Objectives: Public healthcare data have become crucial to the advancement of medicine, and recent changes in legal structure on privacy protection have expanded access to these data with pseudonymization. Recent debates on public healthcare data use by private insurance companies have shown large discrepancies in perceptions among the general public, healthcare professionals, private companies, and lawmakers. This study examined public attitudes toward the secondary use of public data, focusing on differences between public and private entities. Methods: An online survey was conducted from January 11 to 24, 2022, involving a random sample of adults between 19 and 65 of age in 17 provinces, guided by the August 2021 census. Results: The final survey analysis included 1,370 participants. Most participants were aware of health data collection (72.5%) and recent changes in legal structures (61.4%) but were reluctant to share their pseudonymized raw data (51.8%). Overall, they were favorable toward data use by public agencies but disfavored use by private entities, notably marketing and private insurance companies. Concerns were frequently noted regarding commercial use of data and data breaches. Among the respondents, 50.9% were negative about the use of public healthcare data by private insurance companies, 22.9% favored this use, and 1.9% were “very positive.” Conclusions This survey revealed a low understanding among key stakeholders regarding digital health data use, which is hindering the realization of the full potential of public healthcare data. This survey provides a basis for future policy developments and advocacy for the secondary use of health data.

Background: There are limited data on the accuracy of cloud-based speech recognition (SR) open application programming interfaces (APIs) for medical terminology. This study aimed to evaluate the medical term recognition accuracy of current available cloud-based SR open APIs in Korean. Methods: We analyzed the SR accuracy of currently available cloud-based SR open APIs using real doctor–patient conversation recordings collected from an outpatient clinic at a large tertiary medical center in Korea. For each original and SR transcription, we analyzed the accuracy rate of each cloud-based SR open API (i.e., the number of medical terms in the SR transcription per number of medical terms in the original transcription). Results: A total of 112 doctor–patient conversation recordings were converted with three cloud-based SR open APIs (Naver Clova SR from Naver Corporation; Google Speech-toText from Alphabet Inc.; and Amazon Transcribe from Amazon), and each transcription was compared. Naver Clova SR (75.1%) showed the highest accuracy with the recognition of medical terms compared to the other open APIs (Google Speech-to-Text, 50.9%, P < 0.001; Amazon Transcribe, 57.9%, P < 0.001), and Amazon Transcribe demonstrated higher recognition accuracy compared to Google Speech-to-Text (P< 0.001). In the sub-analysis, Naver Clova SR showed the highest accuracy in all areas according to word classes, but the accuracy of words longer than five characters showed no statistical differences (Naver Clova SR, 52.6%; Google Speech-to-Text, 56.3%; Amazon Transcribe, 36.6%). Conclusion Among three current cloud-based SR open APIs, Naver Clova SR which manufactured by Korean company showed highest accuracy of medical terms in Korean, compared to Google Speech-to-Text and Amazon Transcribe. Although limitations are existing in the recognition of medical terminology, there is a lot of rooms for improvement of this promising technology by combining strengths of each SR engines.

Background: Hyperglycemia has shown a negative association with cognitive dysfunction. We analyzed patients with high preoperative blood glucose level and hemoglobin A1c (HbA1c) level to determine the prevalence of postoperative delirium. Methods: We reviewed a database of 23,532 patients with diabetes who underwent non-cardiac surgery. Acute hyperglycemia was defined as fasting blood glucose > 140 mg/dl or random glucose > 180 mg/dl within 24 h before surgery. Chronic hyperglycemia was defined as HbA1c level above 6.5% within three months before surgery. The incidence of delirium was compared according to the presence of acute and chronic hyperglycemia. Results: Of the 23,532 diabetic patients, 21,585 had available preoperative blood glucose level within 24 h before surgery, and 18,452 patients reported levels indicating acute hyperglycemia. Of the 8,927 patients with available HbA1c level within three months before surgery, 5,522 had levels indicating chronic hyperglycemia. After adjustment with inverse probability weighting, acute hyperglycemia was related to higher incidence of delirium (hazard ratio: 1.33, 95% CI [1.10,1.62], P = 0.004 for delirium) compared with controls without acute hyperglycemia. On the other hand, chronic hyperglycemia did not correlate with postoperative delirium. Conclusions Preoperative acute hyperglycemia was associated with postoperative delirium, whereas chronic hyperglycemia was not significantly associated with postoperative delirium. Irrespective of chronic hyperglycemia, acute glycemic control in surgical patients could be crucial for preventing postoperative delirium.

The quality factors (kQ,Q0) were evaluated by appling the results recently studied for the effect of central electrode in TRS-398 protocol. The PTW-31010 and IBA-CC13 chambers were used in this study. The quality factors were calculated as a function of beam quality for high energy electron and photon beams and compared with data currently used in TRS-398 protocol. In the PTW-31010 chamber using aluminium electrode, appling the new central electrode collections, the quality factors were 0.4% and 0.9% higher than current TRS-398 data for high energy photon and electron beams respectively. In the IBA-CC13 chamber using C-552 electrode, there are no variations in quality factors compared to TRS-398 data currently used.
Electrodes ; Electrons

Background: To evaluate the association between inflammation and nutrition-based biomarkers and postoperative outcomes after non-cardiac surgery. Methods: Between January 2011 and June 2019, a total of 102,052 patients undergoing non-cardiac surgery were evaluated, with C-reactive protein (CRP), albumin, and complete blood count (CBC) measured within six months before surgery. We assessed their CRP-to-albumin ratio (CAR), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and modified Glasgow Prognostic Score (mGPS). We determined the best cut-off values by using the receiver operating characteristic (ROC) curves. Patients were divided into high and low groups according to the estimated threshold, and we compared the one-year mortality. Results: The one-year mortality of the entire sample was 4.2%. ROC analysis revealed areas under the curve of 0.796, 0.743, 0.670, and 0.708 for CAR, NLR, PLR, and mGPS, respectively. According to the estimated threshold, high CAR, NLR, PLR, and mGPS were associated with increased one-year mortality (1.7% vs. 11.7%, hazard ratio [HR]: 2.38, 95% CI [2.05, 2.76], P < 0.001 for CAR; 2.2% vs. 10.3%, HR: 1.81, 95% CI [1.62, 2.03], P < 0.001 for NLR; 2.6% vs. 10.5%, HR: 1.86, 95% CI [1.73, 2.01], P < 0.001 for PLR; and 2.3% vs. 16.3%, HR: 2.37, 95% CI [2.07, 2.72], P < 0.001 for mGPS). Conclusions Preoperative CAR, NRL, PLR, and mGPS were associated with postoperative mortality. Our findings may be helpful in predicting mortality after non-cardiac surgery.

Purpose: This study aimed to evaluate the clinical infrastructure and utilization of radiotherapy (RT) services in Korea between 2017 and 2019. Materials and Methods: We extracted the data of patients who underwent RT between 2017 and 2019 from the Health Insurance Review and Assessment Service. We further analyzed this data according to the diagnosis and treatment modalities of patients diagnosed with International Classification of Disease 10 (ICD-10) diagnostic codes C00–C97 and D00–D48. In addition, we collected statistics on RT facilities in Korea using a nationwide survey. Results: The total number of patients who received RT in 2017, 2018, and 2019 were 77,901, 81,849, and 87,460, respectively. The number of patients diagnosed with ICD 10 C- and D-codes in 2019 was 86,339, of whom 39,467 were men and 46,872 women. The rate of utilization of RT among cancer patients was 30.4% in 2017 and 2018 and 30.9% in 2019. In 2019, the most common types of cancers treated with RT were breast, lung, prostate, colorectal, and liver cancers. Regarding the RT infrastructure in Korea, there were 95 radiation oncology centers, 237 megavoltage (MV) teletherapy units, 35 brachytherapy units, and two proton accelerators in 2019. There were 4.5 MV teletherapy machines per million. Conclusion The number of patients treated with RT has increased consistently from 2017 to 2019. As the number of patients with cancer increases, it is expected that the RT infrastructure will be further expanded in Korea.

Objectives: The need for interoperability at the national level was highlighted in Korea, leading to a consensus on the importance of establishing national standards that align with international technological standards and reflect contemporary needs. This article aims to share insights into the background of the recent national health data standardization policy, the activities of the Health Data Standardization Taskforce, and the future direction of health data standardization in Korea. Methods: To ensure health data interoperability, the Health Data Standardization Taskforce was jointly organized by the public and private sectors in December 2022. The taskforce operated three working groups. It reviewed international trends in interoperability standardization, assessed the current status of health data standardization, discussed its vision, mission, and strategies, engaged in short-term standardization activities, and established a governance system for standardization. Results: On September 15, 2023, the notice of “Health Data Terminology and Transmission Standards” in Korea was thoroughly revised to improve the exchange of health information between information systems and ensure interoperability. This notice includes the Korea Core Data for Interoperability (KR CDI) and the Korea Core Data Transmission Standard (HL7 FHIR KR Core), which are outcomes of the taskforce’s efforts. Additionally, to reinforce the standardized governance system, the Health-Data Standardization Promotion Committee was established. Conclusions Active interest and support from medical informatics experts are needed for the development and widespread adoption of health data standards in Korea.