Application of an electronic Frailty Index in Australian primary care: data quality and feasibility assessment

AbstractMost clinical contacts with chronic obstructive pulmonary disease (COPD) patients take place in primary care, presenting opportunity for proactive clinical management. Electronic health records could be used to risk stratify diagnosed patients in this setting, but may be limited by poor data quality or completeness. We developed a risk stratification database algorithm using the DOSE index (Dyspnoea, Obstruction, Smoking and Exacerbation) with routinely collected primary care data, aiming to calculate up to three repeated risk scores per patient over five years, each separated by at least one year. Among 10,393 patients with diagnosed COPD, sufficient primary care data were present to calculate at least one risk score for 77.4%, and the maximum of three risk scores for 50.6%. Linked secondary care data revealed primary care under-recording of hospital exacerbations, which translated to a slight, non-significant cohort average risk score reduction, and an understated risk group allocation for less than 1% of patients. Algorithmic calculation of the DOSE index is possible using primary care data, and appears robust to the absence of linked secondary care data, if unavailable. The DOSE index appears a simple and practical means of incorporating risk stratification into the routine primary care of COPD patients, but further research is needed to evaluate its clinical utility in this setting. Although secondary analysis of routinely collected primary care data could benefit clinicians, patients and the health system, standardised data collection and improved data quality and completeness are also needed.

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Assessing primary care data quality

International Journal of Health Care Quality Assurance ◽

10.1108/ijhcqa-08-2016-0111 ◽

2018 ◽

Vol 31 (3) ◽

pp. 203-213

Author(s):

Yvonne Mei Fong Lim ◽

Maryati Yusof ◽

Sheamini Sivasampu

Keyword(s):

Primary Care ◽

Data Quality ◽

Developing Nations ◽

Error Rates ◽

Web Based ◽

Content Type ◽

National Medical ◽

Primary Care Data ◽

Care Survey ◽

Cent Error

Purpose The purpose of this paper is to assess National Medical Care Survey data quality. Design/methodology/approach Data completeness and representativeness were computed for all observations while other data quality measures were assessed using a 10 per cent sample from the National Medical Care Survey database; i.e., 12,569 primary care records from 189 public and private practices were included in the analysis. Findings Data field completion ranged from 69 to 100 per cent. Error rates for data transfer from paper to web-based application varied between 0.5 and 6.1 per cent. Error rates arising from diagnosis and clinical process coding were higher than medication coding. Data fields that involved free text entry were more prone to errors than those involving selection from menus. The authors found that completeness, accuracy, coding reliability and representativeness were generally good, while data timeliness needs to be improved. Research limitations/implications Only data entered into a web-based application were examined. Data omissions and errors in the original questionnaires were not covered. Practical implications Results from this study provided informative and practicable approaches to improve primary health care data completeness and accuracy especially in developing nations where resources are limited. Originality/value Primary care data quality studies in developing nations are limited. Understanding errors and missing data enables researchers and health service administrators to prevent quality-related problems in primary care data.

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Further Development and Validation of the electronic Frailty Index using the Secure Anonymised Information Linkage Databank

International Journal for Population Data Science ◽

10.23889/ijpds.v3i4.786 ◽

2018 ◽

Vol 3 (4) ◽

Cited By ~ 1

Author(s):

Joe Hollinghurst ◽

Ashley Akbari ◽

Sarah Rodgers ◽

Andrew Clegg

Keyword(s):

Primary Care ◽

Frailty Index ◽

Time Dependent ◽

Future Research ◽

Evidence Based ◽

Deficit Model ◽

Time Restrictions ◽

Primary Care Data ◽

Cumulative Deficit ◽

Using Data

IntroductionAging populations with increasing frailty have major implications for health services internationally, and evidence-based treatment becomes increasingly important. The development of an electronic Frailty Index (eFI) using routine primary care data facilitates implementation of evidence-based interventions. However, the eFI does not account for time restrictions regarding when information was recorded. Objectives and ApproachOur aim is to implement and further validate the eFI using the Secure Anonymised Information Linkage (SAIL) databank, introducing refinements based on time restrictions. Our implementation of the eFI identifies frailty based on 1574 Read codes, which are mapped amongst 36 categories known as deficits. The eFI is based on the internationally established cumulative deficit model, and each deficit contributes equally to the eFI value. However, although each deficit is equally weighted, only one of them is currently time dependent. We therefore analyse the time at which each deficit is identified, and propose time dependent cut-points based on our findings. ResultsWe were able to successfully implement the eFI using data from over 400,000 individuals from the Welsh population using data held in the SAIL databank. Our results agree with the baseline characteristics and distributions of frailty found in the original development of the eFI. We also found that the percentage of individuals identified as frail increased as the number of years of records included was increased. Furthermore, the increase in percentage year by year was almost linear for a number of the deficits. This led to the identification of time bounds for particular deficits, which could help to refine future implementations of the eFI. Conclusion/ImplicationsOur work validates and refines the eFI, which is a particularly useful resource as it uses existing primary care data to identify frailty, meaning no additional resources are required. Furthermore, our implementation is readily available, meaning that future research related to frailty is easily reproducible and achievable by others.

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Structured yet simple approaches to primary care data quality improvements can indeed strike gold

Australian Journal of Primary Health ◽

10.1071/py20247 ◽

2021 ◽

Vol 27 (2) ◽

pp. 143

Author(s):

Abhijeet Ghosh ◽

Elizabeth Halcomb ◽

Sandra McCarthy ◽

Christine Ashley

Keyword(s):

Primary Care ◽

General Practice ◽

Quality Improvement ◽

Data Quality ◽

Disease Surveillance ◽

Data Entry ◽

Structured Interviews ◽

Improvement Strategy ◽

Quality Improvement Strategy ◽

Primary Care Data

General practice data provide important opportunities for both population health and within-practice initiatives to improve health. Despite its promise, a lack of accuracy affects the use of such data. The Sentinel Practices Data Sourcing (SPDS) project is a structured chronic disease surveillance and data quality improvement strategy in general practice. A mixed-methods approach was used to evaluate data quality improvement in 99 participating practices over 12 months. Quantitative data were obtained by measuring performance against 10 defined indicators, whereas 48 semi-structured interviews provided qualitative data. Aggregated scores demonstrated improvements in all indicators, ranging from minor to substantially significant improvements. Participants reported positively on levels of support provided, and acquisition of new knowledge and skills relating to data entry and cleansing. This evaluation provides evidence of the effectiveness of a structured approach to improve the quality of primary care data. Investing in this targeted intervention has the potential to create sustained improvements in data quality, which can drive clinical practice improvement.

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Measuring daily functioning in older persons using a frailty index: a cohort study based on routine primary care data

British Journal of General Practice ◽

10.3399/bjgp20x713453 ◽

2020 ◽

Vol 70 (701) ◽

pp. e866-e873

Author(s):

Willeke M Ravensbergen ◽

Jeanet W Blom ◽

Andrea WM Evers ◽

Mattijs E Numans ◽

Margot WM de Waal ◽

...

Keyword(s):

Primary Care ◽

Cohort Study ◽

Older Persons ◽

Frailty Index ◽

Routine Care ◽

Community Dwelling ◽

Daily Functioning ◽

Primary Care Data ◽

Observational Cohort ◽

Data Source

BackgroundElectronic health records (EHRs) are increasingly used for research; however, multicomponent outcome measures such as daily functioning cannot yet be readily extracted.AimTo evaluate whether an electronic frailty index based on routine primary care data can be used as a measure for daily functioning in research with community-dwelling older persons (aged ≥75 years).Design and settingCohort study among participants of the Integrated Systemic Care for Older People (ISCOPE) trial (11 476 eligible; 7285 in observational cohort; 3141 in trial; over-representation of frail people).MethodAt baseline (T0) and after 12 months (T12), daily functioning was measured with the Groningen Activities Restriction Scale (GARS, range 18–72). Electronic frailty index scores (range 0–1) at T0 and T12 were computed from the EHRs. The electronic frailty index (electronic Frailty Index — Utrecht) was tested for responsiveness and compared with the GARS as a gold standard for daily functioning.ResultsIn total, 1390 participants with complete EHR and follow-up data were selected (31.4% male; median age = 81 years, interquartile range = 78–85). The electronic frailty index increased with age, was higher for females, and lower for participants living with a partner. It was responsive after an acute major medical event; however, the correlation between the electronic frailty index and GARS at T0 and over time was limited.ConclusionBecause the electronic frailty index does not reflect daily functioning, further research on new methods to measure daily functioning with routine care data (for example, other proxies) is needed before EHRs can be a useful data source for research with older persons.

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Problems with primary care data quality: osteoporosis as an exemplar

Journal of Innovation in Health Informatics ◽

10.14236/jhi.v12i3.120 ◽

2004 ◽

Vol 12 (3) ◽

pp. 147-156 ◽

Cited By ~ 12

Author(s):

Simon DeLusignan ◽

Tom Valentin ◽

Tom Chan ◽

Nigel Hague ◽

Oliver Wood ◽

...

Keyword(s):

Primary Care ◽

Data Quality ◽

Primary Care Data

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Construction and Validation of a Frailty Index in Primary Care in Italy: The Health-Search Frailty Index

Innovation in Aging ◽

10.1093/geroni/igab046.2042 ◽

2021 ◽

Vol 5 (Supplement_1) ◽

pp. 533-534

Author(s):

Davide Vetrano ◽

Alberto Zucchelli ◽

Graziano Onder ◽

Roberto Bernabei ◽

Laura Fratiglioni ◽

...

Keyword(s):

Primary Care ◽

Frailty Index ◽

External Validation ◽

Geographical Area ◽

Population Level ◽

Survival Models ◽

National Study ◽

Personalized Care ◽

Primary Care Data ◽

Data Driven Approach

Abstract Recognizing frailty in primary care is important to implement personalized care pathways and for prognostication. The aim of this study was to build and validate a frailty index based on routinely collected primary care data in Italy. We used clinical data from 308,280 Italian primary care patients 60+ with at least 5 years of follow-up, part of the Health Search Database. A heuristic algorithm was used to select the deficits to be included in a highly performant frailty index. The fitness of the index was assessed through the c-statistics derived by survival models. Results were externally validated using the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K). After testing 3.4 million of deficits combinations, 25 deficits were selected to be included in the Health Search Frailty Index (HS-FI). After adjusting by sex, age and geographical area, the HS-FI was associated with 5-year mortality (HR per 0.1 increase 1.99; 95%CI 1.95-2.02) and hospitalization rate (HR per 0.1 increase 1.25; 95%CI 1.23-1.27). In the external validation cohort, HS-FI independently predicted mortality, hospitalization, incident disability, incident dementia, and incident falls. This is the first frailty index built following a data-driven approach, using national representative primary care data. The implementation of such tool – derived by routinely collected data – in primary care software will ease the prompt, comparable and reliable recognition of frailty at the population level.

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Prediction of Adverse Health Outcomes in Older People Using a Frailty Index Based on Routine Primary Care Data

The Journals of Gerontology Series A ◽

10.1093/gerona/gls161 ◽

2012 ◽

Vol 68 (3) ◽

pp. 301-308 ◽

Cited By ~ 99

Author(s):

I. Drubbel ◽

N. J. de Wit ◽

N. Bleijenberg ◽

R. J. C. Eijkemans ◽

M. J. Schuurmans ◽

...

Keyword(s):

Primary Care ◽

Older People ◽

Health Outcomes ◽

Frailty Index ◽

Adverse Health ◽

Adverse Health Outcomes ◽

Primary Care Data

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CHARGE-AF in a national routine primary care electronic health records database in the Netherlands: validation for 5-year risk of atrial fibrillation and implications for patient selection in atrial fibrillation screening

Open Heart ◽

10.1136/openhrt-2020-001459 ◽

2021 ◽

Vol 8 (1) ◽

pp. e001459

Author(s):

Jelle C L Himmelreich ◽

Wim A M Lucassen ◽

Ralf E Harskamp ◽

Claire Aussems ◽

Henk C P M van Weert ◽

...

Keyword(s):

Atrial Fibrillation ◽

Primary Care ◽

High Risk ◽

The Netherlands ◽

Patient Selection ◽

Low Cost ◽

Calibration Plot ◽

Primary Care Data ◽

C Statistic ◽

Kaplan Meier

AimsTo validate a multivariable risk prediction model (Cohorts for Heart and Aging Research in Genomic Epidemiology model for atrial fibrillation (CHARGE-AF)) for 5-year risk of atrial fibrillation (AF) in routinely collected primary care data and to assess CHARGE-AF’s potential for automated, low-cost selection of patients at high risk for AF based on routine primary care data.MethodsWe included patients aged ≥40 years, free of AF and with complete CHARGE-AF variables at baseline, 1 January 2014, in a representative, nationwide routine primary care database in the Netherlands (Nivel-PCD). We validated CHARGE-AF for 5-year observed AF incidence using the C-statistic for discrimination, and calibration plot and stratified Kaplan-Meier plot for calibration. We compared CHARGE-AF with other predictors and assessed implications of using different CHARGE-AF cut-offs to select high-risk patients.ResultsAmong 111 475 patients free of AF and with complete CHARGE-AF variables at baseline (17.2% of all patients aged ≥40 years and free of AF), mean age was 65.5 years, and 53% were female. Complete CHARGE-AF cases were older and had higher AF incidence and cardiovascular comorbidity rate than incomplete cases. There were 5264 (4.7%) new AF cases during 5-year follow-up among complete cases. CHARGE-AF’s C-statistic for new AF was 0.74 (95% CI 0.73 to 0.74). The calibration plot showed slight risk underestimation in low-risk deciles and overestimation of absolute AF risk in those with highest predicted risk. The Kaplan-Meier plot with categories <2.5%, 2.5%–5% and >5% predicted 5-year risk was highly accurate. CHARGE-AF outperformed CHA2DS2-VASc (Cardiac failure or dysfunction, Hypertension, Age >=75 [Doubled], Diabetes, Stroke [Doubled]-Vascular disease, Age 65-74, and Sex category [Female]) and age alone as predictors for AF. Dichotomisation at cut-offs of 2.5%, 5% and 10% baseline CHARGE-AF risk all showed merits for patient selection in AF screening efforts.ConclusionIn patients with complete baseline CHARGE-AF data through routine Dutch primary care, CHARGE-AF accurately assessed AF risk among older primary care patients, outperformed both CHA2DS2-VASc and age alone as predictors for AF and showed potential for automated, low-cost patient selection in AF screening.

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