How to implement information technology in the operating room and the intensive care unit

Meyfroidt, Geert

doi:10.1016/j.bpa.2008.07.004

Cited by 13 publications

(16 citation statements)

References 37 publications

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“…Of these, according to the outcomes order of priority, 14 articles reported the improving quality of care (2, 3, 4, 5-15), 10 articles discussed the improving ICU Performance (2, 5-7, 11-13, 15-18), and other clinical outcomes were as follows: eight articles on the reducing medical errors (2, 5, 7-9, 11, 15, 17), seven articles on the presenting clinical alerts (3, 5, 6, 8, 11, 15, 18), six articles on the evaluating quality of care (2, 8, 13, 15, 17, 19), five articles on the clinical calculating (5, 7, 16, 18, 20), five articles on the improving medication administration (7, 8, 11, 15, 21), four articles on the interfacing with clinical devices (3, 8, 9, 21), four articles on the patient tracking (2, 5, 11, 15), four articles on the predicting patient outcome (2, 8, 16, 20), four articles on the providing telecare (8, 13, 15, 19), four articles on the specifying patient needs (8, 13, 16, 17), three articles on the reducing clinical risk and improving patient safety (2, 8, 9), three articles on the antibiotic management (5, 15, 16), three articles on the overview patient old and new conditions (13, 16, 21), three articles on the improving (multidisciplinary) communication (9, 13, 22)”, three articles on the care planning (9, 13, 19), two articles on the car...…”

Section: Clinical Outcomesmentioning

confidence: 99%

“…Of these, according to the outcomes order of priority, 18 articles focused on the electronic documenting and automated reporting (2, 3, 5-11, 13, 15-22), 18 articles on the Managing patient data (1, 2, 3, 5-11, 13-16, 19-22), 12 articles on the increasing data quality (2, 3, 7, 8, 11, 13-17, 19, 21), nine articles on the electronic data interchange (2, 5-11, 16), seven articles on the patient’s data measure and transmit (2, 5, 6, 10, 11, 13, 20), six articles on the ICIS integration with other information systems (1, 6-9, 11), and, six articles on the building new knowledge from the gathered data (2, 5-8, 21). Analysis of the content showed that ICIS documented clinical data electronically, automated reporting and managed patient data in 82% of cases.…”

Section: Clinical Outcomesmentioning

confidence: 99%

“…Of these, 11 articles discussed the improving cost-effectiveness (2, 3, 5, 7, 8, 10, 13-16, 21) 10 articles on the time saving (3, 4, 5, 6, 8, 9, 14, 15, 18, 20), eight articles on the optimizing resource utilization (2, 7, 8, 11, 13, 15, 16, 18), two articles on the reducing medical errors (2, 8), two articles on the clinical Calculating (5, 20), two articles on the reducing ICU length of stay (LOS) (2, 15), and, two articles on the improving ICU performance (8, 15). Content analysis of the study showed that ICIS improved cost-effectiveness of intensive care and time saving, respectively in 50% and 45% of cases.…”

Section: Clinical Outcomesmentioning

confidence: 99%

“…Also, Some problems must be considered such as automatic scoring systems failure to predict disease severity and mortality, real time documentation of clinical data, Inefficiency of integrated clinical alerts, Significant clinical workflow interruptions, and Resource constraints (7, 8, 9). …”

Section: Introductionmentioning

confidence: 99%

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Intensive Care Information System Impacts

Ehteshami¹,

Sadoughi²,

Ahmadi³

et al. 2013

Acta Inform Med

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Introduction:Today, intensive care needs to be increased with a prospect of an aging population and socioeconomic factors influencing health intervention, but there are some problems in the intensive care environments, it is essential to resolve. The intensive Care information system has the potential to solve many of ICU problems. The objective of the review was to establish the impact of intensive care information systems on the practitioners practice, patient outcomes and ICU performance.Methods:Scientific databases and electronic journal citations was searched to identify articles that discussed the impacts of intensive care information system on the practices, patient outcomes and ICU performance. A total of 22 articles discussing ICIS outcomes was included in this study from 609 articles initially obtained from the searches.Results:Pooling data across studies, we found that the median impact of ICIS on information management was 48.7%. The median impact of ICIS on user’ outcomes was 36.4%, impact on saving tips by 24%, clinical decision support by a mean of 22.7%, clinical outcomes improved by a mean of 18.6%, and researches improved by 18%.Conclusion:The functionalities of ICIS are growing day by day and new functionalities are available with every major release. Better adoption of ICIS by the intensive care environments emphasizes the opportunity of better intensive care services through patient oriented intensive care clinical information systems. There is an immense need for developing guidelines for standardizing ICIS to to maximize the power of ICISs and to integrate with HISs. This will enable intensivists to use the systems in a more meaningful way for better patient care. This study provides a better understanding and greater insight into the effectiveness of ICIS in improving patient care and reducing health care expenses.

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Section: Clinical Outcomesmentioning

confidence: 99%

Section: Clinical Outcomesmentioning

confidence: 99%

Section: Clinical Outcomesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Intensive Care Information System Impacts

Ehteshami¹,

Sadoughi²,

Ahmadi³

et al. 2013

Acta Inform Med

View full text Add to dashboard Cite

show abstract

“…A Patient Data Management System (PDMS) is a computerized ICU medical record that includes automatic registration of data from monitors and therapeutic devices, clinical observations, laboratory parameters, and drug therapy [17]. This large amount of data, including time series of some of these signals, is stored in a large relational database.…”

Section: Introductionmentioning

confidence: 99%

Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model

Meyfroidt

Güiza

Cottem

et al. 2011

BMC Med Inform Decis Mak

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BackgroundThe intensive care unit (ICU) length of stay (LOS) of patients undergoing cardiac surgery may vary considerably, and is often difficult to predict within the first hours after admission. The early clinical evolution of a cardiac surgery patient might be predictive for his LOS. The purpose of the present study was to develop a predictive model for ICU discharge after non-emergency cardiac surgery, by analyzing the first 4 hours of data in the computerized medical record of these patients with Gaussian processes (GP), a machine learning technique.MethodsNon-interventional study. Predictive modeling, separate development (n = 461) and validation (n = 499) cohort. GP models were developed to predict the probability of ICU discharge the day after surgery (classification task), and to predict the day of ICU discharge as a discrete variable (regression task). GP predictions were compared with predictions by EuroSCORE, nurses and physicians. The classification task was evaluated using aROC for discrimination, and Brier Score, Brier Score Scaled, and Hosmer-Lemeshow test for calibration. The regression task was evaluated by comparing median actual and predicted discharge, loss penalty function (LPF) ((actual-predicted)/actual) and calculating root mean squared relative errors (RMSRE).ResultsMedian (P25-P75) ICU length of stay was 3 (2-5) days. For classification, the GP model showed an aROC of 0.758 which was significantly higher than the predictions by nurses, but not better than EuroSCORE and physicians. The GP had the best calibration, with a Brier Score of 0.179 and Hosmer-Lemeshow p-value of 0.382. For regression, GP had the highest proportion of patients with a correctly predicted day of discharge (40%), which was significantly better than the EuroSCORE (p < 0.001) and nurses (p = 0.044) but equivalent to physicians. GP had the lowest RMSRE (0.408) of all predictive models.ConclusionsA GP model that uses PDMS data of the first 4 hours after admission in the ICU of scheduled adult cardiac surgery patients was able to predict discharge from the ICU as a classification as well as a regression task. The GP model demonstrated a significantly better discriminative power than the EuroSCORE and the ICU nurses, and at least as good as predictions done by ICU physicians. The GP model was the only well calibrated model.

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Staff perceptions of the implementation of a trauma video review program at a level I trauma center

Dainty

Seaton

McGowan³

et al. 2021

AEM Education and Training

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Objectives: Successful implementation of any new technology requires extensive engagement with front-line staff. We explored the perceptions of emergency department and trauma staff about a trauma video review program (TVR) prior to implementation of the first such program in Canada at our level I trauma center. Methods:We conducted semi-structured individual interviews and in situ small group interviews with 35 multidisciplinary ED and trauma staff members of a teaching and research hospital in Toronto, Canada. We sought maximum variation in the sample of purposively selected participants. Interviews were recorded with audiotapes or detailed field notes, transcribed verbatim, coded, and analyzed using standard thematic analysis techniques.Results: Participants expressed overall support for the concept of TVR, but there is a core sense of unease that influenced overall staff perceptions. Despite several departmental presentations, very few participants actually had a solid understanding of how the TVR worked. Many were apprehensive about their own professional privacy, deeply concerned about vulnerable patients being filmed without consent, and questioned how video data would be used. Despite significant hesitancy, ED and trauma staff identified positive opportunities that TVR could bring, including providing an evidence base for quality improvement. Conclusions:TVR is an evolving approach to evaluate quality and patient safety in the trauma bay. As such it brings with it natural concerns and apprehension from staff regarding privacy, confidentiality, and how data will be captured and used. There is opportunity for these types of concerns to be addressed with a robust knowledge translation plan and engagement of staff throughout the implementation process.

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How to implement information technology in the operating room and the intensive care unit

Cited by 13 publications

References 37 publications

Intensive Care Information System Impacts

Intensive Care Information System Impacts

Computerized prediction of intensive care unit discharge after cardiac surgery: development and validation of a Gaussian processes model

Staff perceptions of the implementation of a trauma video review program at a level I trauma center

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