Telemedicine appointments are more likely to be completed than in-person healthcare appointments: a retrospective cohort study.

Objectives: Missed appointments can lead to treatment delays and adverse outcomes. Telemedicine may improve appointment completion because it addresses barriers to in-person visits, such as childcare and transportation. This study compared appointment completion for appointments using telemedicine versus in-person care in a large cohort of patients at an urban academic health sciences center. Materials and Methods: We conducted a retrospective cohort study of electronic health record data to determine whether telemedicine appointments have higher odds of completion compared to in-person care appointments, January 1, 2021, and April 30, 2023. The data were obtained from the University of South Florida (USF), a large academic health sciences center serving Tampa, FL, and surrounding communities. We implemented 1:1 propensity score matching based on age, gender, race, visit type, and Charlson Comorbidity Index (CCI). Results: The matched cohort included 87 376 appointments, with diverse patient demographics. The percentage of completed telemedicine appointments exceeded that of completed in-person care appointments by 9.2 points (73.4% vs 64.2%, P < .001). The adjusted odds ratio for telemedicine versus in-person care in relation to appointment completion was 1.64 (95% CI, 1.59-1.69, P < .001), indicating that telemedicine appointments are associated with 64% higher odds of completion than in-person care appointments when controlling for other factors. Discussion: This cohort study indicated that telemedicine appointments are more likely to be completed than in-person care appointments, regardless of demographics, comorbidity, payment type, or distance. Conclusion: Telemedicine appointments are more likely to be completed than in-person healthcare appointments.

Exploring nurses' emotional reactions to and reporting of patient-on-nurse workplace violence: A mixed-methods study.

BACKGROUND: Workplace violence, including violent, intimidating, and disruptive acts, commonly occurs in healthcare settings. Type 2 workplace violence in nursing refers to patient/visitor behaviors directed toward clinicians, contributing to physical and psychological harm. Nurse victims often do not report these events to employers or law enforcement, making it challenging to address workplace violence. OBJECTIVES: Our research examined nurse reactions to Type 2 workplace violence by identifying what behaviors they perceived as aggressive and reportable. Specific aims included: 1) developing and testing video vignettes to portray realistic patient aggression scenarios; 2) identifying nurse understandings of aggressive events that prompt affective reactions, and; 3) examining clinical characteristics related to the nurse victim's likelihood to report. DESIGN: Through a sequential mixed-methods design, we qualitatively developed novel video vignettes portraying Type 2 workplace violence to experimentally examine how nurses interpreted them within a quantitative repeated measures survey. METHODS: Two expert nurse research panels (n = 10) created five vignettes, from which nurses (n = 282) completed a survey with 1382 unique responses. Analyses included descriptive statistics and repeated measures ANOVA/regression models. RESULTS: Video vignettes realistically portrayed workplace violence events, eliciting negative emotional responses among nurses that increased in magnitude with statistical significance as the level of displayed aggression escalated. Statistically significant factors influencing nurse reporting of workplace violence included; 1) the level of aggression displayed by the patient; 2) the level of harm received by the nurse; 3) whether the nurse felt the patient's actions were intentional, and; 4) the nurse's perceived frequency of exposure to workplace violence. CONCLUSIONS: Results suggested that nurse victims of Type 2 workplace violence experience depression, anger, fear, and anxiety, which may contribute to long-term mental health consequences. Findings also identified factors related to nurse reporting behaviors, which may help mitigate workplace violence in healthcare settings by informing research and promoting workplace practices that encourage reporting and safety. REGISTRATION: Not registered. TWEETABLE ABSTRACT: Nurse reactions to workplace violence: Video vignettes reveal escalating aggression's impact on reporting. #EndNurseAbuse #WorkplaceViolence.

Travel Distance Between Participants in US Telemedicine Sessions With Estimates of Emissions Savings: Observational Study.

BACKGROUND: Digital health and telemedicine are potentially important strategies to decrease health care's environmental impact and contribution to climate change by reducing transportation-related air pollution and greenhouse gas emissions. However, we currently lack robust national estimates of emissions savings attributable to telemedicine. OBJECTIVE: This study aimed to (1) determine the travel distance between participants in US telemedicine sessions and (2) estimate the net reduction in carbon dioxide (CO2) emissions attributable to telemedicine in the United States, based on national observational data describing the geographical characteristics of telemedicine session participants. METHODS: We conducted a retrospective observational study of telemedicine sessions in the United States between January 1, 2022, and February 21, 2023, on the doxy.me platform. Using Google Distance Matrix, we determined the median travel distance between participating providers and patients for a proportional sample of sessions. Further, based on the best available public data, we estimated the total annual emissions costs and savings attributable to telemedicine in the United States. RESULTS: The median round trip travel distance between patients and providers was 49 (IQR 21-145) miles. The median CO2 emissions savings per telemedicine session was 20 (IQR 8-59) kg CO2). Accounting for the energy costs of telemedicine and US transportation patterns, among other factors, we estimate that the use of telemedicine in the United States during the years 2021-2022 resulted in approximate annual CO2 emissions savings of 1,443,800 metric tons. CONCLUSIONS: These estimates of travel distance and telemedicine-associated CO2 emissions costs and savings, based on national data, indicate that telemedicine may be an important strategy in reducing the health care sector's carbon footprint.

Narrative review of telemedicine applications in decentralized research.

Telemedicine enables critical human communication and interaction between researchers and participants in decentralized research studies. There is a need to better understand the overall scope of telemedicine applications in clinical research as the basis for further research. This narrative, nonsystematic review of the literature sought to review and discuss applications of telemedicine, in the form of synchronous videoconferencing, in clinical research. We searched PubMed to identify relevant literature published between January 1, 2013, and June 30, 2023. Two independent screeners assessed titles and abstracts for inclusion, followed by single-reviewer full-text screening, and we organized the literature into core themes through consensus discussion. We screened 1044 publications for inclusion. Forty-eight publications met our inclusion and exclusion criteria. We identified six core themes to serve as the structure for the narrative review: infrastructure and training, recruitment, informed consent, assessment, monitoring, and engagement. Telemedicine applications span all stages of clinical research from initial planning and recruitment to informed consent and data collection. While the evidence base for using telemedicine in clinical research is not well-developed, existing evidence suggests that telemedicine is a potentially powerful tool in clinical research.

Will the United States pass on telemedicine progress?

Background: During the COVID-19 pandemic, federal and state health policies allowed temporary flexibilities for Medicare and Medicaid beneficiaries, leading to a sharp increase in telemedicine use. However, many of the flexibilities that enabled innovation and growth in telemedicine continue temporarily since the federal emergency declaration ended in May 2023, and the United States has not made permanent decisions about telemedicine policy. Analysts have raised concerns about increased spending, program integrity, safety, and equity, and recommend strengthening oversight. Methods: Here, we argue that we must continue the flexibilities to better understand telemedicine's quality, safety, and outcomes, and until the United States can develop an evidence-based digital health strategy. A premature regression to pre-pandemic telemedicine policies risks unintended consequences. Conclusion: We must continue the current policy flexibilities, safeguard against fraud and abuse, and immediately prioritize research and evaluation of telemedicine's quality, safety, and outcomes, to avoid unintended consequences and support more permanent policy decision-making.

Vaccine Effectiveness Against Long COVID in Children.

OBJECTIVES: Vaccination reduces the risk of acute coronavirus disease 2019 (COVID-19) in children, but it is less clear whether it protects against long COVID. We estimated vaccine effectiveness (VE) against long COVID in children aged 5 to 17 years. METHODS: This retrospective cohort study used data from 17 health systems in the RECOVER PCORnet electronic health record program for visits after vaccine availability. We examined both probable (symptom-based) and diagnosed long COVID after vaccination. RESULTS: The vaccination rate was 67% in the cohort of 1 037 936 children. The incidence of probable long COVID was 4.5% among patients with COVID-19, whereas diagnosed long COVID was 0.8%. Adjusted vaccine effectiveness within 12 months was 35.4% (95 CI 24.5-44.7) against probable long COVID and 41.7% (15.0-60.0) against diagnosed long COVID. VE was higher for adolescents (50.3% [36.6-61.0]) than children aged 5 to 11 (23.8% [4.9-39.0]). VE was higher at 6 months (61.4% [51.0-69.6]) but decreased to 10.6% (-26.8% to 37.0%) at 18-months. CONCLUSIONS: This large retrospective study shows moderate protective effect of severe acute respiratory coronavirus 2 vaccination against long COVID. The effect is stronger in adolescents, who have higher risk of long COVID, and wanes over time. Understanding VE mechanism against long COVID requires more study, including electronic health record sources and prospective data.

Telemedicine from Home or the Office: Perceptions of Mental Health Providers.

Introduction: The COVID-19 pandemic led to a rapid transition to telemedicine for mental health care and redefined many providers' work environments and practices. The purpose of the study was to investigate the impact of work location on telemental health (TMH) benefits, disruptions, and concerns to further understand the rapid implementation of telemedicine for mental health treatment. Methods: A sample of 175 practicing TMH providers completed an online survey between July and August 2020. Providers answered questions about personal demographics and practice characteristics. Next, they answered questions about benefits, disruptions, and concerns regarding the use of telemedicine in their practice. Chi-square and independent samples t-test were conducted to identify work location differences for personal demographics and clinical practice characteristics. Three multivariate analyses of covariance were conducted to examine overall differences in perceptions of telemedicine benefits, concerns, and disruptions based on work location while covarying for provider race, ethnicity, percentage of caseload seen through telemedicine, practice type, specialty, and primary method of reimbursement. Results: TMH providers who primarily work from an office reported more benefit of reduced costs/overhead (ηp2 = 0.039), less benefit of limiting the spread of the virus (ηp2 = 0.028), and more concern about reimbursement (ηp2 = 0.046) than those who primarily work from home. We observed no difference in disruptions, patient access to care, quality of care, and work-life balance. Discussion: Exploration into work location of TMH providers aids in understanding of clinical workflows and provider wellbeing. Our findings suggest that telemedicine may be easily integrated into different types of clinical workflows and work locations.

Vaccine Effectiveness Against Long COVID in Children: A Report from the RECOVER EHR Cohort.

Objective: Vaccination reduces the risk of acute COVID-19 in children, but it is less clear whether it protects against long COVID. We estimated vaccine effectiveness (VE) against long COVID in children aged 5-17 years. Methods: This retrospective cohort study used data from 17 health systems in the RECOVER PCORnet electronic health record (EHR) Program for visits between vaccine availability, and October 29, 2022. Conditional logistic regression was used to estimate VE against long COVID with matching on age group (5-11, 12-17) and time period and adjustment for sex, ethnicity, health system, comorbidity burden, and pre-exposure health care utilization. We examined both probable (symptom-based) and diagnosed long COVID in the year following vaccination. Results: The vaccination rate was 56% in the cohort of 1,037,936 children. The incidence of probable long COVID was 4.5% among patients with COVID-19, while diagnosed long COVID was 0.7%. Adjusted vaccine effectiveness within 12 months was 35.4% (95 CI 24.5 - 44.5) against probable long COVID and 41.7% (15.0 - 60.0) against diagnosed long COVID. VE was higher for adolescents 50.3% [36.3 - 61.0]) than children aged 5-11 (23.8% [4.9 - 39.0]). VE was higher at 6 months (61.4% [51.0 - 69.6]) but decreased to 10.6% (-26.8 - 37.0%) at 18-months. Discussion: This large retrospective study shows a moderate protective effect of SARS-CoV-2 vaccination against long COVID. The effect is stronger in adolescents, who have higher risk of long COVID, and wanes over time. Understanding VE mechanism against long COVID requires more study, including EHR sources and prospective data. Article Summary: Vaccination against COVID-19 has a protective effect against long COVID in children and adolescents. The effect wanes over time but remains significant at 12 months. What's Known on This Subject: Vaccines reduce the risk and severity of COVID-19 in children. There is evidence for reduced long COVID risk in adults who are vaccinated, but little information about similar effects for children and adolescents, who have distinct forms of long COVID. What This Study Adds: Using electronic health records from US health systems, we examined large cohorts of vaccinated and unvaccinated patients <18 years old and show that vaccination against COVID-19 is associated with reduced risk of long COVID for at least 12 months. Contributors' Statement: Drs. Hanieh Razzaghi and Charles Bailey conceptualized and designed the study, supervised analyses, drafted the initial manuscript, and critically reviewed and revised the manuscript.Drs. Christopher Forrest and Yong Chen designed the study and critically reviewed and revised the manuscript.Ms. Kathryn Hirabayashi, Ms. Andrea Allen, and Dr. Qiong Wu conducted analyses, and critically reviewed and revised the manuscript.Drs. Suchitra Rao, H Timothy Bunnell, Elizabeth A. Chrischilles, Lindsay G. Cowell, Mollie R. Cummins, David A. Hanauer, Benjamin D. Horne, Carol R. Horowitz, Ravi Jhaveri, Susan Kim, Aaron Mishkin, Jennifer A. Muszynski, Susanna Nagie, Nathan M. Pajor, Anuradha Paranjape, Hayden T. Schwenk, Marion R. Sills, Yacob G. Tedla, David A. Williams, and Ms. Miranda Higginbotham critically reviewed and revised the manuscript.All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work. Authorship statement: Authorship has been determined according to ICMJE recommendations.

Consensus Guidelines for Teledermatology: Scoping Review.

BACKGROUND: Consensus guidelines and recommendations play an important role in fostering quality, safety, and best practices, as they represent an expert interpretation of the biomedical literature and its application to practice. However, it is unclear whether the recent collective experience of implementing telemedicine and the concurrent growth in the evidence base for teledermatology have resulted in more robust guidance. OBJECTIVE: The objective of this review was to describe the extent and nature of currently available guidance, defined as consensus guidelines and recommendations available for telemedicine in dermatology, with guidance defined as consensus or evidence-based guidelines, protocols, or recommendations. METHODS: We conducted a single-reviewer scoping review of the literature to assess the extent and nature of available guidance, consensus guidelines, or recommendations related to teledermatology. We limited the review to published material in English since 2013, reflecting approximately the past 10 years. We conducted the review in November and December of the year 2022. RESULTS: We identified 839 potentially eligible publications, with 9 additional records identified through organizational websites. A total of 15 publications met the inclusion and exclusion criteria. The guidelines focused on varied topics and populations about dermatology and skin diseases. However, the most frequent focus was general dermatology (8/15, 53%). Approximately half of the telemedicine guidance described in the publications was specific to dermatology practice in the context of the COVID-19 pandemic. The publications were largely published in or after the year 2020 (13/15, 87%). Geographical origin spanned several different nations, including Australia, the United States, European countries, and India. CONCLUSIONS: We found an increase in COVID-19-specific teledermatology guidance during 2020, in addition to general teledermatology guidance during the period of the study. Primary sources of general teledermatology guidance reported in the biomedical literature are the University of Queensland's Centre for Online Health and Australasian College of Dermatologists E-Health Committee, and the American Telemedicine Association. There is strong evidence of international engagement and interest. Despite the recent increase in research reports related to telemedicine, there is a relative lack of new guidance based on COVID-19 lessons and innovations. There is a need to review recent evidence and update existing recommendations. Additionally, there is a need for guidance that addresses emerging technologies.

An overview and policy implications of national nurse identifier systems: A call for unity and integration.

There is a clear and growing need to be able record and track the contributions of individual registered nurses (RNs) to patient care and patient care outcomes in the US and also understand the state of the nursing workforce. The National Academies of Sciences, Engineering, and Medicine report, The Future of Nursing 2020-2030: Charting a Path to Achieve Health Equity (2021), identified the need to track nurses' collective and individual contributions to patient care outcomes. This capability depends upon the adoption of a unique nurse identifier and its implementation within electronic health records. Additionally, there is a need to understand the nature and characteristics of the overall nursing workforce including supply and demand, turnover, attrition, credentialing, and geographic areas of practice. This need for data to support workforce studies and planning is dependent upon comprehensive databases describing the nursing workforce, with unique nurse identification to support linkage across data sources. There are two existing national nurse identifiers- the National Provider Identifier and the National Council of State Boards of Nursing Identifier. This article provides an overview of these two national nurse identifiers; reviews three databases that are not nurse specific to understand lessons learned in the development of those databases; and discusses the ethical, legal, social, diversity, equity, and inclusion implications of a unique nurse identifier.

Decentralized research technology use in multicenter clinical research studies based at U.S. academic research centers.

Introduction: During the COVID-19 pandemic, research organizations accelerated adoption of technologies that enable remote participation. Now, there's a pressing need to evaluate current decentralization practices and develop appropriate research, education, and operations infrastructure. The purpose of this study was to examine current adoption of decentralization technologies in a sample of clinical research studies conducted by academic research organizations (AROs). Methods: The setting was three data coordinating centers in the U.S. These centers initiated coordination of 44 clinical research studies during or after 2020, with national recruitment and enrollment, and entailing coordination between one and one hundred sites. We determined the decentralization technologies used in these studies. Results: We obtained data for 44/44 (100%) trials coordinated by the three centers. Three technologies have been adopted across nearly all studies (98-100%): eIRB, eSource, and Clinical Trial Management Systems. Commonly used technologies included e-Signature (32/44, 73%), Online Payments Portals (26/44, 59%), ePROs (23/44, 53%), Interactive Response Technology (22/44, 50%), Telemedicine (19/44, 43%), and eConsent (18/44, 41%). Wearables (7/44,16%) and Online Recruitment Portals (5/44,11%) were less common. Rarely utilized technologies included Direct-to-Patient Portals (1/44, 2%) and Home Health Nurse Portals (1/44, 2%). Conclusions: All studies incorporated some type of decentralization technology, with more extensive adoption than found in previous research. However, adoption may be strongly influenced by institution-specific IT and informatics infrastructure and support. There are inherent needs, responsibilities, and challenges when incorporating decentralization technology into a research study, and AROs must ensure that infrastructure and informatics staff are adequate.

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.

Hospital acquired pressure injury prediction in surgical critical care patients.

BACKGROUND: Hospital-acquired pressure injuries (HAPrIs) are areas of damage to the skin occurring among 5-10% of surgical intensive care unit (ICU) patients. HAPrIs are mostly preventable; however, prevention may require measures not feasible for every patient because of the cost or intensity of nursing care. Therefore, recommended standards of practice include HAPrI risk assessment at routine intervals. However, no HAPrI risk-prediction tools demonstrate adequate predictive validity in the ICU population. The purpose of the current study was to develop and compare models predicting HAPrIs among surgical ICU patients using electronic health record (EHR) data. METHODS: In this retrospective cohort study, we obtained data for patients admitted to the surgical ICU or cardiovascular surgical ICU between 2014 and 2018 via query of our institution's EHR. We developed predictive models utilizing three sets of variables: (1) variables obtained during routine care + the Braden Scale (a pressure-injury risk-assessment scale); (2) routine care only; and (3) a parsimonious set of five routine-care variables chosen based on availability from an EHR and data warehouse perspective. Aiming to select the best model for predicting HAPrIs, we split each data set into standard 80:20 train:test sets and applied five classification algorithms. We performed this process on each of the three data sets, evaluating model performance based on continuous performance on the receiver operating characteristic curve and the F1 score. RESULTS: Among 5,101 patients included in analysis, 333 (6.5%) developed a HAPrI. F1 scores of the five classification algorithms proved to be a valuable evaluation metric for model performance considering the class imbalance. Models developed with the parsimonious data set had comparable F1 scores to those developed with the larger set of predictor variables. CONCLUSIONS: Results from this study show the feasibility of using EHR data for accurately predicting HAPrIs and that good performance can be found with a small group of easily accessible predictor variables. Future study is needed to test the models in an external sample.

Predicting Falls Among Community-Dwelling Older Adults: A Demonstration of Applied Machine Learning.

Data science skills are increasingly needed by informatics nurses and nurse scientists, but techniques such as machine learning can be daunting for those with clinical, rather than computer science or technical, backgrounds. With the increasing quantity of publicly available population-level datasets, identification of factors that predict clinical outcomes is possible using machine learning algorithms. This study demonstrates how to apply a machine learning approach to nursing-relevant questions, specifically an approach to predict falls among community-dwelling older adults, based on data from the 2014 Behavioral Risk Factor Surveillance System. A random forest algorithm, a common approach to machine learning, was compared to a logistic regression model. Explanations of how to interpret the models and their associated performance characteristics are included to serve as a tutorial to readers. Machine learning methods constitute an increasingly important approach for nursing as population-level data are increasingly being made available to the public.

Enabling health information exchange at a US Poison Control Center.

OBJECTIVE: The objective of this project was to enable poison control center (PCC) participation in standards-based health information exchange (HIE). Previously, PCC participation was not possible due to software noncompliance with HIE standards, lack of informatics infrastructure, and the need to integrate HIE processes into workflow. MATERIALS AND METHODS: We adapted the Health Level Seven Consolidated Clinical Document Architecture (C-CDA) consultation note for the PCC use case. We used rapid prototyping to determine requirements for an HIE dashboard for use by PCCs and developed software called SNOWHITE that enables poison center HIE in tandem with a poisoning information system. RESULTS: We successfully implemented the process and software at the PCC and began sending outbound C-CDAs from the Utah PCC on February 15, 2017; we began receiving inbound C-CDAs on October 30, 2018. DISCUSSION: With the creation of SNOWHITE and initiation of an HIE process for sending outgoing C-CDA consultation notes from the Utah Poison Control Center, we accomplished the first participation of PCCs in standards-based HIE in the US. We faced several challenges that are also likely to be present at PCCs in other states, including the lack of a robust set of patient identifiers to support automated patient identity matching, challenges in emergency department computerized workflow integration, and the need to build HIE software for PCCs. CONCLUSION: As a multi-disciplinary, multi-organizational team, we successfully developed both a process and the informatics tools necessary to enable PCC participation in standards-based HIE and implemented the process at the Utah PCC.

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.

Simple Workflow Changes Enable Effective Patient Identity Matching in Poison Control.

BACKGROUND: U.S. poison control centers pose a special case for patient identity matching because they collect only minimal patient identifying information. METHODS: In early 2017, the Utah Poison Control Center (Utah PCC) initiated participation in regional health information exchange by sending Health Level Seven Consolidated Clinical Document Architecture (C-CDA) documents to the Utah Health Information Network and Intermountain Healthcare. To increase the documentation of patient identifiers by the Utah PCC, we (1) adapted documentation practices to enable more complete and consistent documentation, and (2) implemented staff training to improve collection of identifiers. RESULTS: Compared with the same time period in 2016, the Utah PCC showed an increase of 27% (p < 0.001) in collection of birth date for cases referred to a health care facility, while improvements in the collection of other identifiers ranged from 0 to 8%. Automated patient identity matching was successful for 77% (100 of 130) of the C-CDAs. CONCLUSION: Historical processes and procedures for matching patient identities require adaptation or added functionality to adequately support the PCC use case.