| Literature DB >> 29734350 |
Jie Bai1, Fei Bai2, Hongbo Zhu3, Di Xue1.
Abstract
INTRODUCTION: Substantial resources have been expended on clinical pathways (CPs), but the reported effects of CPs on medical care vary considerably. This study sought to determine the effects of CPs on medical care in Chinese hospitals, including the perceived effects of CPs on medical care and the objectively measured patient outcomes.Entities:
Mesh:
Year: 2018 PMID: 29734350 PMCID: PMC5937784 DOI: 10.1371/journal.pone.0196776
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Perceived effects of CPs on medical care (at the hospital and individual levels) .
| Hospital level | Individual level | Individual level | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Managers | Physicians | |||||||||||
| Effects | Mean | Std | Mean | Std | t value | Mean | Std | Mean | Std | t value | ||
| Increase -in medical effectiveness | 3.69 | 0.71 | 3.66 | 0.89 | 0.24 | 3.99 | 0.71 | 3.60 | 0.91 | 8.58 | ||
| Increase in patient safety | 3.78 | 0.70 | 3.70 | 0.87 | 0.69 | 4.04 | 0.69 | 3.63 | 0.89 | 9.07 | ||
| Reduction in readmission of inpatients within 30 days | 3.35 | 0.74 | 3.55 | 0.86 | -1.61 | 3.82 | 0.78 | 3.50 | 0.87 | 6.65 | ||
| Increase in patient satisfaction | 3.57 | 0.85 | 3.59 | 0.90 | -0.17 | 3.87 | 0.78 | 3.54 | 0.92 | 6.75 | ||
| Increase in standard medical care | 3.92 | 0.66 | 3.78 | 0.82 | 1.46 | 4.06 | 0.67 | 3.73 | 0.84 | 7.61 | ||
| Reduction in variations in medical care | 3.88 | 0.65 | 3.67 | 0.87 | 2.23 | * | 4.00 | 0.70 | 3.61 | 0.88 | 8.64 | |
| Facilitation of appropriate use of antibiotics | 3.88 | 0.59 | 3.82 | 0.78 | 0.72 | 4.08 | 0.68 | 3.77 | 0.78 | 7.19 | ||
| Improvement in documentation | 3.69 | 0.71 | 3.79 | 0.82 | -1.01 | 4.05 | 0.69 | 3.74 | 0.83 | 7.22 | ||
| Increase in employee satisfaction | 3.29 | 0.81 | 3.44 | 0.93 | -1.11 | 3.74 | 0.82 | 3.38 | 0.94 | 6.92 | ||
| Reduction in average LOS | 3.82 | 0.62 | 3.70 | 0.88 | 1.35 | 4.03 | 0.68 | 3.64 | 0.90 | 8.87 | ||
| Reduction in medical cost | 3.49 | 0.70 | 3.61 | 0.87 | -0.99 | 3.88 | 0.75 | 3.56 | 0.88 | 6.68 | ||
| Overall positive effects on medical care | 3.57 | 0.64 | 3.66 | 0.86 | -1.03 | 3.95 | 0.70 | 3.61 | 0.88 | 7.57 | ||
† Full score = 5
‡ standard deviation
*** P<0.001.
Logistic models for physician-perceived effects of CPs on medical care.
| Parameters | Increased medical effectiveness | Reduced LOS | Reduced | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| β | SE | χ2 Wald | β | SE | χ2 Wald | β | SE | χ2 Wald | |||
| Intercept | 0.28 | 0.30 | 0.86 | 0.32 | 0.31 | 1.06 | 0.04 | 0.30 | 0.02 | ||
| Shanghai | 0.10 | 0.11 | 0.74 | 0.02 | 0.11 | 0.03 | 0.02 | 0.11 | 0.05 | ||
| Gansu Province | 0.25 | 0.14 | 3.11 | 0.56 | 0.15 | 14.07 | 0.46 | 0.14 | 10.65 | ||
| Tertiary hospitals | 0.15 | 0.11 | 1.85 | 0.07 | 0.11 | 0.41 | 0.05 | 0.11 | 0.26 | ||
| Physicians who implemented CPs (1: yes, 0: no) | -0.11 | 0.10 | 1.30 | 0.07 | 0.10 | 0.43 | 0.09 | 0.10 | 0.87 | ||
| χ2 likelihood | 8.48 | 22.69 | 21.32 | ||||||||
† Logistic models were used, controlling for physician characteristics (gender, age, and education). If the original dependent variables were greater than their corresponding median, then the dependent variables in the model were coded as “1”; otherwise, they were coded as “0”.
** P<0.01
*** P<0.001
Logistic models for the objectively measured effects of CPs at the hospital level.
| Cured or improved rate (%) | Average LOS | Average inpatient cost | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Parameters | β | SE | χ2 Wald | β | SE | χ2 Wald | β | SE | χ2 Wald | |
| Intercept | -0.30 | 0.67 | 0.21 | 0.38 | 0.67 | 0.32 | -1.33 | 0.93 | 2.04 | |
| Shanghai | -1.29 | 0.75 | 2.97 | -0.73 | 0.70 | 1.06 | 4.00 | 1.25 | 10.19 | |
| Gansu Province | 1.05 | 0.72 | 2.12 | -1.20 | 0.72 | 2.75 | -0.91 | 0.97 | 0.88 | |
| Tertiary hospitals | -0.66 | 0.77 | 0.73 | 0.94 | 0.74 | 1.63 | 3.41 | 1.13 | 9.11 | |
| No. of implemented CPs | 0.01 | 0.01 | 0.89 | 0.001 | 0.01 | 0.004 | -0.01 | 0.02 | 0.17 | |
| χ2 likelihood | 10.57 | 5.41 | 37.06 | |||||||
† Logistic models were used for the effects of CPs on medical care at the hospital level. If the original dependent variables were greater than their corresponding median, then the dependent variables in the model were coded as “1”; otherwise, they were coded as “0”.
* P<0.05
** P<0.01
*** P<0.001
Multivariate models for the objectively measured effects of CPs at the case level.
| Effectiveness | LOS | Inpatient cost | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Parameters | β | χ2 Wald | β | t value | β | t value | ||||
| Pneumonia | ||||||||||
| Intercept 2 | -8.422 | 26.62 | - | - | - | - | ||||
| Intercept 1 | -1.702 | 1.25 | 11.462 | 6.56 | 3.411 | 31.16 | ||||
| Sex (1: male, 0: female) | -0.130 | 0.18 | 1.147 | 3.31 | 0.054 | 2.93 | ** | |||
| Age (years) | -0.005 | 0.29 | 0.008 | 0.89 | 0.002 | 4.85 | ||||
| Medical insurance (1: yes, 0: no) | 0.127 | 0.01 | 0.939 | 0.87 | 0.067 | 0.87 | ||||
| Compliance rate | 4.380 | 7.45 | -6.194 | -3.43 | -0.267 | -2.77 | ||||
| Model fit | 479.94 | 4.23 | 23.39 | |||||||
| AMI | ||||||||||
| Intercept 2 | -0.920 | 0.61 | - | - | - | - | ||||
| Intercept 1 | 3.878 | 10.68 | 14.407 | 4.72 | 4.099 | 25.64 | ||||
| Sex (1: male, 0: female) | -0.077 | 0.08 | -0.892 | -1.25 | 0.045 | 1.18 | ||||
| Age (years) | -0.014 | 2.02 | 0.055 | 2.13 | -0.002 | -1.06 | ||||
| Medical insurance (1: yes, 0: no) | 0.268 | 0.74 | 0.646 | 0.77 | 0.100 | 2.15 | ||||
| Compliance rate | -3.071 | 11.61 | -14.186 | -6.28 | -0.805 | -6.77 | ||||
| Model fit | 345.38 | 5.07 | 32.78 | |||||||
| Heart failure | ||||||||||
| Intercept 2 | -2.342 | 4.16 | - | - | - | - | ||||
| Intercept 1 | 2.339 | 4.20 | 15.405 | 3.55 | 3.590 | 23.3 | ||||
| Sex (1: male, 0: female) | 0.069 | 0.08 | -0.095 | -0.10 | -0.026 | -0.76 | ||||
| Age (years) | -0.003 | 0.08 | 0.049 | 1.37 | 0.001 | 0.82 | ||||
| Medical insurance (1: yes, 0: no) | 0.024 | 0.004 | 2.617 | 1.78 | 0.163 | 3.12 | ||||
| Compliance rate | -0.484 | 0.21 | -16.393 | -4.11 | -0.352 | -2.46 | ||||
| Model fit | 224.92 | 5.51 | 14.45 | |||||||
| C-Section | ||||||||||
| Intercept 2 | 3.198 | 3.09 | - | - | - | - | ||||
| Intercept 1 | 4.072 | 4.95 | 11.535 | 1.15 | 3.600 | 67.99 | ||||
| Age (years) | -0.030 | 0.47 | 0.047 | 0.03 | 0.004 | 3.04 | ||||
| Medical insurance (1: yes, 0: no) | -0.396 | 0.69 | -0.334 | 0.27 | -0.053 | -4.18 | ||||
| Compliance rate | -1.392 | 0.41 | -11.071 | 1.42 | -0.175 | -2.66 | ||||
| Model fit | 323.37 | 5.32 | 92.29 | |||||||
| Cholecystectomy | ||||||||||
| Intercept 2 | -4.113 | 2.26 | - | - | - | - | ||||
| Intercept 1 | 0.337 | 0.02 | 26.471 | 10.8 | 4.138 | 56.88 | ||||
| Sex (1: male, 0: female) | 0.596 | 0.94 | -0.787 | -1.76 | -0.004 | -0.34 | ||||
| Age (years) | 0.023 | 1.37 | 0.038 | 2.31 | 0.001 | 2.58 | ||||
| Medical insurance (1: yes, 0: no) | 0.729 | 0.70 | 0.688 | 1.07 | 0.026 | 1.29 | ||||
| Compliance rate | 6.130 | 3.54 | -27.141 | -10.41 | -0.574 | -7.42 | ||||
| Model fit | 388.21 | 14.38 | 60.13 | |||||||
† Hospitals were controlled as fixed effects
‡ logistic models were used, and model fitness was tested using likelihood χ2
§ linear regression models were used, and model fitness was tested by the F value
※the dependent variable (inpatient cost) was log10-transformed in the models
# percentage of KPIs that complied with national CPs
* P<0.05
** P<0.01
*** P<0.001