Using Clinical Practice Analysis to Improve Care

Englert, Jan; Davis, Ken; Koch, Karen

doi:10.1016/s1070-3241(01)27025-4

Cited by 21 publications

(12 citation statements)

References 16 publications

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“…20 Studies like these may put increasing pressure on hospitals and medical centers to manage patients with low-risk CAP as outpatients. 21,22 Our study points out the limitations of using the PSI risk stratification alone to direct medical decisions or to audit medical practice. The PSI did perform well in regards to risk of hospital mortality; however, use of the PSI to decrease low-risk CAP admissions would have had little impact given the pertinent comorbidities and psychosocial characteristics of this patient population.…”

Section: Commentmentioning

confidence: 89%

Cost and Incidence of Social Comorbidities in Low-Risk Patients With Community-Acquired Pneumonia Admitted to a Public Hospital

Goss

Rubenfeld

Park

et al. 2003

Chest

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Section: Commentmentioning

confidence: 89%

Cost and Incidence of Social Comorbidities in Low-Risk Patients With Community-Acquired Pneumonia Admitted to a Public Hospital

Goss

Rubenfeld

Park

et al. 2003

Chest

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“…Finally, highvolume practitioners may simply be better able to anticipate problems, rather than react postoccurrence. The ability to emulate high-volume practice may represent another strategy to help physicians follow practice guidelines [43]. This may be particularly important since most cases of hospitalised pneumonia are cared for by primary-care physicians in community hospitals [44].…”

Section: Discussionmentioning

confidence: 99%

Mortality during hospitalisation for pneumonia in Alberta, Canada, is associated with physician volume

Marrie

Carrière²,

Jin³

et al. 2003

Eur Respir J

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The association of mortality with patient factors (severity of illness, comorbidity), physician factors (specialty training, prehospitalisation visit, in-hospital consultation, volume of patients seen per physician) and healthcare organisation factors (patient-travel distances, regional beds per capita, admitting hospital-bed occupancy, admitting hospital-bed turnover, hospital location, volume of pneumonia cases per hospital) after hospital admission with community-acquired pneumonia was investigated using administrative data from Alberta, Canada from April 1, 1994-March 31, 1999.During the 5-yr study period there were 43,642 pneumonia hospitalisations, with an 11% in-hospital and 26% 1-yr mortality. Patient severity of illness and comorbidity were the strongest predictors of increased mortality. Physicians with the highest inhospital pneumonia patient volume (w27 patients?yr -1 ) cared for patients with greater severity/comorbidity, but with decreased odds of in-hospital mortality, compared with the lowest volume physicians (less than seven patients per year).The effects of internal medicine specialist or subspecialist care were mixed, with a reduction in deaths for the first 72 h and an increase in in-hospital deaths. Prehospitalisation visit by a physician was associated with decreased mortality. Healthcare organisation factors were the least strong predictor of mortality, demonstrating an effect only for 1-yr mortality in those discharged alive from hospital. Admissions to larger volume or metropolitan hospitals were associated with a decrease in mortality.Severity of illness and comorbidity had the strongest association with mortality. The first association of high-volume physician and pre-hospital care with decreased inhospital mortality for community-acquired pneumonia is reported. Eur Respir J 2003; 22: 148-155.

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“…Initially, the clusters were inspected with respect to the potential impact of TTS on LOS. The inpatient LOS is of significant concern in healthcare as a measure of care outcomes (Englert et al 2001). From an operational standpoint, the LOS was considered as a valid proxy to measure the consumption of hospital resources (Faddy and McClean 1999).…”

Section: Learning Insights From Clustersmentioning

confidence: 99%

Designing Care Pathways Using Simulation Modeling and Machine Learning

Elbattah¹,

Molloy²,

Zeigler³

2018

2018 Winter Simulation Conference (WSC)

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The development of care pathways is increasingly becoming an instrumental artefact towards improving the quality of care and cutting costs. This paper presents a framework that incorporates Simulation Modeling along with Machine Learning (ML) for the purpose of designing pathways and evaluating the return on investment of implementation. The study goes through a use case in relation to elderly healthcare in Ireland, with a particular focus on the hip-fracture care scheme. Initially, unsupervised ML is utilized to extract knowledge from the Irish Hip Fracture Database. Data clustering is specifically applied to learn potential insights pertaining to patient characteristics, care-related factors, and outcomes. Subsequently, the data-driven knowledge is utilized within the process of simulation model development. Generally, the framework is conceived to provide a systematic approach for developing healthcare policies that help optimize the quality and cost of care.

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Using Clinical Practice Analysis to Improve Care

Cited by 21 publications

References 16 publications

Cost and Incidence of Social Comorbidities in Low-Risk Patients With Community-Acquired Pneumonia Admitted to a Public Hospital

Cost and Incidence of Social Comorbidities in Low-Risk Patients With Community-Acquired Pneumonia Admitted to a Public Hospital

Mortality during hospitalisation for pneumonia in Alberta, Canada, is associated with physician volume

Designing Care Pathways Using Simulation Modeling and Machine Learning

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