Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways.

The Covid-19 pandemic has pushed many hospitals to their capacity limits. Therefore, a triage of patients has been discussed controversially primarily through an ethical perspective. The term triage contains many aspects such as urgency of treatment, severity of the disease and pre-existing conditions, access to critical care, or the classification of patients regarding subsequent clinical pathways starting from the emergency department. The determination of the pathways is important not only for patient care, but also for capacity planning in hospitals. We examine the performance of a human-made triage algorithm for clinical pathways which is considered a guideline for emergency departments in Germany based on a large multicenter dataset with over 4,000 European Covid-19 patients from the LEOSS registry. We find an accuracy of 28 percent and approximately 15 percent sensitivity for the ward class. The results serve as a benchmark for our extensions including an additional category of palliative care as a new label, analytics, AI, XAI, and interactive techniques. We find significant potential of analytics and AI in Covid-19 triage regarding accuracy, sensitivity, and other performance metrics whilst our interactive human-AI algorithm shows superior performance with approximately 73 percent accuracy and up to 76 percent sensitivity. The results are independent of the data preparation process regarding the imputation of missing values or grouping of comorbidities. In addition, we find that the consideration of an additional label palliative care does not improve the results.

A Simulation-Based Cost-Effectiveness Analysis of Severe Acute Respiratory Syndrome Coronavirus 2 Infection Prevention Strategies for Visitors of Healthcare Institutions.

OBJECTIVES: The aim is to quantitatively evaluate different infection prevention strategies in the context of hospital visitor management during pandemics and to provide a decision support system for strategic and operational decisions based on this evaluation. METHODS: A simulation-based cost-effectiveness analysis is applied to the data of a university hospital in Southern Germany and published COVID-19 research. The performance of different hospital visitor management strategies is evaluated by several decision-theoretic methods with varying objective functions. RESULTS: Appropriate visitor restrictions and infection prevention measures can reduce additional infections and costs caused by visitors of healthcare institutions by >90%. The risk of transmission of severe acute respiratory syndrome coronavirus 2 by visitors of terminal care (ie, palliative care) patients can be reduced almost to 0 if appropriate infection prevention measures are implemented. Antigen tests do not seem to be beneficial from both a cost and an effectiveness perspective. CONCLUSIONS: Hospital visitor management is crucial and effectively prevents infections while maintaining cost-effectiveness. For terminal care patients, visitor restrictions can be omitted if appropriate infection prevention measures are taken. Antigen testing plays a subordinate role, except in the case of a pure focus on additional infections caused by visitors of healthcare institutions. We provide decision support to authorities and hospital visitor managers to identify appropriate visitor restriction and infection prevention strategies for specific local conditions, incidence rates, and objectives.

Homogeneity and Best Practice Analyses in Hospital Performance Management: An Analytical Framework.

Performance modeling of hospitals using data envelopment analysis (DEA) has received steadily increasing attention in the literature. As part of the traditional DEA framework, hospitals are generally assumed to be functionally similar and therefore homogenous. Accordingly, any identified inefficiency is supposedly due to the inefficient use of inputs to produce outputs. However, the disparities in DEA efficiency scores may be a result of the inherent heterogeneity of hospitals. Additionally, traditional DEA models lack predictive capabilities despite having been frequently used as a benchmarking tool in the literature. To address these concerns, this study proposes a framework for analyzing hospital performance by combining two complementary modeling approaches. Specifically, we employ a self-organizing map artificial neural network (SOM-ANN) to conduct a cluster analysis and a multilayer perceptron ANN (MLP-ANN) to perform a heterogeneity analysis and a best practice analysis. The applicability of the integrated framework is empirically shown by an implementation to a large dataset containing more than 1,100 hospitals in Germany. The framework enables a decision-maker not only to predict the best performance but also to explore whether the differences in relative efficiency scores are ascribable to the heterogeneity of hospitals.

Balancing control and autonomy in master surgery scheduling: Benefits of ICU quotas for recovery units.

When scheduling surgeries in the operating theater, not only the resources within the operating theater have to be considered but also those in downstream units, e.g., the intensive care unit and regular bed wards of each medical specialty. We present an extension to the master surgery schedule, where the capacity for surgeries on ICU patients is controlled by introducing downstream-dependent block types - one for both ICU and ward patients and one where surgeries on ICU patients must not be performed. The goal is to provide better control over post-surgery patient flows through the hospital while preserving each medical specialty's autonomy over its operational surgery scheduling. We propose a mixed-integer program to determine the allocation of the new block types within either a given or a new master surgery schedule to minimize the maximum workload in downstream units. Using a simulation model supported by seven years of data from the University Hospital Augsburg, we show that the maximum workload in the intensive care unit can be reduced by up to 11.22% with our approach while maintaining the existing master surgery schedule. We also show that our approach can achieve up to 79.85% of the maximum workload reduction in the intensive care unit that would result from a fully centralized approach. We analyze various hospital setting instances to show the generalizability of our results. Furthermore, we provide insights and data analysis from the implementation of a quota system at the University Hospital Augsburg.

Managing admission and discharge processes in intensive care units.

The intensive care unit (ICU) is one of the most crucial and expensive resources in a health care system. While high fixed costs usually lead to tight capacities, shortages have severe consequences. Thus, various challenging issues exist: When should an ICU admit or reject arriving patients in general? Should ICUs always be able to admit critical patients or rather focus on high utilization? On an operational level, both admission control of arriving patients and demand-driven early discharge of currently residing patients are decision variables and should be considered simultaneously. This paper discusses the trade-off between medical and monetary goals when managing intensive care units by modeling the problem as a Markov decision process. Intuitive, myopic rule mimicking decision-making in practice is applied as a benchmark. In a numerical study based on real-world data, we demonstrate that the medical results deteriorate dramatically when focusing on monetary goals only, and vice versa. Using our model, we illustrate the trade-off along an efficiency frontier that accounts for all combinations of medical and monetary goals. Coming from a solution that optimizes monetary costs, a significant reduction of expected mortality can be achieved at little additional monetary cost.

Simulation-based evaluation of operating room management policies.

BACKGROUND: Since operating rooms are a major bottleneck resource and an important revenue driver in hospitals, it is important to use these resources efficiently. Studies estimate that between 60 and 70% of hospital admissions are due to surgeries. Furthermore, staffing cannot be changed daily to respond to changing demands. The resulting high complexity in operating room management necessitates perpetual process evaluation and the use of decision support tools. In this study, we evaluate several management policies and their consequences for the operating theater of the University Hospital Augsburg. METHODS: Based on a data set with 12,946 surgeries, we evaluate management policies such as parallel induction of anesthesia with varying levels of staff support, the use of a dedicated emergency room, extending operating room hours reserved as buffer capacity, and different elective patient sequencing policies. We develop a detailed simulation model that serves to capture the process flow in the entire operating theater: scheduling surgeries from a dynamically managed waiting list, handling various types of schedule disruptions, rescheduling and prioritizing postponed and deferred surgeries, and reallocating operating room capacity. The system performance is measured by indicators such as patient waiting time, idle time, staff overtime, and the number of deferred surgeries. RESULTS: We identify significant trade-offs between expected waiting times for different patient urgency categories when operating rooms are opened longer to serve as end-of-day buffers. The introduction of parallel induction of anesthesia allows for additional patients to be scheduled and operated on during regular hours. However, this comes with a higher number of expected deferrals, which can be partially mitigated by employing additional anesthesia teams. Changes to the sequencing of elective patients according to their expected surgery duration cause expectable outcomes for a multitude of performance indicators. CONCLUSIONS: Our simulation-based approach allows operating theater managers to test a multitude of potential changes in operating room management without disrupting the ongoing workflow. The close collaboration between management and researchers in the design of the simulation framework and the data analysis has yielded immediate benefits for the scheduling policies and data collection efforts at our practice partner.

Defining biomarkers to predict symptoms in subjects with and without allergy under natural pollen exposure.

BACKGROUND: Pollen exposure induces local and systemic allergic immune responses in sensitized individuals, but nonsensitized individuals also are exposed to pollen. The kinetics of symptom expression under natural pollen exposure have never been systematically studied, especially in subjects without allergy. OBJECTIVE: We monitored the humoral immune response under natural pollen exposure to potentially uncover nasal biomarkers for in-season symptom severity and identify protective factors. METHODS: We compared humoral immune response kinetics in a panel study of subjects with seasonal allergic rhinitis (SAR) and subjects without allergy and tested for cross-sectional and interseasonal differences in levels of serum and nasal, total, and Betula verrucosa 1-specific immunoglobulin isotypes; immunoglobulin free light chains; cytokines; and chemokines. Nonsupervised principal component analysis was performed for all nasal immune variables, and single immune variables were correlated with in-season symptom severity by Spearman test. RESULTS: Symptoms followed airborne pollen concentrations in subjects with SAR, with a time lag between 0 and 13 days depending on the pollen type. Of the 7 subjects with nonallergy, 4 also exhibited in-season symptoms whereas 3 did not. Cumulative symptoms in those without allergy were lower than in those with SAR but followed the pollen exposure with similar kinetics. Nasal eotaxin-2, CCL22/MDC, and monocyte chemoattactant protein-1 (MCP-1) levels were higher in subjects with SAR, whereas IL-8 levels were higher in subjects without allergy. Principal component analysis and Spearman correlations identified nasal levels of IL-8, IL-33, and Betula verrucosa 1-specific IgG4 (sIgG4) and Betula verrucosa 1-specific IgE (sIgE) antibodies as predictive for seasonal symptom severity. CONCLUSIONS: Nasal pollen-specific IgA and IgG isotypes are potentially protective within the humoral compartment. Nasal levels of IL-8, IL-33, sIgG4 and sIgE could be predictive biomarkers for pollen-specific symptom expression, irrespective of atopy.

Analyzing economies of scale and scope in hospitals by use of case mix planning.

This study analyzes the effect of economies of scale and scope on the optimal case mix of a hospital or hospital system. With respect to the ideal volume and patient composition, the goal is to evaluate (i) the impact of changes in the efficiency of resource use with increasing scale, and (ii) to determine the potential effects of spreading fixed costs over a greater number of patients. The problem is formulated as a non-linear mixed integer program. It turns out that this non-linear program is too difficult to be solved with standard software. As an alternative, an iterative procedure using piecewise linear approximations to derive lower and upper bounds is proposed and shown to converge to the optimum. The procedure is applied using a public database on German hospital costs and performance statistics. Results indicate that changes in the efficiency of resource use with increasing scale have a considerable impact if similar services can be consolidated, e.g., among different departments. However, if the scope for decision-making regarding the case mix of a hospital is limited, such changes may be negligible.

Correction to: The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals.

The original version of this article unfortunately contained errors. The first column of Tables 5 and 6 in the Appendix section should contain the year of publication instead of the reference number in brackets. The reference citations were then placed in the second column.

Determinants of the utilization of allergy management measures among hay fever sufferers: a theory-based cross-sectional study.

BACKGROUND: The quality of life of chronically ill individuals, such as hay fever sufferers, is significantly dependent on their health behavior. This survey aimed to explain the health-related behavior of allergic individuals using the protection motivation theory (PMT) and the transtheoretical model (TTM). METHODS: The influencing variables stated by PMT were operationalized based on data from semistructured pilot interviews and a pretest with 12 individuals from the target population. The final questionnaire inquired perceived seriousness and severity of hay fever, response efficacy, response costs, self-efficacy, and the use of various hay fever management measures in relation to the TTM stages. Multivariate logistic regression was performed to investigate the relationships between the PMT constructs and the examined health behavior. RESULTS: A total of 569 allergic individuals completed the online questionnaire. Only 33.26% of allergic individuals were in the maintenance stage for treatment under medical supervision, and almost 60% preferred hay fever self-management. A total of 67.56% had a well-established habit of taking anti-allergic medication, but only 25.31% had undergone specific immunotherapy. The likelihood of seeking medical supervision was positively influenced by perceived severity (OR = 1.35, 95% CI: 1.02-1.81), perceived seriousness (OR = 2.12, 95% CI: 1.56-2.89), and self-efficacy (OR = 4.52, 95% CI: 3.11-6.65). The perceived severity of symptoms predicted the practice of hay fever self-management (OR = 1.60, 95% CI: 1.21-2.11), as well as anti-allergic medication intake (OR = 1.65, 95% CI: 1.16-2.35). The latter measure was also positively influenced by self-efficacy (OR = 1.52, 95% CI: 1.01-2.28) and hay fever self-management (OR = 4.76, 95% CI: 2.67-7.49). Undergoing specific immunotherapy was significantly predicted only by medical supervision (OR = 9.80, 95% CI: 8.16-13.80). Allergen avoidance was a strategy used by allergic individuals who preferred hay fever self-management (OR = 2.56, 95% CI: 1.87-3.52) and experienced notable symptom severity (OR = 2.12, 95% CI: 1.60-2.81). CONCLUSION: Educational interventions that increase the awareness of health risks associated with inadequate hay fever management and measures to increase self-efficacy might be beneficial for the promotion of appropriate hay fever management among allergic individuals.

The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals.

The healthcare sector in general and hospitals in particular represent a main application area for Data Envelopment Analysis (DEA). This paper reviews 262 papers of DEA applications in healthcare with special focus on hospitals and therefore closes a gap of over ten years that were not covered by existing review articles. Apart from providing descriptive statistics of the papers, we are the first to examine the research purposes of the publications. These research goals can be grouped into four distinct clusters according to our proposed framework. The four clusters are (1) "Pure DEA efficiency analysis", i.e. performing a DEA on hospital data, (2) "Developments or applications of new methodologies", i.e. applying new DEAy approaches on hospital data, (3) "Specific management question", i.e. analyzing the effects of managerial specification, such as ownership, on hospital efficiency, and (4) "Surveys on the effects of reforms", i.e. researching the impact of policy making, such as reforms of health systems, on hospital efficiency. Furthermore, we analyze the methodological settings of the studies and describe the applied models. We analyze the chosen inputs and outputs as well as all relevant downstream techniques. A further contribution of this paper is its function as a roadmap to important methodological literature and publications, which provide crucial information on the setup of DEA studies. Thus, this paper should be of assistance to researchers planning to apply DEA in a hospital setting by providing information on a) what has been published between 2005 and 2016, b) possible pitfalls when setting up a DEA analysis, and c) possible ways to apply the DEA analysis in practice. Finally, we discuss what could be done to advance DEA from a scientific tool to an instrument that is actually utilized by managers and policymakers.

Hospital physicians can't get no long-term satisfaction - an indicator for fairness in preference fulfillment on duty schedules.

Physicians are a scarce resource in hospitals. In order to minimize physician attrition, schedulers incorporate individual physician preferences when creating the physicians' duty roster. The manual creation of a roster is very time-consuming and often produces suboptimal results. Many schedulers therefore use model-based software to assist in planning. The planning horizon for duty schedules is usually a single month. Many models optimize the plan for the current planning horizon, without taking into account data on preference fulfillment and work load distribution from previous months. It is therefore possible that, when looking at a longer time horizon, some physicians are disadvantaged in terms of preference fulfillment more often than their peers, simply because this generates better results for the individual months. This may be perceived as unfair by the disadvantaged physicians. In order to eliminate this imbalance, we introduce a satisfaction indicator for preference fulfillment in physician scheduling. This indicator is computed for each physician on each monthly plan and is then used to inform decisions regarding preference fulfillment on the current and future plans. As a result, a more equal distribution of preference fulfillment among physicians is achieved. We run a computational study with three different update strategies for our satisfaction indicator. Our study uses 24 months of data from a German university hospital and derives additional generated data from it. Results indicate that our satisfaction indicator, combined with the right update strategy, can achieve an equal distribution of satisfaction over all physicians within a peer group, as well as stable satisfaction levels for each individual physician over a longer time horizon. As our main contribution, we identify that our satisfaction indicator is more effective in creating equal distribution of long-term satisfaction the higher the rate of conflicting preferences is.

Operations research in intensive care unit management: a literature review.

The intensive care unit (ICU) is a crucial and expensive resource largely affected by uncertainty and variability. Insufficient ICU capacity causes many negative effects not only in the ICU itself, but also in other connected departments along the patient care path. Operations research/management science (OR/MS) plays an important role in identifying ways to manage ICU capacities efficiently and in ensuring desired levels of service quality. As a consequence, numerous papers on the topic exist. The goal of this paper is to provide the first structured literature review on how OR/MS may support ICU management. We start our review by illustrating the important role the ICU plays in the hospital patient flow. Then we focus on the ICU management problem (single department management problem) and classify the literature from multiple angles, including decision horizons, problem settings, and modeling and solution techniques. Based on the classification logic, research gaps and opportunities are highlighted, e.g., combining bed capacity planning and personnel scheduling, modeling uncertainty with non-homogenous distribution functions, and exploring more efficient solution approaches.

Case mix planning in hospitals: a review and future agenda.

The case mix planning problem deals with choosing the ideal composition and volume of patients in a hospital. With many countries having recently changed to systems where hospitals are reimbursed for patients according to their diagnosis, case mix planning has become an important tool in strategic and tactical hospital planning. Selecting patients in such a payment system can have a significant impact on a hospital's revenue. The contribution of this article is to provide the first literature review focusing on the case mix planning problem. We describe the problem, distinguish it from similar planning problems, and evaluate the existing literature with regard to problem structure and managerial impact. Further, we identify gaps in the literature. We hope to foster research in the field of case mix planning, which only lately has received growing attention despite its fundamental economic impact on hospitals.

The AI ethics of digital COVID-19 diagnosis and their legal, medical, technological, and operational managerial implications.

The COVID-19 pandemic has given rise to a broad range of research from fields alongside and beyond the core concerns of infectiology, epidemiology, and immunology. One significant subset of this work centers on machine learning-based approaches to supporting medical decision-making around COVID-19 diagnosis. To date, various challenges, including IT issues, have meant that, notwithstanding this strand of research on digital diagnosis of COVID-19, the actual use of these methods in medical facilities remains incipient at best, despite their potential to relieve pressure on scarce medical resources, prevent instances of infection, and help manage the difficulties and unpredictabilities surrounding the emergence of new mutations. The reasons behind this research-application gap are manifold and may imply an interdisciplinary dimension. We argue that the discipline of AI ethics can provide a framework for interdisciplinary discussion and create a roadmap for the application of digital COVID-19 diagnosis, taking into account all disciplinary stakeholders involved. This article proposes such an ethical framework for the practical use of digital COVID-19 diagnosis, considering legal, medical, operational managerial, and technological aspects of the issue in accordance with our diverse research backgrounds and noting the potential of the approach we set out here to guide future research.

Simulation of the mortality after different ex ante (secondary) and ex post (tertiary) triage methods in people with disabilities and pre-existing diseases.

The significant increase in patients during the COVID-19 pandemic presented the healthcare system with a variety of challenges. The intensive care unit is one of the areas particularly affected in this context. Only through extensive infection control measures as well as an enormous logistical effort was it possible to treat all patients requiring intensive care in Germany even during peak phases of the pandemic, and to prevent triage even in regions with high patient pressure and simultaneously low capacities. Regarding pandemic preparedness, the German Parliament passed a law on triage that explicitly prohibits ex post (tertiary) triage. In ex post triage, patients who are already being treated are included in the triage decision and treatment capacities are allocated according to the individual likelihood of success. Legal, ethical, and social considerations for triage in pandemics can be found in the literature, but there is no quantitative assessment with respect to different patient groups in the intensive care unit. This study addressed this gap and applied a simulation-based evaluation of ex ante (primary) and ex post triage policies in consideration of survival probabilities, impairments, and pre-existing conditions. The results show that application of ex post triage based on survival probabilities leads to a reduction in mortality in the intensive care unit for all patient groups. In the scenario close to a real-world situation, considering different impaired and prediseased patient groups, a reduction in mortality of approximately 15% was already achieved by applying ex post triage on the first day. This mortality-reducing effect of ex post triage is further enhanced as the number of patients requiring intensive care increases.

[Simulation of mortality after different ex-ante and ex-post-triage methods in people with disabilities and comorbidities]. / Simulation der Letalität nach verschiedenen Ex-ante- und Ex-post-Triage-Verfahren bei Menschen mit Behinderungen und Vorerkrankungen.

The significant increase in patients during the COVID-19 pandemic presented the healthcare system with a variety of challenges. The intensive care unit is one of the areas particularly affected in this context. Only through extensive infection control measures as well as an enormous logistical effort was it possible to treat all patients requiring intensive care in Germany even during peak phases of the pandemic, and to prevent triage even in regions with high patient pressure and simultaneously low capacities. Regarding pandemic preparedness, the German Parliament passed a law on triage that explicitly prohibits ex post (tertiary) triage. In ex post triage, patients who are already being treated are included in the triage decision and treatment capacities are allocated according to the individual likelihood of success. Legal, ethical, and social considerations for triage in pandemics can be found in the literature, but there is no quantitative assessment with respect to different patient groups in the intensive care unit. This study addressed this gap and applied a simulation-based evaluation of ex ante (primary) and ex post triage policies in consideration of survival probabilities, impairments, and pre-existing conditions. The results show that application of ex post triage based on survival probabilities leads to a reduction in mortality in the intensive care unit for all patient groups. In the scenario close to a real-world situation, considering different impaired and prediseased patient groups, a reduction in mortality of approximately 15% was already achieved by applying ex post triage on the first day. This mortality-reducing effect of ex post triage is further enhanced as the number of patients requiring intensive care increases.

Airborne pollen grain detection from partially labelled data utilising semi-supervised learning.

Airborne pollen monitoring has been conducted for more than a century now, as knowledge of the quantity and periodicity of airborne pollen has diverse use cases, like reconstructing historic climates and tracking current climate change, forensic applications, and up to warning those affected by pollen-induced respiratory allergies. Hence, related work on automation of pollen classification already exists. In contrast, detection of pollen is still conducted manually, and it is the gold standard for accuracy. So, here we used a new-generation, automated, near-real-time pollen monitoring sampler, the BAA500, and we used data consisting of both raw and synthesised microscope images. Apart from the automatically generated, commercially-labelled data of all pollen taxa, we additionally used manual corrections to the pollen taxa, as well as a manually created test set of bounding boxes and pollen taxa, so as to more accurately evaluate the real-life performance. For the pollen detection, we employed two-stage deep neural network object detectors. We explored a semi-supervised training scheme to remedy the partial labelling. Using a teacher-student approach, the model can add pseudo-labels to complete the labelling during training. To evaluate the performance of our deep learning algorithms and to compare them to the commercial algorithm of the BAA500, we created a manual test set, in which an expert aerobiologist corrected automatically annotated labels. For the novel manual test set, both the supervised and semi-supervised approaches clearly outperform the commercial algorithm with an F1 score of up to 76.9 % compared to 61.3 %. On an automatically created and partially labelled test dataset, we obtain a maximum mAP of 92.7 %. Additional experiments on raw microscope images show comparable performance for the best models, which potentially justifies reducing the complexity of the image generation process. Our results bring automatic pollen monitoring a step forward, as they close the gap in pollen detection performance between manual and automated procedure.

Towards automatic airborne pollen monitoring: From commercial devices to operational by mitigating class-imbalance in a deep learning approach.

Allergic diseases have been the epidemic of the century among chronic diseases. Particularly for pollen allergies, and in the context of climate change, as airborne pollen seasons have been shifting earlier and abundances have been becoming higher, pollen monitoring plays an important role in generating high-risk allergy alerts. However, this task requires labour-intensive and time-consuming manual classification via optical microscopy. Even new-generation, automatic, monitoring devices require manual pollen labelling to increase accuracy and to advance to genuinely operational devices. Deep Learning-based models have the potential to increase the accuracy of automated pollen monitoring systems. In the current research, transfer learning-based convolutional neural networks were employed to classify pollen grains from microscopic images. Given a high imbalance in the dataset, we incorporated class weighted loss, focal loss and weight vector normalisation for class balancing as well as data augmentation and weight penalties for regularisation. Airborne pollen has been routinely recorded by a Bio-Aerosol Analyzer (BAA500, Hund GmbH) located in Augsburg, Germany. Here we utilised a database referring to manually classified airborne pollen images of the whole pollen diversity throughout an annual pollen season. By using the cropped pollen images collected by this device, we achieved an unweighted average F1 score of 93.8% across 15 classes and an unweighted average F1 score of 75.9% across 31 classes. The majority of taxa (9 of 15), being also the most abundant and allergenic, showed a recall of at least 95%, reaching up to a remarkable 100% in pollen from Taxus and Urticaceae. The recent introduction of novel pollen monitoring devices worldwide has pointed to the necessity for real-time, automatic measurements of airborne pollen and fungal spores. Thus, we may improve everyday clinical practice and achieve the most efficient prophylaxis of allergic patients.