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News Article | October 28, 2016
Site: co.newswire.com

A book co-authored by Mertie L. Potter, DNP, APRN, PMHCNS-BC, a Clinical Professor at the MGH Institute of Health Professions School of Nursing, has been chosen as one of the 2011 American Journal of Nursing Books of the Year.


News Article | September 22, 2016
Site: www.rdmag.com

For children with speech and language disorders, early-childhood intervention can make a great difference in their later academic and social success. But many such children—one study estimates 60 percent—go undiagnosed until kindergarten or even later. Researchers at the Computer Science and Artificial Intelligence Laboratory at MIT and Massachusetts General Hospital's Institute of Health Professions hope to change that, with a computer system that can automatically screen young children for speech and language disorders and, potentially, even provide specific diagnoses. This week, at the Interspeech conference on speech processing, the researchers reported on an initial set of experiments with their system, which yielded promising results. "We're nowhere near finished with this work," says John Guttag, the Dugald C. Jackson Professor in Electrical Engineering and senior author on the new paper. "This is sort of a preliminary study. But I think it's a pretty convincing feasibility study." The system analyzes audio recordings of children's performances on a standardized storytelling test, in which they are presented with a series of images and an accompanying narrative, and then asked to retell the story in their own words. "The really exciting idea here is to be able to do screening in a fully automated way using very simplistic tools," Guttag says. "You could imagine the storytelling task being totally done with a tablet or a phone. I think this opens up the possibility of low-cost screening for large numbers of children, and I think that if we could do that, it would be a great boon to society." The researchers evaluated the system's performance using a standard measure called area under the curve, which describes the tradeoff between exhaustively identifying members of a population who have a particular disorder, and limiting false positives. (Modifying the system to limit false positives generally results in limiting true positives, too.) In the medical literature, a diagnostic test with an area under the curve of about 0.7 is generally considered accurate enough to be useful; on three distinct clinically useful tasks, the researchers' system ranged between 0.74 and 0.86. To build the new system, Guttag and Jen Gong, a graduate student in electrical engineering and computer science and first author on the new paper, used machine learning, in which a computer searches large sets of training data for patterns that correspond to particular classifications—in this case, diagnoses of speech and language disorders. The training data had been amassed by Jordan Green and Tiffany Hogan, researchers at the MGH Institute of Health Professions, who were interested in developing more objective methods for assessing results of the storytelling test. "Better diagnostic tools are needed to help clinicians with their assessments," said Green, himself a speech-language pathologist. "Assessing children's speech is particularly challenging because of high levels of variation even among typically developing children. You get five clinicians in the room and you might get five different answers." Unlike speech impediments that result from anatomical characteristics such as cleft palates, speech disorders and language disorders both have neurological bases. But, Green explains, they affect different neural pathways: Speech disorders affect the motor pathways, while language disorders affect the cognitive and linguistic pathways. Green and Hogan had hypothesized that pauses in children's speech, as they struggled to either find a word or string together the motor controls required to produce it, were a source of useful diagnostic data. So that's what Gong and Guttag concentrated on. They identified a set of 13 acoustic features of children's speech that their machine-learning system could search, seeking patterns that correlated with particular diagnoses. These were things like the number of short and long pauses, the average length of the pauses, the variability of their length, and similar statistics on uninterrupted utterances. The children whose performances on the storytelling task were recorded in the data set had been classified as typically developing, as suffering from a language impairment, or as suffering from a speech impairment. The machine-learning system was trained on three different tasks: identifying any impairment, whether speech or language; identifying language impairments; and identifying speech impairments. One obstacle the researchers had to confront was that the age range of the typically developing children in the data set was narrower than that of the children with impairments: Because impairments are comparatively rare, the researchers had to venture outside their target age range to collect data. Gong addressed this problem using a statistical technique called residual analysis. First, she identified correlations between subjects' age and gender and the acoustic features of their speech; then, for every feature, she corrected for those correlations before feeding the data to the machine-learning algorithm. "The need for reliable measures for screening young children at high risk for speech and language disorders has been discussed by early educators for decades," said Thomas Campbell, a professor of behavioral and brain sciences at the University of Texas at Dallas and executive director of the university's Callier Center for Communication Disorders. "The researchers' automated approach to screening provides an exciting technological advancement that could prove to be a breakthrough in speech and language screening of thousands of young children across the United States."


News Article | September 22, 2016
Site: www.chromatographytechniques.com

For children with speech and language disorders, early-childhood intervention can make a great difference in their later academic and social success. But many such children — one study estimates 60 percent — go undiagnosed until kindergarten or even later. Researchers at the Computer Science and Artificial Intelligence Laboratory at MIT and Massachusetts General Hospital’s Institute of Health Professions hope to change that, with a computer system that can automatically screen young children for speech and language disorders and, potentially, even provide specific diagnoses. This week, at the Interspeech conference on speech processing, the researchers reported on an initial set of experiments with their system, which yielded promising results. “We’re nowhere near finished with this work,” says John Guttag, the Dugald C. Jackson Professor in Electrical Engineering and senior author on the new paper. “This is sort of a preliminary study. But I think it’s a pretty convincing feasibility study.” The system analyzes audio recordings of children’s performances on a standardized storytelling test, in which they are presented with a series of images and an accompanying narrative, and then asked to retell the story in their own words. “The really exciting idea here is to be able to do screening in a fully automated way using very simplistic tools,” Guttag says. “You could imagine the storytelling task being totally done with a tablet or a phone. I think this opens up the possibility of low-cost screening for large numbers of children, and I think that if we could do that, it would be a great boon to society.” The researchers evaluated the system’s performance using a standard measure called area under the curve, which describes the tradeoff between exhaustively identifying members of a population who have a particular disorder, and limiting false positives. (Modifying the system to limit false positives generally results in limiting true positives, too.) In the medical literature, a diagnostic test with an area under the curve of about 0.7 is generally considered accurate enough to be useful; on three distinct clinically useful tasks, the researchers’ system ranged between 0.74 and 0.86. To build the new system, Guttag and Jen Gong, a graduate student in electrical engineering and computer science and first author on the new paper, used machine learning, in which a computer searches large sets of training data for patterns that correspond to particular classifications — in this case, diagnoses of speech and language disorders. The training data had been amassed by Jordan Green and Tiffany Hogan, researchers at the MGH Institute of Health Professions, who were interested in developing more objective methods for assessing results of the storytelling test. “Better diagnostic tools are needed to help clinicians with their assessments,” says Green, himself a speech-language pathologist. “Assessing children’s speech is particularly challenging because of high levels of variation even among typically developing children. You get five clinicians in the room and you might get five different answers.” Unlike speech impediments that result from anatomical characteristics such as cleft palates, speech disorders and language disorders both have neurological bases. But, Green explains, they affect different neural pathways: Speech disorders affect the motor pathways, while language disorders affect the cognitive and linguistic pathways. Green and Hogan had hypothesized that pauses in children’s speech, as they struggled to either find a word or string together the motor controls required to produce it, were a source of useful diagnostic data. So that’s what Gong and Guttag concentrated on. They identified a set of 13 acoustic features of children’s speech that their machine-learning system could search, seeking patterns that correlated with particular diagnoses. These were things like the number of short and long pauses, the average length of the pauses, the variability of their length, and similar statistics on uninterrupted utterances. The children whose performances on the storytelling task were recorded in the data set had been classified as typically developing, as suffering from a language impairment, or as suffering from a speech impairment. The machine-learning system was trained on three different tasks: identifying any impairment, whether speech or language; identifying language impairments; and identifying speech impairments. One obstacle the researchers had to confront was that the age range of the typically developing children in the data set was narrower than that of the children with impairments: Because impairments are comparatively rare, the researchers had to venture outside their target age range to collect data. Gong addressed this problem using a statistical technique called residual analysis. First, she identified correlations between subjects’ age and gender and the acoustic features of their speech; then, for every feature, she corrected for those correlations before feeding the data to the machine-learning algorithm. “The need for reliable measures for screening young children at high risk for speech and language disorders has been discussed by early educators for decades,” says Thomas Campbell, a professor of behavioral and brain sciences at the University of Texas at Dallas and executive director of the university’s Callier Center for Communication Disorders. “The researchers’ automated approach to screening provides an exciting technological advancement that could prove to be a breakthrough in speech and language screening of thousands of young children across the United States."


Lof G.L.,MGH Institute of Health Professions
International Journal of Speech-Language Pathology | Year: 2011

Evidence-based practice (EBP) is a well established concept in the field of speech-language pathology. However, evidence from research may not be the primary information that practitioners use to guide their treatment selection from the many potential options. There are various alternative therapy procedures that are strongly promoted, so clinicians must become skilled at identifying pseudoscience from science in order to determine if a treatment is legitimate or actually quackery. In order to advance the use of EBP, clinicians can gather practice-based evidence (PBE) by using the scientific method. By adhering to the principles of science, speech-language pathologists can incorporate science-based practice (SBP) into all aspects of their clinical work. © 2011 The Speech Pathology Association of Australia Limited.


Goodman J.H.,MGH Institute of Health Professions | Chenausky K.L.,MGH Institute of Health Professions | Freeman M.P.,Harvard University
Journal of Clinical Psychiatry | Year: 2014

Objective: To systematically evaluate the literature on anxiety disorders during pregnancy. Data Sources: MEDLINE, PsycINFO, and CINAHL were searched through October 2013 for original research studies published in English using combinations of the terms pregnancy, prenatal, or pregnancy outcomes; anxiety disorder; and generalized anxiety. Reference lists of included studies were hand-searched and a PubMed search for in-process reports was conducted. Study Selection: Relevant studies of anxiety disorders during pregnancy as determined by diagnostic interview were included if they reported on prevalence; course, onset, and/or risk factors; maternal, obstetric, or fetal/child outcomes; and/or treatment trial results. Data Extraction: Two reviewers independently extracted relevant data and assessed methodological quality of each study. Results: Fifty-seven reports were included. Reports provided information on panic disorder (25 reports), generalized anxiety disorder (17 reports), obsessive-compulsive disorder (OCD) (23 reports), agoraphobia (6 reports), specific phobia (10 reports), social phobia (14 reports), posttraumatic stress disorder (14 reports), and any anxiety disorder (18 reports). Twenty reports provided information on prevalence, 16 on course, 10 on risk factors, and 22 on outcomes. Only 1 treatment study was identified. High anxiety disorder prevalence in pregnancy was found; however, estimates vary considerably, and evidence is inconclusive as to whether prevalence among pregnant women differs from that of nonpregnant populations. Considerable variation in prenatal course of OCD and panic disorder was found. Substantial heterogeneity limits conclusions regarding risk factors or outcomes. Conclusions: Additional research of higher methodological quality is required to more accurately determine prevalence, understand course, identify risk factors and outcomes, and determine effective treatments for anxiety disorders in pregnancy. © Copyright 2014 Physicians Postgraduate Press, Inc.


Cahn P.S.,MGH Institute of Health Professions
Journal of Interprofessional Care | Year: 2014

Although international reports have called for making interprofessional education an integral part of health professions education, most interprofessional learning activities remain voluntary and occur a single time. Barriers to implementing comprehensive interprofessional education come from forces both internal and external to institutions. Understanding the historical context for how one graduate health professions school attempted to overcome these barriers will provide a longitudinal perspective that may assist other institutions with their interprofessional education efforts. The case of the Massachusetts General Hospital Institute of Health Professions shows that, despite being founded with a mission to educate students from different professions together, interprofessional education does not emerge naturally. An analysis of archival documents, academic catalogs and oral history interviews revealed that early attempts focused on requiring students to take common courses. Later, the faculty created voluntary interprofessional learning activities. Neither approach achieved its intended goals until the Institute developed deliberate strategies to counter the internal and external barriers to integrating interprofessional education. This historical case study suggests that sustainable interprofessional education initiatives require both an organizational home and a permanent place in the curriculum. © 2014 Informa UK Ltd. All rights reserved: reproduction in whole or part not permitted.


Grobecker P.A.,MGH Institute of Health Professions
Nurse Education Today | Year: 2016

Introduction: The rigorous efforts students put into baccalaureate nursing programs to become a professional nurse is compounded by their need to have a sense of belonging in their clinical placements. In addition, the students' perceived stress may contribute to their physiological and psychological wellbeing undermining academic achievements and confidence. Background: A sense of belonging and perceived stress have research history in psychological and sociological realms; but not used together in the nursing profession as applied in clinical placements. The Perceived Stress Scale is a psychological instrument used globally; however, the Belongingness Scale-Clinical Placement Experience (BES-CPE) measurement tool has not been used in published research in the United States. Methods: A descriptive correlational research design examining the relationship between a sense of belonging and perceived stress among baccalaureate nursing students in clinical placements. Three measurement tools were used for data collection: BES-CPE, Perceived Stress Scale (PSS-10) and demographic questionnaire. Students were able to access the online survey through SurveyMonkey®. Participants: A national study was conducted using 1296 volunteer nursing students from the National Student Nurses Association (NSNA) database. These nursing students were currently enrolled in a baccalaureate nursing program, 18. years of age and completed at least one clinical experience. Results: The findings from this study revealed a statistically significant low inverse relationship (r = -.277) between a sense of belonging and perceived stress among baccalaureate nursing students in their clinical placements. The findings also supported the use of BES-CPE as a reliable and valid measurement tool for nursing students in clinical placements. Conclusion: The results of this study supported the concept of a sense of belonging as a fundamental human need, having a positive influence and impact on students' learning, motivation and confidence. In contrast, perceived stress has negative consequences on the students' self-concept, learning skills and competence. © 2015 Elsevier Ltd.


Godfrey M.M.,Dartmouth Institute for Health Policy and Clinical Practice | Oliver B.J.,MGH Institute of Health Professions
BMJ Quality and Safety | Year: 2014

Introduction: The Learning and Leadership Collaborative (LLC) supports cystic fibrosis (CF) centres' responses to the variation in CF outcomes in the USA. Between 2002 and 2013, the Cystic Fibrosis Foundation (CFF) designed, tested and modified the LLC to guide front line staff efforts in these efforts. This paper describes the CFF LLC evolution and essential elements that have facilitated increased improvement capability of CF centres and improved CF outcomes. Methods: CF centre improvement teams across the USA have participated in 11 LLCs of 12 months' duration since 2002. Based on the Dartmouth Microsystem Improvement Curriculum, the original LLC included face to face meetings, an email listserv, conference calls and completion of between learning session task books. The LLCs evolved over time to include internet based learning, an electronic repository of improvement resources and examples, change ideas driven by evidence based clinical practice guidelines, benchmarking site visits, an applied QI measurement curriculum and team coaching. Results: Over 90% of the CF centres in the USA have participated in the LLCs and have increased their improvement capabilities. Ten essential elements were identified as contributors to the successful LLCs: LLC national leadership and coordination, local leadership, people with CF and families involvement, registry data transparency, standardised improvement curriculum with evidence based change ideas, internet resources with reminders, team coaching, regular progress reporting and tracking, benchmarking site visits and applied improvement measurement. Conclusions: The LLCs have contributed to improved medical and process outcomes over the past 10 years. Ten essential elements of the LLCs may benefit improvement efforts in other chronic care populations and health systems.


Hildebrand M.W.,MGH Institute of Health Professions
American Journal of Occupational Therapy | Year: 2015

This evidence-based review was conducted to evaluate the effectiveness of occupational therapy interventions to prevent or mitigate the effects of psychological or emotional impairments after stroke. Thirty-nine journal articles met the inclusion criteria. Six types of interventions were identified that addressed depression, anxiety, or mental health-related quality of life: exercise or movement based, behavioral therapy and stroke education, behavioral therapy only, stroke education only, care support and coordination, and community-based interventions that included occupational therapy. Evidence from well-conducted research supports using problem-solving or motivational interviewing behavioral techniques to address depression. The evidence is inconclusive for using multicomponent exercise programs to combat depression after stroke and for the use of stroke education and care support and coordination interventions to address poststroke anxiety. One study provided support for an intensive multidisciplinary home program in improving depression, anxiety, and health-related quality of life. The implications of the findings for practice, research, and education are discussed.


News Article | October 28, 2016
Site: co.newswire.com

CFHI collaborates with MGH Institute of Health Professions in Boston to expand elective global health opportunities for students in nursing, occupational therapy, physical therapy, physician assistant studies, and speech-language pathology

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