scholarly journals Secondary Use and Analysis of Big Data Collected for Patient Care

2017 ◽  
Vol 26 (01) ◽  
pp. 28-37 ◽  
Author(s):  
F. J. Martin-Sanchez ◽  
V. Aguiar-Pulido ◽  
G. H. Lopez-Campos ◽  
N. Peek ◽  
L. Sacchi
Keyword(s):  
Big Data ◽  
Environmental Factors ◽  
Health Services Research ◽  
Health Services ◽  
Clinical Research ◽  
Economic Benefits ◽  
Patient Data ◽  
Genomic Research ◽  
Services Research ◽  
Secondary Use

Summary Objectives: To identify common methodological challenges and review relevant initiatives related to the re-use of patient data collected in routine clinical care, as well as to analyze the economic benefits derived from the secondary use of this data. Through the use of several examples, this article aims to provide a glimpse into the different areas of application, namely clinical research, genomic research, study of environmental factors, and population and health services research. This paper describes some of the informatics methods and Big Data resources developed in this context, such as electronic phenotyping, clinical research networks, biorepositories, screening data banks, and wide association studies. Lastly, some of the potential limitations of these approaches are discussed, focusing on confounding factors and data quality. Methods: A series of literature searches in main bibliographic databases have been conducted in order to assess the extent to which existing patient data has been repurposed for research. This contribution from the IMIA working group on “Data mining and Big Data analytics” focuses on the literature published during the last two years, covering the timeframe since the working group’s last survey. Results and Conclusions: Although most of the examples of secondary use of patient data lie in the arena of clinical and health services research, we have started to witness other important applications, particularly in the area of genomic research and the study of health effects of environmental factors. Further research is needed to characterize the economic impact of secondary use across the broad spectrum of translational research.

scholarly journals Secondary Use and Analysis of Big Data Collected for Patient Care

2017 ◽  
Vol 26 (01) ◽  
pp. 28-37
Author(s):  
F. J. Martin-Sanchez ◽  
V. Aguiar-Pulido ◽  
G. H. Lopez-Campos ◽  
N. Peek ◽  
L. Sacchi
Keyword(s):  
Big Data ◽  
Environmental Factors ◽  
Health Services Research ◽  
Health Services ◽  
Clinical Research ◽  
Economic Benefits ◽  
Patient Data ◽  
Genomic Research ◽  
Services Research ◽  
Secondary Use

Summary Objectives: To identify common methodological challenges and review relevant initiatives related to the re-use of patient data collected in routine clinical care, as well as to analyze the economic benefits derived from the secondary use of this data. Through the use of several examples, this article aims to provide a glimpse into the different areas of application, namely clinical research, genomic research, study of environmental factors, and population and health services research. This paper describes some of the informatics methods and Big Data resources developed in this context, such as electronic phenotyping, clinical research networks, biorepositories, screening data banks, and wide association studies. Lastly, some of the potential limitations of these approaches are discussed, focusing on confounding factors and data quality. Methods: A series of literature searches in main bibliographic databases have been conducted in order to assess the extent to which existing patient data has been repurposed for research. This contribution from the IMIA working group on “Data mining and Big Data analytics” focuses on the literature published during the last two years, covering the timeframe since the working group’s last survey. Results and Conclusions: Although most of the examples of secondary use of patient data lie in the arena of clinical and health services research, we have started to witness other important applications, particularly in the area of genomic research and the study of health effects of environmental factors. Further research is needed to characterize the economic impact of secondary use across the broad spectrum of translational research.

scholarly journals Stakeholder views on publication bias in health services research

2020 ◽  
Vol 25 (3) ◽  
pp. 162-171 ◽  
Author(s):  
Iestyn Williams ◽  
Abimbola A Ayorinde ◽  
Russell Mannion ◽  
Magdalena Skrybant ◽  
Fujian Song ◽  
...  
Keyword(s):  
Focus Group ◽  
Health Services Research ◽  
Health Services ◽  
Clinical Research ◽  
Publication Bias ◽  
Quantitative Research ◽  
Decision Makers ◽  
Structured Interviews ◽  
Services Research ◽  
General Acceptance

Objectives While the presence of publication bias in clinical research is well documented, little is known about its role in the reporting of health services research. This paper explores stakeholder perceptions and experiences with regard to the role of publication and related biases in quantitative research relating to the quality, accessibility and organization of health services. Methods We present findings from semi-structured interviews with those responsible for the funding, publishing and/or conduct of quantitative health services research, primarily in the UK. Additional data collection includes interviews with health care decision makers as ‘end users’ of health services research, and a focus group with patient and service user representatives. The final sample comprised 24 interviews and eight focus group participants. Results Many study participants felt unable to say with any degree of certainty whether publication bias represents a significant problem in quantitative health services research. Participants drew broad contrasts between externally funded and peer reviewed research on the one hand, and end user funded quality improvement projects on the other, with the latter perceived as more vulnerable to selective publication and author over-claiming. Multiple study objectives, and a general acceptance of ‘mess and noise’ in the data and its interpretation was seen to reduce the importance attached to replicable estimates of effect sizes in health services research. The relative absence of external scrutiny, either from manufacturers of interventions or health system decision makers, added to this general sense of ‘low stakes’ of health services research. As a result, while many participants advocated study pre-registration and using protocols to pre-identify outcomes, others saw this as an unwarranted imposition. Conclusions This study finds that incentives towards publication and related bias are likely to be present, but not to the same degree as in clinical research. In health services research, these were seen as being offset by other forms of ‘novelty’ bias in the reporting and publishing of research findings.

Health Services Research – What it is, How to Do it, and Why it Matters

1994 ◽  
Vol 7 (4) ◽  
pp. 214-219 ◽  
Author(s):  
Richard J. Lilford
Keyword(s):  
Health Policy ◽  
Health Services Research ◽  
Health Services ◽  
Clinical Research ◽  
Services Research ◽  
Short Article ◽  
Policy Makers ◽  
The Uk

The term ‘Health Services Research’ (HSR) has achieved only recent currency in the UK. The purpose of this short article is to explain what it is, to say how it differs from clinical research even when using similar methods, and to argue that it is likely to become of rapidly increasing importance to health policy-makers and managers.

scholarly journals Comparative health services research across populations: the unused opportunities in big data

2015 ◽  
Vol 87 (6) ◽  
pp. 1094-1096 ◽  
Author(s):  
Benjamin A. Goldstein ◽  
Wolfgang C. Winkelmayer
Keyword(s):  
Big Data ◽  
Health Services Research ◽  
Health Services ◽  
Services Research

How Can Payback from Health Services Research Be Assessed?

1996 ◽  
Vol 1 (1) ◽  
pp. 35-43 ◽  
Author(s):  
Martin Buxton ◽  
Steve Hanney
Keyword(s):  
Health Services Research ◽  
Health Services ◽  
Case Studies ◽  
Research Utilization ◽  
Health Sector ◽  
Economic Benefits ◽  
Research Needs ◽  
Services Research ◽  
Complex Concept ◽  
Health Research And Development

Throughout the world there is a growing recognition that health care should be research-led. This strengthens the requirement for expenditure on health services research to be justified by demonstrating the benefits it produces. However, payback from health research and development is a complex concept and little used term. Five main categories of payback can be identified: Knowledge; research benefits; political and administrative benefits; health sector benefits; and broader economic benefits. Various models of research utilization together with previous assessments of payback from research helped in the development of a new conceptual model of how and where payback may occur. The model combines an input-output perspective with an examination of the permeable interfaces between research and its environment. The model characterizes research projects in terms of Inputs, Processes, and Primary Outputs. The last consist of knowledge and research benefits. There are two interfaces between the project and its environment. The first (Project Specification, Selection and Commissioning) is the link with Research Needs Assessment. The second (Dissemination) should lead to Secondary Outputs (which are policy or administrative decisions), and usually Applications (which take the form of behavioural changes), from which Impacts or Final Outcomes result. It is at this final stage that health and wider economic benefits can be measured. A series of case studies were used to assess the feasibility both of applying the model and the payback categorization. The paper draws various conclusions from the case studies and identifies a range of issues for further work.

scholarly journals Clinical research lags behind biomedical, population-based health, and health services research at multiple levels

2007 ◽  
Vol 30 (4) ◽  
pp. 152 ◽  
Author(s):  
Malathi Raghavan ◽  
J. Dean Sandham
Keyword(s):  
Health Services Research ◽  
Health Services ◽  
Clinical Research ◽  
Growth Rates ◽  
Population Based ◽  
P Value ◽  
Services Research ◽  
Level Ii ◽  
Multi Level ◽  
Level I

Purpose: Concerns regarding a decline in clinical research have been raised internationally. In this study, research initiatives and competitiveness of investigators seeking funding for clinical research were compared with those for three other health research themes in Canada, namely, biomedical, population-based, and health services research. Methods: A retrospective, multi-level descriptive study was conducted using administrative data from the Canadian Institutes for Health Research (CIHR) research grants program. Annual growth rates in numbers of proposals submitted since year 2000 (level I of comparison), success rates of submissions (level II), and growth rates in funding received since fiscal-year 1999-00 (level III) were compared across themes. Results: Proposal submission (Level I): The average annual rate of growth in proposal submissions for biomedical, clinical, population-based and health services research was 11.8%, 6.3%, 105.0% and 43.2%, respectively. Success rate (Level II) was lower in clinical research (24%; P-value < 0.001) compared with biomedical (34%), population-based (29%), and health services (28%) research. Funding (Level III) grew at an average rate of 16.1% per year for biomedical, 28.2% for clinical, 65.9% for population-based, and 86.2% for health services research. However, the median amount funded for clinical projects (CAD $154,535) was less (P-value < 0.0001) than that for biomedical projects ($225,346). Conclusion: The overall growth of research activities in clinical theme was slower than with research in other themes—fewer proposals were submitted and lower proportion of submissions was successful. Smaller amounts of funding were received for clinical projects compared with biomedical projects, but a handful of large-scale clinical projects influenced the growth rate in funding for all clinical research. This report underscores the concern that multi-level problems plague clinical research.

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