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

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.

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

Injury Prevention ◽

10.1136/injuryprev-2019-043521 ◽

2021 ◽

Vol 27 (Suppl 1) ◽

pp. i56-i61 ◽

Cited By ~ 2

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.

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

Injury Prevention ◽

10.1136/injuryprev-2019-043507 ◽

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.

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ICD-10-CM–Based Definitions for Emergency Department Opioid Poisoning Surveillance: Electronic Health Record Case Confirmation Study

Public Health Reports ◽

10.1177/0033354920904087 ◽

2020 ◽

Vol 135 (2) ◽

pp. 262-269

Author(s):

Svetla Slavova ◽

Dana Quesinberry ◽

Julia F. Costich ◽

Emilia Pasalic ◽

Pedro Martinez ◽

...

Keyword(s):

Emergency Department ◽

Drug Testing ◽

International Classification Of Diseases ◽

Case Definitions ◽

Health Care Networks ◽

Classification Of Diseases ◽

Icd 10 ◽

Ed Visits ◽

Representative Samples

Objectives: Valid opioid poisoning morbidity definitions are essential to the accuracy of national surveillance. The goal of our study was to estimate the positive predictive value (PPV) of case definitions identifying emergency department (ED) visits for heroin or other opioid poisonings, using billing records with International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes. Methods: We examined billing records for ED visits from 4 health care networks (12 EDs) from October 2015 through December 2016. We conducted medical record reviews of representative samples to estimate the PPVs and 95% confidence intervals (CIs) of (1) first-listed heroin poisoning diagnoses (n = 398), (2) secondary heroin poisoning diagnoses (n = 102), (3) first-listed other opioid poisoning diagnoses (n = 452), and (4) secondary other opioid poisoning diagnoses (n = 103). Results: First-listed heroin poisoning diagnoses had an estimated PPV of 93.2% (95% CI, 90.0%-96.3%), higher than secondary heroin poisoning diagnoses (76.5%; 95% CI, 68.1%-84.8%). Among other opioid poisoning diagnoses, the estimated PPV was 79.4% (95% CI, 75.7%-83.1%) for first-listed diagnoses and 67.0% (95% CI, 57.8%-76.2%) for secondary diagnoses. Naloxone was administered in 867 of 1055 (82.2%) cases; 254 patients received multiple doses. One-third of all patients had a previous drug poisoning. Drug testing was ordered in only 354 cases. Conclusions: The study findings suggest that heroin or other opioid poisoning surveillance definitions that include multiple diagnoses (first-listed and secondary) would identify a high percentage of true-positive cases.

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

Injury Prevention ◽

10.1136/injuryprev-2019-043510 ◽

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.

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Interrupted time series design to evaluate ICD-9-CM to ICD-10-CM coding changes on trends in Colorado emergency department visits related to traumatic brain injury

Injury Epidemiology ◽

10.1186/s40621-021-00308-y ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Lauren Alexis De Crescenzo ◽

Barbara Alison Gabella ◽

Jewell Johnson

Keyword(s):

Public Health ◽

Traumatic Brain Injury ◽

Emergency Department ◽

Brain Injury ◽

Interrupted Time Series ◽

Emergency Department Visits ◽

Control And Prevention ◽

Icd 10 ◽

Ed Visits ◽

The Impact

Abstract Background The transition in 2015 to the Tenth Revision of the International Classification of Disease, Clinical Modification (ICD-10-CM) in the US led the Centers for Disease Control and Prevention (CDC) to propose a surveillance definition of traumatic brain injury (TBI) utilizing ICD-10-CM codes. The CDC’s proposed surveillance definition excludes “unspecified injury of the head,” previously included in the ICD-9-CM TBI surveillance definition. The study purpose was to evaluate the impact of the TBI surveillance definition change on monthly rates of TBI-related emergency department (ED) visits in Colorado from 2012 to 2017. Results The monthly rate of TBI-related ED visits was 55.6 visits per 100,000 persons in January 2012. This rate in the transition month to ICD-10-CM (October 2015) decreased by 41 visits per 100,000 persons (p-value < 0.0001), compared to September 2015, and remained low through December 2017, due to the exclusion of “unspecified injury of head” (ICD-10-CM code S09.90) in the proposed TBI definition. The average increase in the rate was 0.33 visits per month (p < 0.01) prior to October 2015, and 0.04 visits after. When S09.90 was included in the model, the monthly TBI rate in Colorado remained smooth from ICD-9-CM to ICD-10-CM and the transition was no longer significant (p = 0.97). Conclusion The reduction in the monthly TBI-related ED visit rate resulted from the CDC TBI surveillance definition excluding unspecified head injury, not necessarily the coding transition itself. Public health practitioners should be aware that the definition change could lead to a drastic reduction in the magnitude and trend of TBI-related ED visits, which could affect decisions regarding the allocation of TBI resources. This study highlights a challenge in creating a standardized set of TBI ICD-10-CM codes for public health surveillance that provides comparable yet clinically relevant estimates that span the ICD transition.

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

Injury Prevention ◽

10.1136/injuryprev-2019-043512 ◽

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.

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A Mixed-Methods Comparison of a National and State Opioid Overdose Surveillance Definition

Public Health Reports ◽

10.1177/00333549211018181 ◽

2021 ◽

Vol 136 (1_suppl) ◽

pp. 31S-39S

Author(s):

Danielle M. Brathwaite ◽

Catherine S. Wolff ◽

Amy I. Ising ◽

Scott K. Proescholdbell ◽

Anna E. Waller

Keyword(s):

North Carolina ◽

False Positive ◽

False Positive Rate ◽

International Classification Of Diseases ◽

Opioid Overdose ◽

The North ◽

Classification Of Diseases ◽

Positive Rate ◽

Icd 10 ◽

Ed Visits

Objectives We assessed the differences between the first version of the Centers for Disease Control and Prevention (CDC) opioid surveillance definition for suspected nonfatal opioid overdoses (hereinafter, CDC definition) and the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) surveillance definition to determine whether the North Carolina definition should include additional International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes and/or chief complaint keywords. Methods Two independent reviewers retrospectively reviewed data on North Carolina emergency department (ED) visits generated by components of the CDC definition not included in the NC DETECT definition from January 1 through July 31, 2018. Clinical reviewers identified false positives as any ED visit in which available evidence supported an alternative explanation for patient presentation deemed more likely than an opioid overdose. After individual assessment, reviewers reconciled disagreements. Results We identified 2296 ED visits under the CDC definition that were not identified under the NC DETECT definition during the study period. False-positive rates ranged from 2.6% to 41.4% for codes and keywords uniquely identifying ≥10 ED visits. Based on uniquely identifying ≥10 ED visits and a false-positive rate ≤10.0%, 4 of 16 ICD-10-CM codes evaluated were identified for NC DETECT definition inclusion. Only 2 of 25 keywords evaluated, “OD” and “overdose,” met inclusion criteria to be considered a meaningful addition to the NC DETECT definition. Practice Implications Quantitative and qualitative trends in coding and keyword use identified in this analysis may prove helpful for future evaluations of surveillance definitions.

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

Public Health Reports ◽

10.1177/0033354919826562 ◽

2019 ◽

Vol 134 (2) ◽

pp. 132-140 ◽

Cited By ~ 1

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.

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Leveraging Shannon Entropy to Validate the Transition between ICD-10 and ICD-11

Entropy ◽

10.3390/e20100769 ◽

2018 ◽

Vol 20 (10) ◽

pp. 769 ◽

Cited By ~ 1

Author(s):

Donghua Chen ◽

Runtong Zhang ◽

Xiaomin Zhu

Keyword(s):

Shannon Entropy ◽

Information Gain ◽

International Classification Of Diseases ◽

Validation Methods ◽

Coding Systems ◽

Precise Mapping ◽

Before And After ◽

Classification Of Diseases ◽

Icd 10

This study aimed to propose a mapping framework with entropy-based metrics for validating the effectiveness of the transition between International Classification of Diseases 10th revision (ICD-10)-coded datasets and a new context of ICD-11. Firstly, we used tabular lists and mapping tables of ICD-11 to establish the framework. Then, we leveraged Shannon entropy to propose validation methods to evaluate information changes during the transition from the perspectives of single-code, single-disease, and multiple-disease datasets. Novel metrics, namely, standardizing rate (SR), uncertainty rate (UR), and information gain (IG), were proposed for the validation. Finally, validation results from an ICD-10-coded dataset with 377,589 records indicated that the proposed metrics reduced the complexity of transition evaluation. The results with the SR in the transition indicated that approximately 60% of the ICD-10 codes in the dataset were unable to map the codes to standard ICD-10 codes released by WHO. The validation results with the UR provided 86.21% of the precise mapping. Validation results of the IG in the dataset, before and after the transition, indicated that approximately 57% of the records tended to increase uncertainty when mapped from ICD-10 to ICD-11. The new features of ICD-11 involved in the transition can promote a reliable and effective mapping between two coding systems.

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Efficiency of International Classification of Diseases, Ninth Revision, Billing Code Searches to Identify Emergency Department Visits for Blood or Body Fluid Exposures through a Statewide Multicenter Database

Infection Control and Hospital Epidemiology ◽

10.1086/665722 ◽

2012 ◽

Vol 33 (6) ◽

pp. 581-588 ◽

Cited By ~ 4

Author(s):

Lisa M. Rosen ◽

Tao Liu ◽

Roland C. Merchant

Keyword(s):

Body Fluid ◽

Recursive Partitioning ◽

International Classification Of Diseases ◽

Emergency Department Visits ◽

International Classification ◽

Misclassification Rate ◽

Classification Of Diseases ◽

Ed Visits ◽

Code Searches

Background.Blood and body fluid exposures are frequently evaluated in emergency departments (EDs). However, efficient and effective methods for estimating their incidence are not yet established.Objective.Evaluate the efficiency and accuracy of estimating statewide ED visits for blood or body fluid exposures using International Classification of Diseases, Ninth Revision (ICD-9), code searches.Design.Secondary analysis of a database of ED visits for blood or body fluid exposure.Setting.EDs of 11 civilian hospitals throughout Rhode Island from January 1, 1995, through June 30, 2001.Patients.Patients presenting to the ED for possible blood or body fluid exposure were included, as determined by prespecified ICD-9 codes.Methods.Positive predictive values (PPVs) were estimated to determine the ability of 10 ICD-9 codes to distinguish ED visits for blood or body fluid exposure from ED visits that were not for blood or body fluid exposure. Recursive partitioning was used to identify an optimal subset of ICD-9 codes for this purpose. Random-effects logistic regression modeling was used to examine variations in ICD-9 coding practices and styles across hospitals. Cluster analysis was used to assess whether the choice of ICD-9 codes was similar across hospitals.Results.The PPV for the original 10 ICD-9 codes was 74.4% (95% confidence interval [CI], 73.2%–75.7%), whereas the recursive partitioning analysis identified a subset of 5 ICD-9 codes with a PPV of 89.9% (95% CI, 88.9%–90.8%) and a misclassification rate of 10.1%. The ability, efficiency, and use of the ICD-9 codes to distinguish types of ED visits varied across hospitals.Conclusions.Although an accurate subset of ICD-9 codes could be identified, variations across hospitals related to hospital coding style, efficiency, and accuracy greatly affected estimates of the number of ED visits for blood or body fluid exposure.

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