The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set

Ambagtsheer, Rachel C.; Shafiabady, Niusha; Dent, Elsa; Seiboth, C.; Beilby, Justin

doi:10.1016/j.ijmedinf.2020.104094

Cited by 58 publications

(49 citation statements)

References 40 publications

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“…Thus, the evaluation of several machine learning methodologies of automated risk strati ication and referral intervention led to a predictive model with an accuracy of 78% [12]. This is very similar to the accuracy of an AI algorithm used in identifying frailty among residents aged 75 years and over (75%) [13]. Another study, based on demographic and psychometric data from 284 patients, aimed to predict the likelihood of older adults having persistent depressive symptoms after 12 months, using a ML approach.…”

Section: Examples Of ML and Dl Implementation In The Medical Care Of mentioning

confidence: 58%

Artificial Intelligence in the healthcare of older people

Makaetova-Ladinska¹,

Harwood²,

Maltby³

2020

Arch Psychiatr Ment Health

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Clinical applications of Artifi cial Intelligence (AI) in healthcare are relatively rare. The high expectations in relation to data analysis infl uencing general healthcare have not materialized, with few exceptions, and then predominantly in the fi eld of rare diseases, oncology and pathology, and interpretation of laboratory results. While electronic health records, introduced over the last decade or so in the UK have increased access to medical and treatment histories of patients, diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, laboratory and test results, these have potential for evidence-based tools that providers can use to make decisions about a patient's care, as well as streamline workfl ow. In the following text, we review the advances achieved using machine learning and deep learning technology, as well as robot use and telemedicine in the healthcare of older people. Key points: 1. Artifi cial Intelligence use is extensively explored in prevention, diagnosis, novel drug designs and after-care. 2. AI studies on older adults include a small number of patients and lack reproducibility needed for their wider clinical use in different clinical settings and larger populations. 3. Telemedicine and robot assisted technology are well received by older service users. 4. Ethical concerns need to be resolved prior to wider AI use in routine clinical setting. seem to face a number of barriers in contacting communitybased primary health care, such as limited access, lack of standardized information systems and care pathways [3], all necessary to address their complex health care and social care needs. Indeed, older adults have much higher prevalence of nearly all major chronic and long-term conditions. In addition, they are more likely to succumb to adverse health events, such as a falls or infections, and these can lead to dramatic changes in their physical and mental wellbeing even after an apparently minor incident [4]. However, person and familyfocused care, self-management resources, and successful collaborative practice have been all highlighted as facilitators of good health care provision both by older people and their families [3]. All the above places the importance of diagnosis, monitoring of disease risks and their prevention, as well as management and optimizing of geriatric syndromes in the community for both older people living independenly or in 24h care facilities. In particular, identi ication of health issues/ diagnosis, support/treatment needs evaluation, development

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Section: Examples Of ML and Dl Implementation In The Medical Care Of mentioning

confidence: 58%

Artificial Intelligence in the healthcare of older people

Makaetova-Ladinska¹,

Harwood²,

Maltby³

2020

Arch Psychiatr Ment Health

View full text Add to dashboard Cite

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“…In our study, gradient boosted machines achieved the highest sensitivity (78.14%) and specificity (74.41%) in predicting frailty. Other research has been completed using machine learning methods to classify frailty, but frailty was defined using other instruments such as the Clinical Frailty Scale [ 23 ], the Frailty Phenotype [ 31 ], or the electronic frailty index [ 32 ]. Our study is the first to use pan-Canadian primary care EMR data to create a frailty case definition using machine learning.…”

Section: Methodsmentioning

confidence: 99%

Strategies for working across Canadian practice-based research and learning networks (PBRLNs) in primary care: focus on frailty

et al. 2021

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Background Practice based research and learning networks (PBRLNs) are groups of learning communities that focus on improving delivery and quality of care. Accurate data from primary care electronic medical records (EMRs) is crucial in forming the backbone for PBRLNs. The purpose of this work is to: (1) report on descriptive findings from recent frailty work, (2) describe strategies for working across PBRLNs in primary care, and (3) provide lessons learned for engaging PBRLNs. Methods We carried out a participatory based descriptive study that engaged five different PBRLNs. We collected Clinical Frailty Scale scores from a sample of participating physicians within each PBRLN. Descriptive statistics were used to analyze frailty scores and patients’ associated risk factors and demographics. We used the Consolidated Framework for Implementation Research to inform thematic analysis of qualitative data (meeting minutes, notes, and conversations with co-investigators of each network) in recognizing challenges of working across networks. Results One hundred nine physicians participated in collecting CFS scores across the five provinces (n = 5466). Percentages of frail (11-17%) and not frail (82-91%) patients were similar in all networks, except Ontario who had a higher percentage of frail patients (25%). The majority of frail patients were female (65%) and had a significantly higher prevalence of hypertension, dementia, and depression. Frail patients had more prescribed medications and numbers of healthcare encounters. There were several noteworthy challenges experienced throughout the research process related to differences across provinces in the areas of: numbers of stakeholders/staff involved and thus levels of burden, recruitment strategies, data collection strategies, enhancing engagement, and timelines. Discussion Lessons learned throughout this multi-jurisdictional work included: the need for continuity in ethics, regular team meetings, enhancing levels of engagement with stakeholders, the need for structural support and recognizing differences in data sharing across provinces. Conclusion The differences noted across CPCSSN networks in our frailty study highlight the challenges of multi-jurisdictional work across provinces and the need for consistent and collaborative healthcare planning efforts.

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“…This research obtained sensitivity estimates ranging between 65.7% to 86.7%, and specificity ranging between 58.1% to 85.6% [ 16 ]. Ambagtsheer et al used the electronic Frailty Index [ 17 ] for the identification of frailty, while also using supervised machine learning methods on EMR data [ 18 ]. The best performing model was able to achieve a sensitivity of 97.8% and a specificity of 89.1%.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for identification of frailty in Canadian primary care practices

Aponte-Hao

Wong

Thandi

et al. 2021

IJPDS

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IntroductionFrailty is a medical syndrome, commonly affecting people aged 65 years and over and is characterized by a greater risk of adverse outcomes following illness or injury. Electronic medical records contain a large amount of longitudinal data that can be used for primary care research. Machine learning can fully utilize this wide breadth of data for the detection of diseases and syndromes. The creation of a frailty case definition using machine learning may facilitate early intervention, inform advanced screening tests, and allow for surveillance. ObjectivesThe objective of this study was to develop a validated case definition of frailty for the primary care context, using machine learning. MethodsPhysicians participating in the Canadian Primary Care Sentinel Surveillance Network across Canada were asked to retrospectively identify the level of frailty present in a sample of their own patients (total n = 5,466), collected from 2015-2019. Frailty levels were dichotomized using a cut-off of 5. Extracted features included previously prescribed medications, billing codes, and other routinely collected primary care data. We used eight supervised machine learning algorithms, with performance assessed using a hold-out test set. A balanced training dataset was also created by oversampling. Sensitivity analyses considered two alternative dichotomization cut-offs. Model performance was evaluated using area under the receiver-operating characteristic curve, F1, accuracy, sensitivity, specificity, negative predictive value and positive predictive value. ResultsThe prevalence of frailty within our sample was 18.4%. Of the eight models developed to identify frail patients, an XGBoost model achieved the highest sensitivity (78.14%) and specificity (74.41%). The balanced training dataset did not improve classification performance. Sensitivity analyses did not show improved performance for cut-offs other than 5. ConclusionSupervised machine learning was able to create well performing classification models for frailty. Future research is needed to assess frailty inter-rater reliability, and link multiple data sources for frailty identification.

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The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set

Cited by 58 publications

References 40 publications

Artificial Intelligence in the healthcare of older people

Artificial Intelligence in the healthcare of older people

Strategies for working across Canadian practice-based research and learning networks (PBRLNs) in primary care: focus on frailty

Machine learning for identification of frailty in Canadian primary care practices

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