A standardised graphic method for describing data privacy frameworks in primary care research using a flexible zone model

Natural language processing systems for extracting information from electronic health records about activities of daily living. A systematic review

Objective

Natural language processing (NLP) can enhance research on activities of daily living (ADL) by extracting structured information from unstructured electronic health records (EHRs) notes. This review aims to give insight into the state-of-the-art, usability, and performance of NLP systems to extract information on ADL from EHRs.

Materials and Methods

A systematic review was conducted based on searches in Pubmed, Embase, Cinahl, Web of Science, and Scopus. Studies published between 2017 and 2022 were selected based on predefined eligibility criteria.

Results

The review identified 22 studies. Most studies (65%) used NLP for classifying unstructured EHR data on 1 or 2 ADL. Deep learning, combined with a ruled-based method or machine learning, was the approach most commonly used. NLP systems varied widely in terms of the pre-processing and algorithms. Common performance evaluation methods were cross-validation and train/test datasets, with F1, precision, and sensitivity as the most frequently reported evaluation metrics. Most studies reported relativity high overall scores on the evaluation metrics.

Discussion

NLP systems are valuable for the extraction of unstructured EHR data on ADL. However, comparing the performance of NLP systems is difficult due to the diversity of the studies and challenges related to the dataset, including restricted access to EHR data, inadequate documentation, lack of granularity, and small datasets.

Conclusion

This systematic review indicates that NLP is promising for deriving information on ADL from unstructured EHR notes. However, what the best-performing NLP system is, depends on characteristics of the dataset, research question, and type of ADL.

Generalizability of a Musculoskeletal Therapist Electronic Health Record for Modelling Outcomes to Work-Related Musculoskeletal Disorders

PURPOSE

Electronic Health Records (EHRs) can contain vast amounts of clinical information that could be reused in modelling outcomes of work-related musculoskeletal disorders (WMSDs). Determining the generalizability of an EHR dataset is an important step in determining the appropriateness of its reuse. The study aims to describe the EHR dataset used by occupational musculoskeletal therapists and determine whether the EHR dataset is generalizable to the Australian workers' population and injury characteristics seen in workers' compensation claims.

METHODS

Variables were considered if they were associated with outcomes of WMSDs and variables data were available. Completeness and external validity assessment analysed frequency distributions, percentage of records and confidence intervals.

RESULTS

There were 48,434 patient care plans across 10 industries from 2014 to 2021. The EHR collects information related to clinical interventions, health and psychosocial factors, job demands, work accommodations as well as workplace culture, which have all been shown to be valuable variables in determining outcomes to WMSDs. Distributions of age, duration of employment, gender and region of birth were mostly similar to the Australian workforce. Upper limb WMSDs were higher in the EHR compared to workers' compensation claims and diagnoses were similar.

CONCLUSION

The study shows the EHR has strong potential to be used for further research into WMSDs as it has a similar population to the Australian workforce, manufacturing industry and workers' compensation claims. It contains many variables that may be relevant in modelling outcomes to WMSDs that are not typically available in existing datasets.

Lessons learned from the development of a national registry on dementia care and support based on linked national health and administrative data

Introduction

This paper provides insight into the development of the Dutch Dementia Care and Support Registry and the lessons that can be learned from it. The aim of this Registry was to contribute to quality improvement in dementia care and support.

Methods

This paper describes how the Registry was set up in four stages, reflecting the four FAIR principles: the selection of data sources (Findability); obtaining access to the selected data sources (Accessibility); data linkage (Interoperability); and the reuse of data (Reusability).

Results

The linkage of 16 different data sources, including national routine health and administrative data appeared to be technically and legally feasible. The linked data in the Registry offers rich information about (the use of) care for persons with dementia across various healthcare settings, including but not limited to primary care, secondary care, long-term care and medication use, that cannot be obtained from single data sources.

Conclusions

A key lesson learned is that in order to reuse the data for quality improvement in practice, it is essential to involve healthcare professionals in setting up the Registry and to guide them in the interpretation of the data.

Provision of and trust in COVID-19 vaccines information: Perspectives of people who have had COVID-19

AIM

The aim of this study was to understand the provision and need, quality of and trust in COVID-19 vaccines information from the perspectives of people who have had COVID-19 infection.

METHOD

People who have had a COVID-19 infection were approached via their general practice and invited to participate in the Nivel Corona Cohort. They completed questionnaires at baseline (Q1), and at three months (Q2). Outcome measures were based on health information-seeking behaviour, as used in the Comprehensive Model of Information Seeking. Antecedents (i.e., gender, age, education level, health literacy) were used from Q1, and one's beliefs and experiences (i.e., trust in the information and healthcare system, how applicable the information is), information carrier factors (i.e., information quality perceptions and via which sources), health-information seeking actions (i.e., decision to vaccinate and information sufficiency) and vaccination status from Q2. Data were analysed using descriptive analyses, analysis of variance tests (F-tests) and χ² tests with the statistical software STATA.

RESULTS

Of the respondents (N = 314), 96% were vaccinated at least once, mostly after having had the virus. Most retrieved information about COVID-19 vaccines on the website of the National Institute for Public Health and the Environment (79%), broader via the internet (56%), or from family and friends (35%). Almost all had trust in the information (89%) and healthcare system (94%). Most found the information applicable to their situation (67%). Moreover, most perceived the information as correct (71%) and did not perceive the information to be misleading (85%), while fewer people found the information reliable (59%) and clear (58%). Overall, the majority indicated that the information met their expectations to make a well-informed decision to vaccinate (89%).

CONCLUSION

Different characteristics of people who had COVID-19 and sought information were identified, which is important to offer tailored information. People who had COVID-19 in this study, mainly middle-aged, vaccinated and highly educated, were generally positive about the vaccines information, but overall the reliability and clarity could be improved. This is important for a high vaccination uptake, booster programs and coming pandemics.

PATIENT OR PUBLIC CONTRIBUTION

The questionnaire was reviewed by patients who had COVID-19, one of whom is a health services researcher.

Pattern of oral anticoagulant prescribing for atrial fibrillation in general practice: an observational study in The Netherlands

BACKGROUND

In the Dutch atrial fibrillation (AF) guideline for GPs, vitamin K antagonists (VKAs) and direct oral anticoagulants (DOACs) are seen as equivalent, while in cardiology there is a preference for DOACs.

AIM

To describe the pattern of oral anticoagulant (OAC) prescribing for AF by GPs and assess whether GPs proactively convert between VKAs and DOACs in patients with AF.

DESIGN & SETTING

Observational study using routine practice data from 214 general practices, from 2017 until 2019.

METHOD

Patients aged ≥60 years diagnosed with AF, who had been prescribed OACs by their GPs in 2018 were included. A distinction was made between starters, who were participants who did not use OACs in 2017, and prevalent users. It was observed and recorded whether patients switched between VKAs and DOACs.

RESULTS

A total of 12 516 patients with AF were included. Four hundred and seventy-six patients (4%) started OACs in 2018; 12 040 patients were prevalent OAC users. When GPs started patients on OACs, DOACs were prescribed the most (88%). Among prevalent users, more than half of the patients used VKAs (60%). GPs switched between OACs for 1% of starters and 0.6% of prevalent users in 2018 and 2019.

CONCLUSION

Dutch GPs predominantly start with DOACs in newly diagnosed patients with AF. Prevalent patients predominantly use VKAs and switching from a DOAC to a VKA is unusual. Consequently, the number of patients using VKAs will decline in the upcoming years. This trend raises questions about the future of organising frequent international normalised ratio (INR) checks for VKA users.

Opt-In and Opt-Out Consent Procedures for the Reuse of Routinely Recorded Health Data in Scientific Research and Their Consequences for Consent Rate and Consent Bias: Systematic Review

BACKGROUND

Scientific researchers who wish to reuse health data pertaining to individuals can obtain consent through an opt-in procedure or opt-out procedure. The choice of procedure may have consequences for the consent rate and representativeness of the study sample and the quality of the research, but these consequences are not well known.

OBJECTIVE

This review aimed to provide insight into the consequences for the consent rate and consent bias of the study sample of opt-in procedures versus opt-out procedures for the reuse of routinely recorded health data for scientific research purposes.

METHODS

A systematic review was performed based on searches in PubMed, Embase, CINAHL, PsycINFO, Web of Science Core Collection, and the Cochrane Library. Two reviewers independently included studies based on predefined eligibility criteria and assessed whether the statistical methods used in the reviewed literature were appropriate for describing the differences between consenters and nonconsenters. Statistical pooling was conducted, and a description of the results was provided.

RESULTS

A total of 15 studies were included in this meta-analysis. Of the 15 studies, 13 (87%) implemented an opt-in procedure, 1 (7%) implemented an opt-out procedure, and 1 (7%) implemented both the procedures. The average weighted consent rate was 84% (60,800/72,418 among the studies that used an opt-in procedure and 96.8% (2384/2463) in the single study that used an opt-out procedure. In the single study that described both procedures, the consent rate was 21% in the opt-in group and 95.6% in the opt-out group. Opt-in procedures resulted in more consent bias compared with opt-out procedures. In studies with an opt-in procedure, consenting individuals were more likely to be males, had a higher level of education, higher income, and higher socioeconomic status.

CONCLUSIONS

Consent rates are generally lower when using an opt-in procedure compared with using an opt-out procedure. Furthermore, in studies with an opt-in procedure, participants are less representative of the study population. However, both the study populations and the way in which opt-in or opt-out procedures were organized varied widely between the studies, which makes it difficult to draw general conclusions regarding the desired balance between patient control over data and learning from health data. The reuse of routinely recorded health data for scientific research purposes may be hampered by administrative burdens and the risk of bias.

Impact of influenza vaccination on GP-diagnosed COVID-19 and all-cause mortality: a Dutch cohort study

OBJECTIVES

As clinical presentation and complications of both viruses overlap, it was hypothesised that influenza vaccination was associated with lower general practitioner (GP)-diagnosed COVID-19 rates and lower all-cause mortality rates.

STUDY DESIGN

From a primary care population-based cohort in the Netherlands, GP-diagnosed COVID-19 (between 10 March and 22 November 2020) and all-cause mortality events (between 30 December 2019 and 22 November 2020) were recorded. 223 580 persons were included, representing the influenza vaccination 2019 target group (all aged ≥60 years, and those <60 years with a medical indication). Proportional hazards regression analyses evaluated associations between influenza vaccination in 2019 and two outcomes: GP-diagnosed COVID-19 and all-cause mortality. Covariables were sex, age, comorbidities and number of acute respiratory infection primary care consultations in 2019.

RESULTS

A slightly positive association (HR 1.15; 95% CI 1.08 to 1.22) was found between influenza vaccination in 2019 and GP-diagnosed COVID-19, after adjusting for covariables. A slightly protective effect for all-cause mortality rates (HR 0.90; 95% CI 0.83 to 0.97) was found for influenza vaccination, after adjusting for covariables. A subgroup analysis among GP-diagnosed COVID-19 cases showed no significant association between influenza vaccination in 2019 and all-cause mortality.

CONCLUSIONS

Our hypothesis of a possibly negative association between influenza vaccination in 2019 and GP-diagnosed COVID-19 was not confirmed as we found a slightly positive association. A slightly protective effect on all-cause mortality was found after influenza vaccination, possibly by a wider, overall protective effect on health. Future research designs should include test-confirmed COVID-19 cases and controls, adjustments for behavioural, socioeconomic and ethnic factors and validated cause-specific mortality cases.

Detection of primary Sjögren’s syndrome in primary care: developing a classification model with the use of routine healthcare data and machine learning

Background

Primary Sjögren’s Syndrome (pSS) is a rare autoimmune disease that is difficult to diagnose due to a variety of clinical presentations, resulting in misdiagnosis and late referral to specialists. To improve early-stage disease recognition, this study aimed to develop an algorithm to identify possible pSS patients in primary care. We built a machine learning algorithm which was based on combined healthcare data as a first step towards a clinical decision support system.

Method

Routine healthcare data, consisting of primary care electronic health records (EHRs) data and hospital claims data (HCD), were linked on patient level and consisted of 1411 pSS and 929,179 non-pSS patients. Logistic regression (LR) and random forest (RF) models were used to classify patients using age, gender, diseases and symptoms, prescriptions and GP visits.

Results

The LR and RF models had an AUC of 0.82 and 0.84, respectively. Many actual pSS patients were found (sensitivity LR = 72.3%, RF = 70.1%), specificity was 74.0% (LR) and 77.9% (RF) and the negative predictive value was 99.9% for both models. However, most patients classified as pSS patients did not have a diagnosis of pSS in secondary care (positive predictive value LR = 0.4%, RF = 0.5%).

Conclusion

This is the first study to use machine learning to classify patients with pSS in primary care using GP EHR data. Our algorithm has the potential to support the early recognition of pSS in primary care and should be validated and optimized in clinical practice. To further enhance the algorithm in detecting pSS in primary care, we suggest it is improved by working with experienced clinicians.

Opioid prescribing in out-of-hours primary care in Flanders and the Netherlands: A retrospective cross-sectional study

Background

Increased opioid prescribing has raised concern, as the benefits of pain relief not always outweigh the risks. Acute and chronic pain is often treated in a primary care out-of-hours (OOH) setting. This setting may be a driver of opioid use but the extent to which opioids are prescribed OOH is unknown. We aimed to investigate weak and strong opioid prescribing at OOH primary care services (PCS) in Flanders (Northern, Dutch-speaking part of Belgium) and the Netherlands between 2015 and 2019.

Methods

We performed a retrospective cross sectional study using data from routine electronic health records of OOH-PCSs in Flanders and the Netherlands (2015–2019). Our primary outcome was the opioid prescribing rate per 1000 OOH-contacts per year, in total and for strong (morphine, hydromorphone, oxycodone, oxycodone and naloxone, fentanyl, tapentadol, and buprenorphine and weak opioids (codeine combinations and tramadol and combinations) and type of opioids separately.

Results

Opioids were prescriped in approximately 2.5% of OOH-contacts in both Flanders and the Netherlands. In Flanders, OOH opioid prescribing went from 2.4% in 2015 to 2.1% in 2017 and then increased to 2.3% in 2019. In the Netherlands, opioid prescribing increased from 1.9% of OOH-contacts in 2015 to 2.4% in 2017 and slightly decreased thereafter to 2.1% of OOH-contacts. In 2019, in Flanders, strong opioids were prescribed in 8% of the OOH-contacts with an opioid prescription. In the Netherlands a strong opioid was prescribed in 57% of these OOH-contacts. Two thirds of strong opioids prescriptions in Flanders OOH were issued for patients over 75, in the Netherlands one third was prescribed to this age group.

Conclusion

We observed large differences in strong opioid prescribing at OOH-PCSs between Flanders and the Netherlands that are likely to be caused by differences in accessibility of secondary care, and possibly existing opioid prescribing habits. Measures to ensure judicious and evidence-based opioid prescribing need to be tailored to the organisation of the healthcare system.

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.

Prevalence of Health Problems and Health-Care Use in Partners of People with Dementia: Longitudinal Analysis with Routinely Recorded Health and Administrative Data

INTRODUCTION

This study aims to provide insight into the prevalence of health problems and the frequency of general practitioner (GP) contacts in cohabiting partners of persons with dementia, during the year prior to the dementia diagnosis and up to 3 years after the diagnosis.

METHODS

Partners of persons with dementia and a matched control group of partners of persons without dementia were identified in the routinely recorded electronic health records of 451 Dutch general practices in 2008-2015. These data were used to examine the prevalence of the partners' health problems. Differences between these partners and comparison partners in the prevalence of 16 groups of health problems (diagnostic chapters) and in the frequency of GP contacts were examined using generalized estimating equation models.

RESULTS

1,711 partners of persons with dementia and 6,201 comparison partners were included in the analyses. Social problems, more specifically problems related to the illness and/or the loss of the partner, were significantly more prevalent in partners than in comparison partners across the years (p < 0.01), as were musculoskeletal problems (p < 0.01). Respiratory and psychological problems increased over time in partners and remained stable in comparison partners. Across the years, partners contacted their GP more often than comparison partners (p < 0.01).

DISCUSSION/CONCLUSION

Having a cohabiting partner with dementia has consequences for caregiver's physical and psychosocial health. The specific health problems found in this study and the increase in GP contacts might be relevant indicators of overburdening in partners of persons with dementia.

Identifying multimorbid patients with high care needs - A study based on electronic medical record data

Background

Patients with multimorbidity who frequently contact the general practice, use emergency care or have unplanned hospitalisations, may benefit from a proactive integrated care intervention. General practitioners are not always aware of who these ‘high need’ patients are. Electronic medical records are a potential source to identify them.

Objectives

To find predictors of high care needs in general practice electronic medical records of patients with multimorbidity and assess their predictive value.

Methods

General practice electronic medical records of 245,065 patients with ≥2 chronic diseases were linked to hospital claims data. Probit regression analysis was conducted to predict i) having at least 12 general practice contacts per year, ii) emergency department visit(s), and iii) unplanned hospitalisation(s). Predictors were patients’ age, sex, morbidity, health services and medication use in the previous year.

Results

11% of multimorbid patients had ≥12 general practice contacts, which could be reliably predicted by the number of contacts in the previous year (PPV 42%). The model containing all predictors had only slightly better predictive value (PPV 44%). Emergency department visits and unplanned hospitalisations (12% and 7% of multimorbid patients, respectively) could be predicted less accurately (PPV 27% and 20%). Those with frequent contact with the general practice hardly overlapped with ED visitors (29%) or persons with unplanned hospitalisations (17%).

Conclusion

Among multimorbid populations various ‘high need’ groups exist. Patients with high needs for general practice care can be identified by their previous use of general practice care. To identify frequent ED visitors and persons with unplanned hospitalisations, additional information is needed.

Socioeconomic inequalities in out-of-hours primary care use: an electronic health records linkage study

BACKGROUND

Low socioeconomic position (SEP) is related to higher healthcare use in out-of-hours primary care services (OPCSs). We aimed to determine whether inequalities persist when taking the generally poorer health status of socioeconomically vulnerable individuals into account. To put OPCS use in perspective, this was compared with healthcare use in daytime general practice (DGP).

METHODS

Electronic health record (EHR) data of 988 040 patients in 2017 (251 DGPs, 27 OPCSs) from Nivel Primary Care Database were linked to socio-demographic data (Statistics, The Netherlands). We analyzed associations of OPCS and DGP use with SEP (operationalized as patient household income) using multilevel logistic regression. We controlled for demographic characteristics and the presence of chronic diseases. We additionally stratified for chronic disease groups.

RESULTS

An income gradient was observed for OPCS use, with higher probabilities within each lower income group [lowest income, reference highest income group: odds ratio (OR) = 1.48, 95% confidence interval (CI): 1.45-1.51]. Income inequalities in DGP use were considerably smaller (lowest income: OR = 1.17, 95% CI: 1.15-1.19). Inequalities in OPCS were more substantial among patients with chronic diseases (e.g. cardiovascular disease lowest income: OR = 1.60, 95% CI: 1.53-1.67). The inequalities in DGP use among patients with chronic diseases were similar to the inequalities in the total population.

CONCLUSIONS

Higher OPCS use suggests that chronically ill patients with lower income had additional healthcare needs that have not been met elsewhere. Our findings fuel the debate how to facilitate adequate primary healthcare in DGP and prevent vulnerable patients from OPCS use.

Use of out-of-hours primary care in affluent and deprived neighbourhoods during reforms in long-term care: an observational study from 2013 to 2016

OBJECTIVES

Major long-term care (LTC) reforms in the Netherlands in 2015 may specifically have disadvantaged socioeconomically deprived groups to acquire LTC, possibly impacting the use of acute care. We aimed to demonstrate whether LTC reforms coincided with changes in the use of out-of-hours (OOH) primary care services (PCSs), and to compare changes between deprived versus affluent neighbourhoods.

DESIGN

Ecological observational retrospective study using routinely recorded electronic health records data from 2013 to 2016 and population registry data.

SETTING

Data from 15 OOH PCSs participating in the Nivel Primary Care Database (covering approximately 6.5 million inhabitants) in the Netherlands. PCS utilisation data on neighbourhood level were matched with sociodemographic characteristics, including neighbourhood socioeconomic status (SES).

PARTICIPANTS

Electronic health records from 6 120 384 OOH PCS contacts in 2013-2016, aggregated to neighbourhood level.

OUTCOME MEASURES AND ANALYSES

Number of contacts per 1000 inhabitants/year (total, high/low-urgency, night/evening-weekend-holidays, telephone consultations/consultations/home visits).Multilevel linear regression models included neighbourhood (first level), nested within PCS catchment area (second level), to account for between-PCS variation, adjusted for neighbourhood characteristics (for instance: % men/women). Difference-in-difference in time-trends according to neighbourhood SES was assessed with addition of an interaction term to the analysis (year×neighbourhood SES).

RESULTS

Between 2013 and 2016, overall OOH PCS use increased by 6%. Significant increases were observed for high-urgency contacts and contacts during the night. The largest change was observed for the most deprived neighbourhoods (10% compared with 4%-6% in the other neighbourhoods; difference not statistically significant). The increasing trend in OOH PCS use developed practically similar for deprived and affluent neighbourhoods. A a stable gradient reflected more OOH PCS use for each lower stratum of SES.

CONCLUSIONS

LTC reforms coincided with an overall increase in OOH PCS use, with nearly similar trends for deprived and affluent neighbourhoods. The results suggest a generalised spill over to OOH PCS following LTC reforms.

Privacy of Clinical Research Subjects: An Integrative Literature Review

With changes in clinical research practice, the importance of a study-subject's privacy and the confidentiality of their personal data is growing. However, the body of research is fragmented, and a synthesis of work in this area is lacking. Accordingly, an integrative review was performed, guided by Whittemore and Knafl's work. Data from PubMed, Scopus, and CINAHL searches from January 2012 to February 2017 were analyzed via the constant comparison method. From 16 empirical and theoretical studies, six topical aspects were identified: the evolving nature of health data in clinical research, sharing of health data, the challenges of anonymizing data, collaboration among stakeholders, the complexity of regulation, and ethics-related tension between social benefits and privacy. Study subjects' privacy is an increasingly important ethics principle for clinical research, and privacy protection is rendered even more challenging by changing research practice.

Observational health research in Europe: understanding the General Data Protection Regulation and underlying debate

Insights into the incidence and survival of cancer, the influence of lifestyle and environmental factors and the interaction of treatment regimens with outcomes are hugely dependent on observational research, patient data derived from the healthcare system and from volunteers participating in cohort studies, often non-selective. Since 25th May 2018, the European General Data Protection Regulation (GDPR) applies to such data. The GDPR focusses on more individual control for data subjects of 'their' data. Yet, the GDPR was preceded by a long debate. The research community participated actively in that debate, and as a result, the GDPR has research exemptions as well. Some of those apply directly; other exemptions need to be implemented into national law. Those exemptions will be discussed together with a general outline of the GDPR. I propose a substantive definition of research-absent in the GDPR-which can warrant its special status in the GDPR. The debate is not over yet. Most legal texts exhibit ambiguity and are interpreted against a background of values. In this case, those could be subsumed under informational self-determination versus solidarity and the deeper meaning of autonomy. Values will also guide national implementation and their interpretation. The value of individual control or informational self-determination should be balanced by nuanced visions about our mutual dependency in healthcare, as an ever-learning system, especially in the European solidarity-based healthcare systems. Good research governance might be a way forward to escape the consent or anonymise dichotomy.

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 these 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 are recorded routinely as part of the health care process in primary care are actually fit to use for other purposes such as research and quality of health care indicators, how the original purpose may affect the extent to which the data are 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 use and reuse of these type of data.

METHODS

This paper is based on the authors' experience as users of electronic health records data, as general practitioners, health informatics experts, and health services researchers. It is a product of the discussions they had during the Translational Research and Patient Safety in Europe (TRANSFoRm) project, which was funded by the European Commission and sought to develop, pilot, and evaluate a core information architecture for the learning health system in Europe, based on primary care electronic health records.

RESULTS

We first describe the different stages in the processing of electronic health record data, as well as the different purposes for which these data are 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 13 possible sources of bias. Four of them are related to the organization of a health care system, whereas some are of a more technical nature.

CONCLUSIONS

There are a substantial number of possible sources of bias; very little is known about the size and direction of their impact. However, anyone that uses or reuses data that were 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 health care 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.

A novel metadata management model to capture consent for record linkage in longitudinal research studies

Background: Informed consent is an important feature of longitudinal research studies as it enables the linking of the baseline participant information with administrative data. The lack of standardized models to capture consent elements can lead to substantial challenges. A structured approach to capturing consent-related metadata can address these. Objectives: a) Explore the state-of-the-art for recording consent; b) Identify key elements of consent required for record linkage; and c) Create and evaluate a novel metadata management model to capture consent-related metadata. Methods: The main methodological components of our work were: a) a systematic literature review and qualitative analysis of consent forms; b) the development and evaluation of a novel metadata model. Discussion: We qualitatively analyzed 61 manuscripts and 30 consent forms. We extracted data elements related to obtaining consent for linkage. We created a novel metadata management model for consent and evaluated it by comparison with the existing standards and by iteratively applying it to case studies. Conclusion: The developed model can facilitate the standardized recording of consent for linkage in longitudinal research studies and enable the linkage of external participant data. Furthermore, it can provide a structured way of recording consent-related metadata and facilitate the harmonization and streamlining of processes.

Factors influencing the development of primary care data collection projects from electronic health records: a systematic review of the literature

BACKGROUND

Primary care data gathered from Electronic Health Records are of the utmost interest considering the essential role of general practitioners (GPs) as coordinators of patient care. These data represent the synthesis of the patient history and also give a comprehensive picture of the population health status. Nevertheless, discrepancies between countries exist concerning routine data collection projects. Therefore, we wanted to identify elements that influence the development and durability of such projects.

METHODS

A systematic review was conducted using the PubMed database to identify worldwide current primary care data collection projects. The gray literature was also searched via official project websites and their contact person was emailed to obtain information on the project managers. Data were retrieved from the included studies using a standardized form, screening four aspects: projects features, technological infrastructure, GPs' roles, data collection network organization.

RESULTS

The literature search allowed identifying 36 routine data collection networks, mostly in English-speaking countries: CPRD and THIN in the United Kingdom, the Veterans Health Administration project in the United States, EMRALD and CPCSSN in Canada. These projects had in common the use of technical facilities that range from extraction tools to comprehensive computing platforms. Moreover, GPs initiated the extraction process and benefited from incentives for their participation. Finally, analysis of the literature data highlighted that governmental services, academic institutions, including departments of general practice, and software companies, are pivotal for the promotion and durability of primary care data collection projects.

CONCLUSION

Solid technical facilities and strong academic and governmental support are required for promoting and supporting long-term and wide-range primary care data collection projects.

Health Data for Public Health: Towards New Ways of Combining Data Sources to Support Research Efforts in Europe

Objectives: To present the European landscape regarding the re-use of health administrative data for research. Methods: We present some collaborative projects and solutions that have been developed by Nordic countries, Italy, Spain, France, Germany, and the UK, to facilitate access to their health data for research purposes. Results: Research in public health is transitioning from siloed systems to more accessible and re-usable data resources. Following the example of the Nordic countries, several European countries aim at facilitating the re-use of their health administrative databases for research purposes. However, the ecosystem is still a complex patchwork, with different rules, policies, and processes for data provision. Conclusion: The challenges are such that with the abundance of health administrative data, only a European, overarching public health research infrastructure, is able to efficiently facilitate access to this data and accelerate research based on these highly valuable resources.

eSource for clinical trials: Implementation and evaluation of a standards-based approach in a real world trial

OBJECTIVE

The Learning Health System (LHS) requires integration of research into routine practice. 'eSource' or embedding clinical trial functionalities into routine electronic health record (EHR) systems has long been put forward as a solution to the rising costs of research. We aimed to create and validate an eSource solution that would be readily extensible as part of a LHS.

MATERIALS AND METHODS

The EU FP7 TRANSFoRm project's approach is based on dual modelling, using the Clinical Research Information Model (CRIM) and the Clinical Data Integration Model of meaning (CDIM) to bridge the gap between clinical and research data structures, using the CDISC Operational Data Model (ODM) standard. Validation against GCP requirements was conducted in a clinical site, and a cluster randomised evaluation by site nested into a live clinical trial.

RESULTS

Using the form definition element of ODM, we linked precisely modelled data queries to data elements, constrained against CDIM concepts, to enable automated patient identification for specific protocols and pre-population of electronic case report forms (e-CRF). Both control and eSource sites recruited better than expected with no significant difference. Completeness of clinical forms was significantly improved by eSource, but Patient Related Outcome Measures (PROMs) were less well completed on smartphones than paper in this population.

DISCUSSION

The TRANSFoRm approach provides an ontologically-based approach to eSource in a low-resource, heterogeneous, highly distributed environment, that allows precise prospective mapping of data elements in the EHR.

CONCLUSION

Further studies using this approach to CDISC should optimise the delivery of PROMS, whilst building a sustainable infrastructure for eSource with research networks, trials units and EHR vendors.

The European Institute for Innovation through Health Data

The European Institute for Innovation through Health Data (i~HD, www.i-hd.eu) has been formed as one of the key sustainable entities arising from the Electronic Health Records for Clinical Research (IMI-JU-115189) and SemanticHealthNet (FP7-288408) projects, in collaboration with several other European projects and initiatives supported by the European Commission. i~HD is a European not-for-profit body, registered in Belgium through Royal Assent. i~HD has been established to tackle areas of challenge in the successful scaling up of innovations that critically rely on high-quality and interoperable health data. It will specifically address obstacles and opportunities to using health data by collating, developing, and promoting best practices in information governance and in semantic interoperability. It will help to sustain and propagate the results of health information and communication technology (ICT) research that enables better use of health data, assessing and optimizing their novel value wherever possible. i~HD has been formed after wide consultation and engagement of many stakeholders to develop methods, solutions, and services that can help to maximize the value obtained by all stakeholders from health data. It will support innovations in health maintenance, health care delivery, and knowledge discovery while ensuring compliance with all legal prerequisites, especially regarding the insurance of patient's privacy protection. It is bringing multiple stakeholder groups together so as to ensure that future solutions serve their collective needs and can be readily adopted affordably and at scale.

Legal assessment tool (LAT): an interactive tool to address privacy and data protection issues for data sharing

BACKGROUND

In an unprecedented rate data in the life sciences is generated and stored in many different databases. An ever increasing part of this data is human health data and therefore falls under data protected by legal regulations. As part of the BioMedBridges project, which created infrastructures that connect more than 10 ESFRI research infrastructures (RI), the legal and ethical prerequisites of data sharing were examined employing a novel and pragmatic approach.

METHODS

We employed concepts from computer science to create legal requirement clusters that enable legal interoperability between databases for the areas of data protection, data security, Intellectual Property (IP) and security of biosample data. We analysed and extracted access rules and constraints from all data providers (databases) involved in the building of data bridges covering many of Europe's most important databases. These requirement clusters were applied to five usage scenarios representing the data flow in different data bridges: Image bridge, Phenotype data bridge, Personalised medicine data bridge, Structural data bridge, and Biosample data bridge. A matrix was built to relate the important concepts from data protection regulations (e.g. pseudonymisation, identifyability, access control, consent management) with the results of the requirement clusters. An interactive user interface for querying the matrix for requirements necessary for compliant data sharing was created.

RESULTS

To guide researchers without the need for legal expert knowledge through legal requirements, an interactive tool, the Legal Assessment Tool (LAT), was developed. LAT provides researchers interactively with a selection process to characterise the involved types of data and databases and provides suitable requirements and recommendations for concrete data access and sharing situations. The results provided by LAT are based on an analysis of the data access and sharing conditions for different kinds of data of major databases in Europe.

CONCLUSIONS

Data sharing for research purposes must be opened for human health data and LAT is one of the means to achieve this aim. In summary, LAT provides requirements in an interactive way for compliant data access and sharing with appropriate safeguards, restrictions and responsibilities by introducing a culture of responsibility and data governance when dealing with human data.

Factors associated with variation in urgency of primary out-of-hours contacts in the Netherlands: a cross-sectional study

OBJECTIVES

Dutch primary out-of-hours care is provided by general practice cooperatives (GPCs). Although most GPCs use the same standardised triage system, differences between GPCs exist in the urgency assigned to patients' health problems. This cross-sectional study aims to provide insight into factors associated with the variation in assigned urgency between GPCs.

DESIGN AND METHODS

Data were derived from routine electronic health records of 895 253 patients who attended 17 GPCs in 2012. Patients' gender, age, travel distance to the GPC, and the use of a computer-based decision support system for triage were investigated as possibly affecting assigned urgency. Multilevel linear regression analyses were executed for the 3 most frequently presented health problems (cystitis/other urinary infection, laceration/cut and fever).

RESULTS

Variation in urgency levels between GPCs was significant for the selected health problems (p=0.00). Assigned urgency was mainly related to patient gender and age. It was not associated with the use of a computer-based decision support system, or with travel distance to the GPC. Most variation in urgency (93.4-96.7%) could be ascribed to variation in patient characteristics.

CONCLUSIONS

There is significant variation in urgency levels between GPCs, even for the same health problem. This variation is mainly associated with differences in characteristics of individuals contacting the GPCs, rather than with variables such as patients' travel distance or the use of a computer-based decision support system. Since patient characteristics are likely to affect patients' clinical need, our results are an indication of the adequate functioning of the triage system.

Translational Medicine and Patient Safety in Europe: TRANSFoRm--Architecture for the Learning Health System in Europe

The Learning Health System (LHS) describes linking routine healthcare systems directly with both research translation and knowledge translation as an extension of the evidence-based medicine paradigm, taking advantage of the ubiquitous use of electronic health record (EHR) systems. TRANSFoRm is an EU FP7 project that seeks to develop an infrastructure for the LHS in European primary care. Methods. The project is based on three clinical use cases, a genotype-phenotype study in diabetes, a randomised controlled trial with gastroesophageal reflux disease, and a diagnostic decision support system for chest pain, abdominal pain, and shortness of breath. Results. Four models were developed (clinical research, clinical data, provenance, and diagnosis) that form the basis of the projects approach to interoperability. These models are maintained as ontologies with binding of terms to define precise data elements. CDISC ODM and SDM standards are extended using an archetype approach to enable a two-level model of individual data elements, representing both research content and clinical content. Separate configurations of the TRANSFoRm tools serve each use case. Conclusions. The project has been successful in using ontologies and archetypes to develop a highly flexible solution to the problem of heterogeneity of data sources presented by the LHS.

To what extent does sociodemographic composition of the neighbourhood explain regional differences in demand of primary out-of-hours care: a multilevel study

BACKGROUND

In the Netherlands, primary out-of-hours (OOH) care is provided by large scale General Practitioner (GP) cooperatives. GP cooperatives can be contacted by patients living in the area surrounding the GP cooperative (catchment area) at hours when the patient's own general practice is closed. The frequency of primary OOH care use substantially differs between GP cooperative catchment areas. To enable a better match between supply and demand of OOH services, understanding of the factors associated with primary OOH care use is essential. The present study evaluated the contribution of sociodemographic composition of the neighbourhood in explaining differences in primary OOH care use between GP cooperative catchment areas.

METHODS

Data about patients' contacts with primary OOH services (n = 1,668,047) were derived from routine electronic health records of 21 GP cooperatives participating in the NIVEL Primary Care Database in 2012. The study sample is representative for the Dutch population (for age and gender). Data were matched with sociodemographic characteristics (e.g. gender, age, low-income status, degree of urbanisation) on postcode level. Multilevel linear regression models included postcode level (first level), nested within GP cooperative catchment areas (second level). We investigated whether contacts in primary OOH care were associated with neighbourhood sociodemographic characteristics.

RESULTS

The demand of primary OOH care was significantly higher in neighbourhoods with more women, low-income households, non-Western immigrants, neighbourhoods with a higher degree of urbanisation, and low neighbourhood socioeconomic status. Conversely, lower demand was associated with neighbourhoods with more 5 to 24 year old inhabitants. Sociodemographic neighbourhood characteristics explained a large part of the variation between GP cooperatives (R-squared ranging from 8% to 52%). Nevertheless, the multilevel models also showed that a considerable amount of variation in demand between GP cooperatives remained unexplained by sociodemographic characteristics, particularly regarding high-urgency contacts.

CONCLUSIONS

Although part of the variation between GP cooperatives could not be attributed to neighbourhood characteristics, the sociodemographic composition of the neighbourhood is a fair predictor of the demand of primary OOH care. Accordingly, this study provides a useful starting point for an improved planning of the supply of primary OOH care.