***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: Background : The aim was to analyse the magnitude , direction and predictors of change in the main hospital discharge diagnosis ( HDD ) after a clinical expert review , among patients included in a multicentre molecular epidemiologic study of biliopancreatic diseases . Methods : A total of 602 patients with a suspicion diagnosis of pancreas cancer ( PC ) , cancer of the extrahepatic biliary system ( CEBS ) or benign biliopancreatic pathologies ( BPP ) were prospect ively recruited at five general hospitals . A structured form was used to collect information from medical records . A panel of experts revised all diagnostic information and established the main clinico-pathological diagnosis ( CPD ) by consensus . Results : Of the 204 cases with a HDD of PC , 176 ( 86 % ) were deemed to have a CPD of PC , eight of CEBS , twelve a neoplasm of different origin , four BPP and four syndromic diagnoses . Thus , 28 cases ( 14 % ) were false positives . Of the 129 patients with a HDD of CEBS , 15 ( 12 % ) were false positives . Nine of the 396 cases with a HDD of non-PC ( 2 % ) had a CPD of PC ( false negatives ) , whilst 14 of 471 patients with a HDD of non-CEBS ( 3 % ) were deemed to have CEBS . Overall , sensitivity and specificity of HDD for PC were , respectively , 95 and 93 % , and for CEBS , 89 and 97 % . Cytohistological confirmation and laparotomy were independent predictors of diagnostic change . Conclusions : Validity of the HDD was high , but its association with some clinical variables suggests that sole reliance on HDD can significantly bias results , and highlights the need to review all HDDs . Alternatively , only patients at high risk of misdiagnosis could be review ed : primarily , those lacking a cytohistological diagnosis or a laparotomy . No exclusions appear warranted solely on the basis of age , gender or tumour spread Clinical data repositories represent a potential gold mine of information and knowledge . Rapid access to such information can help bridge the gap between clinical care and research , support clinical and executive decision making , and improve the quality of care . A clinical data base can be used in four ways : to display information about an individual patient ( results reporting ) ; to find data on a patient with similarities to one being seen ( case finding ) ; to describe a group of patients with at least one attribute in common ( cohort description ) ; and to analyze data patterns in terms of trends or relationships ( predictive modeling ) . It seems unlikely that many important clinical questions will be subject to r and omized clinical trials because of the ethics , logistics , and expense that would be involved . Evolving statistical and epidemiological methods allow us to approach these clinical data repositories with the purpose of building predictive models , but a clear underst and ing of the limitations of routinely collected clinical data and the inherent biases is necessary . The largest barrier to using routinely collected clinical data is not the limitations of the data themselves , but rather the lack of a data paradigm for the decision-maker . We present some of the problems and pitfalls in obtaining and using routinely collected data , based upon the use of ClinQuery at Boston 's Beth Israel Hospital and the re sources and traditions at the Mayo Clinic Information on coronary heart disease ( CHD ) obtained from the Finnish Hospital Discharge and Cause-of-Death Registers was compared with that collected in the Helsinki Heart Study ( HHS ) during an 8.5-year follow-up . The purpose of the comparison was two-fold , firstly , to study the accuracy of registration of CHD and secondly , to find out what diagnostic codes to use for CHD in register-based follow-up studies . The HHS cases were used as the ' golden st and ard ' and the CHD deaths and definite nonfatal acute myocardial infa rct ions ( AMIs ) ( all diagnoses ) were taken from the registers to establish the sensitivity of the Hospital Discharge and Cause-of-Death Registers combined . The sensitivity was 0.84 during the period 1980 - 86 and 0.87 during 1987 - 90 , with the positive predictive values 0.94 and 0.92 respectively . The treatment effects seen in the HHS were compared with the effects that would have emerged , if register-based information only had been used with different definitions of CHD . Of the register-based calculations , the one with the definition ' all CHD deaths and hospitalizations with the ICD-8 code 410 ' came closest to the HHS result , with a 32 % reduction ( P=0.028 one-sided ) of CHD incidence , while the original HHS result was a 34 % reduction ( P=0.008 one sided ) . However , when comparing Kaplan-Meier plots of cumulative hazards of CHD , the plot with a wider definition of CHD ( ICD-8 and ICD-9 codes 410 - 414 ) came closest to the HHS experience , especially if revascularizations were included in the latter . Definite AMI as a single definition of CHD might thus not be sufficient when study ing CHD risk , instead , at least two parallel definitions of CHD should be used OBJECTIVE : To assess the validity of obstetric complications , including the Joint Commission on Accreditation of Healthcare Organizations ( JCAHO ) Core Measure on perineal lacerations , in the California Patient Discharge Data Set . METHODS : We r and omly sample d 1,611 deliveries from 52 of the 267 hospitals that performed more than 678 eligible deliveries in California in 1992–1993 . We compared hospital-reported complications against our recoding of the same records . RESULTS : Third- and fourth-degree perineal lacerations were reported accurately , with estimated sensitivities exceeding 90 % and positive predictive values exceeding 65 % ( weighted to account for the stratified sampling design ) or 85 % ( unweighted ) . Based on in-depth review of discrepant cases , we estimate the actual positive predictive value at over 90 % . Most coding discrepancies were between no injury and first degree , or between first and second degree . Most postpartum complications , including urinary tract and wound infections , endometritis , anesthesia complications , and postpartum hemorrhage were reported with less than 70 % sensitivity , but at least 80 % positive predictive value . Composite measures from Health Grade s and Solucient , which include these complication codes , also suffer from high false-negative rates . CONCLUSION : Third- and fourth-degree perineal lacerations are accurately reported on hospital discharge abstract s , confirming the validity of related quality indicators sponsored by the Agency for Healthcare Research and Quality and JCAHO . Administrative data seem less useful for monitoring other in-hospital postpartum complications . LEVEL OF EVIDENCE : In order to measure the coverage and accuracy of the  Hospital In-patient Enquiry Scheme data in a large acute hospital in Dublin , 793 patient charts were selected r and omly from all the discharges in 1990 . The capture rate was 56 % . Whether a diagnosis was captured for the Hospital In-patient Enquiry Scheme depended partly upon geographical factors within the hospital and partly upon the nature of the diagnosis . " Non-capture " was not r and om and was particularly likely to occur where the case was complex , e.g. oncology , HIV infection . The coding accuracy of primary diagnoses was 59 % and the completeness of recording of secondary diagnoses was 56 % . Both were significantly associated with the presence in the hospital chart of discharge summaries and with the clarity and source of written diagnoses . The factors that affected accuracy were not the same as those affecting the capture rate BACKGROUND Concept-based Indexing is purported to provide more granular data representation for clinical records.1,2 This implies that a detailed clinical terminology should be able to provide improved access to clinical records . To date there is no data to show that a clinical reference terminology is superior to a precoordinated terminology in its ability to provide access to the clinical record . Today , ICD9-CM is the most commonly used method of retrieving clinical records . OBJECTIVE In this study , we compare the sensitivity , specificity , positive likelihood ratio , positive predictive value and accuracy of SNOMED-RT vs. ICD9-CM in retrieving ten diagnoses from a r and om sample of 2,022 episodes of care . METHOD We r and omly selected 1,014 episodes of care from the inpatient setting and 1,008 episodes of care from the outpatient setting . Each record had associated with it , the free text final diagnoses from the  Master Sheet Index  at the Mayo Clinic and the ICD9-CM codes used to bill for the encounters within the episode of care . The free text diagnoses were coded by two expert indexers ( disagreements were addressed by a Staff Clinician ) as to whether queries regarding one of 5 common or 5 uncommon diagnoses should return this encounter . The free text entries were automatically coded using the Mayo Vocabulary Processor . Each of the ten diagnoses was exploded in both SNOMED-RT and ICD9-CM and using these entry points , a retrieval set was generated from the underlying corpus of records . Each retrieval set was compared with the Gold St and ard created by the expert indexers . RESULTS SNOMED-RT produced significantly greater specificity in its retrieval sets ( 99.8 % vs. 98.3 % , p<0.001 McNemar Test ) . The positive likelihood ratios were significantly better for SNOMED-RT retrieval sets ( 264.9 vs. 33.8 , p<0.001 McNemar Test ) . The positive predictive value of a SNOMED-RT retrieval was also significantly better than ICD9-CM ( 92.9 % vs. 62.4 % , p<0.001 McNemar Test ) . The accuracy defined as 1 ( the total error rate ( FP+FN ) / Total # episodes queried ( 20,220 ) ) was significantly greater for SNOMED-RT ( 98.2 % vs. 96.8 % , p=0.002 McNemar Test ) . Interestingly , the sensitivity of the SNOMED-RT generated retrieval set was not significantly different from ICD9-CM , but there was a trend toward significance ( 60.4 % vs. 57.6 % , p=0.067 McNemar Test ) . However , if we examine only the outpatient practice SNOMED-RT produced a more sensitive retrieval set than ICD9-CM ( 54.8 % vs. 46.4 % , p=0.002 McNemar Test ) . CONCLUSIONS Our data clearly shows that information regarding both common and rare disorders is more accurately identified with automated SNOMED-RT indexing using the Mayo Vocabulary Processor than it is with traditional h and picked constellations of codes using ICD9-CM . SNOMED-RT provided more sensitive retrievals of outpatient episodes of care than ICD9-CM The accuracy of the discharge diagnoses recorded in hospital medical records , and the accuracy of their ICD codes , were retrospectively examined in 174 r and omly chosen hospital patients . There was a 29 % error rate in the recorded ICD codes when compared at a three digit level to the results obtained by retrospectively review ing the records , establishing diagnoses and coding them . The error rate increased when medical records coders , rather than medically trained personnel , undertook the task of establishing the discharge diagnoses How well can hospital discharge abstract s be used to estimate patient health status ? This paper compares information on comorbidity obtained from hospital discharge abstract s for patients undergoing prostatectomy or cholecystectomy at a Winnipeg teaching hospital with clinical data on preoperative medical conditions prospect ively collected during an Anesthesia Follow-up study . The diagnostic information on cardiovascular disease , respiratory disease , and metabolic disorders showed considerable agreement , ranging from 65 to over 90 % correspondence across the two data sets . Certain conditions noted by the anesthesiologist were often absent from the cl aims data ; cardiovascular disease was recorded in the clinical data but absent from the cl aims for 31 % of prostatectomy and 17 % of cholecystectomy cases . Such patients were less likely to have been assigned a high score on the ASA Physical Status measure or to have high-risk diagnoses on the hospital file . Similar findings result ed from comparing the two sources in their ability to predict such adverse outcomes as mortality and readmission to hospital : the anesthesia file generally included less serious comorbidity AIMS To investigate the accuracy of admission and discharge coding of traumatic brain injury ( TBI ) in a New Zeal and hospital . METHOD Prospect i ve study of all patients over fifteen years of age admitted to Hutt Hospital over a six-month period with an actual or potential diagnosis suggesting TBI . RESULTS During the six month period of study , 65 patients with the diagnosis of TBI were admitted to Hutt Hospital . Of these , 21 ( 32.3 % ) met the criteria for diagnosis of TBI ( ' Definite TBI ' ) . A further eighteen patients , not admitted with a diagnosis of TBI , met the TBI criteria . Only 14/39 ( 35.9 % ) of ' Definite TBI ' cases were identified at both admission and discharge .  Discharge diagnosis of TBI  identified correctly 26/39 ( 66.7 % ) of definite cases , with 34/60 ( 56.7 % ) cases with a discharge diagnosis of TBI not meeting our criteria for the diagnosis of TBI . Six out of 39 ' Definite TBI ' cases ( 15.4 % ) were not identified by either admission or discharge diagnosis . Thirty of the 65 patients ( 46.2 % ) admitted to hospital with the diagnosis of TBI showed clinical evidence of having taken alcohol , although only 12 had blood alcohol concentration measured . 
    Output:
    
Variability in quality of care between setting s can easily be overshadowed by variability in data quality .
However , the use of DHDD by physicians to evaluate their own medical practice may be useful .
But only if physicians are willing to critically interpret the meaning of the information for their medical practice assessment