Use Of Administrative Data Sets Research Articles

EUROlinkCAT: Common Data Model

BackgroundOver 130,000 children are born in Europe every year with congenital anomalies which are a major cause of infant mortality, childhood morbidity and long-term disability. A European data linkage study (EUROlinkCAT) aims to investigate the health and educational outcomes of children up to 10 years of age with congenital anomalies, born between 1995 and 2014. While congenital anomaly data including information on potential risk factors are standardised across the EUROCAT network, information on mortality, morbidity and educational outcomes are not.
 ObjectiveTo create a common data model that transforms key variables in local databases to standardised formats enabling data on health and educational outcomes to be pooled and analysed across multiple registries.
 MethodTwenty-two EUROCAT registries in fourteen countries are participating in the study. Each registry records uniformly coded data on cases of congenital anomaly registered in their local population using the EUROCAT Data Management Program. The registries will link their congenital anomaly data to their local mortality, hospital discharge, prescriptions and educational data. The linked individual case data cannot leave the local institution or “safe haven” environment, therefore verification and validation of all derived variables, data transformations and proxy variables must be performed locally.
 FindingsCreating a common data model is challenging as there are diverse coding classification systems, languages, healthcare and educational systems in Europe. As with many administrative datasets, the common data model is based on coded data rather than the often richer “free text” information.
 ConclusionThe use of administrative datasets across Europe enables pooling of data on rare outcomes and allows hypotheses on the health and education of children to be investigated. However, a common data model must be applied to ensure that data from multiple sites conform to a standard format.

Identification of adults with congenital heart disease of moderate or great complexity from administrative data.

There is relatively sparse literature on the use of administrative datasets for research in patients with adult congenital heart disease (ACHD). The goal of this analysis is to examine the accuracy of administrative data for identifying patients with ACHD who died. A list of the International Classification of Diseases codes representing ACHD of moderate- or great-complexity was created. A search for these codes in the electronic health record of adults who received care in 2010-2016 was performed, and used state death records to identify patients who died during this period. Manual record review was completed to evaluate performance of this search strategy. Identified patients were also compared with a list of patients with moderate- or great-complexity ACHD known to have died. About 134 patients were identified, of which 72 had moderate- or great-complexity ACHD confirmed by manual review, yielding a positive predictive value of 0.54 (95% CI 0.45, 0.62). Twenty six patients had a mild ACHD diagnosis. Thirty six patients had no identified ACHD on record review. Misidentifications were attributed to coding error for 19 patients (53%), and to acquired ventricular septal defects for 11 patients (31%). Diagnostic codes incorrect more than 50% of the time were those for congenitally corrected transposition, endocardial cushion defect, and hypoplastic left heart syndrome. Only 1 of 21 patients known to have died was not identified by the search, yielding a sensitivity of 0.95 (0.76, 0.99). Use of administrative data to identify patients with ACHD of moderate or great complexity who have died had good sensitivity but suboptimal positive predictive value. Strategies to improve accuracy are needed. Administrative data is not ideal for identification of patients in this group, and manual record review is necessary to confirm these diagnoses.

Research at Work: Administrative Data and Behavioral Sciences Research

Administrative data may provide rich, relatively inexpensive, unbiased, and accurate information that can be used to explore various research questions without imposing any additional burden to the participants. Understanding the data, having the ability to deal with its complexity, and ensuring data security are the keys to the successful use of administrative data sets.

Integration of Administrative Data and Chart Review for Reporting Health Care Utilization Among Children With Sickle Cell Disease

Health care utilization of children with sickle cell disease (SCD) has been well documented due to an increase in the use of administrative data sets. While use of such data sources is relatively efficient and low cost, questions remain as to whether they provide sufficient information to fully characterize health care use. The aim of this study was to determine whether administrative data have the capacity to fully assess health care utilization among children with SCD. We studied the health care utilization of 154 low-income children with SCD in a managed care organization combining administrative data and medical record review. In our comparison, we found that administrative claims provided key information on the scope and location of health service use and that sole reliance on medical record review may undercount unique members and encounters.

Accuracy and usefulness of ICD-10 death certificate coding for the identification of patients with ALS: Results from the South Carolina ALS Surveillance Pilot Project

The purpose if this study was to investigate the positive predictive value and sensitivity of the ICD-10 code G12.2, which is used to identify patients who have possibly died from ALS. All patients with a motor neuron disease diagnosis code during the study period (2001-2005) were identified using administrative data. South Carolina death certificate data were used to assess the positive predictive value and sensitivity of the ICD-10 code G12.2. Two hundred and seventy known cases of ALS linked to the death certificate data file. G12.2 was coded as either the underlying or contributing cause of death for 229 cases, sensitivity = 85%. There were 318 deaths due to ALS identified by the G12.2 code where a medical record was available for review. Of those, 205 contained information supporting the diagnosis of ALS, positive predictive value = 65%. This evaluation raises questions concerning the validity of using mortality data in forming epidemiological conclusions in this patient population. However, it does appear that mortality data can be used in the development of case-finding algorithms to identify ALS patients through the use of administrative data sets.

The use of large administrative data sets in nursing research

A NALYSIS OF large administrative data sets has been a method used primarily by health services researchers to examine issues such as access, regional differences in care, and health outcomes. Most administrative data sets were first developed in the 1970s for the collection of data regarding health plans, health system utilization, and claims data. Although they were not originally intended for conducting research, administrative data sets were found to be an attractive avenue for health services research studies. Technologic advances in personal computers, modern statistical analysis software, and uniform International Classification of Diseases, Ninth Revision nomenclature have widened the horizons for researchers. In the past, investigations using national or state data sets required access to mainframe computer resources, but today, analyses of this type may be conducted using one’s personal computer. The purpose of this article is to discuss the use of large administrative data sets as a tool in nursing research. As early as 1985, the limited use of secondary data analysis in nursing research was noted (McArt & McDougal, 1985). Jacobson et al.,1993 conducted a Medline search (1986-1992) for nursing citations using the key words secondary data analysis and identified 11 citations, 3 of which appeared in nursing journals. We conducted a similar literature search (1997-2003) using PUBMED with the key words secondary data analysis and nursing and identified 82 nursing studies using secondary data analysis. Of those, only 15 used existing administrative data sets. Fourteen of the 15 were published in nursing journals. Although the use of secondary analysis as a research method has increased in nursing, the use of administrative data sets has not kept equal tempo. Large administrative data sets embody information about groups of people and often describe entire populations at either state and/or national levels, thus enhancing generalizability of research findings. The data are representative of care delivered in many settings and can be useful to examine patterns of health service and resource utilization. Longitudinal administrative data sets, which collect person-level information, can be used to track subjects both over time and across settings of care. Finally, these data sets already exist, can be obtained inexpensively, and are easily adapted for use with analytical software. Various types of administrative data sets are available. Thirty-six states presently are legislatively mandated as part of public health law to collect discharge information on all hospitalized patients. Patient identifiers are removed from the data to protect patient confidentiality, and most state hospital inpatient data sets lack identifiers to track recurrent hospitalizations. Two examples of state data sets are the New York State Department

Performance measurement in cancer care: uses and challenges.

Unnecessary, inappropriate, and futile care are given in all areas of health care including cancer care. Not only does such care increase costs and waste precious resources, but patients may have adverse outcomes when the wrong care is given. One of the ways to address this issue is to measure performance with the use of administrative data sets. Through performance measurement, the best providers can be chosen, providers can be rewarded on the basis of the quality of their performance, opportunities for improvement can be identified, and variation in practice can be minimized. Purchasers should take leadership role in creating data sets that will enhance, clinical performance. Specifically, purchasers should require the following from payers: 1) staging information; 2) requirements and/or incentives for proper International Classification of Diseases coding, including other important (comorbid) conditions; 3) incentives or requirements for proper data collection if the payer is using a reimbursement strategy that places the risk on the provider; and 4) a willingness to collect and report information to providers of care, with a view toward increasing quality and decreasing the costs of cancer care. Demanding better clinical performance can lead to better outcomes. Once good data is presented to patients and providers, better clinical behavior and improved cancer care systems will quickly follow.

Performance measurement in cancer care

Unnecessary, inappropriate, and futile care are given in all areas of health care including cancer care. Not only does such care increase costs and waste precious resources, but patients may have adverse outcomes when the wrong care is given. One of the ways to address this issue is to measure performance with the use of administrative data sets. Through performance measurement, the best providers can be chosen, providers can be rewarded on the basis of the quality of their performance, opportunities for improvement can be identified, and variation in practice can be minimized. Purchasers should take leadership role in creating data sets that will enhance, clinical performance. Specifically, purchasers should require the following from payers: 1) staging information; 2) requirements and/or incentives for proper International Classification of Diseases coding, including other important (comorbid) conditions; 3) incentives or requirements for proper data collection if the payer is using a reimbursement strategy that places the risk on the provider; and 4) a willingness to collect and report information to providers of care, with a view toward increasing quality and decreasing the costs of cancer care. Demanding better clinical performance can lead to better outcomes. Once good data is presented to patients and providers, better clinical behavior and improved cancer care systems will quickly follow.

Health care report cards. Validity of case definitions.

Administrative databases are increasingly being used to construct health care report cards. We analyzed information from one of the original report cards, the Medicare Hospital Information Project. Assessment of mortality statistics for three clinical entities--coronary artery bypass surgery, hip reconstruction, and treatment of sepsis--demonstrated widespread outcome variances that reflected imperfect definitions rather than performance issues in clinical care. The use of administrative data sets to design report cards requires clinical expertise to ensure validity of the data. Designers of report card measures should share preliminary data with providers to enable feedback in methods and uncover definitional and validity concerns before widespread dissemination.