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

Kharrazi, Hadi; Anzaldi, Laura; Hernandez, Leilani; Davison, Ashwini; Boyd, Cynthia M.; Leff, Bruce; Kimura, Joe; Weiner, Jonathan P.

doi:10.1111/jgs.15411

Cited by 99 publications

(90 citation statements)

References 43 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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“…While from a research perspective it would be ideal for most or all EHR data to be captured via structured fields, there are practical barriers to this, including physician resistance to SDES use [7] and lack of ability to capture contextual information [17,18]. Hence, EHR systems such as SCM generally have the ability to capture unstructured data as well.…”

Section: Structured Ehr Componentsmentioning

confidence: 99%

Unlocking the Potential of Electronic Health Records for Health Research

Lee

D’Souza

et al. 2020

IJPDS

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Electronic health records (EHRs), originally designed to facilitate health care delivery, are becoming a valuable data source for health research. EHR systems have two components: the front end, where the data is entered by healthcare workers including physicians and nurses, and the back-end electronic data warehouse where the data is stored in a relational database. EHR data elements can be of many types, which can be categorized as structured, unstructured free-text, and imaging data. The Sunrise Clinical Manager (SCM) EHR is one example of an inpatient EHR system, which covers the city of Calgary (Alberta, Canada). This system, under the management of Alberta Health Services, is now being explored for research use. The purpose of the present paper is to describe the SCM EHR for research purposes, showing how this generalizes to EHRs in general. We further discuss advantages, challenges (e.g. potential bias and data quality issues), and analytical capacities and requirements associated with using EHRs.

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“…In the absence of routine assessment, these documentations tend to be inconsistently available or for a subset of patients in specific clinical contexts (e.g., after a fall event, hospitalization, or major surgery), which may not represent an individual's usual state of health. A recent study by Kharrazi et al 79) showed that the prevalence of geriatric syndromes was underestimated when only claims and structured EHR data were analyzed; natural language processing of unstructured EHR data substantially improved detection by 1.5-fold for dementia, 3.2-fold for falls, 18.0-fold for malnutrition, and 455.9-fold for lack of social support. While these findings are promising, the contribution of unstructured EHR data for case identification depends on the health information technology infrastructure and completeness of documentation by health care providers.…”

Section: Areas Of Uncertaintymentioning

confidence: 99%

“…While these findings are promising, the contribution of unstructured EHR data for case identification depends on the health information technology infrastructure and completeness of documentation by health care providers. 79) How to best combine clinical information with administrative claims data or structured EHR data requires further investigation.…”

Section: Areas Of Uncertaintymentioning

confidence: 99%

Measuring Frailty in Health Care Databases for Clinical Care and Research

Kim

2020

Ann Geriatr Med Res

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Frailty is a clinical state resulting from age-related changes in multiple physiologic systems and accumulation of diseases that reduces patient ability to maintain homeostasis in response to stressors. 1) Frailty is common in older adults, affecting one in every 10 community-dwellers 2,3) and one in every two nursing home residents, 4) and is associated with increased risks of death (relative risk [RR], 1.6-6.0), disability (RR, 1.8-2.8), institutionalization (RR, 2.6-24.0), and falls (RR, 1.2-2.4). 1) Health care costs for older adults with frailty increase by up to 2-fold compared to those in their non-frail counterparts, 5,6) mainly due to inpatient care, post-acute care, and care for potentially preventable conditions. 7,8) Given the considerable clinical and societal consequences of frailty in the ever-growing aging population, assessment of frailty in clinical and population settings offers valuable opportunities for prevention and treatment through efficient use of evidence-based interventions and resources. 9-11) Considering the increasing burden and serious consequences of frailty in aging populations, there is increasing interest in measuring frailty in health care databases for clinical care and research. This review synthesizes the latest research on the development and application of 21 frailty measures for health care databases. Frailty measures varied widely in terms of target population (16 ambulatory, 1 long-term care, and 4 inpatient), data source (16 claims-based and 5 electronic health records [EHR]-based measures), assessment period (6 months to 36 months), data types (diagnosis codes required for 17 measures, health service codes for 7 measures, pharmacy data for 4 measures, and other information for 9 measures), and outcomes for validation (clinical frailty for 7 measures, disability for 7 measures, and mortality for 16 measures). These frailty measures may be useful to facilitate frailty screening in clinical care and quantify frailty for large database research in which clinical assessment is not feasible.

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

Cited by 99 publications

References 43 publications

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

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

Unlocking the Potential of Electronic Health Records for Health Research

Measuring Frailty in Health Care Databases for Clinical Care and Research

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