Area Of Health Care Research Research Articles

‚Interlocking practice, science and teaching‘

The Willy Robert Pitzer Institute for Health Services Research and Rehabilitation was founded in 2022 with the establishment of the endowed professorship for Health Services Research and Rehabilitation at the Faculty of Health at the Technical University of Central Hesse (THM) in Giessen. The director of the institute is Prof Dr Susanne Hanefeld. In addition to filling the endowed professorship, she is also responsible for the Master‘s degree programme in Public Health, which focuses on the areas of healthcare research and rehabilitation. The founding father of the non-profit foundation is Willy Robert Pitzer, a pioneer in the German rehabilitation sector. The Willy Robert Pitzer Foundation, which is one of the largest foundations in the state of Hesse, has a strong scientific and medical focus.

Evaluating Google trends as a primary care research tool.

Google searches represent a snapshot of the world's interest in different topics and are reflected by Google Trends (GT). GT has gained recent popularity across various areas of healthcare research of interest to GPs, such as the seasonality of particular physical symptoms and population-level suicide risk. As a real-time data source, GT is appealing to healthcare researchers, but whether it has a place in primary care research remains largely unaddressed. To explore how GT can be used to research topics relating to primary care in order to evaluate its utility as a future research tool and inform primary care researchers and GPs on the uses and implications of GT as a research tool. Collect relative search volume data from GT on topics relating to health behaviours and access to/experiences of primary care in order to evaluate the utility of GT as a tool for future primary care research. GT was useful for identifying patterns in health behaviours across time but did not offer any useful information on access to/experiences of primary care. Conducting numerous searches on GT identified various limitations, including internet bias and news bias. GT is an accessible and vast data source. This study demonstrated the potential for GT to play a role in researching health behaviours. However, this study also revealed key limitations of GT, and caution should be exercised by primary care researchers and GPs when using GT for research.

Help me to come out gracefully! Working with lesbian, gay, and bisexual, people affected by cancer to develop a national practitioner guide supporting inclusive care

IntroductionThe healthcare support needs of the lesbian, gay, bisexual (LGB) and transgender community are becoming an emerging area of healthcare research. Providing person-centred care is World Health Organisation policy and as such it is important that Radiography services can demonstrate areas in which they are working with people to design, develop and feedback on the services that they receive. This research aimed to establish how cancer treatment impacted on the identities of LGB people, their experiences of care, and their engagement with developing a practitioner guide. MethodsThis cooperative inquiry is underpinned by person-centred philosophy and participatory research principles. Participants were nine lesbian, gay, and bisexual people affected by cancer. Each engaged in two facilitated, audio-recorded conversations to explore their experiences of cancer care. An analytical framework based on Mezirow's Transformational Theory was used to organise the data, followed by detailed content analysis to develop themes. ResultsParticipants included men and women, aged 45–68, who had experienced different cancers. They explored how cancer treatment had impacted on them, and worked with the researcher and stakeholders to establish a seven-recommendation practitioner guide aimed at improving LGB people's care experiences. Their accounts revealed a broad range of issues that both corroborate and build on existing evidence. Themes highlighted expectations and experiences of both assumptions and prejudice in healthcare interactions. These experiences, along with misinterpretation of relationships with significant others, led to feelings of discomfort and reserve about self-expression. Findings of the research are presented in the following key areas: Dilemmas of attending oncology appointments; Inclusive experiences of care; and Formulation of the practitioner guide. ConclusionThe research findings enabled development of a national practitioner guide with the participants and key stakeholders to raise awareness of the needs of LGB persons affected by cancer and support better care. Implications for practiceBy providing real-life accounts this research adds to understanding of how LGB persons interact with services, developing evidence to support cultural competence within the profession of Radiography and oncology services more broadly.

Development of a core outcome set for quality of life for adults with drug-resistant epilepsy: A multistakeholder Delphi consensus study.

In 2017, the American Academy of Neurology (AAN) convened the AAN Quality Measurement Set working group to define the improvement and maintenance of quality of life (QOL) as a key outcome measure in epilepsy clinical practice. A core outcome set (COS), defined as an accepted, standardized set of outcomes that should be minimally measured and reported in an area of health care research and practice, has not previously been defined for QOL in adult epilepsy. A cross-sectional Delphi consensus study was employed to attain consensus from patients and caregivers on the QOL outcomes that should be minimally measured and reported in epilepsy clinical practice. Candidate items were compiled from QOL scales recommended by the AAN 2017 Quality Measurement Set. Inclusion criteria to participate in the Delphi study were adults with drug-resistant epilepsy diagnosed by a physician, no prior diagnosis of psychogenic nonepileptic seizures or a cognitive and/or developmental disability, or caregivers of patients meeting these criteria. A total of 109 people satisfied inclusion/exclusion criteria and took part in Delphi Round 1 (patients, n= 95, 87.2%; caregivers, n= 14, 12.8%), and 55 people from Round 1 completed Round 2 (patients, n= 43, 78.2%; caregivers, n= 12, 21.8%). One hundred three people took part in the final consensus round. Consensus was attained by patients/caregivers on a set of 36 outcomes that should minimally be included in the QOL COS. Of these, 32 of the 36 outcomes (88.8%) pertained to areas outside of seizure frequency and severity. Using patient-centered Delphi methodology, this study defines the first COS for QOL measurement in clinical practice for adults with drug-resistant epilepsy. This set highlights the diversity of factors beyond seizure frequency and severity that impact QOL in epilepsy.

Constructivist Grounded Theory or Interpretive Phenomenology? Methodological Choices Within Specific Study Contexts

Constructivist research methodologies are useful in discerning meanings of experience to subsequently inform and improve healthcare practice. For researchers who philosophically align with the constructivist paradigm, numerous methodologies are available from which to choose to address research questions. However, it can be challenging for researchers, especially novice ones, to choose the most appropriate methodology that aligns with the current state of knowledge of the identified topic, proposed research question, and the study purpose. To reduce the confusion faced by health researchers when choosing an appropriate methodology for a specific study, this paper compares two popular qualitative health research approaches: constructivist grounded theory and interpretive phenomenology. Philosophical underpinnings and the epistemological and ontological evolution of each methodology are explored with similarities and differences highlighted. Manifestation of the philosophical foundations of constructivist grounded theory and interpretive phenomenology are described in relation to data collection, analysis, and the research findings. To illustrate distinctions of each approach and support researchers in the navigation of methodological decision-making, a specific healthcare study context is presented: the rural family members’ experiences of a relative’s interhospital transfer for advanced critical care services. This study context is increasingly being recognized as an important area of healthcare research and practice. However, gaps in knowledge persist, specifically in relation to the experiences of rural family members when a critically ill relative requires an interhospital transfer to a distant urban center for advanced critical care services. Improved understanding of such experiences is necessary to inform the care provided to rural family members, potentially mitigating short and long-term negative consequences for these individuals. Within this example, the importance of the research purpose and research question within a specific study context is underscored as central to appropriate methodological decision-making.

Potential Bias in Image-Guided Procedure Research: A Retrospective Analysis of Disclosed Conflicts of Interest and Open Payment Records

PurposeTo assess the prevalence of positive conflicts of interest (COI) disclosures in United States–based interventional radiology (IR) research as well as the level of agreement between disclosed financial relationships and Open Payment Data for top-cited image-guided procedure research. Materials and MethodsAll publications in volume 30 (2019) of the Journal of Vascular and Interventional Radiology (JVIR) were reviewed to estimate the prevalence of COI disclosures in IR research. Publications were categorized as primary research, systematic review, or other. The prevalence was subsequently compared across JVIR publication subtypes and categories and on the basis of whether they were device-focused publications using χ2 tests. Additionally, the Web of Science database was searched for the top 10 most cited studies of 10 common image-guided procedures with available U.S. physician payment data. The payments were categorized as historical (>1 year prior to publication) or active (<1 year prior to publication) and compared with the disclosed financial COIs using 1-way analysis of variance. ResultsPositive COI disclosures were present in 114 (29%) of the 397 publications in JVIR volume 30. Positive COI disclosures were most prevalent in standards of practice (50%, P = .01) and more prevalent in device-focused publications (54% vs 23%, P < .01). Among the 396 authors of 100 United States–based top-cited image-guided procedure publications, 383 (97%) failed to disclose at least 1 active financial relationship, with an average of $57,937 in undisclosed payments per publication. ConclusionsCOI are prevalent in IR, similar to other areas of healthcare research, and COI in top-cited image-guided procedure research are often underreported.

A Convolutional Neural Network-Based Triplet-Mining Metric Learning Schema for Measuring Patient Similarity Addressing Personalized Healthcare

Prediction of the development risk of some diseases is an important area of Health Care Research. When exploring the personalized care of the patients, precise identification and classification of similarity in patients from their past report is an important process. Electronically stored health information EHRs that has been sampled unevenly as well as which has variable appointment durations, is considered to be unsuitable for measuring the similarity among patients directly, as there is no proper representation that are fitting. In addition, a technique is required that is efficient to evaluate similarities in patient. We propose two new similarities learning environments using deep learning that learn simultaneously the representations of the patients as well as measurement of similarity in pairs. A Convolutional Neural Network (CNN) is used to understand EHRs that contains crucial information which are local thereby providing scholastic illumination in the triplet loss otherwise entropy loss. When the training is completed, distances are calculated as well as similarities scores. Using this similarity information, disease predictions along with patient grouping is performed. Experimentally the results gives an idea that CNN can represent the EHR sequences in a better way and the schema offered are more efficient than the modern metric distance learning.

THE ANTECEDENTS AND CONSEQUENCES OF E-HEALTH LITERACY IN THE PHARMACEUTICAL INDUSTRY: AN AGENDA FOR FUTURE RESEARCH

As a promising area in healthcare research, electronic health (e-health) has received more research attention recently. The purpose of this paper is to develop and validate a proposed conceptual framework for digital health literacy. This conceptual framework is planned as a guide for future studies to use and validated as a foundation for quantitative studies to investigate the e-Health Literacy as perceived by citizens in Asia amid the outback of the world’s high-risk pandemic crisis such as Coronavirus (Covid19). This conceptual analysis applied Technology Acceptance Model as a basis to develop the antecedents of a healthy lifestyle among the citizens of Asian countries. This conceptual paper proposed that Information quality, system quality, and service quality will affect the citizens’ perceived ease of use and their perceived usefulness, which can affect their intention to use e-health and consequently results in a healthy lifestyle among the citizens. This conceptual paper submitted research hypotheses that will be a basis for future researches in Asia and if the framework is validated, recommendations will be offered to various stakeholders on how to improve a healthy lifestyle in Asia. Specifically, the proposed conceptual framework if validated will help policymakers to offer positive policies and procedures for the improvement of thriving healthcare industries in Asia.

Community-Based Participatory Research and Human-Centered Design Principles to Advance Hearing Health Equity.

Inclusive and equitable research is an ethical imperative. Community-based participatory research (CBPR) as well as human-centered design are approaches that center partnership between community members and academic researchers. Together, academic-community research teams iteratively study community priorities, collaboratively develop ethical study designs, and co-create innovations that are accessible and meaningful to the community partners while advancing science. The foundation of the CBPR approach is reliant on its core principles of equity, colearning, shared power in decision-making, reciprocity, and mutual benefit. While the CBPR approach has been used extensively in public health and other areas of healthcare research, the approach is relatively new to audiology, otolaryngology, and hearing health research. The purpose of the present article is to advance an understanding of the CBPR approach, along with principles from human-centered design, in the context of research aimed to advance equity and access in hearing healthcare. The literature is reviewed to provide an introduction for auditory scientists to the CBPR approach and human-centered design, including discussion of the underlying principles of CBPR and where it fits along a community-engaged continuum, theoretical and evaluation frameworks, as well as applications within auditory research. Recent applications of CBPR have been framed broadly within the theoretical positions of the socioecological model for a systems-level approach to community-engaged research and the Health Services Utilization model within health services and disparities research using CBPR. Utilizing human-centered design strategies can work in tandem with a CBPR approach to engage a wide range of people in the research process and move toward the development of innovative yet feasible solutions. Leveraging the principles of CBPR is an intricate and dynamic process, may not be a fit for some topics, some researchers' skillsets, and may be beyond some projects' resources. When implemented skillfully and authentically, CBPR can be of benefit by elevating and empowering community voices and cultural perspectives historically marginalized in society and underrepresented within research. With a focus on health equity, this review of CBPR in the study of hearing healthcare emphasizes how this approach to research can help to advance inclusion, diversity, and access to innovation.

Logistic Regression Model as Classifier for Early Detection of Gestational Diabetes Mellitus

Gestational diabetes mellitus (GDM) is any degree of glucose intolerance during pregnancy. In view of maternal morbidity and mortality as well as fetal complications, early diagnosis is an utmost necessity one in the present scenario. In a developing country like India, early detection and prevention will be more cost effective. Oral glucose tolerance test (OGTT) is the crucial method for diagnosing GDM done usually between 24th and 28th week of pregnancy. The proposed work focuses on early detection of GDM without a visit to the hospital for women who are pregnant for the second time onwards (multigravida patients). In recent years, prediction models using multivariate logistic regression analysis have been developed in many areas of healthcare research. With an accuracy of 82.45%, the classifier has proved to be an efficient model for diagnosis of GDM without the conventional method of blood test by providing newly designed parameters as inputs to the model.

Logistic regression model as classifier for early detection of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is any degree of glucose intolerance during pregnancy. In view of maternal morbidity and mortality as well as fetal complications, early diagnosis is an utmost necessity one in the present scenario. In a developing country like India, early detection and prevention will be more cost effective. Oral glucose tolerance test (OGTT) is the crucial method for diagnosing GDM done usually between 24th and 28th week of pregnancy. The proposed work focuses on early detection of GDM without a visit to the hospital for women who are pregnant for the second time onwards (multigravida patients). In recent years, prediction models using multivariate logistic regression analysis have been developed in many areas of healthcare research. With an accuracy of 82.45%, the classifier has proved to be an efficient model for diagnosis of GDM without the conventional method of blood test by providing newly designed parameters as inputs to the model.

Diabetes and the direct secondary use of electronic health records: Using routinely collected and stored data to drive research and understanding.

IntroductionElectronic health records provide an unparalleled opportunity for the use of patient data that is routinely collected and stored, in order to drive research and develop an epidemiological understanding of disease. Diabetes, in particular, stands to benefit, being a data-rich, chronic-disease state. This article aims to provide an understanding of the extent to which the healthcare sector is using routinely collected and stored data to inform research and epidemiological understanding of diabetes mellitus.MethodsNarrative literature review of articles, published in both the medical- and engineering-based informatics literature.ResultsThere has been a significant increase in the number of papers published, which utilise electronic health records as a direct data source for diabetes research. These articles consider a diverse range of research questions. Internationally, the secondary use of electronic health records, as a research tool, is most prominent in the USA. The barriers most commonly described in research studies include missing values and misclassification, alongside challenges of establishing the generalisability of results.DiscussionElectronic health record research is an important and expanding area of healthcare research. Much of the research output remains in the form of conference abstracts and proceedings, rather than journal articles. There is enormous opportunity within the United Kingdom to develop these research methodologies, due to national patient identifiers. Such a healthcare context may enable UK researchers to overcome many of the barriers encountered elsewhere and thus to truly unlock the potential of electronic health records.

Participatory Bioethics Research and its Social Impact: The Case of Coercion Reduction in Psychiatry.

In this article we address the social value of bioethics research and show how a participatory approach can achieve social impact for a wide audience of stakeholders, involving them in a process of joint moral learning. Participatory bioethics recognizes that research co-produced with stakeholders is more likely to have impact on healthcare practice. These approaches aim to engage multiple stakeholders and interested partners throughout the whole research process, including the framing of ideas and research questions, so that outcomes are tailored to the interests and context, and the type of impact stakeholders envisage. There is an emphasis on realizing social change through the conduct (not merely the results) of the research, and it is believed that the engagement of stakeholders in the research process will promote their intrinsic motivation to change their practice. Another distinctive feature of participatory bioethics research is that its central normative commitment is to reflection and dialogue, not to a particular substantive ethical approach. In reflection and dialogue there is an emphasis on inclusion and the co-production of knowledge. Furthermore, empirical and normative research are combined, and there is a deliberate attempt to give voice to otherwise marginalized positions. This provides a model of social impact which is relevant not only for bioethics research, but also for other areas of health care research. We will show the merits of a participatory approach to bioethics research with a case example. It concerns the reduction of coercion and in particular seclusion in Dutch mental healthcare.

Comparison of Artificial Neural Network and Logistic Regression Models for Prediction of Psychological Symptom Six Months after Mild Traumatic Brain Injury

Background: Nowadays, outcome prediction models using logistic regression (LR) and artificial neural network (ANN) analysis have been developed in many areas of healthcare research. Objectives: In this study, we have compared the performance of multivariable LR and ANN models, in prediction of psychological symptoms six months after mild traumatic brain injury. Methods: In a prospective cohort study, information of 100 mild traumatic brain injury patients, during a six months period between 2014 and 2016 were included. Data were divided into two training (n = 50) and testing (n = 50) groups, randomly. 300 ANNs and LRs were studied in the first group and then the predicted values were compared in the second group using the two final models. The receiver operating characteristic (ROC) curve and accuracy rate were used to compare these models. Results: The results showed that accuracy rate for the neural network model was 90.65%, while it was 75.96% for the LR model. Conclusions: The ANN models appeared to be more powerful in predicting psychological symptoms versus the LR models.

The Effects of Hospital-Level Factors on Patientsʼ Ratings of Physician Communication

The quality of physician-patient communication influences patient health outcomes and satisfaction with healthcare delivery. Yet, little is known about contextual factors that influence physicians' communication with their patients. The main purpose of this article is to examine organizational-level factors that influence patient perceptions of physician communication in inpatient settings. We used the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey and American Hospital Association data to determine patients' ratings of physician communication at the hospital level, and to collect information about hospital-level factors that can potentially influence physician communication. Our sample consisted of 2,756 hospitals. We ran a regression analysis to determine the predictors of poor physician communication, measured as the percentage of patients in a hospital who reported that physicians sometimes or never communicated well. In our sample of hospitals, this percentage ranged between 0% and 21%, with 25% of hospitals receiving poor ratings from more than 6% of patients. Three organizational factors had statistically significant negative associations with physician communication: for-profit ownership, hospital size, and hospitalists providing care in the hospital, On the other hand, the number of full-time-equivalent physicians and dentists per 10,000 inpatient days, physician ownership of the hospital, Medicare share of inpatient days, and public ownership were positively associated with patients' ratings of physician communication. Physician staffing levels are an understudied area in healthcare research. Our findings indicate that physician staffing levels affect the quality of physician communication with patients. Moreover, for-profit and larger hospitals should invest more in physician communication given the role that HCAHPS plays in value-based purchasing.

Reembursing health-care service provider networks

Health-care service provider networks are regarded as an important instrument to overcome the widely criticised fragmentation and sectoral partition of the German health-care system. The first part of this paper incorporates health-care service provider networks in the field of health-care research. The system theoretical model and basic functions of health-care research are used for this purpose. Furthermore already established areas of health-care research with strong relations to health-care service provider networks are listed. The second part of this paper introduces some innovative options for reimbursing health-care service provider networks which can be regarded as some results of network-oriented health-care research. The origins are virtual budgets currently used in part to reimburse integrated care according to §§ 140a ff. SGB V. Describing and evaluating this model leads to real budgets (capitation) - a reimbursement scheme repeatedly demanded by SVR-Gesundheit (German governmental health-care advisory board), for example, however barely implemented. As a final step a direct reimbursement of networks by the German sickness fund is discussed. Advantages and challenges are shown. The development of the different reimbursement schemes is partially based on models from the USA.

E.4. Round table: Measuring and optimizing the impact of European health care research on policy and practice

With health care systems becoming increasingly focused on effectiveness and efficiency, it is a logical development that the same requirements apply more and more to research production in the health care field. Health care research needs to show that investments are well spent and that it produces value for money, especially so in current times with funding budgets under pressure. But what value, and for whom, is something that is not easily defined. The terms research impact and valorisation are often used, but in different manners by different parties. From the perspective of funding bodies, both at national and European level, there is a growing focus on economic impact, emphasizing the importance of patents, products or spin-off companies. As this is less suitable for the wider domain of health care research, including health services and systems research as well as public health research, a working group of experts from international associations and national research institutes across Europe has been installed with the aim to come to a better definition and measurement of impact for the area of health care research. The workshop will contain a presentation on the results of this working group, among others addressing suitable frameworks to measure impact as well as concrete indicators in order to monitor and compare performance of projects, institutes or research programmes. Two panelists will provide additional perspectives on measuring research impact from their own backgrounds and will also provide feedback on the proposed impact methodology. What elements do they miss, is the framework feasible and suitable for the broad area of health care research. To test this latter element, another crucial means of validation is through the perspective of the audience itself. As the research domain is so wide and multidisciplinary, any proposal for measuring impact should be shared with the wider research community. For that purpose, reflections by the audience are of crucial importance. Do the findings and recommendations of the working group do justice to the particularities of this area of research? If so, then a strategy should be discussed on how to ensure that funding bodies across Europe can make use of this broader perspective of impact measurement. Key messages: The workshop will present a methodology to measure the impact of health care research. Participants can help shape this framework and thus its use by funding bodies at European and national level.

Contributions to research on medical tourism sought

Medical tourism is a relatively neglected yet incredibly important area of healthcare research. It has enormous ramifications for large numbers of people globally.

Artificial neural networks based prediction of cerebral palsy in infants with central coordination disturbance

BackgroundIn a previous study we demonstrated that heart variability parameters (HRV) could be helpful clinically as well as a prognostic tool in infants with central coordination disturbance (CCD). In recent years, outcome predictions using artificial neural networks (ANN) have been developed in many areas of health care research, but there are no published studies considered ANN models for prediction of cerebral palsy (CP) development. ObjectiveTo compare the results of an ANN analysis with results of regression analysis, using the same data set and the same clinical and HRV parameters. MethodsThe study included 35 infants with CCD and 37 healthy age and sex-matched controls. Time-domain HRV indices were analyzed from 24h electrocardiography recordings. Clinical parameters and selected time domain HRV parameters are used to predict CP by logistic regression, and then an ANN analysis was applied to the same data set. Input variables were age, gender, postural responses, heart rate parameters (minimum, maximum and average), and time domain parameters of HRV (SDNN, SDANN and RMSSD). For each of one the pairs of ANN and clinical predictors, the area under the receiver operating characteristic (ROC) curves with test accuracy parameters were calculated and compared. ResultsIn the observed dataset, ANN model overall correctly classified all infants, compared with 86.11% correct classification for the logistic regression model, and compared with 67.65% and 77.14% for SDANN and SDNN respectively. ConclusionsANN model, based on clinical and HRV data can predict development of CP in patients with CCD with accuracy greater than 90%. Our results strongly indicate that a well-validated ANN may have a role in the clinical prediction of CP in infants with CCD.

Versorgungsforschung zur angemessenen Gesundheitsversorgung im Alter

This article addresses the contribution that health care research can make to facilitating appropriate health care provision for older adults. First, the major risks in this age group are described. These include multiple illnesses, the increasing need for nursing care with age, but also the growing numbers of older adults with psychological disorders, primarily dementia. The second section of the article presents a critical assessment of the current health care situation in light of the risks identified. On this basis, the third section specifies the areas of health care research that can contribute to improving the quality of the health care provision for this population. The article is based on a presentation made by the author at the 2010 Berlin Talks on Social Medicine: "The New Old--Health Care Research for a Changed Society."