Nurses' Roles in mHealth App Development: Scoping Review.

BACKGROUND: Although mobile health (mHealth) apps for both health consumers and health care providers are increasingly common, their implementation is frequently unsuccessful when there is a misalignment between the needs of the user and the app's functionality. Nurses are well positioned to help address this challenge. However, nurses' engagement in mHealth app development remains unclear. OBJECTIVE: This scoping review aims to determine the extent of the evidence of the role of nurses in app development, delineate developmental phases in which nurses are involved, and to characterize the type of mHealth apps nurses are involved in developing. METHODS: We conducted a scoping review following the 6-stage methodology. We searched 14 databases to identify publications on the role of nurses in mHealth app development and hand searched the reference lists of relevant publications. Two independent researchers performed all screening and data extraction, and a third reviewer resolved any discrepancies. Data were synthesized and grouped by the Software Development Life Cycle phase, and the app functionality was described using the IMS Institute for Healthcare Informatics functionality scoring system. RESULTS: The screening process resulted in 157 publications being included in our analysis. Nurses were involved in mHealth app development across all stages of the Software Development Life Cycle but most frequently participated in design and prototyping, requirements gathering, and testing. Nurses most often played the role of evaluators, followed by subject matter experts. Nurses infrequently participated in software development or planning, and participation as patient advocates, research experts, or nurse informaticists was rare. CONCLUSIONS: Although nurses were represented throughout the preimplementation development process, nurses' involvement was concentrated in specific phases and roles.

Integrating Patient-generated Digital Health Data into Electronic Health Records (EHRs) in Ambulatory Care Settings: EHR Vendor Survey and Interviews.

Data traditionally collected in a clinic or hospital setting is now collected electronically in everyday environments from patients, known as patient-generated health data (PGHD). We conducted informal interviews and collected survey data from major ambulatory care EHR vendors that serve the majority of the U.S. market to collect information on how their clients are integrating PGHD into EHRs. Of the 9 EHR vendors contacted, 6 completed the survey and 5 participated in a 45-minute interview. Feedback from the vendors included how PGHD use has steadily risen over the past decade and how the COVID-19 pandemic accelerated PGHD use. Pathways for data from devices or surveys to be brought securely into the EHR are increasing. While promising, adoption of health IT systems has its challenges. There are disparities in EHRs, devices, and applications. We concluded that more supportive policies are needed to advance PGHD integration.

Nurses' role in addressing social determinants of health.

ABSTRACT: Nurses have a vital role in addressing social and health inequities to promote quality healthcare for all. This article discusses the tools to screen for social determinants of health (SDOH) and key considerations for nurses and nurse leaders to advance the integration of SDOH information into their workflows.

Making the case for workforce diversity in biomedical informatics to help achieve equity-centered care: a look at the AMIA First Look Program.

Developing a diverse informatics workforce broadens the research agenda and ensures the growth of innovative solutions that enable equity-centered care. The American Medical Informatics Association (AMIA) established the AMIA First Look Program in 2017 to address workforce disparities among women, including those from marginalized communities. The program exposes women to informatics, furnishes mentors, and provides career resources. In 4 years, the program has introduced 87 undergraduate women, 41% members of marginalized communities, to informatics. Participants from the 2019 and 2020 cohorts reported interest in pursuing a career in informatics increased from 57% to 86% after participation, and 86% of both years' attendees responded that they would recommend the program to others. A June 2021 LinkedIn profile review found 50% of participants working in computer science or informatics, 4% pursuing informatics graduate degrees, and 32% having completed informatics internships, suggesting AMIA First Look has the potential to increase informatics diversity.

Provider Preferences for Patient-Generated Health Data Displays in Pediatric Asthma: A Participatory Design Approach.

OBJECTIVE: There is a lack of evidence on how to best integrate patient-generated health data (PGHD) into electronic health record (EHR) systems in a way that supports provider needs, preferences, and workflows. The purpose of this study was to investigate provider preferences for the graphical display of pediatric asthma PGHD to support decisions and information needs in the outpatient setting. METHODS: In December 2019, we conducted a formative evaluation of information display prototypes using an iterative, participatory design process. Using multiple types of PGHD, we created two case-based vignettes for pediatric asthma and designed accompanying displays to support treatment decisions. Semi-structured interviews and questionnaires with six participants were used to evaluate the display usability and determine provider preferences. RESULTS: We identified provider preferences for display features, such as the use of color to indicate different levels of abnormality, the use of patterns to trend PGHD over time, and the display of environmental data. Preferences for display content included the amount of information and the relationship between data elements. CONCLUSION: Overall, provider preferences for PGHD include a desire for greater detail, additional sources, and visual integration with relevant EHR data. In the design of PGHD displays, it appears that the visual synthesis of multiple PGHD elements facilitates the interpretation of the PGHD. Clinicians likely need more information to make treatment decisions when PGHD displays are introduced into practice. Future work should include the development of interactive interface displays with full integration of PGHD into EHR systems.

Patient-Generated Health Data in Pediatric Asthma: Exploratory Study of Providers' Information Needs.

BACKGROUND: Adolescents are using mobile health apps as a form of self-management to collect data on symptoms, medication adherence, and activity. Adding functionality to an electronic health record (EHR) to accommodate disease-specific patient-generated health data (PGHD) may support clinical care. However, little is known on how to incorporate PGHD in a way that informs care for patients. Pediatric asthma, a prevalent health issue in the United States with 6 million children diagnosed, serves as an exemplar condition to examine information needs related to PGHD. OBJECTIVE: In this study we aimed to identify and prioritize asthma care tasks and decisions based on pediatric asthma guidelines and identify types of PGHD that might support the activities associated with the decisions. The purpose of this work is to provide guidance to mobile health app developers and EHR integration. METHODS: We searched the literature for exemplar asthma mobile apps and examined the types of PGHD collected. We identified the information needs associated with each decision in accordance with consensus-based guidelines, assessed the suitability of PGHD to meet those needs, and validated our findings with expert asthma providers. RESULTS: We mapped guideline-derived information needs to potential PGHD types and found PGHD that may be useful in meeting information needs. Information needs included types of symptoms, symptom triggers, medication adherence, and inhaler technique. Examples of suitable types of PGHD were Asthma Control Test calculations, exposures, and inhaler use. Providers suggested uncontrolled asthma as a place to focus PGHD efforts, indicating that they preferred to review PGHD at the time of the visit. CONCLUSIONS: We identified a manageable list of information requirements derived from clinical guidelines that can be used to guide the design and integration of PGHD into EHRs to support pediatric asthma management and advance mobile health app development. Mobile health app developers should examine PGHD information needs to inform EHR integration efforts.

The efficacy of mobile health interventions used to manage acute or chronic pain: A systematic review.

Chronic pain is a significant health issue that affects approximately 50 million adults in the United States. Traditional interventions are not always an effective treatment strategy for pain control. However, the wide adoption of smartphones and the rapid growth of health information technologies over the past decade have created opportunities to use mobile health (mHealth) applications (apps) for pain tracking and self-management. In this PRISMA-compliant systematic review, we assessed the current U.S.-based research on pain-related mHealth apps to describe the app components and determine the efficacy of these interventions for persons with acute or chronic pain. We conducted a comprehensive search of five databases based on methodological guidelines from the Joanna Briggs Institute. We included articles reporting original data on mHealth interventions with pain intensity as a primary or secondary outcome and excluded articles that utilized multimodal interventions. Of the original 4959 articles, only five studies met the eligibility criteria. Most of the interventions included feasibility or pilot studies, and all studies were published between 2015 and 2018. Two of the five studies used visual analog scales. Only two of the studies reported statistically significant pain intensity outcomes, and considerable heterogeneity between the studies limited our ability to generalize findings or conduct a meta-analysis. Research investigating the components and efficacy of pain-related mHealth apps as interventions is an emerging field. To better understand the potential clinical benefits of mHealth apps designed to manage pain, further research is needed.

Impact of Hearing Loss on Patient Falls in the Inpatient Setting.

INTRODUCTION: The objectives of this study were to investigate an association between the risk of patient falls and self-reported hearing loss and to examine whether self-reported hearing loss with versus without hearing aids predicts patient falls in an inpatient setting. METHODS: This retrospective cohort analysis was conducted in 2018 in a large, urban, academic medical center. Participants included unique inpatients (N=52,805) of adults aged >18 years between February 1, 2017, and February 1, 2018. Outcome measures were falls in the inpatient setting and hearing loss with versus without hearing aids as predictors for patient falls. RESULTS: Self-reported hearing loss was associated with falls in the inpatient setting (OR=1.74, 95% CI=1.46, 2.07, p<1.43 × 10-9). Among patients with hearing impairment, a lack of hearing aids increased the risk for falls in the inpatient setting (OR=2.70, 95% CI=1.64, 4.69, p<1.41 × 10-5). After accounting for the risk of fall using the Morse Fall Scale (which does not include hearing impairment) and controlling for age and sex, patients with hearing loss and no hearing aids were significantly more likely to fall (OR=2.44, 95% CI=1.002, 5.654, p<0.042), but patients with hearing loss who did have hearing aids were not significantly more likely to fall (p<0.889). Hearing loss together with the Morse Fall Scale better predicted falls than the Morse Fall Scale alone (p<0.017). CONCLUSIONS: In the inpatient setting, there was a positive association between hearing loss and falls. However, among patients with hearing loss, only those without hearing aids were significantly more likely to fall, accounting for the Morse Fall Scale score and demographics characteristics. These findings support adding hearing loss as a modifiable risk factor in risk assessment tools for falls and exploring the use of amplification devices as an intervention.

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.

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

INTRODUCTION: The objective of this study is to determine the extent and describe the nature of patient-generated health data (PGHD) integration into electronic health records (EHRs) using systematic scoping methods to review the available literature. PGHD have the potential to enhance decision making by providing the valuable information that may not be ordinarily captured during a routine care visit. These data which are captured from mobile devices, such as smartphones, activity trackers and other sensors, should be integrated into clinical workflows to allow for optimal use by clinicians. METHODS AND ANALYSIS: This study aims to conduct a rigorous scoping review to explore evidence related to the integration of PGHD into EHRs. Using the framework developed by Arksey and O'Malley, we will create a systematic search strategy, chart data from the relevant articles, and use a qualitative, thematic approach to analyse the data. This review will enable the identification of types of integration and describe challenges and barriers to integrating PGHD. ETHICS AND DISSEMINATION: Database searches will be initiated in June 2019. The review is expected to be completed by October 2019. As the content of the full-text articles emerges, the authors will summarise the characteristics related to the integration of PGHD. The findings of this scoping review will identify research gaps and present implications for future research.

Navigating the Search for Patient Generated Health Data.

With massive amounts of mobile health data generated by patients, there is a growing amount of research conducted to understand their impact on patient care. The MeSH heading for patient generated health data was established in early 2018, complicating searches for PGHD research prior to 2018. In conducting a search of scientific databases, keywords are presented along with their degree of representation in the literature to help inform future searches.

Development and psychometric testing of the Readiness to Engage with Patient-Facing Health Information Technology Tools (RE-PHIT) scale.

OBJECTIVE: With the proliferation of patient-facing health information technology (HIT) tools, nurses are in a position to support the technology needs of both patients and families. The purpose of this study was to develop and examine the psychometric properties of the Readiness to Engage with Patient-Facing Health Information Technology (RE-PHIT) instrument intended to measure nurse readiness to support patient and family use of HIT tools. MATERIALS AND METHODS: Content for the 10-item instrument was derived from the literature, notably from Hibbard's Patient Activation Measure and from expert nurse informaticists. Instrument validation was achieved through an expert panel approach for assessing content validity, exploratory factor analysis for assessing the construct validity and Cronbach's alpha were used to estimate the internal consistency reliability. RESULTS: Content validity produced indices ranging from 0.86 to 1.00 for all items. Exploratory factor analysis yielded a one-factor solution consisting of 10 items that explained 62.6% of the total variance and internal consistency reliability was high with a α = 0.93. DISCUSSION: Findings support the validity and reliability of a new instrument, RE-PHIT, which can be used to measure nurses' readiness to engage with patients and families with HIT tools. Future testing of this instrument should be conducted in additional care settings with different types of nursing clinical workflows and HIT tools. CONCLUSION: A 10-item instrument, RE-PHIT, was developed to measure nurse readiness to support patient and family use of HIT tools. Results of the psychometric testing confirmed that the RE-PHIT scale is a valid and reliable tool.

Developing and maintaining clinical decision support using clinical knowledge and machine learning: the case of order sets.

Development and maintenance of order sets is a knowledge-intensive task for off-the-shelf machine-learning algorithms alone. We hypothesize that integrating clinical knowledge with machine learning can facilitate effective development and maintenance of order sets while promoting best practices in ordering. To this end, we simulated the revision of an "AM Lab Order Set" under 6 revision approaches. Revisions included changes in the order set content or default settings through 1) population statistics, 2) individualized prediction using machine learning, and 3) clinical knowledge. Revision criteria were determined using electronic health record (EHR) data from 2014 to 2015. Each revision's clinical appropriateness, workload from using the order set, and generalizability across time were evaluated using EHR data from 2016 and 2017. Our results suggest a potential order set revision approach that jointly leverages clinical knowledge and machine learning to improve usability while updating contents based on latest clinical knowledge and best practices.