PRAISE: providing a roadmap for automated infection surveillance in Europe.
暂无分享,去创建一个
M. Bonten | M. Abbas | J. Reilly | S. V. van Rooden | J. Lucet | S. Mookerjee | P. Gastmeier | E. Tacconelli | E. Presterl | M. V. van Mourik | E. Carrara | P. Astagneau | H. Humphreys | B. Kristensen | M. Pujol | A. Lepape | S. Gubbels | S. D. de Greeff | O. Aspevall | A. Gomila-Grange | W. Harrison | Anders F Johansson | M. Koek | P. Nauclér | Z. Palacios-Baena | Christopher Roberts | D. Teixeira | T. Tängdén | J. Valik | M. Behnke | Thomas Tängdén | Daniel Teixeira | Zaira R Palacios-Baena
[1] M. Abbas,et al. Information technology aspects of large-scale implementation of automated surveillance of healthcare-associated infections. , 2021, Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases.
[2] M. Abbas,et al. Governance aspects of large-scale implementation of automated surveillance of healthcare-associated infections. , 2021, Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases.
[3] M. Bonten,et al. Validation of an algorithm for semiautomated surveillance to detect deep surgical site infections after primary total hip or knee arthroplasty—A multicenter study , 2020, Infection Control & Hospital Epidemiology.
[4] V. Herasevich,et al. Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population: observational study using electronic health records data , 2020, BMJ Quality & Safety.
[5] H. Verbrugh,et al. Electronically assisted surveillance systems of healthcare-associated infections: a systematic review , 2020, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.
[6] S. V. van Rooden,et al. A framework to develop semiautomated surveillance of surgical site infections: An international multicenter study , 2019, Infection Control & Hospital Epidemiology.
[7] E. Ricchizzi,et al. Use of health databases to deal with underreporting of surgical site infections due to suboptimal post-discharge follow-up. , 2020, The Journal of hospital infection.
[8] Henry A. Kautz,et al. Natural Language Processing for the Identification of Surgical Site Infections in Orthopaedics. , 2019, The Journal of bone and joint surgery. American volume.
[9] Huifen Li,et al. Evaluation of manual and electronic healthcare-associated infections surveillance: a multi-center study with 21 tertiary general hospitals in China. , 2019, Annals of translational medicine.
[10] Henk Scheper,et al. A mobile app for postoperative wound care after arthroplasty: Ease of use and perceived usefulness , 2019, Int. J. Medical Informatics.
[11] S. Gordon,et al. Beyond the abacus: Leveraging the electronic medical record for central line day surveillance. , 2019, American journal of infection control.
[12] Matthias Becker,et al. Natural language processing of German clinical colorectal cancer notes for guideline-based treatment evaluation , 2019, Int. J. Medical Informatics.
[13] D. Pittet,et al. Impact of participation in a surgical site infection surveillance network: results from a large international cohort study. , 2019, The Journal of hospital infection.
[14] S. Seo,et al. Hospital epidemiologists’ and infection preventionists’ opinions regarding hospital-onset bacteremia and fungemia as a potential healthcare-associated infection metric , 2019, Infection Control & Hospital Epidemiology.
[15] Philip E. Bourne,et al. Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review , 2019, J. Am. Medical Informatics Assoc..
[16] M. Bonten,et al. A diagnostic algorithm for the surveillance of deep surgical site infections after colorectal surgery , 2019, Infection Control & Hospital Epidemiology.
[17] Raymund B. Dantes,et al. Preventability of hospital onset bacteremia and fungemia: A pilot study of a potential healthcare-associated infection outcome measure , 2019, Infection Control & Hospital Epidemiology.
[18] C. Suetens,et al. Prevalence of healthcare-associated infections, estimated incidence and composite antimicrobial resistance index in acute care hospitals and long-term care facilities: results from two European point prevalence surveys, 2016 to 2017 , 2018, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.
[19] Julia E. Vogt,et al. Introduction to Machine Learning in Digital Healthcare Epidemiology , 2018, Infection Control & Hospital Epidemiology.
[20] K. Peck,et al. Validation of semiautomated surgical site infection surveillance using electronic screening algorithms in 38 surgery categories , 2018, Infection Control & Hospital Epidemiology.
[21] W. Zingg,et al. Process and outcome indicators for infection control and prevention in European acute care hospitals in 2011 to 2012 – Results of the PROHIBIT study , 2018, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.
[22] Peter Christen,et al. A note on using the F-measure for evaluating record linkage algorithms , 2017, Statistics and Computing.
[23] E. Perencevich,et al. Designing Surveillance of Healthcare-Associated Infections in the Era of Automation and Reporting Mandates , 2018, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.
[24] Philip L. Russo,et al. Impact of electronic healthcare-associated infection surveillance software on infection prevention resources: a systematic review of the literature. , 2017, The Journal of hospital infection.
[25] Jörg Tiedemann,et al. Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting , 2016, Health Informatics J..
[26] M. Bonten,et al. Semiautomated Surveillance of Deep Surgical Site Infections After Primary Total Hip or Knee Arthroplasty , 2017, Infection Control & Hospital Epidemiology.
[27] K. Mølbak,et al. National Automated Surveillance of Hospital-Acquired Bacteremia in Denmark Using a Computer Algorithm , 2017, Infection Control & Hospital Epidemiology.
[28] William B. Lober,et al. Electronic Surveillance For Catheter-Associated Urinary Tract Infection Using Natural Language Processing , 2017, AMIA.
[29] M. Egger,et al. Core components for effective infection prevention and control programmes: new WHO evidence-based recommendations , 2017, Antimicrobial Resistance & Infection Control.
[30] Barbara Sheehan,et al. Natural Language Processing–Enabled and Conventional Data Capture Methods for Input to Electronic Health Records: A Comparative Usability Study , 2016, JMIR medical informatics.
[31] C. Suetens,et al. Burden of Six Healthcare-Associated Infections on European Population Health: Estimating Incidence-Based Disability-Adjusted Life Years through a Population Prevalence-Based Modelling Study , 2016, PLoS medicine.
[32] A. Gardner,et al. Time Spent by Infection Control Professionals Undertaking Healthcare Associated Infection Surveillance: A Multi-centred Cross Sectional Study , 2016 .
[33] Klaus-Peter Adlassnig,et al. Detecting borderline infection in an automated monitoring system for healthcare-associated infection using fuzzy logic , 2016, Artif. Intell. Medicine.
[34] P. Bossuyt,et al. Anticipating missing reference standard data when planning diagnostic accuracy studies , 2016, British Medical Journal.
[35] W. Trick,et al. Probabilistic Measurement of Central Line–Associated Bloodstream Infections , 2015, Infection Control & Hospital Epidemiology.
[36] Kerri A. Thom,et al. A Multicenter Longitudinal Study of Hospital-Onset Bacteremia: Time for a New Quality Outcome Measure? , 2015, Infection Control & Hospital Epidemiology.
[37] P. Gastmeier,et al. Case vignettes to evaluate the accuracy of identifying healthcare-associated infections by surveillance persons. , 2015, The Journal of hospital infection.
[38] M. Bonten,et al. Accuracy of administrative data for surveillance of healthcare-associated infections: a systematic review , 2015, BMJ Open.
[39] Valmeek Kudesia,et al. Natural Language Processing for Real-Time Catheter-Associated Urinary Tract Infection Surveillance: Results of a Pilot Implementation Trial , 2015, Infection Control & Hospital Epidemiology.
[40] H. Syrjälä,et al. Incidence of healthcare-associated infections in a tertiary care hospital: results from a three-year period of electronic surveillance. , 2015, The Journal of hospital infection.
[41] Didier Pittet,et al. Hospital organisation, management, and structure for prevention of health-care-associated infection: a systematic review and expert consensus. , 2015, The Lancet. Infectious diseases.
[42] A. Troelstra,et al. Validation of an Automated Surveillance Approach for Drain-Related Meningitis: A Multicenter Study , 2015, Infection Control & Hospital Epidemiology.
[43] Joshua A. Doherty,et al. Multicenter Evaluation of Computer Automated versus Traditional Surveillance of Hospital-Acquired Bloodstream Infections , 2014, Infection Control & Hospital Epidemiology.
[44] Jeroen S. de Bruin,et al. Data use and effectiveness in electronic surveillance of healthcare associated infections in the 21st century: a systematic review , 2014, J. Am. Medical Informatics Assoc..
[45] Michael Y. Lin,et al. Data Requirements for Electronic Surveillance of Healthcare-Associated Infections , 2014, Infection Control & Hospital Epidemiology.
[46] M. Klompas,et al. Improving ventilator-associated event surveillance in the National Healthcare Safety Network and addressing knowledge gaps: update and review , 2014, Current opinion in infectious diseases.
[47] P. Astagneau,et al. Quality Assessment of Hospital Discharge Database for Routine Surveillance of Hip and Knee Arthroplasty–Related Infections , 2014, Infection Control & Hospital Epidemiology.
[48] M. Bonten,et al. Electronic implementation of a novel surveillance paradigm for ventilator-associated events. Feasibility and validation. , 2014, American journal of respiratory and critical care medicine.
[49] Bryan C. Knepper,et al. Time-Saving Impact of an Algorithm to Identify Potential Surgical Site Infections , 2013, Infection Control & Hospital Epidemiology.
[50] William E Trick,et al. Decision making during healthcare-associated infection surveillance: a rationale for automation. , 2013, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.
[51] Philippe Ravaud,et al. Agreement among Healthcare Professionals in Ten European Countries in Diagnosing Case-Vignettes of Surgical-Site Infections , 2013, PloS one.
[52] A. Troelstra,et al. Automated surveillance for healthcare-associated infections: opportunities for improvement. , 2013, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.
[53] K. Woeltje. Moving into the future: electronic surveillance for healthcare-associated infections. , 2013, The Journal of hospital infection.
[54] Steven H. Brown,et al. Exploring the Frontier of Electronic Health Record Surveillance: The Case of Postoperative Complications , 2013, Medical care.
[55] A Charlett,et al. Advances in electronic surveillance for healthcare-associated infections in the 21st Century: a systematic review. , 2013, The Journal of hospital infection.
[56] Alan M Stamm,et al. A comparison of 3 metrics to identify health care-associated infections. , 2012, American journal of infection control.
[57] M. Dixon-Woods,et al. What counts? An ethnographic study of infection data reported to a patient safety program. , 2012, The Milbank quarterly.
[58] Susan Mallett,et al. QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies , 2011, Annals of Internal Medicine.
[59] C. Hollenbeak,et al. Electronic Measures of Surgical Site Infection: Implications for Estimating Risks and Costs , 2011, Infection Control & Hospital Epidemiology.
[60] Stephan Harbarth,et al. Use of benchmarking and public reporting for infection control in four high-income countries. , 2011, The Lancet. Infectious diseases.
[61] Joshua A. Doherty,et al. Quality of traditional surveillance for public reporting of nosocomial bloodstream infection rates. , 2010, JAMA.
[62] E. Larson,et al. Electronic surveillance systems in infection prevention: organizational support, program characteristics, and user satisfaction. , 2010, American journal of infection control.
[63] A. Boonstra,et al. Barriers to the acceptance of electronic medical records by physicians from systematic review to taxonomy and interventions , 2010, BMC health services research.
[64] Haibo He,et al. Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.
[65] B. Tserenpuntsag,et al. Infection control resources in New York State hospitals, 2007. , 2008, American journal of infection control.
[66] Joshua A. Doherty,et al. Automated Surveillance for Central Line–Associated Bloodstream Infection in Intensive Care Units , 2008, Infection Control & Hospital Epidemiology.
[67] Margaret A Dudeck,et al. CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting. , 2008, American journal of infection control.
[68] Mary F. Wisniewski,et al. Computer Algorithms To Detect Bloodstream Infections , 2004, Emerging infectious diseases.
[69] R. Gaynes,et al. Accuracy of Reporting Nosocomial Infections In Intensive-Care–Unit Patients to the National Nosocomial Infections Surveillance System: A Pilot Study , 1998, Infection Control & Hospital Epidemiology.
[70] N. J. Ehrenkranz,et al. Recorded Criteria as a “Gold Standard” for Sensitivity and Specificity Estimates of Surveillance of Nosocomial Infection: A Novel Method to Measure Job Performance , 1995, Infection Control & Hospital Epidemiology.
[71] J M Hughes,et al. CDC definitions for nosocomial infections, 1988. , 1988, American journal of infection control.
[72] R M Gardner,et al. Computer surveillance of hospital-acquired infections and antibiotic use. , 1986, JAMA.
[73] R. Haley,et al. The efficacy of infection surveillance and control programs in preventing nosocomial infections in US hospitals. , 1985, American journal of epidemiology.