Implications of Data Quality Problems within Hospital Administrative Databases

Silva-Costa, Tiago; Marques, Bernardo; Costa‐Pereira, Altamiro

doi:10.1007/978-3-642-13039-7_208

Cited by 18 publications

(10 citation statements)

References 13 publications

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“…One of the challenges hospitals face nowadays is the quality of the data they collect [36-38]. Some variables can have missing values, can be biased (for instance, because of their main purpose), or may not be possible to collect, and so adjustments can be difficult.…”

Section: Discussionmentioning

confidence: 99%

Definition of a core set of quality indicators for the assessment of HIV/AIDS clinical care: a systematic review

Catumbela

Certal

Costa

et al. 2013

BMC Health Serv Res

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BackgroundSeveral organizations and individual authors have been proposing quality indicators for the assessment of clinical care in HIV/AIDS patients. Nevertheless, the definition of a consensual core set of indicators remains controversial and its practical use is largely limited. This study aims not only to identify and characterize these indicators through a systematic literature review but also to propose a parsimonious model based on those most used.MethodsMEDLINE, SCOPUS, Cochrane databases and ISI Web of Knowledge, as well as official websites of organizations dealing with HIV/AIDS care, were searched for articles and information proposing HIV/AIDS clinical care quality indicators. The ones that are on patient’s perspective and based on services set were excluded. Data extraction, using a predefined data sheet based on Cochrane recommendations, was done by one of the authors while a second author rechecked the extracted data for any inconsistency.ResultsA total of 360 articles were identified in our search query but only 12 of them met the inclusion criteria. We also identified one relevant site. Overall, we identified 65 quality indicators for HIV/AIDS clinical care distributed as following: outcome (n=15) and process-related (n=50) indicators; generic (n=36) and HIV/AIDS disease-specific (n=29) indicators; baseline examinations (n=19), screening (n=9), immunization (n=4), prophylaxis (n=5), HIV monitoring (n=16), and therapy (=12) indicators.ConclusionsThere are several studies that set up HIV clinical care indicators, with only a part of them useful to assess the HIV clinical care. More importantly, HIV/AIDS clinical care indicators need to be valid, reliable and most of all feasible.

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Section: Discussionmentioning

confidence: 99%

Definition of a core set of quality indicators for the assessment of HIV/AIDS clinical care: a systematic review

Catumbela

Certal

Costa

et al. 2013

BMC Health Serv Res

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“…12 The quality of information in these systems depends on incentives of data reporting such as financial and systematic support. 13 This has meant that expensive medical procedures are documented more thoroughly than less costly health services, such as ambulatory services, 14 One of the most common types of error in these databases is absent values, which occurs when required information is missing or not inputted. This shows that administrative data may be incomplete and may not be the most accurate source of information.…”

Section: Reliability Of the Data And Code Accuracymentioning

confidence: 99%

“…One of the core elements of the administrative data system is the International Statistical Classification of Diseases and Related Health Problems (ICD; the most current version is ICD-11), which is a medical classification list by the World Health Organization (WHO). 1,2,13 Using various algorithms to link specific ICD codes can provide a cohort of patients with the disease of interest, such as chronic obstructive pulmonary disease (COPD). 15 For example, information such as cumulative patient profile, ICD codes, and prescriptions can be linked to identify individuals with a certain type of disease.…”

Section: Reliability Of the Data And Code Accuracymentioning

confidence: 99%

Challenges to Using Big Data in Health Services Research

Kang¹,

Sibbald

2019

UWOMJ

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Given the shift in current healthcare trends toward digitization of storing information, there has been an increase in the number of studies using administrative databases. These databases provide a powerful tool to conduct research on outcomes, health services, and epidemiology. However, these databases have limitations and biases that should be considered. Given the sensitive information regarding patients’ health in the database, security clearances must be granted before data is accessed. Furthermore, algorithms to link the different variables to create a cohort of people with specific disease are imperfect and may not yield an accurate representation. Due to a large volume of records, a statistically significant finding may be observed, but may provide insignificant clinical results. Despite the current limitations, administrative databases provide powerful data that researchers can use to identify gaps in performance to improve the healthcare system.

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“…2 We argue that collecting episode-level data in SRES would be beneficial because their data could be applied to: health statistics, hospital management, clinical and epidemiological research, informing policy and funding. [4][5][6] It is still unclear whether it would be possible for SRES to electronically collect episode-level data, and whether their data could be accurate enough to be used. Implementing electronic episode-level data collection would be difficult because some still rely entirely on paper-based systems.…”

Section: Introductionmentioning

confidence: 99%