Utilization of the MOXXI System by Complexity of Patient Drug Treatment and Fragmentation of Care
| Patient Characteristics | No. (%) | Drug Profile Utilization Rate | e-Rx Utilization Rate |
| Complexity of drug treatment | |||
| 1 drug/day | 628 (15.0%) | 5.9 | 27.0 |
| 2–4 drugs/day | 1,757 (41.9%) | 9.5 | 33.4 |
| >4 drugs/day | 1,811 (43.1%) | 12.2 | 32.1 |
| Relative rate per additional medication [95% CI] (p-value) | 1.14 [1.06–1.21] (p = 0.001) | 1.01 [0.99–1.10] (p = 0.54) | |
| Fragmentation of care | |||
| No. of prescribing MDs | |||
| 1 | 838 (20.0%) | 10.7 | 37.3 |
| 2–3 | 1,007 (24.0%) | 9.8 | 34.4 |
| 4–5 | 1,405 (33.5%) | 10.2 | 30.9 |
| >5 | 946 (22.5%) | 11.4 | 28.2 |
| Relative rate per additional physician [95% CI] (p-value) | 1.09 [1.04–1.15] (p = 0.007) | 0.99 [0.96–1.02] (p = 0.60) | |
| No. of emergency department visits | |||
| 0 | 3,366 (80.2%) | 9.9 | 32.9 |
| 1 | 347 (8.3%) | 11.8 | 29.3 |
| 2 | 245 (5.8%) | 11.9 | 31.1 |
| 3–4 | 110 (2.6%) | 13.2 | 29.3 |
| >4 | 128 (3.1%) | 13.1 | 26.8 |
| Relative rate per additional emergency department visit [95% CI] (p-value) | 1.04 [1.01–1.09] (p = 0.006) | 1.0 [0.98–1.03] (p = 0.90) | |
| Continuity of care | |||
| >68% visits to study MD | 1,039 (24.8%) | 8.9 | 32.6 |
| 51%–67% visits to study MD | 1,056 (25.2%) | 9.5 | 31.5 |
| 34%–50% visits to study MD | 1,043 (24.8%) | 9.9 | 30.8 |
| ≤33% visits to study MD | 1,058 (25.2%) | 15.0 | 34.0 |
| Relative rate per 50% decrease in continuity [95% CI] (p-value) | 1.48 [1.07–2.04] (p = 0.02) | 1.0 [0.95–1.05] (p = 0.96) |
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Relative rates were estimated by Poisson regression models within a generalized estimating equation (GEE) framework. Models were estimated separately for each attribute of fragmented care and drug use, adjusted for age and gender in relation to drug profile use, and electronic prescribing (e-Rx) use. The mean age of patients was 68.2 (standard deviation 15.4), and 60.8% were female. For number of drugs used per day, adjusted estimates by age were not possible as age is collinear with drug use; therefore, estimates presented are adjusted for patient gender only.
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The estimated working correlation for patients clustered within physicians was r = 0.26 for drug profile use and r = 0.59 for e-Rx use. GEE estimates of the corrected standard error could not be obtained for the model of the e-Rx and number of medications association because of a lack of convergence; thus, uncorrected standard errors and p-values are presented.
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e-Rx = electronic prescribing; CI = confidence interval; MD = physician.
