For better or worse: Governing healthcare organisations in times of financial distress.

Due to processes of financialisation, financial parties increasingly penetrate the healthcare domain and determine under which conditions care is delivered. Their influence becomes especially visible when healthcare organisations face financial distress. By zooming-in on two of such cases, we come to know more about the considerations, motives and actions of financial parties in healthcare. In this research, we were able to examine the social dynamics between healthcare executives, banks and health insurers involved in a Dutch hospital and mental healthcare organisation on the verge of bankruptcy. Informed by interviews, document analysis and translation theory, we reconstructed the motives and strategies of executives, banks and health insurers and show how they play a crucial role in decision-making processes surrounding the survival or downfall of healthcare organisations. While parties are bound by legislation and company procedures, the outcome of financial distress can still be influenced. Much depends on how executives are perceived by financial stakeholders and how they deal with threats of destabilisation of the network. We further draw attention to the consequences of financialisation processes on the practices of healthcare organisations in financial distress.

Predicting Future Service Use in Dutch Mental Healthcare: A Machine Learning Approach.

A mental healthcare system in which the scarce resources are equitably and efficiently allocated, benefits from a predictive model about expected service use. The skewness in service use is a challenge for such models. In this study, we applied a machine learning approach to forecast expected service use, as a starting point for agreements between financiers and suppliers of mental healthcare. This study used administrative data from a large mental healthcare organization in the Netherlands. A training set was selected using records from 2017 (N = 10,911), and a test set was selected using records from 2018 (N = 10,201). A baseline model and three random forest models were created from different types of input data to predict (the remainder of) numeric individual treatment hours. A visual analysis was performed on the individual predictions. Patients consumed 62 h of mental healthcare on average in 2018. The model that best predicted service use had a mean error of 21 min at the insurance group level and an average absolute error of 28 h at the patient level. There was a systematic under prediction of service use for high service use patients. The application of machine learning techniques on mental healthcare data is useful for predicting expected service on group level. The results indicate that these models could support financiers and suppliers of healthcare in the planning and allocation of resources. Nevertheless, uncertainty in the prediction of high-cost patients remains a challenge.

A 3D Renal Proximal Tubule on Chip Model Phenocopies Lowe Syndrome and Dent II Disease Tubulopathy.

Lowe syndrome and Dent II disease are X-linked monogenetic diseases characterised by a renal reabsorption defect in the proximal tubules and caused by mutations in the OCRL gene, which codes for an inositol-5-phosphatase. The life expectancy of patients suffering from Lowe syndrome is largely reduced because of the development of chronic kidney disease and related complications. There is a need for physiological human in vitro models for Lowe syndrome/Dent II disease to study the underpinning disease mechanisms and to identify and characterise potential drugs and drug targets. Here, we describe a proximal tubule organ on chip model combining a 3D tubule architecture with fluid flow shear stress that phenocopies hallmarks of Lowe syndrome/Dent II disease. We demonstrate the high suitability of our in vitro model for drug target validation. Furthermore, using this model, we demonstrate that proximal tubule cells lacking OCRL expression upregulate markers typical for epithelial-mesenchymal transition (EMT), including the transcription factor SNAI2/Slug, and show increased collagen expression and deposition, which potentially contributes to interstitial fibrosis and disease progression as observed in Lowe syndrome and Dent II disease.

Symptomatic and Functional Remission in Young Adults with a Psychotic Disorder in a Rehabilitation Focused Team.

The aim of this study is to assess symptomatic remission (SR) and functional remission (FR) in a rehabilitation focused program for young adults with a psychotic disorder in the Netherlands, and to investigate which individual and mental health care factors are associated with SR and/or FR, by using Routine Outcome Monitoring data and data on met needs and unmet needs for care. Data of 287 young adults were collected. Almost 40% achieved or maintained SR, 34% FR, and 26% achieved or maintained both. In addition to sociodemographic factors, living independently, paid employment, higher levels of compliance with treatment, and better fulfillment of unmet needs for care in relation to psychological distress, company and daytime activities were associated with better outcomes on SR and/or FR. Our findings underscore that to successfully improve and sustain remission in young adults with a psychotic disorder, it is needed to conduct specific research into the relationship between SR and FR.

Co-creating a program for teams to maintain and reflect on their flexibility.

To prevent rigidity within teams in health care and to support teams in detecting early warning signs of decreasing flexibility, a program has been co-created in collaboration with mental healthcare teams. This program is intended to systematically monitor team behavior, and by doing so to facilitate team intervention. We aim to lay foundations for the further development of methods that can help teams to recognize and respond to processes going on under the surface. This paper introduces the program to the reader; and describes its premises and the co-creation process, leading to a program of nine steps. Then, it describes the application of the program within a team, what a team needs to use the program, and whether the nine steps are sufficient. This pilot shows that the program is a helpful framework within which teams can talk about rigidity, define indicators of their flexibility, and think about appropriate actions and interventions for maintaining or restoring their flexibility. Team ownership and the customizability of the program are important attributes. The program appears to provide a useful framework that helps a team to observe and discuss processes. Team members become aware of the indicators of their team and make their goals explicit.

Development of a Gut-On-A-Chip Model for High Throughput Disease Modeling and Drug Discovery.

A common bottleneck in any drug development process is finding sufficiently accurate models that capture key aspects of disease development and progression. Conventional drug screening models often rely on simple 2D culture systems that fail to recapitulate the complexity of the organ situation. In this study, we show the application of a robust high throughput 3D gut-on-a-chip model for investigating hallmarks of inflammatory bowel disease (IBD). Using the OrganoPlate platform, we subjected enterocyte-like cells to an immune-relevant inflammatory trigger in order to recapitulate key events of IBD and to further investigate the suitability of this model for compound discovery and target validation activities. The induction of inflammatory conditions caused a loss of barrier function of the intestinal epithelium and its activation by increased cytokine production, two events observed in IBD physiopathology. More importantly, anti-inflammatory compound exposure prevented the loss of barrier function and the increased cytokine release. Furthermore, knockdown of key inflammatory regulators RELA and MYD88 through on-chip adenoviral shRNA transduction alleviated IBD phenotype by decreasing cytokine production. In summary, we demonstrate the routine use of a gut-on-a-chip platform for disease-specific aspects modeling. The approach can be used for larger scale disease modeling, target validation and drug discovery purposes.

Structure-guided design of selective Epac1 and Epac2 agonists.

The second messenger cAMP is known to augment glucose-induced insulin secretion. However, its downstream targets in pancreatic ß-cells have not been unequivocally determined. Therefore, we designed cAMP analogues by a structure-guided approach that act as Epac2-selective agonists both in vitro and in vivo. These analogues activate Epac2 about two orders of magnitude more potently than cAMP. The high potency arises from increased affinity as well as increased maximal activation. Crystallographic studies demonstrate that this is due to unique interactions. At least one of the Epac2-specific agonists, Sp-8-BnT-cAMPS (S-220), enhances glucose-induced insulin secretion in human pancreatic cells. Selective targeting of Epac2 is thus proven possible and may be an option in diabetes treatment.

A Cost-Effectiveness Analysis to Evaluate a System Change in Mental Healthcare in the Netherlands for Patients with Depression or Anxiety.

Over the last decade, the Dutch mental healthcare system has been subject to profound policy reforms, in order to achieve affordable, accessible, and high quality care. One of the adjustments was to substitute part of the specialized care for general mental healthcare. Using a quasi-experimental design, we compared the cost-effectiveness of patients in the new setting with comparable patients from specialized mental healthcare in the old setting. Results showed that for this group of patients the average cost of treatment was significantly reduced by, on average, €2132 (p < 0.001), with similar health outcomes as in the old system.

Basal cells contribute to innate immunity of the airway epithelium through production of the antimicrobial protein RNase 7.

Basal cells play a critical role in the response of the airway epithelium to injury and are recently recognized to also contribute to epithelial immunity. Antimicrobial proteins and peptides are essential effector molecules in this airway epithelial innate immunity. However, little is known about the specific role of basal cells in antimicrobial protein and peptide production and about the regulation of the ubiquitous antimicrobial protein RNase 7. In this study, we report that basal cells are the principal cell type producing RNase 7 in cultured primary bronchial epithelial cells (PBEC). Exposure of submerged cultured PBEC (primarily consisting of basal cells) to the respiratory pathogen nontypeable Haemophilus influenzae resulted in a marked increase in expression of RNase 7, although this was not observed in differentiated air-liquid interface cultured PBEC. However, transient epithelial injury in air-liquid interface-cultured PBEC induced by cigarette smoke exposure led to epidermal growth factor receptor-mediated expression of RNase 7 in remaining basal cells. The selective induction of RNase 7 in basal cells by cigarette smoke was demonstrated using confocal microscopy and by examining isolated luminal and basal cell fractions. Taken together, these findings demonstrate a phenotype-specific innate immune activity of airway epithelial basal cells, which serves as a second line of airway epithelial defense that is induced by airway epithelial injury.

The rationale for targeting TGF-ß in chronic liver diseases.

BACKGROUND: Transforming growth factor (TGF)-ß is a pluripotent cytokine that displays several tissue-specific biological activities. In the liver, TGF-ß is considered a fundamental molecule, controlling organ size and growth by limiting hepatocyte proliferation. It is involved in fibrogenesis and, therefore, in worsening liver damage, as well as in triggering the development of hepatocellular carcinoma (HCC). TGF-ß is known to act as an oncosuppressor and also as a tumour promoter in HCC, but its role is still unclear. DESIGN: In this review, we discuss the potential role of TGF-ß in regulating the tumoural progression of HCC, and therefore the rationale for targeting this molecule in patients with HCC. RESULTS: A considerable amount of experimental preclinical evidence suggests that TGF-ß is a promising druggable target in patients with HCC. To support this hypothesis, a phase II clinical trial is currently ongoing using a TGF-ß pathway inhibitor, and results will soon be available. CONCLUSIONS: The identification of new TGF-ß related biomarkers will help to select those patients most likely to benefit from therapy aimed at inhibiting the TGF-ß pathway. New formulations that may provide a more controlled and sustained delivery of the drug will improve the therapeutic success of such treatments.

The influence of study population and definition of improvement on the smallest detectable change and the minimal important change of the neck disability index.

BACKGROUND: Reported values of the minimal important change (MIC) and the smallest detectable change (SDC) for the neck disability index (NDI) differ strongly, raising questions about the generalizability of these parameters. The SDC and the MIC are possibly influenced by the study design or by the study population. We studied the influence of the type of anchor, the definition of improvement and population characteristics on the SDC and the MIC of the NDI. METHODS: A cohort study including 101 patients with non-specific, chronic neck pain. SDC and MIC were calculated using two types of external anchors. For each anchor we applied two different definitions to dichotomize the population into improved and unimproved patients. The influence of patient characteristics was assessed in relevant subgroups: patients with or without radiating pain and patients with different baseline scores. RESULTS: The influence of different anchors and different definitions of improvement on estimates of the SDC and the MIC was only minimal. The SDC and the MIC were similar for subgroups of patients with or without radiation, but differed strongly for subgroups of patients with higher or lower baseline scores. CONCLUSIONS: Our study shows that estimates of the SDC and the MIC of the NDI can be influenced by population characteristics. It is concluded that we cannot adopt a single change score to define relevant change by combining the result of previous studies.

Emergency care reconfiguration in the Netherlands: conflicting interests and trade-offs from a multidisciplinary perspective.

Many countries are reconfiguring their emergency care systems to improve quality and efficiency of care, and this often includes the concentration of emergency departments (EDs). This trend is evident in the Netherlands, but the best approach is the subject of debate among stakeholders. We (i) examined the views of stakeholders on the concentration of EDs in the Netherlands and (ii) identified the main conflicting interests and trade-offs that are relevant for health policy. To do this, we organised focus groups and semi-structured interviews with emergency care professionals, hospital executives and selected external stakeholders. First, the participants saw both advantages and disadvantages to concentration, but these were also contested and debated. Second, we found that - sometimes conflicting - public health care goals (i.e. quality, accessibility and affordability) and narrower interests (e.g. the interests of specific hospitals, insurers, medical specialists and local administrators) were both pointed out. Third, there was no clear preferred approach to the future organisation of EDs, although most stakeholders mentioned some form of centralised decision-making at the national level, combined with regional customisation. Our findings will facilitate health policy decision-making around the reconfiguration of emergency care with the long-term goal of achieving efficient and high-quality emergency care.

Predicting Undesired Treatment Outcomes With Machine Learning in Mental Health Care: Multisite Study.

Background: Predicting which treatment will work for which patient in mental health care remains a challenge. Objective: The aim of this multisite study was 2-fold: (1) to predict patients' response to treatment in Dutch basic mental health care using commonly available data from routine care and (2) to compare the performance of these machine learning models across three different mental health care organizations in the Netherlands by using clinically interpretable models. Methods: Using anonymized data sets from three different mental health care organizations in the Netherlands (n=6452), we applied a least absolute shrinkage and selection operator regression 3 times to predict the treatment outcome. The algorithms were internally validated with cross-validation within each site and externally validated on the data from the other sites. Results: The performance of the algorithms, measured by the area under the curve of the internal validations as well as the corresponding external validations, ranged from 0.77 to 0.80. Conclusions: Machine learning models provide a robust and generalizable approach in automated risk signaling technology to identify cases at risk of poor treatment outcomes. The results of this study hold substantial implications for clinical practice by demonstrating that the performance of a model derived from one site is similar when applied to another site (ie, good external validation).

Whether and how top management create flexibility in mental healthcare organizations: COVID-19 as a test case.

PURPOSE: Flexibility is essential for healthcare organizations to anticipate the increasing internal and external dynamics. Mental healthcare organizations in the Netherlands face major policy reforms made by the government, increasing involvement from municipalities and gradual replacement of clinical care with outpatient care. Top management plays an important strategic role in creating this flexibility because they make important choices, give direction and structure the organization. To create flexibility, managers have to deal with complexity and paradoxes. In this study, the authors aim to contribute to the knowledge on how healthcare managers can create flexibility in their organizations. DESIGN/METHODOLOGY/APPROACH: This is a qualitative empirical field study. In total, 21 managers of mental healthcare organizations participated in open in-depth interviews. The authors explored flexibility on three perspectives: organizational direction, structure and operations. The COVID-19 pandemic has provided an opportunity to explore flexibility. The authors asked participants to reflect on their organization's response to the pandemic. FINDINGS: Most mental healthcare organizations create flexibility in an implicit way. Flexibility and resilience are closely linked mechanisms. Flexibility ensures a quick response while resilience provides the counterforce and rebound needed to adapt. Adaption ensures that healthcare professionals learn from their experiences and do not return completely to the way things were done before. The primary urge to survive ensured rapid and adequate responses to the COVID-19 pandemic. Whether this is a manifestation of flexibility remains difficult to conclude. PRACTICAL IMPLICATIONS: The complexity theory offers some guidance in creating a flexible organization without losing consistency. Flexibility and resilience are closely linked mechanisms that antagonize and protect each other. With this insight, managers in mental healthcare can utilize the qualities and balance them without falling into the various pitfalls. ORIGINALITY/VALUE: In this research, the authors are concerned with flexibility as a proactive attitude and capacity of organizations. By looking at the response of organizations to the COVID-19 crisis, the authors find out that responding to a disaster out of survival instinct is something else than flexibility. There is an interesting relationship between flexibility, resilience and adaptability, and they can balance each other.

Is change over time in psychotic symptoms related to social functioning?

OBJECTIVE: In psychosis, treatment often focuses on symptom reduction whereas social functioning is also essential. In this study, we investigate positive psychotic symptoms and medication use in relation to social functioning over a 3-year time-period in 531 patients diagnosed with psychosis. Furthermore, relations of positive symptoms with needs for care and quality of life were also investigated. METHOD: Using repeated measures analysis, changes were measured over time. Hereafter, mixed model analyses were performed to determine the associations of social functioning, needs for care, and quality of life with psychotic symptoms and patient characteristics. Finally, we assessed differences in symptoms and medication dose between those with an increase and those with a decrease in social functioning. RESULTS: Patients significantly improved in social functioning, while psychotic symptoms increased. Improvement in social functioning was associated with younger age, higher IQ, and lower social functioning at T1, but not with positive symptoms. Also, improvement in social functioning was found to be related to a decrease in the dose of clozapine. Improvement in social functioning occurs despite worsening of positive symptoms. CONCLUSIONS: The findings suggest the need to further explore the relation between symptomatology, social functioning, and medication use. In the treatment of psychotic disorders, one should reconsider the strong focus on reducing psychotic symptoms. The current focus needs to shift much more toward improving functional outcome, especially when the patient expresses a desire for change in this respect.

High-throughput 3D microvessel-on-a-chip model to study defective angiogenesis in systemic sclerosis.

In early systemic sclerosis (Scleroderma, SSc), the vasculature is impaired. Although the exact etiology of endothelial cell damage in SSc remains unclear, it is hypothesized that endothelial to mesenchymal transition (EndoMT) plays a key role. To perform physiologically relevant angiogenic studies, we set out to develop an angiogenesis-on-a-chip platform that is suitable for assessing disease parameters that are relevant to SSc and other vasculopathies. In the model, we substituted Fetal Bovine Serum (FBS) with Human Serum without impairing the stability of the culture. We showed that 3D microvessels and angiogenic factor-induced sprouts exposed to key pro-inflammatory and pro-fibrotic cytokines (TNFα and TGFß) undergo structural alterations consisting of destructive vasculopathy (loss of small vessels). We also showed that these detrimental effects can be prevented by compound-mediated inhibition of TGFß-ALK5 signaling or addition of a TNFα neutralizing antibody to the 3D cultures. This demonstrates that our in vitro model is suitable for compound testing and identification of new drugs that can protect from microvascular destabilization or regression in disease-mimicking conditions. To support this, we demonstrated that sera obtained from SSc patients can exert an anti-angiogenic effect on the 3D vessel model, opening the doors to screening for potential SSc drugs, enabling direct patient translatability and personalization of drug treatment.

siRNA-based identification of IBD-related targets in human monocyte-derived dendritic cells.

Inflammatory bowel disease (IBD) is thought to be caused by an aberrant host response to the commensal enteric flora in genetically susceptible individuals. Dendritic cells (DCs) play a key role in the regulation of this response as they sample gut commensals. In healthy individuals DCs actively contribute to tolerance upon recognition of these resident bacteria, whereas in individuals with IBD, DCs will initiate an inflammatory response. To mimic the disease response in vitro, human monocyte-derived DCs were matured with E. coli causing the cells to produce high levels of the pro-inflammatory cytokine IL-12/IL-23p40 (p40) and low levels of the anti-inflammatory cytokine IL-10. A siRNA-based screening assay was developed and screened to identify potential therapeutic targets that shift this balance towards an immunosuppressive state with lower levels of p40 and higher levels of IL-10. The screening assay was optimized and quality controlled using non-targeting controls and positive control siRNAs targeting IL12B and TLR4 transcripts. In the primary screen, smartpool siRNAs were screened for reduction in p40 expression, induction of IL-10 levels, or increase in IL-10:p40 ratios without affecting cell viability. All potential targets were taken forward into a confirmation screen in a different DC donor in which four individual siRNAs per target were screened. At least two siRNAs per target should have an effect to be considered a valid target. This screen resulted in a concise list of ten genes, of which their role in DC maturation is currently being investigated.

Realistic evaluation of the Flexmonitor, a team program to maintain and reflect on flexibility.

PURPOSE: Flexibility is necessary in a dynamic healthcare environment. However, balancing flexibility and consistency is difficult for healthcare teams, especially when working in threatening conditions. Methods are needed to help teams create, monitor and maintain flexibility. DESIGN/METHODOLOGY/APPROACH: This study evaluates a practice-based program -- the Flexmonitor - which aims to help teams develop and maintain flexibility. Here, realistic evaluation was used to refine the program and define building blocks for future programs. FINDINGS: The Flexmonitor can be used to monitor implicit criteria and differences in interpretation and beliefs among team members to promote flexibility. It also monitors team behavior and the effects of this behavior on self-defined indicators. Using the Flexmonitor, team members can discuss their beliefs and the definitions and criteria of flexibility. Strikingly, teams were not able to effectively self-manage their flexibility using the Flexmonitor. ORIGINALITY/VALUE: This article contributes to our knowledge of self-managing teams, particularly the question of whether team members can take responsibility for team flexibility.

Using machine learning to predict mental healthcare consumption in non-affective psychosis.

OBJECTIVE: The main goal of the study was to predict individual patients' future mental healthcare consumption, and thereby enhancing the design of an efficient demand-oriented mental healthcare system by focusing on a patient population associated with intensive mental healthcare consumption. Factors that affect the mental healthcare consumption of service users with non-affective psychosis were identified, and subsequently used in a prognostic model to predict future healthcare consumption. METHOD: This study was a secondary analysis of an existing dataset from the GROUP study. Based on mental healthcare consumption, patients with non-affective psychosis were divided into two groups: low (N = 579) and high (N = 488) intensive mental healthcare consumers. Three different techniques from the field of machine learning were applied on crosssectional data to identify risk factors: logistic regression, classification tree and a random forest. Subsequently, the same techniques were applied longitudinally in order to predict future healthcare consumption. RESULTS: Identified variables that affected healthcare consumption were the number of psychotic episodes, paid employment, engagement in social activities, previous healthcare consumption, and met needs. Analyses showed that the random forest method is best suited to model risk factors, and that these relations predict future healthcare consumption (AUC 0.71, PPV 0.65). CONCLUSIONS: Machine learning techniques provide valuable information for identifying risk factors in psychosis. They may thus help clinicians optimize allocation of mental healthcare resources by predicting future healthcare consumption.

Development of a human primary gut-on-a-chip to model inflammatory processes.

Inflammatory bowel disease (IBD) is a complex multi-factorial disease for which physiologically relevant in vitro models are lacking. Existing models are often a compromise between biological relevance and scalability. Here, we integrated intestinal epithelial cells (IEC) derived from human intestinal organoids with monocyte-derived macrophages, in a gut-on-a-chip platform to model the human intestine and key aspects of IBD. The microfluidic culture of IEC lead to an increased polarization and differentiation state that closely resembled the expression profile of human colon in vivo. Activation of the model resulted in the polarized secretion of CXCL10, IL-8 and CCL-20 by IEC and could efficiently be prevented by TPCA-1 exposure. Importantly, upregulated gene expression by the inflammatory trigger correlated with dysregulated pathways in IBD patients. Finally, integration of activated macrophages offers a first-step towards a multi-factorial amenable IBD platform that could be scaled up to assess compound efficacy at early stages of drug development or in personalized medicine.