Top PDF Which ICD-9-CM codes should be used for bronchiolitis research?

Which ICD-9-CM codes should be used for bronchiolitis research?

Which ICD-9-CM codes should be used for bronchiolitis research?

This variability in how first episodes of wheezing in very young children (which will overwhelmingly be bronchiolitis) is not accounted for by recognizable dis- ease or child characteristics contained in administra- tive databases. This could mean that clinicians are distinguishing distinct disease processes. Our other findings, and the requirement for a diagnosis of asthma that children be older and the wheezing be recurrent, argue against such an interpretation. Consequently, re- searchers or health system planners who rely on only on narrow diagnosis codes will miss children who should be included in analyses. The numbers missed would be large – a cohort based on broad diagnosis codes will likely be twice the size of one relying on only narrow codes.
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Validation of ICD 9 CM codes for identification of acetaminophen related emergency department visits in a large pediatric hospital

Validation of ICD 9 CM codes for identification of acetaminophen related emergency department visits in a large pediatric hospital

hospitals over alternative time periods. In addition, we assumed that all visits related to acetaminophen expos- ure or overdose were identified by at least 1 of the codes we selected. Due to the large amount of visits, only one trained research assistant reviewed the medical records and concordance statistics are not available, however, a standardized consensus approach was used to classify these cases. Not all true acetaminophen exposures/ overdoses might have been captured as some may not have been assigned our selected codes, but if any, we be- lieve this proportion would be very small given our broad code selection, including several general codes with low sensitivity and specificity. In interpreting our findings it is important to note distinctions between ex- posure and overdose. Since in most occasions ingestion was not observed, overdose could not be confirmed. While the clinical implications of these differences are important, the purpose of our study was to evaluate the value of diagnostic algorithms to identify a majority of potential overdoses. Finally, we examined only the ICD- 9-CM coding system, which may not be comparable with the ICD-10-CM coding system for future studies.
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A classification of diabetic foot infections using ICD 9 CM codes: application to a large computerized medical database

A classification of diabetic foot infections using ICD 9 CM codes: application to a large computerized medical database

Identification of ICD-9 codes for diabetic foot infections First we looked up alphanumeric diagnoses indicative of diabetic foot infections in the “index to diseases and injuries” of the ICD-9-CM coding manual. This index serves to “map” various alphanumeric diagnoses to their ICD-9-CM codes[2]. Then we reviewed the formal defi- nitions of these ICD-9-CM codes to confirm that they did indeed indicate foot infection. We also examined the definitions of related codes sharing the same initial three-digit root. In addition, we used ICD-9 procedure codes and CPT-4 codes to identify patients with ampu- tation (see appendix for codes). We then examined all the ICD-9-CM codes that had been assigned to these patients in the 90 days before the amputation code appeared in the database. We expected that codes for foot infection would appear, because foot infections are a frequent cause of amputation. Finally, we classified the resulting ICD-9-CM codes for foot infection into two groups, specific and moderately specific.
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Clinical codes combined with procedure codes increase diagnostic accuracy of Crohn’s disease in a US Military health record

Clinical codes combined with procedure codes increase diagnostic accuracy of Crohn’s disease in a US Military health record

positives patients with EHRs that included multiple refer- ences to having CD without an endoscopic confirmation. In our study, we often found intestinal conditions or non- specific radiographs suggestive of CD (ie, thickening on CT) but endoscopic or pathology evidence was non- specific or supported a related diagnosis (ie, eosinophilic gastrointestinal disease). Additionally, our study had rela- tively few patients with UC; this was not surprising given we excluded patients with any ICD-9- CM codes for UC for increased CD specificity. A study of the Manitoba Health database used administrative case definitions and found a 91.3% specificity comparing to a self- report question- naire of patients and a 93.7% specificity compared with a chart review gold standard. 12 A study of the General Practice Research Database to validate the diagnosis of CD using OXMIS codes and surveying general practi- tioners to confirm these diagnoses categorised 86% of 49 patients identified by EHR as having CD. 13 A study of
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Effect of Late-Preterm Birth and Maternal Medical Conditions on Newborn Morbidity Risk

Effect of Late-Preterm Birth and Maternal Medical Conditions on Newborn Morbidity Risk

We examined 8 maternal medical conditions that were caused by the pregnancy itself, by the pregnancy’s man- agement, or by an underlying medical condition that may be exacerbated during pregnancy. The selected pre- existing maternal medical conditions and complications of pregnancy included HDP, diabetes (gestational and established), antepartum hemorrhage, acute or chronic lung disease, maternal infection, cardiac disease, renal disease, and genital herpes. We used ICD-9-CM diagno- sis codes from the maternal delivery hospital discharge data or from the maternal medical risk factors that are reported on the infant birth certificate to classify these maternal conditions (Table 2). When a woman had 1 of the selected medical conditions reported on either data source, she was classified as having the condition. Because these conditions were meant to represent antepartum conditions, we excluded ICD-9-CM codes that indicated conditions that occurred postpartum.
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Cost of Hospitalization for Preterm and Low Birth Weight Infants in the United States

Cost of Hospitalization for Preterm and Low Birth Weight Infants in the United States

Although the use of discharge data has coding limi- tations, this study used strengths of the NIS, including the detailed ICD-9-CM codes for birth weight, to target certain analyses to high-risk infants, as well as admission source and disposition to account for transfers. Analysis of transfers suggests that a small number of all preterm/ LBW stays included in this study were transfers and thus may potentially lead to double counting of infants. Im- portantly, the identified number of infants whose dis- charge disposition indicated transfer to another facility nearly matched the identified number of infants whose admission source indicated a transfer in from another facility, a finding that supports internal validity of the data.
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Modeling of Sic Power Semiconductor Devices For Switching Converter Applications

Modeling of Sic Power Semiconductor Devices For Switching Converter Applications

Measures of safe patient care are important indicators of quality for hospitals. Patient characteristics are more closely aligned with complications than hospital characteristics. Hospitals that have the resources for timely identification with these these complications to prevent patient decline are thought to have a higher quality of care. Nurses constitute 24-hour surveillance specifically designed for early identification and intervention of untoward patient events. Because record review to identify cases of FTR is expensive and time consuming, administrative data such as secondary ICD-9 CM codes provide an efficient mechanism for measuring patient outcomes. However, in limited study, these codes have not performed well when compared to the gold standard of record review in identifying FTR. There is some evidence that the inclusion of clinical data has improved the performance of ICD-9 CM codes with other outcome measures. Therefore, the addition of clinical data to the AHRQ identified ICD-9 CM codes might improve their accuracy in identifying FTR. Further investigation in this area is required before these codes can be used reliably as a measure of FTR. This study evaluated the diagnostic performance of AHRQ secondary ICD-9 CM codes as measures of FTR compared to the gold standard of record review. To address limitations from previous studies, record reviews were conducted by independent experts with no connection to the facilities that were studied. In addition, standardized tools were used for record
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Classification of interventions in traditional Chinese medicine

Classification of interventions in traditional Chinese medicine

The US National Center for Health Statistics created the ICD-9 Clinical Modification (ICD-9-CM), which is an adaption used for assigning diagnostic and procedure codes in the U.S. 6 The ICD-9-CM consists of three volumes; volumes 1 and 2 contain diagnosis codes, while volume 3 contains a classification system for surgical, diagnostic, and therapeutic procedures (with an alphabetical index and a tabular list). The tabular list of procedures includes a limited number of procedures and interventions related to TCM. We searched the table with the keyword „acupuncture‟, which retrieved only the following three items:
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Emergency Department Visits for Heat Stroke in the United States, 2009 and 2010

Emergency Department Visits for Heat Stroke in the United States, 2009 and 2010

For each visit, up to 19 diagnoses were recorded and coded with the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes. ED visits for heat stroke and sun stroke were identified by screening for the corresponding ICD-9-CM code (992.0) in any of the nineteen recorded diagnoses. Excluded from the study were other heat-related illnesses (ICD-9-CM codes 992.1-992.9), such as heat syncope, heat cramps, and heat exhaustion, which are less severe than heat stroke and are likely more susceptible than heat stroke to biases from socioeconomic status, healthcare seeking be- havior, and misclassification. Age in years was categorized into the following groupings: 0–19, 20–29, 30–39, 40–49, 50–59, 60–69, 70–79 and ≥ 80. We also assessed the following variables: sex, urban–rural status, month of visit, diagnosis, and disposition from the ED. Urban–rural sta- tus was based on the Office of Management and Budget (OMB) metropolitan/micropolitan assignment of U.S. counties as updated through the 2005 revisions (Agency for Healthcare Research and Quality 2008a). Geographic region is defined by the US Census Bureau (Agency for Healthcare Research and Quality 2008b). The presence of comorbid conditions was identified using the Clinical Classification Software (CCS) codes that combine similar ICD-9-CM codes (Agency for Healthcare Research and Quality 2014b).
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Validation of ICD 9 CM/ICD 10 coding algorithms for the identification of patients with acetaminophen overdose and hepatotoxicity using administrative data

Validation of ICD 9 CM/ICD 10 coding algorithms for the identification of patients with acetaminophen overdose and hepatotoxicity using administrative data

the entire cohort. Subsequently, data from these pre- sumed acetaminophen overdose cases was used to gener- ate algorithms for hepatotoxicity and ALF. These algorithms were derived using multivariate logistic regres- sion analyses including the most predictive diagnostic codes for these outcomes. Algorithms were compared using areas under receiver operating characteristics curves (c-statistics) and the non-parametric method of DeLong et al [33]. The c-statistic ranges from 0 to 1.0, with 1.0 indi- cating perfect prediction and 0.5 indicating prediction due to chance alone. C-statistics between 0.7 and 0.8 are generally considered acceptable, while those over 0.8 are considered most desirable. We also calculated sensitivi- ties, specificities, and positive (PPV) and negative predic- tive values (NPV) for these algorithms, including exact binomial confidence intervals (CI). Because the coding system changed from ICD-9-CM to ICD-10 in fiscal year 2002, the impact of the study interval (1995–2001 vs. 2002–2004) on the performance of the algorithms was assessed in sensitivity analyses. We also examined patient gender, age (≤ versus > the median), and hospital of admission (to account for different coders) as potential predictors of coding accuracy.
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Validity of ICD-9-CM codes for breast, lung and colorectal cancers in three Italian administrative healthcare databases: a diagnostic accuracy study protocol

Validity of ICD-9-CM codes for breast, lung and colorectal cancers in three Italian administrative healthcare databases: a diagnostic accuracy study protocol

In Italy, all the Regional Health Authorities maintain large healthcare information systems containing patient data from all hospital and territorial sources. These data- bases have the potential to address important issues in postmarketing surveillance, 8 9 epidemiology, 10 quality performance and health services research. 11 However, there is a concern that their considerable potential as a source of reliable healthcare information has not been realised since they have not been widely validated. A sys- tematic review of ICD-9 code validation in Italian admin- istrative databases 12 reported that only a few regional databases have been validated for a limited number of ICD-9 codes of diseases including stroke, 13 14 gastrointes- tinal bleeding, 15 thrombocytopenia, 16 epilepsy, 17 infec- tions, 18 chronic obstructive pulmonary disease, 19 20 Guillain-Barré syndrome 21 and cancers. 22 23 In addition, the use of these databases was scarce, as only six admin- istrative databases served as sources for published research articles based on the validated ICD-9 codes. Hence, it is imperative that Regional Health Authorities systematically validate their databases for critical diseases to productively use the information they contain.
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Transition to international classification of disease version 10, clinical modification: the impact on internal medicine and internal medicine subspecialties

Transition to international classification of disease version 10, clinical modification: the impact on internal medicine and internal medicine subspecialties

had the largest percentage of visits associated with convo- luted (complex) codes and/or no translation. No transla- tion is a troubling category as there is no method to track or report these codes across the transition. Further, hos- pital medicine, endocrinology and rheumatology had the largest percentage of reimbursements associated with con- voluted codes, higher than other medical fields including pediatrics and emergency medicine. [8] The potential fi- nancial disruption will likely not be felt equally across in- ternal medicine subspecialties suggesting more resources should be dedicated to those fields that are at greatest risk for financial disruption by the transition and impact into clinical research. Recent news articles have shown the level of financial disruption to clinics, where 18% of pro- viders have a higher denial rate after the transition. [9] Due to the expected challenges during the transition to ICD-10-CM the Center for Medicare and Medicaid Ser- vices (CMS) announced that claims will not be denied due to lack of coding specificity, and CMS will work to minimize payment disruptions, for the first 12 months. [10] This concession by CMS likely alleviated some anx- iety during the transition; however, now reimbursement will be tied to the level of specificity of the codes. In addition, due to this concession the specificity of the ICD- 10-CM codes from October, 2015 to October, 2016 will be highly variable making future comparisons of code data difficult.
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Rule based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record

Rule based and machine learning algorithms identify patients with systemic sclerosis accurately in the electronic health record

The widespread implementation of electronic health records (EHRs) has enabled the integration of large amounts of patient data across diverse healthcare set- tings and populations [1]. The EHR can function as a practical tool to study rare diseases longitudinally such as SSc. Methods to accurately identify patients with SSc in the EHR have not been fully developed. Two studies aimed to identify SSc patients in the EHR. One specific- ally focused on identifying patients at risk for SSc renal crisis in a veteran’s population [2], and the other only examined the performance of a one-time International Classification of Disease Ninth Version (ICD-9) billing code to identify patients with SSc [3]. While ICD billing codes are often used to define disease cohorts, this method may not accurately identify patients with auto- immune diseases [4, 5]. We sought to use clinically meaningful variables readily available in the EHR that would broadly capture SSc patients across the healthcare system. Specifically, we developed and validated algo- rithms that incorporate ICD-9 and ICD-10-CM codes, laboratory data, and keywords to accurately identify SSc patients in the EHR.
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Trends in Hospitalization Rates and Severity of Injuries From Abuse in Young Children, 1997–2009

Trends in Hospitalization Rates and Severity of Injuries From Abuse in Young Children, 1997–2009

clinically derived algorithms to assign an abbreviated severity score to each of 6 major body regions based on ICD-9- CM diagnosis codes and the age of the individual. Abbreviated injury severity scores (AIS) vary from a low of 1 to high of 6, the latter indicating a non- survivable injury. The 3 highest AISs for a given child are then squared and summed to arrive at an overall ISS for that child. A test for trend using b co- ef fi cient for the year of hospital dis- charge was used to test signi fi cant change in ISS over the study period.

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Interrupted time series design to evaluate the effect of the ICD-9-CM to ICD-10-CM coding transition on injury hospitalization trends

Interrupted time series design to evaluate the effect of the ICD-9-CM to ICD-10-CM coding transition on injury hospitalization trends

encounters of care used in this study are based on consen- sus definitions for hospital injury surveillance and, there- fore, are applicable to other state, facility-specific, or national studies on injury hospitalization trends. However, some results from Kentucky on the transition to ICD-10-CM coding may not be applicable to all states. For example, external-cause-of-injury codes are not mandatory in Kentucky. Therefore, we experienced immediate yet transitory drop in the ECOI completeness during the tran- sition months, which may not be observed in states with mandatory ECOI reporting (e.g., Maryland, Massachusetts). We hope that the SAS code and the sample data set pro- vided in Additional file 1 of this paper will facilitate more studies in different health care settings, populations, and geographical regions, stimulate discussion, and improve our understanding on expected changes versus changes indicat- ing new injury incidence trends.
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Positive predictive value of a case definition for diabetes mellitus using automated administrative health data in children and youth exposed to antipsychotic drugs or control medications: a Tennessee Medicaid study

Positive predictive value of a case definition for diabetes mellitus using automated administrative health data in children and youth exposed to antipsychotic drugs or control medications: a Tennessee Medicaid study

The availability of a valid computer case definition of new-onset type 2 diabetes is crucial for conducting pharmacoepidemiologic studies of type 2 diabetes as a study endpoint using automated databases. Automated databases may be the only efficient means of quantify- ing type 2 diabetes risk associated with specific drug exposures, given how infrequently it occurs. However, there are several challenges to conducting pharma- coepidemiologic studies using automated databases. Among the most serious of these is the potential for bias from endpoint misclassification due to coding errors or other problems [16,17]. Most automated data- bases, including the one used in our study, include medical encounter and healthcare service utilization data that were not collected specifically for research purposes. As such, the quality of the collected data may vary considerably [17]. In one study that used ICD-9 diagnosis codes from one or more outpatient records in the U.S. Indian Health Service Facility Data- base to estimate the prevalence and incidence of dia- betes in Navajo youth, a diagnosis of diabetes was confirmed in less than 50% of cases [23]. The primary reason for misclassification was coding errors.
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Development of an algorithm to identify fall-related injuries and costs in Medicare data

Development of an algorithm to identify fall-related injuries and costs in Medicare data

However, studies have rarely taken all of these factors into account when using claims data to identify fall- related injuries. And while each study estimates the im- pact of falls and fall-related injuries, it is difficult to compare the results across studies since they involve different definition of falls, different information, and different assumptions. Therefore, we developed a com- prehensive algorithm for identifying episodes of care for fall-related injuries, which we defined as a collection of fall-related injury claims that are clustered closely enough within a specified time period to believably stem from the same underlying injury. In this algorithm, we identified fall-related injuries by their healthcare services setting, body sites, and by types of injury. We also used both fee-for-service (FFS) Medicare data from CMS and Medicare Advantage (MA) health plan data, which required integrating datasets from different sources with variation in data format and availability. (For simplicity, we use the term “ claims ” to refer to data from both FFS Medicare and MA health plans, even though not all MA data are generated from re- quests for payment.) In particular, the algorithm involved the use of E codes (when available) as well as general ICD-9-CM diagnosis codes, and identified fall-related injuries as well as episodes of care so that both frequencies and costs of fall-related injuries could be examined in detail.
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IMP-ICDX: an injury mortality prediction based on ICD-10-CM codes

IMP-ICDX: an injury mortality prediction based on ICD-10-CM codes

At present, there are many trauma score methods. For instance, ISS, NISS, and TISS are rapid evaluation methods while TMPM and IMP are retrospective evalu- ation methods, and they are all based on AIS codes. These methods have been widely used in clinical practice. They require that all patients have their injuries described in the AIS lexicon. Otherwise, they cannot be used to calculate, which limits their application. The ICISS and IMPM- ICD9, which are based on ICD-9-CM code, have broken away from the AIS code and opened up a new way of scoring method. TMPM-ICD9 is better than ICISS in pre- dicting death results [7, 9]. The data used in this study was derived from ICD-10-CM instead of ICD-9-CM. The above scoring methods are not suitable. Though ICD-10- CM encoding can be converted to ICD-9-CD code and AIS code can be generated, the result after conversion is bound to be biased. It is not in line with the original
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International Standards for Usability Should Be More Widely Used

International Standards for Usability Should Be More Widely Used

While the guidelines constitute an immense body of knowledge, they are not very easy for designers to use (Carter, 1999; de Souza & Bevan, 1990). In the case of Web design (ISO 9241-151), the U.S. Department of Health and Human Services (HHS, 2006) has developed a free set of guidelines that are superior in presentation and content to the ISO equivalent (Bevan & Spinhof, 2007), which makes the HHS guidelines much more approachable for designers. Unfortunately ISO does not have the resources to develop such a professionally produced document.

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Predictive values of diagnostic codes for identifying serious hypocalcemia and dermatologic adverse events among women with postmenopausal osteoporosis in a commercial health plan database

Predictive values of diagnostic codes for identifying serious hypocalcemia and dermatologic adverse events among women with postmenopausal osteoporosis in a commercial health plan database

In this study, we had expected that limiting our algorithm to the first-position diagnoses on claims would increase the PPV for capturing serious occurrences of adverse events that were the primary reason for seeking care. However, we found that clinically incidental or secondary events are also captured through diagnosis codes recorded in the primary position. Further, it is important to note that outcomes leading to hospitalization or ED visits may have had ICD-9 codes recorded in a secondary position on claims. These cases were not counted in this study, and thus, incidence derived with these code sets will be underestimated.
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