Dataset Information

Patient preferences toward an interactive e-consent application for research using electronic health records.

ABSTRACT: OBJECTIVE:The purpose of this study was to assess patient perceptions of using an interactive electronic consent (e-consent) application when deciding whether or not to grant broad consent for research use of their identifiable electronic health record (EHR) information. MATERIALS AND METHODS:For this qualitative study, we conducted a series of 42 think-aloud interviews with 32 adults. Interview transcripts were coded and analyzed using a modified grounded theory approach. RESULTS:We identified themes related to patient preferences, reservations, and mixed attitudes toward consenting electronically; low- and high-information-seeking behavior; and an emphasis on reassuring information, such as data protections and prohibitions against sharing data with pharmaceutical companies. Participants expressed interest in the types of information contained in their EHRs, safeguards protecting EHR data, and specifics on studies that might use their EHR data. DISCUSSION:This study supports the potential value of interactive e-consent applications that allow patients to customize their consent experience. This study also highlights that some people have concerns about e-consent platforms and desire more detailed information about administrative processes and safeguards that protect EHR data used in research. CONCLUSION:This study contributes new insights on how e-consent applications could be designed to ensure that patients' information needs are met when seeking consent for research use of health record information. Also, this study offers a potential electronic approach to meeting the new Common Rule requirement that consent documents contain a "concise and focused" presentation of key information followed by more details.

SUBMITTER: Harle CA

PROVIDER: S-EPMC5992814 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Patient preferences toward an interactive e-consent application for research using electronic health records.

Harle Christopher A CA Golembiewski Elizabeth H EH Rahmanian Kiarash P KP Krieger Janice L JL Hagmajer Dorothy D Mainous Arch G AG Moseley Ray E RE

Journal of the American Medical Informatics Association : JAMIA 20180301 3

<h4>Objective</h4>The purpose of this study was to assess patient perceptions of using an interactive electronic consent (e-consent) application when deciding whether or not to grant broad consent for research use of their identifiable electronic health record (EHR) information.<h4>Materials and methods</h4>For this qualitative study, we conducted a series of 42 think-aloud interviews with 32 adults. Interview transcripts were coded and analyzed using a modified grounded theory approach.<h4>Resu ...[more]

PMID: 29272408

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Project description:BackgroundEHR phenotyping offers the ability to rapidly assemble a precisely defined cohort of patients prescreened for eligibility to participate in health-related research. Even so, stakeholders in the process must still contend with the practical and ethical challenges associated with research recruitment. Patient perspectives on these matters are particularly important given that the success of research recruitment depends on patients' willingness to participate.MethodsWe conducted 15 focus groups (n =?110 participants) in four counties in diverse regions of the southeastern US: Appalachia, the Mississippi Delta, and the Piedmont area of North Carolina. Based on a hypothetical study of a behavioral intervention for type 2 diabetes, we asked about the acceptability and appropriateness of direct investigator versus physician-mediated contact with patients for research recruitment, and whether patients should be asked to opt in or opt out of further contact in response to recruitment letters.ResultsFor initial contact, nearly all participants said it would be acceptable for researchers to contact patients directly and three-fourths said that it would be acceptable for researchers to contact patients through their physicians. When we asked which would be most appropriate, a substantial majority chose direct contact. Themes that arose in the discussion included trust and transparency, decision-making power, the effect on research, and the effect on patient care. For response expectations, the vast majority of participants said both opt-in and opt-out would be acceptable-typically finding neither especially problematic and noting that both afford patients the opportunity to make their own decisions.ConclusionsExternal validity relies heavily on researchers' success enrolling eligible patients and failure to reach accrual targets is a costly and common barrier to advancing scientific knowledge. Our results suggest that patients recognize multiple advantages and disadvantages of different research recruitment strategies and place value on the implications not just for themselves, but also for researchers and healthcare providers. Our findings, including rich qualitative detail, contribute to the body of empirical and ethical literature on improving research recruitment and suggest specific ways forward as well as important areas for future research.

Project description:BackgroundWith the accumulation of electronic health records and the development of artificial intelligence, patients with cancer urgently need new evidence of more personalized clinical and demographic characteristics and more sophisticated treatment and prevention strategies. However, no research has systematically analyzed the application and significance of artificial intelligence based on electronic health records in cancer care.ObjectiveThe aim of this study was to conduct a review to introduce the current state and limitations of artificial intelligence based on electronic health records of patients with cancer and to summarize the performance of artificial intelligence in mining electronic health records and its impact on cancer care.MethodsThree databases were systematically searched to retrieve potentially relevant papers published from January 2009 to October 2020. Four principal reviewers assessed the quality of the papers and reviewed them for eligibility based on the inclusion criteria in the extracted data. The summary measures used in this analysis were the number and frequency of occurrence of the themes.ResultsOf the 1034 papers considered, 148 papers met the inclusion criteria. Cancer care, especially cancers of female organs and digestive organs, could benefit from artificial intelligence based on electronic health records through cancer emergencies and prognostic estimates, cancer diagnosis and prediction, tumor stage detection, cancer case detection, and treatment pattern recognition. The models can always achieve an area under the curve of 0.7. Ensemble methods and deep learning are on the rise. In addition, electronic medical records in the existing studies are mainly in English and from private institutional databases.ConclusionsArtificial intelligence based on electronic health records performed well and could be useful for cancer care. Improving the performance of artificial intelligence can help patients receive more scientific-based and accurate treatments. There is a need for the development of new methods and electronic health record data sharing and for increased passion and support from cancer specialists.

Project description:BackgroundVirtual Patients are a well-known and widely used form of interactive software used to simulate aspects of patient care that students are increasingly less likely to encounter during their studies. However, to take full advantage of the benefits of using Virtual Patients, students should have access to multitudes of cases. In order to promote the creation of collections of cases, a tablet application was developed which makes use of electronic health records as material for Virtual Patient cases. Because electronic health records are abundantly available on hospital information systems, this results in much material for the basis of case creation.ResultsAn iPad-based Virtual Patient interactive software system was developed entitled Casebook. The application has been designed to read specially formatted patient cases that have been created using electronic health records, in the form of X-ray images, electrocardiograms, lab reports, and physician notes, and present these to the medical student. These health records are organised into a timeline, and the student navigates the case while answering questions regarding the patient along the way. Each health record can also be annotated with meta-information by the case designer, such as insight into the thought processes and the decision-making rationale of the physician who originally worked with the patient. Students learn decision-making skills by observing and interacting with real patient cases in this simulated environment. This paper discusses our approach in detail.ConclusionsOur group is of the opinion that Virtual Patient cases, targeted at undergraduate students, should concern patients who exhibit prototypical symptoms of the kind students may encounter when beginning their first medical jobs. Learning theory research has shown that students learn decision-making skills best when they have access to multitudes of patient cases and it is this plurality that allows students to develop their illness scripts effectively. Casebook emphasises the use of pre-existing electronic health record data as the basis for case creation, thus, it is hoped, making it easier to produce cases in larger numbers. By creating a Virtual Patient system where cases are built from abundantly available electronic health records, collections of cases can be accumulated by institutions.

Project description:BackgroundElectronic health records (EHRs) provide rich data on many domains not routinely available in other data, as such, they are a promising source to study changes in health outcomes using longitudinal study designs (eg, cohort studies, natural experiments, etc.). Yet, patient attrition rates in these data are unknown.ObjectiveThe objective of this study was to estimate overall and among adults with diabetes or hypertension: (1) patient attrition over a 3-year period at community health centers; and (2) the likelihood that patients with Medicaid permanently switched their source of primary care.Research designA retrospective cohort study of 2012-2017 data from the Accelerating Data Value Across a National Community Health Center Network (ADVANCE) Clinical Data Research Network of community health centers were used to assess EHR data attrition. Oregon Medicaid enrollment and claims data were used to estimate the likelihood of changing the source of primary care.SubjectsA total of 827,657 patients aged 19-64 with ≥1 ambulatory visit from 76 community health center systems across 20 states. In all, 232,891 Oregon Medicaid enrollees (aged 19-64) with a gap of ≥6 months following a claim for a visit billed to a primary care source.MeasuresPercentage of patients not returning within 3 years of their qualifying visit (attrition). The probability that a patient with Medicaid permanently changed their primary care source.ResultsAttrition over the 3 years averaged 33.5%; attrition rates were lower (<25%) among patients with diabetes and/or hypertension. Among Medicaid enrollees, the percentage of provider change after a 6-month gap between visits was 12% for community health center patients compared with 39% for single-provider practice patients. Over 3 years, the likelihood of a patient changing to a new provider increased with length of time since their last visit but remained lowest among community health center patients.ConclusionThis study demonstrates the use of the EHR dataset is a reliable source of data to support longitudinal studies while highlighting variability in attrition by primary care source and chronic conditions.

Dataset Information

Patient preferences toward an interactive e-consent application for research using electronic health records.

Publications

Patient preferences toward an interactive e-consent application for research using electronic health records.

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