Characteristics of the Patient's Internet Account (IKP) users in Poland between 2019 and 2021
Bibliographic record
Abstract
Objective This retrospective database analysis aimed to characterize Patient's Internet Account (IKP) users in Poland, before and after the COVID-19 pandemic onset. Methods Data were received from the e-Health Centre – public administration office tasked with the digitization of healthcare in Poland. Data on the number of newly created Patient's Internet Accounts between January 2019 and December 2021 were collected. Moreover, data on the gender and age of the users were also analyzed. Results Between January 2019 and December 2021, the cumulative number of Patient’s Internet Account users increased from 32.6 thousand to 14.1 million. In 2021, December 2021, the cumulative number of Patient’s Internet Account users more than doubled (from 5.6 million in January to 14.1 million in December). In 2019, the monthly number of newly created Patient’s Internet Accounts varied from 17 thousand in February to 180 thousand in December. In the last quarter of 2020 (lifting major anti-epidemic restrictions), a significant increase in the monthly number of newly created Patient’s Internet Accounts was observed (500 thousand accounts per month). The highest number of newly created Patient’s Internet Accounts (over 1.25 million) was in April and July 2021. In December 2021, the highest number (3.3 million) of active Patient’s Internet Accounts was among children aged 0-17 (parental access) and Individuals aged 31-40 years (2.7 million). Conclusions This study revealed high interest in Patient’s Internet Accounts during the COVID-19 pandemic. An increase in the number of Patient’s Internet Account users was related to the new service functionalities such as COVID-19 test results and COVID-19 vaccination appointments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".