Health Care Decisions Research Articles

The impact of discontinuing proprotein convertase subtilisin-kexin type 9 (PCSK9) inhibitors on clinical outcomes

Abstract Background/Introduction Proprotein convertase subtilisin-kexin type 9 (PCSK9) inhibitors have demonstrated efficacy in lowering low-density lipoprotein cholesterol (LDL-C) levels and reducing the risk of cardiovascular events. Despite these advantages, some patients are unable to continue the use of PCSK9 inhibitors due to financial or other reasons. Limited clinical studies have been conducted to investigate the clinical outcomes of patients discontinuing PCSK9 inhibitors for various reasons. Purpose This study aims to specifically investigate patients who were unable to continue PCSK9 inhibitors, with a particular focus on those discontinuing due to financial or other reasons. The primary objective is to assess the impact of discontinuation on major adverse cardiovascular events (MACE). Methods This study focused on 205 Japanese patients who received PCSK9 inhibitors between July 2016 and December 2023. The clinical outcomes of patients who continued or discontinued PCSK9 inhibitors were comprehensively compared. The primary endpoint was major adverse cardiovascular events (MACE), encompassing cardiovascular death, coronary revascularization, target lesion revascularization (TLR), and fatal or non-fatal myocardial infarction (MI). Results Among 205 patients (median age: 69 years; interquartile range [IQR]: 60-76 years 68% male), the median follow-up was 413 days (IQR, 49-1148 days). At one year, the median LDL-C was 43mg/dL, and 158 patients (77%) achieved LDL-C <70 mg/dL. During the follow-up, 64% (131 patients) continued PCSK9 inhibitors, while 36% (74 patients) discontinued. Of the discontinuation group, 30% (22 patients) cited financial problems, 24% (18 patients) clinical stability, and 12% (9 patients) patient preference. Multivariate Cox regression analysis revealed that the group discontinuing PCSK9 inhibitors had a higher incidence of MACE than the group that continued (hazard ratio: 1.84; 95% confidence interval: 1.02 to 3.55; p=0.05). Conclusions This study suggests an increased risk of MACE when PCSK9 inhibitors are discontinued for financial or other reasons. This insight is vital for healthcare management and decision-making among medical practitioners.PCSK9: continuation vs discontinuation

Co-creating stigma-prevention strategies for mental health interventions with migrant populations

Abstract Background The ADVANCE project aims to address mental health disparities among people in a situation of vulnerability in Europe. Through a co-creation approach, workshops were conducted to devise stigma prevention strategies during the implementation of mental health promotion interventions for migrant populations in Italy and Denmark. Methods The methodology involved two rounds of scenario-based workshops, a participatory method that elicit expert opinions to solve local problems and anticipate future ones. Round 1 included two separate workshops with (1) end-users and umbrella organizations, and (2) healthcare practitioners and decision-makers, to ensure diverse perspectives and minimize the impact of power imbalances. Participants brainstormed strategies to minimize stigma and optimize the intervention scenario. In Round 2, a mixed-group workshop was held to refine the strategies. Workshops occurred in Copenhagen and Verona in March 2024. Content analysis of the data was carried out. Results In Denmark, discussions emphasized tailored presentation of the intervention promoting shared experiences and normalizing mental health challenges, language sensitivity, participation through self-identification, and attention to relational aspects in intervention delivery to combat stigma effectively and enhance acceptability and engagement with the intervention. In Italy, proposed strategies included group exercises to raise awareness and promote open dialogue about cultural differences, language barriers, and discrimination. Discussions also considered the intersectionality of stigma, including factors like gender, socioeconomic status, and sexual orientation. Common themes included language sensitivity, facilitator training, and inclusive and safe spaces. Conclusions Scenario-based workshops can be a valuable tool to facilitate the co-creation of stigma-prevention strategies while involving all stakeholders in promoting effective mental health interventions. Key messages • Co-creation approach fostered stakeholder collaboration, yielding tailored strategies that promote inclusivity, acceptability and engagement with mental health interventions for migrant populations. • Context-specific approaches that emphasize trust, confidentiality and cultural sensitivity were prioritised to tackle stigma when implementing interventions and promoting mental health equity.

Differing needs for Advance Care Planning in the Veterans Health Administration: use of latent class analysis to identify subgroups to enhance Advance Care Planning via Group Visits for veterans

BackgroundAdvance Care Planning via Group Visits (ACP-GV) is a patient-centered intervention facilitated by a clinician using a group modality to promote healthcare decision-making among veterans. Participants in the group document a “Next Step” to use in planning for their future care needs. The next step may include documentation of preferences in an advance directive, discussing plans with family, or anything else to fulfill their ACP needs. This evaluation seeks to determine whether there are identifiable subgroups of group participants with differing needs prior to delivery of the ACP-GV program and, if so, to use information about the subgroups to enhance the program offered to veterans in United States Department of Veterans Affairs (VA) healthcare settings.MethodsWe conducted a secondary analysis of national data from a quality improvement evaluation. Patient- and provider-level data from administrative healthcare records for VA users in all 50 states, territories, and the District of Columbia provides data on veterans attending ACP-GV during federal fiscal years 2018–2022 (N = 26,857). Latent class analysis seeks to identify the various subgroups of veterans based on their level of ACP self-efficacy before attending ACP-GV and any demographic differences across the resulting subgroups of veterans attending ACP-GV. ACP self-efficacy is derived from seven items obtained from a participant worksheet used during the group.ResultsAnalysis revealed two distinct groups of veterans, distinguishable by their pre-ACP-GV levels of one aspect of ACP self-efficacy: prior knowledge of ACP. Veterans with higher prior knowledge of ACP are associated with an identified next step focused on checking their current AD status and updating it, and veterans with lower ACP prior knowledge are associated with identifying a next step to discuss ACP more fully with family. Differences in age, sex, race, ethnicity, and marital status exist across subgroups of veterans.ConclusionGreater attention must be paid to ACP and veterans’ prior knowledge of ACP to consistently encourage annual review and status updates.

The Role of Faith in Medical Decision-Making

Abstract Background Ultra-Orthodox Israeli Jews face complex decisions when their doctors’ instructions contradict their rabbis’ advice. This study examines how their belief system affects their trust and decision-making in such situations, using socio-economic club theory and Gilligan’s listening guide. Methods Excerpts from 16 focus groups with 128 ultra-Orthodox Jews were analyzed to understand their perspectives on the relationship between doctors and rabbis. The socio-economic approach of Berman’s club theory was applied to explain their behavior, and Gilligan’s listening guide was used to analyze the focus group results. Results The strict behaviors in the relationships between doctors, rabbis, and patients function as social capital, raising the status of ultra-Orthodox Jews as members of an exclusive club. They balance critical health decisions with the social demand to obey religious leaders. However, listening to different voices (‘I,’ ‘we,’ and ‘they’) provides nuanced insights into their decisions, revealing dynamic changes in the community’s perceptions of the doctor-rabbi relationship and varying perspectives within the heterogeneous ultra-Orthodox community. Conclusions Combining Berman’s theory with Gilligan’s listening guide is an effective method for conducting in-depth qualitative research on sensitive issues with religiously strict groups. Understanding the belief systems underlying healthcare decisions is essential for providing culturally sensitive medical care to religious communities and training medical staff serving these communities. Key messages • Ultra-Orthodox Jews balance complex health decisions with social demands, revealing nuanced perspectives through strict doctor-rabbi relationships. • Combining theories and qualitative methods effectively studies sensitive issues in religious groups, informing culturally sensitive healthcare.

Variability of pre-test probability for obstructive coronary artery disease between referring primary care physicians and estimations from the referral letters

Abstract Background Estimation of Pre-Test Probability (PTP) for obstructive Coronary Artery Disease (CAD), derived from sex, age and characterization of chest pain or dyspnea, is recommended by European guidelines to guide further investigation. When Coronary Computed Tomography Angiography (CCTA) was introduced as first line investigation in patients with PTP <15%, in the primary care of a catchment area of ~470 000 inhabitants in Sweden, attachment of PTP was made compulsory to the referral. The interrater variability, or consistency of agreement, of PTP estimation is not known. The validity of PTP to guide healthcare decisions depends to some extent on the reliability of estimations. Appropriate prioritization of invesigations is reliant on the information included in referral letters. Purpose The purpose was to study the interrater variability of estimations of PTP. We hypothesized: 1) the interrater variability of PTP estimations to be less than excellent (ICC < 0.9), and 2) not statistically different among PTP classes; 3) a significant difference between clinical estimations and estimations made from the referral letters. Methods We studied the 545 patients referred for CCTA from primary care, 1st of June 2021 to 7th of Dec. 2022, with available PTP estimations. The interrater variability, expressed as Intraclass Correlation Coefficients (ICC), was determined for estimations of PTP by the referring physicians (RP) and three external raters (ER). The latter were physicians practising in primary care who rated PTP based on the referral letters to CCTA, independently and blinded. Each rater assessed multiple patients and each patient was assessed by all raters. ICC with a two-way random consistency model for single raters was calculated and evaluated according to a proposed standard(1). Results The ICC for PTP estimations between the RP and the median of three ER was 0.75 (CI 95 % 0.71-0.83, p<0.001). ICC was higher for PTP class > 15% compared to the lower classes: <5% (n = 65, ICC ≈ 0); 5-15% (n = 343, ICC 0.48, CI 95 % [0.39-0.56]); >15% (n = 137, ICC 0.71, CI 95 % [0.62-0.78]) (Figure 1). The ICC for PTP estimations was significantly higher between the three ER (0.88, SE 0.009, CI 95% [0.86-0.89]) than when the RP was added as a rater (ICC 0.80, SE 0.012, CI 95 % [0.77-0.82]). Adding a fixed effect of the difference in ratings between RP and ERs to the ICC model did not change model output (R = 0.80 (SE 0.012, CI 95 % [0.77-0.82]). Conclusions There is a good repeatability, in estimation of PTP based on medical history as described in referral letters, between blinded independent reviewers of CCTA referrals. The repeatability between the external raters and the referring physicians’ compulsory entered PTPs is lower, yet good. The variability of PTP estimations, speculatively from disagreement on main symptom and inconsistency in characterization of chest pain, may impact patient selection and outcomes and is understudied.

Building Collaborative Governance in the Extractive Sector

The extractive sector faces wicked problems — so-called because they are complex, ambiguous, dynamic and evolving. They usually lack one root cause and their solutions often create unintended consequences. However, collaborative governance offers a way to address the sector’s complex challenges. By studying examples of collaboration in other areas — environmental conservation, health care, education, urban planning and disaster response — the extractive sector can adopt best practices for its own purposes. Rather than a top-down, siloed approach, collaborative governance involves working with a variety of stakeholders, such as local communities, government, industry and civil society groups. Collaborative governance is a pragmatic and effective approach to problem-solving and fosters transparency, inclusivity and adaptability by integrating a range of perspectives and expertise, culminating in effective and equitable policy-making. Environmental impacts, social conflicts and a broad range of stakeholder interests are just a few of the challenges the extractive sector faces, making traditional top-down decision-making inadequate. The sector could incorporate collaborative governance in decision-making processes by adopting principles from Complex Adaptive Systems (CAS) theory. These principles include adaptability, use of local knowledge and decentralization to aid in balancing hard and fast regulations with on-the-ground realities, including playing a valuable role in conflict prevention. Among the problems the sector faces are economic dependence on finite, depleting resources, evolving technology, social conflict, a shifting regulatory environment, uncertain global markets and the imperative to respect cultural and Indigenous rights. Thus, a key part of collaborative governance would be training local community leaders to advocate for their interests and to manage their resources responsibly. Many other sectors offer valuable lessons in collaborative governance. For example, inclusive and shared decision-making in health care has produced better, more effective and tailored patient outcomes. Urban planning’s focus on sustainability can guide the extractive sector in managing environmental impacts and promoting community resilience. Stakeholders in conservation and natural resource management, including government, organizations and Indigenous communities, already collaborate to establish protected areas, engage in wildlife management and work on sustainable land use. Collaboration in education encompasses school boards, parent councils and other stakeholders. Disaster management and emergency response also have much to teach about collaborative governance. With NGOs, government, first responders and communities co-ordinating disaster preparedness, response and recovery efforts, disaster management has become much more effective. International development is another field that successfully uses collaborative governance. Collaboration between governments, NGOs, donors and communities helps address poverty, infrastructure development and local social issues, with a focus on local ownership, capacity building and sustainable development. The need for more collaborative approaches in the extractive sector is increasingly evident. New global standards demand collaboration, consultation and even consent from local communities, especially where Indigenous peoples are involved. Impact assessments, community consultations and benefit-sharing agreements are now the norm. Collaborative governance is expanding in the extractive sector; thus it is essential to critically assess current practices, learning from collaboration in other sectors in order to adapt key methods to specific contexts in the extractive sector. A blueprint for future collaboration requires thoughtful reflection on what has worked, what has not and why.

Co-developing frameworks towards environmentally directed pharmaceutical prescribing in Scotland – A mixed methods study

The presence of human pharmaceuticals in the aquatic environment is recognised internationally as an important public health and environmental issue. In Scotland, healthcare sustainability targets call for improvements to medicine prescribing and use to reduce healthcare's impact on the environment. This proof-of-concept study aimed to develop a framework on the environmental impact of pharmaceuticals to use as a knowledge support tool for healthcare professionals, focussing on pharmaceutical pollution. Nominal Group Technique was applied to achieve consensus on pharmaceuticals and modelling factors for the framework, working with a panel of cross-sector stakeholders. Bayesian Belief Network modelling was applied to predict the environmental impact (calculated from hazard and exposure factors) of selected pharmaceuticals, with Scotland-wide mapping for visualisation in freshwater catchments. The model calculated the pollution risk score of the individual pharmaceuticals, using the ratio of prescribed mass vs. mass that would not exceed the predicted no-effect concentration in the freshwater environment. The pharmaceuticals exhibited different risk patterns, and spatial variation of risk was evident (generally related to population density), with the most catchments predicted to exceed the pollution risk score for clarithromycin (probability >80 % in 35 of 40 modelled catchments). Simulated risk scores were compared against observed risk calculated as the ratio of measured environmental concentrations from national regulatory and research monitoring and predicted no-effect concentrations. The model generally overpredicted risk, likely due to missing factors (e.g. solid-phase sorption, temporal variation), low spatial resolution, and low temporal resolution of the monitoring data. This work demonstrates a novel, trans-disciplinary approach to develop tools aiding collation and integration of environmental information into healthcare decision-making, through application of public health, environmental science, and health services research methods. Future work will refine the framework with additional clinical and environmental factors to improve model performance, and develop electronic interfaces to communicate environmental information to healthcare professionals.

Use and impact of clinical pathways across various healthcare settings: A protocol for an umbrella review of global evidence.

The proposed umbrella review aims to assess the use and impact of clinical pathways on professional practice, patient outcomes, length of hospital stay, hospital costs, patient satisfaction, and hospital staff satisfaction through a synthesis of existing systematic reviews and meta-analyses. Following PRIOR guidelines, a systematic search will be conducted in MEDLINE, Epistemonikos, and the Cochrane Library to identify relevant systematic reviews and meta-analyses, from inception till March 2024. Two reviewers will independently screen titles and abstracts, with a third resolving any disagreements. Full-text articles considered potentially relevant will be assessed for eligibility by the same process. The data extraction form will cover information about the review methods, characteristics of the included primary studies, the types of interventions evaluated, and the reported outcomes. This standardized data extraction form will be piloted by the review team on five to ten articles to ensure all relevant information is recorded. The quality of included systematic reviews and meta-analyses will be evaluated using AMSTAR 2. PROSPERO registration number is CRD42024529371. The study will present a narrative synthesis of the findings, addressing the clinical and methodological heterogeneity and assessing the impact of clinical pathways on various healthcare outcomes. This umbrella review will provide evidence-based insights into the effectiveness, challenges, and best practices of clinical pathways, guiding healthcare decision-making and identifying areas for future research. Results will be disseminated widely to inform policy and improve healthcare service delivery. No patient or public contribution, as this paper is a protocol of an umbrella review.

Fuzzy Soft Sets and its Application to Decision Making: A Short Case Study Involving the Health Sector

The health sector faces uncertainty and complex decision-making scenarios, making traditional analytical tools insufficient. The fuzzy soft set theory has emerged as a powerful framework for modeling and reasoning with uncertain information, with promising applications in the health domain. This project explores the application of fuzzy soft sets in various decision-making processes in the health sector, including medical diagnosis, disease classification, treatment planning, risk assessment, patient stratification, and predictive modeling. The study reviews historical development of fuzzy set theory and its extension to soft sets, discussing challenges, limitations, and future research directions. The findings aim to contribute to the growing body of knowledge on the practical relevance and potential of fuzzy soft set theory in addressing healthcare decision-making needs.

Prioritising Patients and Planet: Advocating for Change in Respiratory Care

The global burden of respiratory diseases, particularly asthma and chronic obstructive pulmonary disease (COPD), continues unabated. Suboptimal management places a significant strain on both patients and urgent or emergency care services. With an ageing population in many countries, the demand for these services is set to increase further. At the same time, healthcare systems are striving to reduce their carbon footprint and achieve net zero emissions, as the healthcare sector is a significant contributor to carbon emissions worldwide. Although these two goals may appear contradictory, they need not be in conflict. This article reviews an industry-sponsored symposium held at the European Respiratory Society (ERS) Congress 2024 in Vienna, Austria, in September 2024. The session addressed the urgent need to change the delivery model for respiratory healthcare in response to the increasing prevalence of respiratory diseases and the challenges posed by climate change. Co-chair John Hurst, Professor of Respiratory Medicine at University College London (UCL), UK, underscored the importance of innovative solutions for managing respiratory diseases and highlighted the challenges faced by healthcare decision-makers. This was further elaborated on by Omar Usmani, Professor of Respiratory Medicine at Imperial College London, UK, who emphasised the importance of clinical choice. He stated that inhaled medicines, which form the cornerstone of treatment, should not be considered interchangeable. He also discussed ongoing efforts to maintain access to essential medicines by developing novel next-generation propellants (NGP) for pressurised metered-dose inhaler (pMDI) devices, which will reduce their carbon footprint to levels comparable with dry powder inhalers (DPI). Additionally, he described the European Chemicals Agency (ECHA) proposal to restrict a broad range of chemicals classed as per- and polyfluoroalkyl substances (PFAS). This precautionary measure would affect both current propellants in pMDIs and the transition to NGPs, with global implications for inhaled medicines. Erika Penz, Associate Professor of Respirology, Critical Care, and Sleep Medicine at the University of Saskatchewan, Canada, noted that suboptimal management of respiratory disease is associated with a disproportionately high burden on both patients and the environment. The forthcoming availability of pMDI medicines with NGPs alone will not resolve this larger issue. As every healthcare interaction carries a carbon footprint, which increases with the intensity of treatment, the implementation of guidelines into clinical practice would improve patient outcomes and reduce the demand on healthcare services and the associated carbon emissions. Co-chair Helen Reddel, Clinical Professor and Research Leader at the Woolcock Institute of Medical Research, Australia, concluded by re-emphasising the urgent need to implement guidelines immediately for the benefit of both patients and the environment.

Multimorbidity and Person-Centred care in a socioeconomically deprived community: a qualitative study.

Multimorbidity (>2 long-term conditions) has poorer outcomes in areas of high socioeconomic deprivation (SED). High-quality Person-Centred Care (PCC) is important in multimorbidity but socially vulnerable populations have not informed current PCC models. To explore how wider community factors influence management of multimorbidity in the context of high SED, how high-quality PCC is defined by patients, and whether this influences healthcare management. Ethnographically informed case study in community experiencing high SED in Scotland. Participant observation (hours = 142) within 4 community groups who also took part in 2 participatory workshops. 25 in-depth interviews with people with multimorbidity, recruited from local General Practices; emerging findings discussed with interviewees in one focus group. Field notes/transcripts analysed using inductive thematic analysis. Key aspects of PCC were "patient as person", "therapeutic relationship", "co-ordination" and "power-sharing"; power sharing was particularly enabling but rarely happened (barriers often unseen by practitioners). Shared community experiences of "being known", "stigma" and "none of the systems working" influenced how people approached health services, and healthcare decisions. High quality PCC may have been particularly effective in this setting because of its influence on ameliorating wider shared negative community experiences. In a high SED setting PCC is important and can enhance engagement. Wider community factors have a critical influence on engagement with healthcare in areas of high SED and PCC may be particularly important in this context because of its influence ameliorating these. Policy makers should prioritise and resource PCC.

Inclusion of Veterans Health Administration hospitals in Centers for Medicare & Medicaid Services Overall Hospital Quality Star Ratings.

The Centers for Medicare & Medicaid Services (CMS) Overall Hospital Quality Star Rating, established in 2016, is a summary of publicly available quality information for acute care hospitals. In July 2023, Veterans Health Administration (VHA) hospitals became eligible to receive a CMS Overall Hospital Quality Star Rating for the first time. Our objective was to compare performance in quality ratings among VHA and non-VHA hospitals. We used the hospital quality measure scores posted to Care Compare on Medicare.gov as of January 2023 as our primary data set. We conducted a pair of analyses to characterize the performance of VHA hospitals compared to non-VHA hospitals: an overall analysis including all rated hospitals, and a matched analysis in which only a single nearby hospital was included for each VHA hospital. Of the 4518 non-VHA hospitals, 2962 (65.6%) received a Star Rating, compared to 114 (84%) of 136 VHA hospitals. VHA hospitals tended to receive higher ratings overall (one-star: 8%; two-star: 11%; three-star: 14%; four-star: 35%; five-star: 32%) than non-VHA (one-star: 8%; two-star: 22%; three-star: 29%; four-star: 26%; five-star: 15%). A similar pattern was observed in the matched analysis. VHA hospitals tended to perform better on the Overall Star Rating compared to non-VHA hospitals, as evidenced by being more likely to receive a four- or five-star rating. The eligibility of VHA hospitals to receive an Overall Star Rating signifies an important addition to the program that will allow Veterans to make more informed healthcare decisions.

Guidance for unbiased predictive information for healthcare decision-making and equity (GUIDE): considerations when race may be a prognostic factor.

Clinical prediction models (CPMs) are tools that compute the risk of an outcome given a set of patient characteristics and are routinely used to inform patients, guide treatment decision-making, and resource allocation. Although much hope has been placed on CPMs to mitigate human biases, CPMs may potentially contribute to racial disparities in decision-making and resource allocation. While some policymakers, professional organizations, and scholars have called for eliminating race as a variable from CPMs, others raise concerns that excluding race may exacerbate healthcare disparities and this controversy remains unresolved. The Guidance for Unbiased predictive Information for healthcare Decision-making and Equity (GUIDE) provides expert guidelines for model developers and health system administrators on the transparent use of race in CPMs and mitigation of algorithmic bias across contexts developed through a 5-round, modified Delphi process from a diverse 14-person technical expert panel (TEP). Deliberations affirmed that race is a social construct and that the goals of prediction are distinct from those of causal inference, and emphasized: the importance of decisional context (e.g., shared decision-making versus healthcare rationing); the conflicting nature of different anti-discrimination principles (e.g., anticlassification versus antisubordination principles); and the importance of identifying and balancing trade-offs in achieving equity-related goals with race-aware versus race-unaware CPMs for conditions where racial identity is prognostically informative. The GUIDE, comprising 31 key items in the development and use of CPMs in healthcare, outlines foundational principles, distinguishes between bias and fairness, and offers guidance for examining subgroup invalidity and using race as a variable in CPMs. This GUIDE presents a living document that supports appraisal and reporting of bias in CPMs to support best practice in CPM development and use.

Data analytics-driven innovation: UTAUT model perspectives for advancing healthcare social work

In today’s dynamic and increasingly complex healthcare landscape, leveraging data analytics in decision-making processes has become indispensable for fostering innovation and enabling more effective, data-driven approaches to healthcare delivery. This study explores the factors influencing the adoption of data analytics techniques in healthcare social work practice. It is guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) model and is aligned with key principles of open innovation dynamics. The analysis, conducted through binary logistic regression, revealed that facilitating conditions, effort expectancy, and self-efficacy are the most influential predictors of healthcare social workers’ intention to adopt data analytics. This underscores the critical role of organizational support, perceived ease of use, and individual confidence in fostering the adoption of innovative data-driven practices within healthcare social work. The results also indicate that social influence has a limited impact on shaping adoption decisions. The study’s findings have significant practical implications for healthcare decision-makers, social workers, and other stakeholders aiming to advance service delivery and improve patient outcomes through innovative data-driven solutions.

Disease Prediction and Drug Recommendation Using ML

In today's dynamic healthcare landscape, the integration of technology has become indispensable. The project sets out to create a cutting-edge healthcare solution that will transform the very foundations of healthcare decision-making by utilizing the powerful powers of machine learning. The main objective of this project is to develop a dual-dashboard system that will serve both patients and medical staff. The main objective is to reduce the gap between patients and healthcare professionals, which has existed for a long time. It makes it possible for people to receive highly personalized health assessments, enables efficient appointment scheduling, and provides doctors with recommendations for data-driven treatments. Ultimately, this project aims to profoundly impact the accessibility and quality of healthcare services. It seeks to enhance the patient experience, augment the precision of medical decision-making, and foster seamless collaboration between individuals and their healthcare providers.

Patient expectations and decisional regret in the management of ventral hernias.

Older adult patients have many factors to contemplate when considering elective ventral hernia repair. In this study, we aimed to understand whether our novel shared decision-making (SDM) aid helped reduce this population's decisional regret when choosing hernia management strategy. Patients ≥ 60years of age presenting for ventral hernia evaluation were randomized to two groups. The experimental group had their visit guided by our novel SDM aid. All patients took a survey prior to consultation outlining their treatment expectations. All patients were called within 6months to complete the Decision Regret Scale, which measures remorse after a healthcare decision. Seventy-two patients (36 control, 36 experimental) completed final follow-up. On initial expectations evaluation, 53 patients (74%) reported wanting surgical repair and 58 patients (81%) reported expecting surgical repair. Ultimately, 18 patients in the control group and 17 patients in the experimental group did not undergo surgery. The use of the SDM aid did not affect if patients chose observation (OR 0.44, p = 0.24) or result in a lower decision regret score (9.86 vs 9.31, p = 0.89). Surgery was associated with a lower decision regret score (3.38 vs 16.14; p = 0.001). Of those who did not undergo repair, patients initially wanting or expecting surgery had higher decision regret scores (22.83 vs 3.33, p < 0.001; 20.40 vs 5.50, p = 0.009). Nonoperative patients who chose observation had less regret than those needing medical optimization (9.50 vs 25.00, p = 0.04). There were no differences in decision regret scores based on initial wants or expectations for those who had surgical repair. Decisional regret following ventral hernia management is associated with patients' expectations prior to initial surgical consultation. The use of a decisional aid did not lower decision regret scores. These findings emphasize the need for upfront expectation setting and longitudinal programs to help patients reach their treatment goals.

Omnicare: A Comprehensive ML/DL-Based Prediction System for Healthcare and Agriculture

Effective decision-making in healthcare and agriculture can be challenging due to the complexity and volume of data involved. Traditional methods often require extensive manual analysis and domain-specific expertise, which can be time-consuming and prone to error. The challenge is compounded by the need for real-time insights and accurate predictions to address critical issues such as disease diagnosis and crop management. Omnicare tackles these challenges by using advanced machine learning and deep learning technologies to facilitate predictions. With pretrained models, it delivers precise insights for medical and agricultural needs, eliminating the need for user-provided training data. This streamlined, user-friendly approach enhances decision-making and represents a significant advancement in applying AI to real-world problems

Compliance with the 2016 WHO’s antenatal care recommendation and its determinants among women in Sub-Saharan Africa: a multilevel-analysis of population survey data

BackgroundDespite the positive impact of adhering to the new antenatal care model on pregnancy outcomes and maternal health service uptake, women in resource-limited settings exhibit low levels of compliance with this recommendation. Previous studies on women’s adherence to the new antenatal care recommendation have been limited to individual countries, with no evidence available at Sub-Saharan Africa (SSA) level. Therefore, this study sought to investigate compliance with the 2016 WHO’s recommendation of at least eight antenatal care contacts among women in SSA countries and identify its determinants.MethodsThe study utilized a weighted sample of 101,983 women who had received antenatal care during their index pregnancy, drawn from recent DHS data of sixteen SSA countries. A multilevel mixed-effect analysis was conducted to identify factors that influence compliance with new antenatal care recommendations. Model comparison was performed using deviance and log-likelihood values, and statistical significance was determined at a P-value of less than 0.05.ResultsThe level of compliance with the recommended antenatal care contacts among women in SSA was 9.9% (95% CI: 9.7-10.1%), with the highest rate in Sierra Leone (26.1%) and lowest in Rwanda (< 1%). A multivariable logistic regression analysis showed that age, education, employment status, household wealth, healthcare decisions, the timing of antenatal contacts, consumption of nutritional supplements, residence, community-level women illiteracy, and media exposure were the significant determinants of compliance.ConclusionOnly one in ten pregnant women in SSA countries had attended the recommended number of antenatal contacts, with Sierra Leone having the highest compliance rate and Rwanda and Senegal having the lowest. Therefore, policymakers should focus on improving access to education, especially for women and their partners, and providing exempted services for pregnant women from low-income households. Interventions that target communities with low levels of literacy and media exposure could also be effective in improving the uptake of the services.

Advancing the role of real-world evidence in comparative effectiveness research.

Aim: Comparative effectiveness research (CER) is essential for making informed decisions about drug access. It provides insights into the effectiveness and safety of new drugs compared with existing treatments, thereby guiding better healthcare decisions and ensuring that new therapies meet the real-world needs of patients and healthcare systems. Objective: To provide a tool that assists analysts and decision-makers in identifying the most suitable analytical approach for answering a CER question, given specific data availability contexts. Methods: A systematic literature review of the scientific literature was performed and existing regulatory and health technology assessment (HTA) guidance were evaluated to identify and compare recommendations and best practices. Based on this review a methods flowchart that synthesizes current practices and requirements was proposed. Results: The review did not find any papers that clearly identified the most appropriate analytical approach for answering CER questions under various conditions. Therefore, a methods flowchart was designed to inform analyst and decision makers choices starting from a well-defined scientific question. Conclusion: The proposed methods flowchart offers clear guidance on CER methodologies across a range of settings and research needs. It begins with a well-defined research question and considers multiple feasibility aspects related to CER. This tool aims to standardize methods, ensure rigorous and consistent research quality and promote a culture of evidence-based decision-making in healthcare.

Decoding privacy concerns: the role of perceived risk and benefits in personal health data disclosure

BackgroundPersonal health data is crucial for effective medical care, personalized treatment, and health monitoring. It enables accurate diagnosis, efficient treatment plans, and informed healthcare decisions. Personal health data should be protected to ensure patient privacy, prevent misuse or unauthorized access, and maintain trust in healthcare systems, thereby safeguarding individuals’ sensitive information from potential harm or exploitation. Therefore, this study aimed to investigate whether perceived risk and perceived benefits have mediating roles in the relationships among individuals’ personal health information disclosure behaviour, perceived control, and privacy concerns.MethodThe population of the study consisted of individuals living in the provinces of Izmir, Konya and Adana. The sample of the study consisted of individuals who were reached through a convenience sampling method. The scales for privacy concerns, perceived control, perceived risk, perceived benefits and information disclosure behaviour were used in the study. Cronbach’s alpha and the AVE were calculated, and a confirmatory factor analysis was performed. A path analysis was performed using the structural equation model to test the hypotheses.ResultsThe analysis revealed a significant negative relationship between individuals’ personal health data disclosure behaviour and their privacy concerns. However, perceived risk and perceived benefit did not mediate this relationship. Additionally, a significant positive relationship was found between individuals’ behaviour of disclosing their perceived control and personal health data, with perceived risk and benefits playing a mediating role in this relationship.ConclusionThe study concluded that as individuals’ concerns about sharing personal health data increase, they are less likely to share these data. It was also found that perceived risk and perceived benefit mediate this relationship. Additionally, higher perceived risk intensifies privacy concerns, further discouraging data sharing, while perceived benefits can mitigate these concerns, promoting greater willingness to disclose health information.