The Hazards of Using Administrative Data to Measure Surgical Quality

2006 ◽  
Vol 72 (11) ◽  
pp. 1031-1037
Author(s):  
Donald E. Fry ◽  
Michael B. Pine ◽  
Harmon S. Jordan ◽  
David C. Hoaglin ◽  
Barbara Jones ◽  
...  

Administrative claims data have been used to measure risk-adjusted clinical outcomes of hospitalized patients. These data have been criticized because they cannot differentiate risk factors present at the time of admission from complications that occur during hospitalization. This paper illustrates how valid risk-adjustment can be achieved by enhancing administrative data with a present-on-admission code, admission laboratory data, and admission vital signs. Examples are presented for inpatient mortality rates following craniotomy and rates of postoperative sepsis after elective surgical procedures. Administrative claims data alone yielded a risk-adjustment model with 10 variables and a C-statistic of 0.891 for mortality after craniotomy, and a model with 18 variables and a C-statistic of 0.827 for postoperative sepsis. In contrast, the combination of administrative data and clinical data abstracted from medical records increased the number of variables in the craniotomy model to 21 with a C-statistic of 0.923, and the number of variables in the postoperative sepsis model to 29 with a C-statistic of 0.858. Use of only administrative data resulted in unacceptable amounts of systematic bias in 24 per cent of hospitals for craniotomy and 19 per cent of hospitals for postoperative sepsis. Addition of a present-on-admission code, laboratory data, and vital signs reduced the percentage of hospitals with unacceptable bias to two percent both for craniotomy and for postoperative sepsis. These illustrations demonstrate suboptimal risk stratification with administrative claims data only, but show that present-on-admission coding combined with readily available laboratory data and vital signs can support accurate risk-adjustment for the assessment of surgical outcomes.

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 4856-4856
Author(s):  
Peter A. Lane ◽  
Rodney S Theodore ◽  
Maa-Ohui Quarmyne ◽  
James R. Eckman ◽  
Mei Zhou ◽  
...  

Abstract Background: Health services and outcomes research in sickle cell disease (SCD) has been limited by inadequate population-based surveillance and reliance on ICD-9 codes to identify individuals with SCD. The accuracy of ICD-9 coding in administrative claims datasets and the extent of miscoding is unknown. GA was one of 7 states funded to participate in the Registry and Surveillance for Hemoglobinopathies (RuSH) Project. The RuSH case definition for confirmed cases required laboratory documentation of SCD by hemoglobin electrophoresis (HEP)/HPLC. The RuSH case definition for probable cases required identification from administrative datasets of ≥ 2 encounters with an SCD ICD-9 code plus ≥ 1 encounter with ICD-9 or CPT codes from a predetermined list of SCD-associated treatments, procedures, and complications (SCD-TPC): eg hydroxyurea, RBC transfusion, cholecystectomy, stroke. We report results of an ancillary study designed to evaluate the accuracy of using administrative claims data for SCD surveillance by reviewing the medical records (MR) of a large group of children and adolescents identified by SCD ICD-9 codes. Specific objectives were to determine the effect on accuracy of 1) the n of encounters with SCD ICD-9 code for each individual, 2) the addition of SCD-TPC to the case definition, and 3) the length of the surveillance period. We also sought characterize clinical circumstances in which SCD ICD-9 codes were used inaccurately for individuals without SCD. Methods: GA Medicaid (MC) and State Health Benefits Plan (SHBP) databases were used to identify individuals with ≥1 encounter with SCD ICD-9 codes (282.60-282.69; 282.41-282.42) during 2004-2008. The total n of encounters with SCD ICD-9 codes and encounters with SCD-TPC were determined for both a 5-yr (2004-2008) and 1-yr (2008) period. The MR of the subset of children and adolescents seen at Children's Healthcare of Atlanta (CHOA) or Grady were reviewed. Diagnosis of SCD was confirmed by MR review of clinical and laboratory data, including results of newborn screening (NBS), CBC, reticulocytes (retic), and HEP/HPLC. Criteria used to exclude the diagnosis of SCD included 1) absence of any documentation of SCD in provider notes and 2) NBS, HEP/HPLC, and/or CBC/retic results inconsistent with SCD. Cases with MR inadequate to confirm or exclude SCD were categorized as indeterminate. Results: During the 5-yr period 2004-2008, 1,998 children and adolescents with ≥ 1 encounter with SCD ICD-9 code in MC or SHBP datasets were seen at CHOA/Grady, 1,474 (72.8%) of whom had ≥ 1 SCD ICD-9 code during 2008. The Table shows the relationship between the length of the surveillance period and n of encounters with SCD ICD-9 codes in MC/SHBP data sets and SCD status determined by MR review. Abstract 4856. TableSurveillance period (yr)SCD ICD-9 codes (n)PatientsSCDNot SCDIndeterminate5≥ 11,9981,763 (88.2%)196 (9.8%)39 (2.0%)5≥ 21,8511,735 (93.7%)96 (5.2%)20 (1.1%)5≥ 31,7461,693 (97.0%)46 (2.6%)7 (0.4%)1≥ 11,4541,386 (95.3%)57 (3.9%)11 (0.8%)1≥ 21,3731,333 (97.1%)33 (2.4%)7 (0.5%)1≥ 31,2481,231 (98.6%)14 (1.1%)3 (0.2%) For the 5-yr surveillance period, the accuracy of ≥ 2 SCD ICD-coded encounters (93.7%) increased to (97.0%) with addition ≥ 1 encounter with SCD-TPC (RuSH probable case definition), but the number of missed cases increased from 28 (1.6%) to 251 (14.2%). For the 1-yr surveillance period, the same comparison increased the accuracy from 97.1% to 98.4%, but the number of missed cases of SCD increased from 53 (3.8%) to 329 (23.7%). Of 196 patients inaccurately identified as SCD by ICD-9 coding, 65 (33.2%) were hemoglobinopathy carriers; 11 (5.6%) had non-sickle hemoglobinopathies (e.g. HbCC), 9 (4.6%) thalassemias, 18 (9.2%) other non-malignant hematologic disorders, and 19 (9.7%) malignant disorders. Conclusions: Use of administrative claims data to identify children and adolescents with SCD based on ICD-9 coding has important limitations. Accuracy of identification correlated directly with the n of encounters with SCD ICD-9 codes and indirectly with the length of the surveillance period. The addition of ≥ 1 encounter with SCD-TPC to a case definition of ≥ 2 encounters with SCD ICD-9 codes minimally improved specificity but resulted in a large number of missed cases. False positive identification of SCD by ICD-9 coding occurred most commonly in hemoglobinopathy carriers and those with non-sickle hematologic and malignant disorders. Disclosures No relevant conflicts of interest to declare.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qinli Ma ◽  
Michael Mack ◽  
Sonali Shambhu ◽  
Kathleen McTigue ◽  
Kevin Haynes

Abstract Background The supplementation of electronic health records data with administrative claims data may be used to capture outcome events more comprehensively in longitudinal observational studies. This study investigated the utility of administrative claims data to identify outcomes across health systems using a comparative effectiveness study of different types of bariatric surgery as a model. Methods This observational cohort study identified patients who had bariatric surgery between 2007 and 2015 within the HealthCore Anthem Research Network (HCARN) database in the National Patient-Centered Clinical Research Network (PCORnet) common data model. Patients whose procedures were performed in a member facility affiliated with PCORnet Clinical Research Networks (CRNs) were selected. The outcomes included a 30-day composite adverse event (including venous thromboembolism, percutaneous/operative intervention, failure to discharge and death), and all-cause hospitalization, abdominal operation or intervention, and in-hospital death up to 5 years after the procedure. Outcomes were classified as occurring within or outside PCORnet CRN health systems using facility identifiers. Results We identified 4899 patients who had bariatric surgery in one of the PCORnet CRN health systems. For 30-day composite adverse event, the inclusion of HCARN multi-site claims data marginally increased the incidence rate based only on HCARN single-site claims data for PCORnet CRNs from 3.9 to 4.2%. During the 5-year follow-up period, 56.8% of all-cause hospitalizations, 31.2% abdominal operations or interventions, and 32.3% of in-hospital deaths occurred outside PCORnet CRNs. Incidence rates (events per 100 patient-years) were significantly lower when based on claims from a single PCORnet CRN only compared to using claims from all health systems in the HCARN: all-cause hospitalization, 11.0 (95% Confidence Internal [CI]: 10.4, 11.6) to 25.3 (95% CI: 24.4, 26.3); abdominal operations or interventions, 4.2 (95% CI: 3.9, 4.6) to 6.1 (95% CI: 5.7, 6.6); in-hospital death, 0.2 (95% CI: 0.11, 0.27) to 0.3 (95% CI: 0.19, 0.38). Conclusions Short-term inclusion of multi-site claims data only marginally increased the incidence rate computed from single-site claims data alone. Longer-term follow up captured a notable number of events outside of PCORnet CRNs. The findings suggest that supplementing claims data improves the outcome ascertainment in longitudinal observational comparative effectiveness studies.


2020 ◽  
Vol 9 (4) ◽  
Author(s):  
Sanket S. Dhruva ◽  
Craig S. Parzynski ◽  
Ginger M. Gamble ◽  
Jeptha P. Curtis ◽  
Nihar R. Desai ◽  
...  

2011 ◽  
Vol 28 (4) ◽  
pp. 424-427 ◽  
Author(s):  
S. Amed ◽  
S. E. Vanderloo ◽  
D. Metzger ◽  
J.-P. Collet ◽  
K. Reimer ◽  
...  

Author(s):  
Michael D McCulloch ◽  
Tim Sobol ◽  
Joy Yuhas ◽  
Bill Ahern ◽  
Eric D Hixson ◽  
...  

Background: Administrative claims data are commonly used for measurement of mortality and readmissions in Acute Myocardial Infarction (AMI). With advent of the Electronic Medical Record (EMR), the electronic problem list offers new ways to capture diagnosis data. However, no data comparing the accuracy of administrative claims data and the EMR problem list exists. Methods: Two years of admissions at a single, quaternary medical center were analyzed to compare the presence of AMI diagnosis in administrative claims and EMR problem list data using a 2x2 matrix. To gain insights into this novel method, 25 patient admissions were randomly selected from each group to undergo physician chart review to adjudicate a clinical diagnosis of myocardial infarction based on the universal definition. Results: A total of 105,929 admissions from January 1, 2010 to December 31, 2011 were included. Where EMR problem list and administrative claims data were in agreement for or against AMI diagnosis they were highly accurate. Where administrative claims data, but not EMR problem list, reported AMI the most common explanation was true AMI with missing EMR problem list diagnoses (60%). Less common reasons for discordance in this category include: (1) administrative coding error (20%), (2) computer algorithm error (8%), (3) patient death before EMR problem list created (4%), (4) EMR problem list not used (4%) and (5) AMI diagnosis was removed from EMR problem list (4%). Where EMR problem list, but not administrative claims data, reported AMI the most common explanation was no AMI with historical diagnosis of AMI from a previous admission (60%). Less common reasons for discordance in this category include: (1) AMI present but not the principal diagnosis (32%), (2) administrative coding error (4%) and (3) erroneous EMR problem list entry (4%). Conclusion: Compared to administrative and chart review diagnoses, we found that using the EMR problem list to identify patient admissions with a principal diagnosis of AMI will overlook a subset of patients primarily due to inadequate clinical documentation. Additionally, the EMR problem list does not discriminate the admission principal diagnosis from the secondary diagnoses.


PLoS ONE ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. e0194371 ◽  
Author(s):  
Daniel Schwarzkopf ◽  
Carolin Fleischmann-Struzek ◽  
Hendrik Rüddel ◽  
Konrad Reinhart ◽  
Daniel O. Thomas-Rüddel

Neurology ◽  
2017 ◽  
Vol 89 (14) ◽  
pp. 1448-1456 ◽  
Author(s):  
Susan Searles Nielsen ◽  
Mark N. Warden ◽  
Alejandra Camacho-Soto ◽  
Allison W. Willis ◽  
Brenton A. Wright ◽  
...  

Objective:To use administrative medical claims data to identify patients with incident Parkinson disease (PD) prior to diagnosis.Methods:Using a population-based case-control study of incident PD in 2009 among Medicare beneficiaries aged 66–90 years (89,790 cases, 118,095 controls) and the elastic net algorithm, we developed a cross-validated model for predicting PD using only demographic data and 2004–2009 Medicare claims data. We then compared this model to more basic models containing only demographic data and diagnosis codes for constipation, taste/smell disturbance, and REM sleep behavior disorder, using each model's receiver operator characteristic area under the curve (AUC).Results:We observed all established associations between PD and age, sex, race/ethnicity, tobacco smoking, and the above medical conditions. A model with those predictors had an AUC of only 0.670 (95% confidence interval [CI] 0.668–0.673). In contrast, the AUC for a predictive model with 536 diagnosis and procedure codes was 0.857 (95% CI 0.855–0.859). At the optimal cut point, sensitivity was 73.5% and specificity was 83.2%.Conclusions:Using only demographic data and selected diagnosis and procedure codes readily available in administrative claims data, it is possible to identify individuals with a high probability of eventually being diagnosed with PD.


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