Patients’ willingness to share digital health and non-health data for research: a cross-sectional study

Seltzer, Emily; Goldshear, Jesse L.; Guntuku, Sharath Chandra; Grande, Dave; Asch, David A.; Klinger, Elissa V.; Merchant, Raina M.

doi:10.1186/s12911-019-0886-9

Cited by 56 publications

(50 citation statements)

References 26 publications

(16 reference statements)

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“…The potential to glean such personal information about individuals when they might visit the hospital from social media reveal challenges associated with addressing several ethical and privacy concerns. Research has found that a large proportion of patients are willing to securely share their personal data sources 24 and are open to linking it to their electronic health record 22 for research purposes. In this type of work, privacy is of central importance.…”

Section: Discussionmentioning

confidence: 99%

Variability in Language used on Social Media prior to Hospital Visits

Guntuku

Schwartz

Kashyap

et al. 2020

Sci Rep

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forecasting healthcare utilization has the potential to anticipate care needs, either accelerating needed care or redirecting patients toward care most appropriate to their needs. While prior research has utilized clinical information to forecast readmissions, analyzing digital footprints from social media can inform our understanding of individuals' behaviors, thoughts, and motivations preceding a healthcare visit. We evaluate how language patterns on social media change prior to emergency department (eD) visits and inpatient hospital admissions in this case-crossover study of adult patients visiting a large urban academic hospital system who consented to share access to their history of facebook statuses and electronic medical records. An ensemble machine learning model forecasted eD visits and inpatient admissions with out-of-sample cross-validated AUCs of 0.64 and 0.70 respectively. Prior to an ED visit, there was a significant increase in depressed language (Cohen's d = 0.238), and a decrease in informal language (d = 0.345). Facebook posts prior to an inpatient admission showed significant increase in expressions of somatic pain (d = 0.267) and decrease in extraverted/social language (d = 0.357). These results are a first step in developing methods to utilize user-generated content to characterize patient care-seeking context which could ultimately enable better allocation of resources and potentially early interventions to reduce unplanned visits.

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Section: Discussionmentioning

confidence: 99%

Variability in Language used on Social Media prior to Hospital Visits

Guntuku

Schwartz

Kashyap

et al. 2020

Sci Rep

Self Cite

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“…mHealth applications can have a significant impact aiming to empower patients further. Numerous studies found that individuals are willing to share their medical data with respective healthcare providers to get a more suitable treatment [42,43]. Here we presented two strategies to ensure fast and secure communication between patients and healthcare providers based on established concepts, thereby providing intra-sectoral empowerment of involved parties.…”

Section: Discussionmentioning

confidence: 99%

Perspective on mHealth Concepts to Ensure Users’ Empowerment–From Adverse Event Tracking for COVID-19 Vaccinations to Oncological Treatment

et al. 2021

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HAK acknowledges funding was provided by the Ministry of Science and Art Baden-Württemberg (Zentrum für Innovative Versorgung, ZIV), the German Federal Ministry of Education and Research (BMBF) as part of the DIFUTURE project (Medical Informatics Initiative, grant number 01ZZ1804I), and the Ministry of Social Affairs and Integration Baden-Württemberg as part of the project feelBack (networked, digital, patient-related feedback psycho-oncology).

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“…Specific to psychotic disorders and most relevant to our context, based on semistructured interviews with 112 psychotic disorder patients, in a work by Birnbaum et al [ 33 ], 80.7% (88/109) of patients were open to sharing their social media data with clinical researchers to explore how it can potentially inform their treatment. Drawing on these findings, and because clinical researchers have advocated that it is important to first focus on issues related to the accuracy, interpretability, meaning, and actionability of these data such as social media [ 34 ], in this paper, we focus on co-design exercises with clinicians. Our rationale is further bolstered by Baier’s [ 35 ] observation, “Without judicious consideration, social media use by [clinicians] can lead to inadvertent self-disclosures to [patients] that risk damaging the therapeutic alliance, interfering with therapeutic processes, and placing both the [patient] and clinician at risk”.…”

Section: Methodsmentioning

confidence: 99%

Designing a Clinician-Facing Tool for Using Insights From Patients’ Social Media Activity: Iterative Co-Design Approach

et al. 2020

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Background Recent research has emphasized the need for accessing information about patients to augment mental health patients’ verbal reports in clinical settings. Although it has not been introduced in clinical settings, computational linguistic analysis on social media has proved it can infer mental health attributes, implying a potential use as collateral information at the point of care. To realize this potential and make social media insights actionable to clinical decision making, the gaps between computational linguistic analysis on social media and the current work practices of mental health clinicians must be bridged. Objective This study aimed to identify information derived from patients’ social media data that can benefit clinicians and to develop a set of design implications, via a series of low-fidelity (lo-fi) prototypes, on how to deliver the information at the point of care. Methods A team of clinical researchers and human-computer interaction (HCI) researchers conducted a long-term co-design activity for over 6 months. The needs-affordances analysis framework was used to refine the clinicians’ potential needs, which can be supported by patients’ social media data. On the basis of those identified needs, the HCI researchers iteratively created 3 different lo-fi prototypes. The prototypes were shared with both groups of researchers via a videoconferencing software for discussion and feedback. During the remote meetings, potential clinical utility, potential use of the different prototypes in a treatment setting, and areas of improvement were discussed. Results Our first prototype was a card-type interface that supported treatment goal tracking. Each card included attribute levels: depression, anxiety, social activities, alcohol, and drug use. This version confirmed what types of information are helpful but revealed the need for a glanceable dashboard that highlights the trends of these information. As a result, we then developed the second prototype, an interface that shows the clinical state and trend. We found that focusing more on the changes since the last visit without visual representation can be more compatible with clinicians’ work practices. In addition, the second phase of needs-affordances analysis identified 3 categories of information relevant to patients with schizophrenia: symptoms related to psychosis, symptoms related to mood and anxiety, and social functioning. Finally, we developed the third prototype, a clinical summary dashboard that showed changes from the last visit in plain texts and contrasting colors. Conclusions This exploratory co-design research confirmed that mental health attributes inferred from patients’ social media data can be useful for clinicians, although it also revealed a gap between computational social media analyses and clinicians’ expectations and conceptualizations of patients’ mental health states. In summary, the iterative co-design process crystallized design directions for the future interface, including how we can organize and provide symptom-related information in a way that minimizes the clinicians’ workloads.

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Patients’ willingness to share digital health and non-health data for research: a cross-sectional study

Cited by 56 publications

References 26 publications

Variability in Language used on Social Media prior to Hospital Visits

Variability in Language used on Social Media prior to Hospital Visits

Perspective on mHealth Concepts to Ensure Users’ Empowerment–From Adverse Event Tracking for COVID-19 Vaccinations to Oncological Treatment

Designing a Clinician-Facing Tool for Using Insights From Patients’ Social Media Activity: Iterative Co-Design Approach

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