How many diagnosis fields are needed to capture safety events in administrative data? Findings and recommendations from the WHO ICD-11 Topic Advisory Group on Quality and Safety

Drösler, Saskia E.; Romano, Patrick S.; Sundararajan, Vijaya; Burnand, Bernard; Colin, Cyrille; Pincus, Harold Alan; Ghali, William A.

doi:10.1093/intqhc/mzt090

Cited by 32 publications

(34 citation statements)

References 9 publications

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“…Therefore, hospitalizations and ED visits with marijuana-related billing codes were determined by the presence of any of the four marijuana-related billing codes within the first three listed diagnosis codes for examining rates and in any of the listed diagnosis codes for examining primary diagnosis categories. It is not clear if the rank of the order of the marijuana-related codes is important in capturing healthcare encounters related to marijuana and examining marijuana-related codes within the first three listed diagnosis codes could be an underestimation (Drosler et al, 2014; Slavova et al, 2014). …”

Section: Discussionmentioning

confidence: 99%

Marijuana and acute health care contacts in Colorado

Wang

Hall

Vigil

et al. 2017

Preventive Medicine

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Over 22 million Americans are current users of marijuana; half of US states allow medical marijuana, and several allow recreational marijuana. The objective of this study was to evaluate the impact marijuana has on hospitalizations, emergency department (ED) visits, and regional poison center (RPC) calls in Colorado, a medical and recreational marijuana state. This is a retrospective review using Colorado Hospital Association hospitalizations and ED visits with marijuana-related billing codes, and RPC marijuana exposure calls. Legalization of marijuana in Colorado has been associated with an increase in hospitalizations, ED visits, and RPC calls linked with marijuana exposure. From 2000 to 2015, hospitalization rates with marijuana-related billing codes increased from 274 to 593 per 100,000 hospitalizations in 2015. Overall, the prevalence of mental illness among ED visits with marijuana-related codes was five-fold higher (5.07, 95% CI: 5.0, 5.1) than the prevalence of mental illness without marijuana-related codes. RPC calls remained constant from 2000 through 2009. However, in 2010, after local medical marijuana policy liberalization, the number of marijuana exposure calls significantly increased from 42 to 93; in 2014, after recreational legalization, calls significantly increased by 79.7%, from 123 to 221 (p <0.0001). The age group <17 years old also had an increase in calls after 2014. As more states legalize marijuana, it is important to address public education and youth prevention, and understand the impact on mental health disorders. Improvements in data collection and surveillance methods are needed to more accurately evaluate the public health impact of marijuana legalization.

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Section: Discussionmentioning

confidence: 99%

Marijuana and acute health care contacts in Colorado

Wang

Hall

Vigil

et al. 2017

Preventive Medicine

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“…Although a desired definition with Sn and Sp of 100% would be ideal, modifying and optimizing the data definition to capture sepsis as accurately as possible, with Sn falling above 75%, similar to that of other hospital-acquired infections internationally [ 37 ] and for non-communicable diseases such as hypertension [ 38 ] and diabetes [ 39 ], should be the ultimate goal. Improving the quality of administrative health data and increasing the case capture and validity of sepsis could be accomplished through a number of simple strategies, such as (1) improved physician documentation, including documenting sepsis in the front pages of the chart to get the attention of coders; (2) having a specialized coding procedure for ICU patients, perhaps including specific training of health care coders to improve familiarity with the case mix of patients and conditions that are more prevalent in the ICU to increase Sn and case capture; and (3) for those countries in which a limited number of diagnostic coding fields exist, there should be at least eight coding fields for diagnosis to capture conditions such as sepsis [ 40 ]. These strategies can be used in combination with data linkage to other data sources such as laboratory, pharmacy or microbiology data and the EMRs, and with clinical factors such as heart rate, respiratory rate, body temperature, white blood cell count and markers of organ dysfunction, to try to incorporate the key characteristics of sepsis defined and listed in the ACCP/SCCM definitions [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

Validity of administrative data in recording sepsis: a systematic review

et al. 2015

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IntroductionAdministrative health data have been used to study sepsis in large population-based studies. The validity of these study findings depends largely on the quality of the administrative data source and the validity of the case definition used. We systematically reviewed the literature to assess the validity of case definitions of sepsis used with administrative data.MethodsEmbase and MEDLINE were searched for published articles with International Classification of Diseases (ICD) coded data used to define sepsis. Abstracts and full-text articles were reviewed in duplicate. Data were abstracted from all eligible full-text articles, including ICD-9- and/or ICD-10-based case definitions, sensitivity (Sn), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV).ResultsOf 2,317 individual studies identified, 12 full-text articles met all eligibility criteria. A total of 38 sepsis case definitions were tested, which included over 130 different ICD codes. The most common ICD-9 codes were 038.x, 790.7 and 995.92, and the most common ICD-10 codes were A40.x and A41.x. The PPV was reported in ten studies and ranged from 5.6% to 100%, with a median of 50%. Other tests of diagnostic accuracy were reported only in some studies. Sn ranged from 5.9% to 82.3%; Sp ranged from 78.3% to 100%; and NPV ranged from 62.1% to 99.7%.ConclusionsThe validity of administrative data in recording sepsis varied substantially across individual studies and ICD definitions. Our work may serve as a reference point for consensus towards an improved and harmonized ICD-coded definition of sepsis.Electronic supplementary materialThe online version of this article (doi:10.1186/s13054-015-0847-3) contains supplementary material, which is available to authorized users.

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“…Second, the number of coding fields available in (standardised) billing records has increased in recent years, allowing for more secondary diagnoses to be recorded; however, it is unclear whether expansion beyond 15 fields will benefit the HAI registration and other complications. 60 88 Third, the adoption and accuracy of PoA indicators in the process of code assignment remains to be validated, and they were incorporated in only a few studies included in this review. 78 89 Finally, this systematic review could not provide sufficient data to evaluate changes in coding accuracy since the US introduction of financial disincentives in 2008 for certain HACs that were not present on admission.…”

Section: Discussionmentioning

confidence: 99%

Accuracy of administrative data for surveillance of healthcare-associated infections: a systematic review

et al. 2015

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ObjectiveMeasuring the incidence of healthcare-associated infections (HAI) is of increasing importance in current healthcare delivery systems. Administrative data algorithms, including (combinations of) diagnosis codes, are commonly used to determine the occurrence of HAI, either to support within-hospital surveillance programmes or as free-standing quality indicators. We conducted a systematic review evaluating the diagnostic accuracy of administrative data for the detection of HAI.MethodsSystematic search of Medline, Embase, CINAHL and Cochrane for relevant studies (1995–2013). Methodological quality assessment was performed using QUADAS-2 criteria; diagnostic accuracy estimates were stratified by HAI type and key study characteristics.Results57 studies were included, the majority aiming to detect surgical site or bloodstream infections. Study designs were very diverse regarding the specification of their administrative data algorithm (code selections, follow-up) and definitions of HAI presence. One-third of studies had important methodological limitations including differential or incomplete HAI ascertainment or lack of blinding of assessors. Observed sensitivity and positive predictive values of administrative data algorithms for HAI detection were very heterogeneous and generally modest at best, both for within-hospital algorithms and for formal quality indicators; accuracy was particularly poor for the identification of device-associated HAI such as central line associated bloodstream infections. The large heterogeneity in study designs across the included studies precluded formal calculation of summary diagnostic accuracy estimates in most instances.ConclusionsAdministrative data had limited and highly variable accuracy for the detection of HAI, and their judicious use for internal surveillance efforts and external quality assessment is recommended. If hospitals and policymakers choose to rely on administrative data for HAI surveillance, continued improvements to existing algorithms and their robust validation are imperative.

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How many diagnosis fields are needed to capture safety events in administrative data? Findings and recommendations from the WHO ICD-11 Topic Advisory Group on Quality and Safety

Cited by 32 publications

References 9 publications

Marijuana and acute health care contacts in Colorado

Marijuana and acute health care contacts in Colorado

Validity of administrative data in recording sepsis: a systematic review

Accuracy of administrative data for surveillance of healthcare-associated infections: a systematic review

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