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

An Exploratory Study on the Utility of Patient-Generated Health Data as a Tool for Health Care Professionals in Multiple Sclerosis Care

BACKGROUND

Patient-generated health data (PGHD) are data collected through technologies such as mobile devices and health apps. The integration of PGHD into health care workflows can support the care of chronic conditions such as multiple sclerosis (MS). Patients are often willing to share data with health care professionals (HCPs) in their care team; however, the benefits of PGHD can be limited if HCPs do not find it useful, leading patients to discontinue data tracking and sharing eventually. Therefore, understanding the usefulness of mobile health (mHealth) solutions, which provide PGHD and serve as enablers of the HCPs' involvement in participatory care, could motivate them to continue using these technologies.

OBJECTIVE

The objective of this study is to explore the perceived utility of different types of PGHD from mHealth solutions which could serve as tools for HCPs to support participatory care in MS.

METHOD

A mixed-methods approach was used, combining qualitative research and participatory design. This study includes three sequential phases: data collection, assessment of PGHD utility, and design of data visualizations. In the first phase, 16 HCPs were interviewed. The second and third phases were carried out through participatory workshops, where PGHD types were conceptualized in terms of utility.

RESULTS

The study found that HCPs are optimistic about PGHD in MS care. The most useful types of PGHD for HCPs in MS care are patients' habits, lifestyles, and fatigue-inducing activities. Although these subjective data seem more useful for HCPs, it is more challenging to visualize them in a useful and actionable way.

CONCLUSION

HCPs are optimistic about mHealth and PGHD as tools to further understand their patients' needs and support care in MS. HCPs from different disciplines have different perceptions of what types of PGHD are useful; however, subjective types of PGHD seem potentially more useful for MS care.

A guiding framework for creating a comprehensive strategy for mHealth data sharing, privacy, and governance in low- and middle-income countries (LMICs)

With the numerous advances and broad applications of mobile health (mHealth), establishing concrete data sharing, privacy, and governance strategies at national (or regional) levels is essential to protect individual privacy and data usage. This article applies the recent Health Data Governance Principles to provide a guiding framework for low- and middle-income countries (LMICs) to create a comprehensive mHealth data governance strategy. We provide three objectives: (1) establish data rights and ownership to promote equitable benefits from health data, (2) protect people through building trust and addressing patients' concerns, and (3) promote health value by enhancing health systems and services. We also recommend actions for realizing each objective to guide LMICs based on their unique mHealth data ecosystems. These objectives require adopting a regulatory framework for data rights and protection, building trust for data sharing, and enhancing interoperability to use new datasets in advancing healthcare services and innovation.

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.

Developing a Dietary Lifestyle Ontology (DILON) to Improve the Interoperability of Dietary Data: A Proof-of-Concept Study (Preprint)

Background

Dietary habits offer crucial information on one's health and form a considerable part of the patient-generated health data. Dietary data are collected through various channels and formats; thus, interoperability is a significant challenge to reusing this type of data. The vast scope of dietary concepts and the colloquial expression style add difficulty to standardizing the data. The interoperability issues of dietary data can be addressed through Common Data Elements with metadata annotation to some extent. However, making culture-specific dietary habits and questionnaire-based dietary assessment data interoperable still requires substantial efforts.

Objective

The main goal of this study was to address the interoperability challenge of questionnaire-based dietary data from different cultural backgrounds by combining ontological curation and metadata annotation of dietary concepts. Specifically, this study aimed to develop a Dietary Lifestyle Ontology (DILON) and demonstrate the improved interoperability of questionnaire-based dietary data by annotating its main semantics with DILON.

Methods

By analyzing 1158 dietary assessment data elements (367 in Korean and 791 in English), 515 dietary concepts were extracted and used to construct DILON. To demonstrate the utility of DILON in addressing the interoperability challenges of questionnaire-based multicultural dietary data, we developed 10 competency questions that asked to identify data elements sharing the same dietary topics and assessment properties. We instantiated 68 data elements on dietary habits selected from Korean and English questionnaires and annotated them with DILON to answer the competency questions. We translated the competency questions into Semantic Query-Enhanced Web Rule Language and reviewed the query results for accuracy.

Results

DILON was built with 262 concept classes and validated with ontology validation tools. A small overlap (72 concepts) in the concepts extracted from the questionnaires in 2 languages indicates that we need to pay closer attention to representing culture-specific dietary concepts. The Semantic Query-Enhanced Web Rule Language queries reflecting the 10 competency questions yielded correct results.

Conclusions

Ensuring the interoperability of dietary lifestyle data is a demanding task due to its vast scope and variations in expression. This study demonstrated that we could improve the interoperability of dietary data generated in different cultural contexts and expressed in various styles by annotating their core semantics with DILON.

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.

Data Visualizations to Support Health Practitioners' Provision of Personalized Care for Patients With Cancer and Multiple Chronic Conditions: User-Centered Design Study

BACKGROUND

There exists a challenge of understanding and integrating various types of data collected to support the health of individuals with multiple chronic conditions engaging in cancer care. Data visualization has the potential to address this challenge and support personalized cancer care.

OBJECTIVE

The aim of the study was to assess the health care practitioners' perceptions of and feedback regarding visualizations developed to support the care of individuals with multiple chronic conditions engaging in cancer care.

METHODS

Medical doctors (n=4) and registered nurses (n=4) providing cancer care at an academic medical center in the western United States provided feedback on visualization mock-ups. Mock-up designs were guided by current health informatics and visualization literature and the Munzner Nested Model for Visualization Design. User-centered design methods, a mock patient persona, and a scenario were used to elicit insights from participants. Directed content analysis was used to identify themes from session transcripts. Means and SDs were calculated for health care practitioners' rankings of overview visualizations.

RESULTS

Themes identified were data elements, supportive elements, confusing elements, interpretation, and use of visualization. Overall, participants found the visualizations useful and with the potential to provide personalized care. Use of color, reference lines, and familiar visual presentations (calendars, line graphs) were noted as helpful in interpreting data.

CONCLUSIONS

Visualizations guided by a framework and literature can support health care practitioners' understanding of data for individuals with multiple chronic conditions engaged in cancer care. This understanding has the potential to support the provision of personalized care.

An interpretable health behavioral intervention policy for mobile device users

An increasing number of people use mobile devices to monitor their behavior, such as exercise, and record their health status, such as psychological stress. However, these devices rarely provide ongoing support to help users understand how their behavior contributes to changes in their health status. To address this challenge, we aim to develop an interpretable policy for physical activity recommendations that reduce a user's perceived psychological stress, over a given time horizon. We formulate this problem as a sequential decision-making problem and solve it using a new method that we refer to as threshold Q-learning (TQL). The advantage of the TQL method over traditional Q-learning is that it is "doubly robust" and interpretable. This interpretability is achieved by making model assumptions and incorporating threshold selection into the learning process. Our simulation results indicate that the TQL method performs better than the Q-learning method given model misspecification. Our analyses are performed on data collected from 79 healthy adults over a 7 week period, where the data comprise physical activity patterns collected from mobile devices and self-assessed stress levels of the users. This work serves as a first step toward a computational health coaching solution for mobile device users.