Emerging uses of patient generated health data in clinical research

Factors That Influence Successful Adoption of Real-Time Location Systems for Use in a Dementia Care Setting: Mixed Methods Study

BACKGROUND

Technology has been identified as a potential solution to alleviate resource gaps and augment care delivery in dementia care settings such as hospitals, long-term care, and retirement homes. There has been an increasing interest in using real-time location systems (RTLS) across health care settings for older adults with dementia, specifically related to the ability to track a person's movement and location.

OBJECTIVE

In this study, we aimed to explore the factors that influence the adoption or nonadoption of an RTLS during its implementation in a specialized inpatient dementia unit in a tertiary care rehabilitation hospital.

METHODS

The study included data from a brief quantitative survey and interviews from a convenience sample of frontline participants. Our deductive analysis of the interview used the 3 categories of the Fit Between Individuals, Task, and Technology framework as follows: individual and task, individual and technology, and task and technology. The purpose of using this framework was to assess the quality of the fit between technology attributes and an individual's self-reported intentions to adopt RTLS technology.

RESULTS

A total of 20 health care providers (HCPs) completed the survey, of which 16 (80%) participated in interviews. Coding and subsequent analysis identified 2 conceptual subthemes in the individual-task fit category, including the identification of the task and the perception that participants were missing at-risk patient events. The task-technology fit category consisted of 3 subthemes, including reorganization of the task, personal control in relation to the task, and efficiency or resource allocation. A total of 4 subthemes were identified in the individual-technology fit category, including privacy and personal agency, trust in the technology, user interfaces, and perceptions of increased safety.

CONCLUSIONS

By the end of the study, most of the unit's HCPs were using the tablet app based on their perception of its usefulness, its alignment with their comfort level with technology, and its ability to help them perform job responsibilities. HCPs perceived that they were able to reduce patient search time dramatically, yet any improvements in care were noted to be implied, as this was not measured. There was limited anecdotal evidence of reduced patient risk or adverse events, but greater reported peace of mind for HCPs overseeing patients' activity levels.

Remote Patient-Reported Outcomes and Activity Monitoring to Improve Patient-Clinician Communication Regarding Symptoms and Functional Status: A Randomized Controlled Trial

PURPOSE

Routine collection of patient-generated health data (PGHD) may promote earlier recognition of symptomatic and functional decline. This trial assessed the impact of an intervention integrating remote PGHD collection with patient nudges on symptom and functional status understanding between patients with advanced cancer and their oncology team.

METHODS

This three-arm randomized controlled trial was conducted from November 19, 2020, to December 17, 2021, at a large tertiary oncology practice. We enrolled patients with stage IV GI and lung cancers undergoing chemotherapy. Over 6 months, patients in two intervention arms received PROStep-weekly text message-based symptom surveys and passive activity monitoring using a wearable accelerometer. PGHD were summarized in dashboards given to patients' oncology team before appointments. One intervention arm received an additional text-based active choice prompt to discuss worsening symptoms or functional status with their clinician. Control patients did not receive PROStep. The coprimary outcomes patient perceptions of oncology team symptom and functional understanding at 6 months were measured on a 1-5 Likert scale (5 = high understanding).

RESULTS

One hundred eight patients enrolled: 55% male, 81% White, and 77% had GI cancers. Patient-reported clinician understanding did not differ between control and intervention arms for symptoms (4.5 v 4.5; P = .87) or functional status (4.5 v 4.3; P = .31). In the intervention arms, combined patient adherence to weekly symptom reports and daily activity monitoring was 64% and 53%, respectively. Intervention patients in the PROStep versus PROStep + active choice arms reported low burden from wearing the accelerometer (mean burden [standard deviation], 2.7 [1.3] v 2.1 [1.3]; P = .15) and completing surveys (2.1 [1.2] v 1.9 [1.3]; P = .44).

CONCLUSION

Patients receiving PROStep reported high understanding of symptoms and functional status from their oncology team, although this did not differ from controls.

Big Data in Oncology Nursing Research: State of the Science

OBJECTIVE

To review the state of oncology nursing science as it pertains to big data. The authors aim to define and characterize big data, describe key considerations for accessing and analyzing big data, provide examples of analyses of big data in oncology nursing science, and highlight ethical considerations related to the collection and analysis of big data.

DATA SOURCES

Peer-reviewed articles published by investigators specializing in oncology, nursing, and related disciplines.

CONCLUSION

Big data is defined as data that are high in volume, velocity, and variety. To date, oncology nurse scientists have used big data to predict patient outcomes from clinician notes, identify distinct symptom phenotypes, and identify predictors of chemotherapy toxicity, among other applications. Although the emergence of big data and advances in computational methods provide new and exciting opportunities to advance oncology nursing science, several challenges are associated with accessing and using big data. Data security, research participant privacy, and the underrepresentation of minoritized individuals in big data are important concerns.

IMPLICATIONS FOR NURSING PRACTICE

With their unique focus on the interplay between the whole person, the environment, and health, nurses bring an indispensable perspective to the interpretation and application of big data research findings. Given the increasing ubiquity of passive data collection, all nurses should be taught the definition, characteristics, applications, and limitations of big data. Nurses who are trained in big data and advanced computational methods will be poised to contribute to guidelines and policies that preserve the rights of human research participants.

Real-world data for precision cancer medicine-A European perspective

Leveraging real-world data (RWD) for drug access is necessary to overcome a key challenge of modern precision oncology: tackling numerous low-prevalence oncogenic mutations across cancers. Withholding a potentially active medication in patients with rare mutations for the sake of control chemotherapy or "best" supportive care is neither practicable nor ethically justifiable anymore, particularly as RWD could meanwhile be used instead, according to scientific principles outlined by the US Food and Drug Administration, European Medicines Agency and other stakeholders. However, practical implementation varies, with occasionally opposite recommendations based on the same evidence in different countries. In the face of growing need for precision drugs, more transparency of evaluation, a priori availability of guidance for the academia and industry, as well as a harmonized framework for health technology assessment across the European Union (EU) are imperative. These could in turn trigger infrastructural changes in national and pan-European registries, cancer management guidelines (e.g., frequency of routine radiologic restaging, inclusion of patient-reported outcomes), and the health data space, to ensure conformity with declared standards and facilitate extraction of RWD sets (including patient-level data) suitable for approval and pricing with minimal effort. For an EU-wide unification of precision cancer medicine, collective negotiation of drug supply contracts and funding solidarity would additionally be required to handle the financial burden. According to experience from pivotal European programs, off-label use could potentially also be harmonized across EU-states to accelerate availability of novel drugs, streamline collection of valuable RWD, and mitigate related costs through wider partnerships with pharmaceutical companies.

Accuracy of smartphone camera urine photo colorimetry as indicators of dehydration

Objective

Direct urine color assessment has been shown to correlate with hydration status. However, this method is subject to inter- and intra-observer variability. Digital image colorimetry provides a more objective method. This study evaluated the diagnostic accuracy of urine photo colorimetry using different smartphones under different lighting conditions, and determined the optimal cut-off value to predict clinical dehydration.

Methods

The urine samples were photographed in a customized photo box, under five simulated lighting conditions, using five smartphones. The images were analyzed using Adobe Photoshop to obtain Red, Green, and Blue (RGB) values. The correlation between RGB values and urine laboratory parameters were determined. The optimal cut-off value to predict dehydration was determined using area under the receiver operating characteristic curve.

Results

A total of 56 patients were included in the data analysis. Images captured using five different smartphones under five lighting conditions produced a dataset of 1400 images. The study found a statistically significant correlation between Blue and Green values with urine osmolality, sodium, urine specific gravity, protein, and ketones. The diagnostic accuracy of the Blue value for predicting dehydration were "good" to "excellent" across all phones under all lighting conditions with sensitivity >90% at cut-off Blue value of 170.

Conclusions

Smartphone-based urine colorimetry is a highly sensitive tool in predicting dehydration.

A cervical cancer biorepository for pharmacogenomics research in Zimbabwe

BACKGROUND

Research infrastructures such as biorepositories are essential to facilitate genomics and its growing applications in health research and translational medicine in Africa. Using a cervical cancer cohort, this study describes the establishment of a biorepository consisting of biospecimens and matched phenotype data for use in genomic association analysis and pharmacogenomics research.

METHOD

Women aged > 18 years with a recent histologically confirmed cervical cancer diagnosis were recruited. A workflow pipeline was developed to collect, store, and analyse biospecimens comprising donor recruitment and informed consent, followed by data and biospecimen collection, nucleic acid extraction, storage of genomic DNA, genetic characterization, data integration, data analysis and data interpretation. The biospecimen and data storage infrastructure included shared -20 °C to -80 °C freezers, lockable cupboards, secured access-controlled laptop, password protected online data storage on OneDrive software. The biospecimen or data storage, transfer and sharing were compliant with the local and international biospecimen and data protection laws and policies, to ensure donor privacy, trust, and benefits for the wider community.

RESULTS

This initial establishment of the biorepository recruited 410 women with cervical cancer. The mean (± SD) age of the donors was 52 (± 12) years, comprising stage I (15%), stage II (44%), stage III (47%) and stage IV (6%) disease. The biorepository includes whole blood and corresponding genomic DNA from 311 (75.9%) donors, and tumour biospecimens and corresponding tumour DNA from 258 (62.9%) donors. Datasets included information on sociodemographic characteristics, lifestyle, family history, clinical information, and HPV genotype. Treatment response was followed up for 12 months, namely, treatment-induced toxicities, survival vs. mortality, and disease status, that is disease-free survival, progression or relapse, 12 months after therapy commencement.

CONCLUSION

The current work highlights a framework for developing a cancer genomics cohort-based biorepository on a limited budget. Such a resource plays a central role in advancing genomics research towards the implementation of personalised management of cancer.

Identification of Healthy and Unhealthy Lifestyles by a Wearable Activity Tracker in Type 2 Diabetes: A Machine Learning-Based Analysis

Lifestyle is a critical aspect of diabetes management. We aimed to define a healthy lifestyle using objectively measured parameters obtained from a wearable activity tracker (Fitbit) in patients with type 2 diabetes. This prospective observational study included 24 patients (mean age, 46.8 years) with type 2 diabetes. Expectation-maximization clustering analysis produced two groups: A (n=9) and B (n=15). Group A had a higher daily step count, lower resting heart rate, longer sleep duration, and lower mean time differences in going to sleep and waking up than group B. A Shapley additive explanation summary analysis indicated that sleep-related factors were key elements for clustering. The mean hemoglobin A1c level was 0.3 percentage points lower at the end of follow-up in group A than in group B. Factors related to regular sleep patterns could be possible determinants of lifestyle clustering in patients with type 2 diabetes.

Digital Health Applications in Oncology: An Opportunity to Seize

Digital health advances have transformed many clinical areas including psychiatric and cardiovascular care. However, digital health innovation is relatively nascent in cancer care, which represents the fastest growing area of health-care spending. Opportunities for digital health innovation in oncology include patient-facing technologies that improve patient experience, safety, and patient-clinician interactions; clinician-facing technologies that improve their ability to diagnose pathology and predict adverse events; and quality of care and research infrastructure to improve clinical workflows, documentation, decision support, and clinical trial monitoring. The COVID-19 pandemic and associated shifts of care to the home and community dramatically accelerated the integration of digital health technologies into virtually every aspect of oncology care. However, the pandemic has also exposed potential flaws in the digital health ecosystem, namely in clinical integration strategies; data access, quality, and security; and regulatory oversight and reimbursement for digital health technologies. Stemming from the proceedings of a 2020 workshop convened by the National Cancer Policy Forum of the National Academies of Sciences, Engineering, and Medicine, this article summarizes the current state of digital health technologies in medical practice and strategies to improve clinical utility and integration. These recommendations, with calls to action for clinicians, health systems, technology innovators, and policy makers, will facilitate efficient yet safe integration of digital health technologies into cancer care.

Patient and clinician nudges to improve symptom management in advanced cancer using patient-generated health data: study protocol for the PROStep randomised controlled trial

INTRODUCTION

Patients with advanced cancers often face significant symptoms from their cancer and adverse effects from cancer-associated therapy. Patient-generated health data (PGHD) are routinely collected information about symptoms and activity levels that patients either directly report or passively record using devices such as wearable accelerometers. The objective of this study was to test the impact of an intervention integrating remote collection of PGHD with clinician and patient nudges to inform communication between patients with advanced cancer and their oncology team regarding symptom burden and functional status.

METHODS AND ANALYSIS

This single-centre prospective randomised controlled trial randomises patients with metastatic gastrointestinal or lung cancers into one of three arms: (A) usual care, (B) an intervention that integrates PGHD (including weekly text-based symptom surveys and passively recorded step counts) into a dashboard delivered to oncology clinicians at each visit and (C) the same intervention as arm B but with an additional text-based active choice intervention to patients to encourage discussing their symptoms with their oncology team. The study will enrol approximately 125 participants. The coprimary outcomes are patient perceptions of their oncology team's understanding of their symptoms and their functional status. Secondary outcomes are intervention utility and adherence.

ETHICS AND DISSEMINATION

This study has been approved by the institutional review board at the University of Pennsylvania. Study results will be disseminated using methods that describe the results in ways that key stakeholders can best understand and implement.

TRIAL REGISTRATION NUMBERS

NCT04616768 and 843 616.

Time Synchronization of Multimodal Physiological Signals through Alignment of Common Signal Types and Its Technical Considerations in Digital Health

Background: Despite advancements in digital health, it remains challenging to obtain precise time synchronization of multimodal physiological signals collected through different devices. Existing algorithms mainly rely on specific physiological features that restrict the use cases to certain signal types. The present study aims to complement previous algorithms and solve a niche time alignment problem when a common signal type is available across different devices. Methods: We proposed a simple time alignment approach based on the direct cross-correlation of temporal amplitudes, making it agnostic and thus generalizable to different signal types. The approach was tested on a public electrocardiographic (ECG) dataset to simulate the synchronization of signals collected from an ECG watch and an ECG patch. The algorithm was evaluated considering key practical factors, including sample durations, signal quality index (SQI), resilience to noise, and varying sampling rates. Results: The proposed approach requires a short sample duration (30 s) to operate, and demonstrates stable performance across varying sampling rates and resilience to common noise. The lowest synchronization delay achieved by the algorithm is 0.13 s with the integration of SQI thresholding. Conclusions: Our findings help improve the time alignment of multimodal signals in digital health and advance healthcare toward precise remote monitoring and disease prevention.

Adoption of Patient-Generated Health Data in Oncology: A Report From the NCCN EHR Oncology Advisory Group

BACKGROUND

Collecting, monitoring, and responding to patient-generated health data (PGHD) are associated with improved quality of life and patient satisfaction, and possibly with improved patient survival in oncology. However, the current state of adoption, types of PGHD collected, and degree of integration into electronic health records (EHRs) is unknown.

METHODS

The NCCN EHR Oncology Advisory Group formed a Patient-Reported Outcomes (PRO) Workgroup to perform an assessment and provide recommendations for cancer centers, researchers, and EHR vendors to advance the collection and use of PGHD in oncology. The issues were evaluated via a survey of NCCN Member Institutions. Questions were designed to assess the current state of PGHD collection, including how, what, and where PGHD are collected. Additionally, detailed questions about governance and data integration into EHRs were asked.

RESULTS

Of 28 Member Institutions surveyed, 23 responded. The collection and use of PGHD is widespread among NCCN Members Institutions (96%). Most centers (90%) embed at least some PGHD into the EHR, although challenges remain, as evidenced by 88% of respondents reporting the use of instruments not integrated. Forty-seven percent of respondents are leveraging PGHD for process automation and adherence to best evidence. Content type and integration touchpoints vary among the members, as well as governance maturity.

CONCLUSIONS

The reported variability regarding PGHD suggests that it may not yet have reached its full potential for oncology care delivery. As the adoption of PGHD in oncology continues to expand, opportunities exist to enhance their utility. Among the recommendations for cancer centers is establishment of a governance process that includes patients. Researchers should consider determining which PGHD instruments confer the highest value. It is recommended that EHR vendors collaborate with cancer centers to develop solutions for the collection, interpretation, visualization, and use of PGHD.

A qualitative study of clinician perceptions regarding the potential role for digital health interventions for the management of COPD

Effective self-management of chronic obstructive pulmonary disease (COPD) can lead to increased patient control and reduced health care costs. However, both patients and healthcare professionals encounter significant challenges. Digital health interventions, such as smart oximeters and COPD self-management applications, promise to enhance the management of COPD, yet, there is little evidence to support their use and user-experience issues are still common. Understanding the needs of healthcare professionals is central for increasing adoption and engagement with digital health interventions but little is known about their perceptions of digital health interventions in COPD. This paper explored the perceptions of healthcare professionals regarding the potential role for DHI in the management of COPD. Snowball sampling was used to recruit the participants (n = 32). Each participant underwent a semi-structured interview. Using NVivo 12 software, thematic analysis was completed. Healthcare professionals perceive digital health interventions providing several potential benefits to the management of COPD including the capture of patient status indicators during the interappointment period, providing new patient data to support the consultation process and perceived digital health interventions as a potential means to improve patient engagement. The findings offer new insights regarding potential future use-cases for digital health interventions in COPD, which can help ease user-experience issues as they align with the needs of healthcare professionals.

Symptom Monitoring in Children With Life-Threatening Illness: A Feasibility Study Using mHealth

Children with life-threatening illness (C-LTI) experience considerable symptom distress. Mobile technology may offer opportunities to better obtain symptom data that will lead to better symptom management. A mixed-methods study was conducted to explore the feasibility of monitoring and visualizing symptoms using 2 mobile health devices in C-LTI. Participants engaged with the Apple Watch 56% and recorded in the study app 63% of their study days. Our findings showed feasibility of using mobile technology for monitoring symptoms and further explored opportunities to visualize these data showing symptom occurrences, patterns, and trajectories in C-LTI.

Patient-generated data in the management of HIV: a scoping review

OBJECTIVES

Patient-generated data (PGData) are an emergent research area and may improve HIV care. The objectives of this scoping review were to synthesise, evaluate and make recommendations based on the available literature regarding PGData use in HIV care.

DESIGN

Scoping review.

DATA SOURCES

Embase, Medline, CINAHL Plus, Web of Science, Scopus, PsycINFO and Emcare databases.

ELIGIBILITY CRITERIA

Studies involving PGData use within HIV care for people living with HIV and/or healthcare professionals (HCPs) published before February 2021.

DATA EXTRACTION AND SYNTHESIS

Data were extracted using a table and the Mixed Methods Appraisal Tool was used to assess empirical rigour. We used thematic analysis to evaluate content.

RESULTS

11 articles met the eligibility criteria. Studies were observational, predominantly concerned hypothetical or novel digital platforms, mainly conducted in high-income settings, and had small sample sizes (range=10-160). There were multiple definitions of PGData. In the majority of studies (n=9), participants were people living with HIV, with a few studies including HCPs, informatics specialists or mixed participant groups. Participants living with HIV were aged 23-78 years, mostly men, of diverse ethnicities, and had low educational, health literacy and income levels.We identified four key themes: (1) Perceptions of PGData and associated digital platforms; (2) Opportunities; (3) Anticipated barriers and (4) Potential impact on patient-HCP relationships.

CONCLUSIONS

Use of PGData within HIV care warrants further study, especially with regard to digital inequalities, data privacy and security. There is a need for longitudinal data on use within HIV in a variety of settings with a broad range of users, including impact on clinical outcomes. This will allow greater understanding of the role of PGData use in improving the health and well-being of people living with HIV, which is increasingly pertinent as digital healthcare becomes more widespread as a result of COVID-19.

Factors Affecting the Quality of Person-Generated Wearable Device Data and Associated Challenges: Rapid Systematic Review

Background

There is increasing interest in reusing person-generated wearable device data for research purposes, which raises concerns about data quality. However, the amount of literature on data quality challenges, specifically those for person-generated wearable device data, is sparse.

Objective

This study aims to systematically review the literature on factors affecting the quality of person-generated wearable device data and their associated intrinsic data quality challenges for research.

Methods

The literature was searched in the PubMed, Association for Computing Machinery, Institute of Electrical and Electronics Engineers, and Google Scholar databases by using search terms related to wearable devices and data quality. By using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, studies were reviewed to identify factors affecting the quality of wearable device data. Studies were eligible if they included content on the data quality of wearable devices, such as fitness trackers and sleep monitors. Both research-grade and consumer-grade wearable devices were included in the review. Relevant content was annotated and iteratively categorized into semantically similar factors until a consensus was reached. If any data quality challenges were mentioned in the study, those contents were extracted and categorized as well.

Results

A total of 19 papers were included in this review. We identified three high-level factors that affect data quality—device- and technical-related factors, user-related factors, and data governance-related factors. Device- and technical-related factors include problems with hardware, software, and the connectivity of the device; user-related factors include device nonwear and user error; and data governance-related factors include a lack of standardization. The identified factors can potentially lead to intrinsic data quality challenges, such as incomplete, incorrect, and heterogeneous data. Although missing and incorrect data are widely known data quality challenges for wearable devices, the heterogeneity of data is another aspect of data quality that should be considered for wearable devices. Heterogeneity in wearable device data exists at three levels: heterogeneity in data generated by a single person using a single device (within-person heterogeneity); heterogeneity in data generated by multiple people who use the same brand, model, and version of a device (between-person heterogeneity); and heterogeneity in data generated from multiple people using different devices (between-person heterogeneity), which would apply especially to data collected under a bring-your-own-device policy.

Conclusions

Our study identifies potential intrinsic data quality challenges that could occur when analyzing wearable device data for research and three major contributing factors for these challenges. As poor data quality can compromise the reliability and accuracy of research results, further investigation is needed on how to address the data quality challenges of wearable devices.

Perspectives Toward Seeking Treatment Among Patients With Psoriasis: Protocol for a Twitter Content Analysis

BACKGROUND

Psoriasis is an autoimmune disease estimated to affect more than 6 million adults in the United States. It poses a significant public health problem and contributes to rising health care costs, affecting people's quality of life and ability to work. Previous research showed that nontreatment and undertreatment of patients with psoriasis remain a significant problem. Perspectives of patients toward seeking psoriasis treatment are understudied. Social media offers a new data source of user-generated content. Researchers suggested that the social network Twitter may serve as a rich avenue for exploring how patients communicate about their health issues.

OBJECTIVE

The objective of this study is to conduct a content analysis of Twitter posts (in English) published by users in the United States between February 1, 2016, and October 31, 2018, to examine perspectives that potentially influence the treatment decision among patients with psoriasis.

METHODS

User-generated Twitter posts that include keywords related to psoriasis will be analyzed using text classifiers to identify themes related to the research questions. We will use Symplur Signals, a health care social media analytics platform, to access the Twitter data. We will use descriptive statistics to analyze the data and identify the most prevalent topics in the Twitter content among people with psoriasis.

RESULTS

This study is supported by the National Center for Advancing Translational Science through a Clinical and Translational Science Award award. Study approval was obtained from the institutional review board at the University of Southern California. Data extraction and cleaning are complete. For the time period from February 1, 2016, to October 31, 2018, we obtained 95,040 Twitter posts containing terms related to "psoriasis" from users in the United States published in English. After removing duplicates, retweets, and non-English tweets, we found that 75.51% (52,301/69,264) of the psoriasis-related posts were sent by commercial or bot-like accounts, while 16,963 posts were noncommercial and will be included in the analysis to assess the patient perspective. Analysis was completed in Summer 2020.

CONCLUSIONS

This protocol paper provides a detailed description of a social media research project including the process of data extraction, cleaning, and analysis. It is our goal to contribute to the development of more transparent social media research efforts. Our findings will shed light on whether Twitter provides a promising data source for garnering patient perspective data about psoriasis treatment decisions. The data will also help to determine whether Twitter might serve as a potential outreach platform for raising awareness of psoriasis and treatment options among patients and implementing related health interventions.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/13731.

Factors Affecting the Implementation, Use, and Adoption of Real-Time Location System Technology for Persons Living With Cognitive Disabilities in Long-term Care Homes: Systematic Review

BACKGROUND

As the aging population continues to grow, the number of adults living with dementia or other cognitive disabilities in residential long-term care homes is expected to increase. Technologies such as real-time locating systems (RTLS) are being investigated for their potential to improve the health and safety of residents and the quality of care and efficiency of long-term care facilities.

OBJECTIVE

The aim of this study is to identify factors that affect the implementation, adoption, and use of RTLS for use with persons living with dementia or other cognitive disabilities in long-term care homes.

METHODS

We conducted a systematic review of the peer-reviewed English language literature indexed in MEDLINE, Embase, PsycINFO, and CINAHL from inception up to and including May 5, 2020. Search strategies included keywords and subject headings related to cognitive disability, residential long-term care settings, and RTLS. Study characteristics, methodologies, and data were extracted and analyzed using constant comparative techniques.

RESULTS

A total of 12 publications were included in the review. Most studies were conducted in the Netherlands (7/12, 58%) and used a descriptive qualitative study design. We identified 3 themes from our analysis of the studies: barriers to implementation, enablers of implementation, and agency and context. Barriers to implementation included lack of motivation for engagement; technology ecosystem and infrastructure challenges; and myths, stories, and shared understanding. Enablers of implementation included understanding local workflows, policies, and technologies; usability and user-centered design; communication with providers; and establishing policies, frameworks, governance, and evaluation. Agency and context were examined from the perspective of residents, family members, care providers, and the long-term care organizations.

CONCLUSIONS

There is a striking lack of evidence to justify the use of RTLS to improve the lives of residents and care providers in long-term care settings. More research related to RTLS use with cognitively impaired residents is required; this research should include longitudinal evaluation of end-to-end implementations that are developed using scientific theory and rigorous analysis of the functionality, efficiency, and effectiveness of these systems. Future research is required on the ethics of monitoring residents using RTLS and its impact on the privacy of residents and health care workers.

Interrelationships Between Patients' Data Tracking Practices, Data Sharing Practices, and Health Literacy: Onsite Survey Study

Background

Although the use of patient-generated data (PGD) in the optimization of patient care shows great promise, little is known about whether patients who track their PGD necessarily share the data with their clinicians. Meanwhile, health literacy—an important construct that captures an individual’s ability to manage their health and to engage with their health care providers—has often been neglected in prior studies focused on PGD tracking and sharing. To leverage the full potential of PGD, it is necessary to bridge the gap between patients’ data tracking and data sharing practices by first understanding the interrelationships between these practices and the factors contributing to these practices.

Objective

This study aims to systematically examine the interrelationships between PGD tracking practices, data sharing practices, and health literacy among individual patients.

Methods

We surveyed 109 patients at the time they met with a clinician at a university health center, unlike prior research that often examined patients’ retrospective experience after some time had passed since their clinic visit. The survey consisted of 39 questions asking patients about their PGD tracking and sharing practices based on their current clinical encounter. The survey also contained questions related to the participants’ health literacy. All the participants completed the survey on a tablet device. The onsite survey study enabled us to collect ecologically valid data based on patients’ immediate experiences situated within their clinic visit.

Results

We found no evidence that tracking PGD was related to self-reports of having sufficient information to manage one’s health; however, the number of data types participants tracked positively related to their self-assessed ability to actively engage with health care providers. Participants’ data tracking practices and their health literacy did not relate to their data sharing practices; however, their ability to engage with health care providers positively related to their willingness to share their data with clinicians in the future. Participants reported several benefits of, and barriers to, sharing their PGD with clinicians.

Conclusions

Although tracking PGD could help patients better engage with health care providers, it may not provide patients with sufficient information to manage their health. The gaps between tracking and sharing PGD with health care providers call for efforts to inform patients of how their data relate to their health and to facilitate efficient clinician-patient communication. To realize the full potential of PGD and to promote individuals’ health literacy, empowering patients to effectively track and share their PGD is important—both technologies and health care providers can play important roles.

Incorporating patient generated health data into pharmacoepidemiological research

Epidemiology and pharmacoepidemiology frequently employ Real-World Data (RWD) from healthcare teams to inform research. These data sources usually include signs, symptoms, tests, and treatments, but may lack important information such as the patient's diet or adherence or quality of life. By harnessing digital tools a new fount of evidence, Patient (or Citizen/Person) Generated Health Data (PGHD), is becoming more readily available. This review focusses on the advantages and considerations in using PGHD for pharmacoepidemiological research. New and corroborative types of data can be collected directly from patients using digital devices, both passively and actively. Practical issues such as patient engagement, data linking, validation, and analysis are among important considerations in the use of PGHD. In our ever increasingly patient-centric world, PGHD incorporated into more traditional Real-Word data sources offers innovative opportunities to expand our understanding of the complex factors involved in health and the safety and effectiveness of disease treatments. Pharmacoepidemiologists have a unique role in realizing the potential of PGHD by ensuring that robust methodology, governance, and analytical techniques underpin its use to generate meaningful research results.

Polar labeling: silver standard algorithm for training disease classifiers

MOTIVATION

Expert-labeled data are essential to train phenotyping algorithms for cohort identification. However expert labeling is time and labor intensive, and the costs remain prohibitive for scaling phenotyping to wider use-cases.

RESULTS

We present an approach referred to as polar labeling (PL), to create silver standard for training machine learning (ML) for disease classification. We test the hypothesis that ML models trained on the silver standard created by applying PL on unlabeled patient records, are comparable in performance to the ML models trained on gold standard, created by clinical experts through manual review of patient records. We perform experimental validation using health records of 38 023 patients spanning six diseases. Our results demonstrate the superior performance of the proposed approach.

AVAILABILITY AND IMPLEMENTATION

We provide a Python implementation of the algorithm and the Python code developed for this study on Github.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Harnessing consumer smartphone and wearable sensors for clinical cancer research

As smartphones and consumer wearable devices become more ubiquitous, there is a growing opportunity to capture rich mobile sensor data continuously, passively, and in real-world settings with minimal burden. In the context of cancer, changes in these passively sensed digital biomarkers may reflect meaningful variation in functional status, symptom burden, quality of life, and risk for adverse clinical outcomes. These data could enable real-time remote monitoring of patients between clinical encounters and more proactive, comprehensive, and personalized care. Over the past few years, small studies across a variety of cancer populations support the feasibility and potential clinical value of mobile sensors in oncology. Barriers to implementing mobile sensing in clinical oncology care include the challenges of managing and making sense of continuous sensor data, patient engagement issues, difficulty integrating sensor data into existing electronic health systems and clinical workflows, and ethical and privacy concerns. Multidisciplinary collaboration is needed to develop mobile sensing frameworks that overcome these barriers and that can be implemented at large-scale for remote monitoring of deteriorating health during or after cancer treatment or for promotion and tailoring of lifestyle or symptom management interventions. Leveraging digital technology has the potential to enrich scientific understanding of how cancer and its treatment affect patient lives, to use this understanding to offer more timely and personalized support to patients, and to improve clinical oncology outcomes.

Antibiotic Treatment Response in Chronic Lyme Disease: Why Do Some Patients Improve While Others Do Not?

There is considerable uncertainty regarding treatment of Lyme disease patients who do not respond fully to initial short-term antibiotic therapy. Choosing the best treatment approach and duration remains challenging because treatment response among these patients varies: some patients improve with treatment while others do not. A previous study examined treatment response variation in a sample of over 3500 patients enrolled in the MyLymeData patient registry developed by LymeDisease.org (San Ramon, CA, USA). That study used a validated Global Rating of Change (GROC) scale to identify three treatment response subgroups among Lyme disease patients who remained ill: nonresponders, low responders, and high responders. The present study first characterizes the health status, symptom severity, and percentage of treatment response across these three patient subgroups together with a fourth subgroup, patients who identify as well. We then employed machine learning techniques across these subgroups to determine features most closely associated with improved patient outcomes, and we used traditional statistical techniques to examine how these features relate to treatment response of the four groups. High treatment response was most closely associated with (1) the use of antibiotics or a combination of antibiotics and alternative treatments, (2) longer duration of treatment, and (3) oversight by a clinician whose practice focused on the treatment of tick-borne diseases.

Emerging health data platforms: From individual control to collective data governance

Health data have enormous potential to transform healthcare, health service design, research, and individual health management. However, health data collected by institutions tend to remain siloed within those institutions limiting access by other services, individuals or researchers. Further, health data generated outside health services (e.g., from wearable devices) may not be easily accessible or useable by individuals or connected to other parts of the health system. There are ongoing tensions between data protection and the use of data for the public good (e.g., research). Concurrently, there are a number of data platforms that provide ways to disrupt these traditional health data siloes, giving greater control to individuals and communities. Through four case studies, this paper explores platforms providing new ways for health data to be used for personal data sharing, self-health management, research, and clinical care. The case-studies include data platforms: PatientsLikeMe, Open Humans, Health Record Banks, and unforgettable.me. These are explored with regard to what they mean for data access, data control, and data governance. The case studies provide insight into a shift from institutional to individual data stewardship. Looking at emerging data governance models, such as data trusts and data commons, points to collective control over health data as an emerging approach to issues of data control. These shifts pose challenges as to how “traditional” health services make use of data collected on these platforms. Further, it raises broader policy questions regarding how to decide what public good data should be put towards.

Clinician Perspectives and Design Implications in Using Patient-Generated Health Data to Improve Mental Health Practices: Mixed Methods Study

Background

Patient-generated health data (PGHD) have been largely collected through mobile health (mHealth) apps and wearable devices. PGHD can be especially helpful in mental health, as patients’ illness history and symptom narratives are vital to developing diagnoses and treatment plans. However, the extent to which clinicians use mental health–related PGHD is unknown.

Objective

A mixed methods study was conducted to understand clinicians’ perspectives on PGHD and current mental health apps. This approach uses information gathered from semistructured interviews, workflow analysis, and user-written mental health app reviews to answer the following research questions: (1) What is the current workflow of mental health practice and how are PGHD integrated into this workflow, (2) what are clinicians’ perspectives on PGHD and how do they choose mobile apps for their patients, (3) and what are the features of current mobile apps in terms of interpreting and sharing PGHD?

Methods

The study consists of semistructured interviews with 12 psychiatrists and clinical psychologists from a large academic hospital. These interviews were thematically and qualitatively analyzed for common themes and workflow elements. User-posted reviews of 56 sleep and mood tracking apps were analyzed to understand app features in comparison with the information gathered from interviews.

Results

The results showed that PGHD have been part of the workflow, but its integration and use are not optimized. Mental health clinicians supported the use of PGHD but had concerns regarding data reliability and accuracy. They also identified challenges in selecting suitable apps for their patients. From the app review, it was discovered that mHealth apps had limited features to support personalization and collaborative care as well as data interpretation and sharing.

Conclusions

This study investigates clinicians’ perspectives on PGHD use and explored existing app features using the app review data in the mental health setting. A total of 3 design guidelines were generated: (1) improve data interpretation and sharing mechanisms, (2) consider clinical workflow and electronic health record integration, and (3) support personalized and collaborative care. More research is needed to demonstrate the best practices of PGHD use and to evaluate their effectiveness in improving patient outcomes.

Cardiac Irradiation Predicts Activity Decline in Patients Receiving Concurrent Chemoradiation for Locally Advanced Lung Cancer

PURPOSE

Many patients with lung cancer are inactive due to their disease and underlying comorbidities, and activity levels can decline further during cancer therapy. Here we explore dosimetric predictors of activity decline in a cohort of patients who underwent continuous activity monitoring during definitive concurrent chemoradiotherapy (CRT) for locally advanced lung cancer.

METHODS AND MATERIALS

We identified patients who participated in prospective clinical trials involving the use of a commercial fitness tracker throughout the course of CRT. For each patient, we applied linear regression to log-transformed daily step counts to compute the weekly rate of activity change from 1 week before radiation therapy (RT) initiation to 2 weeks after RT completion. Clinical and dosimetric factors were tested as predictors of activity change using linear regressions.

RESULTS

Forty-six patient met the eligibility criteria. Median age was 66 years (range, 38-90). Pretreatment Eastern Cooperative Oncology Group performance status was 0, 1, and 2 for 17%, 70%, and 13%, respectively. Mean lung dose ranged from 5.0 to 23.5 Gy, mean esophagus dose from 1.1 to 39.6 Gy, and mean heart dose from 0.6 to 31.5 Gy. Median daily step count average was 5861 (interquartile range, 3540-8282) before RT and 3422 (interquartile range, 2364-5395) 2 weeks after RT completion. Rate of activity change was not significantly associated with age, performance status, or mean RT dose received by lungs or esophagus. In multivariate analysis, mean heart dose was significantly associated with rate of activity decline, with a 3.1% reduction in step count per week for every 10 Gy increase in mean heart dose (95% confidence interval: 0.5-5.7, P = .023).

CONCLUSIONS

Extent of cardiac irradiation is associated with the rate of physical activity decline during CRT for lung cancer. Our novel finding contributes to the growing body of evidence that adverse effects of cardiac irradiation may be manifested at early time points.

Harnessing the power of social media: how can it help in axial spondyloarthritis research?

PURPOSE OF REVIEW

Axial spondyloarthritis (axSpA) is a chronic inflammatory rheumatic disease that is relatively unknown among the general public. Most patients with axSpA are young or middle-aged adults and more likely to use some social media. This review highlights trends in the application of social media and different ways in which these tools do already or may benefit clinical research, delivery of care, and education in rheumatology, particularly in the field of axSpA.

RECENT FINDINGS

This article discusses four areas in the biomedical field that social media has infused with novel ideas: (i) the use of patient-generated health data from social media to learn about their disease experience, (ii) delivering health education and interventions, (iii) recruiting study participants, and (iv) reform, transfer, and disseminate medical education. We conclude with promising studies in rheumatology that have incorporated social media and suggestions for future directions.

SUMMARY

Rheumatologists now have the opportunity to use social media and innovate on many aspects of their practice. We propose further exploration of multiple ways in which social media might help with the identification, diagnosis, education, and research study enrollment of axSpA patients. However, standardization in study design, reporting, and managing ethical and regulatory aspects will be required to take full advantage of this opportunity.

Innovations in research and clinical care using patient-generated health data

Patient-generated health data (PGHD), or health-related data gathered from patients to help address a health concern, are used increasingly in oncology to make regulatory decisions and evaluate quality of care. PGHD include self-reported health and treatment histories, patient-reported outcomes (PROs), and biometric sensor data. Advances in wireless technology, smartphones, and the Internet of Things have facilitated new ways to collect PGHD during clinic visits and in daily life. The goal of the current review was to provide an overview of the current clinical, regulatory, technological, and analytic landscape as it relates to PGHD in oncology research and care. The review begins with a rationale for PGHD as described by the US Food and Drug Administration, the Institute of Medicine, and other regulatory and scientific organizations. The evidence base for clinic-based and remote symptom monitoring using PGHD is described, with an emphasis on PROs. An overview is presented of current approaches to digital phenotyping or device-based, real-time assessment of biometric, behavioral, self-report, and performance data. Analytic opportunities regarding PGHD are envisioned in the context of big data and artificial intelligence in medicine. Finally, challenges and solutions for the integration of PGHD into clinical care are presented. The challenges include electronic medical record integration of PROs and biometric data, analysis of large and complex biometric data sets, and potential clinic workflow redesign. In addition, there is currently more limited evidence for the use of biometric data relative to PROs. Despite these challenges, the potential benefits of PGHD make them increasingly likely to be integrated into oncology research and clinical care.

Exploring the barriers and facilitators for the use of digital health technologies for the management of COPD: a qualitative study of clinician perceptions

BACKGROUND

Digital health technology (DHT) promises to support patients and healthcare professionals (HCPs) to optimize the management of chronic obstructive pulmonary disease (COPD). However, there is a lack of evidence demonstrating the effectiveness of DHT for the management of COPD. One reason for this is the lack of user-involvement in the development of DHT interventions in COPD meaning their needs and preferences are rarely accounted for in the design phase. Although HCP adoption issues have been identified in relation to DHT, little is known about the challenges perceived by HCPs providing care to COPD patients. Therefore, this study aims to qualitatively explore the barriers and facilitators HCPs perceive for the use of DHT in the management of COPD.

METHODS

Participants (n = 32) were recruited using snowball sampling from two university hospitals and several general practitioner clinics. A semi-structured interview was conducted with each participant. NVivo 12 software was used to complete thematic analysis on the data.

RESULTS

Themes identified include: data quality; evidence-based care; resource constraints; and digital literacy presented as barriers; and facilitators include the following themes: digital health training and education; improving HCP digital literacy; and Personalized prescribing. Patient-centered approaches, such as pulmonary rehabilitation and shared decision-making were suggested as implementation strategies to ease the adoption of digital health for the management of COPD.

CONCLUSION

These findings contribute new insights about the needs and preferences of HCPs working in COPD regarding DHT. The findings can be used to help mitigate user-experience issues by informing the design of person-centered implementation and adoption strategies for future digital health interventions in COPD.

Converging and diverging needs between patients and providers who are collecting and using patient-generated health data: an integrative review

Objective

This integrative review identifies convergent and divergent areas of need for collecting and using patient-generated health data (PGHD) identified by patients and providers (i.e., physicians, nurses, advanced practice nurses, physician assistants, and dietitians).

Methods

A systematic search of 9 scholarly databases targeted peer-reviewed studies published after 2010 that reported patients’ and/or providers’ needs for incorporating PGHD in clinical care. The studies were assessed for quality and bias with the Mixed-Methods Appraisal Tool. The results section of each article was coded to themes inductively developed to categorize patient and provider needs. Distinct claims were extracted and areas of convergence and divergence identified.

Results

Eleven studies met inclusion criteria. All had moderate to low risk of bias. Three themes (clinical, logistic, and technological needs), and 13 subthemes emerged. Forty-eight claims were extracted. Four were divergent and twenty were convergent. The remainder was discussed by only patients or only providers.

Conclusion

As momentum gains for integrating PGHD into clinical care, this analysis of primary source data is critical to understanding the requirements of the 2 groups directly involved in collection and use of PGHD.

Your data is gold – Data donation for better healthcare?

Today, medical data such as diagnoses, procedures, imaging reports and laboratory tests, are not only collected in context of primary research and clinical studies. In addition, citizens are tracking their daily steps, food intake, sport exercises, and disease symptoms via mobile phones and wearable devices. In this context, the topic of “data donation” is drawing increased attention in science, politics, ethics and practice. This paper provides insights into the status quo of personal data donation in Germany and from a global perspective. As this topic requires a consideration of several perspectives, potential benefits and related, multifaceted challenges for citizens, patients and researchers are discussed. This includes aspects such as data quality & accessibility, privacy and ethical considerations.

Single-Cell Analysis of Circulating Tumor Cells: How Far Have We Come in the -Omics Era?

Tumor cells detach from the primary tumor or metastatic sites and enter the peripheral blood, often causing metastasis. These cells, named Circulating Tumor Cells (CTCs), display the same spatial and temporal heterogeneity as the primary tumor. Since CTCs are involved in tumor progression, they represent a privileged window to disclose mechanisms of metastases, while -omic analyses at the single-cell level allow dissection of the complex relationships between the tumor subpopulations and the surrounding normal tissue. However, in addition to reporting the proof of concept that we can query CTCs to reveal tumor evolution throughout the continuum of treatment for early detection of resistance to therapy, the scientific literature has also been highlighting the disadvantages of CTCs, which hampers a routine use of this approach in clinical practice. To date, an increasing number of CTC technologies, as well as -omics methods, have been employed, mostly lacking strong comparative analyses. The rarity of CTCs also represents a major challenge, because there is no consensus regarding the minimal criteria necessary and sufficient to define an event as CTC; moreover, we cannot often compare data from of one study with that of another. Finally, the availability of an individual tumor profile undermines the traditional histology-based treatment. Applying molecular data for patient benefit implies a collective effort by biologists, bioengineers, and clinicians, to create tools to interpret molecular data and manage precision medicine in every single patient. Herein, we focus on the most recent findings in CTC −omics to learn how far we have come.

A qualitative study of chronic obstructive pulmonary disease patient perceptions of the barriers and facilitators to adopting digital health technology

Objective

Non-adherence to self-management plans in chronic obstructive pulmonary disease (COPD) results in poorer outcomes for patients. Digital health technology (DHT) promises to support self-management by enhancing the sense of control patients possess over their disease. COPD digital health studies have yet to show significant evidence of improved outcomes for patients, with many user-adoption issues still present in the literature. To help better address the adoption needs of COPD patients, this paper explores their perceived barriers and facilitators to the adoption of DHT.

Methods

A sample of convenience was chosen and patients (n = 30) were recruited from two Dublin university hospitals. Each patient completed a qualitative semi-structured interview. Thematic analysis of the data was performed using NVivo 12 software.

Results

Barrier sub-themes included lack of perceived usefulness, digital literacy, illness perception, and social context; facilitator sub-themes included existing digital self-efficacy, personalised education, and community-based support.

Conclusion

The findings represent a set of key considerations for researchers and clinicians to inform the design of patient-centred study protocols that aim to account for the needs and preferences of patients in the development of implementation and adoption strategies for DHT in COPD.

N-of-1 Clinical Trials in Nutritional Interventions Directed at Improving Cognitive Function

Longer life expectancy has led to an increase in the prevalence of age-related cognitive decline and dementia worldwide. Due to the current lack of effective treatment for these conditions, preventive strategies represent a research priority. A large body of evidence suggests that nutrition is involved in the pathogenesis of age-related cognitive decline, but also that it may play a critical role in slowing down its progression. At a population level, healthy dietary patterns interventions, such as the Mediterranean and the MIND diets, have been associated with improved cognitive performance and a decreased risk of neurodegenerative disease development. In the era of evidence-based medicine and patient-centered healthcare, personalized nutritional recommendations would offer a considerable opportunity in preventing cognitive decline progression. N-of-1 clinical trials have emerged as a fundamental design in evidence-based medicine. They consider each individual as the only unit of observation and intervention. The aggregation of series of N-of-1 clinical trials also enables population-level conclusions. This review provides a general view of the current scientific evidence regarding nutrition and cognitive decline, and critically states its limitations when translating results into the clinical practice. Furthermore, we suggest methodological strategies to develop N-of-1 clinical trials focused on nutrition and cognition in an older population. Finally, we evaluate the potential challenges that researchers may face when performing studies in precision nutrition and cognition.

Phenotyping to Facilitate Accrual for a Cardiovascular Intervention

Background

The conventional approach for clinical studies is to identify a cohort of potentially eligible patients and then screen for enrollment. In an effort to reduce the cost and manual effort involved in the screening process, several studies have leveraged electronic health records (EHR) to refine cohorts to better match the eligibility criteria, which is referred to as phenotyping. We extend this approach to dynamically identify a cohort by repeating phenotyping in alternation with manual screening.

Methods

Our approach consists of multiple screen cycles. At the start of each cycle, the phenotyping algorithm is used to identify eligible patients from the EHR, creating an ordered list such that patients that are most likely eligible are listed first. This list is then manually screened, and the results are analyzed to improve the phenotyping for the next cycle. We describe the preliminary results and challenges in the implementation of this approach for an intervention study on heart failure.

Results

A total of 1,022 patients were screened, with 223 (23%) of patients being found eligible for enrollment into the intervention study. The iterative approach improved the phenotyping in each screening cycle. Without an iterative approach, the positive screening rate (PSR) was expected to dip below the 20% measured in the first cycle; however, the cyclical approach increased the PSR to 23%.

Conclusions

Our study demonstrates that dynamic phenotyping can facilitate recruitment for prospective clinical study. Future directions include improved informatics infrastructure and governance policies to enable real-time updates to research repositories, tooling for EHR annotation, and methodologies to reduce human annotation.

Exploring the potential benefits of digital health technology for the management of COPD: a qualitative study of patient perceptions

Engaging chronic obstructive pulmonary disease (COPD) patients to actively participate in self-management has proven difficult. Digital health technology (DHT) promises to facilitate a patient-centred care model for the management of COPD by empowering patients to self-manage effectively. However, digital health studies in COPD have yet to demonstrate significant patient outcomes, suggesting that this research has still to adequately address the needs of patients in the intervention development process. The current study explored COPD patients' perceptions of the potential benefits of DHT in the self-management and treatment of their disease. A sample of convenience was chosen and participants (n=30) were recruited from two Dublin university hospitals and each underwent a qualitative semi-structured interview. Thematic analysis of the data was completed using NVivo 12 software. Six themes were identified: symptom management, anxiety management, interaction with physician, care management, personalising care and preventative intervention. In our findings, patients reported a willingness to take a more active role in self-management using DHT. They perceived DHT potentially enhancing their self-management by improving self-efficacy and engagement and by supporting healthcare professionals to practise preventative care provision. The findings can be used to inform patient-centred COPD digital interventions for researchers and clinicians who wish to develop study aims that align with the needs and preferences of patients.

Characteristics of Future Models of Integrated Outpatient Care

Replacement of fee-for-service with capitation arrangements, forces physicians and institutions to minimize health care costs, while maintaining high-quality care. In this report we described how patients and their families (or caregivers) can work with members of the medical care team to achieve these twin goals of maintaining-and perhaps improving-high-quality care and minimizing costs. We described how increased self-management enables patients and their families/caregivers to provide electronic patient-reported outcomes (i.e., symptoms, events) (ePROs), as frequently as the patient or the medical care team consider appropriate. These capabilities also allow ongoing assessments of physiological measurements/phenomena (mHealth). Remote surveillance of these communications allows longer intervals between (fewer) patient visits to the medical-care team, when this is appropriate, or earlier interventions, when it is appropriate. Systems are now available that alert medical care providers to situations when interventions might be needed.

Sensitivity and specificity of patient-entered red flags for lower back pain

BACKGROUND CONTEXT

Red flags are questions typically ascertained by providers to screen for serious underlying spinal pathologies. The utility of patient-reported red flags in guiding clinical decision-making for spine care, however, has not been studied.

PURPOSE

The aim of this study was to quantify the sensitivity and specificity of patient-reported red flags in predicting the presence of serious spinal pathologies.

STUDY DESIGN

This was a retrospective nested case-control study.

PATIENT SAMPLE

This study consisted of 120 patients with International Classification of Diseases, Ninth Revision, Clinical Modification codes for spinal pathologies and 380 randomly selected patients, from a population of 4,313 patients seen at a large tertiary care spine clinic between October 9, 2013 and June 30, 2014.

OUTCOME MEASURES

The presence of patient-reported red flags and red flags obtained from medical records was verified for chart review. The spinal pathology (ie, malignancy, fractures, infections, or cauda equina syndrome) was noted for each patient.

METHODS

The sensitivity and specificity of patient-reported red flags for detecting serious spinal pathologies were calculated from data obtained from the 500 patients. Youden's J was used to rank performance. Agreement between patient-reported red flags and those obtained from medical record review was assessed via Cohen's kappa statistic.

RESULTS

"History of cancer" was the best performing patient-reported red flag to identify malignancy (sensitivity=0.75 [95% confidence intervals, CI 0.53-0.90], specificity=0.79 [95% CI 0.75-0.82]). The best performing patient-reported red flag for fractures was the presence of at least one of the following: "Osteoporosis," "Steroid use," and "Trauma" (sensitivity=0.59 [95% CI 0.44-0.72], specificity=0.65 [95% CI 0.60-0.69]). The prevalence of infection and cauda equina diagnoses was insufficient to gauge sensitivity and specificity. Red flags from medical records had better performance than patient-reported red flags. There was poor agreement between patient red flags and those obtained from medical record review.

CONCLUSIONS

Patient-reported red flags had low sensitivity and specificity for identification of serious pathologies. They should not be used in insolation to make treatment decisions, although they may be useful to prompt further probing to determine if additional investigation is warranted.

Real-world data and the patient perspective: the PROmise of social media?

Understanding the patient perspective is fundamental to delivering patient-centred care. In most healthcare systems, however, patient-reported outcomes are not regularly collected or recorded as part of routine clinical care, despite evidence that doing so can have tangible clinical benefit. In the absence of the routine collection of these data, research is beginning to turn to social media as a novel means to capture the patient voice. Publicly available social media data can now be analysed with relative ease, bypassing many logistical hurdles associated with traditional approaches and allowing for accelerated and cost-effective data collection. Existing work has shown these data can offer credible insight into the patient experience, although more work is needed to understand limitations with respect to patient representativeness and nuances of captured experience. Nevertheless, linking social media to electronic medical records offers a significant opportunity for patient views to be systematically collected for health services research and ultimately to improve patient care.

Recovery in My Lens: A Study on Stroke Vlogs

Stroke is a chronic condition and a leading cause of disability. After hospital discharge, patients need to transition into home-based rehabilitation, a long and distressful process. However, they are often ill-prepared to manage recovery at home; and many are socially isolated. There is a growing number of stroke patients who utilize social media platforms, YouTube in particular, to publish video blogs (vlogs) to make their stories heard and to share their rehabilitation experience. In this study, we analyzed 246 such YouTube vlogs to better understand this new form of patient story-telling and its value to vloggers, viewers, as well as healthcare professionals. We found that vlogging helps stroke patients overcome physical and speech constraints to self-journal, and to connect with other people online. Based on these findings, we discuss how future health systems may leverage vlogs to design self-tracking technologies, to generate patient health data, and to offer patient-centered education.

Removing the Mask of Average Treatment Effects in Chronic Lyme Disease Research Using Big Data and Subgroup Analysis

Lyme disease is caused by the bacteria borrelia burgdorferi and is spread primarily through the bite of a tick. There is considerable uncertainty in the medical community regarding the best approach to treating patients with Lyme disease who do not respond fully to short-term antibiotic therapy. These patients have persistent Lyme disease symptoms resulting from lack of treatment, under-treatment, or lack of response to their antibiotic treatment protocol. In the past, treatment trials have used small restrictive samples and relied on average treatment effects as their measure of success and produced conflicting results. To provide individualized care, clinicians need information that reflects their patient population. Today, we have the ability to analyze large data bases, including patient registries, that reflect the broader range of patients more typically seen in clinical practice. This allows us to examine treatment variation within the sample and identify groups of patients that are most responsive to treatment. Using patient-reported outcome data from the MyLymeData online patient registry, we show that sub-group analysis techniques can unmask valuable information that is hidden if averages alone are used. In our analysis, this approach revealed treatment effectiveness for up to a third of patients with Lyme disease. This study is important because it can help open the door to more individualized patient care using patient-centered outcomes and real-world evidence.

From smartphone to EHR: a case report on integrating patient-generated health data

Patient-generated health data (PGHD), collected from mobile apps and devices, represents an opportunity for remote patient monitoring and timely interventions to prevent acute exacerbations of chronic illness—if data are seen and shared by care teams. This case report describes the technical aspects of integrating data from a popular smartphone platform to a commonly used EHR vendor and explores the challenges and potential of this approach for disease management. Consented subjects using the Asthma Health app (built on Apple’s ResearchKit platform) were able to share data on inhaler usage and peak expiratory flow rate (PEFR) with a local pulmonologist who ordered this data on Epic’s EHR. For users who had installed and activated Epic’s patient portal (MyChart) on their iPhone and enabled sharing of health data between apps via HealthKit, the pulmonologist could review PGHD and, if necessary, make recommendations. Four patients agreed to share data with their pulmonologist, though only two patients submitted more than one data point across the 4.5-month trial period. One of these patients submitted 101 PEFR readings across 65 days; another submitted 24 PEFR and inhaler usage readings across 66 days. PEFR for both patients fell within predefined physiologic parameters, except once where a low threshold notification was sent to the pulmonologist, who responded with a telephone discussion and new e-prescription to address symptoms. This research describes the technical considerations and implementation challenges of using commonly available frameworks for sharing PGHD, for the purpose of remote monitoring to support timely care interventions.

Patients with asthma who record inhaler usage and lung function scores with a smartphone app and transmit the data to an electronic health record (EHR) can get timelier care and prescription adjustments from their doctors. A team led by Yvonne Chan and Nicholas Genes from the Icahn School of Medicine at Mount Sinai in New York, NY, USA explored the feasibility of having patients self-report health data on an iPhone app called Asthma Health and then share the information with their pulmonologists via an EHR patient portal app. Four patients took part in the study, but only two really engaged in the platform. Those patients submitted multiple measures of peak expiratory flow rate per week. In one instance, the measure triggered a pulmonologist to call the patient and prescribe new allergy medications.

Perceived Benefits and Barriers to Exercise for Recently Treated Adults With Acute Leukemia

PURPOSE/OBJECTIVES

To explore perceived exercise benefits and barriers in adults with acute leukemia who recently completed an inpatient exercise intervention during induction therapy.
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RESEARCH APPROACH

Descriptive, exploratory design using semistructured interviews.
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SETTING

Inpatient hematology/oncology unit at North Carolina Cancer Hospital in Chapel Hill.
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PARTICIPANTS

6 adults with acute leukemia aged 35-67 years.
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METHODOLOGIC APPROACH

Content analyses of semistructured interviews that were conducted with each participant prior to hospital discharge.
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FINDINGS

Most participants were not meeting the recommended physical activity levels of 150 minutes of moderate-intensity exercise per week before their diagnosis. Patients were highly pleased with the exercise intervention and the overall program. Common barriers to exercise were anxiety and aches and pains.
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INTERPRETATION

Overall, participants experienced physical and psychological benefits with the exercise intervention with no adverse events from exercising regularly during induction chemotherapy. Referrals for cancer rehabilitation management will lead to prolonged recovery benefits.
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IMPLICATIONS FOR NURSING

Findings inform the nurses' role in encouraging and supporting adults with acute leukemia to exercise and be physically active during their hospitalization. Nurses should also be responsible for assisting patients with physical function activities to increase mobility and enhance overall health-related quality of life.

Development of the Precision Link Biobank at Boston Children's Hospital: Challenges and Opportunities

Increasingly, biobanks are being developed to support organized collections of biological specimens and associated clinical information on broadly consented, diverse patient populations. We describe the implementation of a pediatric biobank, comprised of a fully-informed patient cohort linking specimens to phenotypic data derived from electronic health records (EHR). The Biobank was launched after multiple stakeholders’ input and implemented initially in a pilot phase before hospital-wide expansion in 2016. In-person informed consent is obtained from all participants enrolling in the Biobank and provides permission to: (1) access EHR data for research; (2) collect and use residual specimens produced as by-products of routine care; and (3) share de-identified data and specimens outside of the institution. Participants are recruited throughout the hospital, across diverse clinical settings. We have enrolled 4900 patients to date, and 41% of these have an associated blood sample for DNA processing. Current efforts are focused on aligning the Biobank with other ongoing research efforts at our institution and extending our electronic consenting system to support remote enrollment. A number of pediatric-specific challenges and opportunities is reviewed, including the need to re-consent patients when they reach 18 years of age, the ability to enroll family members accompanying patients and alignment with disease-specific research efforts at our institution and other pediatric centers to increase cohort sizes, particularly for rare diseases.

Added Value from Secondary Use of Person Generated Health Data in Consumer Health Informatics

Introduction: Various health-related data, subsequently called Person Generated Health Data (PGHD), is being collected by patients or presumably healthy individuals as well as about them as much as they become available as measurable properties in their work, home, and other environments. Despite that such data was originally just collected and used for dedicated predefined purposes, more recently it is regarded as untapped resources that call for secondary use. Method: Since the secondary use of PGHD is still at its early evolving stage, we have chosen, in this paper, to produce an outline of best practices, as opposed to a systematic review. To this end, we identified key directions of secondary use and invited protagonists of each of these directions to present their takes on the primary and secondary use of PGHD in their sub-fields. We then put secondary use in a wider perspective of overarching themes such as privacy, interpretability, interoperability, utility, and ethics. Results: We present the primary and secondary use of PGHD in four focus areas: (1) making sense of PGHD in augmented Shared Care Plans for care coordination across multiple conditions; (2) making sense of PGHD from patient-held sensors to inform cancer care; (3) fitting situational use of PGHD to evaluate personal informatics tools in adaptive concurrent trials; (4) making sense of environment risk exposure data in an integrated context with clinical and omics-data for biomedical research. Discussion: Fast technological progress in all the four focus areas calls for a societal debate and decision-making process on a multitude of challenges: how emerging or foreseeable results transform privacy; how new data modalities can be interpreted in light of clinical data and vice versa; how the sheer mass and partially abstract mathematical properties of the achieved insights can be interpreted to a broad public and can consequently facilitate the development of patient-centered services; and how the remaining risks and uncertainties can be evaluated against new benefits. This paper is an initial summary of the status quo of the challenges and proposals that address these issues. The opportunities and barriers identified can serve as action items individuals can bring to their organizations when facing challenges to add value from the secondary use of patient-generated health data.

Present and Future Trends in Consumer Health Informatics and Patient-Generated Health Data

Objectives: Consumer Health Informatics (CHI) and the use of Patient-Generated Health Data (PGHD) are rapidly growing focus areas in healthcare. The objective of this paper is to briefly review the literature that has been published over the past few years and to provide a sense of where the field is going. Methods: We searched PubMed and the ACM Digital Library for articles published between 2014 and 2016 on the topics of CHI and PGHD. The results of the search were screened for relevance and categorized into a set of common themes. We discuss the major topics covered in these articles. Results: We retrieved 65 articles from our PubMed query and 32 articles from our ACM Digital Library query. After a review of titles, we were left with 47 articles to conduct our full article survey of the activities in CHI and PGHD. We have summarized these articles and placed them into major categories of activity. Within the domain of consumer health informatics, articles focused on mobile health and patient-generated health data comprise the majority of the articles published in recent years. Conclusions: Current evidence indicates that technological advancements and the widespread availability of affordable consumer-grade devices are fueling research into using PGHD for better care. As we observe a growing number of (pilot) developments using various mobile health technologies to collect PGHD, major gaps still exist in how to use the data by both patients and providers. Further research is needed to understand the impact of PGHD on clinical outcomes.

mHealth: Mobile Technologies to Virtually Bring the Patient Into an Oncology Practice

Accompanied by the change in the traditional medical landscape, advances in wireless technology have led to the development of telehealth or mobile health (mHealth), which offers an unparalleled opportunity for health care providers to continually deliver high-quality care. This revolutionary shift makes the patient the consumer of health care and empowers patients to be the driving force of management of their own health through mobile devices and wearable technology. This article presents an overview of technology as it pertains to clinical practice considerations. Telemedicine is changing the way clinical care is delivered without regard for proximity to the patient, whereas nonclinical telehealth applications affect distance education for consumers or clinicians, meetings, research, continuing medical education, and health care management. Technology has the potential to reduce administrative burdens and improve both efficiency and quality of care delivery in the clinic. Finally, the potential for telehealth approaches as cost-effective ways to improve adherence to treatment is explored. As telehealth advances, health care providers must understand the fundamental framework for applying telehealth strategies to incorporate into successful clinical practice.