Validation of diabetes case definitions using administrative claims data

2011 ◽  
Vol 28 (4) ◽  
pp. 424-427 ◽  
Author(s):  
S. Amed ◽  
S. E. Vanderloo ◽  
D. Metzger ◽  
J.-P. Collet ◽  
K. Reimer ◽  
...  
Keyword(s):  
Claims Data ◽  
Administrative Claims ◽  
Case Definitions ◽  
Administrative Claims Data ◽  
Diabetes Case

scholarly journals Characterization of bariatric surgery and outcomes using administrative claims data in the research network of a nationwide commercial health plan

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qinli Ma ◽  
Michael Mack ◽  
Sonali Shambhu ◽  
Kathleen McTigue ◽  
Kevin Haynes
Keyword(s):  
Bariatric Surgery ◽  
Health Systems ◽  
Comparative Effectiveness ◽  
Claims Data ◽  
Research Network ◽  
Hospital Death ◽  
Administrative Claims ◽  
Administrative Claims Data ◽  
Abdominal Operations

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.

scholarly journals Attribution of Adverse Events Following Coronary Stent Placement Identified Using Administrative Claims Data

2020 ◽  
Vol 9 (4) ◽  
Author(s):  
Sanket S. Dhruva ◽  
Craig S. Parzynski ◽  
Ginger M. Gamble ◽  
Jeptha P. Curtis ◽  
Nihar R. Desai ◽  
...  
Keyword(s):  
Adverse Events ◽  
Stent Placement ◽  
Claims Data ◽  
Coronary Stent ◽  
Administrative Claims ◽  
Administrative Claims Data

Abstract 173: A Comparison of Administrative Claims Discharge Diagnosis with Electronic Medical Record Problem List in the Diagnosis of Acute Myocardial Infarction

2014 ◽  
Vol 7 (suppl_1) ◽  
Author(s):  
Michael D McCulloch ◽  
Tim Sobol ◽  
Joy Yuhas ◽  
Bill Ahern ◽  
Eric D Hixson ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Medical Record ◽  
Chart Review ◽  
Claims Data ◽  
Administrative Claims ◽  
Problem List ◽  
Principal Diagnosis ◽  
Administrative Claims Data ◽  
Patient Admissions

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.

scholarly journals A risk-model for hospital mortality among patients with severe sepsis or septic shock based on German national administrative claims data

PLoS ONE ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. e0194371 ◽  
Author(s):  
Daniel Schwarzkopf ◽  
Carolin Fleischmann-Struzek ◽  
Hendrik Rüddel ◽  
Konrad Reinhart ◽  
Daniel O. Thomas-Rüddel
Keyword(s):  
Septic Shock ◽  
Severe Sepsis ◽  
Hospital Mortality ◽  
Claims Data ◽  
Risk Model ◽  
Administrative Claims ◽  
Administrative Claims Data

scholarly journals A predictive model to identify Parkinson disease from administrative claims data

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 ◽  
...  
Keyword(s):  
Parkinson Disease ◽  
Predictive Model ◽  
Claims Data ◽  
Demographic Data ◽  
Area Under The Curve ◽  
Rem Sleep Behavior Disorder ◽  
Administrative Claims ◽  
Sleep Behavior ◽  
Administrative Claims Data ◽  
Procedure Codes

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.

scholarly journals Patterns of Long-Term Prescription Opioid Use Among Older Adults in the United States: A Study of Medicare Administrative Claims Data

2020 ◽  
Vol 1;24 (1;1) ◽  
pp. 31-40
Keyword(s):  
Older Adults ◽  
Claims Data ◽  
The United States ◽  
Prescription Opioid ◽  
Administrative Claims ◽  
Medicare Beneficiaries ◽  
Opioid Use ◽  
Opioid Prescription ◽  
Administrative Claims Data

BACKGROUND: Long-term opioid therapy was prescribed with increasing frequency over the past decade. However, factors surrounding long-term use of opioids in older adults remains poorly understood, probably because older people are not at the center stage of the national opioid crisis. OBJECTIVES: To estimate the annual utilization and trends in long-term opioid use among older adults in the United States. STUDY DESIGN: Retrospective cohort study. SETTING: Data from Medicare-enrolled older adults. METHODS: This study utilized a nationally representative sample of Medicare administrative claims data from the years 2012 to 2016 containing records of health care services for more than 2.3 million Medicare beneficiaries each year. Medicare beneficiaries who were 65 years of age or older and who were enrolled in Medicare Parts A, B, and D, but not Part C, for at least 10 months in a year were included in the study. We measured annual utilization and trends in new long-term opioid use episodes over 4 years (2013–2016). We examined claims records for the demographic characteristics of the eligible individuals and for the presence of chronic non-cancer pain (CNCP), cancer, and other comorbidities. RESULTS: From 2013 to 2016, administrative claims of approximately 2.3 million elderly Medicare beneficiaries were analyzed in each year with a majority of them being women (~56%) and white (~82%) with a mean age of approximately 75 years. The proportion of all eligible beneficiaries with at least one new opioid prescription increased from 6.64% in 2013, peaked at 10.32% in 2015, and then decreased to 8.14% in 2016. The proportion of individuals with long-term opioid use among those with a new opioid prescription was 12.40% in 2013 and 10.20% in 2016. Among new long-term opioid users, the proportion of beneficiaries with a cancer diagnosis during the study years increased from 13.30% in 2013 to 15.67% in 2016, and the proportion with CNCP decreased from 30.25% in 2013 to 27.36% in 2016. Across all years, long-term opioid use was consistently high in the Southern states followed by the Midwest region. LIMITATIONS: This study used Medicare fee-for-service administrative claims data to capture prescription fill patterns, which do not allow for the capture of individuals enrolled in Medicare Advantage plans, cash prescriptions, or for the evaluation of appropriateness of prescribing, or the actual use of medication. This study only examined long-term use episodes among patients who were defined as opioid-naive. Finally, estimates captured for 2016 could only utilize data from 9 months of the year to capture 90-day long-term-use episodes. CONCLUSIONS: Using a national sample of elderly Medicare beneficiaries, we observed that from 2013 to 2016 the use of new prescription opioids increased from 2013 to 2014 and peaked in 2015. The use of new long-term prescription opioids peaked in 2014 and started to decrease from 2015 and 2016. Future research needs to evaluate the impact of the changes in new and long-term prescription opioid use on population health outcomes. KEY WORDS: Long-term, opioids, older adults, trends, Medicare, chronic non-cancer pain, cancer, cohort study

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