sources

The data were obtained from the longitudinal cohort datasets of 1,000,000 beneficiaries (LHID2010) of the National Health Insurance Research Database (NHIRD), managed by the National Health Research Institutes in Miaoli, Taiwan. These 1,000,000 persons were randomly sampled from 23,251,700 persons who had been insured under the NHI in 2010 (http://nhird.nhri.org.tw/date_cohort.htm). The claims belonging to the cohort were extracted from the whole database to form a specific dataset for research use. According to the NHIRD, the cohort did not differ from the population in the distributions of age, sex, and income subject to premium. In the current study, only the datasets of ambulatory visits in 2010 were used. One record of an ambulatory visit contains the patient’s data (identification number, sex, birthday, visit date, and three diagnoses) and the provider’s data (identification number of the healthcare facility and visited specialty). The original identification number of every beneficiary and healthcare facility has been encrypted in the NHIRD to protect privacy. The uniqueness of each identification number remains after encryption. The master file of healthcare facilities (HOSB) was used to know the level of accreditation.


## 2.2. Study Design

In 2010, the 1,000,000-person cohort had 15,431,528 ambulatory visit records. We calculated only those visits with physician consultations of western medicine (WM), dentistry, and traditional Chinese medicine (TCM). The visits to emergency departments were excluded from analysis.The focus in the current study was on the patients who visited two or more healthcare facilities on the same day. The percentage of these patients in the cohort was computed and also stratified by age and sex. The number of visits involved in multiple visits to different healthcare facilities on the same day was additionally calculated.Furthermore, we analyzed the patterns of multiple visits to different healthcare facilities on the same day by comparing the accreditation level of healthcare facility, type of visited specialty, and primary diagnosis in each visit with those in the ensuing visit. The sequence numbers of visits embedded in each patient’s NHI chip card were used to clarify the direction of a patient’s flow from one facility to another, from one specialty to another, and from one diagnosis to another. Four accreditation levels of healthcare facilities exist in Taiwan: academic medical center, metropolitan hospital, local community hospital, and physician clinic. The specialization in Taiwan includes 43 major specialties and 22 subspecialties. To simplify the analysis, the diagnosis in coding of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), was grouped into the chapter of ICD-9-CM. Because a patient might move to and fro between healthcare facilities on the same day, we calculated only the first switch between any two facilities. Because a patient might visit more than one specialty in a facility, we compared only the last specialty of the outgoing facility with the first specialty of the incoming facility. The same method applied to the comparison between diagnoses.


## 2.3. Data Processing and Statistical Analysis

The Microsoft SQL Server 2012 was used for computing. The regular statistics were displayed. In calculating the percentages of patients in each age-sex group, the dominators were taken from the registry for 1,000,000 beneficiaries in 2010.


## 3. Results

Of 1,000,000 persons (507,577 women and 492,423 men) of the cohort with 13,276,928 valid visits, 185,347 patients (107,118 women and 78,229 men) had visited different healthcare facilities within one day in 2010, with a total of 672,478 visits in 329,073 patient-days. The average age was42.6±22.8 years (43.1±21.7 in women and 41.9±24.2 in men). Except in age group 0–9, women were more likely to visit different healthcare facilities within one day than men. Besides, children under 10 and old people of both sexes had a higher percentage of this phenomenon, with the peak in age group 70–79 (Figure 1). The overwhelming majority (97.6%, n=321,258) of 329,073 patient-days with visits to different facilities within one day involved only two facilities, but in 30 cases a patient visited 6 facilities within one day (Table 1). On the other hand, 6.8% (n=22,393) of 329,073 patient-days involved only one specialty; that is, a patient visited the same specialty of different facilities on the same day. In 2 extreme cases, a patient visited 7 specialties within one day (Table 1).Table 1
Distribution of a patient’s visits to different healthcare facilities within one day in the 1,000,000-person cohort in 2010, stratified by numbers of different healthcare facilities and specialties in a day.
Number of patient-days
Percentage
Number of different healthcare facilities visited by a patient in a day
6
6
0.0
5
17
0.0
4
320
0.1
3
7,472
2.3
2
321,258
97.6
Number of different specialties visited by a patient in a day
7
2
0.0
6
8
0.0
5
53
0.0
4
555
0.2
3
10,609
3.2
2
295,453
89.8
1
22,393
6.8Figure 1
Age-sex distribution of patients with visits to different healthcare facilities within one day in the 1,000,000-person cohort in 2010.


### 3.1. Distribution by Accreditation Level of Healthcare Facility

Of 329,073 patient-days with visits to different facilities within one day, there were 337,260 switches from one facility to the next facility. The largest group (n=207,358) of these switches occurred between different physician clinics. While 63.0% (n=212,590) of all switches occurred between facilities of the same accreditation level, 14.1% (n=47,664) moved from a facility of a lower level to another of a higher level, and 22.8% (n=77,006) moved in the opposite direction (Table 2) (Figure 2).Table 2
Distribution of a patient’s switches (n=337,260) between different healthcare facilities within one day in the 1,000,000-person cohort in 2010, stratified by accreditation level of healthcare facility.
Incoming facilities
Total
Academic medical center
Metropolitan hospital
Local community hospital
Physician clinic
Home care
Outgoing facilities
Academic medical center
1,010
1,672
1,788
20,178
0
24,648
Metropolitan hospital
1,660
1,973
2,419
28,719
0
34,771
Local community hospital
1,471
2,066
2,249
22,229
1
28,016
Physician clinic
9,736
17,362
15,369
207,358
0
249,825
Home care
0
0
0
0
0
0
Total
13,877
23,073
21,825
278,484
1
337,260Figure 2
Flow of patients with visits to different accreditation level of healthcare facilities within one day in the 1,000,000-person cohort in 2010.


### 3.2. Distribution by Category of Specialty

Of 337,260 switches from one facility to the next facility within one day, the most frequent destinations by specialty were family medicine (45,524 times), TCM (42,102), and dentistry (39,231). In 33,689 switches, a patient moved to the same specialty of another facility and the most frequent destinations were family medicine (6,608), internal medicine (3,201), dentistry (2,160), pediatrics (2,141), and traditional Chinese medicine (1,839) (Table3).Table 3
Distribution of a patient’s switches (n=337,260) between different healthcare facilities within one day in the 1,000,000-person cohort in 2010, stratified by specialty.
Specialty of incoming facility
Total
Percentage ofswitches between the same specialties
Family medicine
Internal medicine
Pediatrics
Obstetrics and gynecology
Orthopedics
Otolaryngology
Ophthalmology
Dermatology
Dentistry
Traditional Chinese medicine
Other
Specialty of outgoing facility
Family medicine
6,608
4,053
1,488
1,710
1,846
2,692
5,906
2,471
5,290
5,696
7,067
44,827
14.7%
Internal medicine
4,470
3,201
880
1,460
1,308
2,125
4,560
2,137
3,666
4,202
5,746
33,755
9.5%
Pediatrics
1,155
748
2,141
558
322
1,309
2,456
1,226
2,950
1,542
1,578
15,985
13.4%
Obstetrics and gynecology
1,647
1,317
579
1,750
398
1,275
1,089
1,293
1,650
2,530
1,746
15,274
11.5%
Orthopedics
2,112
1,465
367
465
722
1,004
1,516
816
1,205
1,996
2,254
13,922
5.2%
Otolaryngology
2,580
1,951
1,128
1,263
824
1,492
4,592
2,642
4,403
2,995
3,519
27,389
5.4%
Ophthalmology
5,422
3,476
2,367
919
1,043
3,978
1,230
2,789
4,095
4,426
4,017
33,762
3.6%
Dermatology
2,187
1,671
1,042
1,079
526
2,196
2,733
770
2,664
3,084
2,489
20,441
3.8%
Dentistry
5,284
3,085
2,626
1,469
914
3,858
3,785
2,720
2,160
4,982
3,836
34,719
6.2%
Traditional Chinese medicine
4,468
2,963
1,424
1,691
1,162
2,320
3,406
2,505
4,299
1,839
4,112
30,189
6.1%
Other
9,591
7,021
2,073
2,357
2,681
5,287
6,499
4,053
6,849
8,810
11,776
66,997
Total
45,524
30,951
16,115
14,721
11,746
27,536
37,772
23,422
39,231
42,102
48,140
337,260
6.5%


### 3.3. Distribution by Principal Diagnosis

Of 337,260 switches from one facility to the next facility within one day, the most frequent diagnoses at both outgoing and incoming facilities were diseases of the digestive system, diseases of the respiratory system, and diseases of the nervous system and sense organs. In 48,324 switches, a patient moved to another facility with the same diagnosis and the most frequent diagnoses were diseases of the digestive system (11,148) and diseases of the respiratory system (10,393) (Table4).Table 4
Distribution of a patient’s switches (n=337,260) between different healthcare facilities within one day in the 1,000,000-person cohort in 2010, stratified by specialty principal diagnosis*.
Diagnosis at incoming facility
Total
Percentage of switches with the same diagnoses
Ch01
Ch02
Ch03
Ch04
Ch05
Ch06
Ch07
Ch08
Ch09
Ch10
Ch11
Ch12
Ch13
Ch14
Ch15
Ch16
Ch17
V
Unknown
Diagnosis at outgoing facility
Ch01
787
91
192
14
226
1,240
338
1,769
2,015
477
11
599
722
4
1
650
461
48
0
9,645
8.2%
Ch02
169
801
141
18
187
650
283
1,007
1,182
404
5
418
500
4
1
496
279
50
0
6,595
12.1%
Ch03
309
174
725
8
397
2,463
610
1,806
2,239
650
15
945
1,541
8
1
902
704
92
0
13,589
5.3%
Ch04
18
13
16
23
21
76
25
81
144
65
4
57
57
0
2
58
30
6
0
696
3.3%
Ch05
324
119
298
13
1,444
1,293
710
2,349
2,116
864
14
827
1,092
12
0
1,438
582
71
0
13,566
10.6%
Ch06
1,214
350
1,070
59
954
4,041
2,190
9,057
7,314
1,646
27
3,492
3,397
41
1
3,167
1,726
151
0
39,897
10.1%
Ch07
581
248
545
25
629
3,264
1,592
2,624
3,337
863
9
1,314
2,491
29
0
1,598
1,042
144
0
20,335
7.8%
Ch08
1,863
504
988
58
1,591
9,432
1,826
10,393
12,266
2,664
53
4,560
3,382
91
11
3,587
2,940
297
1
56,507
18.4%
Ch09
1,977
647
1,114
95
1,509
7,141
2,220
11,881
11,148
3,278
88
4,995
4,361
61
9
4,524
2,733
273
0
58,054
19.2%
Ch10
550
298
456
53
635
2,020
772
3,090
3,697
2,763
158
1,778
1,551
12
6
1,682
843
129
0
20,493
13.5%
Ch11
18
4
12
3
8
18
2
52
81
145
233
29
16
1
5
79
13
26
0
745
31.3%
Ch12
529
247
464
41
609
3,328
854
4,081
4,264
1,531
31
2,961
1,570
21
4
1,478
1,152
121
0
23,286
12.7%
Ch13
719
314
915
40
857
3,911
1,886
3,903
4,798
1,385
12
1,759
5,249
39
2
2,105
2,064
174
0
30,132
17.4%
Ch14
14
11
9
0
12
69
26
142
103
19
1
44
47
77
3
46
30
5
0
658
11.7%
Ch15
1
1
0
0
1
4
1
20
6
7
3
4
1
0
4
8
3
7
0
71
5.6%
Ch16
666
324
472
45
1,118
3,201
1,172
3,346
4,445
1,363
72
1,458
1,806
16
6
2,803
1,079
137
0
23,529
11.9%
Ch17
411
133
391
32
458
1,590
777
2,453
2,510
684
20
1,224
1,821
13
0
1,091
3,255
97
0
16,960
19.2%
V
83
41
68
3
79
216
133
347
436
188
34
192
248
7
19
211
171
25
0
2,501
1.0%
Unknown
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
Total
10,233
4,320
7,876
530
10,735
43,957
15,417
58,402
62,101
18,996
790
26,656
29,852
436
75
25,923
19,107
1,853
1
337,260
14.3%
*Details of ICD-9-CM chapters in Table 5.Table 5
Chapter
Diagnosis
Code range in ICD-9-CM
Ch01
Infectious and parasitic diseases
001–139
Ch02
Neoplasms
140–239
Ch03
Endocrine, nutritional, and metabolic diseases and immunity disorders
240–279
Ch04
Diseases of the blood and blood-forming organs
280–289
Ch05
Mental disorders
290–319
Ch06
Diseases of the nervous system and sense organs
320–389
Ch07
Diseases of the circulatory system
390–459
Ch08
Diseases of the respiratory system
460–519
Ch09
Diseases of the digestive system
520–579
Ch10
Diseases of the genitourinary system
580–629
Ch11
Complications of pregnancy, child birth, and the puerperium
630–677
Ch12
Diseases of the skin and subcutaneous tissue
680–709
Ch13
Diseases of the musculoskeletal system and connective tissue
710–739
Ch14
Congenital abnormalities
740–759
Ch15
Certain conditions originating in the perinatal period
760–779
Ch16
Symptoms, signs, and ill-defined conditions
780–799
Ch17
Injury and poisoning
800–999
V
Supplementary classification of factors influencing health status and contact with health services
V01–V82


## 3.1. Distribution by Accreditation Level of Healthcare Facility

Of 329,073 patient-days with visits to different facilities within one day, there were 337,260 switches from one facility to the next facility. The largest group (n=207,358) of these switches occurred between different physician clinics. While 63.0% (n=212,590) of all switches occurred between facilities of the same accreditation level, 14.1% (n=47,664) moved from a facility of a lower level to another of a higher level, and 22.8% (n=77,006) moved in the opposite direction (Table 2) (Figure 2).Table 2
Distribution of a patient’s switches (n=337,260) between different healthcare facilities within one day in the 1,000,000-person cohort in 2010, stratified by accreditation level of healthcare facility.
Incoming facilities
Total
Academic medical center
Metropolitan hospital
Local community hospital
Physician clinic
Home care
Outgoing facilities
Academic medical center
1,010
1,672
1,788
20,178
0
24,648
Metropolitan hospital
1,660
1,973
2,419
28,719
0
34,771
Local community hospital
1,471
2,066
2,249
22,229
1
28,016
Physician clinic
9,736
17,362
15,369
207,358
0
249,825
Home care
0
0
0
0
0
0
Total
13,877
23,073
21,825
278,484
1
337,260Figure 2
Flow of patients with visits to different accreditation level of healthcare facilities within one day in the 1,000,000-person cohort in 2010.


## 3.2. Distribution by Category of Specialty

Of 337,260 switches from one facility to the next facility within one day, the most frequent destinations by specialty were family medicine (45,524 times), TCM (42,102), and dentistry (39,231). In 33,689 switches, a patient moved to the same specialty of another facility and the most frequent destinations were family medicine (6,608), internal medicine (3,201), dentistry (2,160), pediatrics (2,141), and traditional Chinese medicine (1,839) (Table3).Table 3
Distribution of a patient’s switches (n=337,260) between different healthcare facilities within one day in the 1,000,000-person cohort in 2010, stratified by specialty.
Specialty of incoming facility
Total
Percentage ofswitches between the same specialties
Family medicine
Internal medicine
Pediatrics
Obstetrics and gynecology
Orthopedics
Otolaryngology
Ophthalmology
Dermatology
Dentistry
Traditional Chinese medicine
Other
Specialty of outgoing facility
Family medicine
6,608
4,053
1,488
1,710
1,846
2,692
5,906
2,471
5,290
5,696
7,067
44,827
14.7%
Internal medicine
4,470
3,201
880
1,460
1,308
2,125
4,560
2,137
3,666
4,202
5,746
33,755
9.5%
Pediatrics
1,155
748
2,141
558
322
1,309
2,456
1,226
2,950
1,542
1,578
15,985
13.4%
Obstetrics and gynecology
1,647
1,317
579
1,750
398
1,275
1,089
1,293
1,650
2,530
1,746
15,274
11.5%
Orthopedics
2,112
1,465
367
465
722
1,004
1,516
816
1,205
1,996
2,254
13,922
5.2%
Otolaryngology
2,580
1,951
1,128
1,263
824
1,492
4,592
2,642
4,403
2,995
3,519
27,389
5.4%
Ophthalmology
5,422
3,476
2,367
919
1,043
3,978
1,230
2,789
4,095
4,426
4,017
33,762
3.6%
Dermatology
2,187
1,671
1,042
1,079
526
2,196
2,733
770
2,664
3,084
2,489
20,441
3.8%
Dentistry
5,284
3,085
2,626
1,469
914
3,858
3,785
2,720
2,160
4,982
3,836
34,719
6.2%
Traditional Chinese medicine
4,468
2,963
1,424
1,691
1,162
2,320
3,406
2,505
4,299
1,839
4,112
30,189
6.1%
Other
9,591
7,021
2,073
2,357
2,681
5,287
6,499
4,053
6,849
8,810
11,776
66,997
Total
45,524
30,951
16,115
14,721
11,746
27,536
37,772
23,422
39,231
42,102
48,140
337,260
6.5%


## 3.3. Distribution by Principal Diagnosis

Of 337,260 switches from one facility to the next facility within one day, the most frequent diagnoses at both outgoing and incoming facilities were diseases of the digestive system, diseases of the respiratory system, and diseases of the nervous system and sense organs. In 48,324 switches, a patient moved to another facility with the same diagnosis and the most frequent diagnoses were diseases of the digestive system (11,148) and diseases of the respiratory system (10,393) (Table4).Table 4
Distribution of a patient’s switches (n=337,260) between different healthcare facilities within one day in the 1,000,000-person cohort in 2010, stratified by specialty principal diagnosis*.
Diagnosis at incoming facility
Total
Percentage of switches with the same diagnoses
Ch01
Ch02
Ch03
Ch04
Ch05
Ch06
Ch07
Ch08
Ch09
Ch10
Ch11
Ch12
Ch13
Ch14
Ch15
Ch16
Ch17
V
Unknown
Diagnosis at outgoing facility
Ch01
787
91
192
14
226
1,240
338
1,769
2,015
477
11
599
722
4
1
650
461
48
0
9,645
8.2%
Ch02
169
801
141
18
187
650
283
1,007
1,182
404
5
418
500
4
1
496
279
50
0
6,595
12.1%
Ch03
309
174
725
8
397
2,463
610
1,806
2,239
650
15
945
1,541
8
1
902
704
92
0
13,589
5.3%
Ch04
18
13
16
23
21
76
25
81
144
65
4
57
57
0
2
58
30
6
0
696
3.3%
Ch05
324
119
298
13
1,444
1,293
710
2,349
2,116
864
14
827
1,092
12
0
1,438
582
71
0
13,566
10.6%
Ch06
1,214
350
1,070
59
954
4,041
2,190
9,057
7,314
1,646
27
3,492
3,397
41
1
3,167
1,726
151
0
39,897
10.1%
Ch07
581
248
545
25
629
3,264
1,592
2,624
3,337
863
9
1,314
2,491
29
0
1,598
1,042
144
0
20,335
7.8%
Ch08
1,863
504
988
58
1,591
9,432
1,826
10,393
12,266
2,664
53
4,560
3,382
91
11
3,587
2,940
297
1
56,507
18.4%
Ch09
1,977
647
1,114
95
1,509
7,141
2,220
11,881
11,148
3,278
88
4,995
4,361
61
9
4,524
2,733
273
0
58,054
19.2%
Ch10
550
298
456
53
635
2,020
772
3,090
3,697
2,763
158
1,778
1,551
12
6
1,682
843
129
0
20,493
13.5%
Ch11
18
4
12
3
8
18
2
52
81
145
233
29
16
1
5
79
13
26
0
745
31.3%
Ch12
529
247
464
41
609
3,328
854
4,081
4,264
1,531
31
2,961
1,570
21
4
1,478
1,152
121
0
23,286
12.7%
Ch13
719
314
915
40
857
3,911
1,886
3,903
4,798
1,385
12
1,759
5,249
39
2
2,105
2,064
174
0
30,132
17.4%
Ch14
14
11
9
0
12
69
26
142
103
19
1
44
47
77
3
46
30
5
0
658
11.7%
Ch15
1
1
0
0
1
4
1
20
6
7
3
4
1
0
4
8
3
7
0
71
5.6%
Ch16
666
324
472
45
1,118
3,201
1,172
3,346
4,445
1,363
72
1,458
1,806
16
6
2,803
1,079
137
0
23,529
11.9%
Ch17
411
133
391
32
458
1,590
777
2,453
2,510
684
20
1,224
1,821
13
0
1,091
3,255
97
0
16,960
19.2%
V
83
41
68
3
79
216
133
347
436
188
34
192
248
7
19
211
171
25
0
2,501
1.0%
Unknown
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
Total
10,233
4,320
7,876
530
10,735
43,957
15,417
58,402
62,101
18,996
790
26,656
29,852
436
75
25,923
19,107
1,853
1
337,260
14.3%
*Details of ICD-9-CM chapters in Table 5.Table 5
Chapter
Diagnosis
Code range in ICD-9-CM
Ch01
Infectious and parasitic diseases
001–139
Ch02
Neoplasms
140–239
Ch03
Endocrine, nutritional, and metabolic diseases and immunity disorders
240–279
Ch04
Diseases of the blood and blood-forming organs
280–289
Ch05
Mental disorders
290–319
Ch06
Diseases of the nervous system and sense organs
320–389
Ch07
Diseases of the circulatory system
390–459
Ch08
Diseases of the respiratory system
460–519
Ch09
Diseases of the digestive system
520–579
Ch10
Diseases of the genitourinary system
580–629
Ch11
Complications of pregnancy, child birth, and the puerperium
630–677
Ch12
Diseases of the skin and subcutaneous tissue
680–709
Ch13
Diseases of the musculoskeletal system and connective tissue
710–739
Ch14
Congenital abnormalities
740–759
Ch15
Certain conditions originating in the perinatal period
760–779
Ch16
Symptoms, signs, and ill-defined conditions
780–799
Ch17
Injury and poisoning
800–999
V
Supplementary classification of factors influencing health status and contact with health services
V01–V82


## 4. Discussion

In our current study, we extended the traditional analyses to observe dynamic changes of patient visits in a large-scale, population-based dataset. Our study was purely descriptive. Although not qualitative in study design, our quantitative analysis did offer some clues.Firstly, about one fifth of the Taiwanese had experience of switching healthcare facilities on the same day in a year and such visits accounted for 5.1% of all ambulatory care visits. Because there was neither formal referral system nor strict referral regulation within the NHI in Taiwan, the freedom of choosing healthcare facilities [10] and the low copayments [11, 12] might contribute to the occurrence of this phenomenon. The longer opening hours of healthcare facilities with walk-in registration [13] on the supply side might also play a role.Secondly, of all switches on the same day, 16,308 (4.8%) occurred between hospitals. Usually the outpatient clinic of a hospital in Taiwan offered a broad spectrum of specialties so that the patients could consult several specialties in one visit (the so-called one-stop visit) [14]. For visits to different hospitals within one day, the patients might either search for a second opinion for the same illness or consult different hospitals for different illnesses. That is, a hospital might be good in some specialties and another in some other specialties. Because Taiwan is a densely populated island and most hospitals are located in cities, the patient could thus utilize the efficient transportation system to reach different hospitals in the shortest possible time.Thirdly, with respect to specialty, 59,332 (17.6%) switches on the same day occurred between traditional Chinese medicine (TCM) and all specialties of western medicine (WM). Past studies had also revealed that patients in Taiwan might visit WM and TCM one after another within a short time [15, 16]. TCM can be traced back more than 2,000 years and still commonly used by people in China, Taiwan, Korea, and Japan [17]. More than 9,000 items of TCM herbal drugs are reimbursable within the NHI in Taiwan. Our finding highlights the importance that WM physicians should pay more attention to TCM drugs taken by patients to avoid drug interaction [5, 18, 19].Furthermore, among switches on the same day with the same diagnosis, the most frequent diagnoses were diseases of the digestive system and diseases of the respiratory system. One of our earlier studies has revealed that these two kinds of diseases accounted for almost a half of ambulatory care visits in Taiwan and the number of visits with diseases of the respiratory system was twice that with diseases of digestive system [5]. However, in our current study, diseases of digestive system caused switches on the same day more frequently than diseases of the respiratory system. Perhaps diseases of digestive system bring more stress to patients. The reason deserves further analysis.Our study with insurance claims of the NHIRD had some limitations. Firstly, the beneficiary’s residence was unknown. The influence of location could not be studied. Secondly, we did not measure the distance between healthcare facilities that a patient visited on the same day. The traffic situation was not taken into consideration, either. Thirdly, the patient’s complaints, symptoms, or reasons for consultations were not available. We could not know whether the switches between healthcare facilities were initiated by formal or informal referral from physicians, either.


## 5. Conclusion

In a densely populated country without strict referral regulation, a high percentage of Taiwanese people had the experience of visiting different healthcare facilities on the same day. It might represent the efficiency of Taiwan’s NHI. However, in absence of communication between healthcare suppliers, the resulting duplicate examination and treatment might bring about problems of financial burden and patient safety. The system of family physicians as personal doctors and gatekeepers to healthcare might ameliorate the harmful impact [20].



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*Source: 627580-2014-04-22.xml*