scholarly journals Descriptive exploration of overdose codes in hospital and emergency department discharge data to inform development of drug overdose morbidity surveillance indicator definitions in ICD-10-CM

2021 ◽  
Vol 27 (S1) ◽  
pp. i27-i34
Author(s):  
Leigh M Tyndall Snow ◽  
Katelyn E Hall ◽  
Cody Custis ◽  
Allison L Rosenthal ◽  
Emilia Pasalic ◽  
...  
Keyword(s):  
Emergency Department ◽  
Adverse Effects ◽  
Drug Overdose ◽  
International Classification Of Diseases ◽  
Primary Diagnosis ◽  
Epidemiological Surveillance ◽  
Discharge Data ◽  
Aggregated Data ◽  
Data Coding ◽  
Icd 10

BackgroundIn October 2015, discharge data coding in the USA shifted to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), necessitating new indicator definitions for drug overdose morbidity. Amid the drug overdose crisis, characterising discharge records that have ICD-10-CM drug overdose codes can inform the development of standardised drug overdose morbidity indicator definitions for epidemiological surveillance.MethodsEight states submitted aggregated data involving hospital and emergency department (ED) discharge records with ICD-10-CM codes starting with T36–T50, for visits occurring from October 2015 to December 2016. Frequencies were calculated for (1) the position within the diagnosis billing fields where the drug overdose code occurred; (2) primary diagnosis code grouped by ICD-10-CM chapter; (3) encounter types; and (4) intents, underdosing and adverse effects.ResultsAmong all records with a drug overdose code, the primary diagnosis field captured 70.6% of hospitalisations (median=69.5%, range=66.2%–76.8%) and 79.9% of ED visits (median=80.7%; range=69.8%–88.0%) on average across participating states. The most frequent primary diagnosis chapters included injury and mental disorder chapters. Among visits with codes for drug overdose initial encounters, subsequent encounters and sequelae, on average 94.6% of hospitalisation records (median=98.3%; range=68.8%–98.8%) and 95.5% of ED records (median=99.5%; range=79.2%–99.8%), represented initial encounters. Among records with drug overdose of any intent, adverse effect and underdosing codes, adverse effects comprised an average of 74.9% of hospitalisation records (median=76.3%; range=57.6%–81.1%) and 50.8% of ED records (median=48.9%; range=42.3%–66.8%), while unintentional intent comprised an average of 11.1% of hospitalisation records (median=11.0%; range=8.3%–14.5%) and 28.2% of ED records (median=25.6%; range=20.8%–40.7%).ConclusionResults highlight considerations for adapting and standardising drug overdose indicator definitions in ICD-10-CM.

scholarly journals Defining indicators for drug overdose emergency department visits and hospitalisations in ICD-10-CM coded discharge data

2021 ◽  
Vol 27 (Suppl 1) ◽  
pp. i56-i61 ◽  
Author(s):  
Alana Vivolo-Kantor ◽  
Emilia Pasalic ◽  
Stephen Liu ◽  
Pedro D Martinez ◽  
Robert Matthew Gladden
Keyword(s):  
Emergency Department ◽  
Drug Overdose ◽  
International Classification Of Diseases ◽  
Case Definition ◽  
Emergency Department Visits ◽  
Health Departments ◽  
Discharge Data ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Key Aspects

IntroductionThe drug overdose epidemic has worsened over the past decade; however, efforts have been made to better understand and track nonfatal overdoses using various data sources including emergency department and hospital admission data from billing and discharge files.Methods and findingsThe Centers for Disease Control and Prevention (CDC) has developed surveillance case definition guidance using standardised discharge diagnosis codes for public health practitioners and epidemiologists using lessons learnt from CDC’s funded recipients and the Council for State and Territorial Epidemiologists (CSTE) International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) Drug Poisoning Indicators Workgroup and General Injury ICD-10-CM Workgroup. CDC’s guidance was informed by health departments and CSTE’s workgroups and included several key aspects for assessing drug overdose in emergency department and hospitalisation discharge data. These include: (1) searching all diagnosis fields to identify drug overdose cases; (2) estimating drug overdose incidence using visits for initial encounter but excluding subsequent encounters and sequelae; (3) excluding underdosing and adverse effects from drug overdose incidence indicators; and (4) using codes T36–T50 for overdose surveillance. CDC’s guidance also suggests analysing intent separately for ICD-10-CM coding.ConclusionsCDC’s guidance provides health departments a key tool to better monitor drug overdoses in their community. The implementation and validation of this standardised guidance across all CDC-funded health departments will be key to ensuring consistent and accurate reporting across all entities.

scholarly journals Identification of work-related injury emergency department visits using International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes

2021 ◽  
Vol 27 (Suppl 1) ◽  
pp. i3-i8
Author(s):  
Ashley M Bush ◽  
Terry L Bunn ◽  
Madison Liford
Keyword(s):  
Emergency Department ◽  
International Classification Of Diseases ◽  
Injury Surveillance ◽  
Discharge Data ◽  
Work Related ◽  
Work Related Injury ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Ed Visits

IntroductionEmergency department (ED) visit discharge data are a less explored population-based data source used to identify work-related injuries. When using discharge data, work-relatedness is often determined by the expected payer of workers’ compensation (WC). In October 2015, healthcare discharge data coding systems transitioned to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). ICD-10-CM’s structure offers potential new work-related codes to enhance work-related injury surveillance. This study identified work-related ED visits using relevant ICD-10-CM work-related injury codes. Cases identified using this method were compared with those identified using the WC expected payer approach.MethodsState ED visit discharge data (2016–2019) were analysed using the CDC’s discharge data surveillance definition. Injuries were identified using a diagnosis code or an external cause-of-injury code in any field. Injuries were assessed by mechanism and expected payer. Literature searches and manual review of ICD-10-CM codes were conducted to identify possible work-related injury codes. Descriptive statistics were performed and assessed by expected payer.ResultsWC was billed for 87 361 injury ED visits from 2016 to 2019. Falls were the most frequent injury mechanism. The 246 ICD-10-CM work-related codes identified 36% more work-related ED injury visits than using WC as the expected payer alone.ConclusionThis study identified potential ICD-10-CM codes to expand occupational injury surveillance using discharge data beyond the traditional WC expected payer approach. Further studies are needed to validate the work-related injury codes and support the development of a work-related injury surveillance case definition.

scholarly journals Interrupted time series analysis to evaluate the performance of drug overdose morbidity indicators shows discontinuities across the ICD-9-CM to ICD-10-CM transition

2021 ◽  
Vol 27 (S1) ◽  
pp. i35-i41
Author(s):  
Hannah Yang ◽  
Emilia Pasalic ◽  
Peter Rock ◽  
James W Davis ◽  
Sarah Nechuta ◽  
...  
Keyword(s):  
Hospital Discharge ◽  
Drug Overdose ◽  
Interrupted Time Series ◽  
International Classification Of Diseases ◽  
Hospital Discharge Data ◽  
Opioid Overdose ◽  
Discharge Data ◽  
Classification Of Diseases ◽  
Icd 10

IntroductionOn 1 October 2015, the USA transitioned from the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) to the International Classification of Diseases, 10th Revision (ICD-10-CM). Considering the major changes to drug overdose coding, we examined how using different approaches to define all-drug overdose and opioid overdose morbidity indicators in ICD-9-CM impacts longitudinal analyses that span the transition, using emergency department (ED) and hospitalisation data from six states’ hospital discharge data systems.MethodsWe calculated monthly all-drug and opioid overdose ED visit rates and hospitalisation rates (per 100 000 population) by state, starting in January 2010. We applied three ICD-9-CM indicator definitions that included identical all-drug or opioid-related codes but restricted the number of fields searched to varying degrees. Under ICD-10-CM, all fields were searched for relevant codes. Adjusting for seasonality and autocorrelation, we used interrupted time series models with level and slope change parameters in October 2015 to compare trend continuity when employing different ICD-9-CM definitions.ResultsMost states observed consistent or increased capture of all-drug and opioid overdose cases in ICD-10-CM coded hospital discharge data compared with ICD-9-CM. More inclusive ICD-9-CM indicator definitions reduced the magnitude of significant level changes, but the effect of the transition was not eliminated.DiscussionThe coding change appears to have introduced systematic differences in measurement of drug overdoses before and after 1 October 2015. When using hospital discharge data for drug overdose surveillance, researchers and decision makers should be aware that trends spanning the transition may not reflect actual changes in drug overdose rates.

scholarly journals Multisite medical record review of emergency department visits for traumatic brain injury

2021 ◽  
Vol 27 (S1) ◽  
pp. i42-i48
Author(s):  
Barbara A Gabella ◽  
Jeanne E Hathaway ◽  
Beth Hume ◽  
Jewell Johnson ◽  
Julia F Costich ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Emergency Department ◽  
Brain Injury ◽  
Skull Fracture ◽  
Signs And Symptoms ◽  
International Classification Of Diseases ◽  
Emergency Department Visits ◽  
Intracranial Injury ◽  
Imaging Results ◽  
Icd 10

BackgroundIn 2016, the CDC in the USA proposed codes from the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for identifying traumatic brain injury (TBI). This study estimated positive predictive value (PPV) of TBI for some of these codes.MethodsFour study sites used emergency department or trauma records from 2015 to 2018 to identify two random samples within each site selected by ICD-10-CM TBI codes for (1) intracranial injury (S06) or (2) skull fracture only (S02.0, S02.1-, S02.8-, S02.91) with no other TBI codes. Using common protocols, reviewers abstracted TBI signs and symptoms and head imaging results that were then used to assign certainty of TBI (none, low, medium, high) to each sampled record. PPVs were estimated as a percentage of records with medium-certainty or high-certainty for TBI and reported with 95% confidence interval (CI).ResultsPPVs for intracranial injury codes ranged from 82% to 92% across the four samples. PPVs for skull fracture codes were 57% and 61% in the two university/trauma hospitals in each of two states with clinical reviewers, and 82% and 85% in the two states with professional coders reviewing statewide or nearly statewide samples. Margins of error for the 95% CI for all PPVs were under 5%.DiscussionICD-10-CM codes for traumatic intracranial injury demonstrated high PPVs for capturing true TBI in different healthcare settings. The algorithm for TBI certainty may need refinement, because it yielded moderate-to-high PPVs for records with skull fracture codes that lacked intracranial injury codes.

scholarly journals Validation of ICD-10-CM codes for injuries complicating pregnancy, childbirth and the puerperium: a medical record review

2021 ◽  
Vol 27 (Suppl 1) ◽  
pp. i9-i12
Author(s):  
Anna Hansen ◽  
Dana Quesinberry ◽  
Peter Akpunonu ◽  
Julia Martin ◽  
Svetla Slavova
Keyword(s):  
Emergency Department ◽  
Medical Record ◽  
International Classification Of Diseases ◽  
Medical Record Review ◽  
Full Sample ◽  
Emergency Department Data ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Record Review

IntroductionThe purpose of this study was to estimate the positive predictive value (PPV) of International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes for injury, poisoning, physical or sexual assault complicating pregnancy, childbirth and the puerperium (PCP) to capture injury encounters within both hospital and emergency department claims data.MethodsA medical record review was conducted on a sample (n=157) of inpatient and emergency department claims from one Kentucky healthcare system from 2015 to 2017, with any diagnosis in the ICD-10-CM range O9A.2-O9A.4. Study clinicians reviewed medical records for the sampled cases and used an abstraction form to collect information on documented presence of injury and PCP complications. The study estimated the PPVs and the 95% CIs of O9A.2-O9A.4 codes for (1) capturing injuries and (2) capturing injuries complicating PCP.ResultsThe estimated PPV for the codes O9A.2-O9A.4 to identify injury in the full sample was 79.6% (95% CI 73.3% to 85.9%) and the PPV for capturing injuries complicating PCP was 72.0% (95% CI 65.0% to 79.0%). The estimated PPV for an inpatient principal diagnosis O9A.2-O9A.4 to capture injuries was 90.7% (95% CI 82.0% to 99.4%) and the PPV for capturing injuries complicating PCP was 88.4% (95% CI 78.4% to 98.4%). The estimated PPV for any mention of O9A.2-O9A.4 in emergency department data to capture injuries was 95.2% (95% CI 90.6% to 99.9%) and the PPV for capturing injuries complicating PCP was 81.0% (95% CI 72.4% to 89.5%).DiscussionThe O9A.2-O9A.4 codes captured high percentage true injury cases among pregnant and puerperal women.

scholarly journals Syndromic Surveillance of Emergency Department Visits for Acute Adverse Effects of Marijuana, Tri-County Health Department, Colorado, 2016-2017

2019 ◽  
Vol 134 (2) ◽  
pp. 132-140 ◽  
Author(s):  
Grace E. Marx ◽  
Yushiuan Chen ◽  
Michele Askenazi ◽  
Bernadette A. Albanese
Keyword(s):  
Emergency Department ◽  
Adverse Effects ◽  
Medical Records ◽  
Surveillance System ◽  
Syndromic Surveillance ◽  
Marijuana Use ◽  
Emergency Department Visits ◽  
Diagnostic Codes ◽  
Icd 10 ◽  
Ed Visits

Objectives: In Colorado, legalization of recreational marijuana in 2014 increased public access to marijuana and might also have led to an increase in emergency department (ED) visits. We examined the validity of using syndromic surveillance data to detect marijuana-associated ED visits by comparing the performance of surveillance queries with physician-reviewed medical records. Methods: We developed queries of combinations of marijuana-specific International Classification of Diseases, Tenth Revision (ICD-10) diagnostic codes or keywords. We applied these queries to ED visit data submitted through the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) syndromic surveillance system at 3 hospitals during 2016-2017. One physician reviewed the medical records of ED visits identified by ≥1 query and calculated the positive predictive value (PPV) of each query. We defined cases of acute adverse effects of marijuana (AAEM) as determined by the ED provider’s clinical impression during the visit. Results: Of 44 942 total ED visits, ESSENCE queries detected 453 (1%) as potential AAEM cases; a review of 422 (93%) medical records identified 188 (45%) true AAEM cases. Queries using ICD-10 diagnostic codes or keywords in the triage note identified all true AAEM cases; PPV varied by hospital from 36% to 64%. Of the 188 true AAEM cases, 109 (58%) were among men and 178 (95%) reported intentional use of marijuana. Compared with noncases of AAEM, cases were significantly more likely to be among non-Colorado residents than among Colorado residents and were significantly more likely to report edible marijuana use rather than smoked marijuana use ( P < .001). Conclusions: ICD-10 diagnostic codes and triage note keyword queries in ESSENCE, validated by medical record review, can be used to track ED visits for AAEM.

scholarly journals Incidence and factors associated with emergency department visits for recurrent skin and soft tissue infections in patients in California, 2005–2011

2016 ◽  
Vol 145 (4) ◽  
pp. 746-754 ◽  
Author(s):  
L. MAY ◽  
E. Y. KLEIN ◽  
E. M. MARTINEZ ◽  
N. MOJICA ◽  
L. G. MILLER
Keyword(s):  
Emergency Department ◽  
Soft Tissue ◽  
Geographical Location ◽  
Risk Groups ◽  
Primary Diagnosis ◽  
Emergency Department Visits ◽  
Soft Tissue Infections ◽  
Discharge Data ◽  
Factors Associated ◽  
Ed Visits

SUMMARYMore than 2 million visits for skin and soft tissue infections (SSTIs) are seen in US emergency departments (EDs) yearly. Up to 50% of patients with SSTIs, suffer from recurrences, but associated factors remain poorly understood. We performed a retrospective study of patients with primary diagnosis of SSTI between 2005 and 2011 using California ED discharge data from the State Emergency Department Databases and State Inpatient Databases. Using a multivariable logistic regression, we examined factors associated with a repeat SSTI ED visits up to 6 months after the initial SSTI. Among 197 371 SSTIs, 16·3% were associated with a recurrent ED visit. We found no trend in recurrent visits over time (χ2 trend = 0·68, P = 0·4). Race/ethnicity, age, geographical location, household income, and comorbidities were all associated with recurrent visits. Recurrent ED visits were associated with drug/alcohol abuse or liver disease [odds ratio (OR) 1·4, 95% confidence interval (CI) 1·3–1·4], obesity (OR 1·3, 95% CI 1·2–1·4), and in infections that were drained (OR 1·1, 95% CI 1·1–1·1) and inversely associated with hospitalization after initial ED visit (OR 0·4, 95% CI 0·3–0·4). In conclusion, we found several patient-level factors associated with recurrent ED visits. Identification of these high-risk groups is critical for future ED-based interventions.

scholarly journals Bowel perforation after single-dose activated charcoal

CJEM ◽  
2006 ◽  
Vol 8 (05) ◽  
pp. 358-360 ◽  
Author(s):  
Jason P. Green ◽  
William McCauley
Keyword(s):  
Emergency Department ◽  
Single Dose ◽  
Case Report ◽  
Adverse Effects ◽  
Drug Overdose ◽  
Activated Charcoal ◽  
Literature Search ◽  
Bowel Perforation ◽  
Gastrointestinal Decontamination

ABSTRACT Patients presenting to the emergency department (ED) after medication overdose are often given activated charcoal initially for gastrointestinal decontamination. Complications of charcoal are rare, but do occur. The following case describes a patient with pre-existing undiagnosed diverticular disease who developed sigmoid perforation after a single dose of activated charcoal, given without cathartic for a drug overdose. A literature search revealed no other cases of bowel perforation associated with single-dose activated charcoal. This case report discusses adverse effects associated with activated charcoal and the role of cathartics in gastrointestinal decontamination.

scholarly journals Improving discharge data fidelity for use in large administrative databases

2014 ◽  
Vol 36 (6) ◽  
pp. E2 ◽  
Author(s):  
Yakov Gologorsky ◽  
John J. Knightly ◽  
Yi Lu ◽  
John H. Chi ◽  
Michael W. Groff
Keyword(s):  
Hospital Admissions ◽  
Care Delivery ◽  
Lumbar Fusion ◽  
International Classification Of Diseases ◽  
Population Based ◽  
Primary Diagnosis ◽  
Administrative Databases ◽  
Disc Disease ◽  
Discharge Data ◽  
Data Fidelity

Object Large administrative databases have assumed a major role in population-based studies examining health care delivery. Lumbar fusion surgeries specifically have been scrutinized for rising rates coupled with ill-defined indications for fusion such as stenosis and spondylosis. Administrative databases classify cases with the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). The ICD-9-CM discharge codes are not designated by surgeons, but rather are assigned by trained hospital medical coders. It is unclear how accurately they capture the surgeon's indication for fusion. The authors first sought to compare the ICD-9-CM code(s) assigned by the medical coder according to the surgeon's indication based on a review of the medical chart, and then to elucidate barriers to data fidelity. Methods A retrospective review was undertaken of all lumbar fusions performed in the Department of Neurosurgery at the authors' institution between August 1, 2011, and August 31, 2013. Based on this review, the indication for fusion in each case was categorized as follows: spondylolisthesis, deformity, tumor, infection, nonpathological fracture, pseudarthrosis, adjacent-level degeneration, stenosis, degenerative disc disease, or disc herniation. These surgeon diagnoses were compared with the primary ICD-9-CM codes that were generated by the medical coders and submitted to administrative databases. A follow-up interview with the hospital's coders and coding manager was undertaken to review causes of error and suggestions for future improvement in data fidelity. Results There were 178 lumbar fusion operations performed in the course of 170 hospital admissions. There were 44 hospitalizations in which fusion was performed for tumor, infection, or nonpathological fracture. Of these, the primary diagnosis matched the surgical indication for fusion in 98% of cases. The remaining 126 hospitalizations were for degenerative diseases, and of these, the primary ICD-9-CM diagnosis matched the surgeon's diagnosis in only 61 (48%) of 126 cases of degenerative disease. When both the primary and all secondary ICD-9-CM diagnoses were considered, the indication for fusion was identified in 100 (79%) of 126 cases. Still, in 21% of hospitalizations, the coder did not identify the surgical diagnosis, which was in fact present in the chart. There are many different causes of coding inaccuracy and data corruption. They include factors related to the quality of documentation by the physicians, coder training and experience, and ICD code ambiguity. Conclusions Researchers, policymakers, payers, and physicians should note these limitations when reviewing studies in which hospital claims data are used. Advanced domain-specific coder training, increased attention to detail and utilization of ICD-9-CM diagnoses by the surgeon, and improved direction from the surgeon to the coder may augment data fidelity and minimize coding errors. By understanding sources of error, users of these large databases can evaluate their limitations and make more useful decisions based on them.

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