Measurement Equivalence of Patient-Reported Outcome Measure Response Scale Types Collected Using Bring Your Own Device Compared to Paper and a Provisioned Device: Results of a Randomized Equivalence Trial.

OBJECTIVES: The aim of this study was to assess the measurement equivalence of individual response scale types by using a patient reported outcome measure (PROM) collected on paper and migrated into electronic format for use on the subject's own mobile device (BYOD) and on a provisioned device (site device). METHODS: Subjects suffering from chronic health conditions causing daily pain or discomfort were invited to participate in this single-site, single visit, three-way crossover study. Association between individual item and instrument subscale scores was assessed by using the intraclass correlation coefficient (ICC) and its CI. Participant attitudes toward the use of BYOD in a clinical trial were assessed through use of a questionnaire. RESULTS: In this study, 155 subjects (females 83 [54%]; males 72 [46%]) ages 19 to 69 years (mean ± SD: 48.6 ± 13.1) were recruited. High association between the modes of administration (paper, BYOD, site device) was shown with analysis of ICCs (0.79-0.98) for each response scale type, including visual analogue scale, numeric rating scale, verbal response scale, and Likert scale. Of the subjects, 94% (146 of 155) stated that they would definitely or probably be willing to download an app onto their own mobile device for a forthcoming clinical trial. Forty-five percent of subjects felt BYOD would be more convenient compared with 15% preferring a provisioned device (40% had no preference). CONCLUSIONS: This study provides strong evidence supporting the use of BYOD for PROM collection in terms of the conservation of instrument measurement equivalence across the most widely used response scale types, and high patient acceptance of the approach.

Developing a smartphone app to support the nursing community.

Healthcare professionals are turning increasingly to the digital sector to access information they need for their work. Mobile technology, such as smartphones and tablets, provides a unique opportunity to place high-quality information directly into users' hands. This article describes the ongoing development of a smartphone app, Preparing for Caring, designed to prepare the future workforce by enabling mentors and nursing students to access supportive material during practice-based learning episodes. The article explores the rationale for designing the app and discusses the challenges and benefits of developing this resource from the perspectives of student, mentor and healthcare manager.

Wearable activity data can predict functional recovery after musculoskeletal injury: Feasibility of a machine learning approach.

Delayed functional recovery after injury is associated with significant personal and socioeconomic burden. Identification of patients at risk for a prolonged recovery after a musculoskeletal injury is thus of high relevance. The aim of the current study was to show the feasibility of using a machine learning assisted model to predict functional recovery based on the pre- and immediate post injury patient activity as measured with wearable systems in trauma patients. Patients with a pre-existing wearable (smartphone and/or body-worn sensor), data availability of at least 7 days prior to their injury, and any musculoskeletal injury of the upper or lower extremity were included in this study. Patient age, sex, injured extremity, time off work and step count as activity data were recorded continuously both pre- and post-injury. Descriptive statistics were performed and a logistic regression machine learning model was used to predict the patient's functional recovery status after 6 weeks based on their pre- and post-injury activity characteristics. Overall 38 patients (7 upper extremity, 24 lower extremity, 5 pelvis, 2 combined) were included in this proof-of-concept study. The average follow-up with available wearable data was 85.4 days. Based on the activity data, a predictive model was constructed to determine the likelihood of having a recovery of at least 50 % of the pre-injury activity state by post injury week 6. Based on the individual activity by week 3 a predictive accuracy of over 80 % was achieved on an independent test set (F1=0,82; AUC=0,86; ACC=8,83). The employed model is feasible to assess the principal risk for a slower recovery based on readily available personal wearable activity data. The model has the potential to identify patients requiring additional aftercare attention early during the treatment course, thus optimizing return to the pre-injury status through focused interventions. Additional patient data is needed to adapt the model to more specifically focus on different fracture entities and patient groups.

A Closed-Loop Digital Health Tool to Improve Depression Care in Multiple Sclerosis: Iterative Design and Cross-Sectional Pilot Randomized Controlled Trial and its Impact on Depression Care.

BACKGROUND: People living with multiple sclerosis (MS) face a higher likelihood of being diagnosed with a depressive disorder than the general population. Although many low-cost screening tools and evidence-based interventions exist, depression in people living with MS is underreported, underascertained by clinicians, and undertreated. OBJECTIVE: This study aims to design a closed-loop tool to improve depression care for these patients. It would support regular depression screening, tie into the point of care, and support shared decision-making and comprehensive follow-up. After an initial development phase, this study involved a proof-of-concept pilot randomized controlled trial (RCT) validation phase and a detailed human-centered design (HCD) phase. METHODS: During the initial development phase, the technological infrastructure of a clinician-facing point-of-care clinical dashboard for MS management (BRIDGE) was leveraged to incorporate features that would support depression screening and comprehensive care (Care Technology to Ascertain, Treat, and Engage the Community to Heal Depression in people living with MS [MS CATCH]). This linked a patient survey, in-basket messages, and a clinician dashboard. During the pilot RCT phase, a convenience sample of 50 adults with MS was recruited from a single MS center with 9-item Patient Health Questionnaire scores of 5-19 (mild to moderately severe depression). During the routine MS visit, their clinicians were either asked or not to use MS CATCH to review their scores and care outcomes were collected. During the HCD phase, the MS CATCH components were iteratively modified based on feedback from stakeholders: people living with MS, MS clinicians, and interprofessional experts. RESULTS: MS CATCH links 3 features designed to support mood reporting and ascertainment, comprehensive evidence-based management, and clinician and patient self-management behaviors likely to lead to sustained depression relief. In the pilot RCT (n=50 visits), visits in which the clinician was randomized to use MS CATCH had more notes documenting a discussion of depressive symptoms than those in which MS CATCH was not used (75% vs 34.6%; χ21=8.2; P=.004). During the HCD phase, 45 people living with MS, clinicians, and other experts participated in the design and refinement. The final testing round included 20 people living with MS and 10 clinicians including 5 not affiliated with our health system. Most scoring targets for likeability and usability, including perceived ease of use and perceived effectiveness, were met. Net Promoter Scale was 50 for patients and 40 for clinicians. CONCLUSIONS: Created with extensive stakeholder feedback, MS CATCH is a closed-loop system aimed to increase communication about depression between people living with MS and their clinicians, and ultimately improve depression care. The pilot findings showed evidence of enhanced communication. Stakeholders also advised on trial design features of a full year long Department of Defense-funded feasibility and efficacy trial, which is now underway. TRIAL REGISTRATION: ClinicalTrials.gov NCT05865405; http://tinyurl.com/4zkvru9x.

Examining 21st century skills in BYOD schools: From programs to practice.

Bring-your-own-device (BYOD) is a common strategy to increase technology integration in schools and give learners more responsibility in using digital devices for educational purposes. In particular, learners are expected to develop domain-general 21st-century skills when using their personal devices. Although there is no consensus regarding a comprehensive framework of 21st-century skills, most conceptual models incorporate aspects such as collaboration, communication, creativity, and critical thinking-so-called 4C competences-as well as self-direction and the use of digital technologies for learning, among other aspects. The importance of these competencies has been stressed in general and vocational education. To study the relationship between BYOD approaches and self-reported 21st-century skills, we conducted a survey of N = 8265 upper-secondary students from 100 schools in Switzerland. Using multilevel linear modeling, we compared the self-reported 21st-century skills of students with or without personal digital devices for learning in general and vocational education tracks. Our results indicate that learners reported higher levels of 21st-century skills by indicating that they brought their own devices to school, especially with regard to creativity, self-direction, and the use of technology for learning. However, an official BYOD program was no significant predictor of students' self-reported skills. The interactions between an official BYOD program and actual BYOD practice in class were not significant. Further, self-reported levels of 21st-century skills seemed to be higher in general than in vocational education. The study indicates that a BYOD concept alone does correspond to students' 21st-century skills but actual BYOD practices do.

Investigating nurses' acceptance of patients' bring your own device implementation in a clinical setting: A pilot study.

Objective: The popularity of the â€‹"bring your own device (BYOD)" â€‹concept has grown in recent years, and its application has extended to the healthcare field. This study was aimed at examining nurses' acceptance of a BYOD-supported system after a 9-month implementation period. Methods: We used the technology acceptance model to develop and validate a structured questionnaire as a research tool. All nurses (n â€‹= â€‹18) responsible for the BYOD-supported wards during the study period were included in our study. A 5-point Likert scale was used to assess the degree of disagreement and agreement. Statistical analysis was performed in SPSS version 24.0. Results: The questionnaire was determined to be reliable and well constructed, on the basis of the item-level content validity index and Cronbach α values above 0.95 and 0.87, respectively. The mean constant values for all items were above 3.95, thus suggesting that nurses had a positive attitude toward the BYOD-supported system, driven by the characteristics of the tasks involved. Conclusions: We successfully developed a BYOD-supported system. Our study results suggested that nursing staff satisfaction with BYOD-supported systems could be effectively increased by providing practical functionalities and reducing clinical burden. Hospitals could benefit from the insights generated by this study when implementing similar systems.

Demographic Imbalances Resulting From the Bring-Your-Own-Device Study Design.

Digital health technologies, such as smartphones and wearable devices, promise to revolutionize disease prevention, detection, and treatment. Recently, there has been a surge of digital health studies where data are collected through a bring-your-own-device (BYOD) approach, in which participants who already own a specific technology may voluntarily sign up for the study and provide their digital health data. BYOD study design accelerates the collection of data from a larger number of participants than cohort design; this is possible because researchers are not limited in the study population size based on the number of devices afforded by their budget or the number of people familiar with the technology. However, the BYOD study design may not support the collection of data from a representative random sample of the target population where digital health technologies are intended to be deployed. This may result in biased study results and biased downstream technology development, as has occurred in other fields. In this viewpoint paper, we describe demographic imbalances discovered in existing BYOD studies, including our own, and we propose the Demographic Improvement Guideline to address these imbalances.

Comparability of a provisioned device versus bring your own device for completion of patient-reported outcome measures by participants with chronic obstructive pulmonary disease: quantitative study findings.

OBJECTIVE: To quantitatively compare equivalence and compliance of patient-reported outcome (PRO) data collected via provisioned device (PD) versus bring your own device (BYOD). METHODS: Participants with stable chronic obstructive pulmonary disease (COPD) completed the EXAcerbations of Chronic Pulmonary Disease Tool (EXACT®) daily and COPD Assessment Test™ (CAT) and Patient Global Impression of Severity (PGIS) of COPD weekly on either PD or BYOD for 15 days, then switched device types for 15 days. EXACT was scored using the Evaluating Respiratory Symptoms in COPD (E-RS®: COPD) algorithm and equivalence assessed using intraclass correlation coefficients (ICCs) adjusting for cross-over sequence, period, and time. Two one-sided tests (TOSTs) used ICC adjusted means with 10%, 20%, and 40% of total score tested as equivalence margins. Compliance and comfort with technology were assessed. Equivalence across 3 device screen sizes was assessed following the second completion period. RESULTS: Participants (N = 64) reported high comfort with technology, with 79.7% reporting being "quite a bit" or "very" comfortable. Weekly compliance was high (BYOD = 89.7-100%; PD = 76.9-100%). CAT and E-RS: COPD scores correlated well with PGIS (r > 0.50) and demonstrated equivalence between PD and BYOD completion (ICC = 0.863-0.908). TOST equivalence was achieved within 10% of the total score (p > 0.05). PRO measure scores were equivalent across 3 different screen sizes (ICC = 0.972-0.989). CONCLUSIONS: Measure completion was high and scores equivalent between PD and BYOD, supporting use of BYOD in addition to PD for collecting PRO data in COPD studies and in demographically diverse patient populations.

Comparability of a provisioned device versus bring your own device for completion of patient-reported outcome measures by participants with chronic obstructive pulmonary disease: qualitative interview findings.

BACKGROUND: There is interest in participants using their own smartphones or tablets ("bring your own device"; BYOD) to complete patient-reported outcome (PRO) measures in clinical studies. Our study aimed to qualitatively evaluate participants' experience using a provisioned device (PD) versus their own smartphone (BYOD) for this purpose. METHODS: Participants with chronic obstructive pulmonary disease (COPD) were recruited for this observational, cross-over study and completed PRO measures daily on one device type for 15 days, then switched to the other device type to complete the same measures for another 15 days. After each 15-day period, semi-structured interviews were conducted about their experience with the device. RESULTS: Of 64 participants enrolled, the final qualitative analysis populations comprised those who participated in an interview without protocol violations. Thus, the qualitative longitudinal population (LP) included n = 57 (89%), while the qualitative cross-sectional population (CSP) included n = 60 (94%). CSP participants found both device types easy to use. Twenty CSP participants (33%) reported missing data entry on at least one day when using PD, and 24 (40%) reported missing at least one day when using BYOD. In the LP, preference for one of the device types was somewhat evenly split; 45.6% (n = 26) preferred PD and 50.9% (n = 29) preferred BYOD. The most common reason for preferring PD was that it was "dedicated" to the study; the "convenience" of carrying a single device was the main reason for preferring BYOD. CONCLUSION: The findings from the interviews demonstrated few differences in participants' experience completing PRO measures on a PD versus BYOD. Our study supports the use of BYOD as a potential addition to PD for collecting PRO data and contributes evidence that BYOD may be employed to collect PRO data in demographically diverse patient populations.

Considerations for Conducting Bring Your Own "Device" (BYOD) Clinical Studies.

Background: Digital health technologies are attracting attention as novel tools for data collection in clinical research. They present alternative methods compared to in-clinic data collection, which often yields snapshots of the participants' physiology, behavior, and function that may be prone to biases and artifacts, e.g., white coat hypertension, and not representative of the data in free-living conditions. Modern digital health technologies equipped with multi-modal sensors combine different data streams to derive comprehensive endpoints that are important to study participants and are clinically meaningful. Used for data collection in clinical trials, they can be deployed as provisioned products where technology is given at study start or in a bring your own "device" (BYOD) manner where participants use their technologies to generate study data. Summary: The BYOD option has the potential to be more user-friendly, allowing participants to use technologies that they are familiar with, ensuring better participant compliance, and potentially reducing the bias that comes with introducing new technologies. However, this approach presents different technical, operational, regulatory, and ethical challenges to study teams. For example, BYOD data can be more heterogeneous, and recruiting historically underrepresented populations with limited access to technology and the internet can be challenging. Despite the rapid increase in digital health technologies for clinical and healthcare research, BYOD use in clinical trials is limited, and regulatory guidance is still evolving. Key Messages: We offer considerations for academic researchers, drug developers, and patient advocacy organizations on the design and deployment of BYOD models in clinical research. These considerations address: (1) early identification and engagement with internal and external stakeholders; (2) study design including informed consent and recruitment strategies; (3) outcome, endpoint, and technology selection; (4) data management including compliance and data monitoring; (5) statistical considerations to meet regulatory requirements. We believe that this article acts as a primer, providing insights into study design and operational requirements to ensure the successful implementation of BYOD clinical studies.

Regulatory Acceptance of Patient-Reported Outcome (PRO) Data from Bring-Your-Own-Device (BYOD) Solutions to Support Medical Product Labeling Claims : Let's Share the Success Stories to Move the Industry Forward.

Bring-your-own-device (BYOD) methods for collecting patient-reported outcome (PRO) data in clinical trials can decrease patient burden and improve data quality. However, adoption of BYOD in clinical trials is limited by the absence of publicly available case studies where BYOD PRO data supported regulatory medical product approvals. Anecdotally, we are aware of multiple examples where efficacy and safety label claims were based on BYOD PRO data; however-except for one-these examples have not been made public. The absence of these case studies can lead sponsors to be hesitant to use BYOD for capturing primary and secondary PRO-based endpoints in their trials. This commentary outlines the context of the issue faced and concludes with a call for sponsor transparency with regard to BYOD use through publicizing where approved labeling claims were based on BYOD data. We suggest how this data could be systematically captured going forward. Sharing this information will benefit the clinical trials enterprise by increasing confidence in the utilization of BYOD and provide opportunities to enhance patient-centricity.

Evaluation of Self-care Activities and Quality of Life in Patients With Type 2 Diabetes Treated With Metformin Using the 2D Matrix Code of Outer Drug Packages as Patient Identifier: the DePRO Proof-of-Concept Observational Study.

BACKGROUND: The use of digital technology to assess patients remotely can reduce clinical study costs. In the European Union, the 2D matrix code on prescription drug packaging serves as a unique identifier of a given package of medication, and thus, also of the patient receiving that medication. Scanning of the 2D matrix code may therefore allow remote patient authentication in clinical studies. OBJECTIVE: The aim of the DePRO study was to assess the feasibility of a fully digital data-capture workflow, the authentication of participants via drug packaging 2D matrix codes, in patients with type 2 diabetes mellitus (T2DM) who use metformin. The primary objective was to describe the self-care activities of these patients. Secondary objectives were to evaluate (1) the self-reported health status of these patients, (2) the association of self-care activities with demographics and disease characteristics, and (3) the usability of the my ePRO app. METHODS: DePRO was an observational, multicenter, cross-sectional, digital, and patient-driven study conducted in Germany from June to December 2020. Adult patients prescribed metformin were invited to participate via their pharmacist or a medication tracker app. Participants downloaded the my ePRO app onto their own mobile device, scanned the 2D matrix code on their metformin package for registration and authentication, and provided informed consent via an electronic form. They were then able to complete a study-specific questionnaire on demographics and clinical characteristics, the German version of the Summary of Diabetes Self-Care Activities measure (SDSCA-G), the Diabetes Treatment Satisfaction Questionnaire (DTSQ), and the EQ-5D-5L. The patients conducted the study without support from a health care professional. Statistical analyses were exploratory and descriptive. RESULTS: In total, 3219 patients were invited to participate. The proportion of patients giving consent was greater among those invited by pharmacists (19/217, 8.8%) than among those invited via the medication tracker app (13/3002, 0.4%). Of the 29 patients eligible for analysis, 28 (97%) completed all study questionnaires. Most of the patients (23/29, 79%) were aged <60 years, and 59% (17/29) were male. The patients spent a mean total of 3.5 (SD 1.3) days out of 7 days on self-care activities (SDSCA-G). Most patients (24/29, 83%) were satisfied to extremely satisfied with their current treatment (DTSQ). Events of perceived hyperglycemia or hypoglycemia were reported by 20 of 29 (69%) patients. The best possible health status (EQ-5D-5L) was reported by 18 of 28 (64%) patients. Age was positively correlated with time spent on general and specific diet (Spearman coefficient 0.390 and 0.434, respectively). CONCLUSIONS: The DePRO study demonstrates the feasibility of fully digital authentication (via 2D matrix codes on drug packaging) and data capture in patients with T2DM. Personal invitations yielded higher recruitment rates than remote invitations via the medication tracker app. A high questionnaire completion rate was realized, based on completion by 28 out of 29 patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT04383041; https://clinicaltrials.gov/ct2/show/NCT04383041. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/21727.

Evaluation of Self-Care Activities and Quality of Life in Patients With Type 2 Diabetes Mellitus Treated With Metformin Using the 2D Matrix Code of Outer Drug Packages as Patient Identifier: Protocol for the DePRO Proof-of-Concept Observational Study.

BACKGROUND: Diabetes mellitus (DM) is one of the most common noncommunicable diseases. DM has a substantial negative impact on patients' quality of life, which is measured using a variety of diabetes-specific measures covering multiple aspects of patients' psychological state, behavior, and treatment satisfaction. A fully digital data collection system, including patient identification, would represent a substantial advance in how these patient-reported outcome (PRO) data are measured. Within the European Union, one way to identify patients without the involvement of health care professionals is to use the unique 2D matrix codes on the packaging of prescription medication-for example, metformin, the recommended initial treatment for patients with type 2 DM (T2DM). OBJECTIVE: In the DePRO study we aim to (1) describe the self-care activities of patients with T2DM using metformin-containing medication; (2) describe the self-reported health status (eg, presence of diabetes complications and quality of life) of these patients; (3) describe associations between self-care activities and demographics and disease characteristics; and (4) assess the usability of the my ePRO app. METHODS: DePRO is an observational, multicenter, cross-sectional, digital, patient-driven study conducted in Germany. Patients with a prescription for a metformin-containing medication will be given a postcard by their pharmacist, which will include a download link for the my ePRO app. In total, 12 diabetes-focused pharmacies, selected to represent urban and rural areas, will be recruited. Participants will use their own mobile device (bring your own device) to download the my ePRO app and access the DePRO study, for which they can register using the 2D matrix code on their medication. An electronic informed consent form will be displayed to the patients and only after giving consent will patients be able to complete the study questionnaires. The PRO instruments used in the study are the Summary of Diabetes Self-Care Activities Scale, the Diabetes Treatment Satisfaction Questionnaire, and the 5 level, 5-dimension EuroQol Questionnaire. Patients will also be asked to complete a questionnaire with items addressing demographics, patient characteristics, disease history, complications, and concomitant medications. Data will be transferred to the study database by the app upon completion of each questionnaire. Statistical analyses of primary and secondary endpoints will be exploratory and descriptive. RESULTS: Enrollment began in June 2020. The estimated study completion date is December 31, 2020, and the planned sample size is 300 patients. CONCLUSIONS: The DePRO study uses completely digital data collection, including authentication of eligible patients and completion of the study questionnaires. Therefore, the design of the DePRO study represents a substantial advance in the evaluation of the digital capturing of PRO data. TRIAL REGISTRATION: ClinicalTrials.gov NCT04383041; https://clinicaltrials.gov/ct2/show/NCT04383041. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/21727.

Hospital Bring-Your-Own-Device Security Challenges and Solutions: Systematic Review of Gray Literature.

BACKGROUND: As familiarity with and convenience of using personal devices in hospitals help improve the productivity, efficiency, and workflow of hospital staff, the health care bring-your-own-device (BYOD) market is growing consistently. However, security concerns owing to the lack of control over the personal mobile devices of staff, which may contain sensitive data such as personal health information of patients, make it one of the biggest health care information technology (IT) challenges for hospital administrations. OBJECTIVE: Given that the hospital BYOD security has not been adequately addressed in peer-reviewed literature, the aim of this paper was to identify key security challenges associated with hospital BYOD usage as well as relevant solutions that can cater to the identified issues by reviewing gray literature. Therefore, this research will provide additional practical insights from current BYOD practices. METHODS: A comprehensive gray literature review was conducted, which followed the stepwise guidelines and quality assessment criteria set out by Garousi et al. The searched literature included tier 1 sources such as health care cybersecurity market reports, white papers, guidelines, policies, and frameworks as well as tier 2 sources such as credible and reputed health IT magazines, databases, and news articles. Moreover, a deductive thematic analysis was conducted to organize the findings based on Schlarman's People Policy Technology model, promoting a holistic understanding of hospitals' BYOD security issues and solutions. RESULTS: A total of 51 sources were found to match the designed eligibility criteria. From these studies, several sociotechnical issues were identified. The major challenges identified were the use of devices with insufficient security controls by hospital staff, lack of control or visibility for the management to maintain security requirements, lack of awareness among hospital staff, lack of direction or guidance for BYOD usage, poor user experience, maintenance of legal requirements, shortage of cybersecurity skills, and loss of devices. Although technologies such as mobile device management, unified endpoint management, containerization, and virtual private network allow better BYOD security management in hospitals, policies and people management measures such as strong security culture and staff awareness and training improve staff commitment in protecting hospital data. CONCLUSIONS: The findings suggest that to optimize BYOD security management in hospitals, all 3 dimensions of the security process (people, policy, and technology) need to be given equal emphasis. As the nature of cybersecurity attacks is becoming more complex, all dimensions should work in close alignment with each other. This means that with the modernization of BYOD technology, BYOD strategy, governance, education, and relevant policies and procedures also need to adapt accordingly.

Bring-your-own-device in medical schools and healthcare facilities: A review of the literature.

BACKGROUND: Enabling personal mobile device use through a bring-your-own device (BYOD) policy can potentially save significant costs for medical schools and healthcare facilities, as they would not always have to acquire facility-owned devices. The BYOD policy is also perceived as a driver for balancing user needs for convenience with institutional needs for security. However, there seems to be a paucity in the literature on BYOD policy development, policy evaluation, and evaluation of mobile device implementation projects. OBJECTIVE: This review explored the literature to identify BYOD policy components (issues, interventions, and guidelines) that could potentially inform BYOD policy development and implementation in medical schools and healthcare facilities. METHODS: A literature search on PubMed, Web of Science, and Ebscohost (Academic Search Premier, ERIC, CINAHL, and MEDLINE) was conducted using the following search terms and their synonyms: healthcare facilities, mobile devices, BYOD, privacy and confidentiality, and health records. We developed a review matrix to capture the main aspects of each article and coded the matrix for emerging themes. The database and hand search yielded 1 594 articles, 14 of which were deemed as meeting the inclusion criteria. RESULTS: Several themes emerging from the analysis include: device management, data security, medical applications, information technology, education and/or curriculum, policy, and guidelines. The guidelines theme seems to provide a direction for BYOD policy development and implementation while the policy theme seems to be the comprehensive solution that synergizes BYOD implementation. CONCLUSION: Rather than an approach of 'chasing' issues with interventions, a more feasible approach towards achieving a safe mobile device use environment is through the development of comprehensive BYOD policies that would balance users' need for convenience with organizational security and patient privacy. The paucity in peer-reviewed literature calls for robust research that uses socio-technical approaches to development and evaluation of BYOD policies in medical schools and healthcare facilities.