Dataset Information

A Simple Bedside Screening Tool for Spasticity Referral.

ABSTRACT:

Background and objectives

Spasticity is common in long-term care facilities; however, this often-disabling condition is largely underdiagnosed in this setting and therefore left untreated. This study aimed to test the ability of a three-question flowchart used at the bedside by primary care providers in the long-term care setting to identify residents in need of referral to a specialist for spasticity consultation.

Methods

All residents of a single long-term care facility were approached for participation in this cross-sectional, observational study. Spasticity diagnostic evaluations by a movement disorders specialist neurologist (reference standard) were compared with referral determinations made by two primary care providers [a primary care physician (PCP) and a nurse practitioner (NP)] using the simple flowchart.

Results

The analysis included 49 residents (80% male, age 78.2±9.0 years) who were evaluated by the reference standard neurologist and at least one primary care provider. The bedside referral tool demonstrated high sensitivity and moderate specificity when used by the PCP (92% and 78%, respectively; AUC=0.84) and NP (80% and 53%, respectively; AUC=0.67).

Conclusion

This simple tool may be useful for primary care providers to identify residents to be referred to a specialist for evaluation and treatment of spasticity. These results warrant further investigation of the potential utility of this screening tool across multiple long-term care facilities and various types of care providers.

SUBMITTER: Hacker ML

PROVIDER: S-EPMC7234972 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Simple Bedside Screening Tool for Spasticity Referral.

Hacker Mallory L ML Ploucher Shelby S Naik Aaditi G AG Turchan Maxim M Meystedt Jacqueline C JC Harper Kelly K Hedera Peter P Pirtle Claude J CJ Stubblefield Kassandra K Charles David D

Clinical interventions in aging 20200513

<h4>Background and objectives</h4>Spasticity is common in long-term care facilities; however, this often-disabling condition is largely underdiagnosed in this setting and therefore left untreated. This study aimed to test the ability of a three-question flowchart used at the bedside by primary care providers in the long-term care setting to identify residents in need of referral to a specialist for spasticity consultation.<h4>Methods</h4>All residents of a single long-term care facility were app ...[more]

PMID: 32523335

Similar Datasets

Project description:The Melillo et al. article: Reliability and validity of the Veterans Administration Mobility Screening and Solutions Tool (MSST) (Melillo et al, BMC Health Serv Res 22:1323, 2022) introduces the MSST, a derivative flowchart tool in which the Banner Bedside Mobility Assessment Tool (BMAT) is embedded. The authors identify similar goals for the MSST as those for the BMAT, i.e. determine real-time mobility status and choose safe patient handling and mobility (SPHM) equipment to complete transfer and mobility tasks. Further Melillo et al. indicate that goals included "revising and enhancing" and addressing "gaps" in the BMAT, as well as creating instructions for the four BMAT levels (sit and shake, stretch and point, stand, step: march-in-place, advance step and return) that are clear, understandable and comfortable to use. The authors indicate that another goal of the VA MSST is it "enables any healthcare worker at any time to accurately measure and communicate patient mobility and transfer equipment needs." The present article addresses inaccuracies observed in the Melillo et al. article and confusing elements found in the VA MSST flowchart. The present article is based on the author's involvement in developing the BMAT starting in 2003 along with teams from Banner Health and Liko/Hill-Rom which incorporated use of the BMAT by unlicensed assistive personnel (UAP) as a recheck or verification of earlier testing. The 2020 BMAT 2.0 clarified confusing elements identified in BMAT 1.0, i.e. items that needed to be revised or enhanced. BMAT 2.0 covered use of SPHM when testing, when completing care tasks and when progressing patients. The BMAT is primarily a nurse-driven mobility assessment tool created to not be overly prescriptive. BMAT was validated and determined to be reliable based on years of clinical utility studies and research in various settings across the continuum of care. It continues to be supported by a large medical equipment manufacturer who maintains the copyright license. Although the VA MSST flowchart has the four BMAT assessment levels embedded within it, for reasons outlined in this article, it is not feasible to use the VA MSST flowchart alongside or in conjunction with the BMAT 2.0.

Project description:Cognitive screening is often a first step to document cognitive status of patients suspected having Alzheimer's disease (AD). Unfortunately, screening neuropsychological tests are often insensitivity in the detection. The goal of this study was to develop a simple and sensitive screening neuropsychological test to facilitate early detection of AD. This study recruited 761 elderly individuals suspected of having AD and presenting various cognitive statuses (mean age: 77.69 ± 8.45 years; proportion of females: 65%; cognitively unimpaired, CU, n = 133; mild cognitive impairment, MCI, n = 231; dementia of Alzheimer's type, DAT, n = 397). This study developed a novel screening neuropsychological test incorporating assessments of the core memory deficits typical of early AD and an interview on memory function with an informant. The proposed History-based Artificial Intelligence-Show Chwan Assessment of Cognition (HAI-SAC) was assessed in terms of psychometric properties, test time, and discriminative ability. The results were compared with those obtained using other common screening tests, including Cognitive Abilities Screening Instrument (CASI), Montreal Cognitive Assessment (MoCA), and an extracted Mini-Mental State Examination score from CASI. HAI-SAC demonstrated acceptable internal consistency. Factor analysis revealed two factors: memory (semantic and contextual) and cognition-related information from informants. The assessment performance of HAI-SAC was strongly correlated with that of the common screening neuropsychological tests addressed in this study. HAI-SAC outperformed the other tests in differentiating CU individuals from patients with MCI (sensitivity: 0.87; specificity: 0.58; area under the curve [AUC]: 0.78) or DAT (sensitivity: 0.99; specificity: 0.89; AUC: 0.98). Performance of HAI-SAC on differentiating MCI from DAT was on par with performances of other tests (sensitivity: 0.78; specificity: 0.84; AUC: 0.87), while the test time was less than one quarter that of CASI and half that of MoCA. HAI-SAC is psychometrically sound, cost-effective, and sensitive in discriminating the cognitive status of AD.

Project description:BackgroundApproximately 14 million individuals in the United States are eligible for lung cancer screening (LCS), but only 5.8% completed screening in 2021. Given the low uptake despite the potential great health benefit of LCS, interventions aimed at increasing uptake are warranted. The use of a patient-facing electronic health record (EHR) patient portal direct messaging tool offers a new opportunity to both engage eligible patients in preventative screening and provide a unique referral pathway for tobacco treatment.ObjectiveThis study sought to develop and pilot an EHR patient-facing self-referral tool for an established LCS program in an academic medical center.MethodsGuided by constructs of the Health Belief Model associated with LCS uptake (eg, knowledge and self-efficacy), formative development of an EHR-delivered engagement message, infographic, and self-referring survey was conducted. The survey submits eligible self-reported patient information to a scheduler for the LCS program. The materials were pretested using an interviewer-administered mixed methods survey captured through venue-day-time sampling in 5 network-affiliated pulmonology clinics. Materials were then integrated into the secure patient messaging feature in the EHR system. Next, a one-group posttest quality improvement pilot test was conducted.ResultsA total of 17 individuals presenting for lung screening shared-decision visits completed the pretest survey. More than half were newly referred for LCS (n=10, 60%), and the remaining were returning patients. When asked if they would use a self-referring tool through their EHR messaging portal, 94% (n=16) reported yes. In it, 15 participants provided oral feedback that led to refinement in the tool and infographic prior to pilot-testing. When the initial application of the tool was sent to a convenience sample of 150 random patients, 13% (n=20) opened the self-referring survey. Of the 20 who completed the pilot survey, 45% (n=9) were eligible for LCS based on self-reported smoking data. A total of 3 self-referring individuals scheduled an LCS.ConclusionsPretest and initial application data suggest this tool is a positive stimulus to trigger the decision-making process to engage in a self-referral process to LCS among eligible patients. This self-referral tool may increase the number of patients engaging in LCS and could also be used to aid in self-referral to other preventative health screenings. This tool has implications for clinical practice. Tobacco treatment clinical services or health care systems should consider using EHR messaging for LCS self-referral. This approach may be cost-effective to improve LCS engagement and uptake. Additional referral pathways could be built into this EHR tool to not only refer patients who currently smoke to LCS but also simultaneously trigger a referral to clinical tobacco treatment.

Project description:This project was done at specialist cancer hospital in Qatar. At a haematology-oncology inpatient department most patients were not getting access to palliative care unless they were at the very end stages of life. Data collected from 2008-2011 showed significant numbers of patients were dying within one month of their transfer to palliative care. There was no standard measure to identify the prospective palliative care patients. A multidisciplinary team developed a Palliative care referral screening tool based on the National Cancer Care Network guideline. Retrospective medical record review done from January to April 2012 showed a mean of 68% of patients who scored more than five were not consulted, 32% of patients who scored more than seven were not transferred to palliative care and seven percent died without any referral. The team used various kinds of quality planning, analysis and improvement tools in the form of process mapping, value analysis, Fish Bone diagrams, stakeholders' analysis and communication, physician survey, "Pareto's principal" (80 / 20 rule, the law of vital few) and other data collection tools. The palliative care referral process was standardised by preparing and implementing an objective scoring tool based on international best practice. It changed the referral culture and helped manage the psychological barriers of patients, families and caregivers. Extensive orientation and education of all key stakeholders was implemented. Monthly auditing of patient records was carried out. The aim has been achieved, exceeded and sustained, and we reduced the percentage of patients who scored more than five without palliative consultation from a mean of 68% to 16% and those who scored more than seven without palliative care transfer from a mean of thirty two percent to three percent, after four months of the project's implementation. Standardising the referral process and creating an objective referral tool is needed to facilitate safe, collaborative, continuous and patient centered care. Timely referral of cancer patients to palliative care minimises patient and caregiver distress, ensures better quality of life, and provides an appropriate measure for end of life care.

Dataset Information

A Simple Bedside Screening Tool for Spasticity Referral.

Background and objectives

Methods

Results

Conclusion

Publications

A Simple Bedside Screening Tool for Spasticity Referral.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets