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 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 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 National rates of non-fatal emergency department visits and hospitalisations due to fall-related injuries in older adults 2010–2014 and 2016: transitioning from ICD-9-CM to ICD-10-CM

2021 ◽  
Vol 27 (S1) ◽  
pp. i75-i78
Author(s):  
Briana L Moreland ◽  
Elizabeth R Burns ◽  
Yara K Haddad
Keyword(s):  
Emergency Department ◽  
International Classification Of Diseases ◽  
Emergency Department Visits ◽  
Injury Rates ◽  
Fall Injury ◽  
The Us ◽  
Before And After ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Ed Visits

BackgroundThis study describes rates of non-fatal fall-injury emergency department (ED) visits and hospitalisations before and after the US 2015 transition from the 9th to 10th revision of the International Classification of Diseases, Clinical Modification (ICD-9-CM to ICD-10-CM).MethodsED visit and hospitalisation data for adults aged 65+ years were obtained from the 2010–2016 Healthcare Cost and Utilisation Project. Differences in fall injury rates between 2010 and 2014 (before transition), and 2014 and 2016 (before and after transition) were analysed using t-tests.ResultsFor ED visits, rates did not differ significantly between 2014 and 2016 (4288 vs 4318 per 100 000, respectively). Hospitalisation rates were lower in 2014 (1232 per 100 000) compared with 2016 (1281 per 100 000).ConclusionIncreased rates of fall-related hospitalisations could be an artefact of the transition or may reflect an increase in the rate of fall-related hospitalisations. Analyses of fall-related hospitalisations across the transition should be interpreted cautiously.

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 An emergency department medical record review for adolescent intentional self-harm injuries

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Anna Hansen ◽  
Dessi Slavova ◽  
Gena Cooper ◽  
Jaryd Zummer ◽  
Julia Costich
Keyword(s):  
Emergency Department ◽  
Suicide Attempt ◽  
Suicide Ideation ◽  
Suicide Attempts ◽  
International Classification Of Diseases ◽  
Case Definition ◽  
Coding System ◽  
Icd 10 ◽  
Self Harm ◽  
Coding Guidelines

Abstract Background Non-suicidal self-injury and suicide attempts are increasing problems among American adolescents. This study developed a definition for identifying intentional self-harm (ISH) injuries in emergency department (ED) records coded with International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes. The definition is based on the injury-reporting framework proposed by the Centers for Disease Control and Prevention. The study sought to estimate the definition’s positive predictive value (PPV), and the proportion of ISH injuries with intent to die (i.e., suicide attempt). Methods The study definition, based on first-valid external cause-of-injury ICD-10-CM codes X71-X83, T14.91, T36-T65, or T71, captured 207 discharge records for initial encounters for ISH in one Kentucky ED. Medical records were reviewed to confirm provider-documented diagnosis for ISH, and identify intent to die or suicide ideation. The PPV of the study definition for capturing provider-documented ISH injuries was reported with its 95% confidence interval (95% CI). Results The estimated PPV for the study definition to capture ISH injuries was 88.9%, 95% CI (83.8%, 92.8%). The estimated percentage of ISH with intent to die was 45.9, 95% CI (47.1, 61.0%). The ICD-10-CM code “suicide attempt” (T14.91) captured only 7 cases, but coding guidelines restrict assignment of this code to cases in which the mechanism of the suicide attempt is unknown. Conclusions The proposed case definition supported a robust PPV for ISH injuries. Our findings add to the evidence that the current ICD-10-CM coding system and coding guidelines do not allow identification of ISH with intent to die; modifications are needed to address this issue.

scholarly journals Development of the Canadian Emergency Department Diagnosis Shortlist

CJEM ◽  
2010 ◽  
Vol 12 (04) ◽  
pp. 311-319 ◽  
Author(s):  
Bernard Unger ◽  
Marc Afilalo ◽  
Jean François Boivin ◽  
Michael Bullard ◽  
Eric Grafstein ◽  
...  
Keyword(s):  
Emergency Department ◽  
International Classification Of Diseases ◽  
University Teaching ◽  
Data Sets ◽  
Modified Delphi ◽  
Classification Of Diseases ◽  
Chief Complaints ◽  
Icd 10 ◽  
The Creation ◽  
Standardized Parameters

ABSTRACTObjective:Managers of emergency departments (EDs), governments and researchers would benefit from reliable data sets that characterize use of EDs. Although Canadian ED lists for chief complaints and triage acuity exist, no such list exists for diagnosis classification. This study was aimed at developing a standardized Canadian Emergency Department Diagnosis Shortlist (CED-DxS), as a subset of the full International Classification of Diseases, 10th revision, with Canadian Enhancement (ICD-10-CA).Methods:Emergency physicians from across Canada participated in the revision of the ICD-10-CA through 2 rounds of the modified Delphi method. We randomly assigned chapters from the ICD-10-CA (approximately 3000 diagnoses) to reviewers, who rated the importance of including each diagnosis in the ED-specific diagnosis list. If 80% or more of the reviewers agreed on the importance of a diagnosis, it was retained for the final revision. The retained diagnoses were further aggregated and adjusted, thus creating the CED-DxS.Results:Of the 83 reviewers, 76% were emergency medicine (EM)–trained physicians with an average of 12 years of experience in EM, and 92% were affiliated with a university teaching hospital. The modified Delphi process and further adjustments resulted in the creation of the CED-DxS, containing 837 items. The chapter with the largest number of retained diagnoses was injury and poisoning (n= 292), followed by gastrointestinal (n= 59), musculoskeletal (n= 55) and infectious disease (n= 42). Chapters with the lowest number retained were neoplasm (n= 18) and pregnancy (n= 12).Conclusion:We report the creation of the uniform CED-DxS, tailored for Canadian EDs. The addition of ED diagnoses to existing standardized parameters for the ED will contribute to homogeneity of data across the country.

scholarly journals Rurality, Death, and Healthcare Utilization in Heart Failure in the Community

2021 ◽  
Vol 10 (4) ◽  
Author(s):  
Sheila M. Manemann ◽  
Jennifer St. Sauver ◽  
Carrie Henning‐Smith ◽  
Lila J. Finney Rutten ◽  
Alanna M. Chamberlain ◽  
...  
Keyword(s):  
Heart Failure ◽  
Emergency Department ◽  
Rural Area ◽  
Healthcare Utilization ◽  
International Classification Of Diseases ◽  
Emergency Department Visits ◽  
Risk Of Death ◽  
Increased Risk ◽  
Classification Of Diseases

Background Prior reports indicate that living in a rural area may be associated with worse health outcomes. However, data on rurality and heart failure (HF) outcomes are scarce. Methods and Results Residents from 6 southeastern Minnesota counties with a first‐ever code for HF ( International Classification of Diseases, Ninth Revision [ ICD‐9 ], code 428, and International Classification of Diseases, Tenth Revision [ ICD‐10 ] code I50) between January 1, 2013 and December 31, 2016, were identified. Resident address was classified according to the rural‐urban commuting area codes. Rurality was defined as living in a nonmetropolitan area. Cox regression was used to analyze the association between living in a rural versus urban area and death; Andersen‐Gill models were used for hospitalization and emergency department visits. Among 6003 patients with HF (mean age 74 years, 48% women), 43% lived in a rural area. Rural patients were older and had a lower educational attainment and less comorbidity compared with patients living in urban areas ( P <0.001). After a mean (SD) follow‐up of 2.8 (1.7) years, 2440 deaths, 20 506 emergency department visits, and 11 311 hospitalizations occurred. After adjustment, rurality was independently associated with an increased risk of death (hazard ratio [HR], 1.18; 95% CI, 1.09–1.29) and a reduced risk of emergency department visits (HR, 0.89; 95% CI, 0.82–0.97) and hospitalizations (HR, 0.78; 95% CI, 0.73–0.84). Conclusions Among patients with HF, living in a rural area is associated with an increased risk of death and fewer emergency department visits and hospitalizations. Further study to identify and address the mechanisms through which rural residence influences mortality and healthcare utilization in HF is needed in order to reduce disparities in rural health.

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