Evaluation of the suitability of a patient data management system for ICUs on a general ward

Jünger, Alexander; Michel, A.; Benson, Matthew A.; Quinzio, L.; Hafer, Johannes; Hartmann, Bernd; Brandenstein, Patrick; Marquardt, Kurt; Hempelmann, G.

doi:10.1016/s1386-5056(01)00202-7

Cited by 11 publications

(14 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ballermann et al [23] Moderate a -Ballermann et al [24] Strong -Benson et al [25] Moderate b, c Bosman et al [26] Strong -Bürkle et al [17] Weak a,d -De Mul et al [27] Strong De Reus [18] Weak e ,f,g ,b, c Doulgas et al [28] Strong Donati et al [29] Strong Strong Eden et al [30] Strong Strong Eden et al [31] Strong Ehrenfeld et al [32] Strong Eslami et al [33] Strong -Fraenkel et al [34] Strong -Fretschner et al [22] Weak h ,d Moderate i, e, g,b Junger et al [19] Weak h ,d Weak i ,e ,g, b,c Lipp and Williams [20] Weak i ,f ,g, b,c Menke et al [21] Weak a ,h -Meyfroidt et al [35] Strong -Nelwan et al [36] Strong Saarinen and Aho [37] Strong -Van Vliet [38] Moderate i, e, f,c Wong et al [39] Strong - remained for the full text review. Following analysis of the full text articles, 23 articles were considered relevant for the systematic literature review.…”

Section: Publication Quality Assessment Ratings Quantitative and Qualmentioning

confidence: 99%

“…In the quality assessment phase, five articles received a weak rating and were eliminated [17][18][19][20][21]. One article received a weak rating for the quantitative or qualitative component, but a moderate to strong rating for their descriptive component; Ballermann et al [23] Pre-post implementation x Ballermann et al [24] Pre-post implementation x Benson et al [25] x Bosman et al [26] Randomized controlled trial x De Mul et al [27] x Donati et al [29] Observational x x x Douglas et al [28] x Eden et al [30] Pre-post implementation x x x Eden et al [31] Pre-post implementation x Ehrenfel et al [32] Pre-post implementation x Eslami et al [33] Pre-post implementation x Fraenkel et al [34] Pre-post implementation x x Fretschner et al [22] x Meyfroidt et al [35] Pre-post implementation x Nelwan et al 2007 [36] x Saarinen and Aho [37] Pre-post implementation x Van Vliet [38] x Wong et al [39] Pre-post implementation x hence only their descriptive information was included [22].…”

Section: Publication Quality Assessment Ratings Quantitative and Qualmentioning

confidence: 99%

See 1 more Smart Citation

The organizational and clinical impact of integrating bedside equipment to an information system: A systematic literature review of patient data management systems (PDMS)

Cheung

Velden

Lagerburg

et al. 2015

International Journal of Medical Informatics

View full text Add to dashboard Cite

Section: Publication Quality Assessment Ratings Quantitative and Qualmentioning

confidence: 99%

Section: Publication Quality Assessment Ratings Quantitative and Qualmentioning

confidence: 99%

The organizational and clinical impact of integrating bedside equipment to an information system: A systematic literature review of patient data management systems (PDMS)

Cheung

Velden

Lagerburg

et al. 2015

International Journal of Medical Informatics

View full text Add to dashboard Cite

“…A typical case of such scenarios is the medical signals generated in ICU of hospitals. Currently, the most advanced ICU systems as generally accepted, the HP CareVue [23] , ICU data [24] and VA quantitative sentinel system [9] , all use traditional databases to store biomedical signals. Due to storage limitation, they can only sample the data with a rather long interval (e.g., twice per hour).…”

Section: Data Stream Management Systemsmentioning

confidence: 99%

“…Furthermore, none of them can analyze the stream data or detect variations. The tasks of variation management are completed manually by nurses [24] , the manner that is error-prone and brings heavy workload as well as high expense.…”

Section: Data Stream Management Systemsmentioning

confidence: 99%

PGG: An Online Pattern Based Approach for Stream Variation Management

Tang

Gui

et al. 2008

J. Comput. Sci. Technol.

View full text Add to dashboard Cite

Many database applications require efficient processing of data streams with value variations and fluctuant sampling frequency. The variations typically imply fundamental features of the stream and important domain knowledge of underlying objects. In some data streams, successive events seem to recur in a certain time interval, but the data indeed evolves with tiny differences as time elapses. This feature, so called pseudo periodicity, poses a new challenge to stream variation management. This study focuses on the online management for variations over such streams. The idea can be applied to many scenarios such as patient vital signal monitoring in medical applications. This paper proposes a new method named Pattern Growth Graph (PGG) to detect and manage variations over evolving streams with following features: 1) adopts the wave-pattern to capture the major information of data evolution and represent them compactly; 2) detects the variations in a single pass over the stream with the help of wave-pattern matching algorithm; 3) only stores different segments of the pattern for incoming stream, and hence substantially compresses the data without losing important information; 4) distinguishes meaningful data changes from noise and reconstructs the stream with acceptable accuracy. Extensive experiments on real datasets containing millions of data items, as well as a prototype system, are carried out to demonstrate the feasibility and effectiveness of the proposed scheme.

show abstract

“…Today, intensive care needs to be increased with a prospect of an aging population and socioeconomic factors influencing health intervention, and there are some problems in the ICUs, it is essential to resolve (1, 2, 3); Such as: Beds and skilled manpower shortcoming, Exorbitant cost of treatment, High rates of mortality, Severe Nosocomial infections, High LOS, Low compliace with standards, Low quality of care and improvements, Epidemic of medical and medication errors, Poor coordination of intensive care team, and the lack of information required for clinical and managerial decisions (4, 5, 6). 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%

Intensive Care Information System Impacts

Ehteshami¹,

Sadoughi²,

Ahmadi³

et al. 2013

Acta Inform Med

View full text Add to dashboard Cite

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.

show abstract

Evaluation of the suitability of a patient data management system for ICUs on a general ward

Cited by 11 publications

References 15 publications

The organizational and clinical impact of integrating bedside equipment to an information system: A systematic literature review of patient data management systems (PDMS)

The organizational and clinical impact of integrating bedside equipment to an information system: A systematic literature review of patient data management systems (PDMS)

PGG: An Online Pattern Based Approach for Stream Variation Management

Intensive Care Information System Impacts

Contact Info

Product

Resources

About