Adaptive interventions for opioid prescription management and consumption monitoring.

OBJECTIVES: While opioid addiction, treatment, and recovery are receiving attention, not much has been done on adaptive interventions to prevent opioid use disorder (OUD). To address this, we identify opioid prescription and opioid consumption as promising targets for adaptive interventions and present a design framework. MATERIALS AND METHODS: Using the framework, we designed Smart Prescription Management (SPM) and Smart Consumption Monitoring (SCM) interventions. The interventions are evaluated using analytical modeling and secondary data on doctor shopping, opioid overdose, prescription quality, and cost components. RESULTS: SPM was most effective (30-90% improvement, for example, prescriptions reduced from 18 to 1.8 per patient) for extensive doctor shopping and reduced overdose events and mortality. Opioid adherence was improved and the likelihood of addiction declined (10-30%) as the response rate to SCM was increased. There is the potential for significant incentives ($2267-$3237) to be offered for addressing severe OUD. DISCUSSION: The framework and designed interventions adapt to changing needs and conditions of the patients to become an important part of global efforts in preventing OUD. To the best of our knowledge, this is the first paper on adaptive interventions for preventing OUD by addressing both prescription and consumption. CONCLUSION: SPM and SCM improved opioid prescription and consumption while reducing the risk of opioid addiction. These interventions will assist in better prescription decisions and in managing opioid consumption leading to desirable outcomes. The interventions can be extended to other substance use disorders and to study complex scenarios of prescription and nonprescription opioids in clinical studies.

Mobile Health Interventions and RCTs: Structured Taxonomy and Research Framework.

Mobile Health Interventions (MHIs) have addressed a range of healthcare challenges and have been evaluated using Randomized Controlled Trials (RCTs) to establish clinical effectiveness. Using PRISMA we conducted a systematic literature review of RCTs for MHIs and identified 70 studies which were analyzed and classified using Nickerson-Varshney-Muntermann (NVM) taxonomy. From the resultant iterations of the taxonomy, we extracted insights from the categorized studies. RCTs cover a wide range of health conditions including chronic diseases, general wellness, unhealthy practices, family planning, end-of-life, and post-transplant care. The MHIs that were utilized by the RCTs were varied as well, although most studies did not find significant differences between MHIs and usual care. The challenges for MHI-based RCTs include the use of technologies, delayed outcomes, patient recruitment, patient retention, and complex regulatory requirements. These variances can lead to a higher rate of Type I/Type II errors. Further considerations are the impact of infrastructure, contextual and cultural factors, and reductions in the technological relevancy of the intervention itself. Finally, due to the delayed effect of most outcomes, RCTs of insufficient duration are unable to measure significant, lasting improvements. Using the insights from seventy identified studies, we developed a classification of existing RCTs along with guidelines for MHI-based RCTs and a research framework for future RCTs. The framework offers opportunities for (a) personalization of MHIs, (b) use of richer technologies, and (c) emerging areas for RCTs.

A comprehensive mobile health intervention to prevent and manage the complexities of opioid use.

OBJECTIVES: The Opioid Use crisis continues to be an epidemic with multiple known influencing and interacting factors. With the need for suitable opioid use interventions, we present a conceptual design of an m-health intervention that addresses the various known interacting factors of opioid use and corresponding evidence-based practices. The visualization of the opioid use complexities is presented as the "Opioid Cube". METHODS: Following Stage 0 to Stage IA of the NIH Stage Model, we used guidelines and extant health intervention literature on opioid apps to inform the Opioid Intervention (O-INT) design. We present our design using system architecture, algorithms, and user interfaces to integrate multiple functions including decision support. We evaluate the proposed O-INT using analytical modeling. RESULTS: The conceptual design of O-INT supports the concept of collaborative care, by providing connections between the patient, healthcare professionals, and their family members. The evaluation of O-INT shows a preference for specific functions, such as overdose detection and potential for high system reliability with minimal side effects. The Opioid Cube provides a visualization of various opioid use states and their influencing and interacting factors. CONCLUSIONS: O-INT is a promising design with a holistic approach to manage opioid use and prevent and treat misuse. With several needed functionalities, O-INT design serves as a decision support system for patients, healthcare professionals, researchers, and policy makers. Together, O-INT and the Opioid Cube may serve as a foundation for development and adoption of highly effective m-health interventions for opioid use.

A proposal for a novel approach to reduce burdens of care for people with dementia: A hypothesis.

Cognitive impairment of dementia patients affects their ability to communicate and cooperate with care. Dementia patients need a specialized communication to increase their cooperation during daily care and a scale to measure cooperation with care. To improve communication, we created and applied a digital communication platform (DCP) in an observational study. To measure cooperation with care, we created and tested psychometric properties of the Cooperation with Care Scale-Revised (CWCS-R) in an institutional review board-approved 6-week prospective study. DCP intervention was associated with decreased agitation and use of psychotropic medications for nursing home (NH) dementia patients. CWCS-R is both a reliable and valid tool to measure cooperation with care in NH dementia patients. Innovations in communication with dementia patients and a scale to measure cooperation with care could reduce burdens of care and improve quality of life for patients, their family members, and staff. It may potentially help decrease cost of dementia care.

Review, Assess, Classify, and Evaluate (RACE): a framework for studying m-health apps and its application for opioid apps.

OBJECTIVE: The proliferation of m-health interventions has led to a growing research area of app analysis. We derived RACE (Review, Assess, Classify, and Evaluate) framework through the integration of existing methodologies for the purpose of analyzing m-health apps, and applied it to study opioid apps. MATERIALS AND METHODS: The 3-step RACE framework integrates established methods and evidence-based criteria used in a successive manner to identify and analyze m-health apps: the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, inter-rater reliability analysis, and Nickerson-Varshney-Muntermann taxonomy. RESULTS: Using RACE, 153 opioid apps were identified, assessed, and classified leading to dimensions of Target Audience, Key Function, Operation, Security & Privacy, and Impact, with Cohen's kappa < 1.0 suggesting subjectivity in app narrative assessments. The most common functions were education (24%), prescription (16%), reminder-monitoring-support (13%), and treatment & recovery (37%). A majority are passive apps (56%). The target audience are patients (49%), healthcare professionals (39%), and others (12%). Security & Privacy is evident in 84% apps. DISCUSSION: Applying the 3-step RACE framework revealed patterns and gaps in opioid apps leading to systematization of knowledge. Lessons learned can be applied to the study of m-health apps for other health conditions. CONCLUSION: With over 350 000 existing and emerging m-health apps, RACE shows promise as a robust and replicable framework for analyzing m-health apps for specific health conditions. Future research can utilize the RACE framework toward understanding the dimensions and characteristics of existing m-health apps to inform best practices for collaborative, connected and continued care.

What is already known?• M-health apps are increasingly utilized to address overall health and well-being.• M-health apps are currently studied using a wide range of methodologies.• There is a need for a framework to guide systematic investigation of m-health apps.What did we find?• Three-step RACE framework is designed for systematic investigation of m-health apps.• By applying RACE to opioid apps, we derived numerous insights for practice and research.• Lessons learned from this implementation can be applied to the study of m-health apps for other health conditions.

An analytical model to evaluate reminders for medication adherence.

OBJECTIVES: Several interventions have been proposed to improve medication adherence including those using reminders. The performance of reminders, including effectiveness and side effects, varies widely in different settings. We must study this for improving decision making on how, when, and where to use what type of reminders. METHODS: Analytical modeling is an effective and low-cost method to derive preliminary or intermediate results and insights for further study of interventions for medication adherence. We developed an analytical model that can be used to evaluate the performance of reminders in various settings, including effectiveness, side effects, and healthcare cost savings for medication adherence. RESULTS: Context-aware reminders perform better than simple reminders for willing patients even when they completely rely on reminders for taking their doses. Simple reminders lead to more side effects than context-aware reminders. Further, context-aware reminders generate more healthcare savings without side effects and a comparable cost of the intervention. The results contribute to an improved understanding of reminders and are used to derive a set of guidelines for patients, healthcare professionals, decision-makers, and mobile app developers. CONCLUSIONS: The proposed model is a low cost and effective tool to derive results and insights for the use of reminders in different settings to improve medication adherence. Therefore, the model can be utilized as a decision-making tool for deciding whether to pursue an RCT on healthcare interventions. The analytical model can be extended for complex scenarios of multiple interdependent medications, adaptation with patients' condition and behavior, and composite interventions.

Medication adherence: A method for designing context-aware reminders.

OBJECTIVES: Several interventions have been proposed to improve medication adherence including those using reminders. Context-aware reminders can be effective because they operate when the dose is due, has not been taken, and is still safe to take. Although very promising, we find that there is no method to design context-aware reminders. To address these, we focus on proposing a method to design context-aware reminders. METHODS: We conducted a systematic review of context-aware reminders for medication adherence using PRISMA approach. The analysis of literature leads to several interesting observations including the need for a method to design context-aware reminders. In this study, we present Method to Design Context-Aware Reminders (MDCAR) that can also meet special requirements. We used domain experts reasoning to evaluate the designed Context-Aware Reminders for Medication Adherence (CARS-MA). Further, we used analytical model to evaluate reliability, side effects, and cost of intervention. RESULTS: This is the first paper that addresses "how to" design context-aware reminders. The proposed design method can lead to range of context-aware reminders including CARS-MA. The verification, validation, and evaluation of CARS-MA indicate that the context-aware reminders perform better than simple reminders in improving medication adherence. CONCLUSIONS: The proposed method for context-aware reminders will help healthcare professionals and researchers to implement and select a suitable intervention to improve medication adherence. Further, it can lead to decision support systems for patients, healthcare professionals, researchers and policy makers for medication adherence. The design method can be extended for complex scenarios of multiple medications, persistent-reminders, and composite interventions.

AlzhaTV: A nonpharmacological intervention to manage behavioral and psychological symptoms in nursing home dementia patients.

BACKGROUND: Currently, 5 million seniors who reside in the community or assisted living or nursing facilities are affected by Alzheimer's disease and other neurocognitive disorders. They experience various psychiatric symptoms. Also, their family members are stressed from caregiving, and may feel guilty for not doing more. METHODS: We created an innovative nonpharmacological intervention, AlzhaTV, to decrease behavioral and psychological symptoms associated with neurocognitive disorders. We offered AlzhaTV to 9 nursing home patients. RESULTS: In our retrospective examination of this case series, we found AlzhaTV was effective in reducing behavioral and psychological symptoms in patients with neurocognitive disorders. We achieved either dose reductions, discontinuation, or avoided initiating the use of antipsychotic medications and benzodiazepines in nearly all patients. CONCLUSIONS: AlzhaTV may help decrease behavioral and psychological symptoms of dementia and decrease or avoid the use of antipsychotic medications or benzodiazepines. It may help in reducing the overall cost of health care in patients with dementia or neurocognitive disorders. AlzhaTV helps patients stay connected to their families, friends, and their past while bringing them joy in the present. It also helps families and caregivers feel more connected and less helpless.

Medication adherence for patients with mental illness.

Medication adherence has been studied for some time; however most research has focused on able-bodied patients or the elderly living independently. What has not been studied nearly as much is medication adherence for people with psychiatric or mental illnesses. In this paper, we present a framework that includes the specific challenges in medication adherence for patients with mental illness, algorithms and protocols for evaluating adherence, and some on-going work in developing effective solutions. The architectural framework and associated algorithms leverage the context-aware computing capabilities available on many mobile devices. The system is designed to be able to collect and offer situation-aware information on medication use and adherence for healthcare professionals and other designated persons.

A framework for wireless monitoring of mental health conditions.

Mental health management is fast becoming a major challenge worldwide as the incidence of mental illness has been increasing. It is affecting the quality of life as well as job productivity for a large number of people. Just like physical illnesses, people with mental illnesses can be monitored for a range of conditions and provided medical care as and when necessary. In this paper, we present an IT-enabled framework to support mental health monitoring. This includes comprehensive monitoring of patients for symptoms, behavior, and medication compliance. We utilize context-awareness as a way to develop a system for mental health monitoring. Several examples of future mental health monitoring are also presented.

Safeguarding patient privacy in electronic healthcare in the USA: the legal view.

The conflict between the sweeping power of technology to access and assemble personal information and the ongoing concern about our privacy and security is ever increasing. While we gradually need higher electronic access to medical information, issues relating to patient privacy and reducing vulnerability to security breaches surmount. In this paper, we take a legal perspective and examine the existing patchwork of laws and obligations governing health information in the USA. The study finds that as Electronic Medical Records (EMRs) increase in scope and dissemination, privacy protections gradually decrease due to the shortcomings in the legal system. The contributions of this paper are (1) an overview of the legal EMR issues in the USA, and (2) the identification of the unresolved legal issues and how these will escalate when health information is transmitted over wireless networks. More specifically, the paper discusses federal and state government regulations such as the Electronic Communications Privacy Act, the Health Insurance Portability and Accountability Act (HIPAA) and judicial intervention. Based on the legal overview, the unresolved challenges are identified and suggestions for future research are included.

A wireless ECG monitoring system for pervasive healthcare.

This paper presents an architectural framework of a system utilising mobile technologies to enable continuous, wireless, electrocardiogram (ECG) monitoring of cardiac patients. The proposed system has the potential to improve patients' quality of life by allowing them to move around freely while undergoing continuous heart monitoring and to reduce healthcare costs associated with prolonged hospitalisation, treatment and monitoring.

Patient monitoring using infrastructure-oriented wireless LANs.

There is considerable interest in using wireless and mobile technologies in patient monitoring in diverse environments including hospitals and nursing homes. However, there has not been much work in determining the requirements of patient monitoring and satisfying these requirements using infrastructure-oriented wireless networks. In this paper, we derive several requirements of patient monitoring and show how infrastructure-oriented wireless LANs, such as versions of IEEE 802.11, can be used to support patient monitoring in diverse environments.