BACKGROUND: Recent studies revealed an elevated likelihood of unintended pregnancies among women with psychiatric disorders compared to their counterparts without such vulnerability. Despite the importance of understanding family planning decision-making in this group, qualitative inquiries are lacking. This study explored family planning decisions among women with psychiatric disorders.
METHODS: Utilizing a qualitative approach, three focus group discussions were conducted with purposive sampling: women with a history of unintended pregnancies (N = 3), women without children (N = 5), and women with a history of intended pregnancies (N = 9), all of whom had self-reported psychiatric disorders. Using thematic framework analysis, we investigated the themes \"Shadow of the past,\" reflecting past experiences, and \"Shadow of the future,\" reflecting future imaginaries, building upon the existing \"Narrative Framework.\"
RESULTS: The Narrative Framework formed the foundation for understanding family planning among women with psychiatric disorders. The retrospective dimension of focus group discussions provided opportunities for reflective narratives on sensitive topics, revealing emotions of regret, grief and relief. Childhood trauma, adverse events, and inadequate parenting enriched the \"Shadow of the past\". The \"Shadow of the present\" was identified as a novel theme, addressing awareness of psychiatric disorders and emotions toward psychiatric stability. Social influences, stigma, and concerns about transmitting psychiatric disorders shaped future imaginaries in the shadow of the future.
CONCLUSIONS: This study enlightens how family planning decision-making in women with psychiatric disorders might be complex, as marked by the enduring impact of past experiences and societal influences in this sample. These nuanced insights underscore the necessity for tailored support for women with psychiatric disorders.
Recent studies show that women with psychiatric disorders are more likely to experience unintended pregnancies. However, the underlying reasons are not fully understood. Understanding those reasons is important to provide better healthcare. Our study explored how women with psychiatric disorders make decisions about family planning.We had conversations with different groups of women—women with unintended pregnancies, women without children, and women with intended pregnancies—through focus group discussions. We partnered with the Dutch mental health organization MIND to capture diverse opinions. Key themes and categories in the discussions were identified and organized.We found four main themes: \"Shadow of the past\" showed how past events, trauma, and lack of knowledge about parenting affect family planning. \"Shadow of the present\" revealed different feelings about family planning, the importance of the awareness of psychiatric disorders, and uncertainty about decisions. \"Shadow of the future\" included thoughts about becoming a mother, the impact of social influences, and concerns about passing on psychiatric disorders. \"Reflections on the decision\" showed how psychiatric disorders, experiences with motherhood, and feelings of regret, grief and relief had an influence on family planning decisions.In conclusion, our study highlighted the complexity of family planning decisions for women with psychiatric disorders. Past experiences and societal influences, like stigma, play a big role. These insights show the need for personalized family planning support for women with psychiatric disorders.

BACKGROUND: Surveys suggest a low level of implementation of clinical guidelines, although they are intended to improve the quality of treatment and patient safety. Which guideline recommendations are not followed and why has yet to be analysed. In this study, we investigate the proportion of European and national guidelines followed in the area of pre-operative anaesthetic evaluation prior to non-cardiac surgery.
METHODS: We conducted this monocentric retrospective observational study at a German university hospital with the help of software that logically links guidelines in such a way that individualised recommendations can be derived from a patient\'s data. We included routine logs of 2003 patients who visited our pre-anaesthesia outpatient clinic between June 2018 and June 2020 and compared the actual conducted pre-operative examinations with the recommendations issued by the software. We descriptively analysed the data for examinations not performed that would have been recommended by the guidelines and examinations that were performed even though they were not covered by a guideline recommendation. The guidelines examined in this study are the 2018 ESAIC guidelines for pre-operative evaluation of adults undergoing elective non-cardiac surgery, the 2014 ESC/ESA guidelines on non-cardiac surgery and the German recommendations on pre-operative evaluation on non-cardiothoracic surgery from the year 2017.
RESULTS: Performed ECG (78.1%) and cardiac stress imaging tests (86.1%) indicated the highest guideline adherence. Greater adherence rates were associated with a higher ASA score (ASA I: 23.7%, ASA II: 41.1%, ASA III: 51.8%, ASA IV: 65.8%, P < 0.001), lower BMI and age > 65 years. Adherence rates in high-risk surgery (60.5%) were greater than in intermediate (46.5%) or low-risk (44.6%) surgery (P < 0.001). 67.2% of technical and laboratory tests performed preoperatively were not covered by a guideline recommendation.
CONCLUSIONS: Guideline adherence in pre-operative evaluation leaves room for improvement. Many performed pre-operative examinations, especially laboratory tests, are not recommended by the guidelines and may cause unnecessary costs. The reasons for guidelines not being followed may be the complexity of guidelines and organisational issues. A software-based decision support tool may be helpful.
BACKGROUND: ClinicalTrials.gov ID NCT04843202.

BACKGROUND: Femoral neck fracture (FNF) accounts for approximately 3.58% of all fractures in the entire body, exhibiting an increasing trend each year. According to a survey, in 1990, the total number of hip fractures in men and women worldwide was approximately 338,000 and 917,000, respectively. In China, FNFs account for 48.22% of hip fractures. Currently, many studies have been conducted on postdischarge mortality and mortality risk in patients with FNF. However, there have been no definitive studies on in-hospital mortality or its influencing factors in patients with severe FNF admitted to the intensive care unit.
OBJECTIVE: In this paper, 3 machine learning methods were used to construct a nosocomial death prediction model for patients admitted to intensive care units to assist clinicians in early clinical decision-making.
METHODS: A retrospective analysis was conducted using information of a patient with FNF from the Medical Information Mart for Intensive Care III. After balancing the data set using the Synthetic Minority Oversampling Technique algorithm, patients were randomly separated into a 70% training set and a 30% testing set for the development and validation, respectively, of the prediction model. Random forest, extreme gradient boosting, and backpropagation neural network prediction models were constructed with nosocomial death as the outcome. Model performance was assessed using the area under the receiver operating characteristic curve, accuracy, precision, sensitivity, and specificity. The predictive value of the models was verified in comparison to the traditional logistic model.
RESULTS: A total of 366 patients with FNFs were selected, including 48 cases (13.1%) of in-hospital death. Data from 636 patients were obtained by balancing the data set with the in-hospital death group to survival group as 1:1. The 3 machine learning models exhibited high predictive accuracy, and the area under the receiver operating characteristic curve of the random forest, extreme gradient boosting, and backpropagation neural network were 0.98, 0.97, and 0.95, respectively, all with higher predictive performance than the traditional logistic regression model. Ranking the importance of the feature variables, the top 10 feature variables that were meaningful for predicting the risk of in-hospital death of patients were the Simplified Acute Physiology Score II, lactate, creatinine, gender, vitamin D, calcium, creatine kinase, creatine kinase isoenzyme, white blood cell, and age.
CONCLUSIONS: Death risk assessment models constructed using machine learning have positive significance for predicting the in-hospital mortality of patients with severe disease and provide a valid basis for reducing in-hospital mortality and improving patient prognosis.

UNASSIGNED: The basic product information, health, safety, and nutritional details, as well as food marketing, advertising, and promotion, are all provided on a packed food label. This study was carried out to assess the knowledge, attitude, and practices of the shoppers regarding food labels as there are few studies done in India and none in Pune.
UNASSIGNED: A cross-sectional study was carried out among 226 participants using a validated semi-structured questionnaire, which consisted of sociodemographic details and questions on their knowledge, attitude, and practice regarding food labels while purchasing a packed food item. Quantitative data are presented in the form of mean, standard deviation, 95% confidence interval (CI) of mean, median, and interquartile range (IQR). Qualitative data are in terms of number (N), percentages (%), and 95% CI of percentage.
UNASSIGNED: Of the 226 participants, 163 (72.12) were aware of the label on packaged foods. One hundred seventy-seven (78.32%) participants in all read the label on the packaged food. The maximum retail price (MRP) and expiration date were the most frequent labels sought. Although 17.7% of consumers did not consider nutrient composition when making a purchase, energy and protein were the most often considered nutrients. The majority (70.35%) falsely believed that juice was healthy. The packed food label\'s tiny font made it difficult to read for 30.38% of the customers.
UNASSIGNED: Despite the increased awareness about packed food labeling, a fraction of shoppers were not concerned about nutritional information. In contrast to the prevailing knowledge, the majority believes packed food has a positive effect on health.

BACKGROUND: How healthcare professionals understand and use concepts of social and cognitive capabilities will influence their behaviour and their understanding of others\' behaviour. Differing understandings of concepts might lead to healthcare professionals not acting in accordance with other healthcare professionals\' expectations. Therefore, part of the problem concerning errors and adverse incidents concerning social and cognitive capabilities might be due to varying understandings of concepts among different healthcare professionals. This study aimed to examine the variations in how educators at the Copenhagen Academy for Medical Education and Simulation talk about social and cognitive capabilities.
METHODS: The study was conducted using semi-structured interviews and directed content analysis. The codes for the analysis process were derived from existing non-technical skills models and used to show variations in how the participants talk about the same concepts.
RESULTS: Educators with a background as nurses and physicians, talked differently about leadership and decision-making, with the nurses paying greater attention to group dynamics and external factors when describing both leadership and decision-making, whereas physicians focus on their individual efforts.
CONCLUSIONS: We found patterned differences in how the participants described leadership and decision-making that may be related to participants\' professional training/background. As it can create misunderstandings and unsafe situations if nurses and physicians disagree on the meaning of leadership and decision-making (without necessarily recognising this difference), it could be beneficial to educate healthcare professionals to be aware of the specificity of their own concepts, and to communicate what exactly they mean by using a particular concept, e.g. \"I want you to coordinate tasks\" instead of \"I want better leadership\".

UNASSIGNED: To qualitatively assess surgeons\' decision making for lip surgery in patients with cleft lip/palate (CL/P).
UNASSIGNED: Prospective, non-randomized, clinical trial.
UNASSIGNED: Clinical data institutional laboratory setting.
UNASSIGNED: The study included both patient and surgeon participants recruited from four craniofacial centers. The patient participants were babies with a CL/P requiring primary lip repair surgery (n=16) and adolescents with repaired CL/P who may require secondary lip revision surgery (n=32). The surgeon participants (n=8) were experienced in cleft care. Facial imaging data that included 2D images, 3D images, videos, and objective 3D visual modelling of facial movements were collected from each patient, and compiled as a collage termed the \'Standardized Assessment for Facial Surgery (SAFS)\' for systematic viewing by the surgeons.
UNASSIGNED: The SAFS served as the intervention. Each surgeon viewed the SAFS for six distinct patients (two babies and four adolescents) and provided a list of surgical problems and goals. Then an in-depth-interview (IDI) was conducted with each surgeon to explore their decision-making processes. IDIs were conducted either \'in person\' or virtually, recorded, and then transcribed for qualitative statistical analyses using the Grounded Theory Method.
UNASSIGNED: Rich narratives/themes emerged that included timing of the surgery; risks/limitations and benefits of surgery; patient/family goals; planning for muscle repair and scarring; multiplicity of surgeries and their impact; and availability of resources. In general, there was surgeon agreement for the diagnoses/treatments.
UNASSIGNED: The themes provided important information to populate a checklist of considerations to serve as a guide for clinicians.

UNASSIGNED: In emergency situations, patients and their next of kin must make complex medical and ethical decisions in a quick and timely way.
UNASSIGNED: To describe the decision-making process during informed consent for emergency surgery among patients and the next of kin of patients who have undergone emergency surgery.
UNASSIGNED: Consecutive sampling of 39 participants and in-depth semi-structured interviews were conducted at two tertiary teaching hospitals in Uganda. There were 22 patients and 17 next of kin of patients who had undergone emergency surgery within 24-72 h. Responses about decision-making were coded into themes using the social constructivist theory and phenomenological approach.
UNASSIGNED: There were four emergent themes; decision-makers, people consulted, documentation of the consent and factors influencing decision-making. Most patients and next of kin made decisions on their own and documented the consent for themselves. Other family members and doctors were consulted during the decision-making process. Decision-making was influenced by reassurance of good outcomes of surgery and disclosure by the doctors.
UNASSIGNED: Decisions were made collaboratively with the patient at the center but with input of health personnel, the next of kin and other family members. A communitarian approach combined with shared decision-making between the doctor and the patient and next of kin with adequate discussion and disclosure of information in simple language would improve decision-making for patients and their next of kin.

Purpose: This paper compares two iterations (in-person and online) of a multi-stage continuing education program for improving high-risk decision-making among mental health workers. Methods: The mixed-methods study analyzed the following: (1) physiological and psychological arousal during simulated patient interviews; (2) physiological and psychological arousal recorded during real-time decision-making over four months; and (3) thoughts on the process and outcomes of the intervention raised in reflective interviews. Findings: Quantitatively, there were no statistical differences in stress measures between in-person and online simulated interviews or decision-making logs, suggesting they were effective in eliciting reactions commonly found in challenging clinical situations. Qualitatively, participants in both iterations indicated that the intervention caused them to reflect on practice, consider a wider range of factors related to the decisions, and enact approaches to improve decision-making. Conclusions: A carefully constructed online continuing education experience can result in outcomes for experienced social workers that are equivalent to an in-person iteration.

BACKGROUND: Nearly one-third of patients with diabetes are poorly controlled (hemoglobin A1c≥9%). Identifying at-risk individuals and providing them with effective treatment is an important strategy for preventing poor control.
OBJECTIVE: This study aims to assess how clinicians and staff members would use a clinical decision support tool based on artificial intelligence (AI) and identify factors that affect adoption.
METHODS: This was a mixed methods study that combined semistructured interviews and surveys to assess the perceived usefulness and ease of use, intent to use, and factors affecting tool adoption. We recruited clinicians and staff members from practices that manage diabetes. During the interviews, participants reviewed a sample electronic health record alert and were informed that the tool uses AI to identify those at high risk for poor control. Participants discussed how they would use the tool, whether it would contribute to care, and the factors affecting its implementation. In a survey, participants reported their demographics; rank-ordered factors influencing the adoption of the tool; and reported their perception of the tool\'s usefulness as well as their intent to use, ease of use, and organizational support for use. Qualitative data were analyzed using a thematic content analysis approach. We used descriptive statistics to report demographics and analyze the findings of the survey.
RESULTS: In total, 22 individuals participated in the study. Two-thirds (14/22, 63%) of respondents were physicians. Overall, 36% (8/22) of respondents worked in academic health centers, whereas 27% (6/22) of respondents worked in federally qualified health centers. The interviews identified several themes: this tool has the potential to be useful because it provides information that is not currently available and can make care more efficient and effective; clinicians and staff members were concerned about how the tool affects patient-oriented outcomes and clinical workflows; adoption of the tool is dependent on its validation, transparency, actionability, and design and could be increased with changes to the interface and usability; and implementation would require buy-in and need to be tailored to the demands and resources of clinics and communities. Survey findings supported these themes, as 77% (17/22) of participants somewhat, moderately, or strongly agreed that they would use the tool, whereas these figures were 82% (18/22) for usefulness, 82% (18/22) for ease of use, and 68% (15/22) for clinic support. The 2 highest ranked factors affecting adoption were whether the tool improves health and the accuracy of the tool.
CONCLUSIONS: Most participants found the tool to be easy to use and useful, although they had concerns about alert fatigue, bias, and transparency. These data will be used to enhance the design of an AI tool.

BACKGROUND: Approximately 14 million individuals in the United States are eligible for lung cancer screening (LCS), but only 5.8% completed screening in 2021. Given the low uptake despite the potential great health benefit of LCS, interventions aimed at increasing uptake are warranted. The use of a patient-facing electronic health record (EHR) patient portal direct messaging tool offers a new opportunity to both engage eligible patients in preventative screening and provide a unique referral pathway for tobacco treatment.
OBJECTIVE: This study sought to develop and pilot an EHR patient-facing self-referral tool for an established LCS program in an academic medical center.
METHODS: Guided by constructs of the Health Belief Model associated with LCS uptake (eg, knowledge and self-efficacy), formative development of an EHR-delivered engagement message, infographic, and self-referring survey was conducted. The survey submits eligible self-reported patient information to a scheduler for the LCS program. The materials were pretested using an interviewer-administered mixed methods survey captured through venue-day-time sampling in 5 network-affiliated pulmonology clinics. Materials were then integrated into the secure patient messaging feature in the EHR system. Next, a one-group posttest quality improvement pilot test was conducted.
RESULTS: A total of 17 individuals presenting for lung screening shared-decision visits completed the pretest survey. More than half were newly referred for LCS (n=10, 60%), and the remaining were returning patients. When asked if they would use a self-referring tool through their EHR messaging portal, 94% (n=16) reported yes. In it, 15 participants provided oral feedback that led to refinement in the tool and infographic prior to pilot-testing. When the initial application of the tool was sent to a convenience sample of 150 random patients, 13% (n=20) opened the self-referring survey. Of the 20 who completed the pilot survey, 45% (n=9) were eligible for LCS based on self-reported smoking data. A total of 3 self-referring individuals scheduled an LCS.
CONCLUSIONS: Pretest and initial application data suggest this tool is a positive stimulus to trigger the decision-making process to engage in a self-referral process to LCS among eligible patients. This self-referral tool may increase the number of patients engaging in LCS and could also be used to aid in self-referral to other preventative health screenings. This tool has implications for clinical practice. Tobacco treatment clinical services or health care systems should consider using EHR messaging for LCS self-referral. This approach may be cost-effective to improve LCS engagement and uptake. Additional referral pathways could be built into this EHR tool to not only refer patients who currently smoke to LCS but also simultaneously trigger a referral to clinical tobacco treatment.