Unexpected Increase in Benzodiazepine Prescriptions Related to the Introduction of an Electronic Prescribing Tool: Evidence from Multicenter Hospital Data
Abstract
:1. Background
2. Methods
2.1. Setting, Study Population, and Design
2.2. Data Analysis
3. Results
3.1. Descriptive and Interrupted Time Series Analyses
3.2. Fixed-Effects Regression Models
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
Abbreviations
EOC | Ente Ospedaliero Cantonale |
BZDs | benzodiazepines |
e-prescribing | electronic prescribing systems |
FE | fixed effect |
References
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Before e-Prescribing Implementation | |||||
Hospital A | Hospital B | Hospital C | Hospital D | Hospital E | |
Admissions, n | 3947 | 3206 | 2654 | 4632 | 5758 |
Age, median IQR | 76 (62–84) | 77 (67−85) | 73 (59−82) | 77 (65-84) | 75 (61-83) |
Age groups, n (%) (admissions) | |||||
<70 years | 1392 (35.3) | 910 (28.4) | 1105 (41.6) | 1506 (32.5) | 2180 (37.9) |
≥70 years | 2555 (64.7) | 2296 (71.6) | 1549 (58.4) | 3126 (67.5) | 3578 (62.1) |
Gender, females (%) | 50.6 | 56.7 | 47.7 | 48.2 | 50.7 |
Case-mix (median, Q1−Q3) | 0.72 (0.53−0.93) | 0.79 (0.59−1.00) | 0.67 (0.50−0.92) | 0.71 (0−52−0.93) | 0.67 (0.48−0.92) |
BZD at admission, n (%) | 32.6 | 29.2 | 31.3 | 30.3 | 29.4 |
New BZD prescriptions, % | 3.8 | 5.7 | 5.7 | 3.6 | 3.3 |
After e-Prescribing Implementation | |||||
Admissions, n | 4182 | 1937 | 7245 | 4733 | 4926 |
Age, median IQR | 78 (67−85) | 80 (69−86) | 75 (62−83) | 77 (66−84) | 76 (63−84) |
Age groups, (admissions) | |||||
<70, y, n (%) | 1235 (29.5) | 489 (25.2) | 2704 (37.3) | 1461 (30.9) | 1745 (35.4) |
≥70, y, n (%) | 2947 (70.5) | 1448 (74.8) | 4541 (62.7) | 3272 (69.1) | 3181 (64.6) |
Gender, females (%) | 51.2 | 56.9 | 47.54 | 49.4 | 50.0 |
Case-mix (median, Q1−Q3) | 0.71 (0.52−0.96) | 0.75 (0.54−1.04) | 0.74 (0.51−1.01) | 0.72 (0.51−0.99) | 0.65 (0.48−0.90) |
BZD at admission, (%) | 33.9 | 30.5 | 31.8 | 30.9 | 28.6 |
New BZD prescriptions, (%) | 3.4 | 7.3 | 5.3 | 2.9 | 3.2 |
Hospital A | β Coefficient | Standard Error | p-Value |
---|---|---|---|
Baseline level (β0) | 0.005 | 0.007 | 0.495 |
Baseline trend of BZD prescriptions before e-prescribing (β1) | −0.002 | 0.004 | <0.001 * |
Change in level at the implementation (β2) | 0.028 | 0.009 | <0.001 * |
Trend change after the implementation (β3) | 0.002 | 0.0005 | <0.001 * |
Hospital B | |||
Baseline level (β0) | 0.047 | 0.005 | <0.001 * |
Baseline trend of BZD prescriptions before e-prescribing (β1) | −0.0008 | 0.026 * | |
Change in level at the implementation (β2) | −0.0389 | 0.0215 | 0.076 |
Trend change after the implementation (β3) | 0.004 | 0.001 | <0.001 * |
Hospital C | |||
Baseline level (β0) | 0.097 | 0.024 | <0.001 * |
Baseline trend of BZD prescriptions before e-prescribing (β1) | 0.001 | 0.001 | 0.096 |
Change in level at the implementation (β2) | −0.038 | 0.025 | 0.017 * |
Trend change after the implementation (β3) | −0.002 | 0.001 | 0.028 * |
Hospital D | |||
Baseline level (β0) | 0.029 | 0.005 | <0.001 * |
Baseline trend of BZD prescriptions before e-prescribing (β1) | −0.0004 | 0.0003 | 0.204 |
Change in level at the implementation (β2) | 0.007 | 0.007 | 0.336 |
Trend change after the implementation (β3) | −0.000025 | 0.0004 | 0.958 |
Hospital E | |||
Baseline level (β0) | 0.037 | 0.004 | <0.001 * |
Baseline trend of BZD prescriptions before e-prescribing (β1) | 0.0002 | 0.0002 | 0.316 |
Change in level at the implementation (β2) | −0.0008 | 0.007 | 0.902 |
Trend change after the implementation (β3) | −0.0005 | 0.0004 | 0.206 |
Fixed Effect Model 1 | ||||||||||
Entire sample | <70 years | ≥70 years | Males | Females | ||||||
Estimate (SE) | p-value | Estimate (SE) | p-value | Estimate (SE) | p-value | Estimate (SE) | p-value | Estimate (SE) | p-value | |
Effect of e-prescribing on new BZD prescriptions | 0.015 (0.005) | <0.001 * | 0.007 (0.010) | 0.459 | 0.016 (0.006) | 0.010 * | 0.023 (0.007) | <0.001 * | 0.018 (0.007) | 0.010 * |
Intercept | 0.035 | 0.056 | 0.027 | 0.035 | 0.031 | |||||
R2 | 0.0279 | 0.002 | 0.028 | 0.0405 | 0.0297 | |||||
Fixed Effect Model 2 | ||||||||||
Entire sample | <70 years | ≥70 years | Males | Females | ||||||
Estimate (SE) | p-value | Estimate (SE) | p-value | Estimate (SE) | p-value | Estimate (SE) | p-value | Estimate (SE) | p-value | |
Effect of e-prescribing on new BZD prescriptions | 0.014 (0.008) | 0.007 | 0.089 (0.014) | 0.527 | 0.028 (0.007) | <0.001 * | 0.020 (0.009) | 0.035 * | 0.004 (0.009) | 0.891 |
Case mix × new BZD prescriptions | −0.004 (0.005) | 0.363 | −0.008 (0.006) | 0.202 | 0.003 (0.004) | 0.477 | −0.003 (0.004) | 0.544 | −0.008 (0.004) | 0.006 |
e-prescribing on New BZD prescriptions × case mix | 0.006 (0.005) | 0.251 | 0.006 (0.008) | 0.448 | −0.003 (0.004) | −0.002 (0.005) | 0.978 | 0.008 (0.005) | 0.063 | |
Intercept | 0.038 | 0.066 | 0.022 | 0.035 | 0.045 | |||||
R2 | 0.068 | 0.021 | 0.082 | 0.036 | 0.031 |
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Del Giorno, R.; Schneiders, C.; Stefanelli, K.; Ceschi, A.; Gyoerik-Lora, S.; Aletto, I.; Gabutti, L. Unexpected Increase in Benzodiazepine Prescriptions Related to the Introduction of an Electronic Prescribing Tool: Evidence from Multicenter Hospital Data. Diagnostics 2019, 9, 190. https://doi.org/10.3390/diagnostics9040190
Del Giorno R, Schneiders C, Stefanelli K, Ceschi A, Gyoerik-Lora S, Aletto I, Gabutti L. Unexpected Increase in Benzodiazepine Prescriptions Related to the Introduction of an Electronic Prescribing Tool: Evidence from Multicenter Hospital Data. Diagnostics. 2019; 9(4):190. https://doi.org/10.3390/diagnostics9040190
Chicago/Turabian StyleDel Giorno, Rosaria, Carmen Schneiders, Kevyn Stefanelli, Alessandro Ceschi, Sandor Gyoerik-Lora, Irene Aletto, and Luca Gabutti. 2019. "Unexpected Increase in Benzodiazepine Prescriptions Related to the Introduction of an Electronic Prescribing Tool: Evidence from Multicenter Hospital Data" Diagnostics 9, no. 4: 190. https://doi.org/10.3390/diagnostics9040190