IntroductionReducing long length of stay (LLOS, or inpatient stays lasting over 30 days) is an important way for hospitals to improve cost efficiency, bed availability and health outcomes. Discharge delays can cost hundreds to thousands of dollars per patient, and LLOS represents a burden on bed availability for other potential patients. However, most research studies investigating discharge barriers are not LLOS-specific. Of those that do, nearly all are limited by further patient subpopulation focus or small sample size. To our knowledge, our study is the first to describe LLOS discharge barriers in an entire Department of Medicine.MethodsWe conducted a chart review of 172 LLOS patients in the Department of Medicine at an academic tertiary care hospital and quantified the most frequent causes of delay as well as factors causing the greatest amount of delay time. We also interviewed healthcare staff for their perceptions on barriers to discharge.ResultsDischarge site coordination was the most frequent cause of delay, affecting 56% of patients and accounting for 80% of total non-medical postponement days. Goals of care issues and establishment of follow-up care were the next most frequent contributors to delay.ConclusionTogether with perspectives from interviewed staff, these results highlight multiple different areas of opportunity for reducing LLOS and maximising the care capacity of inpatient hospitals.
BackgroundHypnosis decreases perioperative pain and has opioid-sparing potential but has not been rigorously studied in knee arthroplasty. This trial investigates the impact of perioperative hypnosis on inpatient opioid use following total knee arthroplasty.MethodsThis prospective randomized controlled trial was conducted at a single academic medical center. The hypnosis arm underwent a scripted 10 min hypnosis session prior to surgery and had access to the recorded script. The control arm received hypnosis education only. The primary outcome was opioid use in milligram oral morphine equivalents per 24 hours during hospital admission. A secondary analysis was performed for patients taking opioids preoperatively.Results64 primary knee arthroplasty patients were randomized 1:1 to hypnosis (n=31) versus control (n=33) and included in the intent-to-treat analysis. The mean (SD) postoperative opioid use in oral morphine equivalents per 24 hours was 70.5 (48.4) in the hypnosis versus 90.7 (74.4) in the control arm, a difference that was not statistically significant (difference −20.1; 95% CI −51.8 to 11.4; p=0.20). In the subgroup analysis of the opioid-experienced patients, there was a 54% daily reduction in opioid use in the hypnosis group (82.4 (56.2) vs 179.1 (74.5) difference of −96.7; 95% CI -164.4 to –29.0; p=<0.01), equivalent to sparing 65 mg of oxycodone per day.ConclusionPerioperative hypnosis significantly reduced inpatient opioid use among opioid-experienced patients only. A larger study examining these findings is warranted.Trial registration numberNCT03308071.
With the progression of diabetic retinopathy (DR) from the non-proliferative (NPDR) to proliferative (PDR) stage, the possibility of vision impairment increases significantly. Therefore, it is clinically important to detect the progression to PDR stage for proper intervention. We propose a segmentation-assisted DR classification methodology, that builds on (and improves) current methods by using a fully convolutional network (FCN) to segment retinal neovascularizations (NV) in retinal images prior to image classification. This study utilizes the Kaggle EyePacs dataset, containing retinal photographs from patients with varying degrees of DR (mild, moderate, severe NPDR and PDR. Two graders annotated the NV (a board-certified ophthalmologist and a trained medical student). Segmentation was performed by training an FCN to locate neovascularization on 669 retinal fundus photographs labeled with PDR status according to NV presence. The trained segmentation model was used to locate probable NV in images from the classification dataset. Finally, a CNN was trained to classify the combined images and probability maps into categories of PDR. The mean accuracy of segmentation-assisted classification was 87.71% on the test set (SD = 7.71%). Segmentation-assisted classification of PDR achieved accuracy that was 7.74% better than classification alone. Our study shows that segmentation assistance improves identification of the most severe stage of diabetic retinopathy and has the potential to improve deep learning performance in other imaging problems with limited data availability.
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