Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse

Background - Enormous amounts of data are recorded routinely in health care as part of the care process, primarily for managing individual patient care. There are significant opportunities to use this data for other purposes, many of which would contribute to establishing a learning health system. This is particularly true for data recorded in primary care settings, as in many countries, these are the first place patients turn to for most health problems. Objective - In this paper, we discuss whether data that is recorded routinely as part of the health care process in primary care is actually fit to use for these other purposes, how the original purpose may affect the extent to which the data is fit for another purpose and the mechanisms behind these effects. In doing so, we want to identify possible sources of bias that are relevant for the (re-)use of this type of data. Methods –This discussion paper is based on the authors’ experience as users of electronic health records data, as a general practitioner, health informatics experts, and health services researchers. It is a product of the discussions they had during the TRANSFoRm project, which was funded by the EU and sought to develop, pilot and evaluate a core information architecture for the Learning Health System (LHS) in Europe, based on primary care electronic health records. Results – We first describe the different stages in the processing of EHR data, as well as the different purposes for which this data is used. Given the different data processing steps and purposes, we then discuss the possible mechanisms for each individual data processing step, that can generate biased outcomes. We identified thirteen possible sources of bias. Four of them are related to the organization of a health care system, some are of a more technical nature. Conclusions - There are a substantial number of possible sources of bias, and very little is known about the size and direction of their impact. However, any (re-)user of data that was recorded as part of the health care process (such as researchers and clinicians) should be aware of the associated data collection process and environmental influences that can affect the quality of the data. Our stepwise, actor and purpose oriented approach may help to identify these possible sources of bias. Unless data quality issues are better understood and unless adequate controls are embedded throughout the data lifecycle, data-driven healthcare will not live up to its expectations. We need a data quality research agenda to devise the appropriate instruments needed to assess the magnitude of each of the possible sources of bias, and then start measuring their impact. The possible sources of bias described in this paper serve as a starting point for this research agenda

The use of out-of-hours primary care during the first year of the COVID-19 pandemic

BACKGROUND: In the Netherlands, General Practitioners (GP) are usually the first point of contact with a health professional for most health problems. Out-of-hours (OOH) primary care is provided by regional OOH services. Changes in consultation rates at OOH services may be regarded as a warning system for failures elsewhere in the healthcare system. Therefore in this study, we investigated how the COVID-19 pandemic changed the use of primary care OOH services during the first year of the pandemic. METHODS: Routine electronic health records data were used from 60% of OOH services in the Netherlands, collected by the Nivel Primary Care Database. We compared consultation rates per week (2020) for COVID-19-like symptoms and other health problems (e.g. small traumas, urinary tract infections), for different age groups, the proportion of remote consultations, and different levels of urgency during the pandemic compared to the same period in 2019. RESULTS: The number of consultations for COVID-19-like symptoms peaked at the start of the COVID-19 pandemic, while consultations for other health problems decreased. These changes in consultation rates differed between age groups. Remote consultations took place more frequently for all health problems, while the proportion of non-urgent health problems increased. CONCLUSION: There were significant changes in the number of consultations and the proportion that were remote for COVID-19-like symptoms and other health problems. Especially care for babies and young children decreased, while the number of consultations for older adults remained stable. The continued use of OOH services by older adults suggests there were unmet care needs elsewhere in our healthcare system. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-022-08096-x

Immediate and long-term health impact of exposure to gas-mining induced earthquakes and related environmental stressors

BACKGROUND: Little is known about the public health impact of chronic exposure to physical and social stressors in the human environment. Objective of this study was to investigate the immediate and long-term health effects of living in an environment with gas-mining induced earthquakes and related stressors in the Netherlands. METHODS: Data on psychological, somatic and social problems recorded routinely in electronic health records by general practitioners during a 6-year period (2010–2015) were combined with socioeconomic status and seismicity data. To assess immediate health effects of exposure to M(L)≥1.5 earthquakes, relative risk ratios were calculated for patients in the week of an earthquake and the week afterwards, and compared to the week before the earthquake. To analyse long-term health effects, relative risks of different groups, adjusted for age, sex and socioeconomic status, were computed per year and compared. RESULTS: Apart from an increase in suicidality, few immediate health changes were found in an earthquake week or week afterwards. Generally, the prevalence of health problems was higher in the mining province in the first years, but dropped to levels equal to or even below the control group in subsequent years, with lower relative risks observed in more frequently exposed patients. CONCLUSIONS: From a public health perspective, the findings are fascinating. Contrary to our expectation, health problems presented in general practice in the earthquake province decreased during the study period. More frequently exposed populations reported fewer health issues to general practitioners, which might point at health adaptation to chronic exposure to stressors

Developing a clinical prediction rule for repeated consultations with functional somatic symptoms in primary care:A cohort study

OBJECTIVES: Patients who present in primary care with chronic functional somatic symptoms (FSS) have reduced quality of life and increased health care costs. Recognising these early is a challenge. The aim is to develop and internally validate a clinical prediction rule for repeated consultations with FSS. DESIGN AND SETTING: Records from the longitudinal population-based ('Lifelines') cohort study were linked to electronic health records from general practitioners (GPs). PARTICIPANTS: We included patients consulting a GP with FSS within 1 year after baseline assessment in the Lifelines cohort. OUTCOME MEASURES: The outcome is repeated consultations with FSS, defined as ≥3 extra consultations for FSS within 1 year after the first consultation. Multivariable logistic regression, with bootstrapping for internal validation, was used to develop a risk prediction model from 14 literature-based predictors. Model discrimination, calibration and diagnostic accuracy were assessed. RESULTS: 18 810 participants were identified by database linkage, of whom 2650 consulted a GP with FSS and 297 (11%) had ≥3 extra consultations. In the final multivariable model, older age, female sex, lack of healthy activity, presence of generalised anxiety disorder and higher number of GP consultations in the last year predicted repeated consultations. Discrimination after internal validation was 0.64 with a calibration slope of 0.95. The positive predictive value of patients with high scores on the model was 0.37 (0.29-0.47). CONCLUSIONS: Several theoretically suggested predisposing and precipitating predictors, including neuroticism and stressful life events, surprisingly failed to contribute to our final model. Moreover, this model mostly included general predictors of increased risk of repeated consultations among patients with FSS. The model discrimination and positive predictive values were insufficient and preclude clinical implementation

Gezondheid van gevangenen voor en na detentie

Mensen met een slechte lichamelijke en geestelijke gezondheid zijn oververtegenwoordigd in de gevangenispopulatie. Onduidelijk is of die gezondheidsproblemen er al waren voor de detentie, of juist in de gevangenis zijn ontstaan of verergerd. In een gematcht cohortonderzoek op basis van Nivel Zorgregistraties Eerste Lijn en gevangenisgegevens is de gezondheid van gedetineerden in het jaar voor en het jaar na hun detentie vergeleken met die van niet-gedetineerden. Gedetineerden bleken al voor hun detentie relatief vaak psychische en sociale problemen te hebben. De gevangenschap zelf veranderde daar niet veel aan. (Ex-)gedetineerden blijven een kwetsbare patiëntengroep die ook buiten de gevangenis extra aandacht behoeft

Electronic Health Record-Triggered Research Infrastructure Combining Real-world Electronic Health Record Data and Patient-Reported Outcomes to Detect Benefits, Risks, and Impact of Medication:Development Study

BACKGROUND: Real-world data from electronic health records (EHRs) represent a wealth of information for studying the benefits and risks of medical treatment. However, they are limited in scope and should be complemented by information from the patient perspective. OBJECTIVE: The aim of this study is to develop an innovative research infrastructure that combines information from EHRs with patient experiences reported in questionnaires to monitor the risks and benefits of medical treatment. METHODS: We focused on the treatment of overactive bladder (OAB) in general practice as a use case. To develop the Benefit, Risk, and Impact of Medication Monitor (BRIMM) infrastructure, we first performed a requirement analysis. BRIMM’s starting point is routinely recorded general practice EHR data that are sent to the Dutch Nivel Primary Care Database weekly. Patients with OAB were flagged weekly on the basis of diagnoses and prescriptions. They were invited subsequently for participation by their general practitioner (GP), via a trusted third party. Patients received a series of questionnaires on disease status, pharmacological and nonpharmacological treatments, adverse drug reactions, drug adherence, and quality of life. The questionnaires and a dedicated feedback portal were developed in collaboration with a patient association for pelvic-related diseases, Bekkenbodem4All. Participating patients and GPs received feedback. An expert meeting was organized to assess the strengths, weaknesses, opportunities, and threats of the new research infrastructure. RESULTS: The BRIMM infrastructure was developed and implemented. In the Nivel Primary Care Database, 2933 patients with OAB from 27 general practices were flagged. GPs selected 1636 (55.78%) patients who were eligible for the study, of whom 295 (18.0% of eligible patients) completed the first questionnaire. A total of 288 (97.6%) patients consented to the linkage of their questionnaire data with their EHR data. According to experts, the strengths of the infrastructure were the linkage of patient-reported outcomes with EHR data, comparison of pharmacological and nonpharmacological treatments, flexibility of the infrastructure, and low registration burden for GPs. Methodological weaknesses, such as susceptibility to bias, patient selection, and low participation rates among GPs and patients, were seen as weaknesses and threats. Opportunities represent usefulness for policy makers and health professionals, conditional approval of medication, data linkage to other data sources, and feedback to patients. CONCLUSIONS: The BRIMM research infrastructure has the potential to assess the benefits and safety of (medical) treatment in real-life situations using a unique combination of EHRs and patient-reported outcomes. As patient involvement is an important aspect of the treatment process, generating knowledge from clinical and patient perspectives is valuable for health care providers, patients, and policy makers. The developed methodology can easily be applied to other treatments and health problems

Incentivizing appropriate prescribing in primary care:Development and first results of an electronic health record-based pay-for-performance scheme

Objective Part of the funding of Dutch General Practitioners (GPs) care is based on pay-for-performance, including an incentive for appropriate prescribing according to guidelines in national formularies. Aim of this paper is to describe the development of an indicator and an infrastructure based on prescription data from GP Electronic Health Records (EHR), to assess the level of adherence to formularies and the effects of the pay-for-performance scheme, thereby assessing the usefulness of the infrastructure and the indicator. Methods Adherence to formularies was calculated as the percentage of first prescriptions by the GP for medications that were included in one of the national formularies used by the GP, based on prescription data from EHRs. Adherence scores were collected quarterly for 2018 and 2019 and subsequently sent to health insurance companies for the pay-for-performance scheme. Adherence scores were used to monitor the effect of the pay-for-performance scheme. Results 75% (2018) and 83% (2019) of all GP practicesparticipated. Adherence to formularies was around 85% or 95%, depending on the formulary used. Adherence improved significantly, especially for practices that scored lowest in 2018. Discussion We found high levels of adherence to national formularies, with small improvements after one year. The infrastructure will be used to further stimulate formulary-based prescribing by implementing more actionable and relevant indicators on adherence scores for GPs

Increased incidence of kidney diseases in general practice after a nationwide albuminuria self-test program

<p>Abstract</p> <p>Background</p> <p>To study the influence of a nationwide albuminuria self-test program on the number of GP contacts for urinary complaints and/or kidney diseases and the number of newly diagnosed patients with kidney diseases by the GP.</p> <p>Methods</p> <p>Data were used from the Netherlands Information Network of General Practice (LINH), including a representative sample of general practices with a dynamic population of approximately 300.000 listed patients. Morbidity data were retrieved from electronic medical records, kept in a representative sample of general practices. The incidence of kidney diseases and urinary complaints before and after the albuminuria self-test program was compared with logistic regression analyses.</p> <p>Results</p> <p>Data were used from 139 general practices, including 444,220 registered patients. The number of GP consultations for kidney diseases and urinary complaints was increased in the year after the albuminuria self-test program and particularly shortly after the start of the program. Compared with the period before the self-test program, more patients have been diagnosed by the GP with symptoms/complaints of kidney disease and urinary diseases (OR = 1.7 (CI 1.4 - 2.0) and OR = 2.1 (CI 1.9 - 2.3), respectively). The odds on an abnormal urine-test in the period after the self-test program was three times higher than the year before (OR = 3.0 (CI 2.4 - 3.6)). The effect of the self-test program on newly diagnosed patients with an abnormal urine test was modified by both the presence of the risk factors hypertension and diabetes mellitus. For this diagnosis the highest OR was found in patients without both conditions (OR = 4.2 (CI 3.3 - 5.4)).</p> <p>Conclusions</p> <p>A nationwide albuminuria self-test program resulted in an increasing number of newly diagnosed kidney complaints and diseases the year after the program. The highest risks were found in patients without risk factors for kidney diseases.</p

Inter-practice variation in diagnosing hypertension and diabetes mellitus: a cross-sectional study in general practice

BACKGROUND: Previous studies of inter-practice variation of the prevalence of hypertension and diabetes mellitus showed wide variations between practices. However, in these studies inter-practice variation was calculated without controlling for clustering of patients within practices and without adjusting for patient and practice characteristics. Therefore, in the present study inter-practice variation of diagnosed hypertension and diabetes mellitus prevalence rates was calculated by 1) using a multi-level design and 2) adjusting for patient and practice characteristics. METHODS: Data were used from the Netherlands Information Network of General Practice (LINH) in 2004. Of all 168.045 registered patients, the presence of hypertension, diabetes mellitus and all available ICPC coded symptoms and diseases related to hypertension and diabetes, were determined. Also, the characteristics of practices were used in the analyses. Multilevel logistic regression analyses were performed. RESULTS: The 95% prevalence range for the practices for the prevalence of diagnosed hypertension and diabetes mellitus was 66.3 to 181.7 per 1000 patients and 22.2 to 65.8 per 1000 patients, respectively, after adjustment for patient and practice characteristics. The presence of hypertension and diabetes was best predicted by patient characteristics. The most important predictors of hypertension were obesity (OR = 3.5), presence of a lipid disorder (OR = 3.0), and diabetes mellitus (OR = 2.6), whereas the presence of diabetes mellitus was particularly predicted by retinopathy (OR = 8.5), lipid disorders (OR = 2.8) and hypertension (OR = 2.7). CONCLUSION: Although not the optimal case-mix could be used in this study, we conclude that even after adjustment for patient (demographic variables and risk factors for hypertension and diabetes mellitus) and practice characteristics (practice size and presence of a practice nurse), there is a wide difference between general practices in the prevalence rates of diagnosed hypertension and diabetes mellitu