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Your search keyword '"Dik, Jan-Willem H"' showing total 19 results
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19 results on '"Dik, Jan-Willem H"'

10. Admissions for ambulatory care sensitive conditions: a national observational study in the general and COPD population.

Author

Paul, Marieke C, Dik, Jan-Willem H, Hoekstra, Trynke, and Dijk, Christel E van

Subjects
*SCIENTIFIC observation, *FAMILY medicine, *PATIENTS, *HOSPITAL admission & discharge, *HEALTH insurance reimbursement, *PRIMARY health care, *OBSTRUCTIVE lung diseases, *HEALTH insurance, *LOGISTIC regression analysis
Abstract

Background Hospital admissions for ambulatory care sensitive conditions (ACSCs) may be prevented by effective ambulatory management and treatment. ACSC admissions is used as indicator for primary care quality and accessibility. However, debate continues to which extent these admissions are truly preventable. The aim of this study was to provide more objective insight into the preventability of ACSC admissions. Methods Observational study using 2012–15 health insurer claim data of 13 182 602 Dutch insured inhabitants. Logistic multilevel regression analyses were conducted to investigate factors (ambulatory care and characteristics of inhabitants) possibly associated with ACSC admissions. Prior ambulatory care use was examined for patients with an ACSC contributing to the highest number of ACSC admissions: chronic obstructive pulmonary disease (COPD). Results In 2014, 89.8 hospital admissions for ACSCs per 10 000 insured inhabitants were claimed. Percentage of inhabitants with ACSC admissions varied between general practices from 0.58–0.84%. ASCS admissions were hardly associated with ambulatory care. One month prior to admission, 97% of admitted COPD patients had at least one ambulatory care contact. Conclusions Variation in ACSC admissions between general practitioners was observed, indicating that certain hospital admissions may be prevented. However, we found no indication that ACSC admissions were preventable, as no link was found with the provision of ambulatory care and ACSC admissions. This may indicate that this indicator is country and health care system specific. Before including ACSC admission as quality indicator of primary care in the Netherlands, more insight into the causes of variation is required. [ABSTRACT FROM AUTHOR]

Published
2019
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13. Cross-border comparison of antibiotic prescriptions among children and adolescents between the north of the Netherlands and the north-west of Germany

Author

Dik, Jan-Willem H., primary, Sinha, Bhanu, additional, Friedrich, Alex W., additional, Lo-Ten-Foe, Jerome R., additional, Hendrix, Ron, additional, Köck, Robin, additional, Bijker, Bert, additional, Postma, Maarten J., additional, Freitag, Michael H., additional, Glaeske, Gerd, additional, and Hoffmann, Falk, additional

Published
2016
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19. Rapid Analysis of Diagnostic and Antimicrobial Patterns in R (RadaR): Interactive Open-Source Software App for Infection Management and Antimicrobial Stewardship

Author

Luz, Christian Friedemann, Berends, Matthijs S, Dik, Jan-Willem H, Lokate, Mariëtte, Pulcini, Céline, Glasner, Corinna, Sinha, Bhanu, University Medical Center Groningen [Groningen] (UMCG), Maladies chroniques, santé perçue, et processus d'adaptation (APEMAC), Université de Lorraine (UL), Service des Maladies Infectieuses et Tropicales [CHRU Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Critical care, Anesthesiology, Peri-operative and Emergency medicine (CAPE), and Microbes in Health and Disease (MHD)

Subjects
020205 medical informatics, IMPROVE, Computer science, Process (engineering), Health Informatics, Context (language use), 02 engineering and technology, medical informatics applications, Tertiary referral hospital, law.invention, Data visualization, Software, Anti-Infective Agents, law, PROGRAM, 0202 electrical engineering, electronic engineering, information engineering, Humans, data visualization, Antimicrobial stewardship, Radar, Original Paper, infection, medical informatics applications, software, business.industry, hospital records, CARE, Data science, infection, Visualization, antimicrobial stewardship, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business
Abstract

Background: Analyzing process and outcome measures for all patients diagnosed with an infection in a hospital, including those suspected of having an infection, requires not only processing of large datasets but also accounting for numerous patient parameters and guidelines. Substantial technical expertise is required to conduct such rapid, reproducible, and adaptable analyses; however, such analyses can yield valuable insights for infection management and antimicrobial stewardship (AMS) teams.Objective: The aim of this study was to present the design, development, and testing of RadaR (Rapid analysis of diagnostic and antimicrobial patterns in R), a software app for infection management, and to ascertain whether RadaR can facilitate user-friendly, intuitive, and interactive analyses of large datasets in the absence of prior in-depth software or programming knowledge.Methods: RadaR was built in the open-source programming language R, using Shiny, an additional package to implement Web-app frameworks in R. It was developed in the context of a 1339-bed academic tertiary referral hospital to handle data of more than 180,000 admissions.Results: RadaR enabled visualization of analytical graphs and statistical summaries in a rapid and interactive manner. It allowed users to filter patient groups by 17 different criteria and investigate antimicrobial use, microbiological diagnostic use and results including antimicrobial resistance, and outcome in length of stay. Furthermore, with RadaR, results can be stratified and grouped to compare defined patient groups on the basis of individual patient features.Conclusions: AMS teams can use RadaR to identify areas within their institutions that might benefit from increased support and targeted interventions. It can be used for the assessment of diagnostic and therapeutic procedures and for visualizing and communicating analyses. RadaR demonstrated the feasibility of developing software tools for use in infection management and for AMS teams in an open-source approach, thus making it free to use and adaptable to different settings.

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