Computer-assisted assessment of patient care in the hospital

Gabrieli, Elmer R.

doi:10.1007/bf00996636

Cited by 14 publications

(6 citation statements)

References 12 publications

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“…[30][31][32] A number of natural language processing systems are based on symbolic methods such as pattern matching or rule-based techniques and have been applied to health care. [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45] These systems have varied in approach: pure pattern matching, syntactic grammar, semantic grammar, or probabilistic methods, with different tradeoffs in accuracy, robustness, scalability, and maintainability. These systems have done well in domains, such as radiology, in which the narrative text is focused, and the results for more complex narrative such as discharge summaries are promising.…”

Section: Electronic Tools For Detecting Adverse Eventsmentioning

confidence: 99%

Detecting Adverse Events Using Information Technology

Bates

Evans

Murff

et al. 2003

J Am Med Inform Assoc

370

255

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Section: Electronic Tools For Detecting Adverse Eventsmentioning

confidence: 99%

Detecting Adverse Events Using Information Technology

Bates

Evans

Murff

et al. 2003

J Am Med Inform Assoc

370

255

View full text Add to dashboard Cite

show abstract

“…Even if it might be possible, as a second step, to extract the pertinent facts by an advanced computerized analysis this is an indirect and also halfhearted approach. Gabrieli (1988) describes a linguistic analysis of natural language in the patient record for assessing the capability of the medical care. Problems of this kind seem very difficult.…”

Section: Can Computerization Fulfill Its Foremost Practical 'Raison Dmentioning

confidence: 99%

Computerized patient record system for the glaucoma ward

Holmin

Krakau

Strom

1991

Acta Ophthalmologica

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A system for computerized glaucoma patient record, developed from a data base called 'Advanced Revelation', is described. The record is made up from free text as well as from choices among listed alternatives. All data are stored on magnetic media and also printed out on paper in a well disposed and easily surveyable form. Apart from saving personnel and time in the clinical routine, the main advantages of the system is a powerful tool for clinical research. Data retrieval occurs on 'list', 'count', or 'sort' orders, facilitating retrospective studies. The system has been used for over a year at a glaucoma ward (Lund) to increasing satisfaction.

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“…Generic screening had a high potential in automated screening systems; as hospital information systems advanced, the various adverse events monitored as a part of generic screening systems could be integrated into a system that automatically alerted for cases that needed further clinical review. Gabrieli [ 56 ] asserted that computers had the potential to mechanize a major portion of the quality assurance work, and the challenge was to codify the medical intelligence necessary to monitor the quality of care provided for a price that was affordable. Gabrieli had developed computer-based patient records and automated analysis techniques for discharge summaries, and had concluded that for audit purposes the discharge summary was well suited as a source document.…”

Section: Administration Decision Supportmentioning

confidence: 99%

Decision Support Systems (DSS)

Collen¹,

McCray

2015

Health Informatics

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In the 1970s, to control costs and comply with Medicare rules, hospitals began to use computer-based systems for utilization review. In the early 1980s, a diagnostic related group (DRG) approach was applied to hospital payments, and increasingly complex data reporting requirements led hospitals to establish quality assurance programs. Medical information, or medical knowledge, databases for clinical decision support (CDS) were developed in the 1980s. The capabilities provided by online real time monitoring, vastly increased data storage, and physician order entry and results reporting (OE/RR) came together in CDS programs in the 2000s; by 2010 most physicians were using systems that provided online practice guidelines as well as clinical reminders and alerts about potential adverse clinical events. These advances build upon techniques from other fi elds, including artifi cial intelligence; the emergence of huge clinical databases in the 1990s led to the use of data mining to uncover previously unknown important relationships between clinical data elements, and the use of effi cient knowledge discovery algorithms to extract knowledge from data by identifying and describing patterns in a meaningful manner. To support knowledge creation in biomedicine, the National Library of Medicine produces and maintains hundreds of up-to-date scientifi c databases, including MEDLINE (over 23 million citations with 700,000 posted in 2013 alone), human genome resources, toxicology and environmental databases (searched billions of times a year by millions around the globe).

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Computer-assisted assessment of patient care in the hospital

Cited by 14 publications

References 12 publications

Detecting Adverse Events Using Information Technology

Detecting Adverse Events Using Information Technology

Computerized patient record system for the glaucoma ward

Decision Support Systems (DSS)

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