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3 ( ) 92 2 ( ) ( ) ( ) : : : ( ) ( ) : ( ) (106-36) ( : ) ( ) (85) 712 II

4 92 2 () ()A Study on Medical Claim Payments Auditing Procedure in Taiwan National Health Insurance () () Hwai-Hui Fu () () Hsien-Tang Tsai, Duan Wei () () Iuan-Yuan Lu, Huei-Mei Liang, Tsuang Kuo, Ben-Chang Shia, Ching-Wei Lin III

5 ( ) 2004/06/03 IV

6 8 96% 70% : MCPA Medical Claim Payments Auditing procedure 2 3) [ ] V

7 Abstract National Health Insurance (NHI) has been implemented in Taiwan for nearly eight years; since then, over 96% of 23 million residents of Taiwan have benefited from this program, and 70% of them are satisfied. Recently, the growth rate of healthcare expenditure, however, has been phenomenally rapid, owing to the ageing population, the economic development, the expansion of health insurance, the increased supply of healthcare resources, and the innovation of medical technology. Under the circumstance that the bill of raising the insurance premium rate could not be passed by the legislative congress, the Bureau of NHI (BNHI) was forced to economize on expense to achieve the financial balance. Currently, the BNHI implements total amount control to control the total medical claimed payments of each medical healthcare provider. Facing the increasing volume of documentary auditing, the BNHI has to make its efforts on how to improve its auditing efficiency. This is also one of the purposes of this thesis. This study aimed to establish a reasonable and fair auditing procedure of medical claim payment, termed medical claim payments auditing (MCPA) procedure. At the stage of professional auditing, adopted the MIL-STD-105E sampling plan to select data for professional audit and used the auditing results as a payment criterion. To verify the adaptability of the MCPA procedure, the researcher used the data provided by the institutes of Kaohsiung and Pintong as simulation objects. Further, the estimated cost model was adopted to increase the possibility of using this procedure. The MCPA procedure consists of the following characteristics: 1) The number of sampling is much lower than that of the current system used by the NHI, thus the audit labor-force and time can be reduced significantly. 2) The incentive mechanism design encourages the healthcare providers to honestly apply their medical claim payments and avoids inappropriate healthcare services. 3) Adopting international standards of sampling technology makes the MCPA procedure trustworthy and simultaneously can reduce the VI

8 implementing obstructs. Keywords: National Health Insurance; Sampling plan; Incentive mechanism design VII

9 ... I... II... III... IV... V Abstract... VI... XI... XIII OECD MIL-STD-105E VIII

10 1MCPA-S USL OC MCPA-S MCPA-S MCPA-S MCPA-D MCPA-D MCPA-D MCPA-D MCPA-D MCPA-D AQL USL MCPA MCPA AQL USL IX

11 MCPA-S MCPA-D MCPA-D MCPA-D X

12 GDP % OC OC OC OC K USL OC MCPA-S MCPA-S ln ln MCPA-D OC AQL XI

13 5-6USL BNHI MCPA / BNHI MCPA Q Q XII

14 2-1OECD GDP BC MCPA-S Excel AQL USL MCPA-S MCPA-D I MCPA-D XIII

15 5-3MCPA-D MCPA-D I MCPA-D MCPA-D MCPA-D MCPA-D MCPA-D AQL USL BNHI MCPA / BNHI MCPA XIV

16 7-1 MCPA MCPA-S MCPA-D XV

17 % 92.91% 70% 6.26% 4.26%

18 2300 ( ) 2

19 MCPA MCPA 3

20 2. : MCPA 4

21 MIL-STD-105E 1MCPA-S USL OC MCPA-S MCPA-S MCPA-S 2MCPA-D MCPA-D MCPA-D MCPA-D MCPA-D MCPA-D AQL USL MCPA MCPA MCPA 5

22 MCPA MCPA MIL-STD-105E I MCPA 6

23 Healthcare service system (organization) 1. (NHS,The National Health Service Model) 2. (NHI,The Social Insurance Model) 3. (The Private Insurance Model) reposbility 1. (the voluntary out-of-pocket model) 2. 7

24 (risk-related premiums) 3. (income-related premiums or tax) (public insured or funding bodies) 1. (the voluntary reimbursement-of-patient model) 2. (the voluntary contract model) 3. (the voluntary integrated model) 3 OECD OECD

25 (1) (2) (3) (4) (1) (2) (expenditure target) (1) 25%(2) 75% (expenditure cap) (Phelps, 1997) 9

26 ( 2000) (National Health Services, NHS) (1) (2) (3) (Regional Health Authorities, RHA) (District Health Authorities, DHA)RHA DHA 1990 (general practitioner, GP) (GP fund holder) (NHS trust) % 12.2% 2.1% 3% 10

27 1997 GDP 6.7% 14% (Maynard, 1990) ( 2000) 95% % 47% 28% 11

28 65 (OECD,1994; Raffel, 1984) 1997 GDP 9.3% 1971 GDP 7.6% 14%Phelps(1997) (Bishai, Lang, 2000) ( 2000) (OECD, 1994) 12

29 (Diagnosis Related Group, DRG) (Resource- Based Relative Value Scale, RBRVS) (Health Maintenance Organization, HMO) HMO 1997 GDP 14% OECD ( 2000) 1961 (Employees Health Insurance System, EHI) (National Health Insurance System, NHI)(National Federation of Health Insurance Societies, 1996) EHI (1) 30.2% (2) 26%(3) 13

30 0.3% (4) 9.4% NHI 34.2% NHI 1994 OECD 56% 25% 7% 12% 7.0%~8.7% (National Federation of Health Insurance Societies, 1996) (1) (2) 1.5% GDP 14

31 ( 2000) 1995 GDP 5.27% % 15

32 % of GDP % % 80.0% 70.0% 60.0% 65.4% 61.0% 66.5% 74.6% 68.3% 67.2% 68.4% 63.3% 63.8% 65.6% 71.1% 50.0% 40.0% 30.0% 47.0% 39.0% 50.2% 36.5% 27.0% 23.4% 25.9% 26.3% 24.5% 24.2% 20.0% 10.0% 22.5% 22.0% 19.6% 20.1% 17.2% 0.0% 16

33 OECD GDP OECD (RHA) (DHA), (EHI) (NHI) 3/4 1/4 DRG,RBRVS GDP (1) (2) (3) GDP 17

34 2-2 GDP GDP 1/3 1/6 2-2 GDP GDP US$PPP US$PPP : OECD 2001 (PPP) BC 1996 (Hausarzt) (ordinationsgebühr) (konsultationsgebühr) (fallpunktzahlen) 50% 10% 50%150% 10% 150% 20% 18

35 X Y a ( 1+ 10% ) + X Y ( 1 10% ) + XY ( 1 20% ) b c X Y (Y=Y a +Y b +Y c ) Z Y a 50%Z Y a <0.5Z Y b 50%Z~150%Z 0.5ZY b 1.5Z Y c 150%Z Y c >1.5Z (richtigkeitsprüfung) (prüfungsausschuβ) (wirtschaftlichkeitsprüfung) 1. (einzelfallprüfung) 25% 2. (beispielfallprüfung) 25%50% 3. (vergleichsprüfung) 50% (offensichtliches miβverhältnis) 19

36 4. (stichprobenprüfung) 2% ( ) (repräsentative(eingeschränkte)einzelfallprüfung) 25% (unwirtschaft-lich) (hochrenchnung) 20% 100 BC BC 1. (adjudication) BC 14, 2. (audit) 3. (1) (protocol) (2) (guideline) 20

37 4. (1) (2) 1. (1) (2) (3) (4) (5)

38 Yes No Yes No Yes Yes No 22

39 2. (1) (2) (3) (4) 3. 23

40 BC BC 2-3 BC BC MIL-STD-105E MIL-STD-105E 1942 JAN-STD MIL-STD-105E 1969 CNS ISO/DIS-2859 (N,n,Ac,Re) N n Ac Re 24

41 MIL-STD-105 MIL-STD AQL 3.,, 4. A,B,C ( ) 6. (N,n,Ac,Re) OC Oprating characteristic curve OC OC infinite population N n 100% % OC curve 25

42 p possion distribution OC 100% OC 2-4 MIL-STD-105E 1. N n N c d N 2. d c d>c 2-5 d c d>c c OC d P = n e p() c = p ( a) np' ( np' ) c! c ( np' ) c np c e = ' = p... 0 c! ( C = 0) + p( C = 1) + + p( C = c) 26

43 OC % AQL1- % OC curve LTPD % 2-6 OC OC Nnc N OCnc AQL LTPD 1. AQLacceptable quality level: AQL 1-α 2. LTPDlot tolerance percent defective level: LTPD 3. producer s riskpr: 27

44 Type error α AQL 4. consumer s riskcr: Type error β LTPD AQL1-α LTPDβ acceptable lot unacceptable lot p x c p = p 0 = c x= 0 ( np ) 0 x e x! np0 = 1 α p x c p = p 1 = c x= 0 ( np ) 1 x e x! np1 = β x p 0, p,α, β nc, 1 MIL-STD-105E 1. N n 1 c 1 d 1 n 1 r 1 n 2 28

45 c 2 d 2 n 1 r 2 r 2 = c d 1 c 1 d 1 r 1 c 1 <d 1 < r 1 n 2 d 2 d 1 + d 2 c 2 d 1 + d 2 r c 1 r 1 c 2 r 2 = c OC (Pa) (Pa) 29

46 (Pa)c ' d p = n p e p() c = ( a) e np' np' c1 = Ι 0 1 ( np' ) c! ( np' ) c! c1 c = p ( C = 0) + p( C = 1) p( C = c ) p ( a) ΙΙ = p( C1 = c1 + 1) ( p ) + + ( = ) ( ) Ι 0 p1... pc2 c p C ΙΙ 1 r1 1 p Ι 0... pc2 r1 1 ΙΙ ( a) = p( a) Ι + p( a) ΙΙ p c OC % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% OC lot-by-lot acceptance sampling plans for attributes MIL-STD-105E 30

47 (AQL) OC 2-9% L N S %

48 (04) (08) 32

49 33 (02) (03) (09) (1) (2) (3) 84 3 (1)

50 a. 5% b. 10% (2) (3) / / / / / / / / / / / / / / /

51 / / / / / / / (40-12) (4) /50 1/ / / / / /

52 (5) /50 1/100 1/10 1/ / / / / (6) / /10 1/ /

53 (7) / /10 1/ / (1). (2). 20 1/

54 (1) (a) (b) (c) [ ] (d) (2) (a) Σ( ) (b) (c) (3) N 5% M 5% n T n 5% 38

55 n N +1 5% 1 n M n + (a) N + 1 n Tn + N + 1 ( ) ( ) ( ) ( ) N + 1 n (b) ( T n ) N + 1 N + 1 n ( M n ) N + 1 (4)87 4 a N 6% 10% b M 1 T 1 M 2 (T 2 )M 3 (T 3 )..Mn(Tn) 1 N N N + 1 M c = T N N +1 n N + 1 n N + 1 d = ( ) ( ) + ( ) ( ) + ( ) n n N + 1 n T n N + 1 N + 1 n M n N + 1 = ( ) [ ] 39

56 e ( ) + ( ) ( ) + ( ) [ ] (1) x ij i j xi i yij i j yi i p ˆ = x / y i i i i Di i D = k D i i= 1 Xˆ p ˆ / = x ij y ij 40

57 Xˆ = pd ˆ (2) k ~ X = pˆ i D i (3) i= 1 D / D y / y Xˆ ~ = X i = i i Xˆ = Dpˆ = D = D Di pˆ i( ) = D x y ij ij = D ~ pˆ D = X i i x y i i = D pˆ y i y i i = D pˆ ( i yi ) y i (1) (point estimation) (2), 41

58 ( ) (K) 90 7 Excel (K) % K 5 K K 5 K K K d K 3-13 K d d/k

59 3-11 K K 3-1 K (outlier) 3-1 K 43

60 1MCPA-S Medical Claim Payments Auditing on Single sampling procedure MCPA-S bias correct MIL-STD-105E 44

62 Pa 0 1 Continuous Variable 0 1 USLUpper Specification Limit USL USL 4-3 USL OC OC OC OC 5-3 USL OC Pa USL OC 4-3 AQL1- OC curve LTPD USL 46

63 MCPA-S MCPA-S 4-4 (5N, 2reject) (5T, 5accept) (1L, 1reject) (10N, 10accept) AQL DR > USL MCPA-S 47

64 MCPA-S (lots) homogeneous... (non-homogeneous MCPA-S MCPA-S homogeneous homogeneous MCPA-S N sampling population lot size N 48

65 P(n,Ac,Re) N n Ac Re MCPA-S P(n,Ac,Re) P(n,Ac,Re) 1. N Stratified Random Sampling 2. MCPA-S MIL-STD-105E MCPA-S II 3. N II 4. AQL 5. (n,ac,re) MCPA-S N (n,ac,re) 1. USL DR deduct ratedr DR USL, DR USL, 2. (n,ac,re) 49

66 MCPA-S 1. = 2. = = Feedback Control MCAP-S Feedback Control MCPA-S MCPA-S 50

67 1200 MCPA-S MCPA-S MCPA-S MCPA-S N / / / / / / / / ,000 1/ ,001-35,000 1/ , , ,000 1/15 2,333-10, , ,000 1/15 10,000-33, ,001 & above 1/15 33,333 & above BNHI system Tightened MCPA Normal MCPA Reduced MCPA sample size lot size MCAP-S 51

68 MCAP-S USL MCAP-S MCAP-S MCPA-S % medical expenditure Valid N Mean Minimum Maximum Std.Dev. Skewness Kurtosis MEDICAL$ DELETE$ S_RATIO

69 MEDICAL$ 0.000* DELETE$ 0.000* RATIO Cell Contents: Correlation P-Value * p <0.05 MEDICAL$ DELETE$ RATIO * * * * 1. Kolmogorov-Smirnov 12.3% 0 0 ln Kolmogorov-Smirnov Normal ln 53

4-7 0 ln 2. p = 0.8822 5% ln K-S d = 0.054936p = n.s.

70 4-7 0 ln 2. p = % ln K-S d = p = n.s. not significant 5% ln 3. lognormal 12.3% 0 >12.3% Excel

71 sim-lognormal 0-rate sim-ratio random no 4-4 Excel Excel LOGINV(RAND(), ,1.2775) IF(RAND()<0.123,0,1) sim-lognormal *0-rate INT(RAND()*713) no sim-lognormal 0-rate sim-ratio random no MCPA-S II J AQL% P(n,c) DR USL 5%, 9%, 10%, 15%, 20%, 25%, 30%, 35%, and 40% A R 4-6 AQL%= 4 % P(n=80,c=7)USL =15% R 75.26% 380,791 = 1 = 1 = =

72 AQL%= 25 % P(n=50,c=21)USL =15% Sampling size A 97.75% 329,212 = 1 = 1 = = ,644, AQL USL MCPA-S Ac AQL% USL R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R R A R R R R R R A A A R R R R R A A A A R R R A A A A A A R R R A A A A A A R R R A A A A A A R R R A A A A A A R R R A A A A A A 56

73 2MCPA-D MCPA-S Medical Claim Payments Auditing on Double sampling procedure MCPA-D MCPA-S MCPA-D MCPA-D MCPA-D P(n 1, c 1, r 1 n 2, c 2, r 2 ) 1. N sampling population lot size N 2. N Stratified Random Sampling 3. inspection level

74 "AQL i = i+1 i = 1 DR > USL i=1 " d1 A1 " or d1 A1 " d1 > R1" or else d 1 > R 1 d=d 1 +d 2 d A MCPA-D 58

75 =5% % 5-2 OC 4. N 5. AQL 6. P(n 1, c 1, r 1 n 2, c 2, r 2 ) N n 1 n 2 c 1 c 2 r 1 r 2 d 1 d 2 1. USL DR deduct ratedr DR USL, DR USL, 59

76 MCPA-D MCPA-D 1/15 MCPA-D

77 5-1 MCPA-D I MCPA-D I N , , , ,000 2, ,000 3, ,000 6, MCPA-D MCPA-D II N , , , ,000 2, ,000 3, ,000 6, MCPA-D MCPA-D III N , , , ,000 2, ,000 3, ,000 6,

78 5-4 MCPA-D I MCPA-D I N , , , , , , MCPA-D N MCPA-D II , , , , , , MCPA-D MCPA-D III N , , , , , ,

79 5-7 MCPA-D MCPA-D I N , , , , , , MCPA-D MCPA-D II N , , , , , , MCPA-D MCPA-D III N , , , , , ,

80 5-10 MCPA-D I MCPA-D II MCPA-D III N , , , , , , / MCPA-D α 0.05 β MCPA-D 5-11 α 0.05 MCPA-D N P Pa P Pa

81 5-11 N P , , , , , , MCPA-D AQL0.65% 100% USL0.05~ AQL% first Cum-second size A R size A R

82 5-13 MCPA-D decision AQL% USL0.05 USL0.09 USL0.10 USL0.15 USL0.20 USL0.25 USL0.30 USL0.35 USL0.40 F S F S F S F S F S F S F S F S F S 0.65 R R R R R R R R A 1 R R R R R R R R R 1.5 R R R R R R R R A 2.5 R R R R R R A A A 4 R R R R R A A A A 6.5 R R R R A A A A A 10 R R R R A A A A A 15 R R R A A A A A A 25 R R R A A A A A A 40 A A A A A A A A A 65 A A A A A A A A A 100 A A A A A A A A A 5-14 MCPA-D AQL% USL0.05 USL0.09 USL0.10 USL0.15 USL0.20 USL0.25 USL0.30 USL0.35 USL ~ ~ AQL0.65%100%USL0.05~

83 5-15 AQL% first second size A R size A R decision AQL% USL0.05 USL0.09 USL0.10 USL0.15 USL0.20 USL0.25 USL0.30 USL0.35 USL0.40 F S F S F S F S F S F S F S F S F S 0.65 R R R R R R R R R 1 R R R R R R R R R 1.5 R R R R R R R R R 2.5 R R R R R R R R R 4 R R R R R R R R R 6.5 R R R R R R R R R 10 R R R R R R A A A 15 R R R R R A A A A 25 R R R A A A A A A 40 A A A A A A A A A 65 A A A A A A A A A 100 A A A A A A A A A 5-17 AQL% USL0.05 USL0.09 USL0.10 USL0.15 USL0.20 USL0.25 USL0.30 USL0.35 USL

84 ~ ~ ~ ~ AQL0.65%100%USL0.05~ AQL% first second size A R size A R decision AQL% USL0.05 USL0.09 USL0.10 USL0.15 USL0.20 USL0.25 USL0.30 USL0.35 USL0.40 F S F S F S F S F S F S F S F S F S 0.65 R R R R R R R R R 1 R R R R R R R R R 1.5 R R R R R R R R R 2.5 R R R R R R R R R 4 R R R R R R R R R 6.5 R R R R R R R R A 10 R R R R R R R A A 15 R R R R R R R A A 25 R R R R R R R A A 40 R R R R R R R A A 65 R R R R R R R A A 100 R R R R R R R A A 68

85 5-20 AQL% USL0.05 USL0.09 USL0.10 USL0.15 USL0.20 USL0.25 USL0.30 USL0.35 USL ~ ~ ~ ~

86 MCPA-D AQL USL USL

87 USL USL 60 USL % th 60th 68th 75th 77th 85th 87th 90th AQL USL AQL USL ANOVA analysisaql 4%6.5%10% USL 75th77th85th AQL 5-6 USL 5-23 AQL USL df Mean Square F p-value AQL * USL * AQL*USL Error AQL 71

88 5-6 USL ANOVA AQL p α ANOVA USL p α ANOVA AQL USL , USL AQL AQL , AQL USL USL( ) 72

89 MCPA MCPA MCPA MCPA MCPA MCPA AQL USL MCPA MCPA MCPA , , , , , , , , , , , , , , , , ,725 1,023 34, , , ,602 1,136 40, , , ,225 1,090 42, , ,743 73

90 BNHI MCPA-S / MCPA-D / BNHI MCPA / ,000 1,136, ,000 81, / BNHI-n ,185,050 14,220,600 MCPA-S-n ,348,900 16,186,800 MCPA-D-n ,350 10,156,200 MCPA-D-n 1 +n ,692,700 20,312,400 BNHI MCPA / MCPA-D-n 1 <BNHI-n<MCPA-S-n<MCPA-D-n 1 +n 2 MCPA-S-n MCPA-D-n 1 MCPA-D-n 1 +n 2 MCPA-D-n 1 BNHI-n BNHIMCPA / 74

91 BNHI MCPA-S MCPA-D BNHI MCPA / BNHI-n ,185,050 14,220,600 MCPA-S-n ,100 6,781,200 MCPA-D-n ,100 4,441,200 MCPA-D-n 1 +n ,200 8,882,400 BNHI MCPA MCPA-D-n 1 <MCPA-S-n<MCPA-D-n 1 +n 2 <BNHI-n MCPA-S MCPA-D-n 1 MCPA-D-n 1 +n 2 MCP BNHI MCPA MCPA-S BNHI NT$7,439,400 MCPA-D-n 1 BNHI NT$9,779,400 MCPA-D-n 1 +n 2 BNHI NT5338,

92 BNHI MCPA ,

93 13, moments N Sum Wgts mean Sum Std Dev Variance Skewness Kurtosis USS CSS CV Std mwan Quantiles 100% Max % % Q % % Mid % % Q % % Min % Range % 0 Q3-Q % 0 Mod 0 0.0%

94 MCPA-D MCPA-D 5-1 Pa 1-Pa =(1-Pa)* + Pa*...(1) (Pa) (Pa) (Pa)c d P = n e p() c = p ( a) Ι np' e ( np' ) c! np' c1 = 0 1 c ( np' ) c! c 1 = p ( C = 0) + p( C = 1) p( C = c )

95 [( n c ) n * U c / n L] p i i / i 1 *...(2) 1 p ( a) ΙΙ = p( C1 = c1 + 1) ( p ) + + ( = ) ( ) Ι 0 p1... pc2 c p C ΙΙ 1 r1 1 p Ι 0... pc2 r1 1 ΙΙ [( n c ) ( n + n )* U + c /( n n ) L] p2 j 2 2 j / j *...(3) [() + ( 2) ]/[ P i P ] (4) = j (4)...(5) (4)(5) (1) AQL USL MCPA-D AQL USL AQL USL USL AQL % AQL=10%USL Q % percentile USL= p=0.25 Q Q 3 75% L= Q 3 25% U= AQL=10% =0.25L=0.0476U= =(1-Pa)* + Pa* =( ) * * =

96 Q 3 AQL=10%USL Q % percentile USL= p=0.50 Q Q 2 50% L= Q 2 50% U= AQL=10% =0.50L=0.0181U= =(1-Pa)* + Pa* = 1 * * =

97 Q 2 AQL USL MCPA-D AQL=10%USL= AQL USL 81

98 82

99 1. MCPA MIL-STD-105E 1973 ISO/DIS , MCPA-S MCPA-D 4. MCPA 5. MCPA 83

100 1. AQL 2. USL 3. USL AQL USL AQL 1. α β MCPA 1. 1/ MCPA OC (USL)

101 7-1 MCPA MCPA * 7 * QC * * * 1/15 15 * OC * * * * USL * * * * * $14,220,600 * MCPA-S-n : $ 6,781,200 MCPA-D-n1 : $ 4,441,200 MCPA-D-n1+n2 : $ 8,882,400 * * * MCPA-S MCPA-D MCPA-S MCPA-D MCPA-S MCPA-D MCPA-S MCPA-D * * * * * * * * * * * * * MCPA-D-n1 MCPA-D-n1+n2 * 1 MCPA-S * 2 MCPA-S 85

102 MCPA AQL 10%USL( )0.20 AQL USL MCPA ()

103 AHP MCPA-S MCPA-D 87

104 1. (1988) 2. (1998) 3. (1998) 4. (2001) 5. (2000) 6. (1998) pp (2000) 43(2) (1998) - 6(4) (1997) 30(5) (1997) 30(6) (1998) 31(6) (1997) 30(6) (1998) 31(3) pp3-7 88

105 pp , (1997) 31(1) (1997) 30(5) (1996) 6(10) (2001) 30(6) Lorenz von Stein( ) NSC H (1995) 26. (1993) 27. (1997) 28. (1997) 30(5) (1998) 30. (1996) (1997) 30(5) (1997) 30(5) (1999) 32(6) (1998) 31(5)

106 35. (2001) 36. (1996) 37. (1997) ISO (1998) - 2(1) (1999) ABC 32(6) (1995) 28(2) (2000) 42. (2001) 43. (1998) 90

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108 20. Montgomery DC. Introduction to Statistical Quality Control, 4th ed., Wiley Press; Montgomery, D.C (1991), Introduction to Statistical Quality Control, Wiley. 22. Moskowitz, H. and H. T. Tsai (1988).A One-Sided Double Screening Procedure Using Individual Unit Misclassification Error. Management Science, 34, 9: Moskowitz, H., R. Plante and H.T. Tsai (1993).A Multistage Screening Model for Evaluation and Control of Misclassification Error in the Detection of Hypertension. Management Science, 39: Naohiro Yashiro. Aging of the population in Japan and its implications to the other Asian countries. Journal of Asian Economics 1997; 8(2): OECD(1994), The reform of health care systems: a review of seventeen OECD countries, Paris: OECD. 26. Owen, D.B., D. McIntire and E. Seymour(1975).Tables using one or two screening variables to increase acceptable product under one-sided specification. Journal of Quality Technology, 7, 3: Phelps, C.E.(1997), Health Economics, new York, NY: Addison-Wesley Educational Publishers Inc. 28. Ryan, T.P.(1989), Statistical Methods for Quality Improvement, Wiley. 29. Taylor, H.C. and J.T. Russel(1939).The relationship of validity coefficients to the practical effectiveness of tests in selection: Discussion and tables. Journal of Applied Psychology, 23: Valadez JJ, Brown LD, Vargas WV, Morley D. Using lot quality assurance sampling to assess measurements for growth monitoring in a developing country's primary health care system. Int J Epidemiol1996; 25(2): Wadworth, Stephen and Godfrey(1986), Modern Methods for Quality Control and Improvement, John Wiley. 32. Weber RT. An Easy Approach to Acceptance Sampling: How to Use MIL-Std-105E, 1th ed., American Society for Quality Press;

109 ( )12:00-15: :30~13:00 & 13:00~13:10 13:10~13:20 13:20~13:30 13:30~15:00 15:00~ 1. (1) (2) (3)70%-80%

110 2. (1) (2) (3) (4) (5) (6) (7) 3. (1) / (2) (3) / (4) (5) (6) data mining 4. (1) 20 (2) & 3 (3) (4) (5) 5. (1) 94

111 6. (1) (2) (3) 15 / 2/3 (4)

112

113 :00-14:00 1. MIL-STD-105E MIL-STD-105E 2. MIL-STD-105E MIL-STD-105E 97

114 :00-16:30 1. MIL-STD-105E MIL-STD-105E 2. MIL-STD-105E 98

115 Sample Size Code Letters Special Inspection levels General Inspection levels Lot size N S-1 S-2 S-3 S A A A A A A B 9-15 A A A A A B C A A B B B C D A B B C C D E B B C C C E F B B C D D F G B C D E E G H B C D E F H J C C E F G J K C D E G H K L ,000 C D F G J L M 10,001-35,000 C D F H K M N 35, ,000 D E G J L N P 150, ,000 D E G J M P Q 500,001 and over D E H K N Q R Source: Weber RT. An Easy Approach to Acceptance Sampling: How to Use MIL-Std-105E 99

116 MCPA-S Master table for Normal Inspection Single Sample P(n,Ac,Re) AQLdefects per hundred units Sample Size Code letter Sample Size Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re Ac Re A B C D E F G H J K L M N P Q 1, R 2, = Use first sampling plan below arrow. If sample size equals, or exceeds, lot or batch size, do 100% inspection = Use first sampling plan above arrow. Ac = Acceptance number Re = Rejection number Source: Weber RT. An Easy Approach to Acceptance Sampling: How to Use MIL-Std-105E 100

117 MCPA-D AQL A R A R A R A R A R A R A R A R A R A R A R A R A R A R A R A B C D E F G H J K L M N P Q R First Second First Second First Second First Second First Second First Second First Second First Second First Second First Second First Second First Second First Second First Second First Second (a) (b) A R 1 A R 1 n>n 100% (c) + Source: Weber RT. An Easy Approach to Acceptance Sampling: How to Use MIL-Std-105E 101

118 MCPA-D AQL A R A R A R A R A R A R A R A R A R A R A R A R A R A R A R A B C D E F G H J K L M N P Q R First Second First Second First Second First Second First Second First Second First Second First Second First Second First Second First Second First Second First Second First Second First Second A 2 < d 1 +d 2 < R 2 Source: Weber RT. An Easy Approach to Acceptance Sampling: How to Use MIL-Std-105E 102

119 MCPA-D AQL A R A R A R A R A R A R A R A R A R A R A R A R A R A R A R A B First Second C First Second D First Second E First Second F First Second G First Second H First Second J First Second K First Second L First Second M First Second N First Second P First Second Q First Second R First Second Source: Weber RT. An Easy Approach to Acceptance Sampling: How to Use MIL-Std-105E 103

120 Vol. 5, p63-p72 2. Chin-Feng Lin & Hwai-Hui Fu, 2000 September, Improve hierarchical Value Map to Developing Valuable Marketing Strategies Business Journal for Entrepreneurs, Vol. 2000,Issue3,Section one,pp Chin-Feng Lin & Hwai-Hui Fu, 2001 Exploring logic construction on MECs to enhance marketing strategy Marketing Intelligence and Planning, Vol.19.No.5, pp abi Sigma Vol. 38, No.8, pp PZB Vol. 54, No.1, p207-p

121 6. Hwai-Hui Fu, Hsien-Tang Tsai, Ching-Wei Lin, Duan Wei, 2004 Application of Single Sampling Plan for Auditing Medical Claimed Payments of Taiwan National Health Insurance. Health policy SSCI, 2004/02/25accepted 2001/08/01~2002/07/31 NSC H H 2002/08/01~2003/07/31 NSC H H 2002/08/01~2003/07/31 NSC H /11/01~1992/04/

122 p4-29~p / p 329-p (Core Competence) 11. Hsien-Tang Tsai, Duan Wei,, Hsin-Hung Chen, Hwai-Hui FuJune 24-25, 2004 Applying Cauchy-Schwarz Maximization to Optimal Portfolio Decision with Short Sales Allowed The XIII South Symposium of Statistics. Taipei. 12. Hsien-Tang Tsai, Duan Wei,, Hwai-Hui Fu, Hsin-Hung ChenJune 24-25, 2004 Applying Lot-by-Lot Double Sampling Plan to Taiwan National Health Insurance Auditing System The XIII South Symposium of Statistics. Taipei

892213E006146

892213E006146 Modular Design of Quality Control Systems NSC89-2213-E006-146 89 8 1 90 7 31 ( ) ISO14000, and OHSAS18000. Keywords: ISO9000; ISO14000; OHSAS18000; Quality ISO9000 ISO14000 OHSAS18000 Management; Environment