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Since mid-April, screening mammograms taken at the Medical Center have been read by a radiologist specializing in mammography, with additional guidance from the computer system. Computer-aided diagnosis is expected to reduce--by as much as one-half--the number of breast cancers missed by radiologists.

Like the spell-check program on a word processor, which helps writers avoid typographical errors, the computer provides "an automated second opinion, alerting the radiologist to take a closer look at subtle warning signs that might have been missed on the first pass," said project leader Kunio Doi, PhD, Ralph W. This system should eventually make even the other half comparable to the experts. Although mammography is currently the best method for the early detection of breast cancer, even skilled mammographers fail to spot between 10 and 30 percent of the cancers present in women who get screening mammograms.

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In as many as two-thirds of these "false negative" mammograms, the radiologist missed signs of cancer that, when the mammogram was more closely examined months or years later, were present and visible. Eye fatigue and inattentiveness can explain some of those reading errors, but in other cases the mammographic abnormalities caused by the cancer were subtle enough to escape detection, sometimes even by the experts. Up to half of early breast cancers contain little clusters of microcalcifications, tiny deposits of calcium that on a mammogram look like a constellation of faint stars.

Although these minute specks can be obscured by the wealth of information contained in a typical mammogram, the University of Chicago research team has taught the computer to recognize these stars, even through the "clouds" of normal tissue, and to alert the radiologist to their presence. Another 40 percent of early breast cancers will appear on a mammogram as a mass.

The computer can detect such masses by contrasting the architectural symmetry of the right and left breasts.

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A series of preliminary studies has demonstrated the power of the computer. In the first study, an earlier version of the current system detected 85 to 90 percent of breast lesions in a set of subtly abnormal mammograms, comparable to an experienced radiologist. In a second study, the computer correctly identified more than 50 percent of breast cancers that had been missed by radiologists. In a third study, the combination of computer and radiologist outperformed a radiologist alone, increasing sensitivity from a quite respectable 85 percent up to 92 percent.


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A fourth pre-clinical study, looking at 1, consecutive screening patients who had mammograms at the Medical Center, confirmed the system's usefulness and reliability in the clinical setting. It typically directs the radiologist's scrutiny to one or two benign areas on each mammogram, what physicians call false positives.

The computer's weaknesses are sufficiently different from radiologists' shortcomings that the two complement each other well. It doesn't get distracted by the phone, or by frequent interruptions. It doesn't get tired or cranky, or worry about its career choice. Therefore, the total patient sample size of the controlled trial was set at Randomization was performed on the basis of sequential patient attendance to a trauma resuscitation bay by the senior nurse in charge of patient admission. True random allocation of trauma resuscitation bays was limited by service demands in a busy trauma center, with some bays occupied by patients waiting for further imaging or an intensive care unit bed.

Sequential allocation removed the potential for clinical staff to bias allocation of trauma patients into study bays or control bays. Bay allocation was monitored by the project team to ensure there was uniform compliance. Each trauma bay had an identical, automated, audiovisual recording system that was activated before or on arrival of the trauma patient; the recording length was limited to 60 minutes. A custom-built video audit tool VAT was developed to allow correlation of video data with data outputs from the Trauma Reception and Resuscitation System eg, diagnoses and treatments Figure 2.

Compliance templates Figure 2 ; step 1 defined errors and exceptions to ensure concordance across audits. The use of standard algorithm and reference data Figure 2 ; steps 2 and 3 permitted conformity across templates. This enabled the auditors to compare patient treatment on the video with the information displayed to the trauma care professionals.

Patient data Figure 2 ; step 6 including diagnoses, treatments, and vital signs and events were linked to audit templates by the video time code and stored as the audit file Figure 2 ; step 7. If classified as compliant , the event was compliant with the algorithm.

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Exception indicated the event was not compliant with the algorithm but met the definition criteria for an exception for that flag. Error indicated the event was not compliant with the algorithm, met the definition criteria for error, and did not meet any of the criteria for exceptions. For example, it was expected that patients experiencing major trauma would be log-rolled during the first 30 minutes of care for posterior and spinal assessment. If this was observed on video audit, this flag would be classified as compliant.

The patient may not have been rolled because of an unstable pelvic fracture and associated hypovolemic shock. This would be classified as exception. A classification for error would occur if the patient was not log-rolled and did not meet the predetermined criteria for exception. The VAT was linked to existing patient databases Figure 2 ; step 8 and automatically extracted relevant data for the auditors, including prehospital treatments, incident details, and follow-up data, such as hospital length of stay and outcome. On completion of an audit, a summary Figure 2 ; steps 9 and 10 was viewed by the auditor to check that all audit measures had been assessed.

All audits were checked by a second auditor. The audit data were then stored in an external database for independent statistical analysis. The researchers involved in the algorithm and software development were not involved in the audit process. All researchers and auditors were masked to results undertaken by independent statisticians. The primary outcome variable measured was the rate of noncompliance with agreed algorithms per patient error rate. A secondary goal was to demonstrate that improved algorithm compliance altered morbidity and mortality.

Data regarding the incidence of aspiration pneumonia, sepsis, and adult respiratory distress syndrome, functional independence measure score, intensive care unit and hospital length of stay, and death were prospectively collected. Patient follow-up was limited to the hospital admission after initial trauma presentation. Continuous measures are presented as mean SD or median interquartile range , as appropriate, and compared using the t test or Wilcoxon rank sum test, as appropriate. The error count per patient was modeled using a negative binomial distribution, with the number of compliance events per patient considered to be the exposure.

All statistical analyses were performed using Stata statistical software, version A total of videos of trauma patient resuscitation were recorded in the trauma resuscitation bays between January 24, , and February 25, Of these, 71 were secondary transfers from another hospital and therefore did not meet the inclusion criteria, did not meet the trauma callout criteria, and had inadequate audiovisual quality or data. Nine other videos were excluded because the auditors were unable to agree on their usability.

The remaining videos were classified as usable.

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There was no retrospective inclusion of videos, and the auditors were masked to data analysis. The sample size was patient resuscitations, including baseline control patients January 24 to October 22, and control and study group patients November 20, , to February 25, Audit lagged behind video acquisition. Therefore, once the audited sample size had been reached, the remaining resuscitations were not analyzed Figure 3.

Analysis of these videos was beyond the project's resources and human research ethics committee approval. Patients were distributed evenly between trauma resuscitation bays 1 and 2 control group; and patients, respectively and bays 3 and 4 study group; and patients, respectively.

The mean SD age of patients was The median injury severity score was 13 interquartile range, , the same as that of all patients historically mandating trauma callout when checked against the Alfred Hospital Trauma Registry. The overall mortality rate was 5. Ten medical and nursing professionals 8 critical-care nurses and 2 emergency physicians were employed and trained to perform the video auditing.

The audit team took approximately hours to complete video audits in 6 months. All audit results were checked by a second auditor. When auditors disagreed, a consensus was eventually reached. The interuser reliability of the VAT was evaluated using error concordance. A sample size of 50 audits with flag decisions was assessed independently by 2 auditors to confirm error detection concordance.

This indicated that in the first 30 minutes of trauma reception and resuscitation, a critical decision was required, on average, every 72 seconds. There were algorithm exceptions Overall, the incidence of algorithm deviation was errors per patients, or 2. The study group had the least number of errors per patient errors for patients compared with the control for patients and baseline control for patients groups. Without algorithmic prompts, This increased to The predicted mortality rate was The study mortality rate was 5. This lower-than-expected mortality occured partly because the study population did not include interhospital transfers a group with a higher mortality.

The incidence of sepsis and adult respiratory distress syndrome, the functional independence measure score, and the hospital length of stay were not different among the groups. There was no significant reduction in the incidence of sepsis. Aspiration pneumonia was reduced in the study group from 5. There was a Ours is the first randomized controlled trial, to our knowledge, to demonstrate that computer-aided decision support for experienced trauma teams results in improved protocol compliance and reduced errors.

Errors in trauma resuscitation have persisted during the past 20 years despite major improvements in training, facilities, guidelines, and systems of care. It was unclear whether decision support would alter trauma care professionals' behavior in this high-volume facility. It seems unlikely that the reason for poor protocol compliance was lack of awareness of protocols. A more likely explanation is the high speed of the decision making and the complex prioritization required in trauma resuscitation. Anecdotally, the clearly displayed computer prompts supported shared awareness among team members, 26 facilitated information exchange, highlighted abnormal physiologic variables, and clarified diagnostic and therapeutic decision making.

Although the study design anticipated that behavioral change in the study bays would alter performance in adjacent, nonprompted control bays, the error rate reduction between the study group and the control group remained significant Table 2. This real-time system is an advance beyond traditional clinical decision support, which tends to target individuals in more static patient care scenarios. It is also likely that outcomes from this study are applicable to other clinical areas with high-volume, high-intensity decision-making processes, such as the management of sepsis or cardiogenic shock.


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The implications for improving trauma care are great. The US Senate Finance Committee has identified the great potential for improving health outcomes, including identifying strategies and best practices to improve patient safety and reduce medical errors. Further multicenter studies are required to support and promote the development and use of this type of automated electronic health record collection system for trauma patient resuscitation. Standardization of the resuscitation environment through the use of computerized decision support may also help determine the therapeutic value of single interventions whose effect is currently difficult to determine in such complex environments—a key requirement for comparative-effectiveness research in trauma resuscitation.

This study has some limitations. It is exceptionally difficult to randomize trauma resuscitation bays in a busy trauma center by random number allocation.

However, the evenly matched characteristics of patients in each bay indicate unbiased allocation. In subsequent multicenter studies, designed with larger populations to demonstrate mortality differences, improved randomization techniques should be developed for use. The exclusion of videos recorded after randomization was also a potential limitation.

Inadequate audiovisual quality resulted from technical difficulties arising from filming in a working noisy trauma center. Individual microphones were considered impractical, with a potential to compromise standard infection control precautions. Therefore, 1 unidirectional and 1 omnidirectional microphone were used and the sounds mixed. It initially took considerable time to fine-tune the audio recording and mixing to ensure voices of the 6 or more trauma team members could be adequately heard for auditing purposes. If the senior nurse was distracted by the demands of trauma resuscitation bay preparation or by the arrival of the trauma patient, system activation may have been delayed.

This meant that a substantial number of videos started after trauma resuscitation had commenced. Therefore, if resuscitative efforts were missed on the recording, these videos had to be excluded to ensure homogeneity of the data. However, videos of complete trauma resuscitations with adequate audiovisual quality were sequentially collected until the numbers required for the study were reached.

Subsequent analyses demonstrated no significant difference in injury severity between the groups. The system was designed to be transportable and miniaturized for in-field use. The use of voice recognition software rather than a nurse scribe may improve the system interface. Medicine has lagged behind aviation and other industries in standardization and error avoidance.

The introduction of computer-assisted decision support will reduce error morbidity and improve patient safety and outcome in trauma resuscitation—even with experienced trauma teams. Further large, multicenter trials will determine whether mortality can also be reduced. Such trials will have implications for all clinical areas involving rapid, complex, and critical decision making. Obtained funding : Fitzgerald and Cameron.