Impact of a learning health system on acute care and medical complications after intracerebral hemorrhage

Changes in oral health status with dental intervention during the acute to subacute stages of stroke

OBJECTIVES

To investigate the changes in oral health status with dental intervention during the acute and subacute stages of stroke and their associations with oral intake status.

BACKGROUND

Oral health may deteriorate easily in patients following a stroke. However, data are scarce on the changes in oral health with dental intervention throughout the acute and subacute stages of stroke recovery.

MATERIAL AND METHODS

We prospectively recruited 98 stroke patients who were admitted to an acute hospital and referred to a dental team and then could be followed at a subacute rehabilitation unit in the same hospital. Provided dental intervention, including oral health care and other general dental treatments, was recorded. Oral health was assessed with Oral Health Assessment Tool (OHAT), and the changes in OHAT score during the acute and subacute stages were statistically tested. Oral feeding status was examined using Functional Oral Intake Scale (FOIS). The cohort was divided into the non-oral feeding, dysphagia and regular diet groups based on FOIS score. Differences in OHAT among the oral intake groups were statistically tested.

RESULTS

Gross OHAT score did not differ among the oral intake groups at the time of admission (mean ± standard deviation score: 6.5 ± 2.8 for non-oral diet, 5.6 ± 2.4 for dysphagia diet and 5.3 ± 2.1 for regular diet), but improved more in the regular diet group (2.4 ± 1.5) than in the other groups (5.8 ± 3.0 for non-oral diet and 4.0 ± 2.1 for dysphagia diet) at the last evaluation. Oral hygiene scores improved significantly in the acute stage, while scores for dentures and natural teeth ameliorated significantly in the subacute stage.

CONCLUSION

Our findings suggest that appropriate dental intervention in the acute and subacute stages of stroke may contribute to improved oral health and oral food intake.

Development of artificial intelligence prognostic model for surgically resected non-small cell lung cancer

There are great expectations for artificial intelligence (AI) in medicine. We aimed to develop an AI prognostic model for surgically resected non-small cell lung cancer (NSCLC). This study enrolled 1049 patients with pathological stage I-IIIA surgically resected NSCLC at Kyushu University. We set 17 clinicopathological factors and 30 preoperative and 22 postoperative blood test results as explanatory variables. Disease-free survival (DFS), overall survival (OS), and cancer-specific survival (CSS) were set as objective variables. The eXtreme Gradient Boosting (XGBoost) was used as the machine learning algorithm. The median age was 69 (23-89) years, and 605 patients (57.7%) were male. The numbers of patients with pathological stage IA, IB, IIA, IIB, and IIIA were 553 (52.7%), 223 (21.4%), 100 (9.5%), 55 (5.3%), and 118 (11.2%), respectively. The 5-year DFS, OS, and CSS rates were 71.0%, 82.8%, and 88.7%, respectively. Our AI prognostic model showed that the areas under the curve of the receiver operating characteristic curves of DFS, OS, and CSS at 5 years were 0.890, 0.926, and 0.960, respectively. The AI prognostic model using XGBoost showed good prediction accuracy and provided accurate predictive probability of postoperative prognosis of NSCLC.

Stroke Learning Health Systems: A Topical Narrative Review With Case Examples

To our knowledge, the adoption of Learning Health System (LHS) concepts or approaches for improving stroke care, patient outcomes, and value have not previously been summarized. This topical review provides a summary of the published evidence about LHSs applied to stroke, and case examples applied to different aspects of stroke care from high and low-to-middle income countries. Our attempt to systematically identify the relevant literature and obtain real-world examples demonstrated the dissemination gaps, the lack of learning and action for many of the related LHS concepts across the continuum of care but also elucidated the opportunity for continued dialogue on how to study and scale LHS advances. In the field of stroke, we found only a few published examples of LHSs and health systems globally implementing some selected LHS concepts, but the term is not common. A major barrier to identifying relevant LHS examples in stroke may be the lack of an agreed taxonomy or terminology for classification. We acknowledge that health service delivery settings that leverage many of the LHS concepts do so operationally and the lessons learned are not shared in peer-reviewed literature. It is likely that this topical review will further stimulate the stroke community to disseminate related activities and use keywords such as learning health system so that the evidence base can be more readily identified.

A functional learning health system in Japan: Experience with processes and information infrastructure toward continuous health improvement

Introduction and definition of the term Learning Health System (LHS) appears to have occurred initially around 2007. Prior to this and the introduction of electronic health records (EHR), a predecessor could be found in the Clinical Pathways concept as a standard medical care plan and a tool to improve medical quality. Since 1997, Japan's Saiseikai Kumamoto Hospital (SKH) has been studying and implementing Clinical Pathways. In 2010, they implemented EHR, which facilitated the collection of structured data in common templates that aligned with outcome measurements defined through Japan's Society of Clinical Pathways. For each patient at this hospital, variances from the desired outcomes have been recorded, producing volumes of structured data in formats that could readily be aggregated and analyzed. A visualization tool was introduced to display graphs on the home page of the EHR such that each patient can be compared to similar patients. Knowledge learned from patient care is shared regularly through Clinical Pathways meetings that are supported by all staff within the hospital. The SKH experience over the past two decades is worth exploring further in the context of the development of a fully functional LHS and the attributes/characteristics thereof. In this report, the SKH experience and processes are compared with previously published attributes of a fully functional LHS (ie, characteristics of an LHS that can indicate maturity). Specific examples of the SKH system are detailed with respect to leveraging knowledge gained to change performance that improves patient care as prescribed by learning health cycles. The SKH experience and its information infrastructure and culture exemplify a functional LHS, which is now being expanded to additional hospitals with the hope that it can be scaled and serve as a solid platform for measures aimed at improving medical care, thus establishing broader and more global learning health systems.