Problems with primary care data quality: osteoporosis as an exemplar

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

Aging Clinical and Experimental Research ◽

10.1007/s40520-018-1023-9 ◽

2018 ◽

Vol 31 (5) ◽

pp. 653-660 ◽

Cited By ~ 9

Author(s):

Rachel C. Ambagtsheer ◽

Justin Beilby ◽

Julia Dabravolskaj ◽

Marjan Abbasi ◽

Mandy M. Archibald ◽

...

Keyword(s):

Primary Care ◽

Data Quality ◽

Frailty Index ◽

Feasibility Assessment ◽

Primary Care Data

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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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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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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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