Patient-generated health data and electronic health record integration: protocol for a scoping review

The potential of three-dimensional printing for pediatric oral solid dosage forms

Pediatric patients often require individualized dosing of medicine due to their unique pharmacokinetic and developmental characteristics. Current methods for tailoring the dose of pediatric medications, such as tablet splitting or compounding liquid formulations, have limitations in terms of dosing accuracy and palatability. This paper explores the potential of 3D printing as a solution to address the challenges and provide tailored doses of medication for each pediatric patient. The technological overview of 3D printing is discussed, highlighting various 3D printing technologies and their suitability for pharmaceutical applications. Several individualization options with the potential to improve adherence are discussed, such as individualized dosage, custom release kinetics, tablet shape, and palatability. To integrate the preparation of 3D printed medication at the point of care, a decentralized manufacturing model is proposed. In this setup, pharmaceutical companies would routinely provide materials and instructions for 3D printing, while specialized compounding centers or hospital pharmacies perform the printing of medication. In addition, clinical opportunities of 3D printing for dose-finding trials are emphasized. On the other hand, current challenges in adequate dosing, regulatory compliance, adherence to quality standards, and maintenance of intellectual property need to be addressed for 3D printing to close the gap in personalized oral medication.

The role of artificial intelligence for the application of integrating electronic health records and patient-generated data in clinical decision support

This narrative review aims to identify and understand the role of artificial intelligence in the application of integrated electronic health records (EHRs) and patient-generated health data (PGHD) in clinical decision support. We focused on integrated data that combined PGHD and EHR data, and we investigated the role of artificial intelligence (AI) in the application. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search articles in six databases: PubMed, Embase, Web of Science, Scopus, ACM Digital Library, and IEEE Computer Society Digital Library. In addition, we also synthesized seminal sources, including other systematic reviews, reports, and white papers, to inform the context, history, and development of this field. Twenty-six publications met the review criteria after screening. The EHR-integrated PGHD introduces benefits to health care, including empowering patients and families to engage via shared decision-making, improving the patient-provider relationship, and reducing the time and cost of clinical visits. AI's roles include cleaning and management of heterogeneous datasets, assisting in identifying dynamic patterns to improve clinical care processes, and providing more sophisticated algorithms to better predict outcomes and propose precise recommendations based on the integrated data. Challenges mainly stem from the large volume of integrated data, data standards, data exchange and interoperability, security and privacy, interpretation, and meaningful use. The use of PGHD in health care is at a promising stage but needs further work for widespread adoption and seamless integration into health care systems. AI-driven, EHR-integrated PGHD systems can greatly improve clinicians' abilities to diagnose patients' health issues, classify risks at the patient level by drawing on the power of integrated data, and provide much-needed support to clinics and hospitals. With EHR-integrated PGHD, AI can help transform health care by improving diagnosis, treatment, and the delivery of clinical care, thus improving clinical decision support.

The Use of Patient-Generated Health Data From Consumer-Grade Mobile Devices in Clinical Workflows: Protocol for a Systematic Review

BACKGROUND

With the rapid advancement of mobile technology, the scope of mobile health (mHealth) has expanded to include consumer-grade devices such as smartphones and wearable sensors. These solutions have typically been used for fitness purposes; however, due to their ubiquitous capabilities for data collection, they have the potential to bridge information gaps and supplement data from clinical visits. Patient-generated health data (PGHD) can be derived from mHealth solutions and be used by health care professionals (HCPs) as complementary tools in the care process, yet their integration into clinical workflows presents a myriad of challenges. PGHD might be a new and unfamiliar source of information for most HCPs, and the majority of mHealth solutions have not been designed to be used by HCPs as active reviewers. As mHealth solutions become more available and attractive to patients, HCPs may see an increase in the influx of data and related inquiries from their patients. This mismatch in expectations can result in disruptions to clinical workflows and negatively impact patient-clinician relationships. For PGHD to be integrated into clinical workflows, its use should be proven beneficial for both patients and HCPs. However, so far, only limited research has been done on the concrete experiences of HCPs as active reviewers of PGHD from consumer-grade mobile devices.

OBJECTIVE

We aimed to systematically guide the review of existing literature to identify what types of PGHD from consumer-grade mobile devices are currently being used by HCPs as complementary tools in the care process.

METHODS

The PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) 2015 was followed for the design of the search, selection, and data synthesis processes. Electronic searches will be done on PubMed, ACM Digital Library, IEEE Xplore, and Scopus.

RESULTS

Preliminary searches have been conducted, and previous related systematic and scoping reviews have been found and evaluated. The review is expected to be completed in February 2023.

CONCLUSIONS

This protocol will guide the review of existing literature on the use of PGHD produced by consumer-grade mobile devices. Although there have been previous reviews related to this topic, our proposed approach seeks to understand the specific opinions and experiences of different types of HCPs who are already using PGHD in their clinical practice and the motives for deeming these data useful and worth reviewing. Depending on the studies that will be included, there may be an opportunity to provide a wider understanding of what types of HCPs trust PGHD, despite the possible challenges that its use might convey, potentially contributing with the knowledge to support the design strategies of mHealth tools that could be integrated into clinical workflows.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/39389.

Collecting and sharing self-generated health and lifestyle data: Understanding barriers for people living with long-term health conditions - a survey study

Background

The growing popularity of collecting self-generated health and lifestyle data presents a valuable opportunity to develop our understanding of long-term health conditions and improve care. Barriers remain to the effective sharing of health and lifestyle data by those living with long-term health conditions which include beliefs around concepts of Trust, Identity, Privacy and Security, experiences of stigma, perceptions of risk and information sensitivity.

Method

We surveyed 250 UK adults who reported living with a range of long-term health conditions. We recorded data to assess self-reported behaviours, experiences, attitudes and motivations relevant to sharing self-generated health and lifestyle data. We also asked participants about their beliefs about Trust, Identity, Privacy and Security, stigma, and perceptions of risk and information sensitivity regarding their health and lifestyle data.

Results

Three-quarters of our sample reported recording information about their health and lifestyle on a daily basis. However, two-thirds reported never or rarely sharing this information with others. Trust, Identity, Privacy and Security concerns were considered to be ‘very important’ by those with long-term health conditions when deciding whether or not to share self-generated health and lifestyle data with others, with security concerns considered most important. Of those living with a long-term health condition, 58% reported experiencing stigma associated with their condition. The greatest perceived risk from sharing with others was the potential for future harm to their social relationships.

Conclusions

Our findings suggest that, in order for health professionals and researchers to benefit from the increased prevalence of self-generated health and lifestyle data, more can be done to address security concerns and to understand perceived risks associated with data sharing. Digital platforms aimed at facilitating the sharing of self-generated health and lifestyle data may look to highlight security features, enable users to control the sharing of certain information types, and emphasise the practical benefits to users of sharing health and lifestyle data with others.

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.

Patient-generated health data and electronic health record integration: a scoping review

Objectives

Patient-generated health data (PGHD) are clinically relevant data captured by patients outside of the traditional care setting. Clinical use of PGHD has emerged as an essential issue. This study explored the evidence to determine the extent of and describe the characteristics of PGHD integration into electronic health records (EHRs).

Methods

In August 2019, we conducted a systematic scoping review. We included studies with complete, partial, or in-progress PGHD and EHR integration within a clinical setting. The retrieved articles were screened for eligibility by 2 researchers, and data from eligible articles were abstracted, coded, and analyzed.

Results

A total of 19 studies met inclusion criteria after screening 9463 abstracts. Most of the study designs were pilots and all were published between 2013 and 2019. Types of PGHD were biometric and patient activity (57.9%), questionnaires and surveys (36.8%), and health history (5.3%). Diabetes was the most common patient condition (42.1%) for PGHD collection. Active integration (57.9%) was slightly more common than passive integration (31.6%). We categorized emergent themes into the 3 steps of PGHD flow. Themes emerged concerning resource requirements, data delivery to the EHR, and preferences for review.

Discussion

PGHD integration into EHRs appears to be at an early stage. PGHD have the potential to close health care gaps and support personalized medicine. Efforts are needed to understand how to optimize PGHD integration into EHRs considering resources, standards for EHR delivery, and clinical workflows.