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.

Association between uncontrolled diabetes and periodontal disease in US adults: NHANES 2009-2014.

This study examined the relationship between uncontrolled diabetes and periodontal disease (PD) among adults in the United States. We used data from the 2009-2014 National Health and Nutrition Examination Survey (NHANES) with a sample of 6108 adults ages 30 and over. To measure PD status, we used the Centers for Disease Control and Prevention/American Academy of Periodontology's standards. To classify DM status (no DM, DM with HbA1c < 9%, diabetes with HbA1c ≥ 9%),we used self-reported Diabetes Mellitus (DM) diagnosis and laboratory report of HbA1c. Approximately 8.5% of the sample had controlled DM, and 1.7% had uncontrolled DM, for a total of 10.2% DM in the analysis. Multivariate logistic regression showed that compared to those without DM, PD was significantly increased with controlled DM (adjusted odds ratio (aOR) = 1.32, 95% confidence interval (CI) 1.01-1.73, p < 0.05) and even more with uncontrolled DM (aOR = 2.48, 95% CI 1.52-4.04, p < 0.001), after adjusting for covariates. Factors that reduced the prevalence of PD included annual dental visits, female gender, and college education. Factors that significantly increased PD prevalence were cigarette smoking, non-white race, income < 200% Federal Poverty Level, and older age (age > 50 years). In conclusion, uncontrolled DM was significantly associated with higher odds of PD among adults in the US.

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.

Understanding the Difference Between Healthcare Informatics and Healthcare Data Analytics in the Present State of Health Care Management.

The purpose of this article is to perform a scientific analysis of the definitions associated with healthcare informatics and healthcare data analytics. Additionally, the authors attempt to redefine the scientific pursuit of healthcare informatics and healthcare data analytics. This commentary can assist the thinking of informaticians and data analysts working in healthcare management and practice. The authors also provide a brief insight on the possible future direction of informatics and analytics associated with healthcare.

Inherent Bias in Artificial Intelligence-Based Decision Support Systems for Healthcare.

The objective of this article is to discuss the inherent bias involved with artificial intelligence-based decision support systems for healthcare. In this article, the authors describe some relevant work published in this area. A proposed overview of solutions is also presented. The authors believe that the information presented in this article will enhance the readers' understanding of this inherent bias and add to the discussion on this topic. Finally, the authors discuss an overview of the need to implement transdisciplinary solutions that can be used to mitigate this bias.

A Longitudinal Analysis of Total Pain Scores for a Panel of Patients Treated by Pain Clinics.

BACKGROUND: There is a critical necessity to identify psychometric properties of the total pain score as a measurement of pain management effectiveness in the clinic. PURPOSE: In this article, we perform the analysis of the global pain scores from a panel of patients treated by 10 pain management physicians in a single group practice. BASIC PROCEDURES: The pain measurement consists of 4 pain subscales, namely physical pain, emotions, clinical outcome, and activities. A panel of 130 patients with 4 pain measurements is available to perform longitudinal analysis of the total pain scores. The analysis includes the following: (1) confirmatory factor analysis of the global pain scores with 4 related dimensions, (2) the stability of the pain scores between 2 clinical visits, (3) the change trajectories of pain scores in 4 waves of the pain measurement, and (4) the detection of physician variability in patients' treatment outcomes measured by the reduction of total pain scores. MAIN FINDINGS: The global pain scores were relatively stable between time 1 and time 2 clinical visits. The analysis indicated that there was a decrease in pain with longitudinal advancement in treatment. It also indicated that there was no significant change in this improvement with respect to difference in physicians involved in providing treatment. PRINCIPAL CONCLUSION: While the results indicated a decrease in pain with an alleviation in treatment provided to the patient, the article delineates a well-thought scientific approach to the targeted problem.

CE: Original Research: New Acute Symptoms in Older Adults with Cognitive Impairment: What Should Family Caregivers Do?

: Background: When older adults with cognitive impairment develop new physical or behavioral symptoms, their family caregivers face a difficult decision: whether and when to seek professional medical care. Most family caregivers lack formal training in assessment and may have difficulty making such decisions. The Veterans Health Administration's home-based primary care (HBPC) program, which is widely available, offers community-dwelling frail veterans and their family caregivers guidance, with the goal of reducing hospitalization and institutionalization in long-term care facilities. OBJECTIVE: This study sought to assess the frequency with which family caregivers of cognitively impaired older adults sought prehospital guidance from health care professionals when that resource was available to them, and to describe the characteristics of such events. METHODS: This study used a retrospective chart review of patients who were enrolled in the Orlando Veterans Affairs Medical Center HBPC program for at least one month between October 1, 2013, and September 30, 2014; had a diagnosis indicative of cognitive impairment (Alzheimer's disease, vascular dementia, or mild cognitive impairment); had a dedicated family caregiver; and were not enrolled in hospice care. Data were collected from data collection templates and nurses' narrative notes. Univariate descriptive analyses were conducted regarding the type of staff contacted by family caregivers, the presenting diagnoses, the guidance offered by staff, and the number of unplanned acute care encounters. RESULTS: Among the 215 patients studied, there were 254 unplanned acute care encounters (including ED visits followed by discharge to home and ED visits resulting in hospital admission). Family caregivers sought guidance from a health care professional 22% of the time before such an encounter. The presenting clinical issues were most often new problems (43%) that included falls, feeding tube problems, fever, new pain, rash or other skin problems, and unexplained edema. Overall, 25% of all unplanned acute care encounters were for reasons considered potentially avoidable. About half of the patients who were subsequently hospitalized had symptoms of delirium, indicating that their illness had significantly advanced before presentation. CONCLUSIONS: It's important for health care professionals to ensure that family caregivers of cognitively impaired older adults can access professional guidance readily when facing decisions about a loved one's care, especially when there is an acute onset of new symptoms. Teaching caregivers how to recognize such symptoms early in order to prevent exacerbations of chronic illness and subsequent hospitalization should be a high priority. Our findings underscore the need to do so, so that caregivers can best use the resources that HBPC programs have (or ought to have) in place, in particular 24/7 guidance and decision assistance.

Utilizing telemedicine in oncology settings: Patient favourability rates and perceptions of use analysis using Chi-Square and neural networks.

BACKGROUND: Telemedicine is an alternative to traditional face-to-face doctor-patient office visits. Although telemedicine is becoming more prevalent, few studies have looked at the perceived favorability rate among patients utilizing telemedicine over the traditional office visit to a provider's office considering data samples from more than 5 clinics in northern Louisiana. OBJECTIVE: This study aims to measure patient favorability of using telemedicine to receive care. This study looks at the perceived positive and negative favorability rates of patients in the oncology settings. The researchers analyzed how age, income level, and education level influenced the perceived patient favorability rates and their willingness to utilize telemedicine. METHODS: The investigators used Chi-Square analysis to identify favorability with respect to age education and income levels. In addition to this Artificial Neural Networks were used to identify the threshold for favorability with respect to age, income, and education. RESULTS: Chi-Square tests of association showed that of the variables analyzed, only education level had a statistically significant relationship with a patient's favorability rate of telemedicine utilization. While our neural network analysis indicated that the threshold for income, age, and education are $34,999, 66 years, and a college degree. CONCLUSION: In this article the investigators have successfully demonstrated the use of Artificial Neural Networks in identifying favorability of telemedicine used in addition to the traditional statistical methods such as Chi-Square. Thereby, creating a path for future research using advanced computational techniques like Artificial Neural Networks in analyzing human behavior.

Disparities in patient record completeness with respect to the health care utilization project.

Patient data completeness is an important characteristic in maintaining accurate health records and providing the highest standard of care. Furthermore, finding discrepancies in care based on different subpopulation parameters is important to identify areas of underlying systemic issues in order to address concerns and alleviate those discrepancies. In this project, the investigators use the Data Completeness Analysis Package to find trends in patient record completeness using Healthcare Cost and Utilization Project's State Inpatient Database for the state of Florida, specifically focusing on finding discrepancies among subpopulations along the variables of age, race, and gender. The results from testing Data Completeness Analysis Package with State Inpatient Database show a variety of patterns that provides insights to the health care delivery in Florida.

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.

Identifying the readiness of patients in implementing telemedicine in northern Louisiana for an oncology practice.

This study identified the readiness factors that may create challenges in the use of telemedicine among patients in northern Louisiana with cancer. To identify these readiness factors, the team of investigators developed 19 survey questions that were provided to the patients or to their caregivers. The team collected responses from 147 respondents from rural and urban residential backgrounds. These responses were used to identify the individuals' readiness for utilising telemedicine through factor analysis, Cronbach's alpha reliability test, analysis of variance and ordinary least squares regression. The analysis results indicated that the favourable factor (positive readiness item) had a mean value of 3.47, whereas the unfavourable factor (negative readiness item) had a mean value of 2.76. Cronbach's alpha reliability test provided an alpha value of 0.79. Overall, our study indicated a positive attitude towards the use of telemedicine in northern Louisiana.

A New Paradigm to Analyze Data Completeness of Patient Data.

BACKGROUND: There is a need to develop a tool that will measure data completeness of patient records using sophisticated statistical metrics. Patient data integrity is important in providing timely and appropriate care. Completeness is an important step, with an emphasis on understanding the complex relationships between data fields and their relative importance in delivering care. This tool will not only help understand where data problems are but also help uncover the underlying issues behind them. OBJECTIVES: Develop a tool that can be used alongside a variety of health care database software packages to determine the completeness of individual patient records as well as aggregate patient records across health care centers and subpopulations. METHODS: The methodology of this project is encapsulated within the Data Completeness Analysis Package (DCAP) tool, with the major components including concept mapping, CSV parsing, and statistical analysis. RESULTS: The results from testing DCAP with Healthcare Cost and Utilization Project (HCUP) State Inpatient Database (SID) data show that this tool is successful in identifying relative data completeness at the patient, subpopulation, and database levels. These results also solidify a need for further analysis and call for hypothesis driven research to find underlying causes for data incompleteness. CONCLUSION: DCAP examines patient records and generates statistics that can be used to determine the completeness of individual patient data as well as the general thoroughness of record keeping in a medical database. DCAP uses a component that is customized to the settings of the software package used for storing patient data as well as a Comma Separated Values (CSV) file parser to determine the appropriate measurements. DCAP itself is assessed through a proof of concept exercise using hypothetical data as well as available HCUP SID patient data.

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.