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The aim of this paper is to empirically describe and analyze factors deemed to be relevant for the successful provision of coordinated paediatric oncology care by physicians and nurses involved.

A qualitative case study primarily consisting of interviews.

The paper's findings indicate that certain factors (i.e. distinct mission, clear treatment protocols and support from external stakeholders) relevant for the provision of coordinated paediatric oncology care have not received sufficient attention in previous research. In addition, emphasis is placed on the necessity of facilitating constructive working relationships and a bottom-up perspective when pursuing improved care coordination.

The factors described and analyzed may act as insights for how paediatric oncology might be improved in terms of care coordination and thus facilitate care integration. In addition, the paper's findings identify factors relevant for further empirical studies in order to delineate their generalizability.

Given that the workforce constitutes a principal resource of primary care, appraisal of models of care requires thorough investigation of the health workforce in all Models of Child Health Appraised (MOCHA) countries. This chapter explores this in terms of workforce composition, remuneration, qualifications and training in relation to the needs of children and young people. We have focused on two principal disciplines of primary care; medicine and nursing, with a specific focus on training and skills to care for children in primary care, particularly those with complex care needs, adolescents and vulnerable groups. We found significant disparities in workforce provision and remuneration, in training curricula and in resultant skills of physicians and nurses in European Union and European Economic Area Countries. A lack of overarching standards and recognition of some of the specific needs of children reflected in training of physicians and nurses may lead to suboptimal care for children. There are, of course, many other professions that also contribute to primary care services for children, some of which are discussed in Chapter 15, but we have not had resources to study these to the same detail.

The 30 MOCHA (Models of Child Health Appraised) countries are diverse socially, culturally and economically, and differences exist in their healthcare systems and in the scope and role of primary care. An economic analysis was undertaken that sought to explain differences in child health outcomes between countries. The conceptual framework was that of a production function for health, whereby health outputs (or outcomes) are assumed affected by several ‘inputs’. In the case of health, inputs include personal (genes, health behaviours) and socio-economic (income, living standards) factors and the structure, organisation and workforce of the healthcare system. Random effects regression modelling was used, based on countries as the unit of analysis, with data from 2004 to 2016 from international sources and published categorisations of healthcare system. The chapter describes the data deficiencies and measurement conundrums faced, and how these were addressed. In the absence of consistent indicators of child health outcomes across countries, five mortality measures were used: neonatal, infant, under five years, diabetes (0–19 years) and epilepsy (0–19 years). Factors found associated with reductions in mortality were as follows: gross domestic product per capita growth (neonatal, infant, under five years), higher density of paediatricians (neonatal, infant, under five years), less out-of-pocket expenditure (neonatal, diabetes 0–19), state-based service provision (epilepsy 0–19) and lower proportions of children in the population, a proxy for family size (all outcomes). Findings should be interpreted with caution due to the ecological nature of the analysis and the limitations presented by the data and measures employed.

Diffusion of innovations, defined as the adoption and implementation of new ideas, processes, products, or services in health care, is both particularly important and especially challenging. One known problem with adoption and implementation of new technologies is that, while organizations often make innovations immediately available, organizational actors are more wary about adopting new technologies because these may impact not only patients and practices but also reimbursement. As a result, innovations may remain underutilized, and organizations may miss opportunities to improve and advance. As innovation adoption is vital to achieving success and remaining competitive, it is important to measure and understand factors that impact innovation diffusion. Building on a survey of a national sample of 654 clinicians, our study measures the extent of diffusion of value-enhancing care delivery innovations (i.e., technologies that not only improve quality of care but has potential to reduce care cost by diminishing waste, Faems et al., 2010) for 13 clinical specialties and identifies healthcare-specific individual characteristics such as: professional purview, supervisory responsibility, financial incentive, and clinical tenure associated with innovation diffusion. We also examine the association of innovation diffusion with perceived value of one type of care delivery innovation – artificial intelligence (AI) – for assisting clinicians in their clinical work. Responses indicate that less than two-thirds of clinicians were knowledgeable about and aware of relevant value-enhancing care delivery innovations. Clinicians with broader professional purview, more supervisory responsibility, and stronger financial incentives had higher innovation diffusion scores, indicating greater knowledge and awareness of value-enhancing, care delivery innovations. Higher levels of knowledge of the innovations and awareness of their implementation were associated with higher perceptions of the value of AI-based technology. Our study contributes to our knowledge of diffusion of innovation in healthcare delivery and highlights potential mechanisms for speeding innovation diffusion.

The study aims to summarise the literature on cancer care pathways at the diagnostic and treatment phases. The objectives are to find factors influencing the delivery of cancer care pathways; to highlight any interrelating factors; to find gaps in the literature concerning areas of research; to summarise the strategies and recommendations implemented in the studies.

The study used a qualitative approach and developed a causal loop diagram to summarise the current literature on cancer care pathways, from screening and diagnosis to treatment. A total of 46 papers was finally included in the analysis, which highlights the recurring themes in the literature.

The study highlights the myriad areas of research applied to cancer care pathways. Factors influencing the delivery of cancer care pathways were classified into different albeit interrelated themes. These include access barriers to care, hospital emergency admissions, fast track diagnostics, delay in diagnosis, waiting time to treatment and strategies to increase system efficiency.

As far as the authors know, this is the first study to present a visual representation of the complex relationship between factors influencing the delivery of cancer care pathways.

Art therapy has been recognized as beneficial and effective since first described by Adrian Hill in 1942. Even before this time, art therapy was utilized for moral reinforcement and psychoanalysis. Art therapy aids patients with, but not limited to, chronic illness, physical challenges, and cancer in both pediatric and adult scenarios. Although effective in patient care, the practice of art therapy is extremely underutilized, especially in suburban areas. While conducting our own study in northeastern Ohio, USA, we found that only one out of the five inpatient institutions in the suburban area of Mahoning County, Ohio, that we contacted provided continuous art therapy to it's patients. In the metropolitan area of Cuyahoga County, Ohio, only eight of the twenty-two inpatient institutions in the area provided art therapy. There could be many reasons as to why art therapy is not frequently used in these areas, and medical institutions in general. The cause of this could be the amount of research done on the practice. Although difficult to conduct formal research on such a broad field, the American Art Therapy Association has succeeded in doing such, with studies showing improvement of the patient groups emotionally and mentally in many case types.

The purpose of this study is to identify best practices for innovative primary care models and to describe a potential future primary care (PC) model for Texas to address the burden of chronic disease in a population-based approach.

A systematic literature review was conducted and identified 1,880 published records through PubMed using 26 search terms. After abstract and full-text review, 70 articles remained as potential models.

Although there is already a severe shortage of physicians in Texas, emerging practice patterns and choices among physicians are likely to erode access to primary care services in the state. Health-care leaders are encouraged to consider models such as complex adaptive systems for team-based care, pharmacist hypertension care management program and combined nurse-led care management with group visit structure.

As with any study, this research has its limitations; for example, models that might work in one state, or under a unique state-funded academic medical center, might not be “do-able” in another state within the nuances of a different funding mechanism.

Results of this research provide a model for implementing IPCM for the state of Texas first and will guide IPCM planning and implementation in other states.

This study is “land grant-centric” and focused on carrying out the mission of a major, top-tier research university with an emerging college of medicine at an academic medical center.

Personalized medicine (PM) encompasses a set of procedures, technologies and medications; the term became more prominent from the 2000s onwards and stems from the mapping of the human genome. The purposes of this study were to analyse the development stage of the process of technological innovation for PM and the obstacles that prevent PM from being adopted in the public health system in Brazil.

As a research method, this paper opts for a case study carried out at the Hospital das Clínicas, which belongs to São Paulo Medical School. In total, 22 in-depth interviews were carried out at the hospital to identify current practices in PM, future prospects and barriers imposed to the adoption of PM technologies in public health.

Personalized or precision medicine is already a reality for a small portion of the Brazilian population and is gradually gaining ground in public health care. One finding is that such changes are occurring in a disjointed manner in an incomplete and under development health innovation system. The analysis pointed out that the obstacles identified in Brazil are the same as those faced by high-income countries such as regulation, lack of clinical studies and need to adapt clinical studies to PM. They appear in all stages of the innovation cycle, from research to widespread use.

The research method was a case study, so the findings cannot be extrapolated to other contexts. A limited number of professionals were interviewed, their opinions may not reflect those of their organizations.

There are several studies that discuss how health-care systems in high-income countries could incorporate these new technologies, but only a few focuses on low or middle-income countries such as Brazil.

Mumbai needs to be transformed into a world-class city as stated in the 2005–2025 development plan of Municipal Corporation. For this initiative, hospital management information system (HMIS) has to be implemented across 400+ health facilities in the city.

A case study methodology was adopted to study HMIS implementation. Wave 1 of Phase 1 implementation of HMIS is carried out as a pilot project at Film City’s Hospital, Mumbai, which “go-live” on 21st June 2018. The work for hardware and software implementation was awarded to HardSystems and Solutions Limited and SoftSolutions India Private Limited, respectively, through e-tender.

Provision of inadequate quantity of hardware, slowness of network or system, non-satisfactory training after observation confirmation and sign-off process, lack of data entry operators, mismatch in numbering systems in blood bank and many other challenges concerned with the specific departments had become a major impediment in the efforts to maximize number of patients registered into HMIS.

Even after providing many clinical and managerial benefits, being the first cloud-based centrally located HMIS in any of the hospitals in the city, it imposes a major challenge for the management in terms of resistance of employees toward technology and need for the adoption of theoretical models for implementing change for the overall organizational development.

To the best of the authors’ knowledge, no other teaching case study is conducted to study the HMIS implementation in large-scale public health-care services. This is a dummy case study for teaching exercises. The identity of the stakeholders, organizations and events has been masked to maintain confidentiality.

The authors developed and validated an algorithm using health administrative data to identify patients who are attached or uncertainly attached to a primary care provider (PCP) using patient responses to a survey conducted in Ontario, Canada.

The authors conducted a validation study using as a reference standard respondents to a community-based survey who indicated they did or did not have a PCP. The authors developed and tested health administrative algorithms against this reference standard. The authors calculated the sensitivity, specificity positive predictive value (PPV) and negative predictive value (NPV) on the final patient attachment algorithm. The authors then applied the attachment algorithm to the 2017 Ontario population.

The patient attachment algorithm had an excellent sensitivity (90.5%) and PPV (96.8%), though modest specificity (46.1%) and a low NPV (21.3%). This means that the algorithm assigned survey respondents as being attached to a PCP and when in fact they said they had a PCP, yet a significant proportion of those found to be uncertainly attached had indicated they did have a PCP. In 2017, most people in Ontario, Canada (85.4%) were attached to a PCP but 14.6% were uncertainly attached.

Administrative data for nurse practitioner's encounters and other interprofessional care providers are not currently available. The authors also cannot separately identify primary care visits conducted in walk in clinics using our health administrative data. Finally, the definition of hospital-based healthcare use did not include outpatient specialty care.

Uncertain attachment to a primary health care provider is a recurrent problem that results in inequitable access in health services delivery. Providing annual reports on uncertainly attached patients can help evaluate primary care system changes developed to improve access. This algorithm can be used by health care planners and policy makers to examine the geographic variability and time trends of the uncertainly attached population to inform the development of programs to improve primary care access.

As primary care is an essential component of a person's medical home, identifying regions or high need populations that have higher levels of uncertainly attached patients will help target programs to support their primary care access and needs. Furthermore, this approach will be useful in future research to determine the health impacts of uncertain attachment to primary care, especially in view of a growing body of the literature highlighting the importance of primary care continuity.

This patient attachment algorithm is the first to use existing health administrative data validated with responses from a patient survey. Using patient surveys alone to assess attachment levels is expensive and time consuming to complete. They can also be subject to poor response rates and recall bias. Utilizing existing health administrative data provides more accurate, timely estimates of patient attachment for everyone in the population.