Research Use of Electronic Health Records: Patients' Views on Alternative Approaches to Permission

What is public trust in national electronic health record systems? A scoping review of qualitative research studies from 1995 to 2021

Objective

Public trust in national electronic health record systems is essential for the successful implementation within a healthcare system. Research investigating public trust in electronic health records is limited, leading to a lack of conceptual clarity. In response, the objective of this study is to gain a clearer understanding on the conceptualizations of public trust in electronic health records, which can support the implementation of national electronic health record systems.

Methods

Guided by the PRISMA-ScR checklist, a scoping review of 27 qualitative studies on public trust in electronic health records found between January 2022 and June 2022 was conducted using an inclusive search method. In an iterative process, conceptual themes were derived describing the promoters and outcomes of public trust in electronic health records.

Results

Five major conceptual themes with 15 sub-themes were present across the literature. Comprehension, autonomy, and data protection promote public trust in electronic health record; while personal and system benefits are the outcomes once public trust in electronic health records exists. Additional findings highlight the pivotal role of healthcare actors for the public trust building process.

Conclusions

The results underscore comprehension, autonomy, and data protection as important themes that help ascertain and solidify public trust in electronic health records. As well, health system actors have the capacity to promote or hinder national electronic health record implementation, depending on their actions and how the public perceives those actions. The findings can assist researchers, policymakers, and other health system actors in attaining a better understanding of the intricacies of public trust in electronic health records.

US Residents' Preferences for Sharing of Electronic Health Record and Genetic Information: A Discrete Choice Experiment

OBJECTIVES

The aim to this study was to assess preferences for sharing of electronic health record (EHR) and genetic information separately and to examine whether there are different preferences for sharing these 2 types of information.

METHODS

Using a population-based, nationally representative survey of the United States, we conducted a discrete choice experiment in which half of the subjects (N = 790) responded to questions about sharing of genetic information and the other half (N = 751) to questions about sharing of EHR information. Conditional logistic regression models assessed relative preferences across attribute levels of where patients learn about health information sharing, whether shared data are deidentified, whether data are commercialized, how long biospecimens are kept, and what the purpose of sharing the information is.

RESULTS

Individuals had strong preferences to share deidentified (vs identified) data (odds ratio [OR] 3.26, 95% confidence interval 2.68-3.96) and to be able to opt out of sharing information with commercial companies (OR 4.26, 95% confidence interval 3.42-5.30). There were no significant differences regarding how long biospecimens are kept or why the data are being shared. Individuals had a stronger preference for opting out of sharing genetic (OR 4.26) versus EHR information (OR 2.64) (P = .002).

CONCLUSIONS

Hospital systems and regulatory bodies should consider patient preferences for sharing of personal medical records or genetic information. For both genetic and EHR information, patients strongly prefer their data to be deidentified and to have the choice to opt out of sharing information with commercial companies.

GRANDPA: GeneRAtive network sampling using degree and property augmentation applied to the analysis of partially confidential healthcare networks

Protecting medical privacy can create obstacles in the analysis and distribution of healthcare graphs and statistical inferences accompanying them. We pose a graph simulation model which generates networks using degree and property augmentation and provide a flexible R package that allows users to create graphs that preserve vertex attribute relationships and approximating the retention of topological properties observed in the original graph (e.g., community structure). We illustrate our proposed algorithm using a case study based on Zachary's karate network and a patient-sharing graph generated from Medicare claims data in 2019. In both cases, we find that community structure is preserved, and normalized root mean square error between cumulative distributions of the degrees across the generated and the original graphs is low (0.0508 and 0.0514 respectively).

Research recruitment through the patient portal: perspectives of community focus groups in Seattle and Atlanta

Objective

Research recruitment through patient portals (ie, patient-facing, web-based clinical interfaces) has the potential to be effective, efficient, and inclusive, but best practices remain undefined. We sought to better understand how patients view this recruitment approach.

Materials and Methods

We conducted 6 focus groups in Atlanta, GA and Seattle, WA with members of patient advisory committees and the general public. Discussions addressed acceptability of patient portal recruitment and communication preferences. Focus groups were audio-recorded, transcribed, and analyzed using deductive and inductive codes. Iterative team discussions identified major themes.

Results

Of 49 total participants, 20 were patient advisory committee members. Participants' mean age was 49 (range 18-74); 59% identified as non-Hispanic White and 31% as Black/African American. Participants were supportive of patient portal recruitment and confident that messages were private and legitimate. Participants identified transparency and patient control over whether and how to participate as essential features. Concerns included the frequency of research messages and the ability to distinguish between research and clinical messages. Participants also discussed how patient portal recruitment might affect diversity and inclusion.

Discussion

Focus group participants generally found patient portal recruitment acceptable and perceived it as secure and trustworthy. Transparency, control, and attention to inclusiveness were identified as key considerations for developing best practices.

Conclusion

For institutions implementing patient portal recruitment programs, continued engagement with patient populations can help facilitate translation of these findings into best practices and ensure that implemented strategies accomplish intended goals.

Identifying women with postdelivery posttraumatic stress disorder using natural language processing of personal childbirth narratives

BACKGROUND

Maternal mental disorders are considered a leading complication of childbirth and a common contributor to maternal death. In addition to undermining maternal welfare, untreated postpartum psychopathology can result in child emotional and physical neglect and associated significant pediatric health costs. Some women may experience traumatic childbirth and develop posttraumatic stress disorder symptoms after delivery (childbirth-related posttraumatic stress disorder). Although women are routinely screened for postpartum depression in the United States, there is no recommended protocol to inform the identification of women who are likely to experience childbirth-related posttraumatic stress disorder. Advancements in computational methods of free text have shown promise in informing the diagnosis of psychiatric conditions. Although the language in narratives of stressful events has been associated with posttrauma outcomes, whether the narratives of childbirth processed via machine learning can be useful for childbirth-related posttraumatic stress disorder screening is unknown.

OBJECTIVE

This study aimed to examine the use of written narrative accounts of personal childbirth experiences for the identification of women with childbirth-related posttraumatic stress disorder. To this end, we developed a model based on natural language processing and machine learning algorithms to identify childbirth-related posttraumatic stress disorder via the classification of birth narratives.

STUDY DESIGN

Overall, 1127 eligible postpartum women who enrolled in a study survey during the COVID-19 pandemic provided short written childbirth narrative accounts in which they were instructed to focus on the most distressing aspects of their childbirth experience. They also completed a posttraumatic stress disorder symptom screen to determine childbirth-related posttraumatic stress disorder. After the exclusion criteria were applied, data from 995 participants were analyzed. A machine learning-based Sentence-Transformers natural language processing model was used to represent narratives as vectors that served as inputs for a neural network machine learning model developed in this study to identify participants with childbirth-related posttraumatic stress disorder.

RESULTS

The machine learning model derived from natural language processing of childbirth narratives achieved good performance (area under the curve, 0.75; F1 score, 0.76; sensitivity, 0.8; specificity, 0.70). Moreover, women with childbirth-related posttraumatic stress disorder generated longer narratives (t test results: t=2.30; p=.02) and used more negative emotional expressions (Wilcoxon test: sadness: p=8.90e-04; W=31,017; anger: p=1.32e-02; W=35,005.50) and death-related words (Wilcoxon test: p=3.48e-05; W=34,538) in describing their childbirth experience than those with no childbirth-related posttraumatic stress disorder.

CONCLUSION

This study provided proof of concept that personal childbirth narrative accounts generated in the early postpartum period and analyzed via advanced computational methods can detect with relatively high accuracy women who are likely to endorse childbirth-related posttraumatic stress disorder and those at low risk. This suggests that birth narratives could be promising for informing low-cost, noninvasive tools for maternal mental health screening, and more research that used machine learning to predict early signs of maternal psychiatric morbidity is warranted.

Attitudes of Mental Health Service Users Toward Storage and Use of Electronic Health Records

OBJECTIVE

Electronic health records (EHRs) are used for both clinical practice and research. Because mental health service users' views are underrepresented in perspectives on EHR use, the authors examined service users' awareness, attitudes, and opinions about EHR data storage and sharing.

METHODS

A mixed-methods, cross-sectional design was used to examine attitudes of 253 Norwegian mental health service users who were recruited online to complete a quantitative and qualitative (free-text) survey about EHR utilization.

RESULTS

Most participants were aware that EHRs were stored (95%) and shared (58%). Most thought that patients benefited from EHR storage (84%), trusted authorities with EHR sharing (71%), were willing to share their EHRs to help others (75%), felt they benefited from EHR sharing (75%), and thought EHR sharing was ethical for health care and research (71%). Fewer were aware of EHR sharing for research (36%), and 62% were aware that shared data were anonymized. Of the participants, 69% recognized privacy risks associated with sharing. Lack of transparency and skepticism about anonymization and misuse of EHR data were concerns and perceived risks. Mental health service users thought that EHRs should be shared for policy development (81%), education and training (85%), improving care quality (89%), research (91%), and clinical decision support (81%).

CONCLUSIONS

Participants were aware of and supported EHR sharing for research and clinical care. They supported sharing to help others and were willing to fully participate in clinical care and research, as well as to share EHR information for their own care, research, and the care of others.

Identifying Women with Post-Delivery Posttraumatic Stress Disorder using Natural Language Processing of Personal Childbirth Narratives

Background

Maternal mental disorders are considered a leading complication of childbirth and a common contributor to maternal death. In addition to undermining maternal welfare, untreated postpartum psychopathology can result in child emotional and physical neglect, and associated significant pediatric health costs. Some women may experience a traumatic childbirth and develop posttraumatic stress disorder (PTSD) symptoms following delivery (CB-PTSD). Although women are routinely screened for postpartum depression in the U.S., there is no recommended protocol to inform the identification of women who are likely to experience CB-PTSD. Advancements in computational methods of free text has shown promise in informing diagnosis of psychiatric conditions. Although the language in narratives of stressful events has been associated with post-trauma outcomes, whether the narratives of childbirth processed via machine learning can be useful for CB-PTSD screening is unknown.

Objective

This study examined the utility of written narrative accounts of personal childbirth experience for the identification of women with provisional CB-PTSD. To this end, we developed a model based on natural language processing (NLP) and machine learning (ML) algorithms to identify CB-PTSD via classification of birth narratives.

Study Design

A total of 1,127 eligible postpartum women who enrolled in a study survey during the COVID-19 era provided short written childbirth narrative accounts in which they were instructed to focus on the most distressing aspects of their childbirth experience. They also completed a PTSD symptom screen to determine provisional CB-PTSD. After exclusion criteria were applied, data from 995 participants was analyzed. An ML-based Sentence-Transformer NLP model was used to represent narratives as vectors that served as inputs for a neural network ML model developed in this study to identify participants with provisional CB-PTSD.

Results

The ML model derived from NLP of childbirth narratives achieved good performance: AUC 0.75, F1-score 0.76, sensitivity 0.8, and specificity 0.70. Moreover, women with provisional CB-PTSD generated longer narratives (t-test results: t=2 . 30, p=0 . 02 ) and used more negative emotional expressions (Wilcoxon test: 'sadness': p=8 . 90e- ⁰⁴ , W=31,017 ; 'anger': p=1 . 32e- ⁰² , W=35,005 . 50 ) and death-related words (Wilcoxon test: p=3 . 48e- ⁰⁵ , W=34,538 ) in describing their childbirth experience than those with no CB-PTSD.

Conclusions

This study provides proof of concept that personal childbirth narrative accounts generated in the early postpartum period and analyzed via advanced computational methods can detect with relatively high accuracy women who are likely to endorse CB-PTSD and those at low risk. This suggests that birth narratives could be promising for informing low-cost, non-invasive tools for maternal mental health screening, and more research that utilizes ML to predict early signs of maternal psychiatric morbidity is warranted.

Medical Technologies Past and Present: How History Helps to Understand the Digital Era

This article explores the relationship between medicine's history and its digital present through the lens of the physician-patient relationship. Today the rhetoric surrounding the introduction of new technologies into medicine tends to emphasize that technologies are disturbing relationships, and that the doctor-patient bond reflects a more 'human' era of medicine that should be preserved. Using historical studies of pre-modern and modern Western European medicine, this article shows that patient-physician relationships have always been shaped by material cultures. We discuss three activities - recording, examining, and treating - in the light of their historical antecedents, and suggest that the notion of 'human medicine' is ever-changing: it consists of social attributions of skills to physicians that played out very differently over the course of history.

Mining the Data: Exploring Rural Patients' Attitudes about the Use of Their Personal Information in Research

BACKGROUND

This study examines rural patients' perceived importance of knowing or being consulted about researchers' access and use of their personal data (identifiable and de-identified health information, and identifiable and de-identified non-health information) across five scenarios. This study also examines their views on stewardship or governance of their personal information by researchers in their healthcare systems.

METHODS

We conducted a survey by mail. Data were analyzed using descriptive statistics. Multivariable regression analyses were conducted across each scenario and type of personal data with the same variables included in each model.

RESULTS

The majority of participants said it was "very important/absolutely essential" to know the purpose of the study, to be asked every time, and to know the policies governing researcher access and use of their identifiable health information. Just over two-thirds of respondents thought it "very important/absolutely essential" to know who serves on the data governance committee and to have a community member serve. Distrust in healthcare organizations was positively correlated with the scenarios while willingness to give permission to donate leftover biological specimens was negatively correlated.

CONCLUSION

Our study findings indicate that the type of personal information being accessed and used generally matters to 1,407 patients living in rural Pennsylvania. We also demonstrate that knowing their healthcare organizations' governance policies and practices for managing their personal data is important to many rural Pennsylvania patients. Biomedical researchers need to recognize and attend to those differences as much as possible in order to expand opportunities for and participation in research by residents of these rural communities.

Supplemental data for this article is available online at.

Multicomponent Informed Consent with Marshallese Participants

Pacific Islanders are the second fastest-growing population in the United States; however, Pacific Islanders, and Marshallese specifically, are underrepresented in health research. A community-based participatory research (CBPR) approach was used to engage Marshallese stakeholders and build an academic-community research collaborative to conduct health disparities research. Our CBPR partnership pilot tested a multicomponent consent process that provides participants the option to control the use of their data. Consent forms used concise plain language to describe study information, including participant requirements, risks, and personal health information protections, and were available in both English and Marshallese. This study demonstrates that when provided a multicomponent consent, the vast majority of consenting study participants (89.6%) agreed to all additional options, and only five (10.4%) provided consent for some but not all options. Our description of the development and implementation of a multicomponent consent using a CBPR approach adds a specific example of community engagement and may be informative for other indigenous populations.

The Competing Demands of Patient Privacy and Clinical Research

Privacy and confidentiality of personal medical information are cornerstones of ethical clinical care and ethical research. But real-world research has challenged traditional ways of thinking about privacy and confidentiality of information. In today's world of "big data" and learning health care systems, researchers and others are combining multiple sources of information to address complex problems. We present a case study that highlights the ethical concerns that arise when a patient who is employed by an academic medical center learns through a research invitational letter that her private information was accessed at this center without her consent. We discuss the ethical challenges of balancing patient privacy with advancing clinical research and ask, what level of privacy and confidentiality can and should patients expect from their clinician providers, fellow research colleagues, and institutions?