Early Adopters of Patient-Generated Health Data Upload in an Electronic Patient Portal

Veteran Preferences and Willingness to Share Patient-Generated Health Data

BACKGROUND

Technologies, including mobile health applications (apps) and wearables, offer new potential for gathering patient-generated health data (PGHD) from patients; however, little is known about patient preferences for and willingness to collect and share PGHD with their providers and healthcare systems.

OBJECTIVE

Describe how patients use their PGHD and factors important to patients when deciding whether to share PGHD with a healthcare system.

DESIGN

Cross-sectional mailed longitudinal survey supplemented with administrative data within the Veterans Health Administration (VHA).

SUBJECTS

National sample of Veterans who use VHA healthcare.

MAIN MEASURES

Survey questions asked about demographics, willingness to use different devices to collect and share PGHD, what Veterans do with their PGHD, and factors important to Veterans when deciding whether to share PGHD with VHA. Administrative data provided information on Veteran health conditions. Multiple logistic regression models assessed factors associated with sharing PGHD with VHA.

KEY RESULTS

Overall, 47% of our analytic cohort (n = 383/807) indicated that they share PGHD collected through apps or digital health devices with VHA. In adjusted logistic regression models, Veterans who believed the following factors were Very Important (versus Somewhat/Not At All Important) had higher odds of sharing PGHD with VHA: if their doctor (OR = 1.4; 95%CI, 1.0-2.0) or other healthcare team members (OR = 1.4; 95%CI, 1.0-1.9) recommended they do so; and knowing that their healthcare team would look at the data (OR = 1.4; 95%CI, 1.0-2.0) or use the information to inform their healthcare (OR = 1.5; 95%CI, 1.1-2.1).

CONCLUSIONS

Our data suggest that healthcare team members can influence patient sharing of PGHD, as can a patient's knowledge that PGHD will be used in clinical practice. Efforts to increase the number of patients who share PGHD with a healthcare system may benefit from buy-in among healthcare team members, who appear to play an influential role in patient decisions to share data.

Patient and provider perspectives on the use of patient portals during pregnancy and the postpartum period

INTRODUCTION

Interest in the use of patient portals to support prenatal and postpartum care is growing. This study was conducted to understand patient and provider perspectives about the impact of portal use during pregnancy and the postpartum period.

METHODS

Interviews were conducted with 30 pregnant or postpartum patients and 15 obstetric care providers at an academic medical center that offers its patients access to an outpatient portal. Interview transcripts were analyzed deductively and inductively to categorize findings and identify emergent themes.

RESULTS

Patients and providers described how use of a patient portal during pregnancy and postpartum impacted communication (by supporting convenient communication and access to information), care processes (by aiding appointment attendance and helping with medication management), and care experience (by reducing anxiety and promoting patient involvement). Interviewees provided suggestions to improve patient portal use in obstetric care including using portals to increase access to educational materials and supportive resources, to collect patient-generated data, and to increase patient involvement in postpartum care.

DISCUSSION

Patient portals have particular value for patients' use during pregnancy and the postpartum period due to the frequency of healthcare visits and the heightened attention to one's health during this time. There are opportunities to tailor portal content and functions to patients' needs to improve communication, care processes, and care experiences for this patient population. Further improving the functionality of patient portals for patients' use during pregnancy and the postpartum period has the potential to positively impact patient experiences and health outcomes.

Patient and Provider Perspectives About the Use of Patient-Generated Health Data During Pregnancy: Qualitative Exploratory Study

BACKGROUND

There is increasing interest in using patient-generated health data (PGHD) to improve patient-centered care during pregnancy. However, little research has examined the perspectives of patients and providers as they report, collect, and use PGHD to inform obstetric care.

OBJECTIVE

This study aims to explore the perspectives of patients and providers about the use of PGHD during pregnancy, including the benefits and challenges of reporting, collecting, and using these data, as well as considerations for expanding the use of PGHD to improve obstetric care.

METHODS

We conducted one-on-one interviews with 30 pregnant or postpartum patients and 14 health care providers from 2 obstetrics clinics associated with an academic medical center. Semistructured interview guides included questions for patients about their experience and preferences for sharing PGHD and questions for providers about current processes for collecting PGHD, opportunities to improve or expand the collection of PGHD, and challenges faced when collecting and using this information. Interviews were conducted by phone or videoconference and were audio recorded, transcribed verbatim, and deidentified. Interview transcripts were analyzed deductively and inductively to characterize and explore themes in the data.

RESULTS

Patients and providers described how PGHD, including physiologic measurements and experience of symptoms, were currently collected during and between in-person clinic visits for obstetric care. Both patients and providers reported positive perceptions about the collection and use of PGHD during pregnancy. Reported benefits of collecting PGHD included the potential to use data to directly inform patient care (eg, identify issues and adjust medication) and to encourage ongoing patient involvement in their care (eg, increase patient attention to their health). Patients and providers had suggestions for expanding the collection and use of PGHD during pregnancy, and providers also shared considerations about strategies that could be used to expand PGHD collection and use. These strategies included considering the roles of both patients and providers in reporting and interpreting PGHD. Providers also noted the need to consider the unintended consequences of using PGHD that should be anticipated and addressed.

CONCLUSIONS

Acknowledging the challenges, suggestions, and considerations voiced by patients and providers can inform the development and implementation of strategies to effectively collect and use PGHD to support patient-centered care during pregnancy.

Visualizing machine learning-based predictions of postpartum depression risk for lay audiences

OBJECTIVES

To determine if different formats for conveying machine learning (ML)-derived postpartum depression risks impact patient classification of recommended actions (primary outcome) and intention to seek care, perceived risk, trust, and preferences (secondary outcomes).

MATERIALS AND METHODS

We recruited English-speaking females of childbearing age (18-45 years) using an online survey platform. We created 2 exposure variables (presentation format and risk severity), each with 4 levels, manipulated within-subject. Presentation formats consisted of text only, numeric only, gradient number line, and segmented number line. For each format viewed, participants answered questions regarding each outcome.

RESULTS

Five hundred four participants (mean age 31 years) completed the survey. For the risk classification question, performance was high (93%) with no significant differences between presentation formats. There were main effects of risk level (all P < .001) such that participants perceived higher risk, were more likely to agree to treatment, and more trusting in their obstetrics team as the risk level increased, but we found inconsistencies in which presentation format corresponded to the highest perceived risk, trust, or behavioral intention. The gradient number line was the most preferred format (43%).

DISCUSSION AND CONCLUSION

All formats resulted high accuracy related to the classification outcome (primary), but there were nuanced differences in risk perceptions, behavioral intentions, and trust. Investigators should choose health data visualizations based on the primary goal they want lay audiences to accomplish with the ML risk score.

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.

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.

Patients Managing Their Medical Data in Personal Electronic Health Records: Scoping Review

BACKGROUND

Personal electronic health records (PEHRs) allow patients to view, generate, and manage their personal and medical data that are relevant across illness episodes, such as their medications, allergies, immunizations, and their medical, social, and family health history. Thus, patients can actively participate in the management of their health care by ensuring that their health care providers have an updated and accurate overview of the patients' medical records. However, the uptake of PEHRs remains low, especially in terms of patients entering and managing their personal and medical data in their PEHR.

OBJECTIVE

This scoping review aimed to explore the barriers and facilitators that patients face when deciding to review, enter, update, or modify their personal and medical data in their PEHR. This review also explores the extent to which patient-generated and -managed data affect the quality and safety of care, patient engagement, patient satisfaction, and patients' health and health care services.

METHODS

We searched the MEDLINE, Embase, CINAHL, PsycINFO, Cochrane Library, Web of Science, and Google Scholar web-based databases, as well as reference lists of all primary and review articles using a predefined search query.

RESULTS

Of the 182 eligible papers, 37 (20%) provided sufficient information about patients' data management activities. The results showed that patients tend to use their PEHRs passively rather than actively. Patients refrain from generating and managing their medical data in a PEHR, especially when these data are complex and sensitive. The reasons for patients' passive data management behavior were related to their concerns about the validity, applicability, and confidentiality of patient-generated data. Our synthesis also showed that patient-generated and -managed health data ensures that the medical record is complete and up to date and is positively associated with patient engagement and patient satisfaction.

CONCLUSIONS

The findings of this study suggest recommendations for implementing design features within the PEHR and the construal of a dedicated policy to inform both clinical staff and patients about the added value of patient-generated data. Moreover, clinicians should be involved as important ambassadors in informing, reminding, and encouraging patients to manage the data in their PEHR.

Management Opportunities and Challenges After Achieving Widespread Health System Digitization

The adoption of electronic health records (EHRs) and digitization of health data over the past decade is ushering in the next generation of digital health tools that leverage artificial intelligence (AI) to improve varied aspects of health system performance. The decade ahead is therefore shaping up to be one in which digital health becomes even more at the forefront of health care delivery - demanding the time, attention, and resources of health care leaders and frontline staff, and becoming inextricably linked with all dimensions of health care delivery. In this chapter, we look back and look ahead. There are substantive lessons learned from the first era of large-scale adoption of enterprise EHRs and ongoing challenges that organizations are wrestling with - particularly related to the tension between standardization and flexibility/customization of EHR systems and the processes they support. Managing this tension during efforts to implement and optimize enterprise systems is perhaps the core challenge of the past decade, and one that has impeded consistent realization of value from initial EHR investments. We describe these challenges, how they manifest, and organizational strategies to address them, with a specific focus on alignment with broader value-based care transformation. We then look ahead to the AI wave - the massive number of applications of AI to health care delivery, the expected benefits, the risks and challenges, and approaches that health systems can consider to realize the benefits while avoiding the risks.

Electronic health record adopters: a typology based on patients' privacy concerns and perceived benefits

OBJECTIVES

Patients' adoption of electronic health records (EHRs) varies substantially. Although some countries, such as Estonia and Denmark, are sufficiently advanced in terms of EHR generalisation, others, such as France, are figuring out how to implement and disseminate EHRs. These governments must respond to patients' disparities to achieve the expected performance for healthcare systems and improve the quality of care delivery. This study investigates patients' perceived benefits and privacy concerns related to EHRs to develop a typology of patients, identify the characteristics of different clusters and propose practical measures for public policy-makers.

STUDY DESIGN

We conducted a cross-sectional study using online questionnaires.

METHODS

An online quantitative survey was carried out in France. The final sample of EHR non-users (N = 1076) was fitted to be representative of the French population by age and gender, region and socioprofessional status. Hierarchical and non-hierarchical cluster analyses were performed. Several robustness check analyses were also performed.

RESULTS

Cluster analyses identified four patient clusters: the worried, who show the highest mean privacy concern and risk levels related to health data disclosure; the ready adopters, who lack privacy concerns and risk and are the most motivated by EHR benefits; the concerned adopters, who express far fewer privacy concerns and perceive EHR benefits more favourably than the worried adopters; and the balanced adopters, who are relatively similar to the ready adopters in their EHR motives and are still concerned about their health data, suggesting a segment that is easier to convince. Comparing clusters regarding the intentions to create EHRs and willingness to disclose health data confirms that ready adopters, followed by balanced adopters, are more likely to create an EHR and disclose health data. The concerned adopters and, finally, the worried exhibit the lowest intentions for EHR creation and data disclosure.

CONCLUSIONS

The results provide meaningful insights into patient profiles and expectations. The findings underscore the need to implement targeting policies for each cluster and design concrete solutions for improving EHR performance.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Clinicians' Perspectives in Using Patient-Generated Health Data to Improve Ischemic Heart Disease Management

Patients suffering from ischemic heart disease (IHD) should be monitored closely after being discharged. With recent advances in digital health tools, collecting, using, and sharing patient-generated health data (PGHD) has become more achievable. PGHD can complement the existing clinical data and provide a comprehensive picture of the patient's health status. Despite the potential value of PGHD in healthcare, its implementation currently remains limited due to the clinicians' concern with the reliability and accuracy of the gathered data to support decision-making and concerns regarding the added workload that PGHD might cause to clinical workflow. The main objective of the study was to investigate the clinicians' perspectives towards the use of PGHD for IHD management, focusing on data sharing, interpretation, and efficiency in decision-making. The study consists of semi-structured interviews with seven clinicians. Study results identified four main themes: data generation, data integration, data presentation, data interpretation and utilization in clinical decision-making.

The Impact of Digital Patient Portals on Health Outcomes, System Efficiency, and Patient Attitudes: Updated Systematic Literature Review

Background

Patient portals are becoming increasingly popular worldwide even though their impact on individual health and health system efficiency is still unclear.

Objective

The aim of this systematic review was to summarize evidence on the impact of patient portals on health outcomes and health care efficiency, and to examine user characteristics, attitudes, and satisfaction.

Methods

We searched the PubMed and Web of Science databases for articles published from January 1, 2013, to October 31, 2019. Eligible studies were primary studies reporting on the impact of patient portal adoption in relation to health outcomes, health care efficiency, and patient attitudes and satisfaction. We excluded studies where portals were not accessible for patients and pilot studies, with the exception of articles evaluating patient attitudes.

Results

Overall, 3456 records were screened, and 47 articles were included. Among them, 11 studies addressed health outcomes reporting positive results, such as better monitoring of health status, improved patient-doctor interaction, and improved quality of care. Fifteen studies evaluated the impact of digital patient portals on the utilization of health services with mixed results. Patient characteristics were described in 32 studies, and it was reported that the utilization rate usually increases with age and female gender. Finally, 30 studies described attitudes and defined the main barriers (concerns about privacy and data security, and lack of time) and facilitators (access to clinical data and laboratory results) to the use of a portal.

Conclusions

Evidence regarding health outcomes is generally favorable, and patient portals have the potential to enhance the doctor-patient relationship, improve health status awareness, and increase adherence to therapy. It is still unclear whether the use of patient portals improves health service utilization and efficiency.

Development and Validation of a Useful Taxonomy of Patient Portals Based on Characteristics of Patient Engagement

OBJECTIVE

Taxonomies are classification systems used to reduce complexity and better understand a domain. The present research aims to develop a useful taxonomy for health information managers to classify and compare patient portals based on characteristics appropriate to promote patient engagement. As a result, the taxonomy should contribute to understanding the differences and similarities of the portals. Further, the taxonomy shall support health information managers to more easily define which general type and functionalities of patient portals they need and to select the most suitable solution offered on the market.

METHODS

We followed the formal taxonomy-building method proposed by Nickerson et al. Based on a literature review, we created a preliminary taxonomy following the conceptional approach of the model. We then evaluated each taxa's appropriateness by analyzing and classifying 17 patient portals offered by software vendors and 11 patient portals offered by health care providers. After each iteration, we examined the achievement of the determined objective and subjective ending conditions.

RESULTS

After two conceptional approaches to create our taxonomy, and two empirical approaches to evaluate it, the final taxonomy consists of 20 dimensions and 49 characteristics. To make the taxonomy easy to comprehend, we assigned to the dimensions seven aspects related to patient engagement. These aspects are (1) portal design, (2) management, (3) communication, (4) instruction, (5) self-management, (6) self-determination, and (7) data management. The taxonomy is considered finished and useful after all ending conditions that defined beforehand have been fulfilled. We demonstrated that the taxonomy serves to understand the differences and similarities by comparing patient portals. We call our taxonomy "Taxonomy of Patient Portals based on Characteristics of Patient Engagement (TOPCOP)."

CONCLUSION

We developed the first useful taxonomy for health information managers to classify and compare patient portals. The taxonomy is based on characteristics promoting patient engagement. With 20 dimensions and 49 characteristics, our taxonomy is particularly suitable to discriminate among patient portals and can easily be applied to compare portals. The TOPCOP taxonomy enables health information managers to better understand the differences and similarities of patient portals. Further, the taxonomy may help them to define the type and general functionalities needed. But it also supports them in searching and comparing patient portals offered on the market to select the most suitable solution.

The Computer Will See You Now: Overcoming Barriers to Adoption of Computer-Assisted History Taking (CAHT) in Primary Care

Patient health information is increasingly collected through multiple modalities, including electronic health records, wearables, and connected devices. Computer-assisted history taking could provide an additional channel to collect highly relevant, comprehensive, and accurate patient information while reducing the burden on clinicians and face-to-face consultation time. Considering restrictions to consultation time and the associated negative health outcomes, patient-provided health data outside of consultation can prove invaluable in health care delivery. Over the years, research has highlighted the numerous benefits of computer-assisted history taking; however, the limitations have proved an obstacle to adoption. In this viewpoint, we review these limitations under 4 main categories (accessibility, affordability, accuracy, and acceptability) and discuss how advances in technology, computing power, and ubiquity of personal devices offer solutions to overcoming these.

Preferred Functions of Personal Health Records in Rural Primary Health Clinics in Canada: Health Care Team Perspectives

BACKGROUND

Personal health records (PHR) provide opportunities for improved patient engagement, collection of patient-generated data, and overcome health-system inefficiencies. While PHR use is increasing, uptake in rural populations is lower than in urban areas.

OBJECTIVES

The study aimed to identify priorities for PHR functionality and gain insights into meaning, value, and use of patient-generated data for rural primary care providers.

METHODS

We performed PHR preimplementation focus groups with rural providers and their health care teams from five primary care clinics in a sparsely populated mountainous region of British Columbia, Canada to obtain their understanding of PHR functionality, needs, and perceived challenges.

RESULTS

Eight general practitioners (GP), five medical office assistants, two nurse practitioners (NP), and two registered nurses (14 females and 3 males) participated in focus groups held at their respective clinics. Providers (GPs, NPs, and RNs) had been practicing for a median of 9.5 (range = 1-38) years and had used an electronic medical record for 7.0 (1-20) years. Participants expressed interest in incorporating functionality around two-way communication and appointment scheduling, previsit data gathering, patient and provider data sharing, virtual care including visits using videoconferencing tools, and postvisit sharing of educational materials. Three further themes emerged from the focus groups: (1) the context in which the providers' practice matters, (2) the need for providing patients and providers with choice (e.g., which data to share, who gets to initiate/respond in communications, and processes around virtual care visits), and (3) perceived risks of system use (e.g., increased complexity for older patients and workload barriers for the health care team).

CONCLUSION

Rural primary care teams perceived PHR opportunities for increased patient engagement and access to patient-generated data, while worries about changes in workflow were the biggest perceived risk. Recommendations for PHR adoption in a rural primary health network include setting provider-patient expectations about response times, ability to share notes selectively, and automatically augmented note-taking from virtual-care visits.

Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning

In this review, we aim to assess the current state of science in relation to the integration of patient-generated health data (PGHD) and patient-reported outcomes (PROs) into routine clinical care with a focus on surgical oncology populations. We will also describe the critical role of artificial intelligence and machine-learning methodology in the efficient translation of PGHD, PROs, and traditional outcome measures into meaningful patient care models.

Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications

BACKGROUND

Prediabetes and type 2 diabetes mellitus (T2DM) are one of the major long-term health conditions affecting global healthcare delivery. One of the few effective approaches is to actively manage diabetes via a healthy and active lifestyle.

OBJECTIVES

This research is focused on early detection of prediabetes and T2DM using wearable technology and Internet-of-Things-based monitoring applications.

METHODS

We developed an artificial intelligence model based on adaptive neuro-fuzzy inference to detect prediabetes and T2DM via individualized monitoring. The key contributing factors to the proposed model include heart rate, heart rate variability, breathing rate, breathing volume, and activity data (steps, cadence, and calories). The data was collected using an advanced wearable body vest and combined with manual recordings of blood glucose, height, weight, age, and sex. The model analyzed the data alongside a clinical knowledgebase. Fuzzy rules were used to establish baseline values via existing interventions, clinical guidelines, and protocols.

RESULTS

The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%.

CONCLUSION

We also present a 2-year follow-up observation from the prediction results of the original model. Moreover, the diabetic profile of a participant using M-health applications and a wearable vest (smart shirt) improved when compared to the traditional/routine practice.

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

Background

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

Objective

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

Methods

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

Results

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

Conclusions

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

Analysis of Employee Patient Portal Use and Electronic Health Record Access at an Academic Medical Center

BACKGROUND

Patient portals provide patients and their caregivers online access to limited health results. Health care employees with electronic health record (EHR) access may be able to view their health information not available in the patient portal by looking in the EHR.

OBJECTIVE

In this study, we examine how employees use the patient portal when they also have access to the tethered EHR.

METHODS

We obtained patient portal and EHR usage logs corresponding to all employees who viewed their health data at our institution between January 1, 2013 and November 1, 2017. We formed three cohorts based on the systems that employees used to view their health data: employees who used the patient portal only, employees who viewed health data in the EHR only, and employees who used both systems. We compared system accesses and usage patterns for each employee cohort.

RESULTS

During the study period, 35,172 employees accessed the EHR as part of patients' treatment and 28,631 employees accessed their health data: 25,193 of them used the patient portal and 13,318 accessed their clinical data in EHR. All employees who accessed their records in the EHR viewed their clinical notes at least once. Among EHR accesses, clinical note accesses comprised more than 42% of all EHR accesses. Provider messaging and appointment scheduling were the most commonly used functions in the patient portal. Employees who had access to their health data in both systems were more likely to engage with providers through portal messages.

CONCLUSION

Employees at a large medical center accessed clinical notes in the EHR to obtain information about their health. Employees also viewed other health data not readily available in the patient portal.

A Possible Mobile Health Solution in Orthopedics and Trauma Surgery: Development Protocol and User Evaluation of the Ankle Joint App

Background

Ankle sprains are one of the most frequent sports injuries. With respect to the high prevalence of ankle ligament injuries and patients’ young age, optimizing treatment and rehabilitation is mandatory to prevent future complications such as chronic ankle instability or osteoarthritis.

Objective

In modern times, an increasing amount of smartphone usage in patient care is evident. Studies investigating mobile health (mHealth)–based rehabilitation programs after ankle sprains are rare. The aim of this study was to expose any issues present in the development process of a medical app as well as associated risks and chances.

Methods

The development process of the Ankle Joint App was defined in chronological order using a protocol. The app’s quality was evaluated using the (user) German Mobile App Rating Scale (MARS-G) by voluntary foot and ankle surgeons (n=20) and voluntary athletes (n=20).

Results

A multidisciplinary development team built a hybrid app with a corresponding backend structure. The app’s content provides actual medical literature, training videos, and a log function. Excellent interrater reliability (interrater reliability=0.92; 95% CI 0.86-0.96) was obtained. The mean overall score for the Ankle Joint App was 4.4 (SD 0.5). The mean subjective quality scores were 3.6 (surgeons: SD 0.7) and 3.8 (athletes: SD 0.5). Behavioral change had mean scores of 4.1 (surgeons: SD 0.7) and 4.3 (athletes: SD 0.7). The medical gain value, rated by the surgeons only, was 3.9 (SD 0.6).

Conclusions

The data obtained demonstrate that mHealth-based rehabilitation programs might be a useful tool for patient education and collection of personal data. The achieved (user) MARS-G scores support a high quality of the tested app. Medical app development with an a priori defined target group and a precisely intended purpose, in a multidisciplinary team, is highly promising. Follow-up studies are required to obtain funded evidence for the ankle joints app’s effects on economical and medical aspects in comparison with established nondigital therapy paths.

Impact of telemedicine on assisted reproduction treatment in the public health system

AIM

Telemedicine has helped to make health care more efficient. However, to date no studies have measured its impact on infertility and fertility healthcare. We assessed the potential care benefits and clinical advantages of an initiative implementing electronic patient portal (EPP) for patients scheduled to undergo assisted reproduction treatment, to reduce waiting times for medical consultation and treatment.

METHODS

This was designed as a retrospective cohort study. The experimental group comprised 1972 referral requests received by the assisted reproduction unit of our institution between 2015 and 2016, which were included in the group receiving telemedicine, while the control group was defined by 283 requests received in 2013, all of which were assigned face-to-face care.

RESULTS

We found a statistically significant reduction in the experimental group in terms of the days elapsed between the receipt of the assessment request and the first outpatient visit (68 days vs. 180 days, p<.001). Time to initiation of treatment was also significantly lower in this group (169 days vs. 229 days; p<.001). The experimental group contained around 7 times as many patients receiving treatment as the control group. No differences were observed in the pregnancy rate (29.9% vs. 31.1%; p=.77) or in the complication rate (3.2% vs. 0%; p=.16).

CONCLUSIONS

Use of telemedicine in electronic portal patient form reduces the total waiting time involved in patient requests for infertility treatment and indirectly increases the number of patients treated, causing no negative impact on treatment outcome.