Child maltreatment surveillance following the ICD-10-CM transition, 2016-2018

2020 ◽  
pp. injuryprev-2019-043579
Author(s):  
Amy A Hunter ◽  
Nina Livingston ◽  
Susan DiVietro ◽  
Laura Schwab Reese ◽  
Kathryn Bentivegna ◽  
...  
Keyword(s):  
Child Maltreatment ◽  
Physical Abuse ◽  
Delayed Diagnosis ◽  
Secondary Data ◽  
International Classification Of Diseases ◽  
Medical Coding ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Ed Visits ◽  
Maltreatment Type

BackgroundChild maltreatment is poorly documented in clinical data. The International Classification of Diseases and Related Health Problems, 10th Revision, Clinical Modification (ICD-10-CM) represents the first time that confirmed and suspected child maltreatment can be distinguished in medical coding. The utility of this distinction in practice remains unknown. This study aims to evaluate the application of these codes by patient demographic characteristics and injury type.MethodsWe conducted secondary data analysis of emergency department (ED) discharge records of children under 18 years with an ICD-10-CM code for confirmed (T74) or suspected (T76) child maltreatment. Child age, sex, race/ethnicity, insurance status and co-occurring injuries (S00-T88) were compared by maltreatment type (confirmed or suspected).ResultsFrom 2016 to 2018, child maltreatment was documented in 1650 unique ED visits, or 21.7 per 10 000 child ED visits. Suspected maltreatment was documented most frequently (58%). Half of all maltreatment-related visits involved sexual abuse, most often in females and individuals of non-Hispanic white race. Physical abuse was coded in 36% of visits; injuries to the head were predominant. Non-Hispanic black children were more frequently documented with confirmed physical abuse than suspected (38.7% vs 23.7%, p<0.01). The rate of co-occurring injuries documented with confirmed and suspected maltreatment differed by 30% (9.2 vs 12.5 per 10 000 ED visits, respectively).ConclusionsThe ability to discriminate confirmed and suspected maltreatment may help mitigate clinical barriers to maltreatment surveillance associated with delayed diagnosis and subsequent intervention. Racial disparities in suspected and confirmed cases were identified which may indicate biased diagnostic behaviours in the ED.

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

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.

Symptoms and characteristics of cancer patient visits to an emergency room.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18306-e18306
Author(s):  
Michael Gallaway ◽  
Nimi Idaikkadar
Keyword(s):  
Cancer Patients ◽  
Acute Care ◽  
International Classification Of Diseases ◽  
Population Based ◽  
P Value ◽  
Surveillance Program ◽  
Cancer Type ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Ed Visits

e18306 Background: Cancer patients are increasingly likely to visit an emergency department (ED) for acute care compared to the general population. Cancer patients who visit the ED often have long wait times, increased exposure to infection, and lesser quality treatment. The Centers for Medicare & Medicaid Services (CMS) is encouraging efforts to decrease survivor acute care visits. The purpose of this study was to examine cancer-related ED visits using a national population-based sample to understand why they are seeking care in an ED. Methods: A retrospective cohort study of U.S. patients who visited EDs between June 1, 2017 and May 31, 2018 was conducted using the National Syndromic Surveillance Program (NSSP) BioSense Platform. Cancer patients were identified using International Classification of Diseases, 10th Revision (ICD-10) codes for any cancer type and specifically for cancers of the bladder, female breast, cervix, colon and rectum, kidney, liver, lung, ovary, pancreas, prostate, or uterus. Symptoms were identified using syndromic definitions and key-word queries. Significance testing (p-value ≤0.01), was used to assess differences in the prevalence of symptoms by cancer type. Results: There were 97 million visits to EDs during the study period, 710,297 (0.8%) were among cancer survivors. Slightly more were female (50.1%) than male (49.5%); more were aged 65 or older (53.6%) than 18-64 (1.4-35.3%). The most common symptoms were pain (19%), gastrointestinal (14%), respiratory (12%), neurologic (5%), fever (5%), and injury (4%). Prevalence of symptoms differed significantly by cancer type. Some symptoms were higher among those with specific cancer types compared to all cancers in aggregate: pain (cervical, liver, pancreas); gastrointestinal (pancreas, liver, colorectal); respiratory (lung); neurologic (liver, lung), fever (pancreas, liver), injury (prostate), and bleeding (colorectal, liver). Conclusions: Use of NSSP data enabled a descriptive characterization of more than half of the U.S. ED visits among cancer patients. These comprehensive findings inform best practices to reduce unplanned acute care and help inform possible modifications in treatment and care protocols among survivors of specific cancers.

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 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 The Negative Impact of the COVID-19 Pandemic on Oncology Care at an Academic Cancer Referral Center

ONCOLOGY ◽  
2021 ◽  
pp. 462-470
Author(s):  
John Nakayama ◽  
Gi-Ming Wang ◽  
Gino Cioffi ◽  
Kristin Waite ◽  
Nirav Patil ◽  
...  
Keyword(s):  
Negative Impact ◽  
Controlled Trial ◽  
Delayed Diagnosis ◽  
International Classification Of Diseases ◽  
Cancer Center ◽  
Related Services ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Oncology Care ◽  
Oncologic Treatment

Objectives: COVID-19 created unexpected delays in oncologic treatment. This study sought to assess the volume of missed cancer-related services due to the pandemic. Methods: This case-controlled trial evaluated more than 345,000 oncologic clinic, lab, and radiation appointments from January 1, 2019, through December 31, 2020, and surgery appointments from January 1, 2019, through October 31, 2020. All patients at the Seidman Cancer Center with a cancer diagnosis based on a comprehensive list of 2178 International Classification of Diseases, Ninth Edition (ICD-9) and ICD-10 codes were included in the analysis. Subgroup analyses based on age, race, and sex were also performed. Results: Clinic, lab, and surgical visit cancellations increased by 4.20% (P <.001), 4.84% (P <.001), and 5.22% (P <.001), respectively. In the first 10 months of 2020, there were 703 (9.2%) fewer surgeries compared with the same time period in 2019. The following cancellation rates peaked in March 2020: clinic visits (26.53%), labs (43.66%), surgery (34.00%). Radiation oncology (12.53%) cancellations peaked in April 2020. Prior to the emergence of COVID-19, the group aged 0 to 39 years had the highest clinic cancellation rate (17.85%) compared with patients aged 40 to 64 years (15.95%) and 65 years and older (14.52%; P <.001). Men cancelled (15.63%) significantly more often than women (14.93%; P <.001) in 2019. This reversed during the pandemic: Women (19.56%) cancelled more frequently than men (19.20%; P <.036). Conclusions: There was a large increase in cancelled oncologic care in 2020, which has implications for delayed diagnosis and treatment. This was especially true for patients older than 65 years and for women. These delays could result in patients presenting with more advanced disease, complicating morbidities, and ultimately worse long-term outcomes.

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

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.

scholarly journals Evaluation of Diagnostic Codes in Morbidity and Mortality Data Sources for Heat-Related Illness Surveillance

2017 ◽  
Vol 132 (3) ◽  
pp. 326-335 ◽  
Author(s):  
Laurel Harduar Morano ◽  
Sharon Watkins
Keyword(s):  
International Classification Of Diseases ◽  
International Classification ◽  
Data Sources ◽  
Work Related ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Search Data ◽  
Ed Visits ◽  
Data Source

Objectives: The primary objective of this study was to identify patients with heat-related illness (HRI) using codes for heat-related injury diagnosis and external cause of injury in 3 administrative data sets: emergency department (ED) visit records, hospital discharge records, and death certificates. Methods: We obtained data on ED visits, hospitalizations, and deaths for Florida residents for May 1 through October 31, 2005-2012. To identify patients with HRI, we used codes from the International Classification of Diseases, Ninth Revision, Clinical Modification ( ICD-9-CM) to search data on ED visits and hospitalizations and codes from the International Classification of Diseases, Tenth Revision ( ICD-10) to search data on deaths. We stratified the results by data source and whether the HRI was work related. Results: We identified 23 981 ED visits, 4816 hospitalizations, and 140 deaths in patients with non–work-related HRI and 2979 ED visits, 415 hospitalizations, and 23 deaths in patients with work-related HRI. The most common diagnosis codes among patients were for severe HRI (heat exhaustion or heatstroke). The proportion of patients with a severe HRI diagnosis increased with data source severity. If ICD-9-CM code E900.1 and ICD-10 code W92 (excessive heat of man-made origin) were used as exclusion criteria for HRI, 5.0% of patients with non–work-related deaths, 3.0% of patients with work-related ED visits, and 1.7% of patients with work-related hospitalizations would have been removed. Conclusions: Using multiple data sources and all diagnosis fields may improve the sensitivity of HRI surveillance. Future studies should evaluate the impact of converting ICD-9-CM to ICD-10-CM codes on HRI surveillance of ED visits and hospitalizations.

Angststörungen – von der ICD-10 zur ICD-11

2020 ◽  
pp. 1-5
Author(s):  
Timo D. Vloet ◽  
Marcel Romanos
Keyword(s):  
Mental Disorders ◽  
International Classification Of Diseases ◽  
International Classification ◽  
Statistical Manual ◽  
Diagnostic And Statistical Manual ◽  
Dsm 5 ◽  
Classification Of Diseases ◽  
Icd 10

Zusammenfassung. Hintergrund: Nach 12 Jahren Entwicklung wird die 11. Version der International Classification of Diseases (ICD-11) von der Weltgesundheitsorganisation (WHO) im Januar 2022 in Kraft treten. Methodik: Im Rahmen eines selektiven Übersichtsartikels werden die Veränderungen im Hinblick auf die Klassifikation von Angststörungen von der ICD-10 zur ICD-11 zusammenfassend dargestellt. Ergebnis: Die diagnostischen Kriterien der generalisierten Angststörung, Agoraphobie und spezifischen Phobien werden angepasst. Die ICD-11 wird auf Basis einer Lebenszeitachse neu organisiert, sodass die kindesaltersspezifischen Kategorien der ICD-10 aufgelöst werden. Die Trennungsangststörung und der selektive Mutismus werden damit den „regulären“ Angststörungen zugeordnet und können zukünftig auch im Erwachsenenalter diagnostiziert werden. Neu ist ebenso, dass verschiedene Symptomdimensionen der Angst ohne kategoriale Diagnose verschlüsselt werden können. Diskussion: Die Veränderungen im Bereich der Angsterkrankungen umfassen verschiedene Aspekte und sind in der Gesamtschau nicht unerheblich. Positiv zu bewerten ist die Einführung einer Lebenszeitachse und Parallelisierung mit dem Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Schlussfolgerungen: Die entwicklungsbezogene Neuorganisation in der ICD-11 wird auch eine verstärkte längsschnittliche Betrachtung von Angststörungen in der Klinik sowie Forschung zur Folge haben. Damit rückt insbesondere die Präventionsforschung weiter in den Fokus.

Depression

2017 ◽  
Author(s):  
Philip Cowen
Keyword(s):  
International Classification Of Diseases ◽  
Behavioural Therapy ◽  
Historical Background ◽  
Cognitive Behavioural ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Diagnosis And Classification ◽  
Modern Experience ◽  
Symptoms And Signs

This chapter discusses the symptomatology, diagnosis, and classification of depression. It begins with a brief historical background on depression, tracing its origins to the classical term ‘melancholia’ that describes symptoms and signs now associated with modern concepts of the condition. It then considers the phenomenology of the modern experience of depression, its diagnosis in the operational scheme of ICD-10 (International Classification of Diseases, tenth edition), and current classificatory schemes. It looks at the symptoms needed to meet the criteria for ‘depressive episode’ in ICD-10, as well as clinical features of depression with ‘melancholic’ features or ‘somatic depression’ in ICD-10. It also presents an outline of the clinical assessment of an episode of depression before concluding with an overview of issues that need to be taken into account when addressing approaches to treatment, including cognitive behavioural therapy and the administration of antidepressants.

scholarly journals Garbage codes as causes of death and quality of mortality statistics in Belo Horizonte, Brazil

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
L Ishitani ◽  
R Teixeira ◽  
D Abreu ◽  
L Paixão ◽  
E França
Keyword(s):  
Causes Of Death ◽  
Health Planning ◽  
Age Groups ◽  
International Classification Of Diseases ◽  
Mortality Statistics ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Hospital Deaths ◽  
Belo Horizonte

Abstract Background Quality of cause-of-death information is fundamental for health planning. Traditionally, this quality has been assessed by the analysis of ill-defined causes from chapter XVIII of the International Classification of Diseases - 10th revision (ICD-10). However, studies have considered other useless diagnoses for public health purposes, defined, in conjunction with ill-defined causes, as garbage codes (GC). In Brazil, despite the high completeness of the Mortality Information System, approximately 30% of deaths are attributable to GCs. This study aims to analyze the frequency of GCs in Belo Horizonte municipality, the capital of Minas Gerais state, Brazil. Methods Data of deaths from 2011 to 2013 in Belo Horizonte were analyzed. GCs were classified according to the GBD 2015 study list. These codes were classified in: a) GCs from chapter XVIII of ICD-10 (GC-R), and b) GC from other chapters of ICD-10 (GC-nonR). Proportions of GC were calculated by sex, age, and place of occurrence. Results In Belo Horizonte, from the total of 44,123 deaths, 5.5% were classified as GC-R. The majority of GCs were GC-nonR (25% of total deaths). We observed a higher proportion of GC in children (1 to 4 years) and in people aged over 60 years. GC proportion was also higher in females, except in the age-groups under 1 year and 30-59 years. Home deaths (n = 7,760) had higher proportions of GCs compared with hospital deaths (n = 30,182), 36.9% and 28.7%, respectively. The leading GCs were the GC-R other ill-defined and unspecified causes of death (ICD-10 code R99)), and the GCs-nonR unspecified pneumonia (J18.9), unspecified stroke (I64), and unspecified septicemia (A41.9). Conclusions Analysis of GCs is essential to evaluate the quality of mortality information. Key messages Analysis of ill-defined causes (GC-R) is not sufficient to evaluate the quality of information on causes of death. Causes of death analysis should consider the total GC, in order to advance the discussion and promote adequate intervention on the quality of mortality statistics.

Sign in / Sign up

Export Citation Format

Share Document