Formalization and computation of quality measures based on electronic medical records

Ambiguous definitions of quality measures in natural language impede their automated computability and also the reproducibility, validity, timeliness, traceability, comparability, and interpretability of computed results. Therefore, quality measures should be formalized before their release. We have previously developed and successfully applied a method for clinical indicator formalization (CLIF). The objective of our present study is to test whether CLIF is generalizable--that is, applicable to a large set of heterogeneous measures of different types and from various domains. We formalized the entire set of 159 Dutch quality measures for general practice, which contains structure, process, and outcome measures and covers seven domains. We relied on a web-based tool to facilitate the application of our method. Subsequently, we computed the measures on the basis of a large database of real patient data. Our CLIF method enabled us to fully formalize 100% of the measures. Owing to missing functionality, the accompanying tool could support full formalization of only 86% of the quality measures into Structured Query Language (SQL) queries. The remaining 14% of the measures required manual application of our CLIF method by directly translating the respective criteria into SQL. The results obtained by computing the measures show a strong correlation with results computed independently by two other parties. The CLIF method covers all quality measures after having been extended by an additional step. Our web tool requires further refinement for CLIF to be applied completely automatically. We therefore conclude that CLIF is sufficiently generalizable to be able to formalize the entire set of Dutch quality measures for general practic

Assessment of the Adjusted Clinical Groups system in Dutch primary care using electronic health records: a retrospective cross-sectional study

Background: Within the Dutch health care system the focus is shifting from a disease oriented approach to a more population based approach. Since every inhabitant in the Netherlands is registered with one general practice, this offers a unique possibility to perform Population Health Management analyses based on general practitioners’ (GP) registries. The Johns Hopkins Adjusted Clinical Groups (ACG) System is an internationally used method for predictive population analyses. The model categorizes individuals based on their complete health profile, taking into account age, gender, diagnoses and medication. However, the ACG system was developed with non-Dutch data. Consequently, for wider implementation in Dutch general practice, the system needs to be validated in the Dutch healthcare setting. In this paper we show the results of the first use of the ACG system on Dutch GP data. The aim of this study is to explore how well the ACG system can distinguish between different levels of GP healthcare utilization.Methods: To reach our aim, two variables of the ACG System, the Aggregated Diagnosis Groups (ADG) and the mutually exclusive ACG categories were explored. The population for this pilot analysis consisted of 23,618 persons listed with five participating general practices within one region in the Netherlands. ACG analyses were performed based on historical Electronic Health Records data from 2014 consisting of primary care diagnoses and pharmaceutical data. Logistic regression models were estimated and AUC’s were calculated to explore the diagnostic value of the models including ACGs and ADGs separately with GP healthcare utilization as the dependent variable. The dependent variable was categorized using four different cut-off points: zero, one, two and three visits per year.Results: The ACG and ADG models performed as well as models using International Classification of Primary Care chapters, regarding the association with GP utilization. AUC values were between 0.79 and 0.85. These modelsmperformed better than the base model (age and gender only) which showed AUC values between 0.64 and 0.71.Conclusion: The results of this study show that the ACG system is a useful tool to stratify Dutch primary care populations with GP healthcare utilization as the outcome variable.Prevention, Population and Disease management (PrePoD)Public Health and primary car

Overdreven gezondheidsnieuws. Relatie tussen overdrijving in academische persberichten en in nieuwsmedia

Objective: To determine how often press releases and news articles contain exaggeration and to locate its origin in the trajectory from research paper to news article. Design: Retrospective quantitative content analysis Method: Sample used consists of press releases on biomedical research, published by  15 Dutch universities and university medical centers in 2015 (N=129), and associated news articles (N=185) and peer reviewed research papers. Quantitative content analysis was performed using Rstudio. Results: 20% of press releases and 29% of news articles contain exaggeration of conclusion of causal claim. Explicit health advice was, when present, exaggerated in 7% of press releases and 10% of news articles. When the press releases contained an exaggeration of conclusion of causal claim, 92% of associated news articles was exaggerated as well (N=34). When the conclusion or causal claim in press releases was not exaggerated, 6% of associated news articles contained exaggeration (N=6). The relative chance of exaggeration in news is 16.1 when the associated press release is exaggerated. Additionally we found that exaggerated press releases have a higher number of associated news articles. The relative chance of news uptake for exaggerated press releases compared with non-exaggerated press releases is 1.45 (1,02-2,04). Conclusion: Exaggeration of health related news is strongly associated with exaggeration in the associated press release and occurs in more than 1 in 5 articles. Monitoring and, if necessary, improving the accuracy of academic press releases are likely to be important measures to improve the quality of health news. Science Communication and Societ

Caffeine consumption and behavioral symptoms in nursing home residents: a cross-sectional analysis

Objective Although behavioral changes are common in nursing home residents with dementia and caffeine is known to influence behavior in healthy adults, the effects of caffeine on the behavior of persons with dementia has received little attention. In this study we assessed the relationship of caffeine and behavioral symptoms in older persons with dementia. Design A multicenter sub-cohort study embedded in the Elderly Care Physicians (ECP) training program. Setting Dutch nursing homes associated with the ECP training program. Participants A total of 206 individuals with both diabetes and dementia resident in Dutch nursing homes. Measurements Trainee ECPs collected data on caffeine consumption, cognition and behavioral symptoms using the NPI-NH, MDS-DRS and AES-C. Data on factors known to influence behavior in persons with dementia (e.g. marital status, kidney function, urinary tract infection and medication) were also collected. Results Of the 206 participants, 70% showed behavioral symptoms. An increase in caffeine consumption was associated with a decrease in the presence of behavioral symptoms in the NPI-NH cluster affect and NPI-NH item agitation. Caffeine consumption groups also differed on the presence of disinhibition and depression. In addition, the severity of dementia influenced agitation, anxiety and the clusters affect and psychomotor. Conclusion In a large group of older persons with dementia resident in nursing homes, a low daily consumption of caffeine was associated with greater behavioral symptoms.Public Health and primary careGeriatrics in primary car

Comparing antibiotic prescriptions in primary care between SARS-CoV-2 and influenza: a retrospective observational study

Background: Antibiotics are frequently prescribed during viral respiratory infection episodes in primary care. There is limited information about antibiotic prescription during the SARS-CoV-2 pandemic in primary care and its association with risk-factors for an adverse course.Aim: To compare the proportion of antibiotic prescriptions between patients with COVID-19 and influenza or influenza-like-symptoms, and to assess the association between antibiotic prescriptions and risk-factors for an adverse course of COVID-19.DesignAn observational cohort study using pseudonymised and coded routine healthcare data extracted from 85 primary care practices in the Netherlands.Method: Adult patients with influenza, influenza-like-symptoms, and suspected or confirmed COVID-19 during the period 2017 up until 2020 were included. We calculated proportions of antibiotic prescriptions for influenza and COVID-19 patients and odds ratios (ORs) comparing the associations of antibiotic prescriptions in COVID-19 patients with risk factors, hospital admission, intensive care (IC) admission, and mortality.Results: The proportion of antibiotic prescriptions during the first SARS-CoV-2 wave was lower than during the 2020 influenza season (9.6% vs 20.7%), difference 11.1% (95%CI:8.7-13.5). During the second SARS-CoV-2 wave, antibiotic prescriptions were associated with being older than 70 (OR 2.05 95% CI:1.43-2.93), the number of comorbidities (OR 1.46 95% CI:1.43-2.93) and admission to hospital (OR 3.19 95% CI:2.02-5.03) or IC (OR 4.64 95% CI:2.02-10.62).Conclusion: Antibiotic prescription was less common during the SARS-CoV-2 pandemic than during influenza seasons and associated with an adverse course and its risk factors. Our findings suggest a relatively targeted prescription policy of antibiotics in primary care during COVID.Keywords: Primary healthcare; SARS-CoV-2; antibiotics; influenza.Immunogenetics and cellular immunology of bacterial infectious disease

Effect of COVID-19 on health system integration in the Netherlands: a mixed-methods study

Objectives Overcrowding in acute care services gives rise to major problems, such as reduced accessibility and delay in treatment. In order to be able to continue providing high-quality health care, it is important that organizations are well integrated at all organizational levels. The objective of this study was to to gain an understanding in which extent cooperation within an urban acute care network in the Netherlands (The Hague) improved because of the COVID-19 crisis. Methods Exploratory mixed-methods questionnaire and qualitative interview study. Semistructured interviews with stakeholders in the acute care network at micro (n = 10), meso (n = 9), and macro (n = 3) levels of organization. Thematic analysis took place along the lines of the 6 dimensions of the Rainbow Model of Integrated Care. Results In this study we identified themes that may act as barriers or facilitators to cooperation: communication, interaction, trust, leadership, interests, distribution of care, and funding. During the crisis many facilitators were identified at clinical, professional, and system level such as clear agreements about work processes, trust in each other's work, and different stakeholders growing closer together. However, at an organizational and communicative level there were many barriers such as interference in each other's work and a lack of clear policies. Conclusion The driving force behind all changes in integration of acute care organizations in an urban context during the COVID-19 crisis seemed to be a great sense of urgency to cooperate in the shared interest of providing the best patient care. We recommend shifting the postcrisis focus from overcoming the crisis to overcoming cooperative challenges.Prevention, Population and Disease management (PrePoD)Public Health and primary car