- Identifying Adverse Events Using International Classification of Diseases, Tenth Revision Y Codes in Korea: A Cross-sectional Study
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Minsu Ock, Hwa Jung Kim, Bomin Jeon, Ye-Jee Kim, Hyun Mi Ryu, Moo-Song Lee
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J Prev Med Public Health. 2018;51(1):15-22. Published online January 4, 2018
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DOI: https://doi.org/10.3961/jpmph.17.118
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 Abstract
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- Objectives
The use of administrative data is an affordable alternative to conducting a difficult large-scale medical-record review to estimate the scale of adverse events. We identified adverse events from 2002 to 2013 on the national level in Korea, using International Classification of Diseases, tenth revision (ICD-10) Y codes.
Methods
We used data from the National Health Insurance Service-National Sample Cohort (NHIS-NSC). We relied on medical treatment databases to extract information on ICD-10 Y codes from each participant in the NHIS-NSC. We classified adverse events in the ICD-10 Y codes into 6 types: those related to drugs, transfusions, and fluids; those related to vaccines and immunoglobulin; those related to surgery and procedures; those related to infections; those related to devices; and others.
Results
Over 12 years, a total of 20 817 adverse events were identified using ICD-10 Y codes, and the estimated total adverse event rate was 0.20%. Between 2002 and 2013, the total number of such events increased by 131.3%, from 1366 in 2002 to 3159 in 2013. The total rate increased by 103.9%, from 0.17% in 2002 to 0.35% in 2013. Events related to drugs, transfusions, and fluids were the most common (19 446, 93.4%), followed by those related to surgery and procedures (1209, 5.8%) and those related to vaccines and immunoglobulin (72, 0.3%).
Conclusions
Based on a comparison with the results of other studies, the total adverse event rate in this study was significantly underestimated. Improving coding practices for ICD-10 Y codes is necessary to precisely monitor the scale of adverse events in Korea.
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Summary
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Citations
Citations to this article as recorded by  - Accuracy assessment of patient safety incident (PSI) codes and present-on-admission (POA) indicators: a cross-sectional analysis using the Patient Safety Incidents Inquiry (PSII) in Korea
Jeehee Pyo, Eun Young Choi, Seung Gyeong Jang, Won Lee, Minsu Ock
BMC Health Services Research.2024;[Epub] CrossRef - Assessing the Accuracy of Diagnosis Codes and Their Present on Admission Indicator for the Occurrence of Pressure Ulcers
Eun Young Choi, Jaeun Jaeun, Hyeran Jeong, Jeong Pyo, Minsu Ock
Quality Improvement in Health Care.2024; 30(2): 3. CrossRef - Development of the Korean Patient Safety Incidents Code Classification System
Eun Young Choi, Jeehee Pyo, Young-Kwon Park, Minsu Ock, Sukyeong Kim
Journal of Patient Safety.2023; 19(1): 8. CrossRef - Use of a hospital administrative database to identify and characterize community-acquired, hospital-acquired and drug-induced acute kidney injury
Amayelle Rey, Valérie Gras-Champel, Thibaut Balcaen, Gabriel Choukroun, Kamel Masmoudi, Sophie Liabeuf
Journal of Nephrology.2022; 35(3): 955. CrossRef - Evaluation of Factors Associated with Adverse Drug Events in South Korea Using a Population-Based Database
Eunkyeong Choi, Siin Kim, Hae Sun Suh
Journal of Clinical Medicine.2022; 11(21): 6248. CrossRef - Feasibility of Capturing Adverse Events From Insurance Claims Data Using International Classification of Diseases, Tenth Revision, Codes Coupled to Present on Admission Indicators
Juyoung Kim, Eun Young Choi, Won Lee, Hae Mi Oh, Jeehee Pyo, Minsu Ock, So Yoon Kim, Sang-il Lee
Journal of Patient Safety.2022; 18(5): 404. CrossRef - The Korea National Patient Safety Incidents Inquiry Survey: Characteristics of Adverse Events Identified Through Medical Records Review in Regional Public Hospitals
Min Ji Kim, Hee Jung Seo, Hong Mo Koo, Minsu Ock, Jee-In Hwang, Sang-Il Lee
Journal of Patient Safety.2022; 18(5): 382. CrossRef - Use of ICD‐10‐CM T codes in hospital claims data to identify adverse drug events in Taiwan
Ya‐Fang Cheng, Chi‐Yuan Cheng, Szu‐Hsuan Wang, Yu‐Ting Lin, Tzu‐Cheng Tsai
Journal of Clinical Pharmacy and Therapeutics.2021; 46(2): 476. CrossRef - Perceptions of Hospital Health Information Managers Regarding Present on Admission Indicators in Korea: A Qualitative Study
Jee-Hee Pyo, Eun-Young Choi, Hae-Mi Oh, Won Lee, Ju-Young Kim, Min-Su Ock, So-Yoon Kim, Sang-Il Lee
Quality Improvement in Health Care.2020; 26(1): 23. CrossRef - Variation between hospitals and reviewers in detection of adverse events identified through medical record review in Korea
Sukyeong Kim, Ho Gyun Shin, A E Jeong Jo, Ari Min, Minsu Ock, Jee-In Hwang, Youngjin Jeong, Moon Sung Park, Jong Bouk Lee, Tae I K Chang, Eunhyang Song, Heungseon Kim, Sang-Il Lee
International Journal for Quality in Health Care.2020; 32(8): 495. CrossRef
- Construction and Validation of Hospital-Based Cancer Registry Using Various Health Records to Detect Patients with Newly Diagnosed Cancer: Experience at Asan Medical Center.
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Hwa Jung Kim, Jin Hee Cho, Yongman Lyu, Sun Hye Lee, Kyeong Ha Hwang, Moo Song Lee
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J Prev Med Public Health. 2010;43(3):257-264.
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DOI: https://doi.org/10.3961/jpmph.2010.43.3.257
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4,274
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64
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Abstract
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- OBJECTIVES
An accurate estimation of cancer patients is the basis of epidemiological studies and health services. However in Korea, cancer patients visiting out-patient clinics are usually ruled out of such studies and so these studies are suspected of underestimating the cancer patient population. The purpose of this study is to construct a more complete, hospital-based cancer patient registry using multiple sources of medical information. METHODS: We constructed a cancer patient detection algorithm using records from various sources that were obtained from both the in-patients and out-patients seen at Asan Medical Center (AMC) for any reason. The medical data from the potentially incident cancer patients was reviewed four months after first being detected by the algorithm to determine whether these patients actually did or did not have cancer. RESULTS: Besides the traditional practice of reviewing the charts of in-patients upon their discharge, five more sources of information were added for this algorithm, i.e., pathology reports, the national severe disease registry, the reason for treatment, prescriptions of chemotherapeutic agents and radiation therapy reports. The constructed algorithm was observed to have a PPV of 87.04%. Compared to the results of traditional practice, 36.8% of registry failures were avoided using the AMC algorithm. CONCLUSIONS: To minimize loss in the cancer registry, various data sources should be utilized, and the AMC algorithm can be a successful model for this. Further research will be required in order to apply novel and innovative technology to the electronic medical records system in order to generate new signals from data that has not been previously used.
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Summary
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Citations
Citations to this article as recorded by - The role of Hospital-Based Cancer Registries (HBCRs) as information systems in the delivery of evidence-based integrated cancer care: a scoping review
Sheela Tripathee, Sara Jane MacLennan, Amudha Poobalan, Muhammad Imran Omar, Aravinda Meera Guntupalli
Health Systems.2024; 13(3): 177. CrossRef - The clinical behavior and survival of patients with hepatocellular carcinoma and a family history of the disease
Jihyun An, Seheon Chang, Ha Il Kim, Gi‐Won Song, Ju Hyun Shim
Cancer Medicine.2019; 8(15): 6624. CrossRef - Chronic hepatitis B infection and non-hepatocellular cancers: A hospital registry-based, case-control study
Jihyun An, Jong Woo Kim, Ju Hyun Shim, Seungbong Han, Chang Sik Yu, Jaewon Choe, Danbi Lee, Kang Mo Kim, Young-Suk Lim, Young-Hwa Chung, Yung Sang Lee, Dong Jin Suh, Jin Hyoung Kim, Han Chu Lee, Yury E Khudyakov
PLOS ONE.2018; 13(3): e0193232. CrossRef
- Smoking and Colorectal Cancer Risk in the Korean Elderly.
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Hwa Jung Kim, Seung Mi Lee, Nam Kyong Choi, Seon Ha Kim, Hong Ji Song, Yuong Kyun Cho, Byung Joo Park
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J Prev Med Public Health. 2006;39(2):123-129.
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Abstract
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- OBJECTIVES
The incidence of colorectal cancer increased greatly among the elderly in Korea, but the relationship between smoking and colon cancer remains controversial. Few studies have targeted Asian elderly people. We analyzed the smoking status, the amount smoked, and the smoking duration as risk factors of colorectal cancer to determine their association and causality. METHODS: The cohort members (n=14,103) consisted of 4,694 males and 9,409 females, and they were derived from the Korea Elderly Phamacepidemilogic Cohort (KEPEC), which was a population-based dynamic cohort. They were aged 65 years or more and they lived in Busan Metropolitan City between from 1993-1998; they were beneficiaries of the Korean Medical Insurance Corporation (KMIC). The baseline information was surveyed by a selfadministered mailed questionnaire; after 8.7 person-years of mean follow up period, 100 cases of colorectal cancer occurred. The adjusted relative ratio (aRR) of smoking status, the smoking amount and the smoking duration were calculated from the Cox's proportional hazard model with the never-smokers as a reference group and the Cox model controlled for age, gender, precancerous lesions of CRC, medication history of NSAIDs and antibiotics, the alcohol drinking status and BMI. RESULTS: Compared with the never smokers, the aRRs were 2.03 (95% CI=1.02-4.03) and 1.36 (95% CI=0.80-2.32) for the ex-smokers and current smokers, respectively. Statistical significant trends were not observed for the dose-relationship among the elderly, either for the mean daily amount smoked (p for trend=0.28) or for the total amount (p for trend=0.15). Still, the aRRs were 1.51 (95% CI=0.97-2.34) for the elderly who smoked less than 40 years and 2.35 (95% CI=1.16-4.74) for the elderly who had 40 years or more of smoking (p for trend=0.06). Smokers who started smoking before the age 20 had an increased aRR of 2.15 (95% CI=1.17-3.93) compared to the never smokers. CONCLUSIONS: After controlling for age, gender, precancerous lesion of CRC, medication history of NSAIDs and antibiotics, the alcohol drinking status and BMI, smoking increases the risk of colorectal cancer among elderly people. The age when starting smoking is also important.
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