Dataset Information

The impact of a named GP scheme on continuity of care and emergency hospital admission: a cohort study among older patients in England, 2012-2016.

ABSTRACT: OBJECTIVE:To investigate whether the introduction of a named general practitioner (GP, family physician) improved patients' healthcare for patients aged 75 and over in England. SETTING:Random sample of 27?500 patients aged 65 to 84 in 2012 within 139 English practices from the Clinical Practice Research Datalink linked with Hospital Episode Statistics. DESIGN:Prospective cohort approach, measuring patients' GP consultations and emergency hospital admissions 2?years before/after the intervention. Patients were grouped in (i) aged over 74 and (ii) younger than 75 in both periods in order to compare who were or were not subject to the intervention. Adjusted associations between the named GP scheme, continuity of care and emergency hospital admission were examined using multilevel modelling. INTERVENTION:National Health Service policy to introduce a named accountable GP for patients aged over 74 in April 2014. MAIN OUTCOME MEASURES:(A) Continuity of care index-score, (B) risk of emergency hospital admissions, (C) number of emergency hospital admissions. RESULTS:The intervention was associated with a decrease in continuity index-scores of -0.024 (95% CI -0.030 to -0.018, p<0.001); there were no differences in the decrease between the two age groups (-0.005, 95%?CI -0.014 to 0.005). In the pre-intervention and post-intervention periods, respectively, 15.4% and 19.4% patients had an emergency admission. The probability of an emergency hospital admission increased after the intervention (OR 1.156, 95%?CI 1.064 to 1.257, p=0.001); this increase was bigger for patients over 74 (relative OR 1.191, 95%?CI 1.066 to 1.330, p=0.002). The average number of emergency hospital admissions increased after the intervention (rate ratio (RR) 1.178, 95%?CI 1.103 to 1.259, p<0.001); this increase was greater for patients over 74 (relative RR 1.143, 95%?CI 1.052 to 1.242, p=0.001). CONCLUSION:The introduction of the named GP scheme was not associated with improvements in either continuity of care or rates of unplanned hospitalisation.

SUBMITTER: Tammes P

PROVIDER: S-EPMC6773345 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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The impact of a named GP scheme on continuity of care and emergency hospital admission: a cohort study among older patients in England, 2012-2016.

Tammes Peter P Payne Rupert A RA Salisbury Chris C Chalder Melanie M Purdy Sarah S Morris Richard W RW

BMJ open 20190923 9

<h4>Objective</h4>To investigate whether the introduction of a named general practitioner (GP, family physician) improved patients' healthcare for patients aged 75 and over in England.<h4>Setting</h4>Random sample of 27 500 patients aged 65 to 84 in 2012 within 139 English practices from the Clinical Practice Research Datalink linked with Hospital Episode Statistics.<h4>Design</h4>Prospective cohort approach, measuring patients' GP consultations and emergency hospital admissions 2 years before/a ...[more]

PMID: 31548353

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Project description:ObjectiveTo predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.MethodsThis retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model.ResultsA total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91).ConclusionMachine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.

Dataset Information

The impact of a named GP scheme on continuity of care and emergency hospital admission: a cohort study among older patients in England, 2012-2016.

Publications

The impact of a named GP scheme on continuity of care and emergency hospital admission: a cohort study among older patients in England, 2012-2016.

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