Measuring mild cognitive impairment using routinely collected health-care data: A conceptual framework and guidance for embedded pragmatic clinical trials.
8 affiliations
- 1The Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Division of Gerontology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
- 2Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, Rhode Island, USA.
- 3Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
- 4Center for Health Outcomes and Interdisciplinary Research, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
- 5Division of Geriatric Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA.
- 6Department of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, Connecticut, USA.
- 7Department of Management, Policy, and Community Health, School of Public Health, Institute on Aging, The University of Texas Health Science Center in Houston, Houston, Texas, USA.
- 8Department of Epidemiology, Harvard T.H. Chan School of Public Health, Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine and Department of Psychiatry, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Abstract
Mild cognitive impairment (MCI) is increasingly measured using routinely collected health-care data in embedded pragmatic clinical trials. However, accurate ascertainment remains challenging because key diagnostic elements, such as cognitive function and functional status, are often incompletely captured. We identified three broad categories for measuring MCI using routinely collected health-care data: documentation-based approaches, which identify clinician-recognized MCI; clinical criteria-based approaches, which reconstruct the diagnostic construct using available clinical information; and prediction-based approaches, which estimate the likelihood of MCI using statistical or machine learning models. These approaches measure distinct constructs and differ in validity and scalability, and susceptibility to bias. For eligibility and outcome ascertainment, clinical criteria-based approaches should be prioritized whenever feasible. Documentation-based approaches may serve as complementary measures of clinician-recognized MCI, whereas prediction-based approaches are best suited for screening and risk stratification. Continued advances in phenotype development, data standardization, and validation are needed to support accurate and equitable MCI ascertainment.
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BibTeX
@article{kim2026measuring,
title = {{Measuring mild cognitive impairment using routinely collected health-care data: A conceptual framework and guidance for embedded pragmatic clinical trials.}},
author = {Dae Hyun Kim and Kevin W McConeghy and Jennifer L Lund and Ryan Mace and David H Lynch and Elizabeth M White and Hua Xu and Rafael Samper-Ternent and Phillip Wang and Ellen P McCarthy and NIA IMPACT Collaboratory Technical Data-Health Care Systems Core},
journal = {Alzheimer's \& dementia : the journal of the Alzheimer's Association},
year = {2026},
month = oct,
doi = {10.1002/alz.71942},
url = {https://europepmc.org/article/MED/42967498},
note = {CC BY},
}
RIS
TY - JOUR
TI - Measuring mild cognitive impairment using routinely collected health-care data: A conceptual framework and guidance for embedded pragmatic clinical trials.
AU - Dae Hyun Kim
AU - Kevin W McConeghy
AU - Jennifer L Lund
AU - Ryan Mace
AU - David H Lynch
AU - Elizabeth M White
AU - Hua Xu
AU - Rafael Samper-Ternent
AU - Phillip Wang
AU - Ellen P McCarthy
AU - NIA IMPACT Collaboratory Technical Data-Health Care Systems Core
PY - 2026
DA - 2026/10/01
JO - Alzheimer's & dementia : the journal of the Alzheimer's Association
DO - 10.1002/alz.71942
UR - https://europepmc.org/article/MED/42967498
AB - Mild cognitive impairment (MCI) is increasingly measured using routinely collected health-care data in embedded pragmatic clinical trials. However, accurate ascertainment remains challenging because key diagnostic elements, such as cognitive function and functional status, are often incompletely captured. We identified three broad categories for measuring MCI using routinely collected health-care data: documentation-based approaches, which identify clinician-recognized MCI; clinical criteria-based approaches, which reconstruct the diagnostic construct using available clinical information; and prediction-based approaches, which estimate the likelihood of MCI using statistical or machine learning models. These approaches measure distinct constructs and differ in validity and scalability, and susceptibility to bias. For eligibility and outcome ascertainment, clinical criteria-based approaches should be prioritized whenever feasible. Documentation-based approaches may serve as complementary measures of clinician-recognized MCI, whereas prediction-based approaches are best suited for screening and risk stratification. Continued advances in phenotype development, data standardization, and validation are needed to support accurate and equitable MCI ascertainment.
SN - 1552-5260
SN - 1552-5279
ER -