THNN - A Neural Network Model for Telehealth Data Incompleteness Prediction.

In modern-day medical practices, practitioners and physicians are adapting to new technologies and utilizing new methods of communication with patients. Telemedicine, or telehealth, is one of the newest innovations in medical technology, enabling practitioners to communicate with their patients over the phone, video conferencing, or chat. However, clinical data and sentiments/attitudes are often not reflected in the practitioner's analysis and diagnosis of the patients they serve. As a solution to the problem of data incompleteness in telehealth, THNN allows medical practices to accommodate for possible missing or incomplete data and provide a greater quality of care overall. Through an ensemble of Natural Language Processing (NLP) and AI-enabled systems, THNN produces sentiment and incompleteness mapping to provide seamless results.Clinical relevance- The method presented utilizes telehealth natural language data to process the sentiments of patients and the incompleteness found in the conversations, increasing the possibility of improved healthcare outcomes.

PC-LSTM: Ontology-based Long Short-Term Memory State Model for Data Incompleteness Prediction.

Medical practices are engaged and motivated by new technologies and methods to enhance patient care as efficiently as possible. These new methods and technologies give way for medical practices and clinicians to have the insight, comprehension, and projections to develop better decisions and overall levels of care. In this paper, we propose a model, PatientCentered-LSTM (or PC-LSTM), using the states of the LSTM model to produce a novel, ontology-based state system for data incompleteness. The overall architecture and system design are based around utilizing the hidden and cell states of the LSTM model to produce a network of states for each of the corresponding hierarchies in an Electronic Health Record (EHR) system. The resulting methodology allows for an accurate and precise approach to predicting data incompleteness in electronic health records. Clinical relevance- The method presented uses the hierarchical nature of electronic health record systems to positively influence the analysis of its data completeness; thereby, increasing the possibility of improved healthcare outcomes.

Key observations in terms of management of electronic health records from a mHealth perspective.

The article is a narrative review that briefly describes some of the recent advances in healthcare data management that will have positive effect on mHealth. The advances described in this article are in fact innovation introduced by the author to the field of data management with respect to electronic health records. The research delineated is transdisciplinary in nature and will potentially have positive impact on healthcare outcomes. Also, the article illustrates the necessity for an out of the box thinking approach to improve mHealth while discussing the current impending issues related to data incompleteness of electronic health records and the much-needed decision support systems for mHealth. It is to be noted that most of the electronic health records are now accessed by patients through mobile devices. These mobile devices will run as clients while much of the heavy computing is performed using servers. Here it is important to discuss some of the important technologies and methods used for decision making. The article attempts to present a discussion on how this myriad of intertwining technologies support this decision making with respect to electronic health records. More importantly it is these processes that assist in decision making and efficiency for both mHealth users and providers. In this respect, the article first provides insights on the complexities of decision making involved with electronic health records. This is followed by a discussion on the problem of data incompleteness of electronic health records. Finally, the author provides some insights into the gravity of the problem of data incompleteness in terms of revenue loss/gain for healthcare providers.

A brief analysis of challenges in implementing telehealth in a rural setting.

Available literature clearly indicates that successful implementation of telemedicine and telehealth has been a challenge. This challenge is further amplified if the reader must consider this implementation in a rural setting. In this article the authors discuss some of the key challenges associated with this implementation. The article sheds light on a few key studies and commentaries associated with the use of telehealth in a rural setting. Critically, the article summarizes these critical findings; thereby, informing the reader on the bottlenecks associated with the use of telehealth in a geographically rural area. Also, briefly summarizing the existing body of knowledge on this topic of study. Furthermore, a case study briefly narrating the use of telemedicine and telehealth for rural Oklahoma is presented to advance our understanding of the situation in this field. Some of the critical details associated with this case study provides insights on some of the key challenges associated with the implementation of telehealth in a rural setting. This case study also provides insights on key workflow processes that helped the implementation of telehealth. Finally, the authors summarize the key challenges in the implementation of telehealth based on their perspective. Here it is important to inform the readers that this article is not a scientific review on the topic instead presents an opinion backed by facts and existing literature. Overall, the authors present a key discussion that can lead to advances in research and required innovations that might help in providing easy access to healthcare through telehealth.

Challenges in implementing mHealth interventions: a technical perspective.

Over the years, the healthcare community has witnessed many improvements in methods and technologies used in healthcare delivery, including mHealth as an emerging area of healthcare applications to improve access to health services. However, challenges involved in implementing mHealth to optimal advantage do exist. In this article, we identify some of the most important challenges and propose feasible solutions.

C-PHIS: a concept map-based knowledge base framework to develop personal health information systems.

In this paper we describe the development of a Personal Health Information System using a knowledge base developed using concept maps. Here we describe a solution for providing the critical need to develop an information capturing system that helps domain experts in developing a graphical representation of the aforementioned knowledge base which can then be converted to a machine-actable form of information. A prototype application has been developed using this information capturing system that clearly demonstrates the use of the knowledge base framework using concept maps to develop Personal Health Information System for lung cancer patients.

An approach for building a personal health information system using conceptual domain knowledge.

In this paper we present the development of a Personal Health Information System (PHIS) by capturing the domain knowledge in the form of concept maps. The software architecture based on capturing the conceptual domain knowledge is demonstrated using a working prototype for patients suffering from diabetes mellitus. Cited current literature predicts that this user based information system has the potential to improve patient care, reduce medical errors, and lower health care costs.

A system for building clinical research applications using semantic web-based approach.

In this paper we present a system using Semantic Web by which applications can be effectively constructed for clinical research purposes. We are aware of the immense difficulties and variations involved in clinical research applications. With a purpose of mitigating some of these difficulties in the process of developing clinical research applications we are presenting an approach for building information systems based on Semantic Web. We have developed a working prototype using C-Map tools leveraging the underlying principles of Abstract Software Design Framework to convert domain knowledge into machine-actable information.

Enhancing medical research efficiency by using concept maps.

Even with today's advances in technology, the processes involved in medical research continue to be both time consuming and labor intensive. We have built an experimental integrated tool to convert the textual information available to the researchers into a concept map using the Web Ontology Language as an intermediate source of information. This tool is based on building semantic models using concept maps. The labor-intensive sequence of processes involved in medical research is suitably replaced by using this tool built by a suitable integration of concept maps and Web Ontology Language. We analyzed this tool by considering the example of linking vitamin D deficiency with prostate cancer. This tool is intended to provide a faster solution in building relations and concepts based on the existing facts.