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: 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.

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.