Characteristics of high-need, high-cost patients: a “best-fit” framework synthesis
ND Berkman, E Chang, J Seibert… - Annals of internal medicine, 2022 - acpjournals.org
Background: Accurately identifying high-need, high-cost (HNHC) patients to reduce their
preventable or modifiable health care use for their chronic conditions is a priority and a …
preventable or modifiable health care use for their chronic conditions is a priority and a …
Use of latent class analysis and k-means clustering to identify complex patient profiles
RW Grant, J McCloskey, M Hatfield, C Uratsu… - JAMA network …, 2020 - jamanetwork.com
Importance Medically complex patients are a heterogeneous group that contribute to a
substantial proportion of health care costs. Coordinated efforts to improve care and reduce …
substantial proportion of health care costs. Coordinated efforts to improve care and reduce …
Applying machine learning algorithms to segment high-cost patient populations
Background Efforts to improve the value of care for high-cost patients may benefit from care
management strategies targeted at clinically distinct subgroups of patients. Objective To …
management strategies targeted at clinically distinct subgroups of patients. Objective To …
Use of data-driven methods to predict long-term patterns of health care spending for Medicare patients
JC Lauffenburger, M Mahesri… - JAMA network open, 2020 - jamanetwork.com
Importance Current approaches to predicting health care costs generally rely on a single
composite value of spending and focus on short time horizons. By contrast, examining …
composite value of spending and focus on short time horizons. By contrast, examining …
High‐need phenotypes in medicare beneficiaries: drivers of variation in utilization and outcomes
T Keeney, E Belanger, RN Jones… - Journal of the …, 2020 - Wiley Online Library
OBJECTIVES High‐need (HN) Medicare beneficiaries heavily use healthcare services at a
high cost. This population is heterogeneous, composed of individuals with varying degrees …
high cost. This population is heterogeneous, composed of individuals with varying degrees …
[HTML][HTML] Simulation-derived best practices for clustering clinical data
Introduction Clustering analyses in clinical contexts hold promise to improve the
understanding of patient phenotype and disease course in chronic and acute clinical …
understanding of patient phenotype and disease course in chronic and acute clinical …
Identifying subgroups of adult high-cost health care users: a retrospective analysis
Background: Few studies have categorized high-cost patients (defined by accumulated
health care spending above a predetermined percentile) into distinctive groups for which …
health care spending above a predetermined percentile) into distinctive groups for which …
Characterizing potentially preventable hospitalizations of high-cost patients in rural China
S Lu, Y Zhang, L Zhang, NS Klazinga… - Frontiers in public …, 2022 - frontiersin.org
Introduction High-cost patients are characterized by repeated hospitalizations, and inpatient
cost accounts for a large proportion of their total health care spending. This study aimed to …
cost accounts for a large proportion of their total health care spending. This study aimed to …
Finding social need-les in a haystack: ascertaining social needs of Medicare patients recorded in the notes of care managers
Background Unmet social needs may impair health and access to health care, and
intervening on these holds particular promise in high-risk patient populations, such as those …
intervening on these holds particular promise in high-risk patient populations, such as those …
[HTML][HTML] Exploring patient multimorbidity and complexity using health insurance claims data: a cluster analysis approach
A Nicolet, D Assouline, MA Le Pogam… - JMIR Medical …, 2022 - medinform.jmir.org
Background Although the trend of progressing morbidity is widely recognized, there are
numerous challenges when studying multimorbidity and patient complexity. For multimorbid …
numerous challenges when studying multimorbidity and patient complexity. For multimorbid …