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Abstract BackgroundOver 200 health information exchanges (HIEs) are currently operational in Japan. The most common feature of HIEs is remote on-demand viewing or searching of aggregated patient health data from multiple institutions. However, the usage of this feature by individual users and institutions remains unknown. ObjectiveThis study aims to understand usage of the on-demand patient data viewing feature of large-scale HIEs by individual health care workers and institutions in Japan. MethodsWe conducted audit log analyses of large-scale HIEs. The research subjects were HIEs connected to over 100 institutions and with over 10,000 patients. Each health care worker’s profile and audit log data for HIEs were collected. We conducted four types of analyses on the extracted audit log. First, we calculated the ratio of the number of days of active HIE use for each hospital-affiliated doctor account. Second, we calculated cumulative monthly usage days of HIEs by each institution in financial year (FY) 2021/22. Third, we calculated each facility type’s monthly active institution ratio in FY2021/22. Fourth, we compared the monthly active institution ratio by medical institution for each HIE and the proportion of cumulative usage days by user type for each HIE. ResultsWe identified 24 HIEs as candidates for data collection and we analyzed data from 7 HIEs. Among hospital doctors, 93.5% (7326/7833) had never used HIEs during the available period in FY2021/22, while 19 doctors used them at least 30% of days. The median (IQR) monthly active institution ratios were 0.482 (0.470‐0.487) for hospitals, 0.243 (0.230‐0.247) for medical clinics, and 0.030 (0.024‐0.048) for dental clinics. In 51.9% (1781/3434) of hospitals, the cumulative monthly usage days of HIEs was 0, while in 26.8% (921/3434) of hospitals, it was between 1 and 10, and in 3% (103/3434) of hospitals, it was 100 or more. The median (IQR) monthly active institution ratio in medical institutions was 0.511 (0.487‐0.529) for the most used HIE and 0.109 (0.0927‐0.117) for the least used. The proportion of cumulative usage days of HIE by user type was complex for each HIE, and no consistent trends could be discerned. ConclusionsIn the large-scale HIEs surveyed in this study, the overall usage of the on-demand patient data viewing feature was low, consistent with past official reports. User-level analyses of audit logs revealed large disparities in the number of days of HIE use among health care workers and institutions. There were also large disparities in HIE use by facility type or HIE; the percentage of cumulative HIE usage days by user type also differed by HIE. This study indicates the need for further research into why there are large disparities in demand for HIEs in Japan as well as the need to design comprehensive audit logs that can be matched with other official datasets.

BackgroundHereditary angioedema (HAE), a rare genetic disease, induces acute attacks of swelling in various regions of the body. Its prevalence is estimated to be 1 in 50,000 people, with no reported bias among different ethnic groups. However, considering the estimated prevalence, the number of patients in Japan diagnosed with HAE remains approximately 1 in 250,000, which means that only 20% of potential HAE cases are identified. ObjectiveThis study aimed to develop an artificial intelligence (AI) model that can detect patients with suspected HAE using medical history data (medical claims, prescriptions, and electronic medical records [EMRs]) in the United States. We also aimed to validate the detection performance of the model for HAE cases using the Japanese dataset. MethodsThe HAE patient and control groups were identified using the US claims and EMR datasets. We analyzed the characteristics of the diagnostic history of patients with HAE and developed an AI model to predict the probability of HAE based on a generalized linear model and bootstrap method. The model was then applied to the EMR data of the Kyoto University Hospital to verify its applicability to the Japanese dataset. ResultsPrecision and sensitivity were measured to validate the model performance. Using the comprehensive US dataset, the precision score was 2% in the initial model development step. Our model can screen out suspected patients, where 1 in 50 of these patients have HAE. In addition, in the validation step with Japanese EMR data, the precision score was 23.6%, which exceeded our expectations. We achieved a sensitivity score of 61.5% for the US dataset and 37.6% for the validation exercise using data from a single Japanese hospital. Overall, our model could predict patients with typical HAE symptoms. ConclusionsThis study indicates that our AI model can detect HAE in patients with typical symptoms and is effective in Japanese data. However, further prospective clinical studies are required to investigate whether this model can be used to diagnose HAE.

Background: The damage that may be caused to the operating table and patients under general anaesthesia when a large earthquake occurs is unclear. We aimed to evaluate the movement and damage to operating tables and patients under general anaesthesia during an earthquake. Methods: An operating table with a manikin resembling a patient on it was placed on a shaking table, and seismic waves were input into the shaking table. The effects of seismic waves were evaluated by altering surgical positions (supine and head-down positions), operating tables, flooring material, seismic waves, and output. We observed the movement of the operating table and measured the acceleration of the operating table and manikin head. Results: Under 90% output of long-period seismic waves, the operating table with the supine manikin was overturned. Under experimental conditions that did not cause rocking, shaking such as tilting of the operating table caused stronger acceleration in the manikin's head than in the operating table. There was no clear relationship between operating table rocking and maximum acceleration as a result of programmed seismic waves. In long-period earthquakes, rocking and overturning occurred >60 s after the onset of shaking, whereas in direct earthquakes, rocking occurred within 10 s. Conclusions: An earthquake could cause strong acceleration of the patient's head under general anaesthesia, and operating tables may overturn or shake violently. Regarding patient safety, further measures to prevent overturning should be considered.

BackgroundMultiple studies have examined the impact of deferral on the motivation of prospective blood donors, proposing various policies and strategies to support individuals who undergo this experience. However, existing information and communications technology systems focused on blood donation have not yet integrated these ideas or provided options to assist with the deferral experience. ObjectiveThis study aims to propose an initial gamified design aimed at mitigating the impact of the deferral experience by addressing the drivers of awareness and knowledge, interaction and validation, and motivation. Additionally, the study explores the feasibility of implementing such a system for potential users. MethodsWe conducted a literature review focusing on the dynamics of motivation and intention related to blood donation, as well as the deferral situation and its impact on citizens. Through this review, we identified weak donor identity, lack of knowledge, and reduced motivation as key factors requiring support from appropriate interventions. These factors were then defined as our key drivers. Taking these into account, we proposed a gamification approach that incorporates concepts from the MDA framework. The aim is to stimulate the aforementioned drivers and expand the concept of contribution and identity in blood donation. For a preliminary evaluation, we designed a prototype to collect feedback on usability, usefulness, and interest regarding a potential implementation of our proposed gamification approach. ResultsAmong the participants, a total of 11 citizens interacted with the app and provided feedback through our survey. They indicated that interacting with the app was relatively easy, with an average score of 4.13 out of 5 when considering the 11 tasks of interaction. The SUS results yielded a final average score of 70.91 from the participants’ answers. Positive responses were received when participants were asked about liking the concept of the app (3.82), being likely to download it (3.55), and being likely to recommend it to others (3.64). Participants expressed positivity about the implementation of the design but also highlighted current shortcomings and suggested possible improvements in both functionality and usability. ConclusionsAlthough deferral is a common issue in blood donation, there is a missed opportunity in existing ICT services regarding how to effectively handle such experiences. Our proposed design and implementation seem to have captured the interest of prospective users due to its perceived positive usefulness and potential. However, further confirmation is needed. Improving the design of activities that currently rely heavily on extrinsic motivation elements and integrating more social components to create an enhanced activity loop for intrinsic motivation could further increase the value of the proposed project. Future research could involve conducting a more specialized and longitudinal design evaluation with a larger sample size.

Abstract To improve patients’ adherence to continuous positive airway pressure (CPAP) therapy, this study aimed to clarify whether machine learning-based data analysis can identify the factors related to poor CPAP adherence (i.e., CPAP usage that does not reach four hours per day for five days a week). We developed a CPAP adherence prediction model using logistic regression and learn-to-rank machine learning with a pairwise approach. We then investigated adherence prediction performance targeting a 12-week period and the top ten factors correlating to poor CPAP adherence. The CPAP logs of 219 patients with obstructive sleep apnea (OSA) obtained from clinical treatment at Kyoto University Hospital were used. The highest adherence prediction accuracy obtained was an F1 score of 0.864. Out of the top ten factors obtained with the highest prediction accuracy, four were consistent with already-known clinical knowledge. The factors for better CPAP adherence indicate that air leakage should be avoided, mask pressure should be kept constant, and CPAP usage duration should be longer and kept constant. The results indicate that machine learning is an adequate method for investigating factors related to poor CPAP adherence.

Abstract Poor implementation and variable quality of cardiac rehabilitation (CR) for coronary heart disease (CHD) have been a global concern. This nationwide study aimed to clarify the implementation of and participation in CR among CHD patients and associated factors in Japan. We conducted a retrospective cohort study using data extracted from the National Database of Health Insurance Claims and Specific Health Checkups of Japan. Patients who underwent percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) in 2017–2018 were included. Aspects of CR were assessed in terms of (1) participation in exercise-based CR, (2) pharmacological education, and (3) nutritional education. Of 87,829 eligible patients, 32% had participated in exercise-based CR, with a mean program length of 40 ± 71 days. CABG was associated with higher CR participation compared to PCI (OR 10.2, 95% CI 9.6–10.8). Patients living in the Kyushu region were more likely to participate in CR (OR 2.59, 95% CI 2.39–2.81). Among patients who participated in CR, 92% received pharmacological education, whereas only 67% received nutritional education. In Japan, the implementation of CR for CHD is insufficient and involved varying personal, therapeutic, and geographical factors. CR implementation needs to be promoted in the future.

Purpose: To elucidate the incidence and treatment pattern of active exudative age-related macular degeneration (AMD). Design: A population-based cohort study conducted using the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB), a national claims database managed by the Japanese Ministry of Health, Labour, and Welfare (MHLW). Participants: The entire Japanese population aged 40 years or older (76 million people). Methods: With the permission of the MHLW, we accessed the complete NDB dataset and identified patients with newly diagnosed active exudative AMD between 2011 and 2018. The incidence of active exudative AMD was categorized by age and sex per year between 2011 and 2018; moreover, details regarding first-line therapy and number of anti-vascular endothelial growth factor (VEGF) injections per elapsed year since initial treatment were obtained and changes in treatment pattern were investigated. Main Outcome Measures: Incidence rate of active exudative AMD. Results: During the specified 8-year period, 246 064 incident cases of active exudative AMD were identified; 61.4% of these patients were men. The overall incidence rate was 40.66 per 100 000 person-years (95% confidence interval [CI], 40.49–40.82) in the general population aged 40 years or older, 53.22 (95% CI, 52.95–53.49) in men, and 29.78 (95% CI, 29.60–29.98) in women. Mean age of onset was lower in men than in women (72.51 ± 10.50 years vs. 73.90 ± 10.46 years). Among patients with newly diagnosed active exudative AMD, 92.9% received anti-VEGF injections for initial treatments, whereas 1.8% underwent combination therapy with photodynamic therapy. The number of anti-VEGF injections in the first year (0–12 months), second year (13–24 months), and third year (25–36 months) after the initial injection was 3.66 ± 2.30, 1.39 ± 2.20, and 1.23 ± 2.19, respectively. Patients who received fewer injections in the first year received fewer injections in subsequent years and vice versa. Conclusions: This is a relatively large population-based study on the detailed epidemiology and actual treatment patterns of active exudative AMD in clinical practice. Our results can be a fundamental information source to ensure healthy eyes and promote well-being for all at all ages.

Azoles are five-membered heterocycles found in peptidic natural products and synthetic peptiodomimetics. Here the authors demonstrate a posttranslational chemical modification method for in vitro ribosomal synthesis of peptides with exotic azole groups at specific positions.

BackgroundHospital bed management is an important resource allocation task in hospital management, but currently, it is a challenging task. However, acquiring an optimal solution is also difficult because intraorganizational information asymmetry exists. Signaling, as defined in the fields of economics, can be used to mitigate this problem. ObjectiveWe aimed to develop an assignment process that is based on a token economy as signaling intermediary. MethodsWe implemented a game-like simulation, representing token economy–based bed assignments, in which 3 players act as ward managers of 3 inpatient wards (1 each). As a preliminary evaluation, we recruited 9 nurse managers to play and then participate in a survey about qualitative perceptions for current and proposed methods (7-point Likert scale). We also asked them about preferred rewards for collected tokens. In addition, we quantitatively recorded participant pricing behavior. ResultsParticipants scored the token economy–method positively in staff satisfaction (3.89 points vs 2.67 points) and patient safety (4.38 points vs 3.50 points) compared to the current method, but they scored the proposed method negatively for managerial rivalry, staff employee development, and benefit for patients. The majority of participants (7 out of 9) listed human resources as the preferred reward for tokens. There were slight associations between workload information and pricing. ConclusionsSurvey results indicate that the proposed method can improve staff satisfaction and patient safety by increasing the decision-making autonomy of staff but may also increase managerial rivalry, as expected from existing criticism for decentralized decision-making. Participant behavior indicated that token-based pricing can act as a signaling intermediary. Given responses related to rewards, a token system that is designed to incorporate human resource allocation is a promising method. Based on aforementioned discussion, we concluded that a token economy–based bed allocation system has the potential to be an optimal method by mitigating information asymmetry.

Abstract Aims/Introduction Although the epidemiological relationship between hypoglycemia and increased risk of acute coronary syndrome (ACS) has been well established, the time period for increased risk of ACS after a severe hypoglycemic episode remains unknown. The present study aimed to determine the ACS risk after a severe hypoglycemic episode. Materials and Methods We carried out a retrospective population‐based cohort study based on national claims data in Japan. We retrieved data of diabetes patients aged ≥35 years collected from April 2014 to March 2016. The absolute risk of ACS was defined as the occurrence of an emergency percutaneous coronary intervention after a severe hypoglycemic episode. Results In total, data of 7,909,626 patients were included in the analysis. The absolute risk of ACS was 2.9 out of 1,000 person‐years in all patients. ACS risk in patients with severe hypoglycemic episodes was 3.0 out of 1,000 person‐years. Severe hypoglycemic episodes increased the absolute risk of ACS in patients aged ≥70 years, but not in patients aged

Cardiovascular and cerebrovascular diseases are frequently interconnected due to underlying pathology involving atherosclerosis and thromboembolism. The aim of this study was to investigate the impact of clinical interactions among cardiovascular and cerebrovascular diseases on patient outcomes using a large-scale nationwide claims-based dataset. Cardiovascular diseases were defined as myocardial infarction, heart failure, atrial fibrillation, and aortic dissection. Cerebrovascular diseases were defined as cerebral infarction, intracerebral hemorrhage, and subarachnoid hemorrhage. This retrospective study included 2,736,986 inpatient records (1,800,255 patients) at 911 hospitals from 2015 to 2016 from Japanese registry of all cardiac and vascular disease-diagnostic procedure combination dataset. Interactions among comorbidities and complications, rehospitalization, and clinical outcomes including in-hospital mortality were investigated. Among hospitalization records that involved cardiovascular disease, 5.9% (32,686 records) had cerebrovascular disease as a comorbidity and 2.1% (11,362 records) included an incident cerebrovascular complication after hospitalization. Cerebrovascular disease as a comorbidity or complication was associated with higher in-hospital mortality than no cerebrovascular disease (adjusted odds ratio (OR) [95% confidence interval]: 1.10 [1.06–1.14], 2.02 [1.91–2.13], respectively). Among 367,904 hospitalization records that involved cerebrovascular disease, 17.7% (63,647 records) had cardiovascular disease listed as comorbidity and 3.3% (11,834 records) as a complication. Only cardiovascular disease as a complication was associated with higher in-hospital mortality (adjusted OR [95% confidence interval]: 1.29 [1.22–1.37]). In addition, in-hospital mortality during rehospitalization due to the other disease was significantly higher than mortality during the hospitalization due to the first disease. In conclusion, substantial associations were observed between cardiovascular and cerebrovascular disease in a large-scale nationwide claims-based dataset; these associations had a significant impact on clinical outcomes. More intensive prevention and management of cardiovascular and cerebrovascular disease might be crucial.

Abstract Epyrifenacil is a novel herbicide that acts as an inhibitor of protoporphyrinogen oxidase (PPO) and produces hepatotoxicity in rodents by inhibiting PPO. Our previous research revealed that the causal substance of hepatotoxicity is S‐3100‐CA, a major metabolite of epyrifenacil, and that human hepatocyte uptake of S‐3100‐CA was significantly lower than rodent one, suggesting less relevant to hepatotoxicity in humans. To clarify the species difference in the uptake of S‐3100‐CA, we focused on organic anion transporting polypeptides (OATPs) and carried out an uptake assay using human, rat, and mouse OATP hepatic isoforms‐expressing 293FT cells. As a result, all the examined OATPs were found to contribute to the S‐3100‐CA uptake, suggesting that the species difference was not due to the differences in selectivity toward OATP isoforms. When [14C]epyrifenacil was administered to mice, the liver concentration of S‐3100‐CA was higher in males than in females. Furthermore, when [14C]epyrifenacil was administered with OATP inhibitors, the liver/plasma ratio of S‐3100‐CA was significantly decreased by rifampicin, an Oatp1a1/Oatp1a4 inhibitor in mice, but not by digoxin, an Oatp1a4‐specific inhibitor. This result indicates that Oatp1a1, the predominant transporter in male mice, is the main contributor to the hepatic transport of S‐3100‐CA, and consequently to the gender difference. Moreover, we conclude that the species difference in the hepatic uptake of S‐3100‐CA observed in our previous research is not due to differences in the selectivity toward OATP isoforms but rather to the significantly higher expression of OATPs which mediate uptake of S‐3100‐CA in rodents than in humans.

Abstract Background Cost effectiveness research is emerging in the chronic kidney disease (CKD) research field. Especially, an individual-level state transition model (microsimulation) is widely used for these researches. Some researchers set CKD grades as discrete health states, and the transition probabilities between these states were dependent on the CKD grades (disease grade-based microsimulation, MSM-dg), while others set estimated glomerular filtration rate value which determines the severity of CKD as a main variable describing patients’ continuous status (kidney function-based microsimulation, MSM-kf). MSM-kf seems to reflect the real world more precisely but is more difficult to implement. We compared the calculation results of these two microsimulation models to evaluate the effect of model selection on CKD cost-effectiveness analysis. Methods We implemented simplified MSM-dg and MSM-kf emulating natural course of CKD in general, and compared models using parameters derived from an IgA nephropathy cohort. After checking these models’ overall behavior, life-years, utilities, and thresholds regarding intervention costs below which the intervention is thought as dominant (V0) or cost-effective (V1) were calculated. In addition, one-way and probabilistic sensitivity analyses were performed. Results With base-case parameters, the calculated life-years was shorter in MSM-dg (73.89 vs. 75.80 years) while the thresholds were almost equal (86.87 vs. 90.43 (V0), 132.29 vs. 146.25 [V1 in 1000 USD]) compared to MSM-kf. Sensitivity analyses showed a tendency of the MSM-dg to show shorter results in life-years. V0 and V1 were distributed by approximately ±100,000 USD (V0) and ± 150,000 USD (V1) between models. Conclusions Estimated cost-effectiveness thresholds by both models were not the same and its difference distributed too wide to be ignored. This result indicated that model selection in CKD cost-effectiveness research has large effect on their conclusions.

Abstract As Japan becomes a super-aging society, presentation of the best ways to provide medical care for the elderly, and the direction of that care, are important national issues. Elderly people have multi-morbidity with numerous medical conditions and use many medical resources for complex treatment patterns. This increases the likelihood of inappropriate medical practices and an evidence-practice gap. The present study aimed to: derive findings that are applicable to policy from an elucidation of the actual state of medical care for the elderly; establish a foundation for the utilization of National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB), and present measures for the utilization of existing databases in parallel with NDB validation. Cross-sectional and retrospective cohort studies were conducted using the NDB built by the Ministry of Health, Labor and Welfare of Japan, private health insurance claims databases, and the Kyoto University Hospital database (including related hospitals). Medical practices (drug prescription, interventional procedures, testing) related to four issues—potential inappropriate medication, cancer therapy, chronic kidney disease treatment, and end-of-life care—will be described. The relationships between these issues and clinical outcomes (death, initiation of dialysis and other adverse events) will be evaluated, if possible.

We aimed to evaluate a computer-aided diagnosis (CADx) system for lung nodule classification focussing on (i) usefulness of the conventional CADx system (hand-crafted imaging feature + machine learning algorithm), (ii) comparison between support vector machine (SVM) and gradient tree boosting (XGBoost) as machine learning algorithms, and (iii) effectiveness of parameter optimization using Bayesian optimization and random search. Data on 99 lung nodules (62 lung cancers and 37 benign lung nodules) were included from public databases of CT images. A variant of the local binary pattern was used for calculating a feature vector. SVM or XGBoost was trained using the feature vector and its corresponding label. Tree Parzen Estimator (TPE) was used as Bayesian optimization for parameters of SVM and XGBoost. Random search was done for comparison with TPE. Leave-one-out cross-validation was used for optimizing and evaluating the performance of our CADx system. Performance was evaluated using area under the curve (AUC) of receiver operating characteristic analysis. AUC was calculated 10 times, and its average was obtained. The best averaged AUC of SVM and XGBoost was 0.850 and 0.896, respectively; both were obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters for achieving high AUC were obtained with fewer numbers of trials when using TPE, compared with random search. Bayesian optimization of SVM and XGBoost parameters was more efficient than random search. Based on observer study, AUC values of two board-certified radiologists were 0.898 and 0.822. The results show that diagnostic accuracy of our CADx system was comparable to that of radiologists with respect to classifying lung nodules.

We developed a computer-aided diagnosis (CADx) method for classification between benign nodule, primary lung cancer, and metastatic lung cancer and evaluated the following: (i) the usefulness of the deep convolutional neural network (DCNN) for CADx of the ternary classification, compared with a conventional method (hand-crafted imaging feature plus machine learning), (ii) the effectiveness of transfer learning, and (iii) the effect of image size as the DCNN input. Among 1240 patients of previously-built database, computed tomography images and clinical information of 1236 patients were included. For the conventional method, CADx was performed by using rotation-invariant uniform-pattern local binary pattern on three orthogonal planes with a support vector machine. For the DCNN method, CADx was evaluated using the VGG-16 convolutional neural network with and without transfer learning, and hyperparameter optimization of the DCNN method was performed by random search. The best averaged validation accuracies of CADx were 55.9%, 68.0%, and 62.4% for the conventional method, the DCNN method with transfer learning, and the DCNN method without transfer learning, respectively. For image size of 56, 112, and 224, the best averaged validation accuracy for the DCNN with transfer learning were 60.7%, 64.7%, and 68.0%, respectively. DCNN was better than the conventional method for CADx, and the accuracy of DCNN improved when using transfer learning. Also, we found that larger image sizes as inputs to DCNN improved the accuracy of lung nodule classification.

We aimed to develop an adaptable prognosis prediction model that could be applied at any time point during the treatment course for patients with cancer receiving chemotherapy, by applying time-series real-world big data.Between April 2004 and September 2014, 4,997 patients with cancer who had received systemic chemotherapy were registered in a prospective cohort database at the Kyoto University Hospital. Of these, 2,693 patients with a death record were eligible for inclusion and divided into training (n = 1,341) and test (n = 1,352) cohorts. In total, 3,471,521 laboratory data at 115,738 time points, representing 40 laboratory items [e.g., white blood cell counts and albumin (Alb) levels] that were monitored for 1 year before the death event were applied for constructing prognosis prediction models. All possible prediction models comprising three different items from 40 laboratory items (40C3 = 9,880) were generated in the training cohort, and the model selection was performed in the test cohort. The fitness of the selected models was externally validated in the validation cohort from three independent settings.A prognosis prediction model utilizing Alb, lactate dehydrogenase, and neutrophils was selected based on a strong ability to predict death events within 1-6 months and a set of six prediction models corresponding to 1,2, 3, 4, 5, and 6 months was developed. The area under the curve (AUC) ranged from 0.852 for the 1 month model to 0.713 for the 6 month model. External validation supported the performance of these models.By applying time-series real-world big data, we successfully developed a set of six adaptable prognosis prediction models for patients with cancer receiving chemotherapy.

Woman-centered antenatal care necessitates Electronic Medical Record (EMR) systems that respect women’s preferences. However, women’s preferences regarding EMR systems in antenatal care remain unknown. This work aims to understand the EMR-related experiences that pregnant Japanese women want. First, we conducted a field-based observational study at an antenatal care clinic at a Japanese university hospital. We analyzed the data following a thematic analysis approach and found multiple EMR-related experiences that pregnant women encounter during antenatal care. Based on the observations’ findings, we administered a web survey to 413 recently pregnant Japanese women to understand their attitudes regarding the EMR-related experiences. Our results show that pregnant Japanese women want accessible, exchangeable, and biopsychosocial EMRs. They also want EMR-enabled explanations and summaries. Interestingly, differences in their demographics and stages of pregnancy affected their attitudes towards some EMR-related experiences. To respect their preferences, we propose amplifying the roles of EMR systems as tools that promote communication and woman-centeredness in antenatal care. We also propose expanding the EMR design mindset from a biomedical to a biopsychosocial-oriented one. Finally, to accommodate the differences in individual needs and preferences, we propose the design of adaptable person-centered EMR systems.

Background: One of the disadvantages of medical documentation in Electronic Medical Record (EMR) systems is that records tend to be redundant by “copying and pasting”, a writing style to duplicate and revise previous records. In this study, we analyzed the similarity between records to identify the factors affecting the writing style of clinical notes. Method: We analyzed 98,038 records of 4,149 patients from two years in the Department of Obstetricians and Gynecology at Kyoto University Hospital, Japan. We observed the correlation between the distribution of the record similarity and string amounts, as well as the disease codes and ratios of outpatient visit. Results: The patient group with high record similarity and large number of strings was the group with reproductive medicine, followed by the group of malignant tumor follow-up or Women's Healthcare. Discussion: In reproductive medicine, physicians have a demand for an overarching evaluation, and in follow-up malignancies or in Women's Healthcare, they have a demand to check for subtle differences from the last time. These facts along with our data insist that the writing style in EMR systems is related to the patient's status. Conclusion: We declared that the writing style in EMR systems is affected by the patient's status. The writing style of duplicating and revising is preferred (1) when there is a clinical demand for an overarching evaluation, and (2) when there is a clinical demand to check for subtle differences from the last time. [ABSTRACT FROM AUTHOR]

Patient perception involves a patient's thoughts and beliefs regarding their health status. It is also associated with medical compliance and outcomes. However, discrepancies often arise between patient perception and physicians' documentation within the medical records, resulting in misunderstanding and suboptimal doctor-patient communication. In this study, we assessed the efficacy of generative artificial intelligence (AI) in comparing the content of patient perception as recorded in patient questionnaires and physicians' records of the Department of Breast Surgery. We evaluated the precision and recall of the generative AI by comparison with human-created ground truth. Our results demonstrated the high performance of the generative AI in comprehending and contrasting symptoms and the entire content recorded differently by patients and physicians, with F1 scores ranging from 0.77 to 0.97. These results highlight the potential contribution of a generative AI to deeper mutual comprehension in healthcare scenarios. [ABSTRACT FROM AUTHOR]