Population Health Solutions for Assessing Cognitive Impairment in Geriatric Patients
Notice bibliographique
Résumé
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; and recognizing the great potential, with appropriate caveats, of electronic health records to augment cognitive screening and tracking of changes in cognitive health over time. Summit Participants Deb Adler1, Christopher Alban, MD, MBA2, Mark Bondi, PhD3, Michelle Braun, PhD4, Xavier Cagigas, PhD5, Morgan Daven6, Robert L. Denney, PsyD7,8, Lisa Drozdick, PhD9, Norman L. Foster, MD10,11, Ula Hwang, MD12–15, Laurie Ivey, PsyD16, Grant Iverson, PhD7,17, Joel Kramer, PsyD18, Laura Lacritz, PhD7,19, Melinda Lantz, MD20, Lisa Latts, MD, MSPH, MBA21, Shari M. Ling, MD22, Ana Maria Lopez, MD23–26, Michael Malone, MD27,28, Lori Martin-Plank, PhD, MSN, MSPH, RN29, Katie Maslow, MSW30, Don Melady, MSc(Ed), MD31–33, Melissa Messer34, John Meyers, PsyD7, Charles E. McConnel, PhD19, Randi Most, PhD36, Margaret P. Norris, PhD37, William Perry, PhD7,85,39, Neil Pliskin, PhD40, David Shafer, MBA41, Nina Silverberg, PhD42, Tresa Roebuck-Spencer, PhD43,44, Colin M. Thomas, MD, MPH45, Laura Thornhill, JD46, Jean Tsai, MD, PhD10,47, Nirav Vakharia, MD48, Martin Waters, MSW49 Organizations Represented Alzheimer’s Association, Chicago, IL AMA/CPT Health Care Professionals Advisory Committee, Chicago, IL American Academy of Clinical Neuropsychology (AACN), Ann Arbor, MI American Academy of Neurology (AAN), Minneapolis, MN American Association of Geriatric Psychiatry (AAGP), McLean, VA American Association of Nurse Practitioners (AANP), Austin, TX American Board of Professional Neuropsychology (ABN), Sarasota, FL American College of Emergency Physicians (ACEP), Philadelphia, PA American College of Physicians (ACP), Philadelphia, PA American Geriatrics Society (AGS), New York, NY American Psychological Association (APA), Washington, DC Beacon Health Options, Boston, MA Canadian Association of Emergency Physicians, Ottawa, ON, Canada Collaborative Family Healthcare Association (CFHA), Rochester, New York Gerontological Society of America, Washington, DC Hispanic Neuropsychological Society (HNS), Los Angeles, CA IBM Watson Health, Denver, CO International Federation of Emergency Medicine, West Melbourne, Australia International Neuropsychological Society (INS), Salt Lake City, UT National Academy of Neuropsychology (NAN), Denver, CO Optum of UnitedHealth Group, Minneapolis, MN Pearson, New York City, New York Psychological Assessment Resources, Inc, Lutz, FL Society for Clinical Neuropsychology, Washington, DC U.S. Department of Veterans Affairs, Washington, DC *Please note that participation in the Summit does not constitute organizational endorsement of this report
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».