scholarly journals Evaluation of DoD Syndrome Mapping and Baseline for ICD-9-CM to ICD-10-CM Transition

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Jessica Deerin ◽  
Jean-Paul Chretien ◽  
Paul Lewis
Keyword(s):  
Department Of Defense ◽  
Human Services ◽  
Military Health ◽  
Health And Human Services ◽  
Diagnostic Codes ◽  
Diagnosis Codes ◽  
Time Period ◽  
Icd 10 ◽  
Icd Codes ◽  
Number Of Visits

ObjectiveThe transition from ICD-9-CM to ICD-10-CM requires evaluationof syndrome mappings to obtain a baseline for syndromic surveillancepurposes. Two syndrome mappings are evaluated in this report.IntroductionThe Department of Defense conducts syndromic surveillanceof health encounter visits of Military Health System (MHS)beneficiaries. Providers within the MHS assign up to 10 diagnosiscodes to each health encounter visit. The diagnosis codes are groupedinto syndrome and sub-syndrome categories. On October 1, 2015,the Health and Human Services-mandated transition from ICD-9-CM to ICD-10-CM required evaluation of the syndrome mappingsto establish a baseline of syndrome rates within the DoD. The DoDdata within the BioSense system currently utilizes DoD ESSENCEsyndrome mappings. The Master Mapping Reference Table (MMRT)was developed by the CDC to translate diagnostic codes across theICD-9-CM and ICD-10-CM encoding systems to prepare for thetransition. The DoD ESSENCE and MMRT syndrome definitions arepresented in this analysis for comparison.MethodsDoD data was pulled from the BioSense Platform through aRStudio server on October 11, 2016, querying data from October1, 2014 to September 30, 2016. This time period provides twelvemonths of ICD-9-CM data and twelve months of ICD-10-CM data.The ICD codes were binned to both DoD ESSENCE syndromes andMMRT macro syndromes for comparison. Although a patient visitmay contain up to 10 ICD codes, only the first four were includedfor this analysis. Providers are trained to prioritize diagnosis codesby position. Only 2.2% of visits had greater than 4 diagnostic codes.Each ICD code in a visit is binned to an applicable syndrome. Thetotal number of visits includes visits that binned and did not bin toa syndrome. Multiple syndromes may be assigned to one patient’shealth encounter visit if multiple ICD codes are binned. Additionally,more than one code per visit may bin to the same syndrome; however,only unique syndromes are counted in the total syndrome rate. Thetotal syndrome rate was calculated by total unique syndrome visitsas the numerator and total number of visits during the ICD-9-CM orICD-10-CM time period as the denominator. The rates per 1000 totalvisits were calculated.ResultsAmong the DoD ESSENCE syndromes, the ICD-9-CM ratefor ILI was 36.3 per 1,000 compared to the ICD-10-CM rate of38.6 per 1,000. The ICD-9-CM rate for neurological was 18.1 per1,000 compared to the ICD-10-CM rate of 0.2 per 1,000.Among the MMRT syndromes, the ICD-9-CM rate for ILI was16.7 per 1,000 compared to the ICD-10-CM rate of 38.4 per 1,000.The ICD-9-CM rate for mental disorders was 73.8 per 1,000 comparedto the ICD-10-CM rate of 73.2 per 1,000.ConclusionsThis analysis provides baseline rates of MMRT syndromes andsub-syndromes for syndromic surveillance during the ICD-9-CM toICD-10-CM transition. These data will serve for future comparisonand tracking of syndrome-specific trends for military-relevant healththreats.

Abstract P250: A Strategy for Reliable Identification of Ischemic Stroke, Thrombolytics, and Thrombectomy in Large Administrative Databases

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Kori S Zachrison ◽  
Sijia Li ◽  
Mathew J Reeves ◽  
Opeolu M Adeoye ◽  
Carlos A Camargo ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Administrative Data ◽  
Administrative Databases ◽  
Stroke Patients ◽  
Inpatient Hospitalization ◽  
Accurate Identification ◽  
Stroke Research ◽  
Diagnosis Codes ◽  
Icd 10 ◽  
Icd Codes

Background: Administrative data are frequently used in stroke research. Ensuring accurate identification of ischemic stroke patients, and those receiving thrombolysis and endovascular thrombectomy (EVT) is critical to ensure representativeness and generalizability. We examined differences in patient samples based on different modes of identification, and propose a strategy for future patient and procedure identification in large administrative databases. Methods: We used nonpublic administrative data from the state of California to identify all ischemic stroke patients discharged from an emergency department or inpatient hospitalization from 2010-2017 based on ICD-9 (2010-2015), ICD-10 (2015-2017), and MS-DRG discharge codes. We identified patients with interhospital transfers, patients receiving thrombolytics, and patients treated with EVT based on ICD, CPT and MS-DRG codes. We determined what proportion of these transfers and procedures would have been identified with ICD versus MS-DRG discharge codes. Results: Of 365,099 ischemic stroke encounters, most (87.7%) had both a stroke-related ICD-9 or ICD-10 code and stroke-related MS-DRG code; 12.3% had only an ICD-9 or ICD-10 code, and 0.02% had only a MS-DRG code. Nearly all transfers (99.9%) were identified using ICD codes. We identified32,433 thrombolytic-treated patients (8.9% of total) using ICD, CPT, and MS-DRG codes; the combination of ICD and CPT codes identified nearly all (98%). We identified 7,691 patients treated with EVT (2.1% of total) using ICD and MS-DRG codes; both MS-DRG and ICD-9/-10 codes were necessary because ICD codes alone missed 13.2% of EVTs. CPT codes only pertain to outpatient/ED patients and are not useful for EVT identification. Conclusions: ICD-9/-10 diagnosis codes capture nearly all ischemic stroke encounters and transfers, while the combination of ICD-9/-10 and CPT codes are adequate for identifying thrombolytic treatment in administrative datasets. However, MS-DRG codes are necessary in addition to ICD codes for identifying EVT, likely due to favorable reimbursement for EVT-related MS-DRG codes incentivizing accurate coding.

scholarly journals Strategy for reliable identification of ischaemic stroke, thrombolytics and thrombectomy in large administrative databases

2020 ◽  
pp. svn-2020-000533
Author(s):  
Kori S Zachrison ◽  
Sijia Li ◽  
Mathew J Reeves ◽  
Opeolu Adeoye ◽  
Carlos A Camargo ◽  
...  
Keyword(s):  
Ischaemic Stroke ◽  
Administrative Data ◽  
Administrative Databases ◽  
Accurate Identification ◽  
Stroke Research ◽  
Diagnosis Codes ◽  
Inpatient Hospitalisation ◽  
Icd 10 ◽  
Related Group ◽  
Icd Codes

BackgroundAdministrative data are frequently used in stroke research. Ensuring accurate identification of patients who had an ischaemic stroke, and those receiving thrombolysis and endovascular thrombectomy (EVT) is critical to ensure representativeness and generalisability. We examined differences in patient samples based on mode of identification, and propose a strategy for future patient and procedure identification in large administrative databases.MethodsWe used non-public administrative data from the state of California to identify all patients who had an ischaemic stroke discharged from an emergency department (ED) or inpatient hospitalisation from 2010 to 2017 based on International Classification of Disease (ICD-9) (2010–2015), ICD-10 (2015–2017) and Medicare Severity-Diagnosis-related Group (MS-DRG) discharge codes. We identified patients with interhospital transfers, patients receiving thrombolytics and patients treated with EVT based on ICD, Current Procedural Terminology (CPT) and MS-DRG codes. We determined what proportion of these transfers and procedures would have been identified with ICD versus MS-DRG discharge codes.ResultsOf 365 099 ischaemic stroke encounters, most (87.70%) had both a stroke-related ICD-9 or ICD-10 code and stroke-related MS-DRG code; 12.28% had only an ICD-9 or ICD-10 code and 0.02% had only an MS-DRG code. Nearly all transfers (99.99%) were identified using ICD codes. We identified 32 433 thrombolytic-treated patients (8.9% of total) using ICD, CPT and MS-DRG codes; the combination of ICD and CPT codes identified nearly all (98%). We identified 7691 patients treated with EVT (2.1% of total) using ICD and MS-DRG codes; both MS-DRG and ICD-9/ICD-10 codes were necessary because ICD codes alone missed 13.2% of EVTs. CPT codes only pertain to outpatient/ED patients and are not useful for EVT identification.ConclusionsICD-9/ICD-10 diagnosis codes capture nearly all ischaemic stroke encounters and transfers, while the combination of ICD-9/ICD-10 and CPT codes are adequate for identifying thrombolytic treatment in administrative datasets. However, MS-DRG codes are necessary in addition to ICD codes for identifying EVT, likely due to favourable reimbursement for EVT-related MS-DRG codes incentivising accurate coding.

408 Validation of claim based algorithms for sleep apnea using ICD codes

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A162-A162
Author(s):  
Rizwana Sultana ◽  
Elizabeth Lam ◽  
Enshuo Hsu ◽  
Erin Gurski ◽  
Gulshan Sharma
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Individual Performance ◽  
Care Utilization ◽  
Office Visits ◽  
Sleep Study ◽  
Diagnostic Codes ◽  
Obstructive Sleep ◽  
Icd 10 ◽  
Icd Codes

Abstract Introduction Obstructive sleep apnea (OSA) is a common condition characterized by repeated episodes of partial or complete obstruction of the respiratory passages during sleep. According to recent studies prevalence of obstructive sleep apnea ranges between 9–38%. OSA is associated with increased all-cause mortality particularly associated with cardiac diseases. In order to provide representation of larger population estimates, administrative data using ICD codes have been utilized. Accurate identification of sleep apnea is important for research related to health care utilization and health outcomes. Our aim is to validate an algorithm for identification of patients with obstructive sleep apnea using ICD 10 codes seen at UTMB. Methods Patient medical records were collected from University of Texas Medical Branch EHR system. We included patients who visited from 6/1/2015 to 5/31/2018 in pulmonary or primary care clinics who had any sleep disorder diagnostic codes (ICD-10: G47.30, G47.31, G47.33, G47.34, G47.36, G47.20, G47.10, G47.39, G47.8, G47.9, F51.13, F51.09, R06.89, J96.90, R40.0, F51.9, R06.83, R06.3, G47.63, G47.39, Z86.69). Two algorithms were created. First algorithm included patient with sleep diagnostic codes used at 2 separate office visits. Second algorithm included patients with sleep diagnostic codes and evidence of sleep study. The performance of most used codes was calculated individually. Results 1200 patients were identified with ICD codes used during two office visits. According to the first algorithm with only ICD codes 75% of patients had sleep apnea. Upon addition of evidence of sleep apnea with ICD codes the % of patients with sleep apnea increased to 95.44. Among most used ICD codes, G47.30 had 86.47% patients with sleep apnea according to first algorithm and 96.01% with second algorithm. The percentages for G47.33 was 80.86% and 96.4%, for G47.10, 78.05% and 87.67%, for R40.0 78.91% and 90.63% respectively. Conclusion In conclusion, claim based algorithms for sleep apnea diagnostic codes showed good test positive percentages overall, but algorithm with ICD 10 codes with sleep study performed better in identifying patients with sleep apnea than ICD-9-CM codes alone. Similarly, the individual performance of most used ICD codes was highly improved when evidence of sleep study was present. Support (if any):

scholarly journals Civilian-military Collaboration: Department of Defense data in the BioSense Platform

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Jessica Deerin ◽  
Jean-Paul Chretien ◽  
Paul E. Lewis
Keyword(s):  
Department Of Defense ◽  
Family Members ◽  
Active Duty ◽  
Surveillance Program ◽  
Age Group ◽  
Diagnosis Codes ◽  
Time Period ◽  
Control And Prevention ◽  
Military Treatment Facility ◽  
Clinic Visits

ObjectiveThe Department of Defense data is available to NationalSyndromic Surveillance Program (NSSP) users to conduct syndromicsurveillance. This report summarizes the demographic characteristicsof DoD health encounter visits.IntroductionThe DoD provides daily outpatient and emergency room data feedsto the BioSense Platform within NSSP, maintained by the Centersfor Disease Control and Prevention. This data includes demographiccharacteristics and diagnosis codes for health encounter visits ofMilitary Health System beneficiaries, including active duty, activeduty family members, retirees, and retiree family members. NSSPfunctions through collaboration with local, state, and federal publichealth partners utilizing the BioSense Platform, an electronic healthinformation system.MethodsDoD data was pulled from the BioSense Platform through aRStudio server on October 11, 2016, querying data from November1, 2015 to September 30, 2016. Appointment type and beneficiarycategory data was not available in BioSense until November 1, 2015.Appointment type was categorized into clinic visits and telephoneconsults. Demographic characteristics (age group, gender, beneficiarycategory) are stratified by appointment type.ResultsDuring the time period of November 1, 2015 to September 30, 2016,data were received from 452 clinics. There is a military treatmentfacility located in 45 states and a military treatment facility mayhave one to 12 clinics. There were a total of 86,840,632 healthcareencounter records. The age group, 25-44 years, accounted for 39.4%of the medical encounters; the mean age was 33.9 (SD=19.1). Malesaccounted for 55.6% of the medical encounters. For the time periodfrom November 1, 2015 to September 30, 2016, 78.9% of medicalencounters were clinic visits. The remaining medical encounterswere telephone consults. Of the clinic visits, 53.7% of the medicalencounters were for active duty personnel.ConclusionsThis report highlights the DoD data available to NSSP users forcollaborative syndromic surveillance efforts, promoting a communityof practice. It is important to understand the population demographicsand limitations to the DoD data when conducting syndromicsurveillance.

scholarly journals Coding reliability and agreement of international classification of disease, 10th revision (ICD-10) codes in emergency department data

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Mingkai Peng ◽  
Cathy Eastwood ◽  
Alicia Boxill ◽  
Rachel Joy Jolley ◽  
Laura Rutherford ◽  
...  
Keyword(s):  
Emergency Department ◽  
Emergency Departments ◽  
International Classification ◽  
Main Diagnosis ◽  
International Classification Of Disease ◽  
Diagnostic Codes ◽  
Clinical Encounters ◽  
Diagnosis Codes ◽  
Icd 10

Introduction: Administrative health data from the emergency department (ED) play important roles in understanding health needs of the public and reasons for health care resource use. International Classification of Disease (ICD) diagnostic codes have been widely used for code reasons of clinical encounters for administrative purposes in EDs. Objective: The purpose of the study is to examine the coding agreement and reliability of ICD diagnosis codes in ED through auditing the routinely collected data. Methods: We randomly sampled 1 percent of records (n=1636) between October and December from 11 emergency departments in Alberta, Canada. Auditors were employed to review the same chart and independently assign main diagnosis codes. We assessed coding agreement and reliability through comparison of codes assigned by auditors and hospital coders using the proportion of agreement and Cohen’s kappa. Error analysis was conducted to review diagnosis codes with disagreement and categorized them into six groups. Results: Overall, the agreement was 86.5% and 82.2% at 3 and 4 digits levels respectively, and reliability was 0.86 and 0.82 respectively. Variation of agreement and reliability were identified across different emergency departments. The major two categories of coding discrepancy were the use of different codes for the same condition (23.6%) and the use of codes at different levels of specificity (20.9%). Conclusions: Diagnosis codes in emergency department show high agreement and reliability. More strict coding guidelines regarding the use of unspecified codes are needed to enhance coding consistency.

scholarly journals The Epidemiology of Osteomyelitis in Children

Children ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 1000
Author(s):  
Nike Walter ◽  
Susanne Bärtl ◽  
Volker Alt ◽  
Markus Rupp
Keyword(s):  
Epidemiological Data ◽  
Incidence Rates ◽  
Treatment Protocols ◽  
Diagnosis Codes ◽  
Medical Institutions ◽  
Time Period ◽  
Appropriate Treatment ◽  
Icd 10 ◽  
Anatomical Localization ◽  
Group 5

Pediatric osteomyelitis remains challenging to treat. Detailed epidemiological data are required to estimate future developments. Therefore, we aimed to analyze how the incidence has changed over the last decade depending on age, gender, osteomyelitis subtype, and anatomical localization. Cases were quantified for patients aged 20 years or younger, using yearly reported ICD-10 diagnosis codes from German medical institutions for the time period 2009 to 2019. Incidence rates of osteomyelitis increased by 11.7% from 8.2 cases per 100,000 children in 2009 to 9.2 cases per 100,000 children in 2019. The age-specific incidence rate revealed the highest occurrence of osteomyelitis in patients aged 10–15 years (15.3/100,000 children), which increased by 23% over the observation period, followed by the age group 5–10 years (9.7/100,000 children). In 2019, out of all diagnoses, 39.2% were classified as acute, 38.4% as chronic, and 22.4% were unspecified, whereby chronic cases increased by 38.7%. The lower extremity was mainly affected, with 58.9% of osteomyelitis diagnoses in 2019. In conclusion, pediatric osteomyelitis is a serious issue, even in a developed and industrialized country such as Germany. Considering the recent incidence increase, the permanent need for appropriate treatment should let pediatricians and orthopedic surgeons deal with diagnosis and treatment protocols.

A Practical Approach to Determine Cutoff Concentrations for Opiate Testing with Simultaneous Detection of Codeine, Morphine, and 6-Acetylmorphine in Urine

1999 ◽  
Vol 45 (4) ◽  
pp. 510-519 ◽  
Author(s):  
Buddha D Paul ◽  
Eric T Shimomura ◽  
Michael L Smith
Keyword(s):  
Department Of Defense ◽  
Extraction Method ◽  
Human Services ◽  
Simultaneous Detection ◽  
Gas Chromatography Mass Spectrometry ◽  
Heroin Use ◽  
Single Extraction ◽  
Health And Human Services ◽  
Department Of Health ◽  
Selective Ion Monitoring

Abstract Background: Both the Department of Defense (DoD) and the Department of Health and Human Services (DHHS) currently require two confirmation tests to verify use of heroin, one test for total morphine and a separate test for 6-acetylmorphine (6-AM). Our aim was to determine appropriate free-codeine, free-morphine, and 6-AM cutoff concentrations that could be substituted for total-morphine, total-codeine, and 6-AM cutoff concentrations and to develop a less labor-intensive method for measuring codeine, morphine, and 6-AM. Methods: Urine samples containing opiates were extracted, derivatized, and analyzed using gas chromatography–mass spectrometry with selective ion monitoring. Results: The limits of detection for codeine, morphine, and 6-AM were 6, 5, and 0.5 μg/L, respectively. Recoveries were >90%. Quantification was linear over the concentration range of 6–1000 μg/L for codeine, 5–5000 μg/L for morphine, and 0.5–800 μg/L for 6-AM. Cutoff concentrations for confirmation of opiates were 100, 100, and 10 μg/L for free codeine, free morphine, and 6-AM. Conclusion: The proposed cutoff concentrations for free morphine and 6-AM provide better detection windows for morphine and heroin use than the cutoff concentrations for total morphine and 6-AM used at present. Detection of free codeine, instead of total codeine, simplifies interpretation of codeine use. The single-extraction method enables simultaneous, less labor-intensive analysis of morphine, codeine, and 6-AM.

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