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Table 2 Effects of the NTUH-IMM model on monthly-measured outcome variables

From: Impact of integrated medication management program on medication errors in a medical center: an interrupted time series study

Outcome variables

Predictor variables

Intervention Ward

Control Ward

Coefficient of determination

Estimation (SE)

95% CI

P-value

Coefficient of determination

Estimation (SE)

95% CI

P-value

Mean number of ME reports

Intercept

0.90

0.51 (0.07)

(0.36 to 0.65)

< 0.001

0.40

0.51 (0.04)

(0.44 to 0.58)

< 0.001

Time

0.00 (0.00)

(0.00 to 0.01)

0.480

0.00 (0.00)

(0.00 to 0.01)

0.021

Intervention

1.02 (0.16)

(0.70 to 1.34)

< 0.001

−0.15 (0.12)

(−0.38 to 0.08)

0.208

Time after intervention

0.03 (0.02)

(−0.02 to 0.07)

0.231

0.00 (0.01)

(−0.03 to 0.03)

0.871

Mean number of daily IPs

Intercept

0.26

8.56 (0.23)

(8.12 to 9.00)

< 0.001

0.17

9.34 (0.32)

(8.71 to 9.97)

< 0.001

Time

0.03 (0.01)

(0.02 to 0.05)

0.001

0.01 (0.01)

(−0.01 to 0.04)

0.369

Intervention

−0.59 (0.49)

(−1.55 to 0.37)

0.235

−0.03 (0.62)

(−1.26 to 1.19)

0.957

Time after intervention

0.01 (0.06)

(−0.11 to 0.13)

0.862

0.00 (0.08)

(−0.15 to 0.15)

0.984

Mean number of daily SPMs

Intercept

0.32

2.13 (0.12)

(1.90 to 2.37)

< 0.001

0.14

2.13 (0.13)

(1.89 to 2.38)

< 0.001

Time

0.01 (0.01)

(0.00 to 0.02)

0.076

0.01 (0.01)

(0.00 to 0.02)

0.011

Intervention

0.37 (0.26)

(−0.14 to 0.87)

0.164

−0.49 (0.27)

(−1.03 to 0.04)

0.078

Time after intervention

−0.01 (0.03)

(−0.07 to 0.05)

0.680

0.01 (0.03)

(−0.05 to 0.08)

0.758

Median daily medication cost

Intercept

0.27

19.45 (1.36)

(16.78 to 22.12)

< 0.001

0.14

22.85 (1.58)

(19.76 to 25.93)

< 0.001

Time

0.14 (0.06)

(0.03 to 0.26)

0.016

0.08 (0.07)

(− 0.05 to 0.21)

0.228

Intervention

−0.48 (2.96)

(−6.29 to 5.33)

0.871

1.65 (3.42)

(−5.06 to 8.36)

0.632

Time after intervention

0.15 (0.36)

(−0.55 to 0.85)

0.679

0.00 (0.41)

(−0.81 to 0.82)

0.991

  1. SE Standard error, CI Confidence interval, ME Medication error, IPs Inpatient prescriptions, SPMs Self-prepared medications
  2. Segment linear regression was used to model the correlation between the longitudinal outcome variable and independent variables (time, intervention, and time after intervention), and the model was as follows: Yt = b0 + b1*Tt + b2*Xt + b3*XTt. The estimations of time, intervention, and time after intervention indicate the point estimations of b1, b2, and b3, respectively