This paper is in the following e-collection/theme issue:

Viewpoints and Perspectives (522) Clinical Information and Decision Making (1426) Big Data (241) Decision Support for Health Professionals (1203)

Published on in Vol 21, No 3 (2019): March

A Human(e) Factor in Clinical Decision Support Systems

A Human(e) Factor in Clinical Decision Support Systems

A Human(e) Factor in Clinical Decision Support Systems

Authors of this article:

Tim Bezemer1 Author Orcid Image ;   Mark C H de Groot1 Author Orcid Image ;   Enja Blasse1 Author Orcid Image ;   Maarten J ten Berg1 Author Orcid Image ;   Teus H Kappen2 Author Orcid Image ;   Annelien L Bredenoord3 Author Orcid Image ;   Wouter W van Solinge1 Author Orcid Image ;   Imo E Hoefer1 Author Orcid Image ;   Saskia Haitjema1 Author Orcid Image
Article Authors Cited by (48) Tweetations (16) Metrics

Journals

  1. Groenhof T, Rittersma Z, Bots M, Brandjes M, Jacobs J, Grobbee D, van Solinge W, Visseren F, Haitjema S, Asselbergs F. A computerised decision support system for cardiovascular risk management ‘live’ in the electronic health record environment: development, validation and implementation—the Utrecht Cardiovascular Cohort Initiative. Netherlands Heart Journal 2019;27(9):435 View
  2. Strohm L, Hehakaya C, Ranschaert E, Boon W, Moors E. Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors. European Radiology 2020;30(10):5525 View
  3. Capobianco E. Imprecise Data and Their Impact on Translational Research in Medicine. Frontiers in Medicine 2020;7 View
  4. Groenhof T, Kofink D, Bots M, Nathoe H, Hoefer I, Van Solinge W, Lely A, Asselbergs F, Haitjema S. Low-Density Lipoprotein Cholesterol Target Attainment in Patients With Established Cardiovascular Disease: Analysis of Routine Care Data. JMIR Medical Informatics 2020;8(4):e16400 View
  5. Sánchez López J, Cambil Martín J, Villegas Calvo M, Luque Martínez F. Conflictos éticos entre autonomía y aprendizaje profundo. Journal of Healthcare Quality Research 2020;35(1):51 View
  6. Khalifa M, Magrabi F, Gallego Luxan B. Evaluating the Impact of the Grading and Assessment of Predictive Tools Framework on Clinicians and Health Care Professionals’ Decisions in Selecting Clinical Predictive Tools: Randomized Controlled Trial. Journal of Medical Internet Research 2020;22(7):e15770 View
  7. Oprea T. Exploring the dark genome: implications for precision medicine. Mammalian Genome 2019;30(7-8):192 View
  8. Hwang Y, Kim H, Choi H, Lee J. Exploring Abnormal Behavior Patterns of Online Users With Emotional Eating Behavior: Topic Modeling Study. Journal of Medical Internet Research 2020;22(3):e15700 View
  9. Groenhof T, Koers L, Blasse E, de Groot M, Grobbee D, Bots M, Asselbergs F, Lely A, Haitjema S, van Solinge W, Hoefer I, Nathoe H, de Borst G, Geerlings M, Emmelot M, de Jong P, Leiner T, van der Kaaij N, Kappelle L, Ruigrok Y, Verhaar M, Visseren F, Westerink J. Data mining information from electronic health records produced high yield and accuracy for current smoking status. Journal of Clinical Epidemiology 2020;118:100 View
  10. Amirmahani F, Ebrahimi N, Molaei F, Faghihkhorasani F, Jamshidi Goharrizi K, Mirtaghi S, Borjian‐Boroujeni M, Hamblin M. Approaches for the integration of big data in translational medicine: single‐cell and computational methods. Annals of the New York Academy of Sciences 2021;1493(1):3 View
  11. Cheng C, Chen C, Cheng F, Chen H, Su Y, Yeh C, Chung I, Liao C. A Human-Algorithm Integration System for Hip Fracture Detection on Plain Radiography: System Development and Validation Study. JMIR Medical Informatics 2020;8(11):e19416 View
  12. Brunekreef T, Otten H, van den Bosch S, Hoefer I, van Laar J, Limper M, Haitjema S. Text Mining of Electronic Health Records Can Accurately Identify and Characterize Patients With Systemic Lupus Erythematosus. ACR Open Rheumatology 2021;3(2):65 View
  13. Manco L, Maffei N, Strolin S, Vichi S, Bottazzi L, Strigari L. Basic of machine learning and deep learning in imaging for medical physicists. Physica Medica 2021;83:194 View
  14. Schaaf J, Sedlmayr M, Sedlmayr B, Prokosch H, Storf H. Evaluation of a clinical decision support system for rare diseases: a qualitative study. BMC Medical Informatics and Decision Making 2021;21(1) View
  15. Nijman S, Groenhof T, Hoogland J, Bots M, Brandjes M, Jacobs J, Asselbergs F, Moons K, Debray T. Real-time imputation of missing predictor values improved the application of prediction models in daily practice. Journal of Clinical Epidemiology 2021;134:22 View
  16. Agreli H, Huising R, Peduzzi M. Role reconfiguration: what ethnographic studies tell us about the implications of technological change for work and collaboration in healthcare. BMJ Leader 2021;5(2):134 View
  17. Burns C, Nix T, Shapiro R, Huber J, Mughal M. MEDLINE search retrieval issues: A longitudinal query analysis of five vendor platforms. PLOS ONE 2021;16(5):e0234221 View
  18. Jongsma K, Bekker M, Haitjema S, Bredenoord A. How digital health affects the patient-physician relationship: An empirical-ethics study into the perspectives and experiences in obstetric care. Pregnancy Hypertension 2021;25:81 View
  19. Abdulaal A, Patel A, Al-Hindawi A, Charani E, Alqahtani S, Davies G, Mughal N, Moore L. Clinical Utility and Functionality of an Artificial Intelligence–Based App to Predict Mortality in COVID-19: Mixed Methods Analysis. JMIR Formative Research 2021;5(7):e27992 View
  20. Amann J, Vayena E, Ormond K, Frey D, Madai V, Blasimme A, Canzan F. Expectations and attitudes towards medical artificial intelligence: A qualitative study in the field of stroke. PLOS ONE 2023;18(1):e0279088 View
  21. Overmars L, Niemantsverdriet M, Groenhof T, De Groot M, Hulsbergen-Veelken C, Van Solinge W, Musson R, Ten Berg M, Hoefer I, Haitjema S. A Wolf in Sheep’s Clothing: Reuse of Routinely Obtained Laboratory Data in Research. Journal of Medical Internet Research 2022;24(11):e40516 View
  22. Catho G, Sauser J, Coray V, Da Silva S, Elzi L, Harbarth S, Kaiser L, Marti C, Meyer R, Pagnamenta F, Portela J, Prendki V, Ranzani A, Centemero N, Stirnemann J, Valotti R, Vernaz N, Suter B, Bernasconi E, Huttner B. Impact of interactive computerised decision support for hospital antibiotic use (COMPASS): an open-label, cluster-randomised trial in three Swiss hospitals. The Lancet Infectious Diseases 2022;22(10):1493 View
  23. Venkatraman S, Sundarraj R, Seethamraju R. Exploring health-analytics adoption in indian private healthcare organizations: An institutional-theoretic perspective. Information and Organization 2022;32(3):100430 View
  24. Liu J, Jiao X, Zeng S, Li H, Jin P, Chi J, Liu X, Yu Y, Ma G, Zhao Y, Li M, Peng Z, Huo Y, Gao Q. Oncological big data platforms for promoting digital competencies and professionalism in Chinese medical students: a cross-sectional study. BMJ Open 2022;12(9):e061015 View
  25. Groenhof T, Haitjema S, Lely A, Grobbee D, Asselbergs F, Bots M, Celi L. Optimizing cardiovascular risk assessment and registration in a developing cardiovascular learning health care system: Women benefit most. PLOS Digital Health 2023;2(2):e0000190 View
  26. Haitjema S, Prescott T, van Solinge W. The Applied Data Analytics in Medicine Program: Lessons Learned From Four Years’ Experience With Personalizing Health Care in an Academic Teaching Hospital. JMIR Formative Research 2022;6(1):e29333 View
  27. Vijlbrief D, Dudink J, van Solinge W, Benders M, Haitjema S. From computer to bedside, involving neonatologists in artificial intelligence models for neonatal medicine. Pediatric Research 2023;93(2):437 View
  28. Ting J, Garnett A. E-Health Decision Support Technologies in the Prevention and Management of Pressure Ulcers. CIN: Computers, Informatics, Nursing 2021;39(12):955 View
  29. Möllmann N, Mirbabaie M, Stieglitz S. Is it alright to use artificial intelligence in digital health? A systematic literature review on ethical considerations. Health Informatics Journal 2021;27(4) View
  30. Egermark M, Blasiak A, Remus A, Sapanel Y, Ho D. Overcoming Pilotitis in Digital Medicine at the Intersection of Data, Clinical Evidence, and Adoption. Advanced Intelligent Systems 2022;4(9) View
  31. Horgan D, Romao M, Morré S, Kalra D. Artificial Intelligence: Power for Civilisation – and for Better Healthcare. Public Health Genomics 2019;22(5-6):145 View
  32. Venkatraman S, Sundarraj R. Assessing organizational health-analytics readiness: artifacts based on elaborated action design method. Journal of Enterprise Information Management 2023;36(1):123 View
  33. Niemantsverdriet M, Khairoun M, El Idrissi A, Koopsen R, Hoefer I, van Solinge W, Uffen J, Bellomo D, Groenestege W, Kaasjager K, Haitjema S. Ambiguous definitions for baseline serum creatinine affect acute kidney diagnosis at the emergency department. BMC Nephrology 2021;22(1) View
  34. Sheu R, Chen L, Wu C, Pardeshi M, Pai K, Huang C, Chen C, Chen W. Multi-Modal Data Analysis for Pneumonia Status Prediction Using Deep Learning (MDA-PSP). Diagnostics 2022;12(7):1706 View
  35. MacDonald I, de Goumoëns V, Marston M, Alvarado S, Favre E, Trombert A, Perez M, Ramelet A. Effectiveness, quality and implementation of pain, sedation, delirium, and iatrogenic withdrawal syndrome algorithms in pediatric intensive care: a systematic review and meta-analysis. Frontiers in Pediatrics 2023;11 View
  36. Hehakaya C, Moors E. Institutionalisation of convergent medical innovation: an empirical study of the MRI-guided linear accelerator in the Netherlands and the United States. Innovation 2023:1 View
  37. He X, Zheng X, Ding H, Liu Y, Zhu H. AI-CDSS Design Guidelines and Practice Verification. International Journal of Human–Computer Interaction 2024;40(18):5469 View
  38. van Velzen M, de Graaf-Waar H, Ubert T, van der Willigen R, Muilwijk L, Schmitt M, Scheper M, van Meeteren N. 21st century (clinical) decision support in nursing and allied healthcare. Developing a learning health system: a reasoned design of a theoretical framework. BMC Medical Informatics and Decision Making 2023;23(1) View
  39. Klarin A, Ali Abadi H, Sharmelly R. Professionalism in artificial intelligence: The link between technology and ethics. Systems Research and Behavioral Science 2024;41(4):557 View
  40. van Rooden S, van der Werff S, van Mourik M, Lomholt F, Møller K, Valk S, dos Santos Ribeiro C, Wong A, Haitjema S, Behnke M, Rinaldi E. Federated systems for automated infection surveillance: a perspective. Antimicrobial Resistance & Infection Control 2024;13(1) View
  41. Wosny M, Aeppli S, Fischer S, Peres T, Rothermundt C, Hastings J. Factors Guiding Clinical Decision‐Making in Genitourinary Oncology. Cancer Medicine 2024;13(20) View
  42. Raszke P, Giebel G, Abels C, Wasem J, Adamzik M, Nowak H, Palmowski L, Heinz P, Mreyen S, Timmesfeld N, Tokic M, Brunkhorst F, Blase N. User-Oriented Requirements for Artificial Intelligence-Based Clinical Decision Support Systems in Sepsis: Study Protocol for a Multimethod Research Project (Preprint). JMIR Research Protocols 2024 View
  43. Uma K, Perumal K. A novel hybrid sparse-neural joint adjustable random forest (Sn-Arf) classification method for medical decision support system. International Journal of System Assurance Engineering and Management 2024 View

Books/Policy Documents

  1. Kalina J. Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems. View
  2. Rainey S, Erden Y, Resseguier A. Artificial Intelligence in Medicine. View
  3. Rainey S, Erden Y, Resseguier A. Artificial Intelligence in Medicine. View
  4. Waefler T. Human Interaction, Emerging Technologies and Future Systems V. View
  5. Cabitza F, Campagner A. Big Data Analysis and Artificial Intelligence for Medical Sciences. View