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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Shared medical appointments to screen for geriatric syndromes: preliminary data from a quality improvement initiative
Suepattra G May1, Peter H Cheng, Caroline K Tietbohl
1Research Institute, Palo Alto Medical Foundation, Palo Alto, California.
Abstract:
Older adults are at greater risk of developing conditions that affect health outcomes, quality of life, and costs of care. Screening for geriatric conditions such as memory loss, fall risk, and depression may contribute to the prevention of adverse physical and mental comorbidities, unnecessary hospitalizations, and premature nursing home admissions. Because screening is not consistently performed in primary care settings, a shared medical appointment (SMA) program was developed to fill this gap in care. The goals of the program were to improve early identification of at-risk individuals and ensure appropriate follow-up for memory loss, fall risk, and depression; facilitate discussion about prevention, diagnosis, and treatment of these conditions; implement strategies to reduce risks for these conditions; and increase access to screening and expand preventive health services for older adults. Between August 2011 and May 2013, 136 individuals aged 60 and older participated in the program. Three case studies highlighting the psychosocial and physiological findings of participation in the program are presented. Preliminary data suggest that SMAs are an effective model of regularly screening at-risk older adults that augments primary care practice by facilitating early detection and referral for syndromes that may otherwise be missed or delayed.
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