| Literature DB >> 35505311 |
S Gallier1, A Topham1, P Nightingale2, M Garrick3, I Woolhouse4, M A Berry5, T Pankhurst6, E Sapey7, S Ball8.
Abstract
BACKGROUND: Venous thromboembolism (VTE) causes significant mortality and morbidity in hospitalised patients. Risk factors for VTE are well known and there are validated risk assessment tools to support the use of prophylactic therapies. In England, reporting the percentage of patients with a completed VTE risk assessment is mandated, but this does not include whether that risk assessment resulted in appropriate prescribing. Full guideline compliance, defined as an assessment which led to an appropriate action-here prescribing prophylactic low molecular weight heparin where indicated, is rarely reported. Education, audit and feedback enhance guideline compliance but electronic prescribing systems (EPS) can mandate guideline-compliant actions. We hypothesised that a systems-based EPS intervention (prescribing rules which mandate approval or rejection of a proposed prescription of prophylactic low molecular weight heparin based on the mandated VTE assessment) would increase full VTE guideline compliance more than interventions which focused on targeting individual prescribers.Entities:
Keywords: Compliance; Deep vein thrombosis; Guidelines; Prescribing errors; Risk assessment
Mesh:
Substances:
Year: 2022 PMID: 35505311 PMCID: PMC9066759 DOI: 10.1186/s12911-022-01865-y
Source DB: PubMed Journal: BMC Med Inform Decis Mak ISSN: 1472-6947 Impact factor: 3.298
Demographics for patient admissions
| Pre- intervention: 2 year run in period for data collection (Jan 2011–Nov 2012) | After intervention 1: introduction of Junior Doctor Clinical Dashboard (Nov 2012–Feb 2014) | After intervention 2: introduction of mandatory VTE assessment and prescribing (Feb 2014–Nov 2015) | After intervention 3: change in order of ‘no reduced mobility’ (Nov 2015–Nov 2020) | |
|---|---|---|---|---|
| n | 31,071 | 26,260 | 39,931 | 137,743 |
| Female, n (%) | 14,740 (47.4%) | 12,675 (48.3%) | 19,135 (47.9%) | 67,340 (49.0%) |
| Age (years) | 74 (59–86) | 73 (57–85) | 72 (57–84) | 68 (53–80) |
Values are counts (and percentages), except for age where they are medians and quartiles
The distribution of prescriber seniority in the four main time intervals
| Time interval | Consultant | Specialty grade | Core medical trainee | Foundation doctor | Staff grade |
|---|---|---|---|---|---|
| Pre-intervention: 2 year run in period for data collection (Jan 2011–Nov 2012) | 19 | 32 | 17 | 29 | 4 |
| After intervention 1: introduction of Junior Doctor Clinical Dashboard (Nov 2012–Feb 2014) | 18 | 33 | 15 | 27 | 7 |
| After intervention 2: introduction of mandatory VTE assessment and prescribing (Feb 2014–Nov 2015) | 17 | 34 | 14 | 27 | 8 |
| After intervention 3: change in order of ‘no reduced mobility’ (Nov 2015–Nov 2020) | 16 | 33 | 11 | 32 | 8 |
Distribution of user type in the four main time intervals (values are percentage frequencies). There were no differences in prescriber seniority over the study period
Fig. 1The proportion of patients who were fully guideline compliant over time. Graph showing the proportion of patients who were fully VTE guideline compliant, meaning they had both a risk assessment and then were either appropriately prescribed VTE prophylaxis or not, depending on that risk assessment. The regression lines are fitted over the time periods before intervention 1 (education/doctor’s dashboard), after intervention 2 (introduction of mandated VTE assessment action), after intervention 3 (change to PICS “no reduced mobility”) and Medication switch 1 (from enoxaparin to tinzaparinand then return to enoxaparin (Medication switch 2) until study end
Observed full VTE guideline compliance over the study period
| Time interval | Length of interval (weeks) | Total number of admissions | Total number with full VTE guideline compliance within 24 h | Compliance (%) |
|---|---|---|---|---|
| Pre-intervention: Run in period for data collection (Jan 2011–Nov 2012) | 95 | 31,071 | 21,809 | 70.2 |
| After intervention 1: Introduction of Junior Doctor Clinical Dashboard (Nov 2012–Feb 2014) | 65 | 26,260 | 20,264 | 77.2 |
| After intervention 2: Introduction of mandatory VTE assessment and prescribing (Feb 2014–Nov 2015) | 89 | 39,931 | 37,801 | 94.7 |
| After intervention 3: Change in order of ‘no reduced mobility’ (Oct 2015–Sept 2017) | 100 | 49,931 | 46,028 | 92.2 |
| After medication change from enoxaparin to tinzaparin (Oct 2017–Mar 2019) | 81 | 45,092 | 41,583 | 92.2 |
| After medication change back to enoxaparin to study end (Mar 2019–Nov 2020) | 83 | 42,234 | 38,955 | 92.2 |
Full VTE compliance is where a VTE risk assessment was completed and the correct action was taken. To be fully compliant, both VTE risk assessment and the correct action is needed. For example, non-compliance would be where a risk assessment was not completed, or a VTE assessment suggested LMWH was required and it was not prescribed or a VTE assessment suggested LMWH was not required (or contraindicated) and it was prescribed
Estimated compliance from the segmented linear regression model and equivalent rates of change immediately before and after each intervention and medication switch
| Intervention | Estimated compliance | Equivalent rate of change in compliance per 52 weeks | ||||
|---|---|---|---|---|---|---|
| Before | After | Before | After | |||
| 1. Intervention 1. Introduction of a Junior doctor dashboard | 73.2 | 70.7 | 0.035 | 3.4 | 9.8 | < 0.001 |
| 2. Intervention 2. Introduction of mandatory VTE assessment and prescribing | 82.9 | 95.4 | < 0.001 | 9.8 | − 0.7 | < 0.001 |
| 3. Intervention 3. Change in order of ‘no reduced mobility’ | 94.2 | 93.2 | 0.010 (0.464) | − 0.7 | − 1.0 | N/A (0.085) |
| Medication switches | ||||||
| 1. Enoxaparin to Tinzaparin | 91.4 | 91.2 | < 0.001 | − 1.0 | 1.4 | < 0.001 |
| 2. Tinzaparin back to Enoxaparin | 93.3 | 92.5 | 0.023 | 1.4 | 0.0 | 0.011 |
Figures in parentheses for intervention 3 are p values if a slope change for intervention 3 is included in the model. As this was not significant, it was excluded and as a result the step change for intervention 3 became significant (the p value changing from 0.464 to 0.010). The final model includes five step changes and four slope changes