BackgroundAtrial fibrillation (AFib) is the most common form of heart arrhythmia and a potent risk factor for stroke. Nonvitamin K antagonist oral anticoagulants (NOACs) are routinely prescribed to manage AFib stroke risk; however, nonadherence to treatment is a concern. Additional tools that support self-care and medication adherence may benefit patients with AFib.ObjectiveThe aim of this study was to evaluate the perceived usability and usefulness of a mobile app designed to support self-care and treatment adherence for AFib patients who are prescribed NOACs.MethodsA mobile app to support AFib patients was previously developed based on early stage interview and usability test data from clinicians and patients. An exploratory pilot study consisting of naturalistic app use, surveys, and semistructured interviews was then conducted to examine patients’ perceptions and everyday use of the app.ResultsA total of 12 individuals with an existing diagnosis of nonvalvular AFib completed the 4-week study. The average age of participants was 59 years. All participants somewhat or strongly agreed that the app was easy to use, and 92% (11/12) reported being satisfied or very satisfied with the app. Participant feedback identified changes that may improve app usability and usefulness for patients with AFib. Areas of usability improvement were organized by three themes: app navigation, clarity of app instructions and design intent, and software bugs. Perceptions of app usefulness were grouped by three key variables: core needs of the patient segment, patient workflow while managing AFib, and the app’s ability to support the patient’s evolving needs.ConclusionsThe results of this study suggest that mobile tools that target self-care and treatment adherence may be helpful to AFib patients, particularly those who are newly diagnosed. Additionally, participant feedback provided insight into the varied needs and health experiences of AFib patients, which may improve the design and targeting of the intervention. Pilot studies that qualitatively examine patient perceptions of usability and usefulness are a valuable and often underutilized method for assessing the real-world acceptability of an intervention. Additional research evaluating the AFib Connect mobile app over a longer period, and including a larger, more diverse sample of AFib patients, will be helpful for understanding whether the app is perceived more broadly to be useful and effective in supporting patient self-care and medication adherence.
Background: Overactive bladder (OAB), defined by urinary urgency with or without urge urinary incontinence (UI), usually with frequency and nocturia, can significantly impact patient's quality of life. Tracking symptoms is an important part of OAB management and has been shown to assist in enhancing patient interaction with health care providers (HCP) when discussing solutions for symptom management. Objective:The primary goal of this study was to assess the usability and acceptability of an Android smartphone mobile app designed to help participants learn about OAB symptom management through tracking and self-management. Secondarily, we also assessed engagement with the app over the three-month study period.
Background: Despite widespread adoption and demonstrated value in a range of industries, machine learning predictive algorithms are yet to be routinely used in frontline medical care. Significant health system and industry-based resources are allocated towards validating and refining predictive algorithms for a range of applications to ensure accuracy and reliability. For these algorithms to be useful and useable, further work is required to understand how and why they might fit into, and augment existing clinical workflows.
BACKGROUND Despite widespread adoption and demonstrated value in a range of industries, machine learning predictive algorithms are yet to be routinely used in frontline medical care. Significant health system and industry-based resources are allocated towards validating and refining predictive algorithms for a range of applications to ensure accuracy and reliability. For these algorithms to be useful and useable, further work is required to understand how and why they might fit into, and augment existing clinical workflows. OBJECTIVE This qualitative study assessed the value and usability of a novel machine learning technology to predict and explain the risk of 30-day hospital readmission in patients with heart failure (HF). It involved exploring opportunities for integration of the technology within existing clinical workflows, and investigating key roles that use current readmission risk scores and may use future scores. METHODS Semi-structured interviews (n=27) and targeted observations (n=3) were carried out with key stakeholders, including physicians, nurses, hospital administration, and non-clinical support staff. Participants were recruited from cardiology and general medicine units at an academic medical center within the Partners HealthCare system. Data was analyzed via inductive thematic and workflow analysis. Findings were validated via member checking across limited key roles (n=3). RESULTS Results highlighted a number of factors that were deemed necessary by staff for successful integration of a risk prediction tool into existing clinical workflow. These included, but were not limited to the following. Staff clearly stated that any new tool must be easily accessible from within the electronic health record, which dictates the majority of existing clinical workflow. Staff emphasized that information should be consistently accurate and that any display must be digestible efficiently, intuitively and quickly (ie, within <5 seconds). Additionally, staff discussed that outputs of the risk prediction tool must match their clinical intuition, experience and interactions with the patient. To be truly valuable, the tool must also provide added value over and above these factors: some staff indicated that provision of role-specific and actionable next steps based on the system output would provide novel value to their daily work. Using these considerations, a number of role groups were identified as potentially able to derive value from the proposed risk prediction tool, including case managers, attending RNs, responding clinicians, hospital administration staff, nursing directors and attending physicians. Acceptability and value varied by role, specialization and clinical context. For example, cardiology-trained clinicians reported feeling well-versed in providing good clinical care and minimizing preventable readmissions, and thus saw less value in the tool. General medicine staff, however, indicated that a HF-specific tool may be impractical for their day-to-day work given the range of clinical presentations seen by them. CONCLUSIONS Findings resonate with existing literature around successful implementation and adoption of technologies in health care. Frontline clinicians are incredibly discerning around proposed changes to their existing workflow. Many HF readmission risk tools and initiatives have been trialled with mixed success; frontline staff demonstrated fatigue around piloting new initiatives. However, given the right conditions, staff reported some perceived value in machine learning-based tools to improve their daily work.
BACKGROUND Atrial fibrillation (AFib) is the most common form of heart arrhythmia and a potent risk factor for stroke. Nonvitamin K antagonist oral anticoagulants (NOACs) are routinely prescribed to manage AFib stroke risk; however, nonadherence to treatment is a concern. Additional tools that support self-care and medication adherence may benefit patients with AFib. OBJECTIVE The aim of this study was to evaluate the perceived usability and usefulness of a mobile app designed to support self-care and treatment adherence for AFib patients who are prescribed NOACs. METHODS A mobile app to support AFib patients was previously developed based on early stage interview and usability test data from clinicians and patients. An exploratory pilot study consisting of naturalistic app use, surveys, and semistructured interviews was then conducted to examine patients’ perceptions and everyday use of the app. RESULTS A total of 12 individuals with an existing diagnosis of nonvalvular AFib completed the 4-week study. The average age of participants was 59 years. All participants somewhat or strongly agreed that the app was easy to use, and 92% (11/12) reported being satisfied or very satisfied with the app. Participant feedback identified changes that may improve app usability and usefulness for patients with AFib. Areas of usability improvement were organized by three themes: app navigation, clarity of app instructions and design intent, and software bugs. Perceptions of app usefulness were grouped by three key variables: core needs of the patient segment, patient workflow while managing AFib, and the app’s ability to support the patient’s evolving needs. CONCLUSIONS The results of this study suggest that mobile tools that target self-care and treatment adherence may be helpful to AFib patients, particularly those who are newly diagnosed. Additionally, participant feedback provided insight into the varied needs and health experiences of AFib patients, which may improve the design and targeting of the intervention. Pilot studies that qualitatively examine patient perceptions of usability and usefulness are a valuable and often underutilized method for assessing the real-world acceptability of an intervention. Additional research evaluating the AFib Connect mobile app over a longer period, and including a larger, more diverse sample of AFib patients, will be helpful for understanding whether the app is perceived more broadly to be useful and effective in supporting patient self-care and medication adherence.
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