In-hospital care pathways for stroke

Kwan, Joseph; Sandercock, Peter

doi:10.1002/14651858.cd002924.pub2

Cited by 86 publications

(29 citation statements)

References 60 publications

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“…The structural use of a screening instrument in the daily care of stroke patients will promote the early recognition of depression. 5,23 The trend toward decreasing hospital stays 6 complicates proper screening for PSD during hospital stay 4 and emphasizes the need for an easy-to-use prediction model to identify the risk for PSD. Our aim was to develop a prediction model that clinicians can apply in the daily care during the first week after stroke.…”

Section: Discussionmentioning

confidence: 99%

In-Hospital Risk Prediction for Post-stroke Depression

Ginkel

Hafsteinsdøttir²,

Lindeman³

et al. 2013

Stroke

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Background and Purpose— The timely detection of post-stroke depression is complicated by a decreasing length of hospital stay. Therefore, the Post-stroke Depression Prediction Scale was developed and validated. The Post-stroke Depression Prediction Scale is a clinical prediction model for the early identification of stroke patients at increased risk for post-stroke depression. Methods— The study included 410 consecutive stroke patients who were able to communicate adequately. Predictors were collected within the first week after stroke. Between 6 to 8 weeks after stroke, major depressive disorder was diagnosed using the Composite International Diagnostic Interview. Multivariable logistic regression models were fitted. A bootstrap-backward selection process resulted in a reduced model. Performance of the model was expressed by discrimination, calibration, and accuracy. Results— The model included a medical history of depression or other psychiatric disorders, hypertension, angina pectoris, and the Barthel Index item dressing. The model had acceptable discrimination, based on an area under the receiver operating characteristic curve of 0.78 (0.72–0.85), and calibration ( P value of the U- statistic, 0.96). Transforming the model to an easy-to-use risk-assessment table, the lowest risk category (sum score, <−10) showed a 2% risk of depression, which increased to 82% in the highest category (sum score, >21). Conclusions— The clinical prediction model enables clinicians to estimate the degree of the depression risk for an individual patient within the first week after stroke.

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Section: Discussionmentioning

confidence: 99%

In-Hospital Risk Prediction for Post-stroke Depression

Ginkel

Hafsteinsdøttir²,

Lindeman³

et al. 2013

Stroke

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“…Benefits were most apparent in units based on a discrete ward with no systematic increase observed in the length of inpatient stay. The Cochrane Review on in-hospital care pathways for stroke [7] has not found sufficient evidence to recommend implementation of care pathways for acute stroke management or rehabilitation.…”

Section: Discussionmentioning

confidence: 99%

An audit of the impact of implementation of a stroke care pathway in an acute teaching hospital

et al. 2007

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“…More evidence is needed on the impact and the return on investment of this integrated approach to care as well as each of its 3 separate components. A Cochrane review of in-hospital pathways for stroke found evidence to be inconclusive, 13 however, it is encouraging that subsequent reports have demonstrated benefit. 14,15 Although seemingly logical, evidence of the value of HIT 16 and of the benefit of integrating PROs within clinical care is currently limited.…”

Section: Summary Of the Experience Future Directions And Challengesmentioning

confidence: 99%

Electronic Stroke CarePath

Katzan

Fan

Speck

et al. 2015

Circ: Cardiovascular Quality and Outcomes

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Abstract-We describe the development, implementation, and outcomes of the first 2 years of the Electronic Stroke CarePath, an initiative developed for management of ischemic stroke patients in an effort to improve efficiency and quality of care for patients. The CarePath consists of care pathways for ischemic stroke that are integrated within the electronic health record. Patient-reported outcomes are collected using an external software platform. Documentation tools, order sets, and clinical decision support were designed to improve efficiency, optimize process measure adherence, and produce clinical data as a byproduct of care that are available for future analyses. Inpatient mortality and length of stay were compared before and after CarePath implementation in ischemic stroke patients after adjustment for case-mix. Postdischarge functional outcomes of patients with ischemic stroke were compared between the first 3 months of rollout and remainder of the study period. From January 2011 to December 2012, there were 1106 patients with ischemic stroke on the CarePath. There was a decline in inpatient mortality in patients with ischemic stroke, but not in control patients with intracerebral or subarachnoid hemorrhage.Completion rate of patient-reported questionnaires at postdischarge stroke follow-up was 72.9%. There was a trend toward improved functional outcomes at follow-up with CarePath implementation. Implementation of the Electronic Stroke CarePath is feasible and may be associated with a benefit in multiple different outcomes after ischemic stroke. This approach may be an important strategy for optimizing stroke care in the future. Goals and Vision of the ProgramImprovements in efficiency, quality, and coordination of care are essential for healthcare institutions to adapt successfully in an era of decreasing reimbursement. Heart disease and stroke, which are leading causes of morbidity, mortality, and healthcare costs in the United States, are often among the first diseases to be involved in healthcare innovations and policy changes. 1,2 They were among the first to have disease-specific process and outcome measures publically reported by the Center for Medicare and Medicaid services (CMS) and to have society supported quality registries. Regional and national systems of care are more advanced in stroke and myocardial infarction than in most other diseases. Solutions developed to improve delivery of care for patients with heart disease, and stroke can serve as models for the treatment of other conditions.One important strategy to improve the efficiency and quality of healthcare is standardization of care. This is often done by implementing evidence-based protocols that incorporate guidelines into care algorithms. Evidence-based protocols can improve quality by providing guidance on indications for tests or interventions. A related method is the use of care paths, also referred to as critical pathways or care pathways. These are management plans that additionally provide the sequence and timing of actions ...

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In-hospital care pathways for stroke

Abstract: Trusted evidence. Informed decisions. Better health.

Cited by 86 publications

References 60 publications

In-Hospital Risk Prediction for Post-stroke Depression

In-Hospital Risk Prediction for Post-stroke Depression

An audit of the impact of implementation of a stroke care pathway in an acute teaching hospital

Electronic Stroke CarePath

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