Self-monitoring Practices, Attitudes, and Needs of Individuals with Bipolar Disorder: Implications for the Design of Technologies to Manage Mental Health

Objective To understand self-monitoring strategies used independently of clinical treatment by individuals with bipolar disorder (BD), in order to recommend technology design principles to support mental health management. Materials and Methods Participants with BD (N = 552) were recruited through the Depression and Bipolar Support Alliance, the International Bipolar Foundation, and WeSearchTogether.org to complete a survey of closed- and open-ended questions. In this study, we focus on descriptive results and qualitative analyses. Results Individuals reported primarily self-monitoring items related to their bipolar disorder (mood, sleep, finances, exercise, and social interactions), with an increasing trend towards the use of digital tracking methods observed. Most participants reported having positive experiences with technology-based tracking because it enables self-reflection and agency regarding health management and also enhances lines of communication with treatment teams. Reported challenges stem from poor usability or difficulty interpreting self-tracked data. Discussion Two major implications for technology-based self-monitoring emerged from our results. First, technologies can be designed to be more condition-oriented, intuitive, and proactive. Second, more automated forms of digital symptom tracking and intervention are desired, and our results suggest the feasibility of detecting and predicting emotional states from patterns of technology usage. However, we also uncovered tension points, namely that technology designed to support mental health can also be a disruptor. Conclusion This study provides increased understanding of self-monitoring practices, attitudes, and needs of individuals with bipolar disorder. This knowledge bears implications for clinical researchers and practitioners seeking insight into how individuals independently self-manage their condition as well as for researchers designing monitoring technologies to support mental health management

Medical device technologies: Who is the user?

A myriad of medical devices deployed by many users play an essential role in healthcare, and they, and their users, need to be defined, classified and coded effectively. This study provides definitions of terms frequently employed to describe the users of medical device technologies (MDT) as well as a classification of such users. Devices are widely used, developed and assessed by many others than clinicians. Thus, users of medical devices need to be classified in various relevant ways, such as primary and secondary users; user groups such as healthcare professionals, patients, carers, persons with disabilities, those with special needs, as well as professionals allied with healthcare. Proper definition and classification of MDT users is particularly important for integrating the users’ perspectives in the process of MDT development and assessment, as well as in relation to the regulatory, health and safety, and insurance perspectives concerning MDT

Evaluating the Impact of a New Interactive Digital Solution for Collecting Care Quality In-formation for Residential Homes

Collecting and analysing timely and accurate information about the quality of care that is delivered to older people in residential homes is a challenge. Most current approaches to collecting this in-formation are manual, and add time, cost and human error to them. Interactive digital technologies have the potential to reduce the time consumed, cost and errors in these processes, which in turn can support resolution of an important social challenge. However, designing these interactions can be problematic. In this paper we report the use of a new interactive digital solution that was de-signed to improve the completeness and timeliness of care quality data that was collected from 233 residential nursing and care homes for older people in and around London. Use of the digital solution led to a significant increase in the numbers of residential homes that submitted the cor-rect care quality information by each quarterly deadline. However, qualitative evidence from the residential homes revealed low-level usability problems experienced by care staff that might be in-dicative of continuing usability issues in the care sector. The paper ends with lessons drawn to improve uptake of digital technologies in residential care

Deprescribing of Medicines in Care Homes - A Five-Year Evaluation of Primary Care Pharmacist Practices

(1) Background: This project evaluates the outcomes of a novel pharmacy-led model of deprescribing unnecessary medications for care home patients. A feasibility study was conducted in 2015 to explore exposure to inappropriate polypharmacy in patients residing in care homes over a one-year timescale. The aim of this study was to present the results of this ongoing service evaluation over a five-year period. (2) Methods: Data collection and risk assessment tools developed during the feasibility study were used to measure the prevalence, nature, and impact of deprescribing interventions by primary care pharmacists over a five-year period. A random sample of approximately 5% of safety interventions were screened twice yearly by the pharmacist leads as part of standard practice. (3) Results: Over a period of five years there were 23,955 interventions (mean 2.3 per patient) reported from the 10,405 patient reviews undertaken. Deprescribing accounted for 53% of total estimated financial drug savings, equating to £431,493; and 16.1% of all interventions were related to safety. (4) Conclusions: Medication reviews in care homes, undertaken by primary care pharmacists who are linked to GP practices, generate a wide range of interventions commonly involving deprescribing, which contributes significantly to the continuous optimisation of the prescribing and monitoring of medicines

DATA ANALYTICS FOR CRISIS MANAGEMENT: A CASE STUDY OF SHARING ECONOMY SERVICES IN THE COVID-19 PANDEMIC

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data

Spreadsheets and Sarbanes-Oxley: Regulations, Risks, and Control Frameworks

The Sarbanes-Oxley Act of 2002 (SOX) forced corporations to examine their spreadsheet use in financial reporting. Corporations do not like what they are seeing. Surveys conducted in response to SOX show that spreadsheets are used widely in corporate financial reporting. Spreadsheet error research, in turn, shows that nearly all large spreadsheets contain multiple errors and that errors of material size are quite common. The first round of Sarbanes-Oxley assessments confirmed concerns about spreadsheet accuracy. Another concern is spreadsheet fraud, which also exists in practice and is easy to perpetrate. Unfortunately, few organizations maintain effective controls to deal with either errors or fraud. This paper examines spreadsheet risks for Sarbanes-Oxley (and other regulations) and discusses how general and IT-specific control frameworks can be used to address the control risks created by spreadsheets

Data Analytics for Crisis Management: A Case Study of Sharing Economy Services in the COVID-19 Pandemic

This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data

Program Evaluation of Patient Safety and Risk Mitigation Educational Interventions for Medical Errors in Primary Care Settings by Patricia Rose Gould

The Institute of Medicine reported in 2016 that medical errors are the 3rd leading cause of death in the United States. In the primary care setting, frequency and severity are unknown. Medical error research is limited related to evaluation of interventions conducted by medical professional liability (MPL) companies of risk mitigation strategies. The purpose of this program evaluation was to determine the impact of multifaceted patient safety and risk mitigation educational interventions conducted in primary care settings on patient safety, reporting, and liability. The program evaluation employed a retrospective secondary analysis of actuarial data from a MPL carrier\u27s educational interventions of 10 randomly selected Midwestern primary care clinics. Actuarial data consisted of nonparametric testing of categorical data to examine means and averages on previously conducted assessments, questionnaire responses, occurrence reports, and claims frequency. Outcome analysis of actuarial data revealed that the study population meet assessment criteria. Further actuarial analysis suggested that actual medical error occurrence reporting was inconsistent. Retrospective analysis of questionnaire responses demonstrated that despite educational interventions, more research is warranted to examine medical error understanding, language, and prevention in the primary care setting. Outcome evaluation conclusions suggest that healthcare providers are in a pivotal position to engage in proactive strategies in the primary care settings to mitigate risk; improve patient safety; and increase overall individual, organizational, and community understanding of medical error prevention. Unrecognized medical errors create a burden on society. Risk mitigation strategies of medical errors promote positive social change through improved community health