37,768 research outputs found
Building Data-Driven Pathways From Routinely Collected Hospital Data:A Case Study on Prostate Cancer
Background: Routinely collected data in hospitals is complex, typically heterogeneous, and scattered across multiple Hospital Information Systems (HIS). This big data, created as a byproduct of health care activities, has the potential to provide a better understanding of diseases, unearth hidden patterns, and improve services and cost. The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood. Nevertheless, using routine data for the construction of data-driven clinical pathways, describing processes and trends, is a key topic receiving increasing attention in the literature. Traditional algorithms do not cope well with unstructured processes or data, and do not produce clinically meaningful visualizations. Supporting systems that provide additional information, context, and quality assurance inspection are needed. Objective: The objective of the study is to explore how routine hospital data can be used to develop data-driven pathways that describe the journeys that patients take through care, and their potential uses in biomedical research; it proposes a framework for the construction, quality assessment, and visualization of patient pathways for clinical studies and decision support using a case study on prostate cancer. Methods: Data pertaining to prostate cancer patients were extracted from a large UK hospital from eight different HIS, validated, and complemented with information from the local cancer registry. Data-driven pathways were built for each of the 1904 patients and an expert knowledge base, containing rules on the prostate cancer biomarker, was used to assess the completeness and utility of the pathways for a specific clinical study. Software components were built to provide meaningful visualizations for the constructed pathways. Results: The proposed framework and pathway formalism enable the summarization, visualization, and querying of complex patient-centric clinical information, as well as the computation of quality indicators and dimensions. A novel graphical representation of the pathways allows the synthesis of such information. Conclusions: Clinical pathways built from routinely collected hospital data can unearth information about patients and diseases that may otherwise be unavailable or overlooked in hospitals. Data-driven clinical pathways allow for heterogeneous data (ie, semistructured and unstructured data) to be collated over a unified data model and for data quality dimensions to be assessed. This work has enabled further research on prostate cancer and its biomarkers, and on the development and application of methods to mine, compare, analyze, and visualize pathways constructed from routine data. This is an important development for the reuse of big data in hospitals
An ideal model of an assistive technology assessment and delivery process
The purpose of the present work is to present some aspects of the Assistive Technology Assessment (ATA) process model compatible with the Position Paper 2012 by AAATE/EASTIN. Three aspects of the ATA process will be discussed in light of three topics of the Position Paper 2012: (i) The dimensions and the measures of the User eXperience (UX) evaluation modelled in the ATA process as a way to verify the efficient and the evidence-based practices of an AT service delivery centre; (ii) The relevance of the presence of the psychologist in the multidisciplinary team of an AT service delivery centre as necessary for a complete person-centred assistive solution empowering users to make their own choices; (iii) The new profession of the psychotechnologist, who explores users needs by seeking a proper assistive solution, leading the multidisciplinary team to observe critical issues and problems. Through the foundation of the Position Paper 2012, the 1995 HEART study, the Matching Person and Technology model, the ICF framework, and the pillars of the ATA process, this paper sets forth a concept and approach that emphasise the personal factors of the individual consumer and UX as key to positively impacting a successful outcome and AT solution
Use of nonintrusive sensor-based information and communication technology for real-world evidence for clinical trials in dementia
Cognitive function is an important end point of treatments in dementia clinical trials. Measuring cognitive function by standardized tests, however, is biased toward highly constrained environments (such as hospitals) in selected samples. Patient-powered real-world evidence using information and communication technology devices, including environmental and wearable sensors, may help to overcome these limitations. This position paper describes current and novel information and communication technology devices and algorithms to monitor behavior and function in people with prodromal and manifest stages of dementia continuously, and discusses clinical, technological, ethical, regulatory, and user-centered requirements for collecting real-world evidence in future randomized controlled trials. Challenges of data safety, quality, and privacy and regulatory requirements need to be addressed by future smart sensor technologies. When these requirements are satisfied, these technologies will provide access to truly user relevant outcomes and broader cohorts of participants than currently sampled in clinical trials
Data Accuracy and Completeness of Monthly Midwifery Returns Indicators of Ejisu Juaben Health Directorate of Ghana
The broad range of activities contained in the provision of Primary Health Care (PHC) places a burden on providers to make optimal use of limited resources to achieve maximal health benefit to the population served. All too often, ad hoc decisions and personal preferences guide PHC resource allocations, making accountability for results impossible. Problems constraining Routine Health Information System (RHIS) performance in low-income countries include: poor data quality; limited use of available information; weaknesses in how data are analyzed and poor RHIS management practices. This study sought to investigate these constraints. A non-experimental before and after study involving bassline assessment of data accuracy and completeness, application of innovative strategies such as mentoring and coaching of Health Information Officers in data quality improvement process. Coincidentally, the intervention improved both data accuracy and completeness performance significantly among the participating facilities. The outstanding performance may be attributed to management's new orientation and growing interest towards quality data. Engaging frontline staff in data quality improvement work and provision of regular feedback leads to improvement in data accuracy and completeness. This has implications for decision-making and resource allocation, especially in low-income countries, where the routine health information management system relies heavily on paper work
The Bionic Radiologist: avoiding blurry pictures and providing greater insights
Radiology images and reports have long been digitalized. However, the potential of the more than 3.6 billion radiology
examinations performed annually worldwide has largely gone unused in the effort to digitally transform health care. The Bionic
Radiologist is a concept that combines humanity and digitalization for better health care integration of radiology. At a practical
level, this concept will achieve critical goals: (1) testing decisions being made scientifically on the basis of disease probabilities and
patient preferences; (2) image analysis done consistently at any time and at any site; and (3) treatment suggestions that are closely
linked to imaging results and are seamlessly integrated with other information. The Bionic Radiologist will thus help avoiding missed
care opportunities, will provide continuous learning in the work process, and will also allow more time for radiologists’ primary
roles: interacting with patients and referring physicians. To achieve that potential, one has to cope with many implementation
barriers at both the individual and institutional levels. These include: reluctance to delegate decision making, a possible decrease in
image interpretation knowledge and the perception that patient safety and trust are at stake. To facilitate implementation of the
Bionic Radiologist the following will be helpful: uncertainty quantifications for suggestions, shared decision making, changes in
organizational culture and leadership style, maintained expertise through continuous learning systems for training, and role
development of the involved experts. With the support of the Bionic Radiologist, disparities are reduced and the delivery of care is
provided in a humane and personalized fashion
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Nursing Home Implementation of Health Information Technology: Review of the Literature Finds Inadequate Investment in Preparation, Infrastructure, and Training.
Health information technology (HIT) is increasingly adopted by nursing homes to improve safety, quality of care, and staff productivity. We examined processes of HIT implementation in nursing homes, impact on the nursing home workforce, and related evidence on quality of care. We conducted a literature review that yielded 46 research articles on nursing homes' implementation of HIT. To provide additional contemporary context to our findings from the literature review, we also conducted semistructured interviews and small focus groups of nursing home staff (n = 15) in the United States. We found that nursing homes often do not employ a systematic process for HIT implementation, lack necessary technology support and infrastructure such as wireless connectivity, and underinvest in staff training, both for current and new hires. We found mixed evidence on whether HIT affects staff productivity and no evidence that HIT increases staff turnover. We found modest evidence that HIT may foster teamwork and communication. We found no evidence that the impact of HIT on staff or workflows improves quality of care or resident health outcomes. Without initial investment in implementation and training of their workforce, nursing homes are unlikely to realize potential HIT-related gains in productivity and quality of care. Policy makers should consider creating greater incentives for preparation, infrastructure, and training, with greater engagement of nursing home staff in design and implementation
From efficacy to equity: Literature review of decision criteria for resource allocation and healthcare decisionmaking
Objectives
Resource allocation is a challenging issue faced by health policy decisionmakers requiring careful consideration of many factors. Objectives of this study were to identify decision criteria and their frequency reported in the literature on healthcare decisionmaking.
Method
An extensive literature search was performed in Medline and EMBASE to identify articles reporting healthcare decision criteria. Studies conducted with decisionmakers (e.g., focus groups, surveys, interviews), conceptual and review articles and articles describing multicriteria tools were included. Criteria were extracted, organized using a classification system derived from the EVIDEM framework and applying multicriteria decision analysis (MCDA) principles, and the frequency of their occurrence was measured.
Results
Out of 3146 records identified, 2790 were excluded. Out of 356 articles assessed for eligibility, 40 studies included. Criteria were identified from studies performed in several regions of the world involving decisionmakers at micro, meso and macro levels of decision and from studies reporting on multicriteria tools. Large variations in terminology used to define criteria were observed and 360 different terms were identified. These were assigned to 58 criteria which were classified in 9 different categories including: health outcomes; types of benefit; disease impact; therapeutic context; economic impact; quality of evidence; implementation complexity; priority, fairness and ethics; and overall context. The most frequently mentioned criteria were: equity/fairness (32 times), efficacy/effectiveness (29), stakeholder interests and pressures (28), cost-effectiveness (23), strength of evidence (20), safety (19), mission and mandate of health system (19), organizational requirements and capacity (17), patient-reported outcomes (17) and need (16).
Conclusion
This study highlights the importance of considering both normative and feasibility criteria for fair allocation of resources and optimized decisionmaking for coverage and use of healthcare interventions. This analysis provides a foundation to develop a questionnaire for an international survey of decisionmakers on criteria and their relative importance. The ultimate objective is to develop sound multicriteria approaches to enlighten healthcare decisionmaking and priority-settin
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