Designing a multifaceted telehealth intervention for a rural population using a model for developing complex interventions in nursing

Telehealth interventions offer an evidenced-based approach to providing cost-effective care, education, and timely communication at a distance. Yet, despite its widespread use, telehealth has not reached full potential, especially in rural areas, due to the complex process of designing and implementing telehealth programs. The objective of this paper is to explore the use of a theory-based approach, the Model for Developing Complex Interventions in Nursing, to design a pilot telehealth intervention program for a rural population with multiple chronic conditions.

In order to develop a robust, evidenced based intervention that suits the needs of the community, stakeholders, and healthcare agencies involved, a design team comprised of state representatives, telehealth experts, and patient advocates was convened. Each design team meeting was guided by major model constructs (i.e., problem identification, defining the target population and objectives, measurement theory selection, building and planning the intervention protocol). Overarching the process was a review of the literature to ensure that the developed intervention was congruent with evidence-based practice and underlying the entire process was scope of practice considerations.

Ten design team meetings were held over a six-month period. An adaptive pilot intervention targeting home and community-based Medicaid Waiver Program participants in a rural environment with a primary objective of preventing re-institutionalizations was developed and accepted for implementation. To promote intervention effectiveness, asynchronous (i.e., remote patient monitoring) and synchronous (i.e., nursing assessment of pain and mental health and care coordination) telehealth approaches were selected to address the multiple comorbidities of the target population. An economic evaluation plan was developed and included in the pilot program to assess intervention cost efficiency.

The Model for Developing Complex Interventions in Nursing provided a simple, structured process for designing a multifaceted telehealth intervention to minimize re-institutionalization of participants with multiple chronic conditions. This structured process may promote efficient development of other complex telehealth interventions in time and resource constrained settings. This paper provides detailed examples of how the model was operationalized.

The full article can be downloaded below.  

Telehealth: Is It Only for the Rural Areas? A Review of Its Wider Use

Telehealth (also known as telemedicine, digital medicine) is a relatively new concept in healthcare provision, which is perceived as a useful mode of managing patients at remote locations. As this technology evolves, the need arises to explore telehealth as part of holistic care. Much literature on telehealth is available but most are specialty-based or focus on one or two aspects of care provision. This article focuses on the four main building blocks of telehealth: perception by healthcare staff, perception by patients, quality of Internet and technology, and cost effectiveness based on 2 years of published literature.

The full article can be downloaded below.  

Predictions for Telehealth in 2020: Will This be the Takeoff Year?

In order to place predictions for telehealth in proper perspective, consider that like all new industries, telehealth is characterized by disparate ideas developed by different people, often for the same medical specialty and/or medicine-related activity. Over time, these ideas must coalesce to provide growth and economies of scale. This occurs at multiple levels. One need only look at the history of banking and finance for an analogous situation that is highlighted by innovation, mergers, and integration. In healthcare, the overall experience with an array of disparate health information exchanges has been less than satisfactory for most observers. While the trend towards consolidation has begun in the telehealth arena, it is at an early stage. With this in mind, our invited experts looked into the future from their shared and unique perspectives to offer their view on the next big thing(s) in telehealth in 2020.​

The full article can be downloaded below.  

AI, 5G, and IoT can help deliver the promise of precision medicine

When my son was a toddler, he went to his pediatrician for a routine CAT scan. Easy stuff. Just a little shot to subdue him for a few minutes. He’d be awake and finished in a jiffy.

Except my son didn’t wake up. He lay there on the clinic table, unresponsive, his vitals slowly falling. The clinic had no ability to diagnose his condition. Five minutes later, he was in the back of an ambulance. My wife and I were powerless to do anything but look on, frantic with worry for our boy’s life.

It turned out that he’d had a bad reaction to a common hydrochloride sedative. Once that was figured out, doctors quickly brought him back around, and he was fine.

But what if, through groundbreaking mixtures of compute, database, and AI technologies, a quick round of analyses on his blood and genome could have revealed his potential for such a reaction before it became a critical issue?

The full VentureBeat article can be viewed at this link.  

Measures of Effectiveness, Efficiency, and Quality of Telemedicine in the Management of Alcohol Abuse, Addiction, and Rehabilitation: Systematic Review

More than 18 million Americans are currently suffering from alcohol use disorder (AUD): a compulsive behavior of alcohol use as a result of a chronic, relapsing brain disease. With alcohol-related injuries being one of the leading causes of preventable deaths, there is a dire need to find ways to assist those suffering from alcohol dependence. There still exists a gap in knowledge as to the potential of telemedicine in improving health outcomes for those patients suffering from AUD.

The purpose of this systematic review was to evaluate the measures of effectiveness, efficiency, and quality that result from the utilization of telemedicine in the management of alcohol abuse, addiction, and rehabilitation.

This review was conducted utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The articles used in this analysis were gathered using keywords inclusive of both telemedicine and alcohol abuse, which were then searched in the Cumulative Index to Nursing and Allied Health Literature, Cochrane, and MEDLINE (PubMed) databases. A total of 22 articles were chosen for analysis.

The results indicated that telemedicine reduced alcohol consumption. Other common outcomes included reduced depression (4/35, 11%), increased patient satisfaction (3/35, 9%), increase in accessibility (3/35, 9%), increased quality of life (2/35, 6%), and decreased cost (1/35, 3%). Interventions included mobile health (11/22, 50%), electronic health (6/22, 27%), telephone (3/33, 14%), and 2-way video (2/22, 9%). Studies were conducted in 3 regions: the United States (13/22, 59%), the European Union (8/22, 36%), and Australia (1/22, 5%).

Telemedicine was found to be an effective tool in reducing alcohol consumption and increasing patients’ accessibility to health care services or health providers. The group of articles for analysis suggested that telemedicine may be effective in reducing health care costs and improving the patient’s quality of life. Although telemedicine shows promise as an effective way to manage alcohol-related disorders, it should be further investigated before implementation.

The full article can be downloaded below.  

Precision Medicine and its Imprecise History

The origins of precision medicine are not precisely known. That’s due in no small part to ongoing confusion about what precision medicine is. Confusion over the boundaries of a new scientific paradigm shouldn’t surprise anyone, but even the basic terminology isn’t clear in this case. What’s the relationship of precision medicine to personalized medicine? What distinction, if any, is being made with evidence-based medicine? Haven’t clinicians always striven to provide precise recommendations? As a systematic survey recently concluded, whether called precision medicine or personalized medicine, the phrase has come to refer to the way personal data and biomarkers—particularly genetic biomarkers—might be used to tailor treatments for individual patients (Schleidgen, Klingler, Bertram, Rogowski, & Marckmann, 2013). Nothing in this definition signals what’s new about precision medicine, however—genetic information and other patient data have long been used to advance medical research and improve treatments. Only by delving deeper into what precision medicine has meant over time might we understand what’s actually new about the age-old attempt to move from individual and seemingly idiosyncratic patient outcomes to generalizable knowledge about health and disease, and the crucial role statisticians have historically played in that process.

The full article can be downloaded below.  

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches

A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an indispensable foundation of medical practices by offering the accurate medication with the accurate dose at the accurate time to patients with psychiatric disorders. In light of the latest advancements in artificial intelligence and machine learning techniques, numerous biomarkers and genetic loci associated with psychiatric diseases and relevant treatments are being discovered in precision psychiatry research by employing neuroimaging and multi-omics. In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, together with multi-omics and neuroimaging data. Firstly, we review precision psychiatry and pharmacogenomics studies that leverage various artificial intelligence and machine learning techniques to assess treatment prediction, prognosis prediction, diagnosis prediction, and the detection of potential biomarkers. In addition, we describe potential biomarkers and genetic loci that have been discovered to be associated with psychiatric diseases and relevant treatments. Moreover, we outline the limitations in regard to the previous precision psychiatry and pharmacogenomics studies. Finally, we present a discussion of directions and challenges for future research.

The full article can be downloaded below.  

ZIP Code v. Genetic Code: How Health Plans Can Use Technology to Address the Social Determinants of Health

When it comes to the state of our health in the U.S., the playing field is anything but level.  

Study after study has shown that life circumstances, such as access to adequate food, education and healthcare, have a bigger impact on our health than our genetic makeup.

In other words, our healthcare destiny may depend more on our ZIP code than our genetic code.

The full Surescripts article can be viewed at this link.  

Artificial Intelligence Is Not Ready For The Intricacies Of Radiology​

Radiology is one of the most essential fields in clinical medicine. Experts in this field are specialists in deciphering and diagnosing disease based on various imaging modalities, ranging from ultrasound, magnetic resonance imaging (MRI), computerized tomography (CT), and x-rays. Studies have shown that the use of radiology in clinical practice has exponentially grown over the years: at the Mayo Clinic, between the years 1999 to 2010, use of CT scans increased by 68%, MRI use increased by 85%, and overall use of imaging modalities for diagnostic purposes increased by 75%, all numbers that have likely continued to rise, and indicate the sheer demand and growth of this robust field.

A unique proposal that has become prominent over the last few years to help alleviate this increased demand is the introduction of artificial intelligence (AI) technology into this field. Simply put, the premise of AI as an addition to the practice of radiology is straightforward, and has been envisioned in two main ways: 1) a system that can be programmed with pre-defined criteria and algorithms by expert radiologists, which can then be applied to new, straightforward clinical situations, or 2) deep learning methods, where the AI system relies on complex machine learning and uses neural-type networks to learn patterns via large volumes of data and previous encounters; this can then be used to interpret even the most complicated and abstract images.

However, while much of the theoretical basis for AI in the practice of radiology is extremely exciting, the reality is that the field has not yet fully embraced it. The most significant issue is that the technology simply isn’t ready, as many of the existing systems have not yet been matured to compute and manage larger data sets or work in more general practice and patient settings, and thus, are not able to perform as promised. Other issues exist on the ethical aspects of AI. Given the sheer volume of data required to both train and perfect these systems, as well as the immense data collection that these systems will engage in once fully mainstream, key stakeholders are raising fair concerns and the call for strict ethical standards to be put into place, simultaneous to the technological development of these systems.

The full Forbes article can be viewed at this link.  

Precision global health for real-time action

Precision global health, augmented with artificial intelligence, has the potential to address transnational problems (eg, outbreaks of emerging infectious diseases, diabetes, addictions, ageing, or mental health) and deliver targeted and effective interventions through integrated approaches which combine life sciences, social sciences, and data sciences with public support. More than half the global population are connected to the Internet, mainly through mobile phones, and several countries in sub-Saharan Africa are leading the annual growth of active mobile social users with over 17% in 2018. Thus, the role of local populations and civil society is more important than ever, to identify challenges and work together to address some of the most pressing global health issues and sustainable development.

The full commentary can be downloaded below.