Grando, Adela

AbstractThe goal of national interoperability is to improve care quality and decrease administrative burden and costs. Patients, providers, and other stakeholders are increasingly concerned that indiscriminate sharing of data may have deleterious, permanent consequences, as well as fail to provide granular control over the sharing of individual health data. Data segmentation and consent standards to date have been limited in scope and implementation, which has hindered efforts to scale data sharing preferences. Shift, an independent expert stakeholder task force, has been convened to mature standards, terminologies, and consensus-driven implementation guidance, which are prerequisites for more robust policy drivers needed to support nationwide sensitive data segmentation and consent capabilities. This paper describes Shift's framework and processes as means to advance equitable interoperability.

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.

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.

Rapid ethnography and data mining approaches have been used individually to study clinical workflows, but have seldom been used together to overcome the limitations inherent in either type of method. For rapid ethnography, how reliable are the findings drawn from small samples? For data mining, how accurate are the discoveries drawn from automatic analysis of big data, when compared with observable data? This paper explores the combined use of rapid ethnography and process mining, aka ethno-mining, to study and compare metrics of a typical clinical documentation task, vital signs charting. The task was performed with different electronic health records (EHRs) used in three different hospital sites. The individual methods revealed substantial discrepancies in task duration between sites. Specifically, means of 159.6(78.55), 38.2(34.9), and 431.3(283.04) seconds were captured with rapid ethnography. When process mining was used, means of 518.6(3,808), 345.5(660.6), and 119.74(210.3) seconds were found. When ethno-mining was applied instead, outliers could be identified, explained and removed. Without outliers, mean task duration was similar between sites (78.1(66.7), 72.5(78.5), and 71.7(75) seconds). Results from this work suggest that integrating rapid ethnography and data mining into a single process may provide more meaningful results than a siloed approach when studying of workflow.

Abstract Objective It is difficult to assess self-management behaviors (SMBs) and incorporate them into a personalized self-care plan. We aimed to develop and apply SMB phenotyping algorithms from data collected by diabetes devices and a mobile health (mHealth) application to create patient-specific SMBs reports to guide individualized interventions. Follow-up interventions aimed to understand patient's reasoning behind discovered SMB choices. Methods This study deals with adults on continuous subcutaneous insulin infusion using a continuous glucose monitor (CGM) who self-tracked SMBs with an mHealth application for 1 month. Patient-generated data were quantified and an SMB report was designed and populated for each participant. A diabetes educator used the report to conduct personalized, data-driven educational interventions. Thematic analysis of the intervention was conducted. Results Twenty-two participants recorded 118 alcohol, 251 exercise, 2,661 meal events, and 1,900 photos. A patient-specific SMB report was created from this data and used to conduct the educational intervention. High variability of SMB was observed between patients. There was variability in the percentage of alcohol events accompanied by a blood glucose check, median 79% (38–100% range), and frequency of changing the bolus waveform, median 11 (7–95 range). Interventions confirmed variability of SMBs. Main emerging themes from thematic analysis were: challenges and barriers, motivators, current SMB techniques, and future plans to improve glycemic control. Conclusion The ability to quantify SMBs and understand patients' rationale may help improve diabetes self-care and related outcomes. This study describes our first steps in piloting a patient-specific diabetes educational intervention, as opposed to the current “one size fits all” approach.

Abstract 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.

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.

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.