Automated integration of continuous glucose monitor data in the electronic health record using consumer technology

Classifying Continuous Glucose Monitoring Documents From Electronic Health Records

BACKGROUND

Clinical use of continuous glucose monitoring (CGM) is increasing storage of CGM-related documents in electronic health records (EHR); however, the standardization of CGM storage is lacking. We aimed to evaluate the sensitivity and specificity of CGM Ambulatory Glucose Profile (AGP) classification criteria.

METHODS

We randomly chose 2244 (18.1%) documents from NYU Langone Health. Our document classification algorithm: (1) separated multiple-page documents into a single-page image; (2) rotated all pages into an upright orientation; (3) determined types of devices using optical character recognition; and (4) tested for the presence of particular keywords in the text. Two experts in using CGM for research and clinical practice conducted an independent manual review of 62 (2.8%) reports. We calculated sensitivity (correct classification of CGM AGP report) and specificity (correct classification of non-CGM report) by comparing the classification algorithm against manual review.

RESULTS

Among 2244 documents, 1040 (46.5%) were classified as CGM AGP reports (43.3% FreeStyle Libre and 56.7% Dexcom), 1170 (52.1%) non-CGM reports (eg, progress notes, CGM request forms, or physician letters), and 34 (1.5%) uncertain documents. The agreement for the evaluation of the documents between the two experts was 100% for sensitivity and 98.4% for specificity. When comparing the classification result between the algorithm and manual review, the sensitivity and specificity were 95.0% and 91.7%.

CONCLUSION

Nearly half of CGM-related documents were AGP reports, which are useful for clinical practice and diabetes research; however, the remaining half are other clinical documents. Future work needs to standardize the storage of CGM-related documents in the EHR.

Integration of Continuous Glucose Monitoring Data into an Electronic Health Record System: Single-Center Implementation

Managing data from continuous glucose monitoring (CGM) systems presents challenges to health care provider teams that rely on the electronic health record (EHR) during patient visits. A method of integrating CGM data with the EHR that relies on the Dexcom API was developed by Northwestern Medicine and Dexcom to address these challenges. Here, we describe the data management steps and user interface of the integrated system. Providers can access patients' historical and latest daily CGM data in the form of modal day plots and stacked columns showing time in various glucose concentration ranges. The integration facilitates the acquisition, storage, analysis, and display of CGM data within an EHR system and may be appropriate for deployment in other health care facilities.

Enhancing EHR Systems with data from wearables: An end-to-end Solution for monitoring post-Surgical Symptoms in older adults

Mobile health (mHealth) apps have gained popularity over the past decade for patient health monitoring, yet their potential for timely intervention is underutilized due to limited integration with electronic health records (EHR) systems. Current EHR systems lack real-time monitoring capabilities for symptoms, medication adherence, physical and social functions, and community integration. Existing systems typically rely on static, in-clinic measures rather than dynamic, real-time patient data. This highlights the need for automated, scalable, and human-centered platforms to integrate patient-generated health data (PGHD) within EHR. Incorporating PGHD in a user-friendly format can enhance patient symptom surveillance, ultimately improving care management and post-surgical outcomes. To address this barrier, we have developed an mHealth platform, ROAMM-EHR, to capture real-time sensor data and Patient Reported Outcomes (PROs) using a smartwatch. The ROAMM-EHR platform can capture data from a consumer smartwatch, send captured data to a secure server, and display information within the Epic EHR system using a user-friendly interface, thus enabling healthcare providers to monitor post-surgical symptoms effectively.

New Horizons for Consumer-Mediated Health Information Exchange

OBJECTIVES

In this paper, we discuss current trends in consumer-mediated health information exchange (HIE) within the U.S. and globally, including new approaches, relevant standards that support HIE and interoperability centered around the patient, remaining challenges, and potential future directions.

METHODS

We conducted a narrative review of the peer-reviewed and gray literature to characterize the current HIE landscape in relation to patient-centered data. Our searches targeted literature in three key areas related to consumer-mediated HIE: policy and initiatives, standards, and the technology landscape.

RESULTS

We discuss current trends in consumer-mediated exchange within the U.S. and globally, focusing on policies, standards, and technology that support information exchange centered around the patient. We also outline remaining challenges and potential future directions.

CONCLUSIONS

The current landscape in the U.S. and globally supports a more patient-centered care model. Ongoing advances in technology and data standards provide the technical infrastructure to empower consumers to electronically exchange their information with different stakeholders in ways not possible just a few years ago. These advancements hold great promise for patients to play a more central role in sharing their information in support of more patient-centered care. Additional research and analyzes along with public policies are needed.

Defining and Improving Outcomes Measurement for Virtual Care: Report from the VHA State-of-the-Art Conference on Virtual Care

Virtual care, including synchronous and asynchronous telehealth, remote patient monitoring, and the collection and interpretation of patient-generated health data (PGHD), has the potential to transform healthcare delivery and increase access to care. The Veterans Health Administration (VHA) Office of Health Services Research and Development (HSR&D) convened a State-of-the-Art (SOTA) Conference on Virtual Care to identify future virtual care research priorities. Participants were divided into three workgroups focused on virtual care access, engagement, and outcomes. In this article, we report the findings of the Outcomes Workgroup. The group identified virtual care outcome areas with sufficient evidence, areas in need of additional research, and areas that are particularly well-suited to be studied within VHA. Following a rigorous process of literature review and consensus, the group focused on four questions: (1) What outcomes of virtual care should we be measuring and how should we measure them?; (2) how do we choose the "right" care modality for the "right" patient?; (3) what are potential consequences of virtual care on patient safety?; and (4) how can PGHD be used to benefit provider decision-making and patient self-management?. The current article outlines key conclusions that emerged following discussion of these questions, including recommendations for future research.

Evaluating Pharmacists' Time Collecting Self-Monitoring Blood Glucose Data

Background

Patients on intensive insulin regimens are encouraged to self-monitor blood glucose (SMBG) to optimize their therapy. Clinical pharmacist practitioners (CPPs) use SMBG data to adjust diabetes medications; however, collecting SMBG data from patients is seen anecdotally as time intensive.

Methods

CPPs involved in diabetes management on primary care teams at the Boise Veterans Affairs Medical Center in Idaho were asked to estimate and record the following: SMBG data collection method, time spent collecting data, extra time spent documenting or formatting SMBG readings, total patient visit time, and visit type. For total patient visit time, pharmacists were asked to estimate only time spent discussing diabetes care and collecting SMBG data. Data were collected for 1 week using a standardized spreadsheet distributed to 24 CPPs.

Results

Eight pharmacists provided data from 120 patient encounters. For all encounters, the mean time spent collecting SMBG data was 3.3 minutes, and completing additional documentation/formatting was 1.3 minutes for a total of 4.6 minutes. Patient visits lasted a mean 20.1 minutes; 16% was spent on data collection and 6% on documentation and formatting.

Conclusions

At the Boise Veterans Affairs Medical Center, CPPs spend relatively little time per patient collecting SMBG data for clinical use. However, this time can be substantial when multiplied over several patient encounters. Opportunities exist to increase efficiency in SMBG data collection and documentation.

Direct-to-patient digital diagnostics in primary care: Opportunities, challenges, and conditions necessary for responsible digital diagnostics

BACKGROUND

Diagnostics are increasingly shifting to patients' home environment, facilitated by new digital technologies. Digital diagnostics (diagnostic services enabled by digital technologies) can be a tool to better respond to the challenges faced by primary care systems while aligning with patients' and healthcare professionals' needs. However, it needs to be clarified how to determine the success of these interventions.

OBJECTIVES

We aim to provide practical guidance to facilitate the adequate development and implementation of digital diagnostics.

STRATEGY

Here, we propose the quadruple aim (better patient experiences, health outcomes and professional satisfaction at lower costs) as a framework to determine the contribution of digital diagnostics in primary care. Using this framework, we critically analyse the advantages and challenges of digital diagnostics in primary care using scientific literature and relevant casuistry.

RESULTS

Two use cases address the development process and implementation in the Netherlands: a patient portal for reporting laboratory results and digital diagnostics as part of hybrid care, respectively. The third use case addresses digital diagnostics for sexually transmitted diseases from an international perspective.

CONCLUSIONS

We conclude that although evidence is gathering, the often-expected value of digital diagnostics needs adequate scientific evidence. We propose striving for evidence-based 'responsible digital diagnostics' (sustainable, ethically acceptable, and socially desirable digital diagnostics). Finally, we provide a set of conditions necessary to achieve it. The analysis and actionable guidance provided can improve the chance of success of digital diagnostics interventions and overall, the positive impact of this rapidly developing field.

Roadmap to Continuous Glucose Monitoring Adoption and Improved Outcomes in Endocrinology: The 4T (Teamwork, Targets, Technology, and Tight Control) Program

Glucose monitoring is essential for the management of type 1 diabetes and has evolved from urine glucose monitoring in the early 1900s to home blood glucose monitoring in the 1980s to continuous glucose monitoring (CGM) today. Youth with type 1 diabetes struggle to meet A1C goals; however, CGM is associated with improved A1C in these youth and is recommended as a standard of care by diabetes professional organizations. Despite their utility, expanding uptake of CGM systems has been challenging, especially in minoritized communities. The 4T (Teamwork, Targets, Technology, and Tight Control) program was developed using a team-based approach to set consistent glycemic targets and equitably initiate CGM and remote patient monitoring in all youth with new-onset type 1 diabetes. In the pilot 4T study, youth in the 4T cohort had a 0.5% improvement in A1C 12 months after diabetes diagnosis compared with those in the historical cohort. The 4T program can serve as a roadmap for other multidisciplinary pediatric type 1 diabetes clinics to increase CGM adoption and improve glycemic outcomes.

An Evidence-Based Framework for Creating Inclusive and Personalized mHealth Solutions-Designing a Solution for Medicaid-Eligible Pregnant Individuals With Uncontrolled Type 2 Diabetes

Mobile health (mHealth) apps can be an evidence-based approach to improve health behavior and outcomes. Prior literature has highlighted the need for more research on mHealth personalization, including in diabetes and pregnancy. Critical gaps exist on the impact of personalization of mHealth apps on patient engagement, and in turn, health behaviors and outcomes. Evidence regarding how personalization, engagement, and health outcomes could be aligned when designing mHealth for underserved populations is much needed, given the historical oversights with mHealth design in these populations. This viewpoint is motivated by our experience from designing a personalized mHealth solution focused on Medicaid-enrolled pregnant individuals with uncontrolled type 2 diabetes, many of whom also experience a high burden of social needs. We describe fundamental components of designing mHealth solutions that are both inclusive and personalized, forming the basis of an evidence-based framework for future mHealth design in other disease states with similar contexts.

Visualization of Patient-Generated Health Data: A Scoping Review of Dashboard Designs

BACKGROUND

Patient-centered clinical decision support (PC CDS) aims to assist with tailoring decisions to an individual patient's needs. Patient-generated health data (PGHD), including physiologic measurements captured frequently by automated devices, provide important information for PC CDS. The volume and availability of such PGHD is increasing, but how PGHD should be presented to clinicians to best aid decision-making is unclear.

OBJECTIVES

Identify best practices in visualizations of physiologic PGHD, for designing a software application as a PC CDS tool.

METHODS

We performed a scoping review of studies of PGHD dashboards that involved clinician users in design or evaluations. We included only studies that used physiologic PGHD from single patients for usage in decision-making.

RESULTS

We screened 468 titles and abstracts, 63 full-text papers, and identified 15 articles to include in our review. Some research primarily sought user input on PGHD presentation; other studies garnered feedback only as a side effort for other objectives (e.g., integration with electronic health records [EHRs]). Development efforts were often in the domains of chronic diseases and collected a mix of physiologic parameters (e.g., blood pressure and heart rate) and activity data. Users' preferences were for data to be presented with statistical summaries and clinical interpretations, alongside other non-PGHD data. Recurrent themes indicated that users desire longitudinal data display, aggregation of multiple data types on the same screen, actionability, and customization. Speed, simplicity, and availability of data for other purposes (e.g., documentation) were key to dashboard adoption. Evaluations were favorable for visualizations using common graphing or table formats, although best practices for implementation have not yet been established.

CONCLUSION

Although the literature identified common themes on data display, measures, and usability, more research is needed as PGHD usage grows. Ensuring that care is tailored to individual needs will be important in future development of clinical decision support.

Real-World Evidence of a Hospital-Linked Digital Health App for the Control of Hypertension and Diabetes Mellitus in South Korea: Nationwide Multicenter Study

BACKGROUND

Digital health care apps have been widely used for managing chronic conditions such as diabetes mellitus and hypertension, providing promising prospects for enhanced health care delivery, increased patient engagement, and improved self-management. However, the impact of integrating these apps within hospital systems for managing such conditions still lacks conclusive evidence.

OBJECTIVE

We aimed to investigate the real-world effectiveness of using hospital-linked digital health care apps in lowering blood pressure (BP) and blood glucose levels in patients with hypertension and diabetes mellitus.

METHODS

Nationwide multicenter data on demographic characteristics and the use of a digital health care app from 233 hospitals were collected for participants aged 20 to 80 years in South Korea between August 2021 and June 2022. We divided the participants into 2 groups: 1 group consisted of individuals who exclusively used the digital health app (control) and the other group used the hospital-linked digital health app. All the patients participated in a 12-week digital health care intervention. We conducted a comparative analysis to assess the real-world effectiveness of the hospital-linked digital health app. The primary outcome was the differences in the systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG) level, and postprandial glucose (PPG) level between baseline and 12 weeks.

RESULTS

A total of 1029 participants were analyzed for the FBG level, 527 participants were analyzed for the PPG level, and 2029 participants for the SBP and DBP were enrolled. After 12 weeks, a hospital-linked digital health app was found to reduce SBP (-5.4 mm Hg, 95% CI -7.0 to -3.9) and DBP (-2.4 mm Hg, 95% CI -3.4 to -1.4) in participants without hypertension and FBG level in all participants (those without diabetes, -4.4 mg/dL, 95% CI -7.9 to -1.0 and those with diabetes, -3.2 mg/dL, 95% CI -5.4 to -1.0); however, there was no statistically significant difference compared to the control group (using only digital health app). Specifically, participants with diabetes using a hospital-linked digital health app demonstrated a significant decrease in PPG after 12 weeks (-10.9 mg/dL, 95% CI -31.1 to -5.3) compared to those using only a digital health app (P=.006).

CONCLUSIONS

Hospital-linked digital interventions have greatly improved glucose control for diabetes compared with using digital health technology only. These hospital-linked digital health apps have the potential to offer consumers and health care professionals cost-effective support in decreasing glucose levels when used in conjunction with self-monitoring.

Digital Therapeutics for the Egocentric and Allocentric Neglects in Patients with Brain Injury: A Mini Review

Various therapeutic approaches have been developed for neglect. Many studies have demonstrated the effect of digital therapeutics (DTx) on neglect. However, few studies have reported the effects of DTx on egocentric and allocentric neglect. The differentiation of types of neglect and separate interventions is crucial in the rehabilitation process. In this article, seven studies on DTx on egocentric and allocentric neglect were reviewed. DTx, which was employed in these studies, could be classified as follows: (1) software adaptation in traditional treatment, (2) VR game using the head-mount display as treatment, and (3) the development of a new digital program like ReMoVES. In addition, more studies and more effective results were reported for egocentric neglect than for allocentric neglect. In future studies, each effect on egocentric and allocentric neglect should be identified in detail with the appropriate use of differential evaluation and long-term application of independent DTx.

The Need for Data Standards and Implementation Policies to Integrate CGM Data into the Electronic Health Record

The current lack of continuous glucose monitor (CGM) data integration into the electronic health record (EHR) is holding back the use of this wearable technology for patient-generated health data (PGHD). This failure to integrate with other healthcare data inside the EHR disrupts workflows, removes the data from critical patient context, and overall makes the CGM data less useful than it might otherwise be. Many healthcare organizations (HCOs) are either struggling with or delaying designing and implementing CGM data integrations. In this article, the current status of CGM integration is reviewed, goals for integration are proposed, and a consensus plan to engage key stakeholders to facilitate integration is presented.

Recommendations for the Quality Management of Patient-Generated Health Data in Remote Patient Monitoring: Mixed Methods Study

BACKGROUND

Patient-generated health data (PGHD) collected from innovative wearables are enabling health care to shift to outside clinical settings through remote patient monitoring (RPM) initiatives. However, PGHD are collected continuously under the patient's responsibility in rapidly changing circumstances during the patient's daily life. This poses risks to the quality of PGHD and, in turn, reduces their trustworthiness and fitness for use in clinical practice.

OBJECTIVE

Using a sociotechnical health informatics lens, we developed a data quality management (DQM) guideline for PGHD captured from wearable devices used in RPM with the objective of investigating how DQM principles can be applied to ensure that PGHD can reliably inform clinical decision-making in RPM.

METHODS

First, clinicians, health information specialists, and MedTech industry representatives with experience in RPM were interviewed to identify DQM challenges. Second, these stakeholder groups were joined by patient representatives in a workshop to co-design potential solutions to meet the expectations of all the stakeholders. Third, the findings, along with the literature and policy review results, were interpreted to construct a guideline. Finally, we validated the guideline through a Delphi survey of international health informatics and health information management experts.

RESULTS

The guideline constructed in this study comprised 19 recommendations across 7 aspects of DQM. It explicitly addressed the needs of patients and clinicians but implied that there must be collaboration among all stakeholders to meet these needs.

CONCLUSIONS

The increasing proliferation of PGHD from wearables in RPM requires a systematic approach to DQM so that these data can be reliably used in clinical care. The developed guideline is an important next step toward safe RPM.

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.

Stratified Multivariate Multiscale Dispersion Entropy for Physiological Signal Analysis

Multivariate entropy quantification algorithms are becoming a prominent tool for the extraction of information from multi-channel physiological time-series. However, in the analysis of physiological signals from heterogeneous organ systems, certain channels may overshadow the patterns of others, resulting in information loss. Here, we introduce the framework of Stratified Entropy to prioritize each channels' dynamics based on their allocation to respective strata, leading to a richer description of the multi-channel time-series. As an implementation of the framework, three algorithmic variations of the Stratified Multivariate Multiscale Dispersion Entropy are introduced. These variations and the original algorithm are applied to synthetic time-series, waveform physiological time-series, and derivative physiological data. Based on the synthetic time-series experiments, the variations successfully prioritize channels following their strata allocation while maintaining the low computation time of the original algorithm. In experiments on waveform physiological time-series and derivative physiological data, increased discrimination capacity was noted for multiple strata allocations in the variations when benchmarked to the original algorithm. This suggests improved physiological state monitoring by the variations. Furthermore, our variations can be modified to utilize a priori knowledge for the stratification of channels. Thus, our research provides a novel approach for the extraction of previously inaccessible information from multi-channel time series acquired from heterogeneous systems.

Advancements and future directions in the teamwork, targets, technology, and tight control-the 4T study: improving clinical outcomes in newly diagnosed pediatric type 1 diabetes

PURPOSE OF REVIEW

The benefits of intensive diabetes management have been established by the Diabetes Control and Complications Trial. However, challenges with optimizing glycemic management in youth with type 1 diabetes (T1D) remain across pediatric clinics in the United States. This article will review our Teamwork, Targets, Technology, and Tight Control (4T) study that implements emerging diabetes technology into clinical practice with a team approach to sustain tight glycemic control from the onset of T1D and beyond to optimize clinical outcomes.

RECENT FINDINGS

During the 4T Pilot study and study 1, our team-based approach to intensive target setting, education, and remote data review has led to significant improvements in hemoglobin A1c throughout the first year of T1D diagnosis in youth, as well as family and provider satisfaction.

SUMMARY

The next steps include refinement of the current 4T study 1, developing a business case, and broader implementation of the 4T study. In study 2, we are including a more pragmatic cadence of remote data review and disseminating exercise education and activity tracking to both English- and Spanish-speaking families. The overall goal is to create and implement a translatable program that can facilitate better outcomes for pediatric clinics across the USA.

Efficacy of Personalized Diabetes Self-care Using an Electronic Medical Record-Integrated Mobile App in Patients With Type 2 Diabetes: 6-Month Randomized Controlled Trial

Background

A system that combines technology and web-based coaching can help treat chronic conditions such as diabetes. However, the effectiveness of apps in mobile health (mHealth) interventions is inconclusive and unclear due to heterogeneous interventions and varying follow-up durations. In addition, randomized controlled trial data are limited, and long-term follow-up is lacking, especially for apps integrated into electronic medical records.

Objective

We aimed to assess the effect of an electronic medical record–integrated mobile app for personalized diabetes self-care, focusing on the self-monitoring of blood glucose and lifestyle modifications, on glycemic control in patients with type 2 diabetes mellitus.

Methods

In a 26-week, 3-arm, randomized, controlled, open-label, parallel group trial, patients with type 2 diabetes mellitus and a hemoglobin A_1c (HbA_1c) level of ≥7.5% were recruited. The mHealth intervention consisted of self-monitoring of blood glucose with the automatic transfer of glucose, diet, and physical activity counseling data (iCareD system). Participants were randomly assigned to the following three groups: usual care (UC), mobile diabetes self-care (MC), and MC with personalized, bidirectional feedback from physicians (MPC). The primary outcome was the change in HbA_1c levels at 26 weeks. In addition, diabetes-related self-efficacy, self-care activities, and satisfaction with the iCareD system were assessed after the intervention.

Results

A total of 269 participants were enrolled, and 234 patients (86.9%) remained in the study at 26 weeks. At 12 weeks after the intervention, the mean decline in HbA_1c levels was significantly different among the 3 groups (UC vs MC vs MPC: −0.49% vs −0.86% vs −1.04%; P=.02). The HbA_1c level decreased in all groups; however, it did not differ among groups after 26 weeks. In a subgroup analysis, HbA_1c levels showed a statistically significant decrease after the intervention in the MPC group compared with the change in the UC or MC group, especially in patients aged <65 years (P=.02), patients with a diabetes duration of ≥10 years (P=.02), patients with a BMI of ≥25.0 kg/m² (P=.004), patients with a C-peptide level of ≥0.6 ng/mL (P=.008), and patients who did not undergo treatment with insulin (P=.004) at 12 weeks. A total of 87.2% (137/157) of the participants were satisfied with the iCareD system.

Conclusions

The mHealth intervention for diabetes self-care showed short-term efficacy in glycemic control, and the effect decreased over time. The participants were comfortable with using the iCareD system and exhibited high adherence.

Trial Registration

Clinical Research Information Service, Republic of Korea KCT0004128; https://tinyurl.com/bdd6pa9m

The Launch of the iCoDE Standard Project

INTRODUCTION

The first meeting of the Integration of Continuous Glucose Monitor Data into the Electronic Health Record (iCoDE) project, organized by Diabetes Technology Society, took place virtually on January 27, 2022.

METHODS

Clinicians, government officials, data aggregators, attorneys, and standards experts spoke in panels and breakout groups. Three themes were covered: 1) why digital health data integration into the electronic health record (EHR) is needed, 2) what integrated continuously monitored glucose data will look like, and 3) how this process can be achieved in a way that will satisfy clinicians, healthcare organizations, and regulatory experts.

RESULTS

The meeting themes were addressed within eight sessions: 1) What Do Inpatient Clinicians Want to See With Integration of CGM Data into the EHR?, 2) What Do Outpatient Clinicians Want to See With Integration of CGM Data into the EHR?, 3) Why Are Data Standards and Guidances Useful?, 4) What Value Can Data Integration Services Add?, 5) What Are Examples of Successful Integration?, 6) Which Privacy, Security, and Regulatory Issues Must Be Addressed to Integrate CGM Data into the EHR?, 7) Breakout Group Discussions, and 8) Presentation of Breakout Group Ideas.

CONCLUSIONS

Creation of data standards and workflow guidance are necessary components of the Integration of Continuous Glucose Monitor Data into the Electronic Health Record (iCoDE) standard project. This meeting, which launched iCoDE, will be followed by a set of working group meetings intended to create the needed standard.

Teamwork, Targets, Technology, and Tight Control in Newly Diagnosed Type 1 Diabetes: the Pilot 4T Study

CONTEXT

Youth with type 1 diabetes (T1D) do not meet glycated hemoglobin A1c (HbA1c) targets.

OBJECTIVE

This work aimed to assess HbA1c outcomes in children with new-onset T1D enrolled in the Teamwork, Targets, Technology and Tight Control (4T) Study.

METHODS

HbA1c levels were compared between the 4T and historical cohorts. HbA1c differences between cohorts were estimated using locally estimated scatter plot smoothing (LOESS). The change from nadir HbA1c (month 4) to 12 months post diagnosis was estimated by cohort using a piecewise mixed-effects regression model accounting for age at diagnosis, sex, ethnicity, and insurance type. We recruited 135 youth with newly diagnosed T1D at Stanford Children's Health. Starting July 2018, all youth within the first month of T1D diagnosis were offered continuous glucose monitoring (CGM) initiation and remote CGM data review was added in March 2019. The main outcomes measure was HbA1c.

RESULTS

HbA1c at 6, 9, and 12 months post diagnosis was lower in the 4T cohort than in the historic cohort (-0.54% to -0.52%, and -0.58%, respectively). Within the 4T cohort, HbA1c at 6, 9, and 12 months post diagnosis was lower in those patients with remote monitoring than those without (-0.14%, -0.18% to -0.14%, respectively). Multivariable regression analysis showed that the 4T cohort experienced a significantly lower increase in HbA1c between months 4 and 12 (P < .001).

CONCLUSION

A technology-enabled, team-based approach to intensified new-onset education involving target setting, CGM initiation, and remote data review statistically significantly decreased HbA1c in youth with T1D 12 months post diagnosis.

Opportunities and Challenges of Integrating Food Practice into Clinical Decision-Making

BACKGROUND

Food practice plays an important role in health. Food practice data collected in daily living settings can inform clinical decisions. However, integrating such data into clinical decision-making is burdensome for both clinicians and patients, resulting in poor adherence and limited utilization. Automation offers benefits in this regard, minimizing this burden resulting in a better fit with a patient's daily living routines, and creating opportunities for better integration into clinical workflow. Although the literature on patient-generated health data (PGHD) can serve as a starting point for the automation of food practice data, more diverse characteristics of food practice data provide additional challenges.

OBJECTIVES

We describe a series of steps for integrating food practices into clinical decision-making. These steps include the following: (1) sensing food practice; (2) capturing food practice data; (3) representing food practice; (4) reflecting the information to the patient; (5) incorporating data into the EHR; (6) presenting contextualized food practice information to clinicians; and (7) integrating food practice into clinical decision-making.

METHODS

We elaborate on automation opportunities and challenges in each step, providing a summary visualization of the flow of food practice-related data from daily living settings to clinical settings.

RESULTS

We propose four implications of automating food practice hereinafter. First, there are multiple ways of automating workflow related to food practice. Second, steps may occur in daily living and others in clinical settings. Food practice data and the necessary contextual information should be integrated into clinical decision-making to enable action. Third, as accuracy becomes important for food practice data, macrolevel data may have advantages over microlevel data in some situations. Fourth, relevant systems should be designed to eliminate disparities in leveraging food practice data.

CONCLUSION

Our work confirms previously developed recommendations in the context of PGHD work and provides additional specificity on how these recommendations apply to food practice.

Feasibility and Impact of Remote Glucose Monitoring Among Patients With Newly Diagnosed Type 1 Diabetes: Single-Center Pilot Study

Background

Caregivers of children with newly diagnosed type 1 diabetes (T1D) maintain close contact with providers for several weeks to facilitate rapid adjustments in insulin dosing regimens. Traditionally, patient glucose values are relayed by telephone for provider feedback, but digital health technology can now enable the remote sharing of glucose data via mobile apps.

Objective

The aim of this study was to test the feasibility of remote glucose monitoring in a population of children and adolescents with newly diagnosed T1D and to explore whether remote monitoring alters habits for self-review of glucose data or perceived ease of provider contact in this population as compared to a nonrandomized control group.

Methods

Data were collected from families who chose to participate in remote monitoring (intervention group) as well as from patients receiving usual care (control group). The intervention group received Bluetooth-capable glucose meters and Apple iPod Touch devices. Patient-generated glucose data were passively relayed from the meter to the iPod Touch and then to both the electronic health record (EHR) and a third-party diabetes data platform, Tidepool. The principal investigator reviewed glucose data daily in the EHR and Tidepool and contacted the participants as needed for insulin dose adjustments during the time between hospital discharge and first clinic appointment. Families in the control group received usual care, which involved keeping written records of glucose values and contacting the diabetes team daily by telephone to relay data and receive treatment recommendations. A total of 40 families (20 for the intervention group and 20 for the control group) participated in the study. All families were surveyed at 1 month and 6 months regarding self-review of glucose data and ease of contacting the diabetes team.

Results

Patient-generated glucose data were remotely accessible for 100% of the participants via Tidepool and for 85% via the EHR. Survey data indicated that families in the intervention group were more likely than those in the control group to review their glucose data using mobile health apps after 1 month (P<.001), but by 6 months, this difference had disappeared. Perceived ease of contacting the clinical team for assistance was lower for the intervention group after 6 months (when receiving usual care) in comparison to during the intervention period (P=.48) and compared with a control group who did not have exposure to remote monitoring (P=.03).

Conclusions

Remote glucose monitoring is feasible among pediatric patients with newly diagnosed T1D and may be associated with the earlier adoption of mobile health apps for self-management. The use of broadscale remote monitoring for T1D in the future will depend on improved access to Bluetooth-enabled mobile devices for all patients, improved interoperability of mobile health apps to enable data transfer on Android as well as Apple devices, and new provider workflows to handle large-scale panel management based on patient-generated health data.

Trial Registration

ClinicalTrials.gov NCT04106440; https://clinicaltrials.gov/ct2/show/NCT04106440

OUP accepted manuscript

Objective

Recent technological development along with the constraints imposed by the coronavirus disease 2019 (COVID-19) pandemic have led to increased availability of patient-generated health data. However, it is not well understood how to effectively integrate this new technology into large health systems. This article seeks to identify interventions to increase utilization of electronic blood glucose monitoring for patients with diabetes.

Materials and Methods

A large randomized controlled trial tested the impact of multiple interventions to promote use of electronic blood glucose tracking. The total study sample consisted of 7052 patients with diabetes across 68 providers at 20 selected primary care offices. The design included 2 stages: First, primary care practices were randomly assigned to have their providers receive education regarding blood glucose flowsheet orders. Then, patients in the treated practices were assigned to 1 of 4 reminder interventions.

Results

Provider education successfully increased provider take-up of an online blood glucose monitoring tool by 64 percentage points, while a comparison of reminder interventions revealed that emphasizing accountability to the provider encouraged patients to track their blood glucose online. An assessment of downstream outcomes revealed impacts of the interventions on prescribing behavior and A1c testing frequency.

Discussion

It is important to understand how health systems can practically promote take-up and awareness of emerging digital health alternatives or those with persistently low utilization in clinical settings.

Conclusion

These results indicate that provider training and support are critical first steps to promote utilization of patient-generated health data, and that patient communications can provide further motivation.

Population-level management of type 1 diabetes via continuous glucose monitoring and algorithm-enabled patient prioritization: Precision health meets population health

OBJECTIVE

To develop and scale algorithm-enabled patient prioritization to improve population-level management of type 1 diabetes (T1D) in a pediatric clinic with fixed resources, using telemedicine and remote monitoring of patients via continuous glucose monitor (CGM) data review.

RESEARCH DESIGN AND METHODS

We adapted consensus glucose targets for T1D patients using CGM to identify interpretable clinical criteria to prioritize patients for weekly provider review. The criteria were constructed to manage the number of patients reviewed weekly and identify patients who most needed provider contact. We developed an interactive dashboard to display CGM data relevant for the patients prioritized for review.

RESULTS

The introduction of the new criteria and interactive dashboard was associated with a 60% reduction in the mean time spent by diabetes team members who remotely and asynchronously reviewed patient data and contacted patients, from 3.2 ± 0.20 to 1.3 ± 0.24 min per patient per week. Given fixed resources for review, this corresponded to an estimated 147% increase in weekly clinic capacity. Patients who qualified for and received remote review (n = 58) have associated 8.8 percentage points (pp) (95% CI = 0.6-16.9 pp) greater time-in-range (70-180 mg/dl) glucoses compared to 25 control patients who did not qualify at 12 months after T1D onset.

CONCLUSIONS

An algorithm-enabled prioritization of T1D patients with CGM for asynchronous remote review reduced provider time spent per patient and was associated with improved time-in-range.

Mobile health in preventive cardiology: current status and future perspective

PURPOSE OF REVIEW

Despite cutting edge acute interventions and growing preventive strategies supported by robust clinical trials, cardiovascular disease (CVD) has stubbornly persisted as a leading cause of death in the United States and globally. The American Heart Association recognizes mobile health technologies (mHealth) as an emerging strategy in the mitigation of CVD risk factors, with significant potential for improving population health. The purpose of this review is to highlight and summarize the latest available literature on mHealth applications and provide perspective on future directions and barriers to implementation.

RECENT FINDINGS

While available randomized controlled trials and systematic reviews tend to support efficacy of mHealth, published literature includes heterogenous approaches to similar problems with inconsistent results. Some of the strongest recent evidence has been focused on the use of wearables in arrhythmia detection. Systematic reviews of mHealth approaches demonstrate benefit when applied to risk factor modification in diabetes, cigarette smoking cessation, and physical activity/weight loss, while also showing promise in multi risk factor modification via cardiac rehabilitation.

SUMMARY

Evidence supports efficacy of mHealth in a variety of applications for CVD prevention and management, but continued work is needed for further validation and scaling. Future directions will focus on platform optimization, data and sensor consolidation, and clinical workflow integration. Barriers include application heterogeneity, lack of reimbursement structures, and inequitable access to technology. Policies to promote access to technology will be critical to evidence-based mHealth technologies reaching diverse populations and advancing health equity.

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.

Future and Development Direction of Digital Healthcare

OBJECTIVES

Digital healthcare is expected to play a pivotal role in patient-centered healthcare. It empowers patients by informing, communicating, and motivating them. However, a pragmatic evaluation of the present status of digital healthcare has not been presented; therefore, we aimed to examine the status of digital healthcare in Korea.

METHODS

This article discusses digital healthcare, examples of assessment in Korea and other countries, the implications of past examples, and future directions for development.

RESULTS

Over the years, various clinical studies have used clinical evidence to assess the feasibility of digital healthcare. If feasible, it is actually clinically effective. If it is effective, can it be commercialized at an acceptable cost? These questions have been investigated in various evidence-based studies. In addition, great efforts are being made to secure ample evidence to assess various aspects of digital healthcare, such as safety, quality, end-user experience, and equity.

CONCLUSIONS

Digital healthcare requires a deep understanding of both the technical and medical aspects. To strengthen the competence of the medical aspect, medical staff, patients, and the government must work together with continuous interest in this goal.

Continuous Glucose Monitoring in the Hospital

Continuous glucose monitors (CGMs) have suddenly become part of routine care in many hospitals. The coronavirus disease 2019 (COVID-19) pandemic has necessitated the use of new technologies and new processes to care for hospitalized patients, including diabetes patients. The use of CGMs to automatically and remotely supplement or replace assisted monitoring of blood glucose by bedside nurses can decrease: the amount of necessary nursing exposure to COVID-19 patients with diabetes; the amount of time required for obtaining blood glucose measurements, and the amount of personal protective equipment necessary for interacting with patients during the blood glucose testing. The United States Food and Drug Administration (FDA) is now exercising enforcement discretion and not objecting to certain factory-calibrated CGMs being used in a hospital setting, both to facilitate patient care and to obtain performance data that can be used for future regulatory submissions. CGMs can be used in the hospital to decrease the frequency of fingerstick point of care capillary blood glucose testing, decrease hyperglycemic episodes, and decrease hypoglycemic episodes. Most of the research on CGMs in the hospital has focused on their accuracy and only recently outcomes data has been reported. A hospital CGM program requires cooperation of physicians, bedside nurses, diabetes educators, and hospital administrators to appropriately select and manage patients. Processes for collecting, reviewing, storing, and responding to CGM data must be established for such a program to be successful. CGM technology is advancing and we expect that CGMs will be increasingly used in the hospital for patients with diabetes.

Diabetology 4.0: Scoping Review of Novel Insights and Possibilities Offered by Digitalization

BACKGROUND

The increasing prevalence of diabetes mellitus and associated morbidity worldwide justifies the need to create new approaches and strategies for diabetes therapy. Therefore, the ongoing digitalization offers novel opportunities in this field.

OBJECTIVE

The aim of this study is to provide an updated overview of available technologies, possibilities, and novel insights into diabetes therapy 4.0.

METHODS

A scoping review was carried out, and a literature search was performed using electronic databases (MEDLINE [PubMed], Cochrane Library, Embase, CINAHL, and Web of Science). The results were categorized according to the type of technology presented.

RESULTS

Different types of technology (eg, glucose monitoring systems, insulin pens, insulin pumps, closed-loop systems, mobile health apps, telemedicine, and electronic medical records) may help to improve diabetes treatment. These improvements primarily affect glycemic control. However, they may also help in increasing the autonomy and quality of life of people who are diagnosed with diabetes mellitus.

CONCLUSIONS

Diabetes technologies have developed rapidly over the last few years and offer novel insights into diabetes therapy and a chance to improve and individualize diabetes treatment. Challenges that need to be addressed in the following years relate to data security, interoperability, and the development of standards.

Assessment of Outliers and Detection of Artifactual Network Segments Using Univariate and Multivariate Dispersion Entropy on Physiological Signals

Network physiology has emerged as a promising paradigm for the extraction of clinically relevant information from physiological signals by moving from univariate to multivariate analysis, allowing for the inspection of interdependencies between organ systems. However, for its successful implementation, the disruptive effects of artifactual outliers, which are a common occurrence in physiological recordings, have to be studied, quantified, and addressed. Within the scope of this study, we utilize Dispersion Entropy (DisEn) to initially quantify the capacity of outlier samples to disrupt the values of univariate and multivariate features extracted with DisEn from physiological network segments consisting of synchronised, electroencephalogram, nasal respiratory, blood pressure, and electrocardiogram signals. The DisEn algorithm is selected due to its efficient computation and good performance in the detection of changes in signals for both univariate and multivariate time-series. The extracted features are then utilised for the training and testing of a logistic regression classifier in univariate and multivariate configurations in an effort to partially automate the detection of artifactual network segments. Our results indicate that outlier samples cause significant disruption in the values of extracted features with multivariate features displaying a certain level of robustness based on the number of signals formulating the network segments from which they are extracted. Furthermore, the deployed classifiers achieve noteworthy performance, where the percentage of correct network segment classification surpasses 95% in a number of experimental setups, with the effectiveness of each configuration being affected by the signal in which outliers are located. Finally, due to the increase in the number of features extracted within the framework of network physiology and the observed impact of artifactual samples in the accuracy of their values, the implementation of algorithmic steps capable of effective feature selection is highlighted as an important area for future research.

Algorithm-Enabled, Personalized Glucose Management for Type 1 Diabetes at the Population Scale: A Prospective Evaluation in Clinical Practice (Preprint)

Background

The use of continuous glucose monitors (CGMs) is recommended as the standard of care by the American Diabetes Association for individuals with type 1 diabetes (T1D). Few hardware-agnostic, open-source, whole-population tools are available to facilitate the use of CGM data by clinicians such as physicians and certified diabetes educators.

Objective

This study aimed to develop a tool that identifies patients appropriate for contact using an asynchronous message through electronic medical records while minimizing the number of patients reviewed by a certified diabetes educator or physician using the tool.

Methods

We used consensus guidelines to develop timely interventions for diabetes excellence (TIDE), an open-source hardware-agnostic tool to analyze CGM data to identify patients with deteriorating glucose control by generating generic flags (eg, mean glucose [MG] >170 mg/dL) and personalized flags (eg, MG increased by >10 mg/dL). In a prospective 7-week study in a pediatric T1D clinic, we measured the sensitivity of TIDE in identifying patients appropriate for contact and the number of patients reviewed. We simulated measures of the workload generated by TIDE, including the average number of time in range (TIR) flags per patient per review period, on a convenience sample of eight external data sets, 6 from clinical trials and 2 donated by research foundations.

Results

Over the 7 weeks of evaluation, the clinical population increased from 56 to 64 patients. The mean sensitivity was 99% (242/245; SD 2.5%), and the mean reduction in the number of patients reviewed was 42.6% (182/427; SD 10.9%). The 8 external data sets contained 1365 patients with 30,017 weeks of data collected by 7 types of CGMs. The rates of generic and personalized TIR flags per patient per review period were, respectively, 0.15 and 0.12 in the data set with the lowest average MG (141 mg/dL) and 0.95 and 0.22 in the data set with the highest average MG (207 mg/dL).

Conclusions

TIDE is an open-source hardware-agnostic tool for personalized analysis of CGM data at the clinical population scale. In a pediatric T1D clinic, TIDE identified 99% of patients appropriate for contact using an asynchronous message through electronic medical records while reducing the number of patients reviewed by certified diabetes care and education specialists by 43%. For each of the 8 external data sets, simulation of the use of TIDE produced fewer than 0.25 personalized TIR flags per patient per review period. The use of TIDE to support telemedicine-based T1D care may facilitate sensitive and efficient guideline-based population health management.

Bridging the integration gap between patient-generated blood glucose data and electronic health records

Telemedicine can facilitate population health management by extending the reach of providers to efficiently care for high-risk, high-utilization populations. However, for telemedicine to be maximally useful, data collected using telemedicine technologies must be reliable and readily available to healthcare providers. To address current gaps in integration of patient-generated health data into the electronic health record (EHR), we examined 2 patient-facing platforms, Epic MyChart and Apple HealthKit, both of which facilitated the uploading of blood glucose data into the EHR as part of a diabetes telemedicine intervention. All patients were offered use of the MyChart platform; we subsequently invited a purposive sample of patients who used the MyChart platform effectively (n = 5) to also use the Apple HealthKit platform. Patients reported both platforms helped with diabetes self-management, and providers appreciated the convenience of the processes for obtaining patient data. Providers stated that the EHR data presentation format for Apple HealthKit was challenging to interpret; however, they also valued the greater perceived accuracy the Apple HealthKit data. Our findings indicate that patient-facing platforms can feasibly facilitate transmission of patient-generated health data into the EHR and support telemedicine-based care.

Top 10 Tips for Successfully Implementing a Diabetes Telehealth Program

Diabetes management is well suited to use of telehealth, and recent improvements in both diabetes technology and telehealth policy make this an ideal time for diabetes providers to begin integrating telehealth into their practices. This article provides background information, specific recommendations for effective implementation, and a vision for the future landscape of telehealth within diabetes care to guide interested providers and practices on this topic. Note: This article was written prior to the COVID19 pandemic, and does not include information about recent telehealth policy changes that occurred during or as a result of this public health crisis.

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.

Present and Future of Digital Health in Diabetes and Metabolic Disease

The use of information and communication technology (ICT) in medical and healthcare services goes beyond everyday life. Expectations of a new medical environment, not previously experienced by ICT, exist in the near future. In particular, chronic metabolic diseases such as diabetes and obesity, have a high prevalence and high social and economic burden. In addition, the continuous evaluation and monitoring of daily life is important for effective treatment and management. Therefore, the wide use of ICTbased digital health systems is required for the treatment and management of these diseases. In this article, we compiled a variety of digital health technologies introduced to date in the field of diabetes and metabolic diseases.

Continuous Glucose Monitors and Automated Insulin Dosing Systems in the Hospital Consensus Guideline

This article is the work product of the Continuous Glucose Monitor and Automated Insulin Dosing Systems in the Hospital Consensus Guideline Panel, which was organized by Diabetes Technology Society and met virtually on April 23, 2020. The guideline panel consisted of 24 international experts in the use of continuous glucose monitors (CGMs) and automated insulin dosing (AID) systems representing adult endocrinology, pediatric endocrinology, obstetrics and gynecology, advanced practice nursing, diabetes care and education, clinical chemistry, bioengineering, and product liability law. The panelists reviewed the medical literature pertaining to five topics: (1) continuation of home CGMs after hospitalization, (2) initiation of CGMs in the hospital, (3) continuation of AID systems in the hospital, (4) logistics and hands-on care of hospitalized patients using CGMs and AID systems, and (5) data management of CGMs and AID systems in the hospital. The panelists then developed three types of recommendations for each topic, including clinical practice (to use the technology optimally), research (to improve the safety and effectiveness of the technology), and hospital policies (to build an environment for facilitating use of these devices) for each of the five topics. The panelists voted on 78 proposed recommendations. Based on the panel vote, 77 recommendations were classified as either strong or mild. One recommendation failed to reach consensus. Additional research is needed on CGMs and AID systems in the hospital setting regarding device accuracy, practices for deployment, data management, and achievable outcomes. This guideline is intended to support these technologies for the management of hospitalized patients with diabetes.

Understanding the Feasibility, Acceptability, and Efficacy of a Clinical Pharmacist-led Mobile Approach (BPTrack) to Hypertension Management: Mixed Methods Pilot Study

BACKGROUND

Hypertension is a prevalent and costly burden in the United States. Clinical pharmacists within care teams provide effective management of hypertension, as does home blood pressure monitoring; however, concerns about data quality and latency are widespread. One approach to close the gap between clinical pharmacist intervention and home blood pressure monitoring is the use of mobile health (mHealth) technology.

OBJECTIVE

We sought to investigate the feasibility, acceptability, and preliminary effectiveness of BPTrack, a clinical pharmacist-led intervention that incorporates patient- and clinician-facing apps to make electronically collected, patient-generated data available to providers in real time for hypertension management. The patient app also included customizable daily medication reminders and educational messages. Additionally, this study sought to understand barriers to adoption and areas for improvement identified by key stakeholders, so more widespread use of such interventions may be achieved.

METHODS

We conducted a mixed methods pilot study of BPTrack, to improve blood pressure control in patients with uncontrolled hypertension through a 12-week pre-post intervention. All patients were recruited from a primary care setting where they worked with a clinical pharmacist for hypertension management. Participants completed a baseline visit, then spent 12 weeks utilizing BPTrack before returning to the clinic for follow-up. Collected data from patient participants included surveys pre- and postintervention, clinical measures (for establishing effectiveness, with the primary outcome being a change in blood pressure and the secondary outcome being a change in medication adherence), utilization of the BPTrack app, interviews at follow-up, and chart review. We also conducted interviews with key stakeholders.

RESULTS

A total of 15 patient participants were included (13 remained through follow-up for an 86.7% retention rate) in a single group, pre-post assessment pilot study. Data supported the hypothesis that BPTrack was feasible and acceptable for use by patient and provider participants and was effective at reducing patient blood pressure. At the 12-week follow-up, patients exhibited significant reductions in both systolic blood pressure (baseline mean 137.3 mm Hg, SD 11.1 mm Hg; follow-up mean 131.0 mm Hg, SD 9.9 mm Hg; P=.02) and diastolic blood pressure (baseline mean 89.4 mm Hg, SD 7.7 mm Hg; follow-up mean 82.5 mm Hg, SD 8.2 mm Hg; P<.001). On average, patients uploaded at least one blood pressure measurement on 75% (SD 25%) of study days. No improvements in medication adherence were noted. Interview data revealed areas of improvement and refinement for the patient experience. Furthermore, stakeholders require integration into the electronic health record and a modified clinical workflow for BPTrack to be truly useful; however, both patients and stakeholders perceived benefits of BPTrack when used within the context of a clinical relationship.

CONCLUSIONS

Results demonstrate that a pharmacist-led mHealth intervention promoting home blood pressure monitoring and clinical pharmacist management of hypertension can be effective at reducing blood pressure in primary care patients with uncontrolled hypertension. Our data also support the feasibility and acceptability of these types of interventions for patients and providers.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02898584; https://clinicaltrials.gov/ct2/show/NCT02898584.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/resprot.8059.

Integrating Continuous Glucose Monitor Data Directly into the Electronic Health Record: Proof of Concept

Background: Continuous glucose monitoring (CGM) systems are widely and increasingly used in diabetes self-management and in the context of clinic visits. However, access to CGM data during visits can be challenging. Clinic inefficiencies can restrict the time available for patient education, and the inability to integrate CGM data into electronic health record (EHR) systems can result in data being lost. In this study, we describe our institution's approach to integrating CGM data directly into the EHR through a partnership with a CGM device manufacturer and without a third-party data aggregation/data visualization platform. Methods: We interviewed key stakeholders with the hospital Information Technology Department, the Division of Pediatric Endocrinology, and a CGM device manufacturer. A collaborative, human-centered design approach was used to define the workflow. Health Level 7 (HL7) standards were used to build all data exchanges. Results: In collaboration with all parties, we created a simple network architecture design for both account linkage and data acquisition. The system uses the standard, computerized, physician order entry interface available in the EHR for both processes. Data acquisition occurs in real time, and customized reports are displayed within the results section of the EHR. The entire process is Health Insurance Portability and Accountability Act (HIPAA) compliant and meets all security requirements. Conclusions: Building scalable data integration using HL7 standards is possible and allows real-time access to CGM data within the diabetes provider's existing workflow and can occur with or without the patient present. This may lead to improved clinical outcomes, increased efficiency, and new revenue opportunities by documenting CGM data capture and review.

Digital Health Technology to Enhance Adolescent and Young Adult Clinical Preventive Services: Affordances and Challenges

The lives of adolescents and young adults (AYAs) have become increasingly intertwined with technology. In this scoping review, studies about digital health tools are summarized in relation to five key affordances-social, cognitive, identity, emotional, and functional. Consideration of how a platform or tool exemplifies these affordances may help clinicians and researchers achieve the goal of using digital health technology to enhance clinical preventive services for AYAs. Across these five affordances, considerable research and development activity exists accompanied by signs of high promise, although the literature primarily reflects demonstration studies of acceptability or small sample experiments to discern impact. Digital health technology may afford an array of functions, yet its potential to enhance AYA clinical preventive services is met with three key challenges. The challenges discussed in this review are the disconnectedness between digital health tools and clinical care, threats to AYA privacy and security, and difficulty identifying high-value digital health products for AYA. The data presented are synthesized in calls to action for the use of digital health technology to enhance clinical preventive services and to ensure that the digital health ecosystem is relevant, effective, safe, and purposed for meeting the health needs of AYA.

Enabling Better Use of Person-Generated Health Data in Stroke Rehabilitation Systems: Systematic Development of Design Heuristics

BACKGROUND

An established and well-known method for usability assessment of various human-computer interaction technologies is called heuristic evaluation (HE). HE has been adopted for evaluations in a wide variety of specialized contexts and with objectives that go beyond usability. A set of heuristics to evaluate how health information technologies (HITs) incorporate features that enable effective patient use of person-generated health data (PGHD) is needed in an era where there is a growing demand and variety of PGHD-enabled technologies in health care and where a number of remote patient-monitoring technologies do not yet enable patient use of PGHD. Such a set of heuristics would improve the likelihood of positive effects from patients' use of PGHD and lower the risk of negative effects.

OBJECTIVE

This study aims to describe the development of a set of heuristics for the design and evaluation of how well remote patient therapeutic technologies enable patients to use PGHD (PGHD enablement). We used the case of Kinect-based stroke rehabilitation systems (K-SRS) in this study.

METHODS

The development of a set of heuristics to enable better use of PGHD was primarily guided by the R3C methodology. Closer inspection of the methodology reveals that neither its development nor its application to a case study were described in detail. Thus, where relevant, each step was grounded through best practice activities in the literature and by using Nielsen's heuristics as a basis for determining the new set of heuristics. As such, this study builds on the R3C methodology, and the implementation of a mixed process is intended to result in a robust and credible set of heuristics.

RESULTS

A total of 8 new heuristics for PGHD enablement in K-SRS were created. A systematic and detailed process was applied in each step of heuristic development, which bridged the gaps described earlier. It is hoped that this would aid future developers of specialized heuristics, who could apply the detailed process of heuristic development for other domains of technology, and additionally for the case of PGHD enablement for other health conditions. The R3C methodology was also augmented through the use of qualitative studies with target users and domain experts, and it is intended to result in a robust and credible set of heuristics, before validation and refinement.

CONCLUSIONS

This study is the first to develop a new set of specialized heuristics to evaluate how HITs incorporate features that enable effective patient use of PGHD, with K-SRS as a key case study. In addition, it is the first to describe how the identification of initial HIT features and concepts to enable PGHD could lead to the development of a specialized set of heuristics.

Confronting the Post-ACA American Health Crisis: Designing Health Care for Value and Equity

The United States is in the midst of a health crisis marked by unprecedented 3-year declines in life expectancy. Addressing this national crisis requires alignment of public policies, public health policies, and health care policies, with the overarching aim of improving national health and health equity. Aligning national polices to support human needs provides a foundation for implementing post-Affordable Care Act national health care reform. Reform should start with the twin goals of improving health care value and equity. A focus on value, that is, outcomes and processes desired by patients, is critical to ensuring that resources are judiciously deployed to optimize individual and population health. A focus on health care equity ensures that the health care system is intentionally designed to minimize inequities in health care processes and outcomes, particularly for member of socially disadvantaged groups. All sectors related to the health care system-from policies and payment mechanisms to delivery design, measurement, patient engagement/democratization, training, and research-should be tightly aligned with improving health care value and equity during this next era of health care reform.

Mobile Health Monitoring of Cardiac Status

Cardiovascular diseases (CVDs) are responsible for more deaths than any other cause, with coronary heart disease and stroke accounting for two-thirds of those deaths. Morbidity and mortality due to CVD are largely preventable, through either primary prevention of disease or secondary prevention of cardiac events. Monitoring cardiac status in healthy and diseased cardiovascular systems has the potential to dramatically reduce cardiac illness and injury. Smart technology in concert with mobile health platforms is creating an environment where timely prevention of and response to cardiac events are becoming a reality.

Patient-Reported Outcome Measures of Utilizing Person-Generated Health Data in the Case of Simulated Stroke Rehabilitation: Development Method

BACKGROUND

Person-generated health data (PGHD) are health data that people generate, record, and analyze for themselves. Although the health benefits of PGHD use have been reported, there is no systematic way for patients to measure and report the health effects they experience from using their PGHD. Patient-reported outcome measures (PROMs) allow patients to systematically self-report their outcomes of a health care service. They generate first-hand evidence of the impact of health care services and are able to reflect the real-world diversity of actual patients and management approaches. Therefore, this paper argues that a PROM of utilizing PGHD, or PROM-PGHD, is necessary to help build evidence-based practice in clinical work with PGHD.

OBJECTIVE

This paper aims to describe a method for developing PROMs for people who are using PGHD in conjunction with their clinical care-PROM-PGHD, and the method is illustrated through a case study.

METHODS

The five-step qualitative item review (QIR) method was augmented to guide the development of a PROM-PGHD. However, using QIR as a guide to develop a PROM-PGHD requires additional socio-technical consideration of the PGHD and the health technologies from which they are produced. Therefore, the QIR method is augmented for developing a PROM-PGHD, resulting in the PROM-PGHD development method.

RESULTS

A worked example was used to illustrate how the PROM-PGHD development method may be used systematically to develop PROMs applicable across a range of PGHD technology types used in relation to various health conditions.

CONCLUSIONS

This paper describes and illustrates a method for developing a PROM-PGHD, which may be applied to many different cases of health conditions and technology categories. When applied to other cases of health conditions and technology categories, the method could have broad relevance for evidence-based practice in clinical work with PGHD.

Augmentation of Dispersion Entropy for Handling Missing and Outlier Samples in Physiological Signal Monitoring

Entropy quantification algorithms are becoming a prominent tool for the physiological monitoring of individuals through the effective measurement of irregularity in biological signals. However, to ensure their effective adaptation in monitoring applications, the performance of these algorithms needs to be robust when analysing time-series containing missing and outlier samples, which are common occurrence in physiological monitoring setups such as wearable devices and intensive care units. This paper focuses on augmenting Dispersion Entropy (DisEn) by introducing novel variations of the algorithm for improved performance in such applications. The original algorithm and its variations are tested under different experimental setups that are replicated across heart rate interval, electroencephalogram, and respiratory impedance time-series. Our results indicate that the algorithmic variations of DisEn achieve considerable improvements in performance while our analysis signifies that, in consensus with previous research, outlier samples can have a major impact in the performance of entropy quantification algorithms. Consequently, the presented variations can aid the implementation of DisEn to physiological monitoring applications through the mitigation of the disruptive effect of missing and outlier samples.

Secondary care provider attitudes towards patient generated health data from smartwatches

Wearable devices, like smartwatches, are increasingly used for tracking physical activity, community mobility, and monitoring symptoms. Data generated from smartwatches (PGHD_SW) is a form of patient-generated health data, which can benefit providers by supplying frequent temporal information about patients. The goal of this study was to understand providers' perceptions towards PGHD_SW adoption and its integration with electronic medical records. In-depth, semi-structured qualitative interviews were conducted with 12 providers from internal medicine, family medicine, geriatric medicine, nursing, surgery, rehabilitation, and anesthesiology. Diffusion of Innovations was used as a framework to develop questions and guide data analysis. The constant comparative method was utilized to formulate salient themes from the interviews. Four main themes emerged: (1) PGHD_SW is perceived as a relative advantage; (2) data are viewed as compatible with current practices; (3) barriers to overcome to effectively use PGHD_SW; (4) assessments from viewing sample data. Overall, PGHD_SW was valued because it enabled access to information about patients that were traditionally unattainable. It also can initiate discussions between patients and providers. Providers consider PGHD_SW important, but data preferences varied by specialty. The successful adoption of PGHD_SW will depend on tailoring data, frequencies of reports, and visualization preferences to correspond with the demands of providers.

An overview of clinical decision support systems: benefits, risks, and strategies for success

Computerized clinical decision support systems, or CDSS, represent a paradigm shift in healthcare today. CDSS are used to augment clinicians in their complex decision-making processes. Since their first use in the 1980s, CDSS have seen a rapid evolution. They are now commonly administered through electronic medical records and other computerized clinical workflows, which has been facilitated by increasing global adoption of electronic medical records with advanced capabilities. Despite these advances, there remain unknowns regarding the effect CDSS have on the providers who use them, patient outcomes, and costs. There have been numerous published examples in the past decade(s) of CDSS success stories, but notable setbacks have also shown us that CDSS are not without risks. In this paper, we provide a state-of-the-art overview on the use of clinical decision support systems in medicine, including the different types, current use cases with proven efficacy, common pitfalls, and potential harms. We conclude with evidence-based recommendations for minimizing risk in CDSS design, implementation, evaluation, and maintenance.

Big Data Everywhere: The Impact of Data Disjunction in the Direct-to-Consumer Testing Model

The recent increase in accessible medical and clinical laboratory "Big Data" has led to a corresponding increase in the use of machine-learning tools to develop integrative diagnostic models incorporating both existing and new test data. The rise of direct-to-consumer (DTC) testing paradigms raises the possibility of predictive models that use these new sources. This article discusses several distinct challenges raised by the DTC approach, including issues of centralized data collection, ascertainment bias, linkage to medical outcomes, and standardization/harmonization of results. Several solutions to maximize the promise of machine-learning data analytics for DTC data are suggested.

Wave of Wearables: Clinical Management of Patients and the Future of Connected Medicine

The future of connected health care will involve the collection of patient data or enhancement of clinician workflows through various biosensors and displays found on wearable electronic devices, many of which are marketed directly to consumers. The adoption of wearables in health care is being driven by efforts to reduce health care costs, improve care quality, and increase clinician efficiency. Wearables have significant potential to achieve these goals but are currently limited by lack of widespread integrations into electronic health records, biosensor data collection types, and a lack of scientifically rigorous literature showing benefit.