Medical Specialist Care Utilization Prior to the Explantation of Cosmetic Silicone Breast Implants: A Nationwide Retrospective Data Linkage Study.

BACKGROUND: Explantation is the proposed treatment for breast implant illness (BII). Little is known about which medical specialists are visited and what diagnoses are made before explantation is provided as the treatment. OBJECTIVES: This study investigated medical specialist care utilization in women with cosmetic breast implants who underwent explantation compared to women who chose breast implant replacement surgery and to women without breast implants. METHODS: Retrospective cohort study using data linkage with the Dutch Breast Implant Registry and the Dutch health insurance claims database. Visits to medical specialists were examined over the 3 years before explantation. A total of 832 explantation patients were matched and compared to 1463 breast implant replacement patients and 1664 women without breast implants. RESULTS: Explantation patients were more likely to have visited > 5 different medical specialties compared to both replacement patients (12.3% vs. 5.7%; p < 0.001) and women without breast implants (12.3% vs. 3.7%; p < 0.001). Among explantation patients, women who underwent explantation because of BII were more likely to have visited > 5 different medical specialties compared to women who underwent explantation because of other reasons (25.0% vs. 11.0%; p < 0.001). CONCLUSIONS: Women who underwent explantation of breast implants had higher utilization of medical specialist care in the years before explantation compared to women who underwent breast implant replacement surgery and women without breast implants. Medical specialist care use was especially high among women for whom BII was the registered reason for explantation. These findings suggest further research is needed into the link between BII and the use of medical specialist care. LEVEL OF EVIDENCE III: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Healthcare utilization patterns of individuals with depression after national policy to increase the mental health workforce in primary care: a data linkage study.

BACKGROUND: The deployment of the mental health nurse, an additional healthcare provider for individuals in need of mental healthcare in Dutch general practices, was expected to substitute treatments from general practitioners and providers in basic and specialized mental healthcare (psychologists, psychotherapists, psychiatrists, etc.). The goal of this study was to investigate the extent to which the degree of mental health nurse deployment in general practices is associated with healthcare utilization patterns of individuals with depression. METHODS: We combined national health insurers' claims data with electronic health records from general practices. Healthcare utilization patterns of individuals with depression between 2014 and 2019 (N = 31,873) were analysed. The changes in the proportion of individuals treated after depression onset were assessed in association with the degree of mental health nurse deployment in general practices. RESULTS: The proportion of individuals with depression treated by the GP, in basic and specialized mental healthcare was lower in individuals in practices with high mental health nurse deployment. While the association between mental health nurse deployment and consultation in basic mental healthcare was smaller for individuals who depleted their deductibles, the association was still significant. Treatment volume of general practitioners was also lower in practices with higher levels of mental health nurse deployment. CONCLUSION: Individuals receiving care at a general practice with a higher degree of mental health nurse deployment have lower odds of being treated by mental healthcare providers in other healthcare settings. More research is needed to evaluate to what extent substitution of care from specialized mental healthcare towards general practices might be associated with waiting times for specialized mental healthcare.

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.

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.

What can we learn from experiences in general practice during the COVID-19 pandemic? A qualitative study.

BACKGROUND: Experiences with organizational changes in daytime general practices and out-of-hours (OOH) services during the COVID-19 pandemic may help to address the challenges in general practice care that were already a concern before the crisis. This study aimed to describe these experiences and the potential usefulness of the organizational changes for future general practice care and any future pandemics. METHODS: Semi-structured interviews were performed among 11 directors of OOH services, and 19 (locum) general practitioners (GPs) or practice managers, who were purposively sampled. Video or telephone interviews were performed in two rounds: between November 2020 and January 2021 and between May 2021 and August 2021. The data were analyzed using thematic analysis methods. RESULTS: Three themes emerged from the data: (1) Changes in the triage procedures; in GP practices and OOH services, stricter triage criteria were implemented, and GPs were more actively involved in the triage process. These measures helped to reduce the number of 'low urgency' face-to-face consultations. (2) Changes in GP care; there was a shift towards video and telephone consultations, allowing GPs to spend more time with patients during the remaining face-to-face consultations. For chronic patients, the shift towards telemonitoring appeared to encourage self-care, and postponing face-to-face consultations for regular checkups appeared to be unproblematic for stable patients. (3) Coordination of GP care and information communication flow during the COVID-19 pandemic; OOH directors perceived a lack of consistency in the information from various governmental and non-governmental parties on containment measures and guidelines related to COVID-19, making it difficult to act on them. The COVID-19 pandemic intensified collaboration between GPs, OOH services, and other healthcare professionals. CONCLUSIONS: The results of this study indicate that some of the organizational changes, such as stricter triage, remote consultations, and changes in managed care of chronic patients, may help in tackling the pre-existing challenges in GP care from before the COVID-19 pandemic. However, more extensive research and continuous monitoring are necessary to establish the effects on patients and their health outcomes. To navigate future pandemics, the intensified collaboration between health professionals should be maintained, while there is considerable room for improvement in the provision of unambiguous information.

Healthcare utilization patterns for knee and hip osteoarthritis before and after changes in national health insurance coverage: a data linkage study from the Netherlands.

INTRODUCTION: Medical guidelines aim to stimulate stepped care for knee and hip osteoarthritis, redirecting treatments from hospitals to primary care. In the Netherlands, this development was supported by changing health insurance coverage for physio/exercise therapy. The aim of this study was to evaluate healthcare utilization patterns before and after health changes in health insurance coverage. METHOD: We analyzed electronic health records and claims data from patients with osteoarthritis in the knee (N = 32,091) and hip (N = 16,313). Changes between 2013 and 2019 in the proportion of patients treated by the general practitioner, physio/exercise therapist or orthopedic surgeon within 6 months after onset were assessed. RESULTS: Joint replacement surgeries decreased for knee (OR 0.47 [0.41-0.54]) and hip (OR 0.81 [0.71-0.93]) osteoarthritis between 2013-2019. The use of physio/exercise therapy increased (knee: OR 1.38 [1.24-1.53], hip: OR 1.26 [1.08-1.47]). However, the proportion treated by a physio/exercise therapist decreased for patients that had not depleted their annual deductibles (knee: OR 0.86 [0.79 - 0.94], hip: OR 0.90 [0.79 - 1.02]). This might be affected by the inclusion of physio/exercise therapy in basic health insurance in 2018. CONCLUSION: We have found a shift from hospitals to primary care in knee and hip osteoarthritis care. However, the use of physio/exercise therapy declined after changes in insurance coverage for patients that had not depleted their deductibles.

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.

The burden of disease of fatal and non-fatal burn injuries for the full spectrum of care in the Netherlands.

BACKGROUND: A comprehensive overview of the burden of disease of burns for the full spectrum of care is not available. Therefore, we estimated the burden of disease of burns for the full spectrum in the Netherlands in 2018, and explored whether the burden of disease changed over the past 5 years (2014-2018). METHODS: Data were collected at four levels: general practice, emergency department, hospital, and mortality data. For each level, years lived with disability (YLD), years of life lost (YLL), and disability-adjusted life-years (DALY) were estimated using a tailored methodology. RESULTS: Burns resulted in a total of 9278 DALYs in the Netherlands in 2018, comprising of 7385 YLDs (80%) and 1892 YLLs (20%). Burn patients who visited the general practice contributed most DALYs (64%), followed by deceased burn patients (20%), burn patients admitted to hospital (14%) and those treated at the emergency department (2%). The burden of disease was comparable in both sexes (4734 DALYs (51%) for females; 4544 DALYs (49%) for males), though the distribution of DALYs by level of care varied; females contributed more DALYs at the general practice level, and males at all other levels of care. Among children boys 0-4 years had the highest burden of disease (784 DALYs (9%)), and among adults, females 18-34 years old (1319 DALYs (14.2%)) had the highest burden of disease. Between 2014 and 2018 there was a marginal increase of 0.8% in the number of DALYs. CONCLUSIONS: Burns cause a substantial burden of disease, with burns requiring care at the general practice level contributing most DALYs. Information on burden of burns by the full level of care as well as by subgroup is important for the development of tailored burn prevention strategies, and the updated figures are recommended to be used for priority setting and resource allocation.

Setting up a Governance Framework for Secondary Use of Routine Health Data in Nursing Homes: Development Study Using Qualitative Interviews.

BACKGROUND: In the nursing home sector, reusing routinely recorded data from electronic health records (EHRs) for knowledge development and quality improvement is still in its infancy. Trust in appropriate and responsible reuse is crucial for patients and nursing homes deciding whether to share EHR data for these purposes. A data governance framework determines who may access the data, under what conditions, and for what purposes. This can help obtain that trust. Although increasing attention is being paid to data governance in the health care sector, little guidance is available on development and implementation of a data governance framework in practice. OBJECTIVE: This study aims to describe the development process of a governance framework for the "Registry Learning from Data in Nursing Homes," a national registry for EHR data on care delivered by nursing home physicians (in Dutch: specialist ouderengeneeskunde) in Dutch nursing homes-to allow data reusage for research and quality improvement of care. METHODS: Relevant stakeholders representing practices, policies, and research in the nursing home sector were identified. Semistructured interviews were conducted with 20 people from 14 stakeholder organizations. The main aim of the interviews was to explore stakeholders' perspectives regarding the Registry's aim, data access criteria, and governing bodies' tasks and composition. Interview topics and analyses were guided by 8 principles regarding governance for reusing health data, as described in the literature. Interview results, together with legal advice and consensus discussions by the Registry's consortium partners, were used to shape the rules, regulations, and governing bodies of the governance framework. RESULTS: Stakeholders valued the involvement of nursing home residents and their representatives, nursing home physicians, nursing homes' boards of directors, and scientists and saw this as a prerequisite for a trustworthy data governance framework. For the Registry, involvement of these groups can be achieved through a procedure in which residents can provide their consent or objection to the reuse of the data, transparency about the decisions made, and providing them a position in a governing body. In addition, a data request approval procedure based on predefined assessment criteria indicates that data reuse by third parties aligns with the aims of the Registry, benefits the nursing home sector, and protects the privacy of data subjects. CONCLUSIONS: The stakeholders' views, expertise, and knowledge of other frameworks and relevant legislation serve to inform the application of governance principles to the contexts of both the nursing home sector and the Netherlands. Many different stakeholders were involved in the development of the Registry Learning from Data in Nursing Homes' governance framework and will continue to be involved. Engagement of the full range of stakeholders in an early stage of governance framework development is important to generate trust in appropriate and responsible data reuse.

Prevalence of Local Postoperative Complications and Breast Implant Illness in Women With Breast Implants.

Importance: It is unknown how often breast implant illness (BII) is the indication for revision in women with silicone breast implants. Objective: To examine how often women with silicone breast implants have their implants explanted or replaced because of BII compared with local postoperative complications. Design, Setting, and Participants: A legacy cohort study on breast implant revision surgery was conducted between April 1, 2015, and December 31, 2020, and a prospective cohort study on breast implantation and revision surgery was conducted between April 1, 2015, and December 31, 2019 (with follow-up until December 31, 2020). Data were obtained from the Dutch Breast Implant Registry. Data analysis was performed from September 2021 to August 2022. Exposures: Silicone breast implant. Main Outcomes and Measures: Breast implant revision with the indication BII or local postoperative complications. Results: All 12 882 cosmetic breast implants (6667 women; mean [SD] age, 50.6 [12.7] years) and 2945 reconstructive breast implants (2139 women, mean [SD] age, 57.9 [11.3] years) in the legacy cohort and all 47 564 cosmetic breast implants (24 120 women, mean [SD] age, 32.3 [9.7] years) and 5928 reconstructive breast implants (4688 women, mean [SD] age, 50.9 [11.5] years) in the prospective cohort were included for analysis. In the prospective cohort, 739 cosmetic breast implants (1.6%) were revised after a median (IQR) time to reoperation of 1.8 (0.9-3.1) years, and 697 reconstructive breast implants (11.8%) were revised after a median (IQR) time to reoperation of 1.1 (0.5-1.9) years. BII was registered as the reason for revision in 35 cosmetic revisions (4.7%) and 5 reconstructive revisions (0.7%) in the prospective cohort, corresponding to 0.1% of the inserted implants. In the legacy cohort, 536 cosmetic revisions (4.2%) and 80 reconstructive breast implant revisions (2.7%) were performed because of BII. Conclusions and Relevance: In this cohort study of women with silicone breast implants, BII was an uncommon indication for revision compared with local complications, both in the short and long term. In contrast to the increasing public interest in BII, these results showed that local complications are a far more common reason for breast implant revision.

Gender and age concordance between patient and GP: an observational study on associations with referral behaviour.

BACKGROUND: Appropriate referral from primary to secondary care is essential for maintaining a healthcare system that is accessible and cost-effective. Social concordance can affect the doctor-patient interaction and possibly also referral behaviour. AIM: To investigate the association of gender concordance and age concordance on referral rates in primary care in The Netherlands. DESIGN & SETTING: Electronic health records data (n = 24 841) were used from 65 GPs in The Netherlands, containing referral information, which was combined with demographics of GPs and patients to investigate factors associated with referral likelihood. METHOD: Health records covered 16 different symptoms and diagnoses, categorised as 'gender sensitive', 'age sensitive', 'both age and gender sensitive', or 'neutral' based on Delphi consensus. Multi-level logistic regressions were performed to calculate the associations of gender and age concordance with referral status. RESULTS: Overall, 16.8% of patients were referred to a medical specialist. The female-male dyad (GP-patient) was associated with a higher referral likelihood (odds ratio [OR] 1.14; 95% confidence interval [CI] = 1.02 to 1.27; P = 0.02) compared with the female-female dyad. Gender discordance was associated with a higher referral likelihood regarding consultations involving 'gender-sensitive' symptoms and diagnoses (OR 1.21; CI = 1.02 to 1.44; P = 0.03), and in duo and group practices (OR 1.08; 95% CI = 1.00 to 1.16; P = 0.05). Age concordance was not a significant predictor of referrals in the main model nor in subgroup analyses. CONCLUSION: Gender discordance was associated with a higher likelihood of referring. This study adds to the evidence that gender concordance affects decisions to refer, particularly with respect to symptoms and diagnoses that can be regarded as 'gender sensitive'.

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.

Optimising the secondary use of primary care prescribing data to improve quality of care: a qualitative analysis.

OBJECTIVES: To explore available data sources, secondary uses and key considerations for optimising the actionability of primary care prescribing data to improve quality of care in the Dutch context. DESIGN: An exploratory qualitative study was undertaken based on semi-structured interviews. We anchored our investigation around three tracer prescription types: antibiotics; benzodiazepines and opioids. Descriptive and explanatory themes were derived from interview data using thematic analysis. SETTING: Stakeholders were sampled from across the micro (clinical), meso (organisational) and macro (policy) contexts of the Dutch primary care system. PARTICIPANTS: The study involved 28 informants representing general practitioners (GPs), community pharmacists, regional chronic care networks (care groups), academia and research institutes, insurers, professional associations, electronic health record (EHR) vendors and national authorities. RESULTS: In the Netherlands, three main sources of data for improving prescribing in primary care are in use: clinical data in the EHRs of GP practices; pharmacy data in community pharmacy databases and claims data of insurers. While the secondary use of pharmacy and claims data is well-established across levels, the use of these data together with EHR data is limited. Important differences in the types of prescribing information needed by micro-meso-macro context are found, though the extent to which current indicators address these varies by prescription type. Five main themes were identified as areas for optimising data use: (1) measuring what matters, (2) increasing data linkages, (3) improving data quality, (4) facilitating data sharing and (5) optimising fit for use analysis. CONCLUSIONS: To make primary care prescribing data useful for improving quality, consolidated patient-specific data on the indication for a prescription and dispensed medicine, over time, is needed. In the Netherlands, the selection of indicators requires further prioritisation to better signal the appropriateness and long-term use of prescription drugs. Prioritising data linkages is critical towards more actionable use.

Impact of a Machine Learning-Based Decision Support System for Urinary Tract Infections: Prospective Observational Study in 36 Primary Care Practices.

BACKGROUND: There is increasing attention on machine learning (ML)-based clinical decision support systems (CDSS), but their added value and pitfalls are very rarely evaluated in clinical practice. We implemented a CDSS to aid general practitioners (GPs) in treating patients with urinary tract infections (UTIs), which are a significant health burden worldwide. OBJECTIVE: This study aims to prospectively assess the impact of this CDSS on treatment success and change in antibiotic prescription behavior of the physician. In doing so, we hope to identify drivers and obstacles that positively impact the quality of health care practice with ML. METHODS: The CDSS was developed by Pacmed, Nivel, and Leiden University Medical Center (LUMC). The CDSS presents the expected outcomes of treatments, using interpretable decision trees as ML classifiers. Treatment success was defined as a subsequent period of 28 days during which no new antibiotic treatment for UTI was needed. In this prospective observational study, 36 primary care practices used the software for 4 months. Furthermore, 29 control practices were identified using propensity score-matching. All analyses were performed using electronic health records from the Nivel Primary Care Database. Patients for whom the software was used were identified in the Nivel database by sequential matching using CDSS use data. We compared the proportion of successful treatments before and during the study within the treatment arm. The same analysis was performed for the control practices and the patient subgroup the software was definitely used for. All analyses, including that of physicians' prescription behavior, were statistically tested using 2-sided z tests with an α level of .05. RESULTS: In the treatment practices, 4998 observations were included before and 3422 observations (of 2423 unique patients) were included during the implementation period. In the control practices, 5044 observations were included before and 3360 observations were included during the implementation period. The proportion of successful treatments increased significantly from 75% to 80% in treatment practices (z=5.47, P<.001). No significant difference was detected in control practices (76% before and 76% during the pilot, z=0.02; P=.98). Of the 2423 patients, we identified 734 (30.29%) in the CDSS use database in the Nivel database. For these patients, the proportion of successful treatments during the study was 83%-a statistically significant difference, with 75% of successful treatments before the study in the treatment practices (z=4.95; P<.001). CONCLUSIONS: The introduction of the CDSS as an intervention in the 36 treatment practices was associated with a statistically significant improvement in treatment success. We excluded temporal effects and validated the results with the subgroup analysis in patients for whom we were certain that the software was used. This study shows important strengths and points of attention for the development and implementation of an ML-based CDSS in clinical practice. TRIAL REGISTRATION: ClinicalTrials.gov NCT04408976; https://clinicaltrials.gov/ct2/show/NCT04408976.

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.

Occurrence and Timing of Advance Care Planning in Persons With Dementia in General Practice: Analysis of Linked Electronic Health Records and Administrative Data.

Background: Advance care planning (ACP) is a process of communication in which patients and family caregivers discuss preferences for future care with the healthcare team. For persons with dementia, it is crucial to timely engage in ACP. Therefore, we study ACP in dementia using electronic health record data. This study aims to determine how often ACP conversations are recorded, analyze time from dementia diagnosis until the first recorded conversation and time from the first recorded conversation to death, and analyze which factors are associated with the timing of ACP. Methods: Electronic records of 15,493 persons with dementia in Dutch general practice between 2008 and 2016 were linked to national administrative databases. ACP conversations and indicators of health deficits to determine frailty were obtained from electronic records coded with the International Classification of Primary Care. Socio-demographic characteristics were derived from the national population registry managed by Statistics Netherlands. Date of death was derived from the Personal Records Database (2008-2018). Results: ACP was recorded as such as 22 (95% CI, 20-23) first conversations per 1,000 person-years of follow-up. The hazard ratio (HR) for the first conversation increased every year after dementia diagnosis, from 0.01 in the first year to 0.07 in the 7th and 8th year after diagnosis. Median time from a first conversation to death was 2.57 years (95% CI, 2.31-2.82). Migrant status [non-Western vs. Western (HR 0.31, 95% CI, 0.15-0.65)] was significantly associated with a longer time from dementia diagnosis to the first conversation. Being pre-frail (HR 2.06, 95% CI, 1.58-2.69) or frail (HR 1.40, 95% CI, 1.13-1.73) vs. non-frail was significantly associated with a shorter time from dementia diagnosis to the first ACP conversation. Conclusion: ACP conversations in Dutch general practice were rare for persons with dementia, or was rarely recorded as such. In particular among persons with a non-Western migration background and those who are non-frail, it started long after diagnosis. We advise further research into public health and practical strategies to engage persons with dementia with a non-Western migration background and non-frail persons early in the disease trajectory in ACP.

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.

Antibiotic Prescribing in Dutch Daytime and Out-of-Hours General Practice during the COVID-19 Pandemic: A Retrospective Database Study.

COVID-19 restrictions have resulted in major changes in healthcare, including the prescribing of antibiotics. We aimed to monitor antibiotic prescribing trends during the COVID-19 pandemic in Dutch general practice, both during daytime and out-of-hours (OOH). Routine care data were used from 379 daytime general practices (DGP) and 28 OOH-services over the period 2019-2021. Per week, we analyzed prescription rates per 100,000 inhabitants, overall, for respiratory and urinary tract infections (RTIs and UTIs) specifically and within age categories. We assessed changes in antibiotic prescribing during different phases of the pandemic using interrupted time series analyses. Both at DGPs and OOH-services significantly fewer antibiotics were prescribed during the COVID-19 pandemic after government measures became effective. Furthermore, the number of contacts decreased in both settings. When restrictions were revoked in 2021 prescription rates increased both at DGP and OOH-services, returning to pre-pandemic levels at OOH-services, but not in DGP. Changes in antibiotic prescribing rates were prominent for RTIs and among children up to 11 years old, but not for UTIs. To conclude, while antibiotic prescribing decreased during the first year of the COVID-19 pandemic both in daytime and out-of-hours, the pandemic does not seem to have a lasting effect on antibiotic prescribing.

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.

Burn injuries in primary care in the Netherlands: Risk factors and trends.

BACKGROUND: Research to date has mainly focused on burn injuries treated in secondary care. This study aims to provide knowledge on the epidemiology of burn injuries in primary care, to give directions for burn prevention. METHODS: Data were derived from routine electronic health records of general practices and their out-of-hours service organisations in the Netherlands that participated in the Nivel Primary Care Database 2010-2015. We studied risk factors and trends. RESULTS: The average burn injury prevalence rate was 4.40 (95% CI 4.27-4.53) per 1000 person-years in daytime general practice care and 1.47 (95% CI 1.46-1.49) per 1000 inhabitants in out-of-hours care. Children of 0-4 years old, especially boys, and young adult women had a higher risk. Burn injury risk was higher during the summer months and around New Year's Eve. Living in low socioeconomic and strongly urbanised neighbourhoods was associated with a higher risk of burn injury than living in other neighbourhoods. CONCLUSION: Dutch general practitioners have a large share in burn care and therefore can play a significant role in burn prevention. Prevention may be most effective in the summer and around New Year's Eve, and specific attention seems to be warranted for low socioeconomic groups and strongly urbanised neighbourhoods.