Scoping review of data privacy risks in COVID-19 apps with digital vaccination certifications.

The goal was to review mobile apps with COVID-19 digital vaccination certificates between November 2022 and March 2023 and evaluate: (a) compliance with the WHO Proof of Vaccination Scenario requirements, (b) risk levels of app permissions using a Permission Accumulated Risk Score (PARS), and (c) readability and transparency of the app's privacy policies using a Privacy Transparency Index (PTI) score. We found 49 mobile apps with COVID-19 digital vaccination certificates from across 32 countries. Most apps were developed by governments (37/49, 75.51%). We discovered a high positive correlation between the country-wide app total installs and the people vaccinated with at least one dose in the country (r = 0.93, P = <.001). Most apps (97.96%) had sources of information available for compliance with WHO Proof of Vaccination Scenario requirements. Only two apps included all the required data items, while most apps (75%) included five or more data out of nine items. We found that most (97.96%) apps had a Google Play link to generate the Exodus platform permission report, and most (95.92%) apps had an associated privacy policy available. We identified 80 unique permissions; some (23.75%) were dangerous or special. We also found 28 types of trackers. The average PARS was 28.58 (IQR 23.25, range 15-38.25). Most of the apps' privacy policies documents were difficult or very difficult to read (median grade level 14, IQR 2.6, range 13-15.6). The average PTI was 50.43 (SD 14.73; range 22.5-75). In conclusion, higher compliance with the WHO Proof of Vaccination Scenario requirements is desirable to support interoperability. Developers should limit the number of permissions for essential needs and disclose their purpose. Developers should write privacy policies that a wider audience can understand.

Physicians' Perspectives on HL7 Information Policy Sensitive Value Set: A Validation Study through Health Concept Categorization.

The Health Level 7 (HL7) organization introduced the Information Sensitivity Policy Value Set with 45 sensitive data categories to facilitate the implementation of granular electronic consent technology. The goal is to allow patients to have control over the sharing of their sensitive medical records. This study represents the first attempt to explore physicians' viewpoints on these categories. Twelve physicians participated in a survey, leading to revisions in 21 HL7 categories. They later classified 600 clinical data items through a second survey using the updated categories. Participants' perspectives were documented, and data analysis included descriptive measures and heat maps. In the first survey, six participants suggested adding 19 new categories (e.g., personality disorder), and modifying 25 category definitions. Two new categories and sixteen revised category definitions were incorporated to support more patient-friendly content and inclusive language. Fifteen new category recommendations were addressed through a revision of category definitions (e.g., personality disorder described as a behavioral health condition). In the second survey, data categorizations led to recommendations for more categories from ten participants. Future revisions of the HL7 categories should incorporate physicians' viewpoints, validate the categories using patient data or/and include patients' perspectives, and develop patient-centric category specifications.

Physicians differ in their perceptions of sensitive medical records: Survey and interview study.

Physician categorizations of electronic health record (EHR) data (e.g., depression) into sensitive data categories (e.g., Mental Health) and their perspectives on the adequacy of the categories to classify medical record data were assessed. One thousand data items from patient EHR were classified by 20 physicians (10 psychiatrists paired with ten non-psychiatrist physicians) into data categories via a survey. Cluster-adjusted chi square tests and mixed models were used for analysis. 10 items were selected per each physician pair (100 items in total) for discussion during 20 follow-up interviews. Interviews were thematically analyzed. Survey item categorization yielded 500 (50.0%) agreements, 175 (17.5%) disagreements, 325 (32.5%) partial agreements. Categorization disagreements were associated with physician specialty and implied patient history. Non-psychiatrists selected significantly (p = .016) more data categories than psychiatrists when classifying data items. The endorsement of Mental Health and Substance Use categories were significantly (p = .001) related for both provider types. During thematic analysis, Encounter Diagnosis (100%), Problems (95%), Health Concerns (90%), and Medications (85%) were discussed the most when deciding the sensitivity of medical information. Most (90.0%) interview participants suggested adding additional data categories. Study findings may guide the evolution of digital patient-controlled granular data sharing technology and processes.

My data choices: Pilot evaluation of patient-controlled medical record sharing technology.

Patients desire greater control over sharing their digital health data. Consent2Share (C2S) is an open-source consent tool offered by SAMHA and the VA to support granular data sharing (GDS) options that align with patient preferences and data privacy regulations. The need to validate this tool exists. We pilot tested C2S with 199 English and Spanish-speaking patients with behavioral health conditions (BHCs) and patient guardians. Data were analyzed using mixed methodology. All participants desired granular control over the sharing of their health data. Most participants (87%) were highly interested in using a tool that offered granular options for executing data sharing decisions, with over half (55%) indicated that being able to specify the data type, data recipient, and data use purpose made them more willing to share their medical records. Majority (83%) indicated that the supported data type sharing categories satisfied their data-sharing privacy preferences. Majority (87%) also reported that knowing the purpose of data use made them more comfortable in sharing. Some participants (28%) accessed the education materials provided on data type sharing options. Patients want granular choices when sharing medical records. Consent2Share and its supported data type sharing categories are adequate to capture patients' data sharing preferences. Further development is needed before deployment in clinical environments.

Recommendations to Inform Substance Use Disorder Data Sharing Research: Scoping Review and Thematic Analysis.

OBJECTIVES: Title 42 Code of Federal Regulations Part 2 (42 CFR Part 2 or Part 2) was enacted in 1975 to protect patients receiving treatment for substance use disorders. A scoping review on Part 2 characterized published work. METHODS: Seven databases were searched. Studies were categorized based on date of publication, primary objectives, methods, and findings. A thematic analysis was conducted using article titles and abstracts. RESULTS: Of the 36 studies identified, the majority (78%) were opinions or legal reviews, (22%) employed quantitative and/or qualitative methods to study the impact of Part 2, and over half (58%) were published in the past 5 years. Only 8% of studies reported stakeholder involvement in Part 2 studies and no study included patients or patient advocates. No study discussed the efforts to align Part 2 with Health Insurance Portability and Accountability Act or provided evidence of the benefit of this alignment. Three main themes emerged from the review: care delivery (46.6%), law and ethics (27.6%), and technology (25.9%). There were no studies focusing on stigma and its effect on substance use treatment and Part 2. CONCLUSIONS: Despite the significance of Part 2, the literature is sparse. It is time to initiate a new era of scholarly research that focuses on the impact of statutes and policies that govern substance use disorder data sharing. Specifically, we recommend research on Part 2 and Health Insurance Portability and Accountability Act alignment, the effects of Part 2 on patients, as well as providers and other stakeholder perceptions on the regulation.

Self-Management Behaviors of Patients with Type 1 Diabetes: Comparing Two Sources of Patient-Generated Data.

OBJECTIVES: This article aims to evaluate adult type 1 diabetes mellitus (T1DM) self-management behaviors (SMBs) related to exercise and alcohol on a survey versus a smartphone app to compare self-reported and self-tracked SMBs, and examine inter- and intrapatient variability. METHODS: Adults with T1DM on insulin pump therapy were surveyed about their alcohol, meal, and exercise SMBs. For 4 weeks, participants self-tracked their alcohol, meal, and exercise events, and their SMBs corresponding with these events via an investigator-developed app. Descriptive statistics and generalized linear mixed-effect models were used to analyze the data RESULTS: Thirty-five participants self-tracked over 5,000 interactions using the app. Variability in how participants perceived the effects of exercise and alcohol on their blood glucose was observed. The congruity between SMBs self-reported on the survey and those self-tracked with the app was measured as mean (SD). The lowest congruity was for alcohol and exercise with 61.9% (22.7) and 66.4% (20.2), respectively. Congruity was higher for meals with 80.9% (21.0). There was significant daily intra- and interpatient variability in SMBs related to preprandial bolusing: recommended bolus, p < 0.05; own bolus choice, p < 0.01; and recommended basal adjustment, p < 0.01. CONCLUSION: This study highlights the variability in intra- and interpatient SMBs obtained through the use of a survey and app. The outcomes of this study indicate that clinicians could use both one-time and every-day assessment tools to assess SMBs related to meals. For alcohol and exercise, further research is needed to understand the best assessment method for SMBs. Given this degree of patient variability, there is a need for an educational intervention that goes beyond the traditional "one-size-fits-all" approach of diabetes management to target individualized treatment barriers.

Patient Perception and Satisfaction With Insulin Pump System: Pilot User Experience Survey.

BACKGROUND: The goal of this study was to assess patient perspectives and satisfaction with the MiniMed 670G insulin pump. Those participants who used the pump as part of a hybrid closed loop were also asked to provide their views on the automatic feature (auto mode). METHODS: Adults with type 1 diabetes mellitus using the Medtronic™ 670G pump were asked about their experience with the device using a semi-structured survey developed by the research team. Responses were quantified to identify emergent themes. RESULTS: Seventeen participants used the pump as part of a hybrid closed loop system, while four participants used the pump in combination with a nonintegrated continuous glucose monitoring system. Overall, participants indicated a high level of satisfaction with the pump (14/21) mostly because of improvements in blood glucose (BG) control (15/21). Least liked features were physical design and structure (6/21), frequency of user input (5/21), alert frequency (4/21), and difficulty of use (3/21). Those using the hybrid closed loop were satisfied with the auto mode feature (11/17), mostly because of improvements in BG control (9/17). The least liked features of the auto mode technology were that blood glucose levels remained elevated (5/17) and the frequency of alerts (4/17). CONCLUSION: Participants indicated a high level of satisfaction with the pump and its auto mode featured mostly because of improvements in BG control. They also pointed out some key aspects of the device that are of potential clinical or commercial relevance. Additional research is needed to further evaluate users' perspectives on this new device.

Comparing Real-Time Self-Tracking and Device-Recorded Exercise Data in Subjects with Type 1 Diabetes.

BACKGROUND: Insulin therapy, medical nutrition therapy, and physical activity are required for the treatment of type 1 diabetes (T1D). There is a lack of studies in real-life environments that characterize patient-reported data from logs, activity trackers, and medical devices (e.g., glucose sensors) in the context of exercise. OBJECTIVE: The objective of this study was to compare data from continuous glucose monitor (CGM), wristband heart rate monitor (WHRM), and self-tracking with a smartphone application (app), iDECIDE, with regards to exercise behaviors and rate of change in glucose levels. METHODS: Participants with T1D on insulin pump therapy tracked exercise for 1 month with the smartphone app while WHRM and CGM recorded data in real time. Exercise behaviors tracked with the app were compared against WHRM. The rate of change in glucose levels, as recorded by CGM, resulting from exercise was compared between exercise events documented with the app and recorded by the WHRM. RESULTS: Twelve participants generated 277 exercise events. Tracking with the app aligned well with WHRM with respect to frequency, 3.0 (2.1) and 2.5 (1.8) days per week, respectively (p = 0.60). Duration had very high agreement, the mean duration from the app was 65.6 (55.2) and 64.8 (54.9) minutes from WHRM (p = 0.45). Intensity had a low concordance between the data sources (Cohen's kappa = 0.2). The mean rate of change of glucose during exercise was -0.27 mg/(dL*min) and was not significantly different between data sources or intensity (p = 0.21). CONCLUSION: We collated and analyzed data from three heterogeneous sources from free-living participants. Patients' perceived intensity of exercise can serve as a surrogate for exercise tracked by a WHRM when considering the glycemic impact of exercise on self-care regimens.

Design and Testing of a Smartphone Application for Real-Time Self-Tracking Diabetes Self-Management Behaviors.

BACKGROUND: Type 1 diabetes (T1D) care requires multiple daily self-management behaviors (SMBs). Preliminary studies on SMBs rely mainly on self-reported survey and interview data. There is little information on adult T1D SMBs, along with corresponding compensation techniques (CTs), gathered in real-time. OBJECTIVE: The article aims to use a patient-centered approach to design iDECIDE, a smartphone application that gathers daily diabetes SMBs and CTs related to meal and alcohol intake and exercise in real-time, and contrast patients' actual behaviors against those self-reported with the app. METHODS: Two usability studies were used to improve iDECIDE's functionality. These were followed by a 30-day pilot test of the redesigned app. A survey designed to capture diabetes SMBs and CTs was administered prior to the 30-day pilot test. Survey results were compared against iDECIDE logs. RESULTS: Usability studies revealed that participants desired advanced features for self-tracking meals and alcohol intake. Thirteen participants recorded over 1,200 CTs for carbohydrates during the 30-day study. Participants also recorded 76 alcohol and 166 exercise CTs. Comparisons of survey responses and iDECIDE logs showed mean% (standard deviation) concordance of 77% (25) for SMBs related to meals, where concordance of 100% indicates a perfect match. There was low concordance of 35% (35) and 46% (41) for alcohol and exercise events, respectively. CONCLUSION: The high variability found in SMBs and CTs highlights the need for real-time diabetes self-tracking mechanisms to better understand SMBs and CTs. Future work will use the developed app to collect SMBs and CTs and identify patient-specific diabetes adherence barriers that could be addressed with individualized education interventions.

Self-Reported Compensation Techniques for Carbohydrate, Exercise, and Alcohol Behaviors in Patients With Type 1 Diabetes on Insulin Pump Therapy.

Studies have found variability in self-care behaviors in patients with type 1 diabetes, particularly when incorporating exercise and alcohol consumption. The objective of this study was to provide results from a survey to understand (1) insulin pump behaviors, (2) reported self-management behaviors for exercise and alcohol, and (3) perceptions of the effects of exercise and alcohol on blood glucose (BG) control. Fourteen participants from an outpatient endocrinology practice were recruited and administered an electronic survey. Compensation techniques for exercise and alcohol, along with reasons for employing the techniques were identified. Also identified were factors that participants said affected BG control with regard to exercise and alcohol. These results confirm the considerable inconsistency patients have about incorporating exercise and alcohol into decisions about self-management behaviors.

Self-Management Behaviors in Adults on Insulin Pump Therapy.

BACKGROUND: Successful diabetes management requires behavioral changes. Little is known about self-management behaviors (SMB) in adults on insulin pump (IP) therapy. OBJECTIVE: Analyze and characterize observed common diabetes SMB in adult participants with type 1 diabetes (T1D) using IPs and to correlate behaviors with glycemic outcomes based on participant's individual glucose targets. MATERIALS AND METHODS: One month of IP data from adults with T1D were downloaded. Computer programs were written to automatically quantify the observed frequency of expected behaviors such as: insulin bolusing, checking blood glucose (BG), and recording carbohydrate intake, and other interactions with the IP. RESULTS: Nineteen participants were recruited and 4,249 IP interactions were analyzed to ascertain behaviors. Intersubject variability of adherence to minimally expected behaviors was observed: daily documentation of carbohydrates and BG checks in 76.6 (31.7)% and 60.0 (32.5)%, respectively, and bolusing without consulting the IPBC in 13.0 (16.9)% of delivered boluses, while daily insulin bolus delivery was consistent 96.8 (5.7)%. Higher frequency of adherence to daily behaviors correlated with a higher number of glucose readings at target. CONCLUSION: Results indicate variability in SMB and do not always match recommendations. Case-scenarios based on observed real-life SMB could be incorporated into interviews/surveys to elucidate ways to improve SMB.

Characterization of Exercise and Alcohol Self-Management Behaviors of Type 1 Diabetes Patients on Insulin Pump Therapy.

BACKGROUND: There is a lack of systematic ways to analyze how diabetes patients use their insulin pumps to self-manage blood glucose to compensate for alcohol ingestion and exercise. The objective was to analyze "real-life" insulin dosing decisions occurring in conjunction with alcohol intake and exercise among patients using insulin pumps. METHODS: We recruited adult type 1 diabetes (T1D) patients on insulin pump therapy. Participants were asked to maintain their daily routines, including those related to exercising and consuming alcohol, and keep a 30-day journal on exercise performed and alcohol consumed. Thirty days of insulin pump data were downloaded. Participants' actual insulin dosing behaviors were compared against their self-reported behaviors in the setting of exercise and alcohol. RESULTS: Nineteen T1D patients were recruited and over 4000 interactions with the insulin pump were analyzed. The analysis exposed variability in how subjects perceived the effects of exercise/alcohol on their blood glucose, inconsistencies between self-reported and observed behaviors, and higher rates of blood glucose control behaviors for exercise versus alcohol. CONCLUSION: Compensation techniques and perceptions on how exercise and alcohol affect their blood glucose levels vary between patients. Improved individualized educational techniques that take into consideration a patient's unique life style are needed to help patients effectively apply alcohol and exercise compensation techniques.

A Sequential Data Analysis Approach to Electronic Health Record Workflow.

Failure to understand clinical workflow across electronic health record (EHR) tasks is a significant contributor to usability problems. In this paper, we employed sequential data analysis methods with the aim of characterizing patterns of 5 clinicians' information-gathering across 66 patients. Two analyses were conducted. The first one characterized the most common sequential patterns as reflected in the screen transitions. The second analysis was designed to mine and quantify the frequency of sequence occurrence. We observed 27 screen-transition patterns that were employed from 2 to 7 times. Documents/Images and Intake/Output screens were viewed for nearly all patients indicating the importance of these information sources. In some cases, they were viewed more than once which may show that users are following inefficient patterns in the information gathering process. New quantitative methods of analysis as applied to interaction data can yield critical insights in robust designs that better support clinical workflow.

Argumentation-logic for creating and explaining medical hypotheses.

OBJECTIVE: While EIRA has proved to be successful in the detection of anomalous patient responses to treatments in the Intensive Care Unit, it could not describe to clinicians the rationales behind the anomalous detections. The aim of this paper is to address this problem. METHODS: Few attempts have been made in the past to build knowledge-based medical systems that possess both argumentation and explanation capabilities. Here we propose an approach based on Dung's seminal calculus of opposition. RESULTS: We have developed a new tool, arguEIRA, which is an extension of the existing EIRA system. In this paper we extend EIRA by providing it with an argumentation-based justification system that formalizes and communicates to the clinicians the reasons why a patient response is anomalous. CONCLUSION: Our comparative evaluation of the EIRA system against the newly developed tool highlights the multiple benefits that the use of argumentation-logic can bring to the field of medical decision support and explanation.

Argumentation logic for the flexible enactment of goal-based medical guidelines.

UNLABELLED: RESEARCH PURPOSE: We have designed a prototype clinical workflow system that allows the specification and enactment of medical guidelines in terms of clinical goals to be achieved, maintained or avoided depending on the patient's disease and treatment evolution. The prototype includes: (1) an argumentation-based decision support system which can be used both to represent medical decisions within guidelines, and to dynamically choose the most suitable plans to achieve clinical goals, and (2) mechanisms to specify a health organization's facilities and health workers skills and roles, which can be taken into account during the decision process in order to improve quality of care. RESULTS: The framework has been fully implemented in the COGENT formal modeling system. The prototype has been evaluated implementing a hypertension guideline. CONCLUSIONS: The framework has shown flexibility and adaptability in (1) advising and tailoring health care based on a health organization's resources and a patient's particular medical condition, (2) delegating health care, and (3) replanning when unexpected situations arise.

Patterns for collaborative work in health care teams.

OBJECTIVE: The problem of designing and managing teams of workers that can collaborate working together towards common goals is a challenging one. Incomplete or ambiguous specification of responsibilities and accountabilities, lack of continuity in teams working in shifts, inefficient organization of teams due to lack of information about workers' competences and lack of clarity to determine if the work is delegated or assigned are examples of important problems related to collaborative work in healthcare teams. Here we address these problems by specifying goal-based patterns for abstracting the delegation and assignment of services. The proposed patterns should provide generic and reusable solutions and be flexible enough to be customizable at run time to the particular context of execution. Most importantly the patterns should support a mechanism for detecting abnormal events (exceptions) and for transferring responsibility and accountability for recovering from exceptions to the appropriate actor. METHOD: To provide a generic solution to the problematic issues arising from collaborative work in teams of health workers we start from definitions of standard terms relevant for team work: competence, responsibility, and accountability. We make explicit the properties satisfied by service assignment and delegation in terms of competences, responsibilities, and accountability in normal scenarios and abnormal situations that require the enactment of recovery strategies. Based on these definitions we specify (1) a basic terminology, (2) design patterns for service assignment and delegation (with and without supervision), and (3) an exception manager for detecting and recovering from exceptions. We use a formal framework to specify design patterns and exceptions. RESULTS: We have proved using Owicki-Gries Theory that the proposed patterns satisfy the properties that characterize service assignment and delegation in terms of competence, responsibility and accountability in normal and abnormal (exceptional) scenarios. We show that although abstract, the proposed patterns can be instantiated in an executable COGENT prototype, and can be mapped into the Tallis tool that enacts PROforma language specifications of medical guidelines. CONCLUSIONS: The proposed patterns are generic and abstract enough to capture the normal and abnormal scenarios of assignment and delegation of tasks in collaborative work in health care teams.