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The European Journal of General Practice Dec 2018In the course of our supervisory work over the years, we have noticed that qualitative research tends to evoke a lot of questions and worries, so-called frequently asked...
In the course of our supervisory work over the years, we have noticed that qualitative research tends to evoke a lot of questions and worries, so-called frequently asked questions (FAQs). This series of four articles intends to provide novice researchers with practical guidance for conducting high-quality qualitative research in primary care. By 'novice' we mean Master's students and junior researchers, as well as experienced quantitative researchers who are engaging in qualitative research for the first time. This series addresses their questions and provides researchers, readers, reviewers and editors with references to criteria and tools for judging the quality of qualitative research papers. The second article focused on context, research questions and designs, and referred to publications for further reading. This third article addresses FAQs about sampling, data collection and analysis. The data collection plan needs to be broadly defined and open at first, and become flexible during data collection. Sampling strategies should be chosen in such a way that they yield rich information and are consistent with the methodological approach used. Data saturation determines sample size and will be different for each study. The most commonly used data collection methods are participant observation, face-to-face in-depth interviews and focus group discussions. Analyses in ethnographic, phenomenological, grounded theory, and content analysis studies yield different narrative findings: a detailed description of a culture, the essence of the lived experience, a theory, and a descriptive summary, respectively. The fourth and final article will focus on trustworthiness and publishing qualitative research.
Topics: Data Collection; Grounded Theory; Humans; Primary Health Care; Qualitative Research; Research Design; Research Personnel; Sample Size
PubMed: 29199486
DOI: 10.1080/13814788.2017.1375091 -
American Journal of Public Health Dec 2021The National Health and Nutrition Examination Survey (NHANES) is a unique source of national data on the health and nutritional status of the US population, collecting...
The National Health and Nutrition Examination Survey (NHANES) is a unique source of national data on the health and nutritional status of the US population, collecting data through interviews, standard exams, and biospecimen collection. Because of the COVID-19 pandemic, NHANES data collection was suspended, with more than a year gap in data collection. NHANES resumed operations in 2021 with the NHANES 2021-2022 survey, which will monitor the health and nutritional status of the nation while adding to the knowledge of COVID-19 in the US population. This article describes the reshaping of the NHANES program and, specifically, the planning of NHANES 2021-2022 for data collection during the COVID-19 pandemic. Details are provided on how NHANES transformed its participant recruitment and data collection plans at home and at the mobile examination center to safely collect data in a COVID-19 environment. The potential implications for data users are also discussed. (. 2021;111(12):2149-2156. https://doi.org/10.2105/AJPH.2021.306517).
Topics: Adult; COVID-19; Communicable Disease Control; Data Collection; Female; Humans; Interviews as Topic; Male; Middle Aged; Nutrition Surveys; Nutritional Status; Pandemics; Physical Examination; SARS-CoV-2; United States; Young Adult
PubMed: 34878854
DOI: 10.2105/AJPH.2021.306517 -
JMIR MHealth and UHealth Sep 2019Due to the adoption of electronic health records (EHRs) and legislation on meaningful use in recent decades, health systems are increasingly interdependent on EHR... (Review)
Review
BACKGROUND
Due to the adoption of electronic health records (EHRs) and legislation on meaningful use in recent decades, health systems are increasingly interdependent on EHR capabilities, offerings, and innovations to better capture patient data. A novel capability offered by health systems encompasses the integration between EHRs and wearable health technology. Although wearables have the potential to transform patient care, issues such as concerns with patient privacy, system interoperability, and patient data overload pose a challenge to the adoption of wearables by providers.
OBJECTIVE
This study aimed to review the landscape of wearable health technology and data integration to provider EHRs, specifically Epic, because of its prevalence among health systems. The objectives of the study were to (1) identify the current innovations and new directions in the field across start-ups, health systems, and insurance companies and (2) understand the associated challenges to inform future wearable health technology projects at other health organizations.
METHODS
We used a scoping process to survey existing efforts through Epic's Web-based hub and discussion forum, UserWeb, and on the general Web, PubMed, and Google Scholar. We contacted Epic, because of their position as the largest commercial EHR system, for information on published client work in the integration of patient-collected data. Results from our searches had to meet criteria such as publication date and matching relevant search terms.
RESULTS
Numerous health institutions have started to integrate device data into patient portals. We identified the following 10 start-up organizations that have developed, or are in the process of developing, technology to enhance wearable health technology and enable EHR integration for health systems: Overlap, Royal Philips, Vivify Health, Validic, Doximity Dialer, Xealth, Redox, Conversa, Human API, and Glooko. We reported sample start-up partnerships with a total of 16 health systems in addressing challenges of the meaningful use of device data and streamlining provider workflows. We also found 4 insurance companies that encourage the growth and uptake of wearables through health tracking and incentive programs: Oscar Health, United Healthcare, Humana, and John Hancock.
CONCLUSIONS
The future design and development of digital technology in this space will rely on continued analysis of best practices, pain points, and potential solutions to mitigate existing challenges. Although this study does not provide a full comprehensive catalog of all wearable health technology initiatives, it is representative of trends and implications for the integration of patient data into the EHR. Our work serves as an initial foundation to provide resources on implementation and workflows around wearable health technology for organizations across the health care industry.
Topics: Biomedical Technology; Data Collection; Electronic Health Records; Humans; Wearable Electronic Devices
PubMed: 31512582
DOI: 10.2196/12861 -
PloS One 2020Data saturation is the most commonly employed concept for estimating sample sizes in qualitative research. Over the past 20 years, scholars using both empirical research...
Data saturation is the most commonly employed concept for estimating sample sizes in qualitative research. Over the past 20 years, scholars using both empirical research and mathematical/statistical models have made significant contributions to the question: How many qualitative interviews are enough? This body of work has advanced the evidence base for sample size estimation in qualitative inquiry during the design phase of a study, prior to data collection, but it does not provide qualitative researchers with a simple and reliable way to determine the adequacy of sample sizes during and/or after data collection. Using the principle of saturation as a foundation, we describe and validate a simple-to-apply method for assessing and reporting on saturation in the context of inductive thematic analyses. Following a review of the empirical research on data saturation and sample size estimation in qualitative research, we propose an alternative way to evaluate saturation that overcomes the shortcomings and challenges associated with existing methods identified in our review. Our approach includes three primary elements in its calculation and assessment: Base Size, Run Length, and New Information Threshold. We additionally propose a more flexible approach to reporting saturation. To validate our method, we use a bootstrapping technique on three existing thematically coded qualitative datasets generated from in-depth interviews. Results from this analysis indicate the method we propose to assess and report on saturation is feasible and congruent with findings from earlier studies.
Topics: Data Collection; Humans; Qualitative Research; Research Design; Sample Size
PubMed: 32369511
DOI: 10.1371/journal.pone.0232076 -
International Journal of Epidemiology Dec 2016Triangulation is the practice of obtaining more reliable answers to research questions through integrating results from several different approaches, where each approach...
Triangulation is the practice of obtaining more reliable answers to research questions through integrating results from several different approaches, where each approach has different key sources of potential bias that are unrelated to each other. With respect to causal questions in aetiological epidemiology, if the results of different approaches all point to the same conclusion, this strengthens confidence in the finding. This is particularly the case when the key sources of bias of some of the approaches would predict that findings would point in opposite directions if they were due to such biases. Where there are inconsistencies, understanding the key sources of bias of each approach can help to identify what further research is required to address the causal question. The aim of this paper is to illustrate how triangulation might be used to improve causal inference in aetiological epidemiology. We propose a minimum set of criteria for use in triangulation in aetiological epidemiology, summarize the key sources of bias of several approaches and describe how these might be integrated within a triangulation framework. We emphasize the importance of being explicit about the expected direction of bias within each approach, whenever this is possible, and seeking to identify approaches that would be expected to bias the true causal effect in different directions. We also note the importance, when comparing results, of taking account of differences in the duration and timing of exposures. We provide three examples to illustrate these points.
Topics: Bias; Causality; Data Collection; Epidemiology; Humans; Mendelian Randomization Analysis; Research Design
PubMed: 28108528
DOI: 10.1093/ije/dyw314 -
The Lancet. Global Health Aug 2021
Topics: COVID-19; Data Collection; Disabled Persons; Global Health; Humans
PubMed: 34297946
DOI: 10.1016/S2214-109X(21)00312-0 -
Journal of the American Medical... Mar 2021
Topics: Data Collection; Electronic Health Records; Health Records, Personal; Humans; Machine Learning; Natural Language Processing; Patient Access to Records; Surveys and Questionnaires
PubMed: 33677514
DOI: 10.1093/jamia/ocab040 -
Public Health Research & Practice Jul 2016Life-history data are quantitative, retrospective and autobiographical data collected through event-history calendars. By mimicking the structure of our memories, these...
Life-history data are quantitative, retrospective and autobiographical data collected through event-history calendars. By mimicking the structure of our memories, these instruments can gather reliable information on different dimensions of the lifecourse. Life-history data enable the duration, timing and ordering of events to be brought to the foreground of analysis. Extending the scope of lifecourse research, life-history data make it possible to examine the long-term effects of past policies with more precision and detail.
Topics: Autobiographies as Topic; Data Collection; Humans; Life Change Events; Mental Recall; Personal Narratives as Topic; Research Design
PubMed: 27421342
DOI: 10.17061/phrp2631630 -
Acta Crystallographica. Section D,... Feb 2019
Topics: Animals; Crystallography, X-Ray; Data Collection; Datasets as Topic; Electrons; Humans; Protein Conformation; Proteins; Software
PubMed: 30821700
DOI: 10.1107/S2059798319002870 -
American Journal of Public Health Dec 2021
Topics: COVID-19; Data Accuracy; Data Collection; Humans; Influenza, Human; Public Health Surveillance; SARS-CoV-2; Self Report; Surveys and Questionnaires; Time Factors; United States; Zika Virus Infection
PubMed: 34878882
DOI: 10.2105/AJPH.2021.306553