Potential and challenges of patient-generated health data for high-quality cancer care

What defines the "value" of robotic surgery for patients with gastrointestinal cancers? Perspectives from a U.S. Cancer Center

The use of robotic surgery has experienced rapid growth across diverse medical conditions, with a notable emphasis on gastrointestinal cancers. The advanced technologies incorporated into robotic surgery platforms have played a pivotal role in enabling the safe performance of complex procedures, including gastrectomy and pancreatectomy, through a minimally invasive approach. However, there exists a noteworthy gap in high-level evidence demonstrating that robotic surgery for gastric and pancreatic cancers has substantial benefits compared to traditional open or laparoscopic methods. The primary impediment hindering the broader implementation of robotic surgery is its cost. The escalating healthcare expenses in the United States have prompted healthcare providers and payors to explore patient-centered, value-based healthcare models and reimbursement systems that embrace cost-effectiveness. Thus, it is important to determine what defines the value of robotic surgery. It must either maintain or enhance oncological quality and improve complication rates compared to open procedures. Moreover, its true value should be apparent in patients' expedited recovery and improved quality of life. Another essential aspect of robotic surgery's value lies in minimizing or even eliminating opioid use, even after major operations, offering considerable benefits to the broader public health landscape. A quicker return to oncological therapy has the potential to improve overall oncological outcomes, while a speedier return to work not only alleviates individual financial distress but also positively impacts societal productivity. In this article, we comprehensively review and summarize the current landscape of health economics and value-based care, with a focus on robotic surgery for gastrointestinal cancers.

Using an mHealth approach to collect patient-generated health data for predicting adverse health outcomes among adult survivors of childhood cancer

Introduction

Cancer therapies predispose childhood cancer survivors to various treatment-related late effects, which contribute to a higher symptom burden, chronic health conditions (CHCs), and premature mortality. Regular monitoring of symptoms between clinic visits is useful for timely medical consultation and interventions that can improve quality of life (QOL). The Health Share Study aims to utilize mHealth to collect patient-generated health data (PGHD; daily symptoms, momentary physical health status) and develop survivor-specific risk prediction scores for mitigating adverse health outcomes including poor QOL and emergency room admissions. These personalized risk scores will be integrated into the hospital-based electronic health record (EHR) system to facilitate clinician communications with survivors for timely management of late effects.

Methods

This prospective study will recruit 600 adult survivors of childhood cancer from the St. Jude Lifetime Cohort study. Data collection include 20 daily symptoms via a smartphone, objective physical health data (physical activity intensity, sleep performance, and biometric data including resting heart rate, heart rate variability, oxygen saturation, and physical stress) via a wearable activity monitor, patient-reported outcomes (poor QOL, unplanned healthcare utilization) via a smartphone, and clinically ascertained outcomes (physical performance deficits, onset of/worsening CHCs) assessed in the survivorship clinic. Participants will complete health surveys and physical/functional assessments in the clinic at baseline, 2) report daily symptoms, wear an activity monitor, measure blood pressure at home over 4 months, and 3) complete health surveys and physical/functional assessments in the clinic 1 and 2 years from the baseline. Socio-demographic and clinical data abstracted from the EHR will be included in the analysis. We will invite 20 cancer survivors to investigate suitable formats to display predicted risk information on a dashboard and 10 clinicians to suggest evidence-based risk management strategies for adverse health outcomes.

Analysis

Machine and statistical learning will be used in prediction modeling. Both approaches can handle a large number of predictors, including longitudinal patterns of daily symptoms/other PGHD, along with cancer treatments and socio-demographics.

Conclusion

The individualized risk prediction scores and added communications between providers and survivors have the potential to improve survivorship care and outcomes by identifying early clinical presentations of adverse events.

Unleashing the Potential for Patient-Generated Health Data (PGHD)

Patient-generated health data (PGHD) is data created, captured, or recorded by patients in between healthcare appointments, and is an important supplement to data generated during periodic clinical encounters. PGHD has potential to improve diagnosis and management of chronic conditions, improve health outcomes, and facilitate more "connected health" between patients and their care teams. Electronic PGHD is rapidly accelerating due to the proliferation of consumer health technologies, remote patient monitoring systems, and personal health platforms. Despite this tremendous growth in PGHD and anticipated benefits, broadscale use of PGHD has been challenging to implement with significant gaps in current knowledge about how PGHD can best be employed in the service of high-quality, patient-centered care. While the role of PGHD in patient self-management continues to grow organically, we need a deeper understanding of how data collection and sharing translate into actionable information that supports shared decision-making and informs clinical care in real-world settings. This, in turn, will foster both clinical adoption and patient engagement with PGHD. We propose an agenda for PGHD-related research in the Veterans Health Administration that emphasizes this clinical value to enhance our understanding of its potential and limitations in supporting shared decision-making and informing clinical care.

How Could Sensor-Based Measurement of Physical Activity Be Used in Cardiovascular Healthcare?

Physical activity and cardiovascular disease (CVD) are intimately linked. Low levels of physical activity increase the risk of CVDs, including myocardial infarction and stroke. Conversely, when CVD develops, it often reduces the ability to be physically active. Despite these largely understood relationships, the objective measurement of physical activity is rarely performed in routine healthcare. The ability to use sensor-based approaches to accurately measure aspects of physical activity has the potential to improve many aspects of cardiovascular healthcare across the spectrum of healthcare, from prediction, prevention, diagnosis, and treatment to disease monitoring. This review discusses the potential of sensor-based measurement of physical activity to augment current cardiovascular healthcare. We highlight many factors that should be considered to maximise the benefit and reduce the risks of such an approach. Because the widespread use of such devices in society is already a reality, it is important that scientists, clinicians, and healthcare providers are aware of these considerations.

Attitudes and perspectives of nurses and physicians in South Korea towards the clinical use of person-generated health data

This study aimed to explore the adoption of person-generated health data in clinical settings and discern the factors influencing clinicians' willingness to use it. A web-based survey containing 48 questions was developed based on prior research and the Unified Theory of Acceptance and Use of Technology 2 model. The survey was administered to a convenience sample of 486 nurses and physicians in South Korea recruited through an online community and snowball sampling. Of these, 70.7% were physicians. While 65% had used mobile health apps and devices, only 12.8% were familiar with person-generated health data. Still, a promising 73.3% expressed interest in incorporating person-generated health data into patient care, particularly data on blood glucose and vital signs. The findings of the study also indicated that clinicians specializing in internal medicine (OR: 1.9, CI: 1.16-3.19), familiar with person-generated health data (OR: 2.6, CI: 1.58-4.29), with a positive view of information and communication technology adoption (OR: 2.6, CI: 1.65-4.13), and who see the value in person-generated health data (OR: 3.9, CI: 2.55-6.09) showed higher inclination to utilize it. However, those in outpatient settings (OR: 0.4, CI: 0.19-0.73) showed less enthusiasm. The findings of this study suggest that despite the willingness of clinicians to use person-generated health data, various barriers must be addressed first, including a lack of knowledge regarding its use, concerns about data reliability and quality, and a lack of provider incentives. Overcoming these challenges demands concerted organizational or policy support. This research underscores person-generated health data's untapped potential in healthcare and the pressing need for strategies that facilitate its clinical integration.

Utilization of real-world data in assessing treatment effectiveness for diffuse large B-cell lymphoma

Direct comparisons of the effectiveness of the numerous novel therapies in the diffuse large B-cell lymphoma (DLBCL) treatment landscape in a range of head-to-head randomized phase 3 trials would be time-consuming and costly. Comparative effectiveness studies using real-world data (RWD) represent a complementary approach. Recently, several studies of relapsed/refractory (R/R) DLBCL have used RWD to create observational cohorts to compare patient outcomes with cohorts derived from single-arm phase 2 trials. Using propensity score methods to balance clinically and prognostically relevant baseline covariates, closely matched patient-level cohorts can be generated. By incorporating appropriate measures to assess covariate balance and address potential bias in comparative effectiveness study designs, robust comparative analyses can be performed. Results from such studies have been used to supplement regulatory approval of therapies assessed in single-arm trials. While RWD studies have a greater susceptibility to bias compared to randomized controlled trials, well-designed and appropriately analyzed studies can provide complementary real-world evidence on treatment effectiveness.

Wearable device use and technology preferences in cancer survivors with or at risk for atrial fibrillation

Background

Cancer survivors face increased risk of heart disease, including atrial fibrillation (AF). Certain types of technology, such as consumer wearable devices, can be useful to monitor for AF, but little is known about wearables and AF monitoring in cancer survivor populations.

Objective

The purpose of this study was to understand technology usage and preferences in cancer survivors with or at risk for AF, and to describe demographic factors associated with wearable device ownership in this population.

Methods

Eligible patients completed a remote survey assessment regarding use of commercial wearable devices. The survey contained questions designed to assess commercial wearable device use, electronic health communications, and perceptions regarding the participant's cardiac health.

Results

A total of 424 cancer survivors (mean age 74.2 years; 53.1% female; 98.8% white) were studied. Although most participants owned a smartphone (85.9%), only 31.8% owned a wearable device. Over half (53.5%) of cancer survivors were worried about their heart health. Overall, patients believed arrhythmias (79.7%) were the most important heart condition for a wearable to detect. Survivors reported being most willing to share blood pressure (95.6%) and heart rate (95.3%) data with their providers and were least willing to share information about their diet, weight, and physical activity using these devices.

Conclusion

Understanding factors such as device ownership, usage, and heart health concerns in cancer survivors can play an important role in improving cardiovascular monitoring and its accessibility. Long-term patient outcomes may be improved by incorporating wearable devices into routine care of cancer survivors.

Communicating Electronic Adherence Data to Physicians-Consensus-Based Development of a Compact Reporting Form

Information on medication adherence is missing in patient files, although it might be helpful to optimize treatment. An adherence report that presents data from electronic adherence monitoring and provides recommendations regarding pharmacological treatment could close this gap. We aimed to develop an adherence reporting form that combines suitable calculations and graphical representations to facilitate the physicians’ interpretation of (non-)adherence. Two consensus development panels were conducted. First, pharmacists with expertise in adherence monitoring debated the items needed to calculate and illustrate electronic adherence data. Second, physicians discussed the items they would need for an adherence report and were encouraged to propose new items. Preference was indicated by raising a green or red card. Voting was repeated until consensus was obtained. Third, first drafts of the adherence reporting form were created by two pharmacists. Seven pharmacists agreed on four metrics to express medication adherence and three graphical representations. Five physicians approved the four metrics and rated the dot chart as the most useful illustration for judging the patient’s adherence patterns. Additionally, they required a clinical–pharmaceutical evaluation of the adherence estimates considering drug-related properties. We developed an adherence reporting form for the first time in a compact format and based on the recommendations of experts. In addition, we considered the preferences of physicians, who appreciated the clarity of the reporting form.

Returning Cardiac Rhythm Data to Patients: Opportunities and Challenges

Spurred by federal legislation, professional organizations, and patients themselves, patient access to data from electronic cardiac devices is increasingly transparent. Patients can collect data through consumer devices and access data traditionally shared only with health care providers. These data may improve screening, self-management, and shared decision-making for cardiac arrhythmias, but challenges remain, including patient comprehension, communication with providers, and sustained engagement. Ways to address these challenges include leveraging visualizations that support comprehension, involving patients in designing and developing patient-facing digital tools, and establishing clear practices and goals for data exchange with health care providers.

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.

The impact of electronic health record-integrated patient-generated health data on clinician burnout

Patient-generated health data (PGHD), such as patient-reported outcomes and mobile health data, have been increasingly used to improve health care delivery and outcomes. Integrating PGHD into electronic health records (EHRs) further expands the capacities to monitor patients' health status without requiring office visits or hospitalizations. By reviewing and discussing PGHD with patients remotely, clinicians could address the clinical issues efficiently outside of clinical settings. However, EHR-integrated PGHD may create a burden for clinicians, leading to burnout. This study aims to investigate how interactions with EHR-integrated PGHD may result in clinician burnout. We identify the potential contributing factors to clinician burnout using a modified FITT (Fit between Individuals, Task and Technology) framework. We found that technostress, time pressure, and workflow-related issues need to be addressed to accelerate the integration of PGHD into clinical care. The roles of artificial intelligence, algorithm-based clinical decision support, visualization format, human-computer interaction mechanism, workflow optimization, and financial reimbursement in reducing burnout are highlighted.

Mobile apps for real-world evidence in health care

The use of real-world evidence for health care research and evaluation is growing. Mobile health apps have often-overlooked potential to contribute valuable real-world data that are not captured by other sources and could provide data that are more cost-effective and generalizable than can randomized controlled trials. However, there are several challenges that must be overcome to realize the potential value of patient-used mobile health app real-world data, including data quality, motivation for long-term use, privacy and security, methods of analysis, and standardization and integration. Addressing these challenges will increase the value of data from mobile health apps to inform real-world evidence and improve patient empowerment, clinical management, disease research, and treatment development.

Sharing patient-generated data with healthcare providers: findings from a 2019 national survey

OBJECTIVE

Our study estimates the prevalence and predictors of wearable device adoption and data sharing with healthcare providers in a nationally representative sample.

MATERIALS AND METHODS

Data were obtained from the 2019 Health Information National Trend Survey. We conducted multivariable logistic regression to examine predictors of device adoption and data sharing.

RESULTS

The sample contained 4159 individuals, 29.9% of whom had adopted a wearable device in 2019. Among adopters, 46.3% had shared data with their provider. Individuals with diabetes (odds ratio [OR], 2.39; 95% CI, 1.66-3.45; P < .0001), hypertension (OR, 2.80; 95% CI, 2.12-3.70; P < .0001), and multiple chronic conditions (OR, 1.55; 95% CI, 1.03-2.32; P < .0001) had significantly higher odds of wearable device adoption. Individuals with a usual source of care (OR, 2.44; 95% CI, 1.95-3.04; P < .0001), diabetes (OR, 1.66; 95% CI, 1.32-2.08; P < .0001), and hypertension (OR, 1.78; 95% CI, 1.44-2.20; P < .0001) had significantly higher odds of sharing data with providers.

DISCUSSION

A third of individuals adopted a wearable medical device and nearly 50% of individuals who owned a device shared data with a provider in 2019. Patients with certain conditions, such as diabetes and hypertension, were more likely to adopt devices and share data with providers. Social determinants of health, such as income and usual source of care, negatively affected wearable device adoption and data sharing, similarly to other consumer health technologies.

CONCLUSIONS

Wearable device adoption and data sharing with providers may be more common than prior studies have reported; however, digital disparities were noted. Studies are needed that test implementation strategies to expand wearable device use and data sharing into care delivery.

Symptom and functional recovery monitoring in thoracic surgery

Curative-intent surgery is the treatment of choice for thoracic malignancies, including lung cancer. There is significant complexity and uncertainty associated with the diagnosis, prognosis, and surgical treatment decision-making in thoracic surgery. From a patient point of view, this complexity and uncertainty can be overwhelming. Therefore, for high-quality cancer care, an emphasis on patient-centered care-including the improvement in quality of life (QOL) through symptom and functional monitoring-is essential. Using the current literature and our previous research, the purpose of this paper is to: (I) review the current evidence on symptom and functional monitoring in surgery; (II) describe strategies to monitor symptoms and functional recovery in surgery; and (III) describe a model of patient-centered care in thoracic surgery.

Adapting the stage-based model of personal informatics for low-resource communities in the context of type 2 diabetes

Growing availability of self-monitoring technologies creates new opportunities for collection of personal health data and their use in personalized health informatics interventions. However, much of the previous empirical research and existing theories of individuals' engagement with personal data focused on early adopters and data enthusiasts. Less is understood regarding ways individuals from medically underserved low-income communities who live with chronic diseases engage with self-monitoring in health. In this research, we adapted a widely used theoretical framework, the stage-based model of personal informatics, to the unique attitudes, needs, and constraints of low-income communities. We conducted a qualitative study of attitudes and perceptions regarding tracking and planning in health and other contexts (e.g., finances) among low-income adults living with type 2 diabetes. This study showed distinct differences in participants' attitudes and behaviors around tracking and planning, as well as wide variability in their sense of being in charge of different areas of one's life. Ultimately, we found a strong connection between these two: perceptions of being in charge seems to be strongly connected to an individual's proactive or reactive tracking and planning in that area. Whereas individuals with a greater sense of being in charge of their health were more proactive, meaning they were likely to engage with all the stages of personal informatics model on their own, those with less of a sense of being in charge were more likely to be reactive-relying on their healthcare providers for several critical stages of self-monitoring (deciding what data to collect, integrating data from multiple sources, reflecting over patterns in collected data, and arriving at conclusions and implications for action). Perhaps as a result, these individuals were less likely to experience increases in self-awareness and self-knowledge, common motivating factors to engaging in self-monitoring in the future. We argue that adapting this framework in a way that highlights gaps in individuals' engagement has a number of important implications for future research in biomedical informatics and for the design of new interventions that promote engagement with self-monitoring, and that are robust in light of fragmented engagement.

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.

Health Tracking and Information Sharing in the Patient-Centered Era: A Health Information National Trends Survey (HINTS) Study

We examined the current state of digital health tracking and information sharing with health professionals among patients with chronic conditions using data from the National Cancer Institute's 2018 Health Information National Trends Survey (HINTS). Descriptive statistics were used to examine the characteristics of health tracking and information sharing, Chi-squared tests were used to compare across groups, and multivariate logistic regression models were used to control for covariates. Between 17.4-37.6% of respondents reported sharing information with a health professional through either e-mail, monitoring device, text message, or online medical record message. There were sociodemographic differences across health tracking and information sharing modalities, and patients with chronic conditions disproportionately lacked Internet access, a basic cell phone, smartphone, or tablet compared to those without chronic conditions (p<0.05). This suggests there are sociodemographic and technology-based disparities for health tracking and information sharing for patients with chronic conditions.

Patient-Reported Outcomes in Surgical Oncology: An Overview of Instruments and Scores

Traditional measures of quality and effectiveness in surgical oncology have focused on morbidity, mortality, and when available, recurrence rates. Measuring patient-reported outcomes (PROs) has become more widely accepted during the last decade. This article reviews instruments commonly used in the surgical treatment of cancer patients to evaluate PROs. In addition, it discusses the linkage of PROs and health-related quality of life measures with traditional surgical complications and highlights future directions related to the expanding use of PROs in the surgical care of cancer patients.

mHealth and patient generated health data: stakeholder perspectives on opportunities and barriers for transforming healthcare

BACKGROUND

Wearable devices, mobile health apps, and geolocation technologies place the ability to track, monitor and report data in the individuals' hands - or on their bodies. These innovations create an opportunity for "connected health," where individuals collect data outside of the healthcare encounter and report it to care providers. Collection of such patient-generated health data (PGHD) has the potential to impact the delivery of healthcare through remote monitoring, and by allowing patients and healthcare teams to provide targeted and efficient care that aligns with the health status of individual patients.

METHODS

To understand the value and barriers associated with clinical integration of PGHD we engaged a range of stakeholders, examining their perspectives and experiences of PGHD use. We conducted open-ended interviews with healthcare consumers (patients and care partners), healthcare providers, and healthcare administrators. Open recruitment and purposive sampling were utilized to identify participants that represented the breadth of PGHD use in research and clinical care. Interview guides focused on the value and barriers of PGHD use. Interviews were recorded, transcribed, and analyzed for emergent themes.

RESULTS

Themes emerged around the value of PGHD to support care decisions and improve patient-provider communication and engagement, and the promise of applying PGHD to formal care pathways and measurement-based care. Significant barriers included data validity and actionability, and the burden of integrating PGHD into existing care processes. Interviews highlighted areas for future research to better understand how PGHD can advance care transformation.

CONCLUSIONS

These findings provide rich context for understanding the experiences and needs of the individuals who interface with PGHD. Translating advances in technology and data tracking into successful clinical implementation requires understanding how stakeholders conceptualize and make use of PGHD, the potential value that PGHD can add to care, and the challenges that may limit PGHD's promise. Our results illustrate the value and challenges associated with health-system implementation of PGHD. Efforts to increase the scale and spread of PGHD will benefit from an approach that addresses the value and challenges PGHD brings to clinical care.

Outcome measures for oncology alternative payment models: practical considerations and recommendations

OBJECTIVES

This paper aims to synthesize existing scholarship on quality measures in oncology, with a specific focus on outcome-based quality measures, which are often underutilized. We also present a set of "core outcome measures" that may be considered in future oncology alternative payment models (APMs).

STUDY DESIGN

Our research consists of a focused literature review, content analysis, and quality measure synthesis and categorization.

METHODS

We conducted a focused literature review to generate key evidence on quality measures in oncology. We studied 7 oncology quality assessment frameworks, encompassing 142 quality metrics, and synthesized recommendations using the Center for Medicare and Medicaid Innovation APM toolkit, focusing on outcome measures.

RESULTS

We present 34 outcome-based oncology quality measures for consideration, which are classified into 5 domains: clinical care (eg, hospital and emergency department visits, treatment effectiveness, mortality), safety (eg, infections, hospital adverse events), care coordination (for hospital and hospice care), patient and caregiver experience, and population health and prevention. Both general and indication-specific outcome measures should be considered in oncology APMs, as appropriate. Utilizing outcome-based measures will require addressing multiple challenges, ranging from risk adjustment to data quality assurance.

CONCLUSIONS

Oncology care will benefit from a more rigorous approach to quality assessment. The success of oncology APMs will require a robust set of quality measures that are relevant to patients, providers, and payers.

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.

Patient generated health data use in clinical practice: A systematic review

BACKGROUND

Precision health calls for collecting and analyzing large amounts of data to capture an individual's unique behavior, lifestyle, genetics, and environmental context. The diffusion of digital tools has led to a significant growth of patient generated health data (PGHD), defined as health-related data created, gathered or inferred by or from patients and for which the patient controls data collection and data sharing.

PURPOSE

We assessed the current evidence of the impact of PGHD use in clinical practice and provide recommendations for the formal integration of PGHD in clinical care.

METHODS

We searched PubMed, Ovid, Embase, CINAHL, Web of Science, and Scopus up to May 2018. Inclusion criteria were applied and four reviewers screened titles and abstracts and consequently full articles.

FINDINGS

Our systematic literature review identified 21 studies that examined the use of PGHD in clinical settings. Integration of PGHD into electronic records was extremely limited, and decision support capabilities were for the most part basic.

DISCUSSION

PGHD and other types of patient-reported data will be part of the health care system narrative and we must continue efforts to understand its impact on health outcomes, costs, and patient satisfaction. Nursing scientists need to lead the process of defining the role of PGHD in the era of precision health.

Counting Steps: A New Way to Monitor Patients with Pulmonary Arterial Hypertension

RATIONALE

Activity levels in patients with pulmonary arterial hypertension (PAH) have correlated with surrogate markers of disease severity. It is not known whether physical activity measures are useful in monitoring patients with PAH.

OBJECTIVES

This pilot study aimed to evaluate whether change in physical activity measured by an accelerometer correlates with changes in six-minute walk distance (6MWD), echocardiographic parameters, NT-proBNP, or health-related quality-of-life measures (HRQOL).

METHODS

The study design was a prospective, observational study in subjects with prevalent PAH. Subjects wore a wrist-worn accelerometer (Fitbit Charge HR®) between two outpatient visits. Daily step count and activity levels were recorded, and the change over time was correlated with changes in 6MWD, echocardiographic parameters, HRQOL, and NT-proBNP.

MEASUREMENTS AND MAIN RESULTS

30 subjects were enrolled, of which 20 patients had adequate accelerometer data to be analyzed over the study duration. The mean duration of follow-up was 136.4 ( ± 47.3) days. The change in daily step count correlated with a change in 6MWD (r 0.43, p 0.05). Changes in duration spent in moderately active (r 0.52, p 0.02), lightly active (r 0.48, p 0.05), and sedentary activity levels (r - 0.54, p 0.02) correlated with a change in HRQOL. Changes in activity levels did not correlate with echocardiographic measures or NT-pro BNP.

CONCLUSIONS

Changes in daily step count and time spent at fairly active, lightly active, and sedentary activity levels correlate with changes in 6MWD, and HRQOL in subjects with PAH suggesting that accelerometry may be a useful monitoring tool.

Efficient Health Information Management Based on Patient-Generated Digital Data

Technology has been a growing part of healthcare for decades. The patient experience is going digital, and consumers are leading the way by accessing EHRs and using digital tools, such as wearables and apps, to manage their health. Patient-generated health data (PGHD) are health-related data created, recorded, or gathered by or from patients (or family members or other caregivers) to help address a health concern. PGHD may have several reported patient engagement and empowerment benefits, but lingering issues may prohibit providers from actually using patients' data. In this paper we review the main recent PGHD uses, challenges and opportunities.

Sleep Tracking and Exercise in Patients With Type 2 Diabetes Mellitus (Step-D): Pilot Study to Determine Correlations Between Fitbit Data and Patient-Reported Outcomes

Background

Few studies assessing the correlation between patient-reported outcomes and patient-generated health data from wearable devices exist.

Objective

The aim of this study was to determine the direction and magnitude of associations between patient-generated health data (from the Fitbit Charge HR) and patient-reported outcomes for sleep patterns and physical activity in patients with type 2 diabetes mellitus (T2DM).

Methods

This was a pilot study conducted with adults diagnosed with T2DM (n=86). All participants wore a Fitbit Charge HR for 14 consecutive days and completed internet-based surveys at 3 time points: day 1, day 7, and day 14. Patient-generated health data included minutes asleep and number of steps taken. Questionnaires assessed the number of days of exercise and nights of sleep problems per week. Means and SDs were calculated for all data, and Pearson correlations were used to examine associations between patient-reported outcomes and patient-generated health data. All respondents provided informed consent before participating.

Results

The participants were predominantly middle-aged (mean 54.3, SD 13.3 years), white (80/86, 93%), and female (50/86, 58%). Use of oral T2DM medication correlated with the number of mean steps taken (r=.35, P=.001), whereas being unaware of the glycated hemoglobin level correlated with the number of minutes asleep (r=−.24, P=.04). On the basis of the Fitbit data, participants walked an average of 4955 steps and slept 6.7 hours per day. They self-reported an average of 2.0 days of exercise and 2.3 nights of sleep problems per week. The association between the number of days exercised and steps walked was strong (r=.60, P<.001), whereas the association between the number of troubled sleep nights and minutes asleep was weaker (r=.28, P=.02).

Conclusions

Fitbit and patient-reported data were positively associated for physical activity as well as sleep, with the former more strongly correlated than the latter. As extensive patient monitoring can guide clinical decisions regarding T2DM therapy, passive, objective data collection through wearables could potentially enhance patient care, resulting in better patient-reported outcomes.

Quantifying and Visualizing Medication Adherence in Patients Following Acute Myocardial Infarction

Medication adherence is a critical component for recovery following acute myocardial infarction (AMI). Currently, numerous smartphone applications are capable of tracking medication adherence through patient-generated data (PGD), but few are integrated with the electronic health record (EHR). Integration of medication adherence PGD into the EHR can give both healthcare providers and patients increased insight into patterns of missed doses, effects on vital signs, and correlation with side effect symptomology to inform healthcare decisions. We propose the generation of a medication adherence "vital sign", a score calculated based upon the patient's reported doses taken collected through a smartphone application and streamed into the EHR on a daily basis. We also propose the creation of Patient Health Reports that incorporate relevant patient history, information from previous visits, and the medication adherence scores to give providers a comprehensive view of patients' health prior to clinic visits. These features are intended to incorporate PGD into clinical care to inform decision making in way that streamlines patient visits, reduces healthcare costs, and improves health outcomes.

It Takes Two to Tango: Engaging Patients and Providers With Portals

Patient portals are designed to be tools to more fully engage patients in their health care and help enable them to better manage their own health information. As the U.S. health care system rapidly adopted electronic health records (EHRs) over the past decade, many with linked patient portals, enthusiasm and expectations for this new technology as a means to engage and empower patients grew. Most patient portals have a set of core features designed to facilitate health care transactions, information tracking, and communication with care team members. The evidence supporting the anticipated benefits of patient portals on patient outcomes, however, remains mixed and incomplete. Moreover, a paradox exists in that, despite a high consumer interest in patient portals, widespread adoption remains relatively low. Potential reasons include the need for greater provider endorsement, examination and adaptation of clinical workflows, and the recognition of patient engagement as a reciprocal process.

Wireless Monitoring Program of Patient-Centered Outcomes and Recovery Before and After Major Abdominal Cancer Surgery

Importance

A combined subjective and objective wireless monitoring program of patient-centered outcomes can be carried out in patients before and after major abdominal cancer surgery.

Objective

To conduct a proof-of-concept pilot study of a wireless, patient-centered outcomes monitoring program before and after major abdominal cancer surgery.

Design, Setting, and Participants

In this proof-of-concept pilot study, patients wore wristband pedometers and completed online patient-reported outcome surveys (symptoms and quality of life) 3 to 7 days before surgery, during hospitalization, and up to 2 weeks after discharge. Reminders via email were generated for all moderate to severe scores for symptoms and quality of life. Surgery-related data were collected via electronic medical records, and complications were calculated using the Clavien-Dindo classification. The study was carried out in the inpatient and outpatient surgical oncology unit of one National Cancer Institute-designated comprehensive cancer center. Eligible patients were scheduled to undergo curative resection for hepatobiliary and gastrointestinal cancers, were English speaking, and were 18 years or older. Twenty participants were enrolled over 4 months. The study dates were April 1, 2015, to July 31, 2016.

Main Outcomes and Measures

Outcomes included adherence to wearing the pedometer, adherence to completing the surveys (MD Anderson Symptom Inventory and EuroQol 5-dimensional descriptive system), and satisfaction with the monitoring program.

Results

This study included a final sample of 20 patients (median age, 55.5 years [range, 22-74 years]; 15 [75%] female) with evaluable data. Pedometer adherence (88% [17 of 20] before surgery vs 83% [16 of 20] after discharge) was higher than survey adherence (65% to 75% [13 of 20 and 15 of 20] completed). The median number of daily steps at day 7 was 1689 (19% of daily steps at baseline), which correlated with the Comprehensive Complication Index, for which the median was 15 of 100 (r = -0.64, P < .05). Postdischarge overall symptom severity (2.3 of 10) and symptom interference with activities (3.5 of 10) were mild. Pain (4.4 of 10), fatigue (4.7 of 10), and appetite loss (4.0 of 10) were moderate after surgery. Quality-of-life scores were lowest at discharge (66.6 of 100) but improved at week 2 (73.9 of 100). While patient-reported outcomes returned to baseline at 2 weeks, the number of daily steps was only one-third of preoperative baseline.

Conclusions and Relevance

Wireless monitoring of combined subjective and objective patient-centered outcomes can be carried out in the surgical oncology setting. Preoperative and postoperative patient-centered outcomes have the potential of identifying high-risk populations who may need additional interventions to support postoperative functional and symptom recovery.

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.

Using Technology to Improve Cancer Care: Social Media, Wearables, and Electronic Health Records

Digital engagement has become pervasive in the delivery of cancer care. Internet- and cellular phone-based tools and systems are allowing large groups of people to engage with each other and share information. Health systems and individual health professionals are adapting to this revolution in consumer and patient behavior by developing ways to incorporate the benefits of technology for the purpose of improving the quality of medical care. One example is the use of social media platforms by oncologists to foster interaction with each other and to participate with the lay public in dialogue about science, medicine, and cancer care. In addition, consumer devices and sensors (wearables) have provided a new, growing dimension of digital engagement and another layer of patient-generated health data to foster better care and research. Finally, electronic health records have become the new standard for oncology care delivery, bringing new opportunities to measure quality in real time and follow practice patterns, as well as new challenges as providers and patients seek ways to integrate this technology along with other forms of digital engagement to produce more satisfaction in the process of care along with measurably better outcomes.

Leveraging cues from person-generated health data for peer matching in online communities

OBJECTIVE

Online health communities offer a diverse peer support base, yet users can struggle to identify suitable peer mentors as these communities grow. To facilitate mentoring connections, we designed a peer-matching system that automatically profiles and recommends peer mentors to mentees based on person-generated health data (PGHD). This study examined the profile characteristics that mentees value when choosing a peer mentor.

MATERIALS AND METHODS

Through a mixed-methods user study, in which cancer patients and caregivers evaluated peer mentor recommendations, we examined the relative importance of four possible profile elements: health interests, language style, demographics, and sample posts. Playing the role of mentees, the study participants ranked mentors, then rated both the likelihood that they would hypothetically contact each mentor and the helpfulness of each profile element in helping the make that decision. We analyzed the participants' ratings with linear regression and qualitatively analyzed participants' feedback for emerging themes about choosing mentors and improving profile design.

RESULTS

Of the four profile elements, only sample posts were a significant predictor for the likelihood of a mentee contacting a mentor. Communication cues embedded in posts were critical for helping the participants choose a compatible mentor. Qualitative themes offer insight into the interpersonal characteristics that mentees sought in peer mentors, including being knowledgeable, sociable, and articulate. Additionally, the participants emphasized the need for streamlined profiles that minimize the time required to choose a mentor.

CONCLUSION

Peer-matching systems in online health communities offer a promising approach for leveraging PGHD to connect patients. Our findings point to interpersonal communication cues embedded in PGHD that could prove critical for building mentoring relationships among the growing membership of online health communities.