Using population register data for health services research – the Finnish experience

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
I Keskimäki ◽  
S Lumme
Keyword(s):  
Health Care ◽  
Health Services Research ◽  
Health Services ◽  
Register Data ◽  
Personal Identification ◽  
Service Research ◽  
Research Use ◽  
Population Census ◽  
Services Research ◽  
Wide Range

Abstract The personal identification number was introduced in Finland in the mid-1960s. By 1970, it was universally used in administrative databases including population census, causes of death, cancer, hospital discharge, and health insurance registries. Early on data protection regulations recognised the use of registries and ID numbers for research and register linkages which allowed expanding the use of register data in population health and health services research. The possibility to analyse electronic health records (eHRs) linked to individual whole-population sociodemographic data has allowed the use of a wide range of study designs from cross-sectional to longitudinal studies and to various hierarchical designs. Some registers extend follow-up time up to over 50 years with detailed mapping of individual event histories. Data also allow detailed disentangling of individual and contextual factors, such as socioeconomics and comorbidities vs. provider characteristics. While some data items draw on administrative decisions or clinical discretion, such as granting insurance benefits or decisions on surgery, the research use of the data requires understanding of processes used to construct data items. The use of population registers in health services research is clearly cost-effective. However, the lack of a high-performance computing capacity and environment suitable for processing large sensitive data, as well as inadequate information on the use of primary health care and eHRs has limited efficient use of extensive and complex linkage schemes and the utilisation of machine learning. The Finnish register authorities have improved computing facilities for research use and the legislative reform on secondary use of health care data is opening eHRs for research use granting higher granularity in describing content and quality of care. These developments enable big-data methods to be an essential part of the future methodological toolbox for health service research.

Health Services Research at United HealthCare Corporation: The Role of the Center for Health Care Policy and Evaluation

1996 ◽  
Vol 53 (1_suppl) ◽  
pp. 65-76 ◽  
Author(s):  
Eileen Peterson ◽  
Deborah Shatin ◽  
Douglas Mccarthy
Keyword(s):  
Health Care ◽  
Managed Care ◽  
Health Services Research ◽  
Health Services ◽  
Health Care Policy ◽  
Research Priorities ◽  
Research Capacity ◽  
Lessons Learned ◽  
Services Research ◽  
And Performance

This article describes collaborative health services research and performance evaluation activities at United HealthCare Corporation, a national health care management services company. We outline the development of a research capacity within our company, the principal data sources used, and the types of research conducted. The importance of health services research within a managed care system is illustrated using two projects as examples. finally, we discuss issues faced by organizations such as ours in defining appropriate research priorities, ensuring health plan participation, and disseminating research findings. Lessons learned should be of interest to health services researchers working in or collaborating with managed care organizations as well as others seeking to understand the dynamics of research in private-sector health care companies.

scholarly journals The contribution of focus group discussions to Aboriginal Australian health service research

2014 ◽  
Vol 7 (2) ◽  
pp. 1-15 ◽  
Author(s):  
Angela Dawson ◽  
John Daniels ◽  
Kathleen Clapham
Keyword(s):  
Focus Group ◽  
Health Services Research ◽  
Health Services ◽  
Aboriginal People ◽  
Good Practice ◽  
Aboriginal Health ◽  
Service Research ◽  
Focus Group Discussions ◽  
Services Research ◽  
Group Discussions

Focus Group Discussions (FGDs) are a common way of gathering qualitative data in Aboriginal health services research; however there have been no studies on the question of whether they are appropriate research tools in such contexts, nor are there are specific guidelines available to ensure that FGDs are delivered to collect data in ways that are consistent with Aboriginal approaches to consultation, ownership and ways of knowing. Furthermore, there is a lack of clarity concerning the theoretical and methodological perspectives that could be operationalised by FGDs to gather data, guide analysis and interpretation in ways that are culturally appropriate, ethically sound and rigorous. We undertook a content analysis of Aboriginal health services research studies using FGDs to determine their use and elements that may provide insight into good practice. A framework is proposed to help guide future FGD research with Aboriginal people.

scholarly journals Qualitative Research Methodology and its Scope in Health Services Research

2020 ◽  
Vol 3 (2) ◽  
pp. 18-21
Author(s):  
Naiya Patel
Keyword(s):  
Health Care ◽  
Qualitative Research ◽  
Clinical Trials ◽  
Health Services Research ◽  
Health Services ◽  
Health Care Providers ◽  
Research Methodology ◽  
Care Providers ◽  
Services Research ◽  
Qualitative Research Methodology

Health services research is a multidisciplinary field which involves policy makers, health care providers, as well as quality outcomes professionals of the health services provided in an organizational setting to name some. Using qualitative research methodology to get insights of both the provider and patient experience down the pipeline can help strengthen what is lacking. Bridging the gap of translation research by not just surveys 1 might be an appropriate research methodology, however, inclusion of case studies, ethnographies might help stakeholders in the field, to visualize in depth phenomenon occurring in health services research field. Telly medicine, commercial digital health status trackr might be some of the inetrventions to improvise health care services, however, knowing what are the actual needs at individual level might efficiently help in redistribution of resources or policy laws. Recruiting for clinical trials through story telling communication technology2,3, might help in recruitment for novel drug therapies to explore possibilities, however, exploring the barriers to enroll for the clinical trials, or why the drug might work effectively in some cultural population and why not on others, can only be efficiently explored through qualitative research methodologies.

scholarly journals Assigning Resources to Health Care Use for Health Services Research

Medical Care ◽  
2009 ◽  
Vol 47 (Supplement) ◽  
pp. S70-S75 ◽  
Author(s):  
Paul A. Fishman ◽  
Mark C. Hornbrook
Keyword(s):  
Health Care ◽  
Health Services Research ◽  
Health Services ◽  
Health Care Use ◽  
Services Research

Interactive visualization applications in population health and health services research: a systematic scoping review (Preprint)

2021 ◽  
Author(s):  
Jawad Chishtie ◽  
Iwona Anna Bielska ◽  
Aldo Barrera ◽  
Jean-Sebastien Marchand ◽  
Muhammad Imran ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Health Services Research ◽  
Health Services ◽  
Population Health ◽  
Scoping Review ◽  
Interactive Visualization ◽  
Resource Planning ◽  
Services Research ◽  
Epidemiologic Surveillance

BACKGROUND Simple visualizations in health research data, such as scatter plots, heat maps and bar charts typically present relationships between two variables. Interactive visualization methods allow for multiple related facets, such as multiple risk factors, to be studied simultaneously, leading to data insights through exploring trends and patterns from complex big healthcare data. The technique presents a powerful tool that can be used in combination with statistical analysis for knowledge discovery, hypothesis generation and testing, and decision support. OBJECTIVE The primary objective of this scoping review is to describe and summarize the evidence of interactive visualization applications, methods and tools being employed in population health and HSR, and their sub-domains in the last 15 years, from 1 January 2005 to 30 March 2019. Our secondary objective is to describe the use cases, metrics, frameworks used, settings, target audience, goals and co-design of applications. METHODS We adapted standard scoping review guidelines, with a peer reviewed search strategy, two independent researchers at each stage of screening and abstraction, with a third independent researcher to arbitrate conflicts and validate findings. A comprehensive abstraction platform was built to capture the data from diverse bodies of literature, primarily from the computer science and health care sector. After screening 11,310 articles, we present findings from 56 applications from interrelated areas of population health and health services research, and their sub-domains such as epidemiologic surveillance, health resource planning, access, utilization and costs, among diverse clinical and demographic populations. RESULTS As a companion review to our earlier systematic synthesis of literature on visual analytic applications, we present findings in six major themes of interactive visualization applications developed for eight major problem categories. We found a wide application of interactive visualization methods, the major being epidemiologic surveillance for infectious disease, resource planning, health service monitoring and quality and studying medication use patterns. Data sources included mostly secondary administrative and electronic medical record data. Additionally, at least two-third applications involved participatory co-design approaches, while introducing a distinct category ‘embedded research’ within co-design initiatives. These applications were in response to an identified need for data-driven insights towards knowledge generation and decision support. We further discuss the opportunities from the use of interactive visualization methods towards studying global health, inequities including social determinants of health, and other related areas. We also allude to the challenges in the uptake of these methods. CONCLUSIONS Visualization in health has strong historical roots, with an upward trend in the use of these methods in population health and health services research. Such applications are being fast utilized by academic and health care agencies for knowledge discovery, hypotheses generation and decision support. CLINICALTRIAL Protocol registration: RR1-10.2196/14019 Related first review: RR2-10.2196/14019 INTERNATIONAL REGISTERED REPORT RR2-10.2196/14019

HMOs and Health Services Research: The Penalty of Taking the Lead

1996 ◽  
Vol 53 (1_suppl) ◽  
pp. 18-43 ◽  
Author(s):  
Amy B. Bernstein ◽  
Jill Bernstein
Keyword(s):  
Health Care ◽  
Health Policy ◽  
Health Services Research ◽  
Health Services ◽  
Information Needs ◽  
Health Maintenance Organization ◽  
Services Research ◽  
Health Maintenance ◽  
Future Evolution ◽  
Different Types

Although health maintenance organization (HMO) structures and databases are not uniform across plans, there are unique characteristics of HMO data in general that make them useful in examining health policy and delivery issues. The authors examine differences in data generated by different types of HMOs. After discussing why health services research using HMO data is needed by HMOs, other providers, practitioners, payers, and consumers of health care, the authors examine ways in which HMOs can provide sound answers to crucially important questions about the future of health care. They conclude that although the need for research on HMOs is compelling, researchers need to understand the information needs of HMOs and the incentives that are shaping the industry's approach to system delivery and clinical outcomes research. If HMOs do not take the lead in conducting health services research, they will diminish their role in shaping policies that will shape their future evolution.

scholarly journals In Memoriam, Michael Pozen

1985 ◽  
Vol 1 (S1) ◽  
pp. xvi-xvii
Author(s):  
Edmund M. Ricci
Keyword(s):  
Health Care ◽  
Health Services Research ◽  
Health Services ◽  
Scientific Field ◽  
Clinical Aspects ◽  
Emergency Medical Care ◽  
Services Research ◽  
Emergency Medical ◽  
In Memoriam ◽  
Career Specialty

The relatively young scientific field of health services research, whose practitioners are still struggling to establish legitimacy within the vast domain of health care, owes much to Michael Pozen. Indeed, Michael must be counted as one of the founders of this new speciality in that his brief but incredibly productive career began as the field of health services research took form and emerged as a distinct career specialty. His very significant contributions to health care can be assessed from two perspectives.The most visible perspective upon Michael Pozen's professional work can be obtained from a review of his research as reported in his published articles and presented papers. Michael clearly established himself as a creative and resourceful scholar and researcher who had a particular interest in improving the design and delivery of emergency medical services as well as the clinical aspects of emergency medical care.

scholarly journals Intersections of machine learning and epidemiological methods for health services research

2020 ◽  
Author(s):  
Sherri Rose
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Services Research ◽  
Health Services ◽  
Machine Learning Techniques ◽  
Epidemiological Methods ◽  
Learning Tools ◽  
Services Research ◽  
Learning Techniques ◽  
Regression Techniques

Abstract The field of health services research is broad and seeks to answer questions about the health care system. It is inherently interdisciplinary, and epidemiologists have made crucial contributions. Parametric regression techniques remain standard practice in health services research with machine learning techniques currently having low penetrance in comparison. However, studies in several prominent areas, including health care spending, outcomes and quality, have begun deploying machine learning tools for these applications. Nevertheless, major advances in epidemiological methods are also as yet underleveraged in health services research. This article summarizes the current state of machine learning in key areas of health services research, and discusses important future directions at the intersection of machine learning and epidemiological methods for health services research.

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