Defining and Assessing Geriatric Risk Factors and Associated Health Care Utilization Among Older Adults Using Claims and Electronic Health Records

Kan, Hongjun; Kharrazi, Hadi; Leff, Bruce; Boyd, Cynthia M.; Davison, Ashwini; Chang, Hsien Yen; Kimura, Joe; Wu, Shannon; Anzaldi, Laura; Richards, Tom; Lasser, Elyse C.; Weiner, Jonathan P.

doi:10.1097/mlr.0000000000000865

Cited by 42 publications

(51 citation statements)

References 36 publications

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“…One of the plausible implementations of frailty into clinical practice is to identify frail patients using electronic health record data. [85][86][87] In a UK study, Clegg et al developed the electronic Frailty Index (eFI) from 36 deficits, 88 based on the Frailty Index of cumulative deficit model. 17 The eFI was automatically populated from routinely collected data stored in the existing primary care electronic health record where general practitioners (GPs) list all patient diagnoses.…”

Section: Implications and Challenges For Health Care Policymentioning

confidence: 99%

<p>Frailty syndrome: implications and challenges for health care policy</p>

Kojima

Liljas

Iliffe

2019

RMHP

275

220

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Older adults are a highly heterogeneous group with variable health and functional life courses. Frailty has received increasing scientific attention as a potential explanation of the health diversity of older adults. The frailty phenotype and the Frailty Index are the most frequently used frailty definitions, but recently new frailty definitions that are more practical have been advocated. Prevalence of frailty among the community-dwelling population aged 65 years and older is ~10% but varies depending on which frailty definitions are used. The mean prevalence of frailty gradually increases with age, but the individual's frailty level can be improved. Older adults, especially frail older adults, form the main users of medical and social care services. However, current health care systems are not well prepared to deal with the chronic and complex medical needs of frail older patients. In this context, frailty is potentially a perfect fit as a risk stratification paradigm. The evidence from frailty studies has not yet been fully translated into clinical practice and health care policy making. Successful implementation would improve quality of care and promote healthy aging as well as diminish the impact of aging on health care systems and strengthen their sustainability. At present, however, there is no effective treatment for frailty and the most effective intervention is not yet known. Based on currently available evidence, multi-domain intervention trials, including exercise component, especially multicomponent exercise, which includes resistance training, seem to be promising. The current challenges in frailty research include the lack of an international standard definition of frailty, further understanding of interventions to reverse frailty, the best timing for intervention, and education/training of health care professionals. The hazards of stigmatization should also be considered. If these concerns are properly addressed, widespread application of public health approaches will be possible, including screening, identification, and treatment of frailty, resulting in better care and healthier aging for older people.

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Section: Implications and Challenges For Health Care Policymentioning

confidence: 99%

<p>Frailty syndrome: implications and challenges for health care policy</p>

Kojima

Liljas

Iliffe

2019

RMHP

275

220

View full text Add to dashboard Cite

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“…38 In addition, the expanded identification of geriatric syndromes using NLP methods will improve current codebased risk assessment not only by finding more persons at risk, but also by better reflecting clinician judgment and intuition and thus improving care management interventions. 3…”

Section: Added Value Of Structured Ehr Datamentioning

confidence: 99%

“…Geriatric syndromes are correlated with high morbidity and mortality and high healthcare use, such as hospitalizations, emergency department (ED) visits, and nursing home admissions . “Geriatric syndromes” is a term used to capture complex and multifactorial clinical conditions in older adults that do not fit into discrete disease categories .…”

mentioning

confidence: 99%

The Value of Unstructured Electronic Health Record Data in Geriatric Syndrome Case Identification

Kharrazi

Anzaldi

Hernandez³

et al. 2018

J American Geriatrics Society

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“…For example, in the case of PACE (Program for All-inclusive Care of the Elderly), the Centers for Medicare and Medicaid Services [CMS] distributes a survey to enrolled beneficiaries to determine the level of ADL dependency in the enrolled population, which it uses as a surrogate measure of frailty [ 6 ]. Even in health systems committed to uncovering multimorbidity, frail health and functional disabilities, practical challenges may limit the availability and quality of records reflecting these risks [ 7 , 8 ]. Such information may lie buried in scans or text fields in many EHR systems, or patient records themselves may still not be integrated across multiple provider and insurer systems, subjecting their population-level uses to indication and selection biases.…”

Section: Introductionmentioning

confidence: 99%

Validation of the JEN frailty index in the National Long-Term Care Survey community population: identifying functionally impaired older adults from claims data

Kinosian

Wieland

et al. 2018

BMC Health Serv Res

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BackgroundUse of a claims-based index to identify persons with physical function impairment and at risk for long-term institutionalization would facilitate population health and comparative effectiveness research. The JEN Frailty Index [JFI] is comprised of diagnosis domains representing impairments and multimorbid clusters with high long-term institutionalization [LTI] risk. We test the index’s discrimination of activities-of-daily-living [ADL] dependency and 1-year LTI and mortality in a nationally representative sample of over 12,000 Medicare beneficiaries, and compare long-term community survival stratified by ADL and JFI.Methods2004 U.S. National Long-Term Care Survey data were linked to Medicare, Minimum Data Set, Veterans Health Administration files and vital statistics. ADL dependencies, JFI score, age and sex were measured at baseline survey. ADL and JFI groups were cross-tabulated generating likelihood ratios and classification statistics. Logistic regression compared discrimination (areas under receiver operating characteristic curves), multivariable calibration and accuracy of the JFI and, separately, ADLs, in predicting 1-year outcomes. Hall-Wellner bands facilitated contrasts of JFI- and ADL-stratified 5-year community survival.ResultsLikelihood ratios rose evenly across JFI risk categories. Areas under the curves of functional dependency at ≥3 and ≥ 2 for JFI, age and sex models were 0.807 [95% c.i.: 0.795, 0.819] and 0.812 [0.801, 0.822], respectively. The area under the LTI curve for JFI and age (0.781 [0.747, 0.815]) discriminated less well than the ADL-based model (0.829 [0.799, 0.860]). Community survival separated by JFI strata was comparable to ADL strata.ConclusionsThe JEN Frailty Index with demographic covariates is a valid claims-based measure of concurrent activities-of-daily-living impairments and future long-term institutionalization risk in older populations lacking functional information.Electronic supplementary materialThe online version of this article (10.1186/s12913-018-3689-2) contains supplementary material, which is available to authorized users.

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Defining and Assessing Geriatric Risk Factors and Associated Health Care Utilization Among Older Adults Using Claims and Electronic Health Records

Abstract: The results demonstrate the feasibility and potential of using EHRs and claims for collecting new types of geriatric risk information that could augment the more commonly collected disease information to identify and move upstream the management of high-risk cases among older patients.

Cited by 42 publications

References 36 publications

<p>Frailty syndrome: implications and challenges for health care policy</p>

<p>Frailty syndrome: implications and challenges for health care policy</p>

The Value of Unstructured Electronic Health Record Data in Geriatric Syndrome Case Identification

Validation of the JEN frailty index in the National Long-Term Care Survey community population: identifying functionally impaired older adults from claims data

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