Background Traditional approaches to mechanical ventilation use tidal volumes of 10 to 15 ml per kilogram of body weight and may cause stretch-induced lung injury in patients with acute lung injury and the acute respiratory distress syndrome. We therefore conducted a trial to determine whether ventilation with lower tidal volumes would improve the clinical outcomes in these patients. Methods Patients with acute lung injury and the acute respiratory distress syndrome were enrolled in a multicenter, randomized trial. The trial compared traditional ventilation treatment, which involved an initial tidal volume of 12 ml per kilogram of predicted body weight and an airway pressure measured after a 0.5-second pause at the end of inspiration (plateau pressure) of 50 cm of water or less, with ventilation with a lower tidal volume, which involved an initial tidal volume of 6 ml per kilogram of predicted body weight and a plateau pressure of 30 cm of water or less. The primary outcomes were death before a patient was discharged home and was breathing without assistance and the number of days without ventilator use from day 1 to day 28. Results The trial was stopped after the enrollment of 861 patients because mortality was lower in the group treated with lower tidal volumes than in the group treated with traditional tidal volumes (31.0 percent vs. 39.8 percent, P=0.007), and the number of days without ventilator use during the first 28 days after randomization was greater in this group (mean [+/-SD], 12+/-11 vs. 10+/-11; P=0.007). The mean tidal volumes on days 1 to 3 were 6.2+/-0.8 and 11.8+/-0.8 ml per kilogram of predicted body weight (P Conclusions In patients with acute lung injury and the acute respiratory distress syndrome, mechanical ventilation with a lower tidal volume than is traditionally used results in decreased mortality and increases the number of days without ventilator use.

Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome.

What do a seventeenth-century mortality table (whose causes of death include "fainted in a bath," "frighted," and "itch"); the identification of South Africans during apartheid as European, Asian, colored, or black; and the separation of machine- from hand-washables have in common? All are examples of classification -- the scaffolding of information infrastructures. In Sorting Things Out, Geoffrey C. Bowker and Susan Leigh Star explore the role of categories and standards in shaping the modern world. In a clear and lively style, they investigate a variety of classification systems, including the International Classification of Diseases, the Nursing Interventions Classification, race classification under apartheid in South Africa, and the classification of viruses and of tuberculosis. The authors emphasize the role of invisibility in the process by which classification orders human interaction. They examine how categories are made and kept invisible, and how people can change this invisibility when necessary. They also explore systems of classification as part of the built information environment. Much as an urban historian would review highway permits and zoning decisions to tell a city's story, the authors review archives of classification design to understand how decisions have been made. Sorting Things Out has a moral agenda, for each standard and category valorizes some point of view and silences another. Standards and classifications produce advantage or suffering. Jobs are made and lost; some regions benefit at the expense of others. How these choices are made and how we think about that process are at the moral and political core of this work. The book is an important empirical source for understanding the building of information infrastructures.

https://www.reciis.icict.fiocruz.br/index.php/reciis/article/download/794/1436

Sorting Things Out: Classification and Its Consequences

ContextDevelopers of health care software have attributed improvements in patient
care to these applications. As with any health care intervention, such claims
require confirmation in clinical trials.ObjectivesTo review controlled trials assessing the effects of computerized clinical
decision support systems (CDSSs) and to identify study characteristics predicting
benefit.Data SourcesWe updated our earlier reviews by searching the MEDLINE, EMBASE, Cochrane
Library, Inspec, and ISI databases and consulting reference lists through
September 2004. Authors of 64 primary studies confirmed data or provided additional
information.Study SelectionWe included randomized and nonrandomized controlled trials that evaluated
the effect of a CDSS compared with care provided without a CDSS on practitioner
performance or patient outcomes.Data ExtractionTeams of 2 reviewers independently abstracted data on methods, setting,
CDSS and patient characteristics, and outcomes.Data SynthesisOne hundred studies met our inclusion criteria. The number and methodologic
quality of studies improved over time. The CDSS improved practitioner performance
in 62 (64%) of the 97 studies assessing this outcome, including 4 (40%) of
10 diagnostic systems, 16 (76%) of 21 reminder systems, 23 (62%) of 37 disease
management systems, and 19 (66%) of 29 drug-dosing or prescribing systems.
Fifty-two trials assessed 1 or more patient outcomes, of which 7 trials (13%)
reported improvements. Improved practitioner performance was associated with
CDSSs that automatically prompted users compared with requiring users to activate
the system (success in 73% of trials vs 47%; P = .02)
and studies in which the authors also developed the CDSS software compared
with studies in which the authors were not the developers (74% success vs
28%; respectively, P = .001).ConclusionsMany CDSSs improve practitioner performance. To date, the effects on
patient outcomes remain understudied and, when studied, inconsistent.

Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review.

Context.—Many computer software developers and vendors claim that their systems
can directly improve clinical decisions. As for other health care interventions,
such claims should be based on careful trials that assess their effects on
clinical performance and, preferably, patient outcomes.Objective.—To systematically review controlled clinical trials assessing the effects
of computer-based clinical decision support systems (CDSSs) on physician performance
and patient outcomes.Data Sources.—We updated earlier reviews covering 1974 to 1992 by searching the MEDLINE,
EMBASE, INSPEC, SCISEARCH, and the Cochrane Library bibliographic databases
from 1992 to March 1998. Reference lists and conference proceedings were reviewed
and evaluators of CDSSs were contacted.Study Selection.—Studies were included if they involved the use of a CDSS in a clinical
setting by a health care practitioner and assessed the effects of the system
prospectively with a concurrent control.Data Extraction.—The validity of each relevant study (scored from 0-10) was evaluated
in duplicate. Data on setting, subjects, computer systems, and outcomes were
abstracted and a power analysis was done on studies with negative findings.Data Synthesis.—A total of 68 controlled trials met our criteria, 40 of which were published
since 1992. Quality scores ranged from 2 to10, with more recent trials rating
higher (mean, 7.7) than earlier studies (mean, 6.4) (P<.001).
Effects on physician performance were assessed in 65 studies and 43 found
a benefit (66%). These included 9 of 15 studies on drug dosing systems, 1
of 5 studies on diagnostic aids, 14 of 19 preventive care systems, and 19
of 26 studies evaluating CDSSs for other medical care. Six of 14 studies assessing
patient outcomes found a benefit. Of the remaining 8 studies, only 3 had a
power of greater than 80% to detect a clinically important effect.Conclusions.—Published studies of CDSSs are increasing rapidly, and their quality
is improving. The CDSSs can enhance clinical performance for drug dosing,
preventive care, and other aspects of medical care, but not convincingly for
diagnosis. The effects of CDSSs on patient outcomes have been insufficiently
studied.

https://jamanetwork.com/journals/jama/articlepdf/188081/JRV71066.pdf

Effects of Computer-Based Clinical Decision Support Systems on Physician Performance and Patient Outcomes: A Systematic Review

Background. Randomized, controlled trials have shown that prophylactic antibiotics are effective in preventing surgical-wound infections. However, it is uncertain how the timing of antibiotic administration affects the risk of surgical-wound infection in actual clinical practice. Methods. We prospectively monitored the timing of antibiotic prophylaxis and studied the occurrence of surgical-wound infections in 2847 patients undergoing elective clean or "clean—contaminated" surgical procedures at a large community hospital. The administration of antibiotics 2 to 24 hours before the surgical incision was defined as early; that during the 2 hours before the incision, as preoperative; that during the 3 hours after the incision, as perioperative; and that more than 3 but less than 24 hours after the incision, as postoperative. Results. Of the 1708 patients who received the prophylactic antibiotics preoperatively, 10 (0.6 percent) subsequently had surgical-wound infections. Of the 282 patients who recei...

https://www.nejm.org/doi/pdf/10.1056/NEJM199201303260501

The timing of prophylactic administration of antibiotics and the risk of surgical-wound infection.

Total lung capacity (TLC), functional residual capacity, residual volume, and corresponding 95% confidence intervals were measured in 245 healthy nonsmoking person (122 women, 123 men) using a single-breath helium technique. Prediction equations for lung volumes were generated by multiple linear regression. The resultant equations are similar to previously published equations using multiple-breath gas equilibration techniques. Measured 95% confidence intervals can be closely approximated by using two times the standard error of the estimate for each equation, but cannot be approximated by using +/- 20% of the predicted value. Radiographic TLC was not significantly different from the helium dilution TLC.

Lung volumes in healthy nonsmoking adults.

Objective: To evaluate the automated detection of clinical conditions described in narrative reports. Design: Automated methods and human experts detected the presence or absence of six clinical co...

Unlocking Clinical Data from Narrative Reports: A Study of Natural Language Processing

Development of a comprehensive computer system for acquiring medical data and implementing medical decision logic has been ongoing for over 15 years at the University of Utah and the LDS Hospital in Salt Lake City, Utah. This system is known as HELP and is currently operational at LDS Hospital, which is a 550-bed tertiary care hospital serving the needs of the intermountain west. This hospital also serves as one of the primary teaching centers for the University of Utah Medical School. Having been developed in this environment, the design of the HELP system was required to meet the administrative, clinical, teaching, and research needs of hospitals, as well as provide the decision-making capability.

https://link.springer.com/content/pdf/10.1007/BF00995116.pdf

The HELP system.

Knowledge-based approaches to the maintenance of a large controlled medical terminology.

http://www.pentex.it/wp-content/uploads/0030139.pdf

Paul D. Clayton

Papers

Lung volumes in healthy nonsmoking adults.

Unlocking Clinical Data from Narrative Reports: A Study of Natural Language Processing

The HELP system.

Knowledge-based approaches to the maintenance of a large controlled medical terminology.

Privacy, Confidentiality: and Electronic Medical Records