Historical evolution of healthcare systems of post-soviet Russia, Belarus, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan, Armenia, and Azerbaijan: A scoping review

This scoping review addresses the transformation and development of new healthcare systems in nine countries -Armenia, Azerbaijan, Belarus, Kazakhstan, Kyrgyzstan, Russia, Tajikistan, Turkmenistan, and Uzbekistan over the period following the collapse of the Soviet Union from 1991 to the present. This assessment focuses on maternal and child health, mental health, communicable diseases, and non-communicable diseases in an effort to highlight the changes in the healthcare status of these nine countries under scrutiny. Considering that all the post-Soviet nations are officially recognized members of the World Health Organization (WHO) and have demonstrated their commitment to attaining the WHO's objectives, the evaluation of healthcare system progress and improvement was carried out utilizing indicators provided by the WHO. This review reveals that the evolution of healthcare systems could be considered sustainable, given that average life expectancy has returned to the level it was in 1991- the year of the USSR's breakup, and people's health has improved since the turn of the twenty-first century. To enhance the potential success of future healthcare reforms, however, governments must monitor implementation of the reform process, evaluate the achievement of objectives, and make necessary adjustments. The success of future healthcare changes will depend on the active involvement of the government, medical community, and patient community, as well as obtaining the support of local health authorities. This study may help identify successful and failed strategies, guiding future healthcare changes and investments.

Internet of medical things and blockchain-enabled patient-centric agent through SDN for remote patient monitoring in 5G network

During the COVID-19 pandemic, there has been a significant increase in the use of internet resources for accessing medical care, resulting in the development and advancement of the Internet of Medical Things (IoMT). This technology utilizes a range of medical equipment and testing software to broadcast patient results over the internet, hence enabling the provision of remote healthcare services. Nevertheless, the preservation of privacy and security in the realm of online communication continues to provide a significant and pressing obstacle. Blockchain technology has shown the potential to mitigate security apprehensions across several sectors, such as the healthcare industry. Recent advancements in research have included intelligent agents in patient monitoring systems by integrating blockchain technology. However, the conventional network configuration of the agent and blockchain introduces a level of complexity. In order to address this disparity, we present a proposed architectural framework that combines software defined networking (SDN) with Blockchain technology. This framework is specially tailored for the purpose of facilitating remote patient monitoring systems within the context of a 5G environment. The architectural design contains a patient-centric agent (PCA) inside the SDN control plane for the purpose of managing user data on behalf of the patients. The appropriate handling of patient data is ensured by the PCA via the provision of essential instructions to the forwarding devices. The suggested model is assessed using hyperledger fabric on docker-engine, and its performance is compared to that of current models in fifth generation (5G) networks. The performance of our suggested model surpasses current methodologies, as shown by our extensive study including factors such as throughput, dependability, communication overhead, and packet error rate.

Hospital food management: a multi-objective approach to reduce waste and costs

Food waste contributes significantly to greenhouse emissions and represents a substantial portion of overall waste within hospital facilities. Furthermore, uneaten food leads to a diminished nutritional intake for patients, that typically are vulnerable and ill. Therefore, this study developed mathematical models for constructing patient meals in a 1000-bed hospital located in Florida. The objective is to minimize food waste and meal-building costs while ensuring that the prepared meals meet the required nutrients and caloric content for patients. To accomplish these objectives, four mixed-integer programming models were employed, incorporating binary and continuous variables. The first model establishes a baseline for how the system currently works. This model generates the meals without minimizing waste or cost. The second model minimizes food waste, reducing waste up to 22.53 % compared to the baseline. The third model focuses on minimizing meal-building costs and achieves a substantial reduction of 37 %. Finally, a multi-objective optimization model was employed to simultaneously reduce both food waste and cost, resulting in reductions of 19.70 % in food waste and 32.66 % in meal-building costs. The results demonstrate the effectiveness of multi-objective optimization in reducing waste and costs within large-scale food service operations.

A meta-analysis of technology acceptance in healthcare from the consumer's perspective

Consumer-oriented health care technologies are increasingly available and transforming global health delivery systems. However, there is a paucity of research that systematically investigates health care technology acceptance from the consumer's perspective. This study conducts a literature review and meta-analysis to examine consumers' adoption intentions toward health care technologies. The findings suggest that technology acceptance models are transferable to health care technology with modifications, and factors such as perceived risks, technology performance expectancy, consumer trust, and habit significantly correlate with consumers' adoption intentions. This study provides valuable insights into health care technology management and practical implications for health care service designers, providers, and regulatory authorities.

Exploring patient safety risk in an emergency ward for substance use through a mixed-method analysis

BACKGROUND

There is limited knowledge of how psychiatric patient safety measures can detect and understand risk as an emergent property within a healthcare system. Overcrowding poses a risk for patient safety in psychiatric emergency wards and is associated with increased mortality and violence. This paper aims to explore patterns of risk emergence in a psychiatric ward and provide insights into the dynamics of workload pressure.

METHOD

A case study was conducted in a psychiatric emergency ward for patients with substance use disorders. The study employed a four-phased mixed-methods design. Phase one used clinical experts to identify patient safety pressure issues. Phase two used data on patient visits extracted from medical records between 2010 and 2020. In phase three, a quantitative analysis of patient visits and diagnosis was made. Phase four used a focus group of clinical experts for a semi-structured interview, analysing the result from phase three.

RESULT

Trend analysis demonstrated a steady growth of patient visits to the emergency ward over the studied ten-year period. The findings showed a decrease in patients being diagnosed with delirium when visiting the emergency ward and an increase in percentage of patients receiving a psychosis diagnosis. The focus group expressed concerns about delayed treatments, increased violence and underestimating patients' needs.

CONCLUSION

This study indicated that increased workload pressure can be predicted at a system level by analysing patient visits and diagnostics trends over time. The study advocates for ongoing awareness of patient safety risks by monitoring factors identified by clinical front-end workers as potential sources of risk. Healthcare management could employ supportive tools to detect and address emerging risks, including expected workload, overcrowding, staffing issues or bed shortages.

Social mood during the Covid-19 vaccination process in Spain. A sentiment analysis of tweets and social network leaders

In accordance with the cognitive orientation contemplated in the resolution of complex problems posed in public decision-making using decision support systems and social networks, this work studies the possibility of identifying the state of mind of society through the state of mind of network leaders. Using sentiment and emotion analysis as research techniques and Twitter as a representative social network, the study corpus considers tweets and retweets in Spanish about COVID-19 in the period from February 27, 2020 to December 31, 2021. As cognitive orientation claims, the proposed techniques will allow us to extract the arguments that support the different positions and decisions from the analysis of the tweets issued exclusively by social leaders. In the case study considered, the COVID-19 vaccination process in Spain, the reduction in the number of tweets' authors (more than 8,000) to the network leaders (just 8) was greater than 99 %; and the subsequent reduction in the number of associated tweets was greater than 88 % from the 18,193 tweets in society to the 2,145 tweets of the eight social leaders. The impressive degree of information compression achieved may be useful to establish new directions of social mood analysis applied to healthcare and business management.

Disentangling organizational levers and economic benefits in transitional care programs: a systematic review and configurational analysis

BACKGROUND

Promoting safe and efficient transitions of care is critical to reducing readmission rates and associated costs and improving the quality of patient care. A growing body of literature suggests that transitional care (TC) programs are effective in improving quality of life and reducing unplanned readmissions for several patient groups. TC programs are highly complex and multidimensional, requiring evidence on how specific practices and system characteristics influence their effectiveness in patient care, readmission reduction and costs.

METHODS

Using a systematic review and a configurational approach, the study examines the role played by system characteristics (size, ownership, professional skills, technology used), the organizational components implemented, analyzing their combinations, and the potential economic impact of TC programs.

RESULTS

The more organizational components are implemented, the greater the likelihood that a TC program will be successful in reducing readmission rates. Not all components have the same effect. The results show that certain components, 'post-discharge symptom monitoring and management' and 'discharge planning', are necessary but not sufficient to achieve the outcome. The results indicate the existence of two different combinations of components that can be considered sufficient for the reduction of readmissions. Furthermore, while system characteristics are underexplored, the study shows different ways of incorporating the skill mix of professionals and their mode of coordination in TC programs. Four organizational models emerge: the health-based monocentric, the social-based monocentric, the multidisciplinary team and the mono-specialist team. The economic impact of the programs is generally positive. Despite an increase in patient management costs, there is an overall reduction in all post-intervention costs, particularly those related to readmissions.

CONCLUSIONS

The results underline the importance of examining in depth the role of system characteristics and organizational factors in facilitating the creation of a successful TC program. The work gives preliminary insights into how to systematize organizational practices and different coordination modes for facilitating decision-makers' choices in TC implementation. While there is evidence that TC programs also have economic benefits, the quality of economic evaluations is relatively low and needs further study.

Thyroidkeeper: a healthcare management system for patients with thyroid diseases

Thyroid diseases, especially thyroid tumors, have a huge population in China. The postoperative patients, under China's incomplete tertiary diagnosis and treatment system, will frequently go to tertiary hospitals for follow-up and medication adjustment, resulting in heavy burdens on both specialists and patients. To help postoperative patients recover better against the above adverse conditions, a novel mobile application ThyroidKeeper is proposed as a collaborative AI-based platform that benefits both patients and doctors. In addition to routine health records and management functions, ThyroidKeeper has achieved several innovative points. First, it can automatically adjust medication dosage for patients during their rehabilitation based on their medical history, laboratory indicators, physical health status, and current medication. Second, it can comprehensively predict the possible complications based on the patient's health status and the health status of similar groups utilizing graph neural networks. Finally, the employing of graph neural network models can improve the efficiency of online communication between doctors and patients, help doctors obtain medical information for patients more quickly and precisely, and make more accurate diagnoses. The preliminary evaluation in both laboratory and real-world environments shows the advantages of the proposed ThyroidKeeper system.

A simulation model for predicting hospital occupancy for Covid-19 using archetype analysis

COVID-19 pandemic has sent millions of people to hospitals worldwide, exhausting on many occasions the capacity of healthcare systems to provide care patients required to survive. Although several epidemiological research works have contributed a variety of models and approaches to anticipate the pandemic spread, very few have tried to translate the output of these models into hospital service requirements, particularly in terms of bed occupancy, a key question for hospital managers. This paper proposes a tool for predicting the current and future occupancy associated with COVID-19 patients of a hospital to help managers make informed decisions to maximize the availability of hospitalization and intensive care unit (ICU) beds and ensure adequate access to services for confirmed COVID-19 patients. The proposed tool uses a discrete event simulation approach that uses archetypes (i.e., empirical models of trajectories) extracted from empirical analysis of actual patient trajectories. Archetypes can be fitted to trajectories observed in different regions or to the particularities of current and forthcoming variants using a rather small amount of data. Numerical experiments on realistic instances demonstrate the accuracy of the tool's predictions and illustrate how it can support managers in their daily decisions concerning the system's capacity and ensure patients the access the resources they require.

Performance analysis of English hospitals during the first and second waves of the coronavirus pandemic

The coronavirus infection COVID-19 killed millions of people around the world in 2019-2022. Hospitals were in the forefront in the battle against the pandemic. This paper proposes a novel approach to assess the effectiveness of hospitals in saving lives. We empirically estimate the production function of COVID-19 deaths among hospital inpatients, applying Heckman's two-stage approach to correct for the bias caused by a large number of zero-valued observations. We subsequently assess performance of hospitals based on regression residuals, incorporating contextual variables to convex quantile regression. Data of 187 hospitals in England over a 35-week period from April to December 2020 is divided in two sub-periods to compare the structural differences between the first and second waves of the pandemic. The results indicate significant performance improvement during the first wave, however, learning by doing was offset by the new mutated virus straits during the second wave. While the elderly patients were at significantly higher risk during the first wave, their expected mortality rate did not significantly differ from that of the general population during the second wave. Our most important empirical finding concerns large and systematic performance differences between individual hospitals: larger units proved more effective in saving lives, and hospitals in London had a lower mortality rate than the national average.

Decision-making on the fly: a qualitative study of physicians in out-of-hospital emergency medical services

BACKGROUND

Out-of-hospital Emergency Medical Services (OHEMS) require fast and accurate assessment of patients and efficient clinical judgment in the face of uncertainty and ambiguity. Guidelines and protocols can support staff in these situations, but there is significant variability in their use. Therefore, the aim of this study was to increase our understanding of physician decision-making in OHEMS, in particular, to characterize the types of decisions made and to explore potential facilitating and hindering factors.

METHODS

Qualitative interview study of 21 physicians in a large, publicly-owned and operated OHEMS in Croatia. Data was subjected to an inductive content analysis.

RESULTS

Physicians (mostly young, female, and early in their career), made three decisions (transport, treat, and if yes on either, how) after an initial patient assessment. Decisions were influenced by patient needs, but to a greater extent by factors related to themselves and patients (microsystem), their organization (mesosystem), and the larger health system (macrosystem). This generated a high variability in quality and outcomes. Participants desired support through further training, improved guidelines, formalized feedback, supportive management, and health system process redesign to better coordinate and align care across organizational boundaries.

CONCLUSIONS

The three decisions were made complex by contextual factors that largely lay outside physician control at the mesosystem level. However, physicians still took personal responsibility for concerns more suitably addressed at the organizational level. This negatively impacted care quality and staff well-being. If managers instead adopt a learning orientation, the path from novice to expert physician could be more ably supported through organizational demands and practices aligned with real-world practice. Questions remain on how managers can better support the learning needed to improve quality, safety, and physicians' journey from novice to expert.

A Markovian model for the spread of the SARS-CoV-2 virus

We propose a Markovian stochastic approach to model the spread of a SARS-CoV-2-like infection within a closed group of humans. The model takes the form of a Partially Observable Markov Decision Process (POMDP), whose states are given by the number of subjects in different health conditions. The model also exposes the different parameters that have an impact on the spread of the disease and the various decision variables that can be used to control it (e.g, social distancing, number of tests administered to single out infected subjects). The model describes the stochastic phenomena that underlie the spread of the epidemic and captures, in the form of deterministic parameters, some fundamental limitations in the availability of resources (hospital beds and test swabs). The model lends itself to different uses. For a given control policy, it is possible to verify if it satisfies an analytical property on the stochastic evolution of the state (e.g., to compute probability that the hospital beds will reach a fill level, or that a specified percentage of the population will die). If the control policy is not given, it is possible to apply POMDP techniques to identify an optimal control policy that fulfils some specified probabilistic goals. Whilst the paper primarily aims at the model description, we show with numeric examples some of its potential applications.

Development and initial validation of the addressing client needs with social determinants of health scale (ACN: SDH)

BACKGROUND

The purpose of this study was to validate a scale that can be used by healthcare service professionals, healthcare systems, educators, and researchers to assess health service professionals' social determinants of health (SDOH) competency; with competency defined as their knowledge, awareness-biases, skills, and actual preparedness to address SDOH challenges.

METHODS

An Exploratory Factor Analysis (EFA) was conducted with a sample of 220 health service professionals, and 6 factors were identified. A Confirmatory Factor Analysis (CFA) was conducted with 303 health service professionals and the 6-factor solution was supported, with 22 items.

RESULTS

The reliability estimates for the 6 factors are as follows: Factor 1, Action Toward Addressing SDOH (a = .85); Factor 2, SDOH Knowledge (a = .94); Factor 3, Negative Attitude toward Addressing SDOH (a = .79); Factor 4, Systemic Accountability (a = .81); Factor 5, School Preparation (a = .86); and Factor 6, Perception of the Cause of SDOH (a = .94).

CONCLUSION

The ACN:SDH scale is the first validated measure that can be used to systematically appraise health service professionals' SDOH competency.

There and back again. Examining the development of employee commitment during a prolonged crisis

To effectively function and adapt in crises, healthcare organizations rely on the skills and commitment of their workforce. Yet, our current understanding of how employees' workplace commitment is affected by and evolves throughout the course of a crisis remains limited. In this paper, we explore the commitment of hospital staff to an important workplace target, the COVID-19 crisis response, and show how this commitment develops over time. We report on an exploratory case study of hospital staff in a heavily hit region of the Netherlands. We conducted interviews with hospital executives, management, medical and support staff to uncover the issues hospitals faced in recruiting staff to provide COVID-19 care throughout the first and second wave of the crisis. Our findings suggest that while staff initially exhibited high levels of commitment to aiding in the crisis effort, staff were perceived to exhibit lower levels of commitment in the second wave, complicating the provision of COVID-19 care. We unveil three contributing factors to this shift, namely: competing demands, energy depletion and a lack of support and appreciation. Our findings suggest that while staff were initially willing to dedicate themselves and take responsibility for the crisis effort, as their other more stable commitments became more salient in the second wave, their willingness to dedicate limited resources to the crisis effort decreased. In our discussion, we examine the implications of our findings for the literature on workplace commitment, and advance our understanding of employee workplace commitment during crises.

Performance prediction and optimization for healthcare enterprises in the context of the COVID-19 pandemic: an intelligent DEA-SVM model

The coronavirus disease (COVID-19) pandemic has caused significant changes in the external environment of enterprises, resulting in tremendous negative impacts. Accordingly, the irregular fluctuation of business data poses a critical challenge to traditional approaches. Therefore, to combat the effects of the COVID-19 pandemic, an effective model is required to proactively predict an enterprise's performance and simultaneously generate scientific performance optimization solutions. Consequently, at the intersection of artificial intelligence algorithms, operations research, and management science, an intelligent DEA-SVM model, which has a theoretical contribution, is developed in this study. The capabilities of this model are verified through sufficient numerical experiments. On the one hand, this model outperforms traditional algorithms in prediction accuracy. On the other hand, effective performance optimization solutions for low-performance enterprises are obtained from the input-output perspective. Moreover, the application value of this model is reflected in its successful implementation in the healthcare industry. Thus, it is a user-friendly tool for realizing the stable operation of enterprises in the context of the COVID-19 pandemic.

An electronic consultation program impacts on heart failure patients' prognosis: implications for heart failure care

AIMS

e-consults are asynchronous, clinician-to-clinician exchanges that answer focused, non-urgent, patient-specific questions using the electronic medical record. We instituted an e-consultation programme (2013-2019) for all general practitioners (GPs) referrals to cardiologists that preceded patients' in-person consultations when considered. In our study, we aimed to analyse the clinical characteristics, 1 year prognosis and the prognostic determinants of patients with a previous diagnosis of HF referred for an e-consult, categorized by their previous HF-related hospitalization status (recent hospitalization, <1 year before; remote hospitalization, >1 year before or never been hospitalized because of HF), and to analyse the impact of reducing the time elapsed between e-consultation and response by the cardiologist in terms of prognosis.

METHODS AND RESULTS

Epidemiological and clinical data were obtained from 4851 HF patients referred by GPs to the cardiology department for an e-consultation 2013 and 2020. The delay of time to e-consults were solved was 8.6 + 8.6 days with 84.3% solved in <14 days. For the 1 year prognosis evaluation after the e-consult were assessed the cardiovascular hospitalizations, HF-related hospitalizations, HF-related mortality, cardiovascular mortality, and all-cause mortality. Compared with the group without a previous hospitalization, patients with recent and remote HF hospitalization were at higher risk of a new HF-related hospitalization (OR: 19.41 [95% CI: 12.95-29.11]; OR: 8.44 [95% CI: 5.14-13.87], respectively), HF-related mortality (OR: 2.47 [95% CI: 1.43-4.27]; OR: 1.25 [95% CI: 0.51-3.06], respectively), as well as cardiovascular hospitalizations and mortality and all-cause mortality. Reduction in the time elapsed because e-consultation was solved was associated with lower risk of HF-related mortality (OR: 0.94 [95% CI: 0.89-0.99]), cardiovascular mortality (OR: 0.96 [95% CI: 0.93-0.98]), and all-cause mortality (OR: 0.98 [95% CI: 0.97-1.00]).

CONCLUSIONS

A clinician-to-clinician e-consultation programme between GPs and cardiologists in patients with HF allows to solve the demand of care in around 25% e-consults without an in-person consultation; the patients with a previous history of HF-related hospitalization showed a worse 1 year outcome. A reduction in the time elapsed because e-consultation was solved was associated with a mortality reduction.

Country versus pharmaceutical company interests for hepatitis C treatment

Hepatitis C virus (HCV) is one of the leading causes of liver disease and is responsible for massive health and economic burden worldwide. The disease is asymptomatic in its early stages, but it can progress over time to fatal end-stage liver disease. Thus, the majority of individuals infected with HCV are unaware of their chronic condition. Recent treatment options for HCV can completely cure the infection but are costly. We developed a game model between a pharmaceutical company (PC) and a country striving to maximize its citizens' utility. First, the PC determines the price of HCV treatment; then, the country responds with corresponding screening and treatment strategies. We employed an analytical framework to calculate the utility of the players for each selected strategy. Calibrated to detailed HCV data from Israel, we found that the PC will gain higher revenue by offering a quantity discount rather than using standard fixed pricing per treatment, by indirectly forcing the country to conduct more screening than it desired. By contrast, risk-sharing agreements, in which the country pays only for successful treatments are beneficial for the country. Our findings underscore that policy makers worldwide should prudently consider recent offers by PCs to increase screening either directly, via covering HCV screening, or indirectly, by providing discounts following a predetermined volume of sales. More broadly, our approach is applicable in other healthcare settings where screening is essential to determine treatment strategies.

The COVID crisis impact on open education in the field of healthcare management

This paper aims to explore how the Hellenic Open University managed to adapt to the new normal of the pandemic and particularly what methods it deployed in its postgraduate Healthcare Management program. The first part introduces the key features of the Hellenic Open University and analyzes the strategic policy deployed by the University during the pandemic through the pillars of academic integrity, openness and excellence. The second part presents the research method and findings regarding both instructors and students of the Healthcare Management postgraduate program. The third part concludes that the policy introduced by University had a positive effect overall both to academic staff and healthcare practitioners. The importance and innovation of this study lies in the fact that it emphasizes on adult students who are in their crashing majority healthcare practitioners and thus have already a saying regarding the pandemic's management. The scope of the research was to demonstrate that this student audience had a significant input in the teaching procedure, since they were at the forefront of the pandemic crisis. In this regard, the instructor-student relationship was more than ever reciprocal based on the knowledge of the first and the experience of the latter.

How can big data analytics be used for healthcare organization management? Literary framework and future research from a systematic review

Background

Multiple attempts aimed at highlighting the relationship between big data analytics and benefits for healthcare organizations have been raised in the literature. The big data impact on health organization management is still not clear due to the relationship’s multi-disciplinary nature. This study aims to answer three research questions: a) What is the state of art of big data analytics adopted by healthcare organizations? b) What about the benefits for both health managers and healthcare organizations? c) What about future directions on big data analytics research in healthcare?

Methods

Through a systematic literature review the impact of big data analytics on healthcare management has been examined. The study aims to map extant literature and present a framework for future scholars to further build on, and executives to be guided by.

Results

The positive relationship between big data analytics and healthcare organization management has emerged. To find out common elements in the studies reviewed, 16 studies have been selected and clustered into 4 research areas: 1) Potentialities of big data analytics. 2) Resource management. 3) Big data analytics and management of health surveillance systems. 4) Big data analytics and technology for healthcare organization.

Conclusions

In conclusion is identified how the big data analytics solutions are considered a milestone for managerial studies applied to healthcare organizations, although scientific research needs to investigate standardization and integration of the devices as well as the protocol in data analysis to improve the performance of the healthcare organization.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08167-z.

[What about the weekend effect? Impact of the day of admission on in-hospital mortality, length of stay and cost of hospitalization]

INTRODUCTION

There is no agreement on the existence of the weekend effect in healthcare or, if it exists, on its possible causes. The objective of the study was to evaluate the differences in healthcare outcomes between patients admitted on weekdays or weekends in a high-complexity hospital.

METHODS

Observational and retrospective study of patients admitted between 2016 and 2019 in a public hospital with more than 1300 beds. Hospitalization episodes were classified according to whether admission took place between Friday at 3:00 p.m. and the following Monday at 8:00 a.m. (weekend admission) or not (admission on weekdays). Mortality, length of stay and associated costs were compared, applying their respective risk-adjustment models.

RESULTS

Of the total 169,495 hospitalization episodes analyzed, 48,201 (28.44%) corresponded to the weekend, presenting an older age (54.9 years vs. 53.9; P<.001), a higher crude mortality rate (5.22% vs. 4.59%; P<0.001), and a longer average length of stay (7.42 days vs. 6.74; P<.001), than those admitted on weekdays. The median crude cost of stay was lower (€731.25 vs. €850.88; P<0.001). No significant differences were found when applying the adjustment models, with a risk-adjusted mortality ratio of 1.03 (0.99-1.08) vs. 0.98 (0.95-1.01), risk-adjusted length of stay of 1.002 (0.98-1.005) vs. 0.999 (0.997-1.002) and risk-adjusted cost of stay of 0.928 (0.865-0.994) vs. 0.901 (0.843-0.962).

CONCLUSION

The results of the study reveal that the assistance provided during the weekends does not imply worse health outcomes or increased costs. Comparing the impact between hospitals will require a future homogenization of temporal criteria and risk adjustment models.

Definition and conceptualization of the patient-centered care pathway, a proposed integrative framework for consensus: a Concept analysis and systematic review

BACKGROUND

Confusion exists over the definition of the care pathway concept and existing conceptual frameworks contain various inadequacies which have led to implementation difficulties. In the current global context of rapidly changing health care systems, there is great need for a standardized definition and integrative framework that can guide implementation. This study aims to propose an accurate and up-to-date definition of care pathway and an integrative conceptual framework.

METHODS

An innovative hybrid method combining systematic review, concept analysis and bibliometric analysis was undertaken to summarize qualitative, quantitative, and mixed-method studies. Databases searched were PubMed, Embase and ABI/Inform. Methodological quality of included studies was then assessed.

RESULTS

Forty-four studies met the inclusion criteria. Using concept analysis, we developed a fine-grained understanding, an integrative conceptual framework, and an up-to-date definition of patient-centered care pathway by proposing 28 subcategories grouped into seven attributes. This conceptual framework considers both operational and social realities and supports the improvement and sustainable transformation of clinical, administrative, and organizational practices for the benefit of patients and caregivers, while considering professional experience, organizational constraints, and social dynamics. The proposed attributes of a fluid and effective pathway are (i) the centricity of patients and caregivers, (ii) the positioning of professional actors involved in the care pathway, (iii) the operation management through the care delivery process, (iv) the particularities of coordination structures, (v) the structural context of the system and organizations, (vi) the role of the information system and data management and (vii) the advent of the learning system. Antecedents are presented as key success factors of pathway implementation. By using the consequences and empirical referents, such as outcomes and evidence of care pathway interventions, we went beyond the single theoretical aim, proposing the application of the conceptual framework to healthcare management.

CONCLUSIONS

This study has developed an up-to-date definition of patient-centered care pathway and an integrative conceptual framework. Our framework encompasses 28 subcategories grouped into seven attributes that should be considered in complex care pathway intervention. The formulation of these attributes, antecedents as success factors and consequences as potential outcomes, allows the operationalization of this model for any pathway in any context.

Machine learning versus regression modelling in predicting individual healthcare costs from a representative sample of the nationwide claims database in France

BACKGROUND

Innovative provider payment methods that avoid adverse selection and reward performance require accurate prediction of healthcare costs based on individual risk adjustment. Our objective was to compare the performances of a simple neural network (NN) and random forest (RF) to a generalized linear model (GLM) for the prediction of medical cost at the individual level.

METHODS

A 1/97 representative sample of the French National Health Data Information System was used. Predictors selected were: demographic information; pre-existing conditions, Charlson comorbidity index; healthcare service use and costs. Predictive performances of each model were compared through individual-level (adjusted R-squared (adj-R²), mean absolute error (MAE) and hit ratio (HiR)), and distribution-level metrics on different sets of covariates in the general population and by pre-existing morbid condition, using a quasi-Monte Carlo design.

RESULTS

We included 510,182 subjects alive on 31st December, 2015. Mean annual costs were 1894€ (standard deviation 9326€) (median 393€, IQ range 95€; 1480€), including zero-claim subjects. All models performed similarly after adjustment on demographics. RF model had better performances on other sets of covariates (pre-existing conditions, resource counts and past year costs). On full model, RF reached an adj-R² of 47.5%, a MAE of 1338€ and a HiR of 67%, while GLM and NN had an adj-R² of 34.7% and 31.6%, a MAE of 1635€ and 1660€, and a HiR of 58% and 55 M, respectively. RF model outperformed GLM and NN for most conditions and for high-cost subjects.

CONCLUSIONS

RF should be preferred when the objective is to best predict medical costs. When the objective is to understand the contribution of predictors, GLM was well suited with demographics, conditions and base year cost.

[Comprehensive management guideline for breast cancer follow-up and healthcare (2022 edition)]

In recent years, with the emphasis on breast cancer screening and the improvement of standardized diagnosis and treatment, cure rate and survival time of breast cancer patients have been significantly improved in China. Accompanied by prolonged follow-up period, patients are facing increasing cancer and other related health issues, which calls for a more accurate and long-term follow-up plan and focus on broader health risks. Health issues on cardiovascular system, bone density, blood lipid and psychology caused by anti-cancer treatment, senescence or changes in hormone levels have become new challenges in the management of patients with breast cancer, not only affecting the life quality, but also impacting disease recurrence and death. Therefore, in addition to standardized treatment, scientific and standardized follow-up, comprehensive management of patients' concomitant diseases, interdisciplinary cooperation and holistic rehabilitation are also vital links to treatment. This could improve the therapeutic efficacy of anti-cancer treatment and the quality of life of patients. Based on the current landscape of treatment and follow-up of breast cancer patients in China, the experts committee updated the "Comprehensive Management Guideline for Breast Cancer Follow-up and Concomitant Diseases (2019 edition)" and revised it as "Guidelines for breast cancer follow-up and health management in China (2022 edition)" according to the latest literature and guidelines and medical advice from interdisciplinary experts. This new guideline is composed of four parts: path diagram, follow-up management, comprehensive healthcare management, and patient reported outcome. It aims to standardize the long-term follow-up of breast cancer patients, guide clinicians to actively provide comprehensive interdisciplinary healthcare management, and further improve the prognosis and life quality of breast cancer patients in China.

Use of a maturity model for facilitating the introduction of CAR T-cell therapy-Results of the START CAR-T project

Introduction

Chimeric antigen receptor (CAR) T-cell therapies are novel immunotherapies for the treatment of hematologic malignancies. They are administered in specialized centers by a multidisciplinary team and require the careful coordination of all steps involved in manufacturing and using cellular therapies. The Maturity Model (MM) is a tool developed and used for assessing the effectiveness of a variety of activities. In healthcare, it may assist clinicians in the gradual improvement of patient management with CAR T-cell therapy and other complex treatments.

Methods

The START CAR-T project was initiated to investigate the potential of a MM in the setting of CAR T-cell therapy. Four Italian clinics participated in the creation of a dedicated MM. Following the development and test of this MM, its validity and generalizability were further tested with a questionnaire submitted to 18 Italian centers.

Results

The START CAR-T MM assessed the maturity level of clinical sites, with a focus on organization, process, and digital support. For each area, the model defined four maturity steps, and indicated the actions required to evolve from a basic to an advanced status. The application of the MM to 18 clinical sites provided a description of the maturity level of Italian centers with regard to the introduction of CAR T-cell therapy.

Conclusion

The START CAR-T MM appears to be a useful and widely applicable tool. It may help centers optimize many aspects of CAR T-cell therapy and improve patient access to this novel treatment option.

Factors associated with diagnosis delay in head and neck cancer

INTRODUCTION AND OBJECTIVES

Tumour stage is an important prognostic factor in head and neck tumours. Many tumours are diagnosed in advanced stages despite almost universal healthcare and their being symptomatic. This paper seeks to determine the diagnostic delay in head and neck tumours in our health department, to analyse factors associated with delay and if it is associated with diagnosis in advanced stages.

METHODS

Retrospective study of 137 patients with head and neck cancer diagnosed from 2016-2018. Patient delay, delay in primary health care, delay in secondary health care, diagnostic delay and possible associated factors (smoking, location, stage, …) were evaluated.

RESULTS

Many patients (44.5%) were diagnosed in advanced stages. The median patient delay was 30 days. The median referral to otorhinolaryngology was 3.5 days. If the referral was made by another specialist (p = .008), the patients were under previous treatment (P=.000) and the tumours were in initial stages (P=.038) this delay was greater. The median from the first visit to otorhinolaryngology was 15 days, higher in regular referrals (43%) (P=.000). The median diagnostic delay was 12 days, higher in surgical biopsies (P=.000). The median professional delay was 58.5 days and total delay was 118.5 days.

CONCLUSIONS

Many head and neck tumours are diagnosed in advanced stages. A relationship was not found between diagnosis in advanced stages and diagnostic delay. However, steps must be taken to reduce these excessive delays.

The neglected contexts and outcomes of evidence-based management: a systematic scoping review in hospital settings

PURPOSE

The coronavirus disease 2019 (COVID-19) pandemic highlighted the necessity of practicing Evidence-based Management (EBMgt) as an approach to decision-making in hospital settings. The literature, however, provides limited insight into the process of EBMgt and its contextual nuances. Such insight is critical for better leveraging EBMgt in practice. Therefore, the authors' aim was to integrate the literature on the process of EBMgt in hospital settings, identify the gaps in knowledge and delineate areas for future research.

DESIGN/METHODOLOGY/APPROACH

The authors conducted a systematic scoping review using an innovative methodology that involved two systematic searches. First using EBMgt terminology and second using terminology associated with the EBMgt concept, which the authors derived from the first search.

FINDINGS

The authors identified 218 relevant articles, which using content analysis, they mapped onto the grounded model of the EBMgt process; a novel model of the EBMgt process developed by Sahakian and colleagues. The authors found that the English language literature provides limited insight into the role of managers' perceptions and motives in EBMgt, the practice of EBMgt in Global South countries, and the outcomes of EBMgt. Overall, this study's findings indicated that aspects of the decision-maker, context and outcomes have been neglected in EBMgt.

ORIGINALITY/VALUE

The authors contributed to the EBMgt literature by identifying these gaps and proposing future research areas and to the systematic review literature by developing a novel scoping review method.

Evolving pancreatic cancer treatment: From diagnosis to healthcare management

The prognosis of pancreatic ductal adenocarcinoma is still the worst among solid tumors. In this review, a panel of experts addressed the main unanswered questions about the clinical management of this disease, with the aim of providing practical decision support for physicians. On the basis of the evidence available from the literature, the main topics concerning pancreatic cancer are discussed: the diagnosis, as the need for a pathological characterization and the role for germ-line and somatic molecular profiling; the therapeutic management of resectable disease, as the role of upfront surgery or neoadjuvant chemotherapy, the post-operative restaging and the optimal timing foradjuvant chemotherapy, the management of the borderline resectable and locally advanced disease; the metastatic disease and the role of surgery for the management of patients with isolated metastasis and the use of biomarkers of metastatic potential; the role of supportive care and the healthcare management of pancreatic ductal adenocarcinoma.

Overbooking for physical examination considering late cancellation and set-resource relationship

BACKGROUND

Late cancellations of physical examination has severe impact on the operations of a physical examination center since it is often too late to fill vacancy. A booking control policy that considers overbooking is then one natural solution. Unlike appointment scheduling problems for clinics and hospitals, in which treating a patient mostly requires only one type of resource, a physical examination set typically requires multiple types of resources. Traditional methods that do not consider set-resource relationship thus may be inapplicable.

METHODS

We formulate a stochastic mathematical programming model that maximizes the expected net reward, which is the examination revenue minus overage cost. A complete search algorithm and a greedy search algorithm are designed to search for optimal booking limits for all examination sets. To estimate the late cancellation probability for each individual consumer, we apply logistic regression to identify significant factors affecting the probability. After clustering is used to estimate individual probabilities, Monte Carlo simulation is conducted to generate probability distributions for the number of consumers without late cancellations. A discrete-event simulation is performance to evaluate the effectiveness of our proposed solution.

RESULTS

We collaborate with a leading physical examination center to collect real data to evaluate our proposed overbooking policies. We show that the proposed overbooking policy may significantly increase the expected net reward. Our simulation results also help us understand the impact of overbooking on the expected number of customers and expected overage. A sensitivity analysis is conducted to demonstrate that the benefit of overbooking is insensitive to the accuracy of cost estimation. A Pareto efficiency analysis gives practitioners suggestions regarding policy determination considering multiple performance indications.

CONCLUSIONS

Our proposed overbooking policies may greatly enhance the overall performance of a physical examination center.

A SIAT3HE model of the COVID-19 pandemic in Bergamo, Italy

The aim of this article is to give a better understanding of the dynamics of the SARS-CoV-2 pandemic in the Bergamo province (Italy), one of the most hit areas of the world, between February and April 2020. A new compartmental model, called SIAT3HE, was designed and fitted on accurate data about the pandemic provided by ATS Bergamo, the health protection agency of the Bergamo province. Our results show that SARS-CoV-2 reached Bergamo in January and infected 318,000 people, the 28.8% of the province population. The 43.1% of the infected individuals stayed asymptomatic. As 6,028 people died due to COVID-19 till April 30th, the infection fatality ratio of SARS-CoV-2 in the Bergamo province was 1.9%. These results are in very good agreement with available information: the number of infections is consistent with the results of recent serological surveys and the number of deaths due to COVID-19 is close to the excess mortality of the considered period.

Evidence-based practice in primary healthcare from the managerial point of view - a national survey

Background

The vast availability of and demand for evidence in modern primary healthcare force clinical decisions to be made based on condensed evidence in the form of policies and guidelines. Primary healthcare managers play a key role in implementing these governing documents. Thus, the aim of this article is to investigate the use and availability of evidence-based practice resources from the perspective of first-line primary healthcare managers.

Methods

The study is based on a national survey of primary healthcare managers, consisting of 186 respondents, recruited nationally from Sweden. The data was analysed using empirically constructed concepts and validated using factor analysis. A chi-square test was utilized to determine the statistical significance of comparisons. Associations between variables were calculated using Spearman’s correlation coefficients. All tests were two-sided, and the significance level was set to 0.05.

Results

A majority (97 %) of managers stated that guidelines and policy documents impacted primary healthcare; 84 % of managers observed a direct effect on daily practices. Most of the managers (70 %) stated that some adaptation was needed when new evidence was introduced. The managers emphasized the importance of keeping themselves updated and open to new information about work routines (96 %).

Conclusions

The study illustrates a nearly unanimous response about the influence of clinical evidence on daily practice. The emphasis on the importance of all staff members keeping their professional knowledge up to date is viewed as a direct result of this effect on daily practice. An information-dense organization such as a primary healthcare organization would have much to gain from increased cooperation with regional information resources such as clinical libraries.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-07023-w.

Multi-level learning in public healthcare medical teams: the role of the social environment

PURPOSE

This study explores how individual, dyad and team levels of learning interact in public healthcare medical teams.

DESIGN/METHODOLOGY/APPROACH

A single interpretive case study is carried out in the public Health Service Executive (HSE) in Ireland, involving three rounds of semi-structured interviews with non-consultant hospital doctors (NCHDs), supported by relevant professional documentation and researcher log entries.

FINDINGS

An experience hierarchy, interpersonal relationships and social dynamics form the backdrop to learning interactions within public healthcare medical teams. Individual and team learning primarily occur in informal settings where interpreting and developing understanding takes place either in dyads, small groups or with the whole team. NCHD learning may vary depending on how effectively they build interpersonal relationships, take advantage of informal learning opportunities and manage the social dynamics within their team. Willingness and confidence to share insights and asking questions are triggers for individual and team learning.

RESEARCH LIMITATIONS/IMPLICATIONS

As a single case study focused on the HSE NCHD individual and team learning experience, this research study represents a relatively small exploration of individual and team learning interplay in the public healthcare medical team environment. The development of learning theory in this domain presents an intriguing avenue of further research, including observation of interactions within a team.

PRACTICAL IMPLICATIONS

The findings have practical relevance to those who are interested in the effectiveness of post-graduate/ NCHD learning in the public healthcare system. Interpersonal relationships and social norms play strong roles in how interaction and learning occurs in a team. These findings highlight the challenge of ensuring consistent quality across individual NCHDs or across hospital sites when training is heavily influenced by the approach of senior colleagues/ consultants to their more junior colleagues and the degree to which they take an active interest in NCHD learning.

ORIGINALITY/VALUE

The proposed learning framework is a key theoretical contribution, which draws upon the multi-levels of learning and provides greater insight into how individual, dyad and team learning interact in public healthcare medical teams when managing patient care. The findings have practical relevance in how to facilitate effective teamwork and learning interactions and for those who are interested in the consistency and quality of the training experience for NCHDs.

Barriers and facilitators in the implementation of an evidence-based health promotion intervention in a primary care setting: a qualitative study

PURPOSE

This study aims to elucidate the health care organization, management and policy barriers and facilitators associated with implementation of an evidence-based health promotion intervention in primary care centers in the Basque Country, Spain.

DESIGN/METHODOLOGY/APPROACH

Seven focus groups were conducted with 49 health professionals from six primary care centers participating in the Prescribing Healthy Life program. Text was analyzed using the Consolidated Framework for Implementation Research (CFIR) focusing on those constructs related to health care organization, management and policy.

FINDINGS

The health promotion intervention was found to be compatible with the values of primary care professionals. However, professionals at all centers reported barriers to implementation related to: (1) external policy and incentives, (2) compatibility with existing workflow and (3) available resources to carry out the program. Specific barriers in these areas related to lack of financial and political support, consultation time constraints and difficulty managing competing day-to-day demands. Other barriers and facilitators were related to the constructs networks and communication, culture, relative priority and leadership engagement. A set of six specific barrier-facilitator pairs emerged.

ORIGINALITY/VALUE

Implementation science and, specifically, the CFIR constructs were used as a guide. Barriers and facilitators related to the implementation of a health promotion program in primary care were identified. Healthcare managers and policy makers can modify these factors to foster a more propitious implementation environment. These factors should be appropriately monitored, both in pre-implementation phases and during the implementation process, in order to ensure effective integration of health promotion into the primary care setting.

COVID-19 prediction models: a systematic literature review

As the world grapples with the problem of the coronavirus disease 2019 (COVID-19) pandemic and its devastating effects, scientific groups are working towards solutions to mitigate the effects of the virus. This paper aimed to collate information on COVID-19 prediction models. A systematic literature review is reported, based on a manual search of 1,196 papers published from January to December 2020. Various databases such as Google Scholar, Web of Science, and Scopus were searched. The search strategy was formulated and refined in terms of subject keywords, geographical purview, and time period according to a predefined protocol. Visualizations were created to present the data trends according to different parameters. The results of this systematic literature review show that the study findings are critically relevant for both healthcare managers and prediction model developers. Healthcare managers can choose the best prediction model output for their organization or process management. Meanwhile, prediction model developers and managers can identify the lacunae in their models and improve their data-driven approaches.

Value configurations for balancing standardization and customization in chronic care: a qualitative study

BACKGROUND

Demands for both customization and standardization are increasing in healthcare. At the same time, resources are scarce, and healthcare managers are urged to improve efficiency. A framework of three value configurations - shop, chain, and network - has been proposed for how healthcare operations can be designed and organized for efficient value creation. In this paper, use of value configurations for balancing of standardization and customization is explored in the context of care for chronic mental conditions.

METHODS

A typical case is presented to illustrate the manifestations of conflicting demands between customization and standardization, and the potential usefulness of the value configurations framework. Qualitative data were collected from managers and care developers in two focus groups and six semi-structured interviews, completed by a national document describing a care pathway. Data were coded and analysed using an insider-outsider approach.

RESULTS

Operationalization of the balance between standardization and customization were found to be highly delegated and ad hoc. Also, the conflict between the two demands was often seen as aggravated by scarce resources. Value configurations can be fruitful as a means of discussing and redesigning care operations if applied at a suitable level of abstraction. Applied adequately, all three value configurations were recognized in the care operations for the patient group, with shop as the overarching configuration. Some opportunities for improved efficiency were identified, yet all configurations were seen as vital in the chronic care process.

CONCLUSIONS

The study challenges the earlier proposed organizational separation of care corresponding to different value configurations. Instead, as dual demand for customization and standardization permeates healthcare, parallel but explicated value configurations may be a path to improved quality and efficiency. Combined and intermediate configurations should also be further investigated.

Lacking Advanced Degrees are Not a Barrier to Entry into Academic Surgery Leadership

BACKGROUND

Undergraduate and graduate medical education offerings continue to create opportunities for medical students to pursue MD+ degree education. These educational endeavors provide formal education in fields related to surgery, which gives trainees and surgeons diverse perspectives on surgical care. This study sought to assess current prevalence of additional advanced degrees among leaders in academic surgery to assess the relationship between dual degree attainment and holding various leadership positions within surgical departments.

METHODS

The Association for Program Directors in Surgery database was used to identify academic surgical programs, which comprised our study population. Each department of surgery website in the APDS database was interrogated for departmental leaders and their reported academic degrees.

RESULTS

Among 3223 identified surgeon leaders, 14.6% (470/3223) were found to possess MD+ degrees. Most common degrees possessed included MBA, MPH, and PhD. In comparing different types of surgeon leaders such as chairs, program directors, and division chiefs, no group was found to have a significantly higher prevalence of MD+ degrees than others.

CONCLUSION

Prevalence of MD+ degrees among current academic surgery leaders is low, and the lack of an advanced degree should not be considered a barrier to entry into leadership positions. We hypothesize that these findings are likely to evolve as larger proportions of trainees obtain MD+ degrees during medical school and academic development time throughout residency.

Factors associated with diagnosis delay in head and neck cancer

INTRODUCTION AND OBJECTIVES

Tumour stage is an important prognostic factor in head and neck tumours. Many tumours are diagnosed in advanced stages despite almost universal healthcare and their being symptomatic. This paper seeks to determine the diagnostic delay in head and neck tumours in our health department, to analyse factors associated with delay and if it is associated with diagnosis in advanced stages.

METHODS

Retrospective study of 137 patients with head and neck cancer diagnosed from 2016-2018. Patient delay, delay in primary health care, delay in secondary health care, diagnostic delay and possible associated factors (smoking, location, stage, …) were evaluated.

RESULTS

Many patients (44.5%) were diagnosed in advanced stages. The median patient delay was 30 days. The median referral to otorhinolaryngology was 3.5 days. If the referral was made by another specialist (p=.008), the patients were under previous treatment (P=.000) and the tumours were in initial stages (P=.038) this delay was greater. The median from the first visit to otorhinolaryngology was 15 days, higher in regular referrals (43%) (P=.000). The median diagnostic delay was 12 days, higher in surgical biopsies (P=.000). The median professional delay was 58.5 days and total delay was 118.5 days.

CONCLUSIONS

Many head and neck tumours are diagnosed in advanced stages. A relationship was not found between diagnosis in advanced stages and diagnostic delay. However, steps must be taken to reduce these excessive delays.

Psychoanalysis: neglected ally in an emerging "critical healthcare management studies" (CHMS)?

PURPOSE

The purpose of this paper is to demonstrate the relevance of psychoanalysis to an emerging sub-field known as "critical healthcare management studies" (CHMS).

DESIGN/METHODOLOGY/APPROACH

Building upon a wave of critical scholarship in the broader field of management, scholars and practitioners of healthcare management have begun to forge a critical scholarly movement of their own. CHMS, short for "critical healthcare management studies," formally denotes a new subfield of inquiry dedicated to challenging entrenched assumptions, exposing power relations, and cultivating critical praxis, all the while serving as a vital counterpoint to mainstream scholarship. This paper seeks to augment the CHMS movement with psychoanalysis, and particularly the critical vein of organizational psychoanalysis already well-established in critical management studies.

FINDINGS

The argument is made that a greater engagement with psychoanalysis offers novel avenues for critical theorizing and practice in healthcare management. Specifically three areas are considered: 1) the exploitative role of guilt in the caring professions, 2) the resurgence of authoritarianism and its implications for unconscious organizational dynamics, and 3) the potential for a psychoanalytically informed critical healthcare praxis.

ORIGINALITY/VALUE

While there remain wide differences of opinion about the utility of psychoanalysis outside of the clinical arena, this paper reveals just how psychoanalysis can inform today's healthcare organizations, and more broadly the social and political organization of health in society.

Managerial features and outcome in neonatal intensive care units: results from a cluster analysis

Background

Healthcare organisations differ in performance even if they are located in the same country or region. Suitable managerial practices and organisational processes can lead to better health outcomes. As a result, hospitals are constantly looking for managerial arrangements that can improve outcomes and keep costs down. This study aims to identify different managerial models in neonatal intensive care units (NICUs) and their impact on a large number of outcomes.

Methods

The research was conducted in Italy, within the SONAR project. SONAR’s aim was to identify the characteristics of NICUs, monitor outcomes and promote best practices. This study includes 51 of the 63 NICUs that took part in the SONAR project. Questionnaires on the activities and managerial features were administered to doctors and nurses working in NICUs. A total of 643 questionnaires were analysed from doctors and a total of 1601 from nurses. A cluster analysis was performed to identify managerial models of NICUs.

Results

Three managerial models emerged from cluster analysis: traditional, collaborative and individualistic. In the “traditional” model the doctor is above the nurse in the hierarchy, and the nurse therefore has exclusively operational autonomy. The “collaborative” model has as key elements professional specialisation and functional coordination. The “individualistic” model considers only individual professional skills and does not concern the organisational conditions necessary to generate organisational effectiveness.

The results also showed that there is an association between managerial model and neonatal outcomes. The collaborative model shows best results in almost all outcomes considered, and the traditional model has the worst. The individualistic model is in the middle, although its values are very close to those of traditional model.

Conclusions

Health management needs to assess NICU strategically in order to develop models to improve outcomes. This study provides insights for management useful for designing managerial characteristics of NICUs in order to achieve better results. NICUs characterised by a collaborative model in fact show better neonatal outcomes.

Methodologically grounded semantic analysis of large volume of chilean medical literature data applied to the analysis of medical research funding efficiency in Chile

Background

Medical knowledge is accumulated in scientific research papers along time. In order to exploit this knowledge by automated systems, there is a growing interest in developing text mining methodologies to extract, structure, and analyze in the shortest time possible the knowledge encoded in the large volume of medical literature. In this paper, we use the Latent Dirichlet Allocation approach to analyze the correlation between funding efforts and actually published research results in order to provide the policy makers with a systematic and rigorous tool to assess the efficiency of funding programs in the medical area.

Results

We have tested our methodology in the Revista Médica de Chile, years 2012-2015. 50 relevant semantic topics were identified within 643 medical scientific research papers. Relationships between the identified semantic topics were uncovered using visualization methods. We have also been able to analyze the funding patterns of scientific research underlying these publications. We found that only 29% of the publications declare funding sources, and we identified five topic clusters that concentrate 86% of the declared funds.

Conclusions

Our methodology allows analyzing and interpreting the current state of medical research at a national level. The funding source analysis may be useful at the policy making level in order to assess the impact of actual funding policies, and to design new policies.

Effects of health insurance on patient demand for physician services

BACKGROUND

In recent years, policymakers have sought to reduce health disparities between the insured and uninsured through a federal health insurance expansion policy; however, disparities continue to persist among the insured population. One potential explanation is that the use of healthcare services varies by the type of health insurance coverage due to differences in the design of coverage. The aim of this study is to examine whether health insurance coverage type is associated with the structure and use of healthcare services.

METHODS

The nationally representative Medical Expenditure Panel Survey and multinomial logistic regression are used to estimate the effects of different types of health coverage on the combinations of routine and emergency care sought and received.

RESULTS

The multinomial logistic regression analysis for the overall sample revealed privately insured respondents reported higher use of routine care only (24.33%; p < 0.001) and lower use of emergency room care only (- 2.13%; p < 0.01) than the uninsured. The publicly insured reported similar trends for use of routine care only (17.93%; p < 0.001) as the privately insured, as compared to the uninsured. Both the privately and publicly insured reported higher use of a mixture of care; however, publicly insured were more likely to use a mixture of care (8.57%, p < 0.001).

CONCLUSION

The results show that health insurance is associated with higher use of the physician services, but does not promote the use of cost-effective schedules of care among the publicly insured.

A snapshot of European neurosurgery December 2019 vs. March 2020: just before and during the Covid-19 pandemic

Background

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 or Covid-19), which began as an epidemic in China and spread globally as a pandemic, has necessitated resource management to meet emergency needs of Covid-19 patients and other emergent cases. We have conducted a survey to analyze caseload and measures to adapt indications for a perception of crisis.

Methods

We constructed a questionnaire to survey a snapshot of neurosurgical activity, resources, and indications during 1 week with usual activity in December 2019 and 1 week during SARS-CoV-2 pandemic in March 2020. The questionnaire was sent to 34 neurosurgical departments in Europe; 25 departments returned responses within 5 days.

Results

We found unexpectedly large differences in resources and indications already before the pandemic. Differences were also large in how much practice and resources changed during the pandemic. Neurosurgical beds and neuro-intensive care beds were significantly decreased from December 2019 to March 2020. The utilization of resources decreased via less demand for care of brain injuries and subarachnoid hemorrhage, postponing surgery and changed surgical indications as a method of rationing resources. Twenty departments (80%) reduced activity extensively, and the same proportion stated that they were no longer able to provide care according to legitimate medical needs.

Conclusion

Neurosurgical centers responded swiftly and effectively to a sudden decrease of neurosurgical capacity due to relocation of resources to pandemic care. The pandemic led to rationing of neurosurgical care in 80% of responding centers. We saw a relation between resources before the pandemic and ability to uphold neurosurgical services. The observation of extensive differences of available beds provided an opportunity to show how resources that had been restricted already under normal conditions translated to rationing of care that may not be acceptable to the public of seemingly affluent European countries.

Electronic supplementary material

The online version of this article (10.1007/s00701-020-04482-8) contains supplementary material, which is available to authorized users.

Not there yet: using data-driven methods to predict who becomes costly among low-cost patients with type 2 diabetes

BACKGROUND

Diabetes is a leading cause of Medicare spending; predicting which individuals are likely to be costly is essential for targeting interventions. Current approaches generally focus on composite measures, short time-horizons, or patients who are already high utilizers, whose costs may be harder to modify. Thus, we used data-driven methods to classify unique clusters in Medicare claims who were initially low utilizers by their diabetes spending patterns in subsequent years and used machine learning to predict these patterns.

METHODS

We identified beneficiaries with type 2 diabetes whose spending was in the bottom 90% of diabetes care spending in a one-year baseline period in Medicare fee-for-service data. We used group-based trajectory modeling to classify unique clusters of patients by diabetes-related spending patterns over a two-year follow-up. Prediction models were estimated with generalized boosted regression, a machine learning method, using sets of all baseline predictors, diabetes predictors, and predictors that are potentially-modifiable through interventions. Each model was evaluated through C-statistics and 5-fold cross-validation.

RESULTS

Among 33,789 beneficiaries (baseline median diabetes spending: $4153), we identified 5 distinct spending patterns that could largely be predicted; of these, 68.1% of patients had consistent spending, 25.3% had spending that rose quickly, and 6.6% of patients had spending that rose progressively. The ability to predict these groups was moderate (validated C-statistics: 0.63 to 0.87). The most influential factors for those with progressively rising spending were age, generosity of coverage, prior spending, and medication adherence.

CONCLUSIONS

Patients with type 2 diabetes who were initially low spenders exhibit distinct subsequent long-term patterns of diabetes spending; membership in these patterns can be largely predicted with data-driven methods. These findings as well as applications of the overall approach could potentially inform the design and timing of diabetes or cost-containment interventions, such as medication adherence or interventions that enhance access to care, among patients with type 2 diabetes.

Evolution of Haemophilia Care in Europe: 10 years of the principles of care

INTRODUCTION

The European principles of care in haemophilia marked their first decade in 2018. These guiding principles were the beginning of the European Haemophilia Consortium (EHC) review of countries' adherence to these principles in 2009, 2012, 2015 and 2018. The aim of this paper was to examine the implementation of the principles and how they have impacted the evolution of care in the last decade, as well as to identify remaining gaps and proposes future directions.

METHODS

In 2018, the EHC distributed a survey to EHC national member organisations in English and Russian and encouraged them to discuss responses with local clinicians for accuracy. Data was also cross-referenced and validated for countries in earlier surveys using additional available resources.

RESULTS

The 10-year-old European principles had a significant impact on the development of care for haemophilia and related bleeding disorders in Europe. They set objectives around which multi-stakeholder groups have established recommendations and specific steps for the progressive improvement of care for bleeding disorders. However, some have been promoted and implemented more than others.

CONCLUSION

Monitoring adherence to, and impact of, the European Principles of Care significantly assists in tracking developments and highlighting gaps. Countries' inability to report consistent and coherent data remains a challenge and hinders both provision of treatment and care for patients as well as optimal national and European healthcare systems.

Insights for the future of health system partnerships in low- and middle-income countries: a systematic literature review

BACKGROUND

Despite growing support for the private sector involvement in the provision of public health services in Low- and Middle-Income Countries (LMICs), a lack of clear information on the future of the provision of such services restricts the ability of managers and policy-makers to assess how feasible integration between public and private actors may be in these countries. This paper presents a systematic literature review which traces the dynamics and boundaries of public-private partnerships for the healthcare sector in LMICs.

METHODS

A total of 723 articles indexed in Scopus were initially submitted to bibliometric analysis. Finally, 148 articles published in several academic journals were selected for independent full-text review by two researchers. Content analysis was made in order to minimise mistakes in interpreting the findings of studies in the sample.

RESULTS

Public-private partnerships identified through the content analysis were categorised into four research areas: 1) Transfer of resources; 2) Co-production of health goods and services; 3) Governance networks; 4) Criteria for successful partnership development.

CONCLUSIONS

The four main research areas supply suggestions for a future research agenda, and managerial and policy implications for partnerships in LMICs.

Valorization of clinical trials from the Italian National Health Service perspective: definition and first application of a model to estimate avoided costs

Introduction

From the perspective of healthcare organizations and public health care systems, the value of a clinical trial can be assessed from a clinical and economical perspective. However, to date, there is no standardized model for systematically capturing the economic value of clinical trials at organizational and system levels. The aim of this study was to develop and test a methodology for estimating the avoided costs deriving from the management of patients as part of a clinical trial.

Methods

Our methodology is based on the assumption that the economic value of a clinical trial derives from 1) the funding received by the experimental site from a trial's sponsor, and from 2) the cost avoided by the experimental site with the treatment of patients within a study and not according to standard care by the experimental site.

Results

By applying the methodology to onco-hematological clinical trials conducted in two academic hospitals from 2011 to 2016, we demonstrate that savings between 2 million and 4 million euros were achieved over a five-year period. Thus, for every 1,000 euros invested by the pharmaceutical company into the clinical studies conducted at these hospitals, the hospitals saved on average 2,200 euros due to costs not incurred as a result of the trials.

Conclusions

The study has proposed and tested a methodology for estimating the economic value of clinical trials by taking into account avoided costs deriving from the treatment of patients enrolled in sponsored trials. The study has proposed a management tool for healthcare institutions to govern clinical trials.

Will neurological care change over the next 5 years due to the COVID-19 pandemic? Key informant consensus survey

INTRODUCTION

The COVID-19 pandemic will give rise to long-term changes in neurological care, which are not easily predictable.

MATERIAL AND METHODS

A key informant survey was used to enquire about the changes expected in the specialty over the next 5 years. The survey was completed by heads of neurology departments with broad knowledge of the situation, having been active during the pandemic.

RESULTS

Despite a low level of consensus between participants, there was strong (85%) and moderate consensus (70%) about certain subjects, mainly the increase in precautions to be taken, the use of telemedicine and teleconsultations, the reduction of care provided in in-person consultations to avoid the presence of large numbers of people in waiting rooms, the development of remote training solutions, and the changes in monitoring visits during clinical trials. There was consensus that there would be no changes to the indication of complementary testing or neurological examination.

CONCLUSION

The key informant survey identified the foreseeable changes in neurological care after the pandemic.

Management of neurological care during the COVID-19 pandemic

INTRODUCTION

The COVID-19 epidemic has led to the need for unprecedented decisions to be made to maintain the provision of neurological care. This article addresses operational decision-making during the epidemic.

DEVELOPMENT

We report the measures taken, including the preparation of a functional reorganisation plan, strategies for hospitalisation and emergency management, the use of telephone consultations to maintain neurological care, provision of care at a unit outside the hospital for priority patients, decisions about complementary testing and periodic in-hospital treatments, and the use of a specific telephone service to prioritise patients with epileptic seizures.

CONCLUSION

Despite the situation of confinement, neurology departments must continue to provide patient care through different means of operation. Like all elements of management, these must be evaluated.

Improving Emergency Department Efficiency by Patient Scheduling Using Deep Reinforcement Learning

Emergency departments (ED) in hospitals usually suffer from crowdedness and long waiting times for treatment. The complexity of the patient’s path flows and their controls come from the patient’s diverse acute level, personalized treatment process, and interconnected medical staff and resources. One of the factors, which has been controlled, is the dynamic situation change such as the patient’s composition and resources’ availability. The patient’s scheduling is thus complicated in consideration of various factors to achieve ED efficiency. To address this issue, a deep reinforcement learning (RL) is designed and applied in an ED patients’ scheduling process. Before applying the deep RL, the mathematical model and the Markov decision process (MDP) for the ED is presented and formulated. Then, the algorithm of the RL based on deep Q-networks (DQN) is designed to determine the optimal policy for scheduling patients. To evaluate the performance of the deep RL, it is compared with the dispatching rules presented in the study. The deep RL is shown to outperform the dispatching rules in terms of minimizing the weighted waiting time of the patients and the penalty of emergent patients in the suggested scenarios. This study demonstrates the successful implementation of the deep RL for ED applications, particularly in assisting decision-makers under the dynamic environment of an ED.

Top ten: A model of dominating factors influencing job satisfaction of emergency nurses

BACKGROUND

Increasing emergency department (ED) visits per capita, combined with an aging population and ongoing budgetary constraints in Canada's health care system necessitate continuous improvements to ensure that patients remain safe and continue to receive high quality care. Addressing ED nursing job satisfaction is a cost-effective way of improving safety and quality of hospital care.

METHODS

A scoping literature review was conducted to examine the breadth of job satisfaction literature and identify the factors that specifically influence the job satisfaction of ED nurses.

RESULTS AND DISCUSSION

A review of 161 journal articles revealed 34 articles fitting the inclusion criteria, which were included in the final analysis. There were ten predominant factors that allowed for workplace interventions, including six that did not achieve consensus. The ten factors are presented in the form of a practical model for implementation based on the Herzberg two-factor theory and the Maslow hierarchy of needs theory.

CONCLUSION

The model informs healthcare leaders how to pragmatically understand job satisfaction specific to ED nurses. This information in turn can be used to design interventions that increase job satisfaction while maintaining safety and quality of care.

Examining health care providers' and middle-level managers' readiness for change: a qualitative study

Background

Readiness is a critical precursor of successful change; it denotes whether those involved in the change are motivated and empowered to participate in the change. Research on readiness tends to focus on frontline providers or individuals in non-managerial positions and offers limited attention to individuals in middle management positions who are expected to lead frontline providers in change implementation. Yet middle-level managers are also recipients of changes that are planned and decreed by those in higher positions. This study sought to examine both frontline provider and middle manager readiness for change in the context of primary care program integration.

Methods

Using a qualitative case study approach, we examined how frontline providers and middle managers experienced six readiness factors: discrepancy, appropriateness, valence, efficacy, fairness and trust in management. Data were collected through documents, meeting observation and semi-structured interviews with frontline providers and middle managers involved in the change.

Results

The findings highlighted similarities and differences in readiness experiences of frontline providers and middle managers. Across both types of participants, we found that the notion of valence should be expanded to consider individuals’ evaluation of benefits to patients and the health system; efficacy applies to both content and process of change; fairness and trust in management findings require further exploration to determine their appropriateness to be incorporated in models of readiness for change; and trust in management (or lack of trust) has a cascading influence across the levels in the organization.

Conclusions

Our study makes a contribution by nuancing and extending conceptualizations of individual readiness factors, and by highlighting the central role of middle manager readiness for change. Implications of the study include the need to consider readiness factors prior to the implementation of change and the importance of fostering readiness throughout all levels of the organization.