Comparing and assessing four AI chatbots' competence in economics.

Artificial Intelligence (AI) chatbots have emerged as powerful tools in modern academic endeavors, presenting both opportunities and challenges in the learning landscape. They can provide content information and analysis across most academic disciplines, but significant differences exist in terms of response accuracy for conclusions and explanations, as well as word counts. This study explores four distinct AI chatbots, GPT-3.5, GPT-4, Bard, and LLaMA 2, for accuracy of conclusions and quality of explanations in the context of university-level economics. Leveraging Bloom's taxonomy of cognitive learning complexity as a guiding framework, the study confronts the four AI chatbots with a standard test for university-level understanding of economics, as well as more advanced economics problems. The null hypothesis that all AI chatbots perform equally well on prompts that explore understanding of economics is rejected. The results are that significant differences are observed across the four AI chatbots, and these differences are exacerbated as the complexity of the economics-related prompts increased. These findings are relevant to both students and educators; students can choose the most appropriate chatbots to better understand economics concepts and thought processes, while educators can design their instruction and assessment while recognizing the support and resources students have access to through AI chatbot platforms.

What is the effectiveness of reporting systems in promoting learning in healthcare?

Patient safety in healthcare remains a top priority. Learning from safety events is vital to move towards safer systems. As a result, reporting systems are recognised as the cornerstone of safety, especially in high-risk industries. However, in healthcare, the benefits of reporting systems in promoting learning remain contentious. Though the strengths of these systems, such as promoting a safety culture and providing information from near misses are noted, there are problems that mean learning is missed. Understanding the factors that both enable and act as barriers to learning from reporting is also important to consider. This review, considers the effectiveness of reporting systems in contributing to learning in healthcare.

Characterization of teaching-learning tutors in public health services and topics of interest for their work.

OBJECTIVE: To describe the profile of teaching-learning tutors in public health services and investigate which topics are of greatest interest in development spaces for these actors. METHOD: Cross-sectional study. Eligible tutors of Health Care Planning. Data collection using an electronic questionnaire composed of closed questions on sociodemographic characteristics, training and performance. Chi-square test used to compare proportions according to tutor typologies. RESULTS: A total of 614 tutors worked in Brazil's five geographic regions, the majority in primary care (82%), followed by state/regional work (13%) and specialized outpatient care (5%). The majority reported being female, of brown skin color, from the nursing field, having worked as a tutor for less than a year, and with no previous experience in preceptorship or similar. The most important topics were Health Care Networks, risk stratification for chronic conditions and the functions of specialized outpatient care. CONCLUSION: The predominance of certain characteristics among tutors was identified, with differences between the types of work. The findings can support managers in the process of selecting and developing tutors in Health Care Planning.

CT Perfusion Map Synthesis from CTP Dynamic Images Using a Learned LSTM Generative Adversarial Network for Acute Ischemic Stroke Assessment.

Computed tomography perfusion (CTP) is a dynamic 4-dimensional imaging technique (3-dimensional volumes captured over approximately 1 min) in which cerebral blood flow is quantified by tracking the passage of a bolus of intravenous contrast with serial imaging of the brain. To diagnose and assess acute ischemic stroke, the standard method relies on summarizing acquired CTPs over the time axis to create maps that show different hemodynamic parameters, such as the timing of the bolus arrival and passage (Tmax and MTT), cerebral blood flow (CBF), and cerebral blood volume (CBV). However, producing accurate CTP maps requires the selection of an arterial input function (AIF), i.e. a time-concentration curve in one of the large feeding arteries of the brain, which is a highly error-prone procedure. Moreover, during approximately one minute of CT scanning, the brain is exposed to ionizing radiation that can alter tissue composition, and create free radicals that increase the risk of cancer. This paper proposes a novel end-to-end deep neural network that synthesizes CTP images to generate CTP maps using a learned LSTM Generative Adversarial Network (LSTM-GAN). Our proposed method can improve the precision and generalizability of CTP map extraction by eliminating the error-prone and expert-dependent AIF selection step. Further, our LSTM-GAN does not require the entire CTP time series and can produce CTP maps with a reduced number of time points. By reducing the scanning sequence from about 40 to 9 time points, the proposed method has the potential to minimize scanning time thereby reducing patient exposure to CT radiation. Our evaluations using the ISLES 2018 challenge dataset consisting of 63 patients showed that our model can generate CTP maps by using only 9 snapshots, without AIF selection, with an accuracy of 84.37 % .

Developing a framework for promoting interest and engagement of scholarship of teaching and learning for medical students.

BACKGROUND: The scholarship of teaching and learning (SoTL) is a field of academic research that focuses on improving learning through reflective and informed teaching. Currently, most SoTL-related work is faculty-driven; however, student involvement in SoTL has been shown to benefit both learners and educators. Our study aims to develop a framework for increasing medical students' interest, confidence, and engagement in SoTL. METHODS: A student-led SoTL interest group was developed and a year-round program of SoTL was designed and delivered by student leaders of the group under the guidance of a faculty advisor. Individual post-session surveys were administered to evaluate participants' perceptions of each session. Pre- and post-program surveys were administered to evaluate the program impact. RESULTS: The year-round SoTL program consistently attracted the participation of medical students and faculty. Survey responses indicated strong medical student interest in the program and positive impact of the program. Increased interest and confidence in medical education research were reported by the student participants. The program design provided opportunities for student participants to network and receive ongoing feedback about medical education research they were interested or involved in. CONCLUSION: Our study provides insights for developing a framework that other institutions can reference and build upon to educate and engage students in SoTL.

Sparsified federated learning with differential privacy for intrusion detection in VANETs based on Fisher Information Matrix.

With the continuous development of vehicular ad hoc networks (VANET) security, using federated learning (FL) to deploy intrusion detection models in VANET has attracted considerable attention. Compared to conventional centralized learning, FL retains local training private data, thus protecting privacy. However, sensitive information about the training data can still be inferred from the shared model parameters in FL. Differential privacy (DP) is sophisticated technique to mitigate such attacks. A key challenge of implementing DP in FL is that non-selectively adding DP noise can adversely affect model accuracy, while having many perturbed parameters also increases privacy budget consumption and communication costs for detection models. To address this challenge, we propose FFIDS, a FL algorithm integrating model parameter pruning with differential privacy. It employs a parameter pruning technique based on the Fisher Information Matrix to reduce the privacy budget consumption per iteration while ensuring no accuracy loss. Specifically, FFIDS evaluates parameter importance and prunes unimportant parameters to generate compact sub-models, while recording the positions of parameters in each sub-model. This not only reduces model size to lower communication costs, but also maintains accuracy stability. DP noise is then added to the sub-models. By not perturbing unimportant parameters, more budget can be reserved to retain important parameters for more iterations. Finally, the server can promptly recover the sub-models using the parameter position information and complete aggregation. Extensive experiments on two public datasets and two F2MD simulation datasets have validated the utility and superior performance of the FFIDS algorithm.

Classification images for aerial images capture visual expertise for binocular disparity and a prior for lighting from above.

Using a novel approach to classification images (CIs), we investigated the visual expertise of surveyors for luminance and binocular disparity cues simultaneously after screening for stereoacuity. Stereoscopic aerial images of hedges and ditches were classified in 10,000 trials by six trained remote sensing surveyors and six novices. Images were heavily masked with luminance and disparity noise simultaneously. Hedge and ditch images had reversed disparity on around half the trials meaning hedges became ditch-like and vice versa. The hedge and ditch images were also flipped vertically on around half the trials, changing the direction of the light source and completing a 2 × 2 × 2 stimulus design. CIs were generated by accumulating the noise textures associated with "hedge" and "ditch" classifications, respectively, and subtracting one from the other. Typical CIs had a central peak with one or two negative side-lobes. We found clear differences in the amplitudes and shapes of perceptual templates across groups and noise-type, with experts prioritizing binocular disparity and using this more effectively. Contrariwise, novices used luminance cues more than experts meaning that task motivation alone could not explain group differences. Asymmetries in the luminance CIs revealed individual differences for lighting interpretation, with experts less prone to assume lighting from above, consistent with their training on aerial images of UK scenes lit by a southerly sun. Our results show that (i) dual noise in images can be used to produce simultaneous CI pairs, (ii) expertise for disparity cues does not depend on stereoacuity, (iii) CIs reveal the visual strategies developed by experts, (iv) top-down perceptual biases can be overcome with long-term learning effects, and (v) CIs have practical potential for directing visual training.

Research on collaborative innovation optimization strategies for digitally enabled higher education ecosystems.

Digitally enabled higher education involves the in-depth use of new-generation digital technology, which has subverted and innovated the traditional teaching mode, driven the development of high-quality teaching and learning, and improved teachers' teaching experience, and increased efficiency. Based on ecosystem theory, this paper constructs a higher education ecosystem with the government, enterprises, and universities as the core participating subjects. It considers the participating subjects' effort level and the ecosystem's overall benefits under the three scenarios of noncooperative research and development (R&D), cost sharing, and cooperative R&D. The results show that (1) the service innovation effort level of the three parties increases with increasing human resource level and technology maturity, and the government's benefit decreases with increasing cost of fulfilling social responsibility. (2) The government's cost subsidies to universities and enterprises can enhance the service innovation level of both parties and increase the optimal returns of the three parties and the ecosystem as a whole. (3) In the cooperative R&D game scenario, the effort level of the three parties and the total ecosystem returns are greater than those in the noncollaborative R&D scenario, and after determining the subsidy coefficients of the government, Pareto optimality of the three parties and the ecosystem as a whole can be achieved. The conclusions of this study can aid in understanding the dynamic evolution mechanism of digitally enabled higher education and provide a realistic decision-making reference for higher education ecosystem managers.

Embedding research capacity strengthening in multi-country studies in low-and middle-income countries: learnings from sexual and reproductive health research.

Research capacity strengthening (RCS) can empower individuals, institutions, networks, or countries to define and prioritize problems systematically; develop and scientifically evaluate appropriate solutions; and reinforce or improve capacities to translate knowledge into policy and practice. However, how to embed RCS into multi-country studies focusing on sexual and reproductive health and rights (SRHR) is largely undocumented. We used findings from a qualitative study, from a review of the literature, and from a validation exercise from a panel of experts from research institutions that work on SRHR RCS. We provide a framework for embedded RCS; suggest a set of seven concrete actions that research project planners, designers, implementers, and funders can utilise to guide embedded RCS activities in low- and middle-income countries; and present a practical checklist for planning and assessing embedded RCS in research projects.

Paper ContextMain findings: Building on findings from a primary qualitative study, a literature review, and a consultation with experts on capacity strengthening in LMICs, we propose a systematic approach to embedded RCS.Added knowledge: We present a framework for embedding RCS in multi-country studies and propose seven action points and a checklist for the implementation of RCS in multi-country research projects with considerations for sexual and reproductive health and rights research.Global health impact for policy and action: An easy-to-use checklist can enable global health researchers and policymakers to ensure RCS is an integral component of multi-country research.

Exploring the learning preferences of farmworker-serving community health workers.

Community health workers are responsible for finding, processing, and transferring health information to communities with limited access to health-related resources, including farmworkers. This paper is the culmination of an undergraduate student research project to explore the learning processes and preferences of farmworker-serving community health workers in the USA. The project was designed for students from farmworker or agricultural backgrounds at two North Carolina universities and was supported by a North Carolina Department of Health and Human Services workforce development grant. Semi-structured interviews were conducted, in person and virtually, with a convenience sample of 17 current and former community health workers. The interview data were analysed thematically and identified a preference for a combination of learning styles, with visual and hands-on learning being the most preferred. Community health workers also identified the importance of learning preferences in relation to their responsibilities as health educators. This study provides librarians, along with public health and medical professionals, with useful information about learning preferences to inform the creation of new and varied learning materials for community health workers.

Reversing the Innovation Pathway Could Be the Key to Cost-Efficient Health Care.

Throughout history, the world's biggest technological innovations have emerged from rich countries. Resource availability, economic prosperity that supports specialization in key areas of science and industry, and the concentrated centers of learning that such economies create all support this model. But history sometimes turns back on itself, and this is one of those moments.

Equitable access to quality injury care; Equi-Injury project protocol for prioritizing interventions in four low- or middle-income countries: a mixed method study.

BACKGROUND: Equitable access to quality care after injury is an essential step for improved health outcomes in low- and middle-income countries (LMICs). We introduce the Equi-Injury project, in which we will use integrated frameworks to understand how to improve equitable access to quality care after injury in four LMICs: Ghana, Pakistan, Rwanda and South Africa. METHODS: This project has 5 work packages (WPs) as well as essential cross-cutting pillars of community engagement, capacity building and cross-country learning. In WP1, we will identify needs, barriers, and facilitators to impactful stakeholder engagement in developing and prioritising policy solutions. In WP2, we will collect data on patient care and outcomes after injuries. In WP3, we will develop an injury pathway model to understand which elements in the pathway of injury response, care and treatment have the biggest impact on health and economic outcomes. In WP4, we will work with stakeholders to gain consensus on solutions to address identified issues; these solutions will be implemented and tested in future research. In WP5, in order to ascertain where learning is transferable across contexts, we will identify which outcomes are shared across countries. The study has received approval from ethical review boards (ERBs) of all partner countries in South Africa, Rwanda, Ghana, Pakistan and the University of Birmingham. DISCUSSION: This health system evaluation project aims to provide a deeper understanding of injury care and develop evidence-based interventions within and across partner countries in four diverse LMICs. Strong partnership with multiple stakeholders will facilitate utilisation of the results for the co-development of sustainable interventions.

Examining Student Preparation for Advancing Care in a Diverse Society.

BACKGROUND: Although many nursing programs include health equity in their curriculum, research investigating the efficacy of such curricula often is lacking. METHOD: Using criterion sampling, school of nursing alumni who could speak about their graduate preparation and current experiences working with diverse patient populations were recruited for this study. Semistructured interviews were conducted with 22 alumni regarding their curricular, clinical, and co-curricular experiences as graduate students to better understand the strengths and gaps in their preparation as health professionals. RESULTS: Four major themes emerged from the analysis of interview transcripts (n = 22). These themes included: (1) diversity and representation; (2) implicit bias and microaggressions; (3) skills and knowledge areas; and (4) supplemental learning through co-curricular experiences. CONCLUSION: Implications for policy, curriculum innovation, and clinical practice can better prepare students to advance care for a diverse society. [J Nurs Educ. 2024;63(4):228-232.].

Concept analysis of health system resilience.

BACKGROUND: There are several definitions of resilience in health systems, many of which share some characteristics, but no agreed-upon framework is universally accepted. Here, we review the concept of resilience, identifying its definitions, attributes, antecedents and consequences, and present the findings of a concept analysis of health system resilience. METHODS: We follow Schwarz-Barcott and Kim's hybrid model, which consists of three phases: theoretical, fieldwork and final analysis. We identified the concept definitions, attributes, antecedents and consequences of health system resilience and constructed an evidence-informed framework on the basis of the findings of this review. We searched PubMed, PsycINFO, CINAHL Complete, EBSCOhost-Academic Search and Premier databases and downloaded identified titles and abstracts on Covidence. We screened 3357 titles and removed duplicate and ineligible records; two reviewers then screened each title, and disagreements were resolved by discussion with the third reviewer. From the 130 eligible manuscripts, we identified the definitions, attributes, antecedents and consequences using a pre-defined data extraction form. RESULTS: Resilience antecedents are decentralization, available funds, investments and resources, staff environment and motivation, integration and networking and finally, diversification of staff. The attributes are the availability of resources and funds, adaptive capacity, transformative capacity, learning and advocacy and progressive leadership. The consequences of health system resilience are improved health system performance, a balanced governance structure, improved expenditure and financial management of health and maintenance of health services that support universal health coverage (UHC) throughout crises. CONCLUSION: A resilient health system maintains quality healthcare through times of crisis. During the coronavirus disease 2019 (COVID-19) epidemic, several seemingly robust health systems were strained under the increased demand, and services were disrupted. As such, elements of resilience should be integrated into the functions of a health system to ensure standardized and consistent service quality and delivery. We offer a systematic, evidence-informed method for identifying the attributes of health system resilience, intending to eventually be used to develop a measuring tool to evaluate a country's health system resilience performance.

Residents-as-teachers: A Needs Assessment of Residents' Teaching Skills in the Clinical Setting Using Direct Observation of Teaching.

BACKGROUND AND OBJECTIVES: Residents play a significant role in teaching undergraduate medical students though most residents have not received formal training in teaching and may be adopting ineffective teaching strategies. Many institutions have established a residents-as-teachers (RaT) programme to improve residents' teaching skills. However, many RaT programmes were established without a context-specific needs assessment. This study describes a need assessment survey of residents' teaching skills. The specific objectives of the study were to determine the residents' selfperceived and true learning needs for facilitating clinical teaching, the preferred important topics and methods of instruction for a RaT programme. METHODS: This cross-sectional, prospective, observational, quantitative study involved self-assessment of residents' teaching skills and assessments of those residents by medical students and an observer during actual clinical bedside teachings using the Maastricht Clinical Teaching Questionnaire. RESULTS: Thirty-nine (78%) out of 50 residents participated in the study, of which 20 agreed to direct observation of teaching. Sixty-two (85%) of the 73 medical students completed 82 evaluations of residents' teaching. The residents' self-perceived and true learning needs were in the domains of exploration, scaffolding, modelling, coaching and reflection. The leading preferred topics were communication skills, leadership, the teaching of procedural skills, and bedside teaching. The preferred methods of instruction were interactive sessions with teachers and working in small groups with a facilitator. CONCLUSIONS: Residents performed well in creating a safe learning environment but were poor in exploration, scaffolding, modelling, coaching and reflection domains of teaching. These findings will inform the design of a tailor-made RaT programme.

CONTEXTE ET OBJECTIFS: Les résidents jouent un rôle important dans l'enseignement des étudiants en médecine de premier cycle, bien que la plupart des résidents n'aient pas reçu de formation formelle en enseignement et puissent adopter des stratégies d'enseignement inefficaces. De nombreuses institutions ont mis en place un programme de résidents en tant qu'enseignants (RaT) pour améliorer les compétences pédagogiques des résidents. Cependant, de nombreux programmes RaT ont été établis sans évaluation préalable des besoins spécifiques au contexte. Cette étude décrit une enquête sur les besoins en compétences pédagogiques des résidents. Les objectifs spécifiques de l'étude étaient de déterminer les besoins d'apprentissage auto-perçus et réels des résidents pour faciliter l'enseignement clinique, les sujets importants préférés et les méthodes d'instruction pour un programme RaT. MÉTHODES: Cette étude quantitative prospective observationnelle transversale impliquait une auto-évaluation des compétences pédagogiques des résidents et des évaluations de ces résidents par des étudiants en médecine et un observateur lors d'enseignements cliniques en direct au lit du patient à l'aide du Questionnaire d'Enseignement Clinique de Maastricht. RÉSULTATS: Trente-neuf (78 %) des 50 résidents ont participé à l'étude, dont 20 ont accepté l'observation directe de l'enseignement. Soixantedeux (85 %) des 73 étudiants en médecine ont complété 82 évaluations de l'enseignement des résidents. Les besoins d'apprentissage auto-perçus et réels des résidents étaient dans les domaines de l'exploration, de l'échafaudage, de la modélisation, du coaching et de la réflexion. Les sujets préférés étaient les compétences en communication, le leadership, l'enseignement des compétences procédurales et l'enseignement au lit du patient. Les méthodes d'instruction préférées étaient les séances interactives avec les enseignants et le travail en petits groupes avec un facilitateur. CONCLUSIONS: Les résidents se sont bien comportés dans la création d'un environnement d'apprentissage sûr mais étaient faibles dans les domaines de l'exploration, de l'échafaudage, de la modélisation, du coaching et de la réflexion de l'enseignement. Ces résultats orienteront la conception d'un programme RaT sur mesure. MOTS-CLÉS: Résidents en tant qu'enseignants, évaluation des besoins, enseignement clinique au lit du patient, évaluation, compétencespédagogiques.

Learning motivation and self-assessment in health economics: a survey on overconfidence in healthcare providers.

INTRODUCTION: Lifelong learning is the foundation for professionals to maintain competence and proficiency in several aspects of economy and medicine. Until now, there is no evidence of overconfidence (the belief to be better than others or tested) and clinical tribalism (the belief that one's own group outperforms others) in the specialty of health economics. We investigated the hypothesis of overconfidence effects and their relation to learning motivation and motivational patterns in healthcare providers regarding healthcare economics. METHODS: We conducted a national convenience online survey of 116 healthcare workers recruited from social and personal networks to detect overconfidence effects and clinical tribalism and to assess learning motivation. Instruments included self-assessments for five learning dimensions (factual knowledge, skills, attitude, problem-solving and behaviour) and a four-item situational motivation scale. The analysis comprised paired t-tests, correlation analyses and two-step cluster analyses. RESULTS: We detected overplacement, overestimation and signs of clinical tribalism. Responders in the physician subgroup rated themselves superior to colleagues and that their professional group was superior to other professions. Participants being educators in other competencies showed high overconfidence in health economics. We detected two groups of learners: overconfident but motivated persons and overconfident and unmotivated learners. Learning motivation did not correlate with overconfidence effects. DISCUSSION: We could show the presence of overconfidence in health economics, which is consistent with studies in healthcare and the economy. The subjective perception of some medical educators, being role models to students and having a superior 'attitude' (eg, morality) concerning the economy may foster prejudice against economists as students might believe them. It also may aggravate moral distress and disrupts interactions between healthcare providers managers and leaders. Considering the study's limitations, lifelong interprofessional and reflective training and train-the-trainer programmes may be mandatory to address the effects.

Exploring the Use of Persuasive System Design Principles to Enhance Medication Incident Reporting and Learning Systems: Scoping Reviews and Persuasive Design Assessment.

BACKGROUND: Medication incidents (MIs) causing harm to patients have far-reaching consequences for patients, pharmacists, public health, business practice, and governance policy. Medication Incident Reporting and Learning Systems (MIRLS) have been implemented to mitigate such incidents and promote continuous quality improvement in community pharmacies in Canada. They aim to collect and analyze MIs for the implementation of incident preventive strategies to increase safety in community pharmacy practice. However, this goal remains inhibited owing to the persistent barriers that pharmacies face when using these systems. OBJECTIVE: This study aims to investigate the harms caused by medication incidents and technological barriers to reporting and identify opportunities to incorporate persuasive design strategies in MIRLS to motivate reporting. METHODS: We conducted 2 scoping reviews to provide insights on the relationship between medication errors and patient harm and the information system-based barriers militating against reporting. Seven databases were searched in each scoping review, including PubMed, Public Health Database, ProQuest, Scopus, ACM Library, Global Health, and Google Scholar. Next, we analyzed one of the most widely used MIRLS in Canada using the Persuasive System Design (PSD) taxonomy-a framework for analyzing, designing, and evaluating persuasive systems. This framework applies behavioral theories from social psychology in the design of technology-based systems to motivate behavior change. Independent assessors familiar with MIRLS reported the degree of persuasion built into the system using the 4 categories of PSD strategies: primary task, dialogue, social, and credibility support. RESULTS: Overall, 17 articles were included in the first scoping review, and 1 article was included in the second scoping review. In the first review, significant or serious harm was the most frequent harm (11/17, 65%), followed by death or fatal harm (7/17, 41%). In the second review, the authors found that iterative design could improve the usability of an MIRLS; however, data security and validation of reports remained an issue to be addressed. Regarding the MIRLS that we assessed, participants considered most of the primary task, dialogue, and credibility support strategies in the PSD taxonomy as important and useful; however, they were not comfortable with some of the social strategies such as cooperation. We found that the assessed system supported a number of persuasive strategies from the PSD taxonomy; however, we identified additional strategies such as tunneling, simulation, suggestion, praise, reward, reminder, authority, and verifiability that could further enhance the perceived persuasiveness and value of the system. CONCLUSIONS: MIRLS, equipped with persuasive features, can become powerful motivational tools to promote safer medication practices in community pharmacies. They have the potential to highlight the value of MI reporting and increase the readiness of pharmacists to report incidents. The proposed persuasive design guidelines can help system developers and community pharmacy managers realize more effective MIRLS.

Big Challenges Meet Big Leadership in 2024.

As we begin the year 2024, we do so with some very big challenges that have spilled over from 2023 and, indeed, many years before that. Every day, we are confronted with concerning experiential and research-based evidence about worsening access to healthcare, pervasive racism and widening disparities. Clearly, there is a great deal of work to be done in our healthcare system to support and improve the health of the diverse populations that we serve. Yet, along with the challenges come opportunities to reflect, collaborate, innovate, evaluate and learn. When I look at issues of the Canadian Journal of Nursing Leadership (CJNL) from the past 20 years, I am astounded at how some concerns have changed and some have remained the same. Can you believe that there was a time when nursing positions in practice and in education were actually being cut? Of course, many of the big issues we face today were emerging even then, and we have long since passed the tipping point that has put the country into a healthcare crisis.