Large Visualizations for System Monitoring of Complex, Heterogeneous Systems

Russell, Daniel M.; Dieberger, Andreas; Bhagwan, Varun; Gruhl, Daniel

doi:10.1007/11555261_74

Cited by 2 publications

(2 citation statements)

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“…These hurdles are further augmented with the facts that data sets are often quite large, e.g., hours of video and audio transcripts and that, in many cases, the time and processing limits (particularly in emergent care situations) require Bbest effort[ optimization of data acquisition rather than a complete draw or ingest. Fortunately, the team was able to leverage and customize its prior work on data acquisition technologies [41][42][43][44][45][46].…”

Section: Evidence Generationmentioning

confidence: 99%

“…The evidence ecosystem (see Figure 1) builds upon work in medical informatics [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], collaborative care [21][22][23][24][25][26][27], data mining [28][29][30][31][32][33], modeling [34][35][36][37][38][39][40], software engineering [41][42][43][44][45][46][47], usability and user interface design [48][49][50][51][52]…”

Section: Introductionmentioning

confidence: 99%

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Information technology for healthcare transformation

et al. 2011

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Rising costs, decreasing quality of care, diminishing productivity, and increasing complexity have all contributed to the present state of the healthcare industry. The interactions between payers (e.g., insurance companies and health plans) and providers (e.g., hospitals and laboratories) are growing and are becoming more complicated. The constant upsurge in and enhanced complexity of diagnostic and treatment information has made the clinical decision-making process more difficult. Medical transaction charges are greater than ever. Population-specific financial requirements are increasing the economic burden on the entire system. Medical insurance and identity theft frauds are on the rise. The current lack of comparative cost analytics hampers systematic efficiency. Redundant and unnecessary interventions add to medical expenditures that add no value. Contemporary payment models are antithetic to outcome-driven medicine. The rate of medical errors and mistakes is high. Slow inefficient processes and the lack of best practice support for care delivery do not create productive settings. Information technology has an important role to play in approaching these problems. This paper describes IBM Research's approach to helping address these issues, i.e., the evidence-based healthcare platform.

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