by Jung-ho Shin, Daisuke Takada, Tetsuji Morishita, Hueiru Lin, Seiko Bun, Emi Teraoka, Takuya Okuno, Hisashi Itoshima, Hiroyuki Nagano, Kenji Kishimoto, Hiromi Segawa, Yuka Asami, Takuya Higuchi, Kenta Minato, Susumu Kunisawa, Yuichi ImanakaBackground
In response to the coronavirus diseases 2019 (COVID-19) pandemic, the Japanese government declared a state of emergency on April 7, 2020. Six days earlier, the Japan Surgical Society had recommended postponing elective surgical procedures. Along with the growing public fear of COVID-19, hospital visits in Japan decreased.Methods
Using claims data from the Quality Indicator/Improvement Project (QIP) database, this study aimed to clarify the impact of the first wave of the pandemic, considered to be from March to May 2020, on case volume and claimed hospital charges in acute care hospitals during this period. To make year-over-year comparisons, we considered cases from July 2018 to June 2020.Results
A total of 2,739,878 inpatient and 53,479,658 outpatient cases from 195 hospitals were included. In the year-over-year comparisons, total claimed hospital charges decreased in April, May, June 2020 by 7%, 14%, and 5%, respectively, compared to the same months in 2019. Our results also showed that per-case hospital charges increased during this period, possibly to compensate for the reduced case volumes. Regression results indicated that the hospital charges in April and May 2020 decreased by 6.3% for hospitals without COVID-19 patients. For hospitals with COVID-19 patients, there was an additional decrease in proportion with the length of hospital stay of COVID-19 patients including suspected cases. The mean additional decrease per COVID-19 patient was estimated to 5.5 million JPY.Conclusion
It is suggested that the hospitals treating COVID-19 patients were negatively incentivized.
Studies addressing the development and/or validation of diagnostic and prognostic prediction models are abundant in most clinical domains. Systematic reviews have shown that the methodological and reporting quality of prediction model studies is suboptimal. Due to the increasing availability of larger, routinely collected and complex medical data, and the rising application of Artificial Intelligence (AI) or machine learning (ML) techniques, the number of prediction model studies is expected to increase even further. Prediction models developed using AI or ML techniques are often labelled as a ‘black box’ and little is known about their methodological and reporting quality. Therefore, this comprehensive systematic review aims to evaluate the reporting quality, the methodological conduct, and the risk of bias of prediction model studies that applied ML techniques for model development and/or validation.
A search will be performed in PubMed to identify studies developing and/or validating prediction models using any ML methodology and across all medical fields. Studies will be included if they were published between January 2018 and December 2019, predict patient-related outcomes, use any study design or data source, and available in English. Screening of search results and data extraction from included articles will be performed by two independent reviewers. The primary outcomes of this systematic review are: (1) the adherence of ML-based prediction model studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD), and (2) the risk of bias in such studies as assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). A narrative synthesis will be conducted for all included studies. Findings will be stratified by study type, medical field and prevalent ML methods, and will inform necessary extensions or updates of TRIPOD and PROBAST to better address prediction model studies that used AI or ML techniques.
Ethical approval is not required for this study because only available published data will be analysed. Findings will be disseminated through peer-reviewed publications and scientific conferences.
Iodinated contrast media are commonly used in medical imaging and can cause hypersensitivity reactions, including rare but severe life-threatening reactions. Although several prophylactic approaches have been proposed for severe reactions, their effects remain unclear. Therefore, we aim to review systematically the preventive effects of pharmacologic and non-pharmacologic interventions and predictors of acute, hypersensitivity reactions.
We will search the PubMed, EMBASE and Cochrane Central Register of Controlled Trials databases from 1 January 1990 through 31 December 2019 and will examine the bibliographies of eligible studies, pertinent review articles and clinical practice guidelines. We will include prospective and retrospective studies of any design that evaluated the effects of pharmacological and non-pharmacological preventive interventions for adverse reactions of non-ionic iodinated contrast media. Two assessors will independently extract the characteristics of the study and intervention and the quantitative results. Two independent reviewers will assess the risk of bias using standard design-specific validity assessment tools. The primary outcome will be reduction in acute contrast media-induced hypersensitivity reactions. The secondary outcomes will include characteristics associated with the development of contrast media-induced acute hypersensitivity reactions, and adverse events associated with specific preventive interventions. Unique premedication regimens (eg, dose, drug and duration) and non-pharmacological strategies will be analysed separately. Average-risk and high-risk patients will be considered separately. A meta-analysis will be performed if appropriate.
Ethics approval is not applicable, as this will be a secondary analysis of publicly available data. The results of the analysis will be submitted for publication in a peer reviewed journal.
To delineate the critical decision-making processes that paediatricians apply when treating children with life-threatening conditions and the psychosocial experience of paediatricians involved in such care.
We conducted semistructured, individual face-to-face interviews for each participant from 2014 to 2015. The content of each interview was subjected to a comprehensive qualitative analysis. The categories of dilemma were extracted from a second-round content analysis.
Participants were board-certified paediatricians with sufficient experience in making decisions in relation to children with severe illnesses or disabilities. We repeated purposive sampling and analyses until we reached saturation of the category data.
We performed interviews with 15 paediatricians. They each reported both unique and overlapping categories of dilemmas that they encountered when making critical decisions. The dilemmas included five types of causal elements: (1) paediatricians’ convictions; (2) the quest for the best interests of patients; (3) the quest for medically appropriate plans; (4) confronting parents and families and (5) socioenvironmental issues. Dilemmas occurred and developed as conflicting interactions among these five elements. We further categorised these five elements into three principal domains: the decision-maker (decider); consensus making among families, colleagues and society (process) and the consequential output of the decision (consequence).
This is the first qualitative study to demonstrate the framework of paediatricians’ decision-making processes and the complex structures of dilemmas they face. Our data indicate the necessity of establishing and implementing an effective support system for paediatricians, such as structured professional education and arguments for creating social consensus that assist them to reach the best plan for the management of severely ill children.