Maximising the value of hospital administrative datasets

2010 ◽  
Vol 34 (2) ◽  
pp. 216 ◽  
Author(s):  
Shyamala G. Nadathur
Keyword(s):  
Administrative Data ◽  
Performance Monitoring ◽  
International Classification Of Diseases ◽  
Audit And Feedback ◽  
Management Tools ◽  
Classification Of Diseases ◽  
Diagnostic Related Groups ◽  
Data Collections ◽  
Hospital Administrative ◽  
Administrative Datasets

Mandatory and standardised administrative data collections are prevalent in the largely public-funded acute sector. In these systems the data collections are used for financial, performance monitoring and reporting purposes. This paper comments on the infrastructure and standards that have been established to support data collection activities, audit and feedback. The routine, local and research uses of these datasets are described using examples from Australian and international literature. The advantages of hospital administrative datasets and opportunities for improvement are discussed under the following headings: accessibility, standardisation, coverage, completeness, cost of obtaining clinical data, recorded Diagnostic Related Groups and International Classification of Diseases codes, linkage and connectivity. In an era of diminishing resources better utilisation of these datasets should be encouraged. Increased study and scrutiny will enhance transparency and help identify issues in the collections. As electronic information systems are increasingly embraced, administrative data collections need to be managed as valuable assets and powerful operational and patient management tools.

Comparing ICD-10 external cause codes for pedal cyclists with self-reported crash details

2017 ◽  
Vol 24 (2) ◽  
pp. 157-160 ◽  
Author(s):  
Ben Beck ◽  
Christina L Ekegren ◽  
Peter Cameron ◽  
Mark Stevenson ◽  
Rodney Judson ◽  
...  
Keyword(s):  
Administrative Data ◽  
International Classification Of Diseases ◽  
Coding System ◽  
Event Information ◽  
Injury Event ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Hospital Administrative ◽  
Crash Characteristics

Accurate coding of injury event information is critical in developing targeted injury prevention strategies. However, little is known about the validity of the most universally used coding system, the International Classification of Diseases (ICD-10), in characterising crash counterparts in pedal cycling events. This study aimed to determine the agreement between hospital-coded ICD-10-AM (Australian modification) external cause codes with self-reported crash characteristics in a sample of pedal cyclists admitted to hospital following bicycle crashes. Interview responses from 141 injured cyclists were mapped to a single ICD-10-AM external cause code for comparison with ICD-10-AM external cause codes from hospital administrative data. The percentage of agreement was 77.3% with a κ value of 0.68 (95% CI 0.61 to 0.77), indicating substantial agreement. Nevertheless, studies reliant on ICD-10 codes from administrative data should consider the 23% level of disagreement when characterising crash counterparts in cycling crashes.

scholarly journals Linking administrative data sets of inpatient infectious diseases diagnoses in far North Queensland: a cohort profile

BMJ Open ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. e034845
Author(s):  
Damon P Eisen ◽  
Emma S McBryde ◽  
Luke Vasanthakumar ◽  
Matthew Murray ◽  
Miriam Harings ◽  
...  
Keyword(s):  
Infectious Diseases ◽  
Relational Database ◽  
Query Language ◽  
Chronic Renal Disease ◽  
Data Extraction ◽  
Clinical Information ◽  
International Classification Of Diseases ◽  
Data Sets ◽  
Classification Of Diseases ◽  
Hospital Administrative

PurposeTo design a linked hospital database using administrative and clinical information to describe associations that predict infectious diseases outcomes, including long-term mortality.ParticipantsA retrospective cohort of Townsville Hospital inpatients discharged with an International Classification of Diseases and Related Health Problems 10th Revision Australian Modification code for an infectious disease between 1 January 2006 and 31 December 2016 was assembled. This used linked anonymised data from: hospital administrative sources, diagnostic pathology, pharmacy dispensing, public health and the National Death Registry. A Created Study ID was used as the central identifier to provide associations between the cohort patients and the subsets of granular data which were processed into a relational database. A web-based interface was constructed to allow data extraction and evaluation to be performed using editable Structured Query Language.Findings to dateThe database has linked information on 41 367 patients with 378 487 admissions and 1 869 239 diagnostic/procedure codes. Scripts used to create the database contents generated over 24 000 000 database rows from the supplied data. Nearly 15% of the cohort was identified as Aboriginal or Torres Strait Islanders. Invasive staphylococcal, pneumococcal and Group A streptococcal infections and influenza were common in this cohort. The most common comorbidities were smoking (43.95%), diabetes (24.73%), chronic renal disease (17.93%), cancer (16.45%) and chronic pulmonary disease (12.42%). Mortality over the 11-year period was 20%.Future plansThis complex relational database reutilising hospital information describes a cohort from a single tropical Australian hospital of inpatients with infectious diseases. In future analyses, we plan to explore analyses of risks, clinical outcomes, healthcare costs and antimicrobial side effects in site and organism specific infections.

scholarly journals Utility of ICD Codes for Stress Cardiomyopathy in Hospital Administrative Databases: What Do They Signify?

2019 ◽  
pp. 160-163
Author(s):  
Anusha G Bhat ◽  
Kevin White ◽  
Kyle Gobeil ◽  
Tara Lagu ◽  
Peter K Lindenauer ◽  
...  
Keyword(s):  
Chart Review ◽  
Clinical Manifestations ◽  
International Classification Of Diseases ◽  
Stress Cardiomyopathy ◽  
Administrative Databases ◽  
Classification Of Diseases ◽  
Icd Codes ◽  
Hospital Administrative ◽  
New Diagnosis

Prior studies of stress cardiomyopathy (SCM) have used International Classification of Diseases (ICD) codes to identify patients in administrative databases without evaluating the validity of these codes. Between 2010 and 2016, we identified 592 patients discharged with a first known principal or secondary ICD code for SCM in our medical system. On chart review, 580 charts had a diagnosis of SCM (positive predictive value 98%; 95% CI: 96.4-98.8), although 38 (6.4%) did not have active clinical manifestations of SCM during the hospitalization. Moreover, only 66.8% underwent cardiac catheterization and 91.5% underwent echocardiography. These findings suggest that, although all but a few hospitalized patients with an ICD code for SCM had a diagnosis of SCM, some of these were chronic cases, and numerous patients with a new diagnosis of SCM did not undergo a complete diagnostic workup. Researchers should be mindful of these limitations in future studies involving administrative databases.

scholarly journals (Dis)concordance of comorbidity data across ICD codes, medical charts, and self-reports

2019 ◽  
Author(s):  
Abdullah Sheriffdeen ◽  
Jeremy Millar ◽  
Catherine Martin ◽  
Melanie Evans ◽  
Gabriella Tikellis ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Administrative Data ◽  
International Classification Of Diseases ◽  
Medical Chart ◽  
Chart Audit ◽  
Standard Data ◽  
Self Reports ◽  
Classification Of Diseases ◽  
Data Source ◽  
Level Of Agreement

Abstract Background Benchmarking outcomes across settings commonly requires risk-adjustment for co-morbidities that must be derived from extant sources that were designed for other purposes. A question arises as to the extent to which differing available sources for health data will be concordant when inferring the type and severity of co-morbidities, how close these are to the “truth”. We studied the level of concordance for same-patient comorbidity data extracted from administrative data (coded from International Classification of Diseases, Australian modification,10th edition [ICD-10AM]), from the medical chart audit, and data self-reported by men with prostate cancer who had undergone a radical prostatectomy.Methods We included six hospitals (5 public and 1 private) contributing to the Prostate Cancer Outcomes Registry-Victoria (PCOR-Vic) in the study. We listed eligible patients from the PCOR-Vic who underwent a radical prostatectomy between January 2017 and April 2018 for the Health Information Manager in each hospital, who provided each patient’s associated ICD-10AM comorbidity codes. Medical charts were reviewed to extract comorbidities used to generate the Charlson Comorbidity Index. The self-reported comorbidity questionnaire (SCQ) was distributed through PCOR-Vic to eligible men.Results The percentage agreement between the administrative data, medical charts and self-reports ranged from 92% to 99% in the 122 patients (from 217 eligible participants, 56%), who responded to the questionnaire. The prevalence-adjusted bias-adjusted kappa (PABAK) coefficient was from 0.83 to 0.98 for all conditions aside from cancer, reflecting a strong level of agreement for the absence of comorbidities. Conversely, the presence of comorbidities showed a poor level of agreement between data sources. There was concordance on 213/277 (77%) comorbidities when comparing medical charts and administrative data; 102/238 (30%) comorbidities when comparing medical chart and self-reports; and 34/150 (23%) comorbidities when comparing administrative data and self-reports.Conclusion Relying on a single data source to generate comorbidity indices for risk-modelling purposes may fail to capture the reality of a patient’s disease profile. There does not appear to be a ‘gold-standard’ data source for the collection of data on comorbidities.

scholarly journals (Dis)concordance of comorbidity data and cancer status across administrative datasets, medical charts, and self-reports

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
A. Sheriffdeen ◽  
J. L. Millar ◽  
C. Martin ◽  
M. Evans ◽  
G. Tikellis ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Radical Prostatectomy ◽  
Administrative Data ◽  
International Classification Of Diseases ◽  
Chart Audit ◽  
Standard Data ◽  
Self Reports ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Data Source

Abstract Background Benchmarking outcomes across settings commonly requires risk-adjustment for co-morbidities that must be derived from extant sources that were designed for other purposes. A question arises as to the extent to which differing available sources for health data will be concordant when inferring the type and severity of co-morbidities, how close are these to the “truth”. We studied the level of concordance for same-patient comorbidity data extracted from administrative data (coded from International Classification of Diseases, Australian modification,10th edition [ICD-10 AM]), from the medical chart audit, and data self-reported by men with prostate cancer who had undergone a radical prostatectomy. Methods We included six hospitals (5 public and 1 private) contributing to the Prostate Cancer Outcomes Registry-Victoria (PCOR-Vic) in the study. Eligible patients from the PCOR-Vic underwent a radical prostatectomy between January 2017 and April 2018.Health Information Manager’s in each hospital, provided each patient’s associated administrative ICD-10 AM comorbidity codes. Medical charts were reviewed to extract comorbidity data. The self-reported comorbidity questionnaire (SCQ) was distributed through PCOR-Vic to eligible men. Results The percentage agreement between the administrative data, medical charts and self-reports ranged from 92 to 99% in the 122 patients from the 217 eligible participants who responded to the questionnaire. The presence of comorbidities showed a poor level of agreement between data sources. Conclusion Relying on a single data source to generate comorbidity indices for risk-modelling purposes may fail to capture the reality of a patient’s disease profile. There does not appear to be a ‘gold-standard’ data source for the collection of data on comorbidities.

scholarly journals Diagnostic Accuracy of Health Care Administrative Diagnosis Codes to Identify Nontuberculous Mycobacteria Disease: A Systematic Review

2021 ◽  
Vol 8 (5) ◽  
Author(s):  
Carlos Mejia-Chew ◽  
Lauren Yaeger ◽  
Kevin Montes ◽  
Thomas C Bailey ◽  
Margaret A Olsen
Keyword(s):  
Health Care ◽  
Diagnostic Accuracy ◽  
Administrative Data ◽  
International Classification Of Diseases ◽  
Administrative Database ◽  
Cochrane Central Register ◽  
Predictive Values ◽  
Diagnosis Codes ◽  
Database Research ◽  
Classification Of Diseases

Abstract Background Health care administrative database research frequently uses standard medical codes to identify diagnoses or procedures. The aim of this review was to establish the diagnostic accuracy of codes used in administrative data research to identify nontuberculous mycobacterial (NTM) disease, including lung disease (NTMLD). Methods We searched Ovid Medline, Embase, Scopus, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov from inception to April 2019. We included studies assessing the diagnostic accuracy of International Classification of Diseases, 9th edition, Clinical Modification (ICD-9-CM) diagnosis codes to identify NTM disease and NTMLD. Studies were independently assessed by 2 researchers, and the Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to assess bias and quality. Results We identified 5549 unique citations. Of the 96 full-text articles reviewed, 7 eligible studies of moderate quality (3730 participants) were included in our review. The diagnostic accuracy of ICD-9-CM diagnosis codes to identify NTM disease varied widely across studies, with positive predictive values ranging from 38.2% to 100% and sensitivity ranging from 21% to 93%. For NTMLD, 4 studies reported diagnostic accuracy, with positive predictive values ranging from 57% to 64.6% and sensitivity ranging from 21% to 26.9%. Conclusions Diagnostic accuracy measures of codes used in health care administrative data to identify patients with NTM varied across studies. Overall the positive predictive value of ICD-9-CM diagnosis codes alone is good, but the sensitivity is low; this method is likely to underestimate case numbers, reflecting the current limitations of coding systems to capture NTM diagnoses.

scholarly journals (Dis)concordance of comorbidity data and cancer status across administrative datasets, medical charts, and self-reports

2020 ◽  
Author(s):  
Abdullah Sheriffdeen ◽  
Jeremy Millar ◽  
Catherine Martin ◽  
Melanie Evans ◽  
Gabriella Tikellis ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Radical Prostatectomy ◽  
Administrative Data ◽  
International Classification Of Diseases ◽  
Chart Audit ◽  
Standard Data ◽  
Self Reports ◽  
Classification Of Diseases ◽  
Data Source ◽  
Single Data

Abstract Background Benchmarking outcomes across settings commonly requires risk-adjustment for co-morbidities that must be derived from extant sources that were designed for other purposes. A question arises as to the extent to which differing available sources for health data will be concordant when inferring the type and severity of co-morbidities, how close these are to the “truth”. We studied the level of concordance for same-patient comorbidity data extracted from administrative data (coded from International Classification of Diseases, Australian modification,10 th edition [ICD-10AM]), from the medical chart audit, and data self-reported by men with prostate cancer who had undergone a radical prostatectomy. Methods We included six hospitals (5 public and 1 private) contributing to the Prostate Cancer Outcomes Registry-Victoria (PCOR-Vic) in the study. We listed eligible patients from the PCOR-Vic who underwent a radical prostatectomy between January 2017 and April 2018 for the Health Information Manager in each hospital, who provided each patient’s associated ICD-10AM comorbidity codes. Medical charts were reviewed to extract comorbidities used to generate the Charlson Comorbidity Index. The self-reported comorbidity questionnaire (SCQ) was distributed through PCOR-Vic to eligible men. Results The percentage agreement between the administrative data, medical charts and self-reports ranged from 92% to 99% in the 122 patients (from 217 eligible participants, 56%), who responded to the questionnaire. The presence of comorbidities showed a poor level of agreement between data sources. Due to a variety of factors, certain conditions were recorded more than others. Conclusion Relying on a single data source to generate comorbidity indices for risk-modelling purposes may fail to capture the reality of a patient’s disease profile. There does not appear to be a ‘gold-standard’ data source for the collection of data on comorbidities.

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