Developing an Intervention Toolbox for the Common Health Problems in the Workplace

Development of the Health ↔ Work Toolbox is described. The toolbox aims to reduce the workplace impact of common health problems (musculoskeletal, mental health, and stress complaints) by focusing on tackling work-relevant symptoms. Based on biopsychosocial principles this toolbox supplements current approaches by occupying the zone between primary prevention and healthcare. It provides a set of evidence-informed principles and processes (knowledge + tools) for tackling work-relevant common health problems. The toolbox comprises a proactive element aimed at empowering line managers to create good jobs, and a ‘just in time’ responsive element for supporting individuals struggling with a work-relevant health problem. The key intention is helping people with common health problems to maintain work participation. The extensive conceptual and practical development process, including a comprehensive evidence review, produced a functional prototype toolbox that is evidence based and flexible in its use. End-user feedback was mostly positive. Moving the prototype to a fully-fledged internet resource requires specialist design expertise. The Health ↔ Work Toolbox appears to have potential to contribute to the goal of augmenting existing primary prevention strategies and healthcare delivery by providing a more comprehensive workplace approach to constraining sickness absence

The Autism Toolbox : An Autism Resource for Scottish Schools

The Autism Toolbox will draw upon a range of practice experience, literature and research to offer guidance for authorities and schools providing for children and young people with Autism Spectrum Disorders (ASD)

Study Protocol: A Randomized Controlled Trial of Patient Navigation-Activation to Reduce Cancer Health Disparities

Abstract Background Cancer health disparities affecting low-income and minority patients are well documented. Root-causes are multifactorial, including diagnostic and treatment delays, social and financial barriers, and poor communication. Patient navigation and communication coaching (activation) are potential interventions to address disparities in cancer treatment. The purpose of this clinical trial is to test the effectiveness of an intervention combining patient navigation and activation to improve cancer treatment. Methods/Design The Rochester Patient Navigation Research Program (PNRP) is a National Cancer Institute-sponsored, patient-level randomized trial (RCT) of patient navigation and activation, targeting newly-diagnosed breast and colorectal cancer patients in Rochester, NY. The goal of the program is to decrease cancer health disparities by addressing barriers to receipt of cancer care and promoting patient self-efficacy. The intervention uses trained, paraprofessional patient navigators recruited from the target community, and a detailed training and supervisory program. Recruited patients are randomly assigned to receive either usual care (except for baseline and follow-up questionnaires and interviews) or intervention. The intervention patients receive tailored assistance from their patient navigators, including phone calls, in-person meetings, and behind-the-scenes coordination of care. A total of 344 patients have been recruited. Outcomes measured at three month intervals include timeliness of care, patient adherence, patient satisfaction, quality of life, self-efficacy, health literacy, and cancer knowledge. Discussion This unique intervention combining patient navigation and patient activation is designed to address the multifactorial problem of cancer health disparities. If successful, this study will affect the design and implementation of patient navigation programs. Trials Registration clinicaltrials.gov identifier NCT00496678http://deepblue.lib.umich.edu/bitstream/2027.42/78254/1/1471-2407-10-551.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78254/2/1471-2407-10-551.pdfPeer Reviewe

Population Health Solutions for Assessing Cognitive Impairment in Geriatric Patients.

In December 2017, the National Academy of Neuropsychology convened an interorganizational Summit on Population Health Solutions for Assessing Cognitive Impairment in Geriatric Patients in Denver, Colorado. The Summit brought together representatives of a broad range of stakeholders invested in the care of older adults to focus on the topic of cognitive health and aging. Summit participants specifically examined questions of who should be screened for cognitive impairment and how they should be screened in medical settings. This is important in the context of an acute illness given that the presence of cognitive impairment can have significant implications for care and for the management of concomitant diseases as well as pose a major risk factor for dementia. Participants arrived at general principles to guide future screening approaches in medical populations and identified knowledge gaps to direct future research. Key learning points of the summit included: recognizing the importance of educating patients and healthcare providers about the value of assessing current and baseline cognition;emphasizing that any screening tool must be appropriately normalized and validated in the population in which it is used to obtain accurate information, including considerations of language, cultural factors, and education; andrecognizing the great potential, with appropriate caveats, of electronic health records to augment cognitive screening and tracking of changes in cognitive health over time

Wavelet Features for Recognition of First Episode of Schizophrenia from MRI Brain Images

Machine learning methods are increasingly used in various fields of medicine, contributing to early diagnosis and better quality of care. These outputs are particularly desirable in case of neuropsychiatric disorders, such as schizophrenia, due to the inherent potential for creating a new gold standard in the diagnosis and differentiation of particular disorders. This paper presents a scheme for automated classification from magnetic resonance images based on multiresolution representation in the wavelet domain. Implementation of the proposed algorithm, utilizing support vector machines classifier, is introduced and tested on a dataset containing 104 patients with first episode schizophrenia and healthy volunteers. Optimal parameters of different phases of the algorithm are sought and the quality of classification is estimated by robust cross validation techniques. Values of accuracy, sensitivity and specificity over 71% are achieved

Preparing for the Merit-Based Incentive Payment Structure: A Value-Based Business Plan

The notion of delivering value-based care while maintaining corporate vitality in health care is a multifaceted process. Shaping a business model to reflect this process requires the alignment of quality implications, reimbursement initiatives, a framework that takes into account stipulations on reimbursement for different types of clinicians, and an understanding and preparedness for emerging guidelines and nationally recognized structures for quality indicators. When organizations recognize the need for change, but are not yet prepared to implement it, a comprehensive business plan can help the organization visualize a path to success. In this project, the foundation of requirements for a value-based business plan as described above was developed for a small home-based primary care practice in southwest Michigan. This project provided a pathway for preparation and compliance with the 2017 Medicare Merit-Based Incentive Structure. Compliance with the Medicare program has potential to improve the quality of care delivered and bring significant financial benefits if done well. As a result of this project, the home-based primary care service in southwest Michigan was considering the possible adoption of 29 new quality initiatives for the year 2017 and unlocked a potential gain of $134,000 from Medicare over succeeding next four years