***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Context The Institute of Medicine report that caused concern about adverse events in hospitals relied on studies that used medical record review to identify adverse events . Many people question whether medical record review s can accurately identify negligent adverse events . Contribution The following changes in the review process markedly reduced the rates of negligent adverse events : 1 ) increasing the number of review ers from one to three and 2 ) requiring review ers to be highly confident that an event was due to negligence . Implication s Because review criteria can affect adverse event rates , the estimates cited by the Institute of Medicine could be inaccurate . The Editors Implicit judgments , based on medical record review , about adverse events caused by medical care have moderate to poor inter-rater reliability ( 1 - 7 ) . However , the influential report on medical errors issued by the U.S. Institute of Medicine ( IOM ) in 2000 ( 8) relied on studies based on medical record review to estimate that 44 000 ( 9 ) to 98 000 ( 10 ) Americans die each year because of medical errors . These studies used one ( 9 ) or up to three ( 10 ) physician review ers per record to detect adverse events . The IOM estimates are important because they have prompted health care providers and administrators to reduce errors and have influenced the U.S. research budget . We report details on the reliability of physician judgments about adverse events and negligent adverse events in the study from which the IOM derived its estimate of 44 000 error-related deaths per year ( 9 ) . We also report the effects of varying criteria for review er confidence in and review er agreement about the presence of adverse events . Methods Data Sources This study was conducted as part of the Utah and Colorado Medical Practice Study ( UCMPS ) ( 5 ) . In UCMPS , trained nurses review ed medical records to identify 1 of 19 screening criteria that could indicate the presence of an adverse event . A trained physician then review ed flagged records by using a structured chart abstract ion form . Previously , the nurse review process was found to be a good screening tool , with a sensitivity of 84 % ( 1 ) . We began the current study with all 2868 records referred for physician review by nurse screeners from the original 15 000-record sample ( 5000 from Utah and 10 000 from Colorado ) ( Appendix Figure ) . Two physician investigators confirmed all adverse events detected during this initial review . As a result of this confirmation process , we eliminated 13 false-positive cases . We eliminated cases only if they failed to meet explicit criteria for an adverse event , not if there was a concern about the review er 's judgment regarding whether medical management caused the adverse event . Next , we r and omly selected 500 of the 2868 records referred for physician review by nurse screeners ( 167 from Utah and 333 from Colorado ) , maintaining the original 1 to 2 ratio of Utah to Colorado records ( Appendix Figure ) . The general characteristics of this sample are described elsewhere ( 5 ) . Of these 500 records , 400 were r and omly sample d from referred records that a single physician review er had previously judged to show no adverse events . Fifty records were r and omly sample d from records initially found to show nonnegligent adverse events , and the remaining 50 were sample d from records initially judged to show negligent adverse events . This mix provided a sample similar to the one originally review ed by the physicians . Record Review We completed three independent physician review s of the original 500 medical records  ( not photocopies ) . The first was the original review for the UCMPS . The two subsequent physician review s were conducted by physicians from the original UCMPS and by newly recruited physician review ers who were trained in the same manner as the original UCMPS physician review ers ( 5 ) . Physicians could participate in more than one review ; however , all review ers were blinded to the purpose of these additional review s , and none of them review ed a record that they had previously review ed . All physician review ers used the same data form , which included the same definition of adverse event : an injury caused by medical management ( rather than the disease process ) that result ed in prolonged hospital stay or disability at discharge . Negligence was defined as care that fell below the st and ard expected of physicians in the community . Because judgments about adverse events may be complex , review ers used a six-point confidence scale that has been used in previous studies ( 5 , 6 ) . We required a confidence score of at least four ( > 50 % chance that medical management caused the adverse event ) to indicate the presence of adverse events or negligent adverse events . For each of the three review s , two investigators independently review ed all detected adverse events to confirm that they met the study criteria . Statistical Analysis We calculated the rates of adverse events and negligent adverse events detected during each review . We report statistics for adverse events and negligent adverse events as the measure of inter-rater reliability . For all statistical analyses , we used SAS software , release 6.12 ( SAS Institute , Inc. , Cary , North Carolina ) . Role of the Funding Source The funding source contributed to the design of the study but had no role in conducting the study or in reporting its results . Results The first independent review of the study sample detected 64 adverse events ; the second , 95 ; and the third , 86 . Table 1 shows the agreement and disagreement among the three review s. Comparisons between sets of review s ( review 1 compared with review 2 , review 1 compared with review 3 , and review 2 compared with review 3 ) demonstrated similar reliability . The statistics ranged from 0.40 to 0.41 ( the lowest bound of the three 95 % CIs was 0.30 , and the highest was 0.51 ) for adverse events and from 0.19 to 0.24 ( the lowest bound of the 95 % CIs was 0.05 , and the highest was 0.37 ) for negligent adverse events . The former is considered moderate ; the latter is considered poor ( 11 ) . We had hypothesized that reliability would increase as the review er confidence score required to indicate an adverse event decreased . However , when adverse events were defined as having a confidence score of two or greater instead of four or greater , the overall statistic for adverse events decreased slightly , from 0.41 to 0.37 ( P = 0.19 ) . Table 1 . Number of Cases Result ing in Agreement and Disagreement among Three Sets of Review s Different review strategies produced different estimates of the total number of adverse events and negligent adverse events . Increasing the confidence score to indicate the presence of an adverse event and increasing the degree of consensus between independent review ers ( agreement of one , two , or three review ers ) substantially affected adverse event rates in this sample of 500 records ( Table 2 ) . For example , if all three review ers had a confidence score of four or greater , the adverse event rate would be 7.58 % and the negligent adverse event rate would be 0.82 % . If we required a score of four or greater on only one of three review s to confirm the presence of an adverse event , the adverse event rate would be 37.68 % and the negligent adverse event rate would be 15.13 % . When the required confidence score was decreased to two , estimates among the three review ers varied even more . Table 2 . Adverse Event Rates Using Different Thresholds Discussion We found moderate to poor inter-rater reliability among physicians trying to identify adverse events and negligent adverse events by medical record review . Increasing the required levels of review er confidence to indicate the presence of an event or increasing the number of physician review ers who detected an event result ed in markedly different event rates . Similar studies of medical record review have found almost identical reliability ( statistics of approximately 0.4 for both studies ) ( 6 , 7 ) . Our study is limited because our data come from hospital records in two states that may not be generalizable to other geographic locations . Furthermore , we could not measure the validity of chart review because there is no true gold st and ard that avoids some type of implicit assessment . Despite the poor to moderate reliability of medical record review , each of the adverse events identified represents a potential opportunity for quality improvement ( 12 , 13 ) . Each case involving an adverse event may contain valuable information for improving patient safety . However , research ers should use caution when estimating incidence and prevalence of errors solely on the basis of these data . For example , the IOM used data from our larger study ( 9 ) , which used  medical record review  , to estimate that 44 000 Americans die each year of preventable adverse events ( or , to use the IOM term , medical errors ) ( 8) . Our results suggest that these estimates are sensitive to the review er consensus and confidence required to indicate the presence of adverse events . The estimate of 44 000 deaths could be approximately 50 % lower if the study used to estimate that figure required independent agreement by two physician review ers and could be 30 % higher if the required review er confidence about the presence of an adverse event was lower ( Table 2 ) . We can not quantify the reduction more precisely because the IOM figures are based on judgments about the number of preventable adverse events and our results are based on judgments about adverse events and negligent adverse events . The figures for error-related death reported by the IOM are imprecise . However , it is shortsighted to focus on the exact number of deaths and thereby ignore the vast additional research on errors and adverse events cited by the IOM ( 8 , 14 ) . Regardless of whether the number of annual U.S. deaths due to medical error is 30 000 or 300 000 , the need to test interventions to reduce errors and adverse events is clear . Our findings suggest that persons and institutions Patient injuries are thought to have a substantial financial impact on the health care system , but recent studies have been limited to estimating the costs of adverse drug events in teaching hospitals . This analysis estimated the costs of all types of patient injuries from a representative sample of hospitals in Utah and Colorado . We detected 459 adverse events ( of which 265 were preventable ) by review ing the medical records of 14,732 r and omly selected 1992 discharges from 28 hospitals . The total costs ( all results are discounted 1996 dollars ) were $ 661,889,000 for adverse events , and $ 308,382,000 for preventable adverse events . Health care costs totaled $ 348,081,000 for all adverse events and $ 159,245,000 for the preventable adverse events . Fifty-seven percent of the adverse event health care costs , and 46 % of the preventable adverse event costs were attributed to outpatient medical care . Surgical complications , adverse drug events , and delayed or incorrect diagnoses and therapies were the most expensive types of adverse events . The costs of adverse events were similar to the national costs of caring for people with HIV/AIDS , and totaled 4.8 % of per capita health care expenditures in these states Background : An unplanned admission to the intensive care unit within 24 h of a procedure ( UIA ) is a recommended clinical indicator in surgical patients . Often regarded as a surrogate marker of adverse events , it has potential as a direct measure of patient safety . Its true validity for such use is currently unknown . Methods : The authors vali date d UIA as an indicator of safety in surgical patients in a prospect i ve cohort study of 44,130 patients admitted to their hospital . They assessed the association of UIA with intraoperative incidents and near misses , increased hospital length of stay , and 30-day mortality as three constructs of patient safety . Results : The authors identified 201 patients with a UIA ; 104 ( 52.2 % ) had at least one incident or near miss . After adjusting for confounders , these incidents were significantly associated with UIA in all categories of surgical procedures analyzed ; odds ratios were 12.21 ( 95 % confidence interval [ CI ] , 6.33–23.58 ) , 4.06 ( 95 % CI , 2.74–6.03 ) , and 2.13 ( 95 % CI , 1.02–4.42 ) , respectively . The 30-day mortality for patients with UIA was 10.9 % , compared with 1.1 % in non-UIA patients . After risk adjustment , UIA was associated with excess mortality in several types of surgical procedures ( odds ratio , 3.89 ; 95 % CI , 2.14–7.04 ) . The median length of stay was increased if UIA occurred : 16 days ( interquartile range , 10–31 ) versus 2 days ( interquartile range , 0.5–9 ) ( P < 0.001 ) . For patients with a UIA , the likelihood of discharge from hospital was significantly decreased in most surgical categories analyzed , with adjusted hazard ratios of 0.41 ( 95 % CI , 0.23–0.77 ) to 0.58 ( 95 % CI , 0.37–0.93 ) . Conclusions : These findings provide strong support for the construct validity of UIA as a measure of patient safety AIM To identify the effect of an ICU Liaison Nurse ( LN ) on major adverse events in patients recently discharged from the ICU . METHODS Case-control study using a chart audit protocol to assess controls retrospectively and cases prospect ively . Controls did not receive ICU-based follow-up care . Cases received at least three visits over 3 days from the ICU LN . The LN service operated 7 days/week 0800 - 1800 . Data on a range of predictors and three major adverse events ( unexpected death , surgical procedure needed , and transfer to a higher level of care ) were collected using a purpose built audit form . RESULTS A total of 388 patients ( 201 controls and 187 cases ) were included in the study . Demographic and clinical characteristics were similar for both groups . A total of 165 major adverse 
    Output:
    
Adverse events are a persistent and an important reason for admission to the ICU .
However , there is relatively weak evidence to estimate an overall incidence and preventability rate of these events .
IMPLICATION S FOR PRACTICE Unplanned intensive care admission within 24 hours of a procedure with an anesthetist in attendance ( UIA ) is a recommended clinical indicator in surgical patients .