Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to investigate the (...) patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

Diabetic ketoacidosis is a serious, medical emergency that can be fatal but treatable, we aimed to evaluate the clinical profile of patients admitted with diabetic ketoacidosis. This case series study enrolled 213 non-pregnant adult and adolescent patients admitted with diabetic ketoacidosis at Tripoli Diabetes Hospital from January to September 2023. Demographic data, clinical characteristics, laboratory findings, precipitating factors, and patient outcomes were extracted from medical records and analyzed. Type 1 diabetes mellitus was present in 187 (87.8%) of patients, (...) the age range 11-84 years, (30.26±13.28), with 130 patients ≤30 years old (61.0%), females accounting for 110 of the patients (51.6%), 109 had a diabetes duration of less than 10 years (51.2%). The most common precipitating factor was insulin omission 92 (43.2%) in patients with known diabetes mellitus, whereas, diabetic ketoacidosis as the first presentation of diabetes mellitus in 24 patients (11.7%), systolic blood pressure ranged 50-160 mmHg (112.82±16.19), diastolic blood pressure varied 30-100 mmHg (70.24±11.32). Plasma glucose at presentation ranged 183-1494 mg/dl (462.92±169.85), with Eu-glycemic diabetic ketoacidosis 100-249 mg/dl were in nine patients (4.2%), whereas most of cases (135 patients, 63.4%) present with plasma glucose 250-500 mg/dl, while hyperosmolar status (≥701 mg/dl) were present in 13 patients (6.1%). Venous pH varied from 6.7-7.42 (7.13±0.14), 132 (62.0%) patients presented with pH <7.24, while severe acidosis was pH < 7 in 32 of patients (14.6%), serum bicarbonate with 10.93±4.95, severe (<5 mmol) in 91 patients (42.7%) and moderate (5-10 mmol) were in 79 patients (37.08%). The mean length of hospital stay was 3.33 days, with an average of 2.11 days spent in the high-dependent unit. Serum potassium varied from 2.4-6.7 meq/L with 3.66± 0.63, most common complications of diabetic ketoacidosis treatment were hypokalemia observed in 82 (35.7%) of cases, and hypoglycemia was detected in 41 patients (19.2%). 170 patients were discharged in good condition (79.8%), and 17.8% of patients were transferred to another hospital to receive further management for co-morbid diseases with diabetes after controlling their hyperglycemic crises. The majority of patients presented with the critical status of diabetic ketoacidosis. The findings emphasize the importance of patient education about prevention measures prompt presentation to the hospital, and clinicians' awareness for early and aggressive treatment of hyperglycemic crises. (shrink)

Abstract: Artificial Intelligence (AI) is rapidly becoming a cornerstone of modern healthcare, offering unprecedented capabilities in diagnostics, treatment planning, patient care, and healthcare management. This paper explores the transformative impact of AI on the healthcare sector, examining how it enhances patient outcomes, improves the efficiency of medical practices, and introduces new ethical and operational challenges. By analyzing current applications such as AI-driven diagnostic tools, personalized medicine, and hospital management systems, this paper highlights the significant advancements AI has (...) brought to the field. However, the integration of AI also raises concerns related to data privacy, algorithmic bias, and the displacement of healthcare professionals. This study provides a comprehensive overview of these developments, offering insights into the future potential of AI in revolutionizing healthcare while addressing the ethical considerations that must accompany its adoption. The findings suggest that while AI holds the promise of transforming healthcare delivery, careful consideration of its implications is essential to ensure equitable and effective implementation. (shrink)

Health services internationally struggle to ensure health care is “person-centered” (or similar). In part, this is because there are many interpretations of “person-centered care” (and near synonyms), some of which seem unrealistic for some patients or situations and obscure the intrinsic value of patients’ experiences of health care delivery. The general concern behind calls for person-centered care is an ethical one: Patients should be “treated as persons.” We made novel use of insights from the capabilities approach to characterize person-centered care (...) as care that recognizes and cultivates the capabilities associated with the concept of persons. This characterization unifies key features from previous characterisations and can render person-centered care applicable to diverse patients and situations. By tying person-centered care to intrinsically valuable capability outcomes, it incorporates a requirement for responsiveness to individuals and explains why person-centered care is required independently of any contribution it may make to health gain. (shrink)

Patients with serious illnesses or injuries may decide to quit their medical treatment if they think paying the fees will put their families into destitution. Without treatment, it is likely that fatal outcomes will soon follow. We call this phenomenon “near-suicide”. This study attempted to explore this phenomenon by examining how the seriousness of the patient’s illness or injury and the subjective evaluation of the patient’s and family’s financial situation after paying treatment fees affect the final decision (...) on the treatment process. Bayesian Mindsponge Framework (BMF) analytics were employed to analyze a dataset of 1042 Vietnamese patients. We found that the more serious the illnesses or injuries of patients were, the more likely they were to choose to quit treatment if they perceived that paying the treatment fees heavily affected their families’ financial status. Particularly, only one in four patients with the most serious health issues who thought that continuing the treatment would push themselves and their families into destitution would decide to continue the treatment. Considering the information-filtering mechanism using subjective cost–benefit judgments, these patients likely chose the financial well-being and future of their family members over their individual suffering and inevitable death. Our study also demonstrates that mindsponge-based reasoning and BMF analytics can be effective in designing and processing health data for studying extreme psychosocial phenomena. Moreover, we suggest that policymakers implement and adjust their policies (e.g., health insurance) following scientific evidence to mitigate patients’ likelihood of making “near-suicide” decisions and improve social equality in the healthcare system. (shrink)

Background: Current practice frequently fails to provide care consistent with the preferences of decisionally-incapacitated patients. It also imposes significant emotional burden on their surrogates. Algorithmic-based patient preference predictors (PPPs) have been proposed as a possible way to address these two concerns. While previous research found that patients strongly support the use of PPPs, the views of surrogates are unknown. The present study thus assessed the views of experienced surrogates regarding the possible use of PPPs as a means to help (...) make treatment decisions for decisionally-incapacitated patients. -/- Methods: This qualitative study used semi-structured interviews to determine the views of experienced surrogates [n=26] who were identified from two academic medical centers and two community hospitals. The primary outcomes were respondents’ overall level of support for the idea of using PPPs and the themes related to their views on how a PPP should be used, if at all, in practice. -/- Results: Overall, 21 participants supported the idea of using PPPs. The remaining five indicated that they would not use a PPP because they made decisions based on the patient’s best interests, not based on substituted judgment. Major themes which emerged were that surrogates, not the patient’s preferences, should determine how treatment decisions are made, and concern that PPPs might be used to deny expensive care or be biased against minority groups. -/- Conclusions: Surrogates, like patients, strongly support the idea of using PPPs to help make treatment decisions for decisionally-incapacitated patients. These findings provide support for developing a PPP and assessing it in practice. At the same time, patients and surrogates disagree over whose preferences should determine how treatment decisions are made, including whether to use a PPP. These findings reveal a fundamental disagreement regarding the guiding principles for surrogate decision-making. Future research is needed to assess this disagreement and consider ways to address it. (shrink)

Patients’ knowledge of hypertension and treatment has been found to affect health outcomes of hypertension. This study aimed to assess the impact of therapeutic patients’ education on knowledge of hypertension and lifestyle/dietary modification among hypertensive patients in Nigeria. The study was conducted among 317 hypertensive patients randomized into controlled and intervention groups (158 vs 159, respectively) between March 2021 and February 2022. Baseline knowledge of the patients was assessed and intervention was provided for the intervention group with a structured (...) educational program at a baseline and six months. Descriptive data were presented with a frequency table in percentage while the chi-square test and univariate logistic regression were used to determine the association between categorical variables. Out of the total 318 patients, 275 completed the study (response rate: 86.8%) with 136 in the control group and 139 in the intervention group. The mean age of the patients was 59.5 (±12.5) and patients >60 years (49.5%) were the most frequent age category. The baseline knowledge score of hypertension was 9.8 (±2.6) and 9.3 (±2.6) on a scale of 16 points in the control group and intervention group, respectively (P=0.060) while at six months 11.9 (±2.3) vs 10.8 (±2.4) (P<0.001) and 12 months 12.6 (±2.5) vs 9.5 (±2.0) (P<0.001), respectively. Knowledge of lifestyle/dietary modification in the control group and intervention group at baseline was 7.0 (±2.1) and 6.6 (±2.0), respectively, while at six months 7.5 (±1.5) vs 9.9 (±1.3) (P<0.001) and at 12 months 7.2 (±1.5) vs 10.4 (±1.2), respectively. Marital status, body mass index, and family history of hypertension were associated with knowledge of hypertension and lifestyle/dietary modification (P<0.001). The educational intervention provided was associated with a significant improvement in knowledge of hypertension and lifestyle/dietary modification. The marital status of the patients, body mass index and family history of hypertension influenced patients’ level of knowledge. (shrink)

Owing to its grappling with a motley of intricate socioeconomic, as well as medico-legal, crises, Haiti has found itself bereft of some of its people, many of whom have had to leave the Caribbean country in search of improved lives elsewhere. Receiving some of the Haitian refugees fleeing abject poverty, unemployment, and other harms and barriers has been the United States, one of Haiti's northern neighbors and a country that has played an outcome-determinative, if not outsized, role in steering the (...) country toward its presently hobbled state. Drawing on the U.S.’s recent treatment of Haitian refugees, this paper argues that U.S. reception of Haitian immigrants rubs salt in the wound of a long history of dehumanizing and oppressive abuses endured by Haitians. Furthermore, and more importantly, this paper posits that U.S. failure to wholly embrace its legal obligation to accept Haitian refugees under international law needs to be understood in the light of the specific horrors inflicted by science, in pre-independence Haiti, on non-consenting, Afro-Haitian experimental subjects. And by extension, such a contextually-nuanced understanding is crucial in shaping the delivery of healthcare services to Haitian refugees fortunate enough to remain in the U.S. —as an awareness and appreciation of the socio-historical context of patients' lived experiences, i.e., their complete social history, can furnish important clues vis-à-vis the presence and etiologies of disease, influence the foci of physical exams, and generally pave the way for the provision of cost-efficient and evidence-based care. (shrink)

With the ageing of the Portuguese population, there are more people in dependency situations and needing long-term care (LTC). In this context, it is important to ensure the quality of life (QoL) of those individuals, and that quality can be measured through their health-related quality of life (HRQoL) and overall well-being. Also, understanding how perceived service quality (PSQ) can be related to how people perceive their QoL is pertinent since service quality is an important factor to achieve patient health (...) outcomes. To develop this project, a LTC unit located in the district of Lisbon was chosen, the LTC unit of Arruda dos Vinhos. The main objective was to assess the relation between the PSQ, the HRQoL and the overall well-being of patients receiving LTC at the LTC unit of Arruda dos Vinhos. A project based on a case study was performed, through interviews based on the items of the SERVPERF, EQ-5D and ICECAP-O questionnaires, which were then analysed through content analysis. Results showed there is a relation between PSQ and HRQoL and between HRQoL and overall well-being. It was also possible to realise that to better evaluate the quality of this specific service, other subjects as the activities performed at the LTC unit, the physiotherapy service and the food provided, should be taken in consideration. Concluding, there is a relation between PSQ and the QoL of the patients receiving LTC at the LTC unit of Arruda dos Vinhos, when quality of life is measured through HRQoL. (shrink)

Background: The complexity of diagnostic presentations of an inpatient psychiatry population requires an integrative approach to health and well-being. In this sense, the primary need of this research aims at developing clinical strategies and healthier coping skills for anger, anxiety, and depression; promoting self-esteem, healthier sleep, and anxiety reduction; as well as enhancing mood and emotional–behavioral regulation via exercise and nutrition education. Objectives: The primary objective is to promote exercise, fitness, and physical health in inpatient psychiatry patients. The secondary objective (...) includes therapeutic management of depressive symptoms and patient-centered approach to mania, angry outbursts, and generalized disruptive behavior. The tertiary objective is promoting research in the psychophysiological effectors of exercise and nutrition education in combination with psychotherapy. Method: Monitoring self-reported changes in mood and general well-being via administration of surveys and questionnaires pre- and postexercise sessions. Results: The research yielded positive outcomes in all areas investigated, suggesting the positive effects of exercise and mind–body strategies in the context of psychotherapy in inpatient psychiatry. Conclusion: Physical exercise may be a helpful way to reduce mental health disorders in the context of inpatient psychiatry by targeting anxiety, depression, anger, psychomotor agitation, and muscle tension and addressing stressors and triggers and to develop a more balanced and integrated sense of self. (shrink)

Introduction Current treatments for pain have limited benefits and worrying side effects. Some studies suggest that pain is reduced when clinicians deliver positive messages. However, the effects of positive messages are heterogeneous and have not been subject to meta-analysis. We aimed to estimate the efficacy of positive messages for pain reduction. -/- Methods We included randomized trials of the effects of positive messages in a subset of the studies included in a recent systematic review of context factors for treating pain. (...) Several electronic databases were searched. Reference lists of relevant studies were also searched. Two authors independently undertook study selection, data extraction, risk of bias assessment, and analyses. Our primary outcome measures were differences in patient- or observer-reported pain between groups who were given positive messages and those who were not. -/- Results Of the 16 randomized trials (1703 patients) that met the inclusion criteria, 12 trials had sufficient data for meta-analysis. The pooled standardized effect size was −0.31 (95% confidence interval [CI] −0.61 to −0.01, p = 0.04, I2 = 82%). The effect size remained positive but not statistically significant after we excluded studies considered to have a high risk of bias (standard effect size −0.17, 95% CI −0.54 to 0.19, P = 0.36, I2 = 84%). -/- Conclusion Care of patients with chronic or acute pain may be enhanced when clinicians deliver positive messages about possible clinical outcomes. However, we have identified several limitations of the present study that suggest caution when interpreting the results. We recommend further high-quality studies to confirm (or falsify) our result. -/- FUNDING -/- Alexander Mebius research has been funded through the ERC grant "Philosophy of Pharmacology: Safety, Statistical Standards, and Evidence Amalgamation" (GA: 639276). (shrink)

In a rigorous systematic review, Dukhanin and colleagues categorize metrics and evaluative tools of the engagement of patient, public, consumer, and community in decision-making in healthcare institutions and systems. The review itself is ably done and the categorizations lead to a useful understanding of the necessary elements of engagement, and a suite of measures relevant to implementing engagement in systems. Nevertheless, the question remains whether the engagement of patient representatives in institutional or systemic deliberations will lead to improved (...) clinical outcomes or increased engagement of individual patients themselves in care. Attention to the conceptual foundations of patient engagement would help make this systematic review relevant to the clinical care of patients. (shrink)

Background: Mental health professionals working in correctional contexts engage a double role to care and control. This dual loyalty conflict has repeatedly been criticized to impede the development of a high-quality alliance. As therapeutic alliance is a robust predictor of outcome measures of psychotherapy, it is essential to investigate the effects of this ethical dilemma. Methods: This qualitative interview study investigates patients’ perceptions of their therapists’ dual role conflict in court-mandated treatment settings. We interviewed 41 older incarcerated persons using a (...) semi-structured interview guide, the interviews were subsequently analyzed following thematic analysis. Results: We first present the patients’ perceptions of their treating psychotherapist’s dual loyalty conflict, which was linked to their overall treatment experience. In a second step, we outline the study participants’ reasons for this judgment, which were most commonly linked to feelings of trust or betrayal. More specifically, they named certain therapist characteristics and activities that enabled them to develop a trustful therapeutic alliance, which we grouped into four topics: respecting the patient’s pace and perceived coercion; patient health needs to be first priority; clarity in roles and responsibilities; and the art of communication – between transparency and unchecked information sharing. Discussion: Developing a high quality alliance in mandatory offender treatment is central due to its relationship with recovery and desistance. Our findings show that some therapists’ characteristics and activities attenuate the negative impact of their double role on the development and maintenance of the alliance. To increase the effectiveness of court-mandated treatments, we need to support clinicians in dealing with their dual role to allow the formation of a high quality therapeutic alliance. Our qualitative interview study contributed to this much-needed empirical research on therapist’ characteristics promoting a trustful relationship in correctional settings. (shrink)

Aim: To elicit preferences for prognostic information, attitudes towards withdrawal of life-sustaining treatment (WLST) and perspectives on acceptable quality of life after post-anoxic coma within the adult general population of Germany, Italy, the Netherlands and the United States of America. Methods: A web-based survey, consisting of questions on respondent characteristics, perspectives on quality of life, communication of prognostic information, and withdrawal of life-sustaining treatment, was taken by adult respondents recruited from four countries. Statistical analysis included descriptive analysis and chi2-tests for (...) dierences between countries. Results: In total, 2012 respondents completed the survey. In each country, at least 84% indicated they would prefer to receive early prognostic information. If a poor outcome was predicted with some uncertainty, 37-54% of the respondents indicated that WLST was not to be allowed. A conscious state with severe physical and cognitive impairments was perceived as acceptable quality of life by 17–44% of the respondents. Clear differences between countries exist, including respondents from the U.S. being more likely to allow WLST than respondents from Germany (OR = 1.99, p < 0.001) or the Netherlands (OR = 1.74, p < 0.001) and preferring to stay alive in a conscious state with severe physical and cognitive impairments more than respondents from Italy (OR = 3.76, p < 0.001), Germany (OR = 2.21, p < 0.001), or the Netherlands (OR = 2.39, p < 0.001). Conclusions: Over one-third of the respondents considered WLST unacceptable when there is any remaining prognostic uncertainty. Respondents had a more positive perspective on acceptable quality of life after coma than what is currently considered acceptable in medical literature. This indicates a need for a closer look at the practice of WLST based on prognostic information, to ensure responsible use of novel prognostic tests. (shrink)

Gallbladder disease commonly affects the general population of Western developed nations and the standard surgical treatment of laparoscopic cholecystectomy normally follows severe cases of this condition. However, the negative health and life quality consequences of this standard line of treatment are lesser known and stand the chance of being taken lightly by healthcare professionals. Complications include bile duct injury, dropped gallstones and a second open invasive surgery, of which could be life threatening for some rare cases. It is, therefore, necessary (...) to highlight the limitations of such a procedure and the need for improvement and widening of the range of treatment options for vulnerable members of the patient population. Additionally, here is a brief overview introduction of a new procedure called endoscopic ultrasound guided gallbladder drainage with promising success rates that can be a potentially feasible alternative to traditional surgical procedures to offer gallbladder disease patients. (shrink)

Abstract: Background: Coronary artery disease remains the leading cause of morbidity and mortality Worldwide. Previous reviews pointed that nursing interventions are beneficial for coronary artery patients. However, most interventions focused on education and counselling, but not consistent with the outcome set; still did not consider patient’s coronary artery disease risky characteristics. Related studies in China also difficult to find. Therefore this study was conducted to investigate kinds of nursing interventions delivered to coronary artery patients and match them with (...) class='Hi'>patient’s risk factors of coronary artery disease. Results of this study were expected to add new knowledge that will alert nurses to consider coronary artery risk factors which in turn might enable the development of appropriate approaches to improve patient’s wellbeing hence reduce frequent coronary artery morbidity and mortality. Methods: A descriptive, cross-sectional, retrospective design using clinical case notes was employed. Study was undertaken in coronary care wards at the teaching hospital in China from November 2017 to September 2018. Structured-literature supported self-designed questionnaire was utilized for data collection. Chi square (χ2) test and multivariate logistic regression for adjusted odds ratio with 95% confidence interval were used to compare the relationship among independent (patient’s risk coronary artery disease factors) and dependent (nursing interventions) categorical variables. Ethical permission was granted accordingly. Results: A total of 300 coronary artery patients’ case notes were audited with mean age 63±11.2 years. Of these 175 (58.3%) were males. 126(42%) were smoking and 224(74.7%) were hypertensive. More evidence based nursing interventions than education and counselling were found to be delivered to these patients. “Administer coronary artery disease medication and their instructions” was mostly delivered to many patients 291(97%) while “counsel to cope with stress” was the least one 60 (20.0%). Three of eight nursing interventions delivered significantly matched with three or all of these patient’s coronary artery risk variables (age, smoking, hypertension and diabetes) (p < 0.05 and/or < 0.01) with Adjusted odds ratio (95% CI) within their significant ranges. Conclusion: This study delivers valuable insight that, nurses in the studied teaching hospital delivered beneficial evidence based nursing interventions to patients with coronary artery disease which significantly matched with their risk factors of coronary artery illness. However, care for stress was low hence needs improvement. Furthermore, research is needed to get consistency of nursing interventions with patient’s end point clinical outcomes for further appraisal of nursing efforts in caring CAD patients . (shrink)

Objective: Vascular disorders severely impair the psycho-social status of individuals. Various laser and light systems, which have advantages and disadvantages, including the pro-yellow laser are used for the therapy of these disorders. Aim: The goal of the present study was to investigate the effectiveness and feasibility of the 577 nm pro-yellow laser for a broad range of indications including erythematelangiectatic rosacea, facial erythema, post-acne erythema, facial telangiectasis, hemangioma, genital angiokeratoma, and port wine stain nevus. Methods: A total of 98 patients (...) (25 male, 73 female) older than 15 years who were treated with the pro-yellow laser for vascular disorders at the cosmetology unit between 2017 and 2019 were retrospectively included in the study. Results: The mean rate of recovery was 100% in genital angiokeratoma, 94.4% in spider angioma, 83.3% in facial telangiectasis, 74.8% in erythematotelangiectatic rosacea, 72% in facial erythema + facial telangiectasis, and 68.3% in facial erythema. Over 60% of improvement was observed in most patients with vascular disorders. There was no significant link between the Fitzpatrick skin type and treatment success. Treatment success was significantly low in cases with nasal involvement. Conclusion: The current study concluded that pro-yellow laser is an efficient and safe laser modality that may yield satisfactory outcomes regardless of the different skin types. (shrink)

Background: Tuberculosis is among the major causes of illness and death worldwide especially in Asia. Smoking is associated with recurrent tuberculosis and its related mortality. Also, it could affect clinical manifestations, bacteriological conversion and outcome of treatment. This study aimed to evaluate the pattern of tobacco smoking, history of previous quit attempts and attitude towards quitting in tuberculosis patients. Materials and Methods: It was a cross-sectional study done amongst tuberculosis patients presented to DOTS corner of Rajshahi Medical College Hospital. 315 (...) patients entered the study as "First Come First Serve". Selfreported questionnaires were designed according to the standard questionnaires of smoking pattern. Results: Sixty one percent (61%) patients (n=192) were smoker before the diagnosis of tuberculosis. 51.1% were current smokers at the time of interview and were continuing smoking after the diagnosis of tuberculosis. Only 10.2% discontinued smoking after diagnosis. 68.2% smokes less than 10 cigarettes per day. 53.1% was between 31-50 age group. 21-30 is the commonest age group (62%) to start smoking is and 66.1% smoked less than 10 years before diagnosis. To reduce stress was the most common cause to continue smoking (48.9%). 71.9% smokers showed keen interest to stop smoking. Conclusion: Considering the prevalence of smoking in tuberculosis patients, evaluation of tobacco smoking status in such patients and motivating them to quit smoking could be considered as important steps in their treatment process. (shrink)

Introduction: The incidence of chronic low back pain (LBP) is very high in Bangladesh. There is a high prevalence of psychiatric diseases among chronic low back pain patients. But primary care physicians and specialists do not screen this association. The aims of this study were to evaluate the incidence and pattern of psychiatric diseases in chronic low back pain patients. Materials and methods: A prospective cross-sectional hospital-based study of 135 chronic low back pain patients using simple, direct, standardized questionnaire including (...) history and detailed psychiatric evaluation by a consultant psychiatrist from January to December 2014. Results: A total 69 chronic low back pain patients (51.1%) were diagnosed with psychiatric problems. Predominant group were female (65.9%), above 40 years of age (60.7%). Depression (44.9 %) and anxiety (31.9%) were the major psychiatric illness diagnosed (76.8%). Only 17.4 % patients expects complete cure. Fear of disease outcome was the commonest precipitating factor found (66.6%). With the adjuvant psychiatric management, 81.1% showed improvement clinically. Conclusion: Detecting the level of psychiatric illness among chronic low back pain patients at early stage will improve the treatment outcome. A referral system to psychiatrists by physicians needs to develop to screen the mental disorder symptoms to treat the problem. (shrink)

rtificial Intelligence (AI) is swiftly becoming a fundamental element in modern healthcare, bringing unparalleled capabilities in diagnostics, treatment planning, patient care, and healthcare management. This paper delves into AI's transformative impact on the healthcare sector, highlighting how it enhances patient outcomes, boosts the efficiency of medical practices, and introduces new ethical and operational challenges. Through an analysis of current applications such as AI-driven diagnostic tools, personalized medicine, and hospital management systems, the paper underscores the significant advancements AI (...) has introduced to the field. However, the integration of AI also presents concerns related to data privacy, algorithmic bias, and the displacement of healthcare professionals. This study provides a thorough overview of these developments, offering insights into AI's future potential to revolutionize healthcare while addressing the ethical considerations necessary for its responsible adoption. The findings suggest that while AI has the potential to transform healthcare delivery, careful consideration of its implications is essential for ensuring its equitable and effective implementation. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into medical imaging represents a transformative shift in healthcare, offering significant improvements in diagnostic accuracy, efficiency, and patient outcomes. This paper explores the application of AI technologies in the analysis of medical images, focusing on techniques such as convolutional neural networks (CNNs) and deep learning models. We discuss how these technologies are applied to various imaging modalities, including X-rays, MRIs, and CT scans, to enhance disease detection, image segmentation, and diagnostic support. (...) Additionally, the paper addresses the challenges faced in AI-driven medical imaging, including data quality, model interpretability, and ethical considerations. By examining recent advancements and real-world case studies, this paper provides insights into the current state of AI in medical imaging and its potential future directions. The findings highlight the ongoing evolution of AI technologies and their crucial role in advancing medical diagnostics and treatment strategies. (shrink)

Abstract: Early detection of breast cancer is vital for improving patient outcomes and reducing mortality rates. Technological advancements have significantly enhanced the accuracy and efficiency of screening methods. This paper explores recent innovations in early detection, focusing on the evolution of digital mammography, the benefits of 3D mammography (tomosynthesis), and the application of advanced imaging techniques such as molecular imaging and MRI. It also examines the role of artificial intelligence (AI) in diagnostic tools, showing how machine learning algorithms (...) are improving imaging analysis and reducing diagnostic errors. Furthermore, the paper discusses advancements in genetic screening, liquid biopsies, and biomarkers, emphasizing their potential in identifying high-risk individuals and supporting personalized treatment plans. The paper provides a forward-looking perspective on the future of breast cancer detection, addressing both the opportunities and challenges associated with the adoption of these cutting-edge technologies to improve patient outcomes. (shrink)

Abstract: Colon cancer is a prevalent and life-threatening disease, necessitating accurate and timely diagnosis for effective treatment and improved patient outcomes. This research paper presents the development of a knowledge-based system for diagnosing colon cancer using the CLIPS language. Knowledge-based systems offer the potential to assist healthcare professionals in making informed diagnoses by leveraging expert knowledge and reasoning mechanisms. The methodology involves acquiring and structuring medical knowledge specific to colon cancer, followed by the implementation of a knowledge- based (...) system using CLIPS. The system's design integrates relevant medical databases and employs advanced reasoning mechanisms to facilitate accurate diagnosis. Evaluation results demonstrate the system's effectiveness, with high accuracy and sensitivity rates compared to existing methods. The findings highlight the potential of knowledge-based systems in improving clinical practice and patient care. The research contributes to the growing field of medical informatics by demonstrating the feasibility and effectiveness of CLIPS-based knowledge systems for colon cancer diagnosis. Future research can focus on enhancing system performance and expanding its scope to encompass other types of cancer diagnoses. (shrink)

Abstract: The integration of Artificial Intelligence (AI) into medical imaging marks a transformative advancement in healthcare, significantly enhancing diagnostic accuracy, efficiency, and patient outcomes. This paper delves into the application of AI technologies in medical image analysis, with a particular focus on techniques such as convolutional neural networks (CNNs) and deep learning models. We examine how these technologies are employed across various imaging modalities, including X-rays, MRIs, and CT scans, to improve disease detection, image segmentation, and diagnostic support. (...) Furthermore, the paper discusses the challenges associated with AI-driven medical imaging, such as data quality, model interpretability, and ethical considerations. By analyzing recent advancements and real-world case studies, this paper offers insights into the current landscape of AI in medical imaging and explores its potential future directions. The findings underscore the ongoing evolution of AI technologies and their pivotal role in advancing medical diagnostics and treatment strategies. (shrink)

Abstract: Early detection of breast cancer plays a pivotal role in improving patient prognosis and reducing mortality rates. Recent technological advancements have significantly enhanced the accuracy and effectiveness of breast cancer screening methods. This paper explores the latest innovations in early detection, including the evolution of digital mammography, the impact of 3D mammography (tomosynthesis), and the use of advanced imaging techniques such as molecular imaging and MRI. Furthermore, the integration of artificial intelligence (AI) in diagnostic tools is discussed, highlighting (...) how machine learning algorithms are refining imaging analyses and reducing diagnostic errors. Advances in genetic screening, liquid biopsies, and biomarkers are also examined, showcasing their potential in identifying high-risk individuals and enabling personalized treatment plans. This paper provides insights into the future of breast cancer detection, outlining both the opportunities and challenges that lie ahead in adopting these innovative technologies to improve patient outcomes. (shrink)

Warts and verrucas are common skin conditions caused by the human papillomavirus (HPV) infection. They present as raised, rough, or bumpy growths on the hands, feet, or other areas subjected to friction or pressure. Plantar warts exhibit a rough surface with small black dots, while genital warts have a cauliflower-like appearance. Pain or itchiness may accompany these lesions. Factors such as close contact with infected individuals and immune compromise can impact the severity and spread of warts. Diagnosis is primarily based (...) on clinical examination, and referral to a dermatologist may be necessary for uncertain cases. Treatment options range from over-the-counter remedies to professional interventions. Persistent or symptomatic lesions should be evaluated by a healthcare professional. Improved understanding and effective management of warts and verrucas are essential for optimal patient outcomes. (shrink)

Abstract: Heart attacks, or myocardial infarctions, are a leading cause of mortality worldwide. Early prediction and accurate analysis of potential risk factors play a crucial role in preventing heart attacks and improving patient outcomes. In this study, we conduct a comprehensive review of datasets related to heart attack analysis and prediction. We begin by examining the various types of datasets available for heart attack research, encompassing clinical, demographic, and physiological data. These datasets originate from diverse sources, including hospitals, (...) research institutions, and public health agencies. Our analysis aims to identify common features, data quality, and potential biases in these datasets. Next, we explore the predictive modeling techniques employed in heart attack prediction. Machine learning algorithms, such as decision trees, support vector machines, and deep neural networks, have gained prominence in predicting heart attacks. We discuss the strengths and limitations of these methods and highlight recent advancements in predictive modeling. Furthermore, we delve into the critical risk factors associated with heart attacks. Factors such as age, gender, hypertension, diabetes, and cholesterol levels are examined for their significance in predicting cardiac events. We also investigate the role of lifestyle factors, including smoking, diet, and physical activity, in heart attack risk assessment. Additionally, this review addresses the importance of data preprocessing and feature engineering in improving prediction accuracy. Feature selection methods, missing data handling, and data scaling techniques are discussed to enhance the robustness of heart attack prediction models. In conclusion, this comprehensive dataset review provides valuable insights into the state of heart attack analysis and prediction. It serves as a resource for researchers and healthcare professionals seeking to better understand the datasets available for heart attack research and the methods employed for accurate prediction. Ultimately, our efforts in dataset analysis and predictive modeling contribute to the advancement of preventive cardiology and the reduction of heart attack-related morbidity and mortality. (shrink)

Five reasons why teaching medical humanities in medical schools improves student performance, enhances wellbeing, and ameliorates patient outcomes.

Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for binary classification, (...) identifying the presence or absence of heart disease. The neural network architecture includes an input layer, a hidden layer, and an output layer, designed to capture intricate relationships within the dataset. Rigorous training and validation processes, accompanied by data preprocessing steps, ensure the model's robustness and generalization capabilities. The results demonstrate promising performance, with an accuracy of 92% and an average error of 0.062. Moreover, an analysis of feature importance highlights key predictors, including "oldpeak," "thalach," "trestbps," "ca," "thal," "cp," "chol," "sex," "restecg," "age," "slope," "fbs," and "exang." This research contributes to the field of predictive healthcare by leveraging neural networks to enhance heart disease prediction. The developed model offers the potential for early identification of individuals at risk, facilitating timely medical interventions and ultimately improving public health. Further exploration of machine learning techniques in healthcare promises to reshape disease prediction and prevention strategies. (shrink)

Kidney stones pose a significant health concern, and early detection can lead to timely intervention and improved patient outcomes. This research endeavours to predict the presence of kidney stones based on urine analysis, utilizing a neural network model. A dataset of 552 urine specimens, comprising six essential physical characteristics (specific gravity, pH, osmolarity, conductivity, urea concentration, and calcium concentration), was collected and prepared. Our proposed neural network architecture, featuring three layers (input, hidden, output), was trained and validated, achieving (...) an impressive accuracy of 98.67% and an average error of 0.012. In addition to model performance, feature importance analysis was conducted to determine the most influential factors in predicting kidney stone presence. The findings underscore the significance of urea concentration, specific gravity, calcium concentration, conductivity, osmolarity, and pH as key indicators. This research contributes to the early diagnosis of kidney stones and demonstrates the potential of neural networks in medical diagnostics. The clinical implications of these findings are discussed, emphasizing the importance of timely intervention in managing kidney stone-related health issues. (shrink)

This abstract is a synopsis of the paper "An Expert System for Diagnosing Whooping Cough Using CLIPS." The bacterium Bordetella pertussis causes whooping cough, a highly infectious respiratory ailment with several phases of symptoms. An accurate and timely diagnosis is critical for effective treatment and the avoidance of future transmission. The construction of an expert system for detecting whooping cough using the CLIPS (C Language Integrated Production System) architecture is highlighted in this abstract. The expert system provides standardized and systematic (...) evaluation, which reduces the chance of misdiagnosis and improves patient outcomes. Maintenance and upgrades are required to keep the system current with growing medical knowledge. More study in this area has the potential to advance expert systems in identifying and managing a variety of medical disorders. (shrink)

In biomedical measurement, biomarkers are used to achieve reliable prediction of, and useful causal information about patient outcomes while minimizing complexity of measurement, resources, and invasiveness. A biomarker is an assayable metric that discloses the status of a biological process of interest, be it normative, pathophysiological, or in response to intervention. The greatest utility from biomarkers comes from their ability to help clinicians (and researchers) make and evaluate clinical decisions. In this paper we discuss a specific methodological use (...) of clinical biomarkers in pharmacological measurement: Some biomarkers, called ‘surrogate markers’, are used to substitute for a clinically meaningful endpoint corresponding to events and their penultimate risk factors. We confront the reliability of clinical biomarkers that are used to gather information about clinically meaningful endpoints. Our aim is to present a systematic methodology for assessing the reliability of multiple surrogate markers (and biomarkers in general). To do this we draw upon the robustness analysis literature in the philosophy of science and the empirical use of clinical biomarkers. -/- After introducing robustness analysis we present two problems with biomarkers in relation to reliability. Next, we propose an intervention-based robustness methodology for organizing the reliability of biomarkers in general. We propose three relevant conditions for a robust methodology for biomarkers: (R1) Intervention-based demonstration of partial independence of modes: In biomarkers partial independence can be demonstrated through exogenous interventions that modify a process some number of “steps” removed from each of the markers. (R2) Comparison of diverging and converging results across biomarkers: By systematically comparing partially-independent biomarkers we can track under what conditions markers fail to converge in results, and under which conditions they successfully converge. (R3) Information within the context of theory: Through a systematic cross-comparison of the markers we can make causal conclusions as well as eliminate competing theories. We apply our robust methodology to currently developing Alzheimer’s research to show its usefulness for making causal conclusions. (shrink)

Heart palpitations, while often benign, can sometimes be indicative of severe underlying conditions requiring immediate intervention. Accurate and swift diagnosis thus remains a clinical priority. "A CLIPS-Based Expert System for Heart Palpitations Diagnosis" represents a novel approach to addressing this challenge, harnessing the power of artificial intelligence and rule-based expert systems. Specifically, this system applies a suite of 7 if-then rules to evaluate potential heart palpitations causes and assign one of three outcomes: 1) A confirmed diagnosis of heart palpitations, (...) 2) A suspected link to cardiovascular diseases, and 3) A possible association with anxiety or stress disorders. The expert system offers an intuitive user interface, allowing for seamless symptom input and instant diagnosis based on user-provided information. This paper explores the various phases of this expert system's lifecycle, including design, implementation, and evaluation. Furthermore, the study situates the system within the broader discourse on rule-based expert systems for heart palpitations diagnosis, critically analyzing their efficiency, potential pitfalls, and ongoing challenges. Through this research, the value of integrating rule-based expert systems in clinical diagnostic processes is highlighted, illustrating its capacity to enhance diagnostic accuracy and patient outcomes. (shrink)

When information on a coma patient’s expected outcome is uncertain, a moral dilemma arises in clinical practice: if life-sustaining treatment is continued, the patient may survive with unacceptably poor neurological prospects, but if withdrawn a patient who could have recovered may die. Continuous electroencephalogram-monitoring is expected to substantially improve neuroprognostication for patients in coma after cardiac arrest. This raises expectations that decisions whether or not to withdraw will become easier. This paper investigates that expectation, exploring cEEG’s impacts (...) when it becomes part of a socio-technical network in an Intensive Care Unit. Based on observations in two ICUs in the Netherlands and one in the USA that had cEEG implemented for research, we interviewed 25 family members, healthcare professionals, and surviving patients. The analysis focuses on the way patient outcomes are constructed, the kind of decision support these outcomes provide, and how cEEG affects communication between professionals and relatives. We argue that cEEG can take away or decrease the intensity of the dilemma in some cases, while increasing uncertainty for others. It also raises new concerns. Since its actual impacts furthermore hinge on how cEEG is designed and implemented, we end with recommendations for ensuring responsible development and implementation. (shrink)

Breast cancer remains a significant health concern worldwide, necessitating the development of effective diagnostic tools. In this study, we employ a neural network-based approach to analyze the Wisconsin Breast Cancer dataset, sourced from Kaggle, comprising 570 samples and 30 features. Our proposed model features six layers (1 input, 1 hidden, 1 output), and through rigorous training and validation, we achieve a remarkable accuracy rate of 99.57% and an average error of 0.000170 as shown in the image below. Furthermore, our investigation (...) identifies the most influential features in breast cancer diagnosis, shedding light on the key determinants of malignancy. Notably, we find that factors such as fractal dimension_se, symmetry worst, compactness_worst, symmetry_se, and smoothness_se play pivotal roles in distinguishing between benign and malignant cases. This research contributes to the ongoing efforts to enhance breast cancer diagnosis, providing valuable insights into feature importance and showcasing the potential of neural networks in medical applications. Our findings have implications for improving early detection and treatment strategies, ultimately contributing to improved patient outcomes. (shrink)

Lung cancer is a serious and deadly disease that affects the lungs, which are responsible for taking in oxygen and expelling carbon dioxide from the body. The disease can develop in any part of the lungs and is usually caused by smoking or exposure to certain chemicals. The main Objective: of this expert system is to provide an accurate diagnosis of lung cancer and the appropriate treatment options. In this paper, Methods: we present the design and implementation of an expert (...) system that can assist physicians in diagnosing lung cancer and determining the best course of treatment. The system provides an overview of the disease, its causes, symptoms, diagnosis, and treatment, along with relevant medical information. Results: The system was developed using the Clips expert system language and evaluated by medical professionals who found it to be an effective tool for diagnosing and treating lung cancer. Conclusion: The proposed expert system has the potential to improve patient outcomes and reduce healthcare costs associated with the treatment of lung cancer. (shrink)

Outcomes research in healthcare has been a topic much addressed in recent years. Efforts in this direction have been supplemented by work in the areas of guidelines for clinical practice and computer-interpretable workflow and careflow models.In what follows we present the outlines of a framework for understanding the relations between organizations, guidelines, individual patients and patient-related functions. The derived framework provides a means to extract the knowledge contained in the guideline text at different granularities, in ways that can (...) help us to assign tasks within the healthcare organization and to assess clinical performance in realizing the guideline. It does this in a way that preserves the flexibility of the organization in the adoption of the guidelines. (shrink)

Since its inception in the early 1990s, evidence-based medicine (EBM) has become the dominant epistemic framework for Western medical practice. However, in light of powerful criticisms against EBM, alternatives such as casuistic medicine have been gaining support in both the medical and philosophical community. In the absence of empirical evidence in support of the claim that EBM improves patient outcomes, and in light of considerations that it is unlikely that such evidence will be forthcoming, another standard is needed (...) to assess EBM against its alternatives. In this paper, I propose a set of criteria for this purpose based on Helen Longino's criteria for assessing the objectivity of a knowledge productive community. I then apply these criteria to assess EBM against a casuistic framework for medical knowledge. I argue that EBM's strict adherence to a hierarchical organization of knowledge can reasonably be expected to block it from fulfilling a high level of objectivity. A casuistic framework, on the other hand, because it emphasizes critical evaluation in conjunction with the flexibility of a case-based approach, could be expected to better facilitate a more optimal epistemic community. (shrink)

The evidence based medicine movement has championed the need for objective and transparent methods of clinical guideline development. The Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) framework was developed for that purpose. Central to this framework is criteria for assessing the quality of evidence from clinical studies and the impact that body of evidence should have on our confidence in the clinical effectiveness of a therapy under examination. Grades of Recommendation, Assessment, Development, and Evaluation has been adopted by a (...) number of professional medical societies and organizations as a means for orienting the development of clinical guidelines. As a result, the method of GRADE has implications on how health care is delivered and patient outcomes. In this paper, we reveal several issues with the underlying logic of GRADE that warrant further discussion. First, the definitions of the “grades of evidence” provided by GRADE, while explicit, are functionally vague. Second, the “criteria for assigning grade of evidence” is seemingly arbitrary and arguably logically incoherent. Finally, the GRADE method is unclear on how to integrate evidence grades with other important factors, such as patient preferences, and trade‐offs between costs, benefits, and harms when proposing a clinical practice recommendation. Much of the GRADE method requires judgement on the part of the user, making it unclear as to how the framework reduces bias in recommendations or makes them more transparent—both goals of the programme. It is our view that the issues presented in this paper undermine GRADE's justificatory scheme, thereby limiting the usefulness of GRADE as a tool for developing clinical recommendations. (shrink)

Both patients and clinicians frequently report problems around communicating and assessing pain. Patients express dissatisfaction with their doctors and doctors often find exchanges with chronic pain patients difficult and frustrating. This chapter thus asks how we could improve pain communication and thereby enhance outcomes for chronic pain patients. We argue that improving matters will require a better appreciation of the complex meaning of pain terms and of the variability and flexibility in how individuals think about pain. We start by (...) examining the various accounts of the meaning of pain terms that have been suggested within philosophy and suggest that, while each of the accounts captures something important about our use of pain terms, none is completely satisfactory. We propose that pain terms should be viewed as communicating complex meanings, which may change across different communicative contexts, and this in turn suggests that we should view our ordinary thought about pain as similarly complex. We then sketch what a view taking seriously this variability in meaning and thought might look like, which we call the “polyeidic” view. According to this view, individuals tacitly occupy divergent stances across a range of different dimensions of pain, with one agent, for instance, thinking of pain in a much more “bodycentric” kind of way, while another thinks of pain in a much more "mindcentric” way. The polyeidic view attempts to expand the multidimensionality recognised in, e.g., biopsychosocial models in two directions: first, it holds that the standard triumvirate— dividing sensory/cognitive/affective factors— needs to be enriched in order to capture important distinctions within the social and psychological dimensions. Second, the polyeidic view attempts to explain (at least in part) why modulation of experience by these social and psychological factors is possible in the first place. It does so by arguing that because the folk concept of pain is complex, different weightings of the different parts of the concept can modulate pain experience in a variety of ways. Finally, we argue that adopting a polyeidic approach to the meaning of pain would have a range of measurable clinical outcomes. (shrink)

Whether anorexic patients should be able to refuse treatment when this potentially has a fatal outcome is a vexed topic. A recent proposal for a new category of ‘terminal anorexia’ suggests criteria when a move to palliative care or even physician assisted suicide might be justified. I argue that this proposed diagnosis presents a false sense of certainty of the illness trajectory by conceptualizing anorexia in analogy with physical disorders and stressing the effects of starvation. Furthermore, this conceptualization is in (...) conflict with the claim that individuals who meet the diagnostic criteria for terminal anorexia have decision making capacity. It should therefore be rejected. (shrink)

Hundreds of evaluative studies of psychotherapy still leave the issue of its effectiveness unsettled. The author argues that such studies have ignored the major determinant of therapeutic effectiveness, the role of a patient’s belief in the successful outcome in therapy. Psychiatrist Thomas Szasz, one of the foremost critics of psychiatry, wrote of this paper: "It is one of the best, if not the best, that I have read on this subject.” It makes little sense to claim that a certain (...) therapy is effective or ineffective in itself; rather, there is a relation of mutual interaction among three terms: a patient’s ability to learn in a psychological context, a therapist’s ability to teach, and the capacity of an approach to therapy to engender in the patient the necessary belief in its effectiveness. -/- The paper is followed by a 4-page 2014 Afterword. (shrink)

Importance: Patients with remitted major depressive disorder (MDD) were previously found to display abnormal functional magnetic resonance imaging connectivity (fMRI) between the right superior anterior temporal lobe (RSATL) and the subgenual cingulate cortex and adjacent septal region (SCSR) when experiencing self-blaming emotions relative to emotions related to blaming others (eg, "indignation or anger toward others"). This finding provided the first neural signature of biases toward overgeneralized self-blaming emotions (eg, "feeling guilty for everything"), known to have a key role in cognitive (...) vulnerability to MDD. It is unknown whether this neural signature predicts risk of recurrence, a crucial step in establishing its potential as a prognostic biomarker, which is urgently needed for stratification into pathophysiologically more homogeneous subgroups and for novel treatments. -/- Objective: To use fMRI in remitted MDD at baseline to test the hypothesis that RSATL-SCSR connectivity for self-blaming relative to other-blaming emotions predicts subsequent recurrence of depressive episodes. -/- Design, setting, and participants: A prospective cohort study from June 16, 2011, to October 10, 2014, in a clinical research facility completed by 75 psychotropic medication-free patients with remitted MDD and no relevant comorbidity. In total, 31 remained in stable remission, and 25 developed a recurring episode over the 14 months of clinical follow-up and were included in the primary analysis. Thirty-nine control participants with no personal or family history of MDD were recruited for further comparison. -/- Main outcomes and measures: Between-group difference (recurring vs stable MDD) in RSATL connectivity, with an a priori SCSR region of interest for self-blaming vs other-blaming emotions. -/- Results: We corroborated our hypothesis that during the experience of self-blaming vs other-blaming emotions, RSATL-SCSR connectivity predicted risk of subsequent recurrence. The recurring MDD group showed higher connectivity than the stable MDD group (familywise error-corrected P < .05 over the a priori SCSR region of interest) and the control group. In addition, the recurring MDD group also exhibited RSATL hyperconnectivity with the right ventral putamen and claustrum and the temporoparietal junction. Together, these regions predicted recurrence with 75% accuracy. -/- Conclusions and relevance: To our knowledge, this study is the first to provide a robust demonstration of an fMRI signature of recurrence risk in remitted MDD. Additional studies are needed for its further optimization and validation as a prognostic biomarker. (shrink)

Background: Postoperative pneumonia is a preventable complication associated with adverse outcomes, that greatly aggravates the medical expenses of patients. The goal of our study is to identify risk factors and outcomes of postoperative pneumonia.

Crises in therapist-patient relationship can also become a challenge in clinical supervision. However, success and failure in establishing and maintaining constructive relationships in therapy and supervision is not only subject to a lucky fit of personal characteristics (therapist A gets along well/badly with client B; supervisee A gets along well/badly with supervisor C). Rather, we can identify determining field conditions in the overall therapeutic and supervisory situation for this outcome. We do not only focus on the persons involved, but (...) also on their environment, the task to be accomplished together, further framework conditions and the power relations resulting from their mutual influence-in the supervised case of therapy as well as in supervision itself. We want to examine the structure and dynamics of these relationships from a genuine Gestalt psychological perspective. What contributes to a cooperative atmosphere? When do goals get out of sight? What can make the atmosphere hostile? How do such developments become accessible in supervision? (shrink)

With the ascent of modern epidemiology in the Twentieth Century came a new standard model of prediction in public health and clinical medicine. In this article, we describe the structure of the model. The standard model uses epidemiological measures-most commonly, risk measures-to predict outcomes (prognosis) and effect sizes (treatment) in a patient population that can then be transformed into probabilities for individual patients. In the first step, a risk measure in a study population is generalized or extrapolated to (...) a target population. In the second step, the risk measure is particularized or transformed to yield probabilistic information relevant to a patient from the target population. Hence, we call the approach the Risk Generalization-Particularization (Risk GP) Model. There are serious problems at both stages, especially with the extent to which the required assumptions will hold and the extent to which we have evidence for the assumptions. Given that there are other models of prediction that use different assumptions, we should not inflexibly commit ourselves to one standard model. Instead, model pluralism should be standard in medical prediction. (shrink)

With the shortage of physicians and surgeons and increase in demand worldwide due to situations such as the COVID-19 pandemic, there is a growing interest in finding solutions to help address the problem. A solution to this problem would be to use neurotechnology to provide them augmented cognition, senses and action for optimal diagnosis and treatment. Consequently, doing so can negatively impact them and others. We argue that applying neurotechnology for human enhancement in physicians and surgeons can cause injustices, and (...) harm to them and patients. In this paper, we will first describe the augmentations and neurotechnologies that can be used to achieve the relevant augmentations for physicians and surgeons. We will then review selected ethical concerns discussed within literature, discuss the neuroengineering behind using neurotechnology for augmentation purposes, then conclude with an analysis on outcomes and ethical issues of implementing human augmentation via neurotechnology in medical and surgical practice. (shrink)

A self-fulfilling prophecy in neuroprognostication occurs when a patient in coma is predicted to have a poor outcome, and life-sustaining treatment is withdrawn on the basis of that prediction, thus directly bringing about a poor outcome for that patient. In contrast to the predominant emphasis in the bioethics literature, we look beyond the moral issues raised by the possibility that an erroneous prediction might lead to the death of a patient who otherwise would have lived. Instead, we (...) focus on the problematic epistemic consequences of neuroprognostic SFPs in settings where research and practice intersect. When this sort of SFP occurs, the problem is that physicians and researchers are never in a position to notice whether their original prognosis was correct or incorrect, since the patient dies anyway. Thus, SFPs keep us from discerning false positives from true positives, inhibiting proper assessment of novel prognostic tests. This epistemic problem of SFPs thus impedes learning, but ethical obligations of patient care make it difficult to avoid SFPs. We then show how the impediment to catching false positive indicators of poor outcome distorts research on novel techniques for neuroprognostication, allowing biases to persist in prognostic tests. We finally highlight a particular risk that a precautionary bias towards early withdrawal of life-sustaining treatment may be amplified. We conclude with guidelines about how researchers can mitigate the epistemic problems of SFPs, to achieve more responsible innovation of neuroprognostication for patients in coma. (shrink)

I argue that generic generalizations expressed in language (i.e. ‘generics’) are apt for clinical reasoning. I introduce generics and describe two problems in the use and interpretation of generics: Generics may license inaccurate judgements about the frequency of events or properties within a group (i.e. a problem with the ‘truth-aptness’ of generics) and may facilitate problematic beliefs about social kinds (e.g. prejudice or essentializing). I provide an account of clinical reasoning and describe some features of what I call ‘good’ clinical (...) reasoning. I offer examples of generics in clinical contexts and examine how the two problems with generics (i.e. of truth-aptness and social generics) can harm patients and impair clinical reasoning. However, I ultimately argue that generics are important for good clinical reasoning because they track ‘conspicuous’ features of disease processes (e.g. severe possible outcomes). Further, I argue that prejudicial generics are often irrational to believe. Social generics that are rational to believe need not lead to problematic implicatures about social kinds and can instead facilitate meaningfully articulating societal injustices. I then argue that statistical statements, which are tempting alternatives to generics in clinical reasoning, are no better than generics for good clinical reasoning. (shrink)

In the last decade, the cases of breast and cervical cancer have been positioned at the top rank of cancer statistics worldwide. Consequently, many husbands become family caregivers (FCGs) and get the burden of cancer caregiving. Being blind and incompetent, they need supportive care from healthcare professionals (HCPs). To support them, HCPs provide various healthcare information to meet their needs. Further, their demand for a specific type of healthcare information may reflect their need for emotional support from the HCPs to (...) reduce the burden. This study aims to examine the predictors of the need for emotional support among FCGs of female cancer patients by analyzing the demanded healthcare information. The mindsponge theory was used in conceptual development and interpretation. Bayesian Mindsponge Framework (BMF) analytics were used for statistical analysis on a dataset of 48 husbands and 12 significant others in five congested communities of Surabaya, Indonesia. Results showed that among the six types of healthcare information, the demanded cancer-specific information and information on cancer physical needs significantly predict the need for emotional support among respondents. Meanwhile, FCGs with a higher demand for information on alternative therapies are less likely to need emotional support. Other types of information have ambiguous effects on the need for emotional support. This study reveals that the demanded cancer-specific information, e.g., cancer prognosis or likely outcome, and information on cancer physical needs must be prioritized in information disclosure to meet the needs for emotional support among FCGs of female cancer patients. (shrink)